BOSTON—The incidence of serotonin syndrome ranged from 0.02% to 0.04% among all patients who were coprescribed triptans and an SSRI/SNRI during any calendar year from 2001 to 2014, according to a study presented at the 59th Annual Scientific Meeting of the American Headache Society. “Our data do not suggest a clinically meaningful risk of serotonin syndrome in patients coprescribed triptans with SSRI/SSNI antidepressants,” said Yulia Y. Orlova, MD, a clinical fellow at the John R. Graham Headache Center at Brigham and Women’s Faulkner Hospital in Boston.

Serotonin syndrome is a drug-induced group of symptoms that can be life-threatening. In 2006, the FDA issued an advisory concerning the risk of serotonin syndrome with concomitant use of triptans and SSRI/SNRI antidepressants. Since then, pharmacy systems and other decision support systems routinely have issued safety alerts when coprescription occurs. “However, all published reports of serotonin syndrome in patients receiving triptans alone or in combination with an SSRI/SNRI are case reports or case series that lack a denominator, so the true risk remains unknown,” said Dr. Orlova on behalf of her study collaborators.

Dr. Orlova and colleagues conducted a population-based study. For each year from 2001 to 2014, they used the Partners Healthcare System Research Patient Data Registry to identify patients receiving coprescriptions. The registry is a centralized data warehouse with clinical information about more than 6.5 million patients. The ICD-9 code for serotonin syndrome (333.99) is not reported separately in the database, but is part of a broader category of “other extrapyramidal diseases and abnormal movement disorders.” The researchers conservatively assumed that all reports of diagnostic code ICD-9 333.99 might represent serotonin syndrome. Among those patients receiving coprescriptions in the database, the researchers searched for those with the 333.99 code. The research team then reviewed detailed medical records to determine whether those patients met Sternbach or Hunter criteria for serotonin toxicity, or both, during the year in which concomitant prescription of a triptan antimigraine medication and SSRI/SNRI antidepressant may have occurred.

Over the 14-year study period, nearly 48,000 patients were prescribed triptans. Among these patients, about 19,000 were also coprescribed SSRI or SNRI antidepressants. A total of 229 received an ICD-9 diagnosis of 333.99. Detailed chart review revealed 17 cases where serotonin syndrome was reported as part of the differential diagnosis, past medical history, or main diagnosis. Seven of the 17 patients met Sternbach criteria (0.04% of all coprescription cases), four met Hunter criteria (0.02% of all coprescription cases), and all of the latter also satisfied Sternbach criteria.

Triptan use was reported in close temporal relation to the onset of symptoms in two cases. One case, involving eletriptan, was self-reported by the patient and recorded by the physician in the medical record. Chart information for this case did not allow assessment of whether the case met diagnostic criteria. The second case satisfied both sets of criteria for serotonin syndrome and involved the use of rizatriptan, although the onset of symptoms preceded rizatriptan use.

BOSTON—The incidence of serotonin syndrome ranged from 0.02% to 0.04% among all patients who were coprescribed triptans and an SSRI/SNRI during any calendar year from 2001 to 2014, according to a study presented at the 59th Annual Scientific Meeting of the American Headache Society. “Our data do not suggest a clinically meaningful risk of serotonin syndrome in patients coprescribed triptans with SSRI/SSNI antidepressants,” said Yulia Y. Orlova, MD, a clinical fellow at the John R. Graham Headache Center at Brigham and Women’s Faulkner Hospital in Boston.

Serotonin syndrome is a drug-induced group of symptoms that can be life-threatening. In 2006, the FDA issued an advisory concerning the risk of serotonin syndrome with concomitant use of triptans and SSRI/SNRI antidepressants. Since then, pharmacy systems and other decision support systems routinely have issued safety alerts when coprescription occurs. “However, all published reports of serotonin syndrome in patients receiving triptans alone or in combination with an SSRI/SNRI are case reports or case series that lack a denominator, so the true risk remains unknown,” said Dr. Orlova on behalf of her study collaborators.

Dr. Orlova and colleagues conducted a population-based study. For each year from 2001 to 2014, they used the Partners Healthcare System Research Patient Data Registry to identify patients receiving coprescriptions. The registry is a centralized data warehouse with clinical information about more than 6.5 million patients. The ICD-9 code for serotonin syndrome (333.99) is not reported separately in the database, but is part of a broader category of “other extrapyramidal diseases and abnormal movement disorders.” The researchers conservatively assumed that all reports of diagnostic code ICD-9 333.99 might represent serotonin syndrome. Among those patients receiving coprescriptions in the database, the researchers searched for those with the 333.99 code. The research team then reviewed detailed medical records to determine whether those patients met Sternbach or Hunter criteria for serotonin toxicity, or both, during the year in which concomitant prescription of a triptan antimigraine medication and SSRI/SNRI antidepressant may have occurred.

Over the 14-year study period, nearly 48,000 patients were prescribed triptans. Among these patients, about 19,000 were also coprescribed SSRI or SNRI antidepressants. A total of 229 received an ICD-9 diagnosis of 333.99. Detailed chart review revealed 17 cases where serotonin syndrome was reported as part of the differential diagnosis, past medical history, or main diagnosis. Seven of the 17 patients met Sternbach criteria (0.04% of all coprescription cases), four met Hunter criteria (0.02% of all coprescription cases), and all of the latter also satisfied Sternbach criteria.

Triptan use was reported in close temporal relation to the onset of symptoms in two cases. One case, involving eletriptan, was self-reported by the patient and recorded by the physician in the medical record. Chart information for this case did not allow assessment of whether the case met diagnostic criteria. The second case satisfied both sets of criteria for serotonin syndrome and involved the use of rizatriptan, although the onset of symptoms preceded rizatriptan use.

BOSTON—The incidence of serotonin syndrome ranged from 0.02% to 0.04% among all patients who were coprescribed triptans and an SSRI/SNRI during any calendar year from 2001 to 2014, according to a study presented at the 59th Annual Scientific Meeting of the American Headache Society. “Our data do not suggest a clinically meaningful risk of serotonin syndrome in patients coprescribed triptans with SSRI/SSNI antidepressants,” said Yulia Y. Orlova, MD, a clinical fellow at the John R. Graham Headache Center at Brigham and Women’s Faulkner Hospital in Boston.

Serotonin syndrome is a drug-induced group of symptoms that can be life-threatening. In 2006, the FDA issued an advisory concerning the risk of serotonin syndrome with concomitant use of triptans and SSRI/SNRI antidepressants. Since then, pharmacy systems and other decision support systems routinely have issued safety alerts when coprescription occurs. “However, all published reports of serotonin syndrome in patients receiving triptans alone or in combination with an SSRI/SNRI are case reports or case series that lack a denominator, so the true risk remains unknown,” said Dr. Orlova on behalf of her study collaborators.

Dr. Orlova and colleagues conducted a population-based study. For each year from 2001 to 2014, they used the Partners Healthcare System Research Patient Data Registry to identify patients receiving coprescriptions. The registry is a centralized data warehouse with clinical information about more than 6.5 million patients. The ICD-9 code for serotonin syndrome (333.99) is not reported separately in the database, but is part of a broader category of “other extrapyramidal diseases and abnormal movement disorders.” The researchers conservatively assumed that all reports of diagnostic code ICD-9 333.99 might represent serotonin syndrome. Among those patients receiving coprescriptions in the database, the researchers searched for those with the 333.99 code. The research team then reviewed detailed medical records to determine whether those patients met Sternbach or Hunter criteria for serotonin toxicity, or both, during the year in which concomitant prescription of a triptan antimigraine medication and SSRI/SNRI antidepressant may have occurred.

Over the 14-year study period, nearly 48,000 patients were prescribed triptans. Among these patients, about 19,000 were also coprescribed SSRI or SNRI antidepressants. A total of 229 received an ICD-9 diagnosis of 333.99. Detailed chart review revealed 17 cases where serotonin syndrome was reported as part of the differential diagnosis, past medical history, or main diagnosis. Seven of the 17 patients met Sternbach criteria (0.04% of all coprescription cases), four met Hunter criteria (0.02% of all coprescription cases), and all of the latter also satisfied Sternbach criteria.

Triptan use was reported in close temporal relation to the onset of symptoms in two cases. One case, involving eletriptan, was self-reported by the patient and recorded by the physician in the medical record. Chart information for this case did not allow assessment of whether the case met diagnostic criteria. The second case satisfied both sets of criteria for serotonin syndrome and involved the use of rizatriptan, although the onset of symptoms preceded rizatriptan use.

More than 44 million pelvic examinations are performed annually in the United States.¹ In March 2017, the United States Preventive Services Task Force (USPSTF) published an updated recommendation statement regarding the need for routine screening pelvic examinations in asymptomatic adult women (18 years and older) receiving primary care: “The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of performing screening pelvic examinations in asymptomatic, nonpregnant adult women.”²

That statement, however, was assigned a grade of I, which means that evidence is lacking, of poor quality, or conflicting, and that the balance of benefits and harms cannot be determined. This USPSTF recommendation statement thus will not change practice for ObGyn providers but likely will renew our commitment to provide individualized well-woman care. There was inadequate or poor quality evidence for benefits related to all-cause mortality, disease-specific morbidity, and quality of life, as well as inadequate evidence on harms related to false-positive findings and anxiety stemming from screening pelvic exams.

Read about coding and billing for a standard pelvic exam

The pelvic examination and insurance coverage

---

Melanie Witt, RN, MA

Coding and billing for the care provided at a well-woman visit can be uncomplicated if you know the right codes for the right program. The information presented here concerns straightforward preventive care and assumes that the patient also has not presented with a significant problem at the same visit.

First, a patient who is not Medicare-eligible might have insurance coverage for an annual preventive care examination every year. Normally, this service would be billed using the Current Procedural Terminology (CPT) preventive medicine codes, but some insurers require the use of special codes for an annual gynecologic exam. These special codes are:

• S0610, Annual gynecological examination, new patient
• S0612, Annual gynecological examination, established patient
• S0613, Annual gynecological examination; clinical breast examination without pelvic evaluation.

Notably, Aetna, Cigna, and UnitedHealthcare require these codes to signify that a pelvic examination has been performed (except for code S0613), but many Blue Cross Blue Shield programs, for whom these codes were originally created, are now reverting to the CPT preventive medicine codes for all preventive care.

CPT outlines the requirements for use of the preventive medicine codes as: an initial or periodic comprehensive preventive medicine evaluation or reevaluation and management (E/M) service, which includes an age- and gender-appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures. The codes are divided into new or established patient categories by age range as follows:

The Medicare E/M documentation guidelines do not apply to preventive services, and a head-to-toe examination also is not required. CPT recognizes the American College of Obstetricians and Gynecologists (ACOG) as an authoritative body to make recommendations for the expected preventive service for women, and if such a service is provided and documented, the preventive care codes are to be reported. The payers who use the S codes for a gynecologic exam will require that a pelvic examination has been performed, but such an examination would not be required when using the CPT codes or ACOG's guidelines if the physician and patient agreed that such an exam was not warranted every year. The other components of a preventive service applicable to the female patient's age, however, should be documented in order to report the CPT codes for preventive medicine services.

If a pelvic examination is not performed, say because the patient is young and not sexually active, but an examination of other areas is carried out, the diagnosis code would change from Z01.411, Encounter for gynecological examination (general) (routine) with abnormal findings, or Z01.419, Encounter for gynecological examination (general) (routine) without abnormal findings, to a general health exam: Z00.00, Encounter for general adult medical examination without abnormal findings, or Z00.01, Encounter for general adult medical examination with abnormal findings.  

What about Medicare?

Medicare requirements are somewhat different. First, Medicare covers only a small portion of the preventive care service; that is, it covers a physical examination of the genital organs and breasts and the collection and conveyance of a Pap specimen to the laboratory every 2 years for a low-risk patient. Second, the codes required to get reimbursed for the examination are:

• G0101, Cervical or vaginal cancer screening; pelvic and clinical breast examination
• Q0091, Screening Papanicolaou smear; obtaining, preparing, and conveyance of cervical or vaginal smear to laboratory.

It is not necessary to perform both of these services every 2 years (for instance, the patient may not need a Pap smear every 2 years based on her age and history), but the benefit is available if the service is performed. If the woman is at high risk for developing cervical or vaginal cancer, Medicare will cover this portion of the encounter every year so long as the Medicare-defined criteria for high risk have been documented at the time of the exam.

Related article:
GYN coding changes to note for your maximized reimbursement

Ms. Witt is an independent coding and documentation consultant and former program manager, department of coding and nomenclature, American Congress of Obstetricians and Gynecologists.

---

The author reports no financial relationships relevant to this article.

Read the authors’ interpretation of the new USPSTF statement

Interpreting the new USPSTF statement

We understand the USPSTF statement to mean that pelvic exams should not be abandoned, but rather should be individualized to each patient for her specific visit. We agree that for visits focused on counseling and routine screening in asymptomatic, nonpregnant women, pelvic exams likely will not increase the early detection and treatment of disease and more benefit likely would be derived by performing and discussing evidence-based and age-appropriate health services. A classic example would be for initiation or maintenance of oral contraception in an 18-year-old patient for whom an exam could cause unnecessary trauma, pain, or psychological distress leading to future avoidance or barriers to seeking health care. For long-acting reversible contraception placement, however, a pelvic exam clearly would be necessary for insertion of an intrauterine device.

Related article:
Women’s Preventive Services Initiative Guidelines provide consensus for practicing ObGyns

Indications for pelvic examination

Remember that the pelvic examination has 3 distinct parts (and that not all parts need to be routinely conducted)³:

• general inspection of the external genitalia and vulva
• speculum examination and evaluation of the vagina and cervix
• bimanual examination with possible rectovaginal examination in age-appropriate or symptomatic women.

According to the Well-Woman Task Force of the American College of Obstetricians and Gynecologists (ACOG), “For women 21 years and older, external exam may be performed annually and that inclusion of speculum examination, bimanual examination, or both in otherwise healthy women should be a shared, informed decision between patient and provider.”⁴

Indications for performing certain parts of the pelvic exam include⁴:

• routine screening for cervical cancer (Pap test)
• routine screening for gonorrhea, chlamydia infection, and other sexually transmitted infections
• evaluation of abnormal vaginal discharge
• evaluation of abnormal bleeding, pelvic pain, and pelvic floor disorders, such as prolapse, urinary incontinence, and accidental bowel leakage
• evaluation of menopausal symptoms, such as dryness, dyspareunia, and the genitourinary syndrome of menopause
• evaluation of women at increased risk for gynecologic malignancy, such as women with known hereditary breast–ovarian cancer syndromes.

In 2016, ACOG launched the Women’s Preventive Services Initiative (WPSI) in conjunction with the Health Resources and Services Administration (HRSA) of the US Department of Health and Human Services. In this 5-year collaboration, the agencies are endeavoring to review and update the recommendations for women’s preventive health care services, including well-woman visits, human papillomavirus testing, and contraception, among many others.⁵ Once the HRSA adopts these recommendations, women will be able to access comprehensive preventive health services without incurring any out-of-pocket expenses.

The pediatric and adolescent gynecologist perspective

---

Roshanak Mansouri Zinn, MD, and Rebekah L. Williams, MD, MS

No literature addresses the utility of screening pelvic examination in the pediatric and adolescent population. According to the American College of Obstetricians and Gynecologists Committee on Adolescent Health Care opinion on the initial reproductive health visit for screening and preventive reproductive health care (reaffirmed in 2016), a screening internal exam is not necessary, but an external genital exam may be indicated and may vary depending on the patient's concerns and prior clinical encounters.¹ The American Academy of Pediatrics promotes annual screening external genital examination for all female patients as part of routine primary care, with internal examinations only as indicated.²

Age-appropriate pelvic examination for girls and nonsexually active adolescents usually is limited to an external genital exam to evaluate the anatomy and note the sexual maturity rating (Tanner stage), an important indicator of normal pubertal development. As in adults, the potential benefits of screening examination in this population include detection of benign gynecologic conditions (including vulvar skin conditions and abnormalities of hymenal or vaginal development). Additionally, early reproductive health visits are an important time for clinicians to build rapport with younger patients and to provide anticipatory education on menstruation, hygiene, and anatomy. These visits can destigmatize and demystify the pelvic examination and help young women seek care more appropriately and more comfortably if problems do arise.

Even when a pelvic exam is indicated, a patient's young age can give providers pause as to what type of exam to perform. Patients with vulvovaginal symptoms, abnormal vaginal bleeding, vaginal discharge, or pelvic or abdominal pain should receive complete evaluation with external genital examination. If external vaginal examination does not allow for complete assessment of the problem, the patient and provider can assess the likelihood of her tolerating an internal exam in the clinic versus undergoing vaginoscopy under sedation. Limited laboratory evaluation and transabdominal pelvic ultrasonography may provide sufficient information for appropriate clinical decision making and management without internal examination. If symptoms persist or do not respond to first-line treatment, an internal exam should be performed.

Patients of any age may experience anxiety or physical discomfort or may even delay or avoid seeking care because of fear of a pelvic exam. However, providers of reproductive health care for children and adolescents can offer early education, reassurance, and a more comfortable experience when pelvic examination is necessary in this population.

References

1. American College of Obstetricians and Gynecologists Committee on Adolescent Health Care. Committee Opinion No. 598: Committee on Adolescent Health Care: the initial reproductive health visit. Obstet Gynecol. 2014;123(5):1143-1147.
2. Braverman PK, Breech L; Committee on Adolescence. American Academy of Pediatrics. Clinical report: gynecologic examination for adolescents in the pediatric office setting. Pediatrics. 2010;126(3):583-590.

 

Dr. Mansouri Zinn is Assistant Professor, Department of Women's Health, University of Texas at Austin.

Dr. Williams is Assistant Professor, Clinical Pediatrics, Section of Adolescent Medicine, Indiana University School of Medicine, Indianapolis.

Developed in collaboration with the North American Society for Pediatric and Adolescent Gynecology

---

The authors report no financial relationships relevant to this article.

How will the USPSTF statement affect practice?

In an editorial in the Journal of the American Medical Association commenting on the USPSTF statement, McNicholas and Peipert stated, “Based on the recommendation from the task force, clinicians may ask whether the pelvic examination should be abandoned. The answer is not found in this recommendation statement, but instead in a renewed commitment to shared decision making.”⁶ We wholeheartedly agree with this statement. The health care provider and the patient should make the decision, taking into consideration the patient’s risk factors for gynecologic cancers and other conditions, her personal preferences, and her overall values.

This new USPSTF recommendation statement will not change how we currently practice, and the statement’s grade I rating should not impact insurance coverage for pelvic exams. Additionally, further research is needed to better elucidate the role of the pelvic exam at well-woman visits, with hopes of obtaining more precise guidelines from the USPSTF and ACOG.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

More than 44 million pelvic examinations are performed annually in the United States.¹ In March 2017, the United States Preventive Services Task Force (USPSTF) published an updated recommendation statement regarding the need for routine screening pelvic examinations in asymptomatic adult women (18 years and older) receiving primary care: “The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of performing screening pelvic examinations in asymptomatic, nonpregnant adult women.”²

That statement, however, was assigned a grade of I, which means that evidence is lacking, of poor quality, or conflicting, and that the balance of benefits and harms cannot be determined. This USPSTF recommendation statement thus will not change practice for ObGyn providers but likely will renew our commitment to provide individualized well-woman care. There was inadequate or poor quality evidence for benefits related to all-cause mortality, disease-specific morbidity, and quality of life, as well as inadequate evidence on harms related to false-positive findings and anxiety stemming from screening pelvic exams.

Read about coding and billing for a standard pelvic exam

The pelvic examination and insurance coverage

---

Melanie Witt, RN, MA

Coding and billing for the care provided at a well-woman visit can be uncomplicated if you know the right codes for the right program. The information presented here concerns straightforward preventive care and assumes that the patient also has not presented with a significant problem at the same visit.

First, a patient who is not Medicare-eligible might have insurance coverage for an annual preventive care examination every year. Normally, this service would be billed using the Current Procedural Terminology (CPT) preventive medicine codes, but some insurers require the use of special codes for an annual gynecologic exam. These special codes are:

• S0610, Annual gynecological examination, new patient
• S0612, Annual gynecological examination, established patient
• S0613, Annual gynecological examination; clinical breast examination without pelvic evaluation.

Notably, Aetna, Cigna, and UnitedHealthcare require these codes to signify that a pelvic examination has been performed (except for code S0613), but many Blue Cross Blue Shield programs, for whom these codes were originally created, are now reverting to the CPT preventive medicine codes for all preventive care.

CPT outlines the requirements for use of the preventive medicine codes as: an initial or periodic comprehensive preventive medicine evaluation or reevaluation and management (E/M) service, which includes an age- and gender-appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures. The codes are divided into new or established patient categories by age range as follows:

The Medicare E/M documentation guidelines do not apply to preventive services, and a head-to-toe examination also is not required. CPT recognizes the American College of Obstetricians and Gynecologists (ACOG) as an authoritative body to make recommendations for the expected preventive service for women, and if such a service is provided and documented, the preventive care codes are to be reported. The payers who use the S codes for a gynecologic exam will require that a pelvic examination has been performed, but such an examination would not be required when using the CPT codes or ACOG's guidelines if the physician and patient agreed that such an exam was not warranted every year. The other components of a preventive service applicable to the female patient's age, however, should be documented in order to report the CPT codes for preventive medicine services.

If a pelvic examination is not performed, say because the patient is young and not sexually active, but an examination of other areas is carried out, the diagnosis code would change from Z01.411, Encounter for gynecological examination (general) (routine) with abnormal findings, or Z01.419, Encounter for gynecological examination (general) (routine) without abnormal findings, to a general health exam: Z00.00, Encounter for general adult medical examination without abnormal findings, or Z00.01, Encounter for general adult medical examination with abnormal findings.  

What about Medicare?

Medicare requirements are somewhat different. First, Medicare covers only a small portion of the preventive care service; that is, it covers a physical examination of the genital organs and breasts and the collection and conveyance of a Pap specimen to the laboratory every 2 years for a low-risk patient. Second, the codes required to get reimbursed for the examination are:

• G0101, Cervical or vaginal cancer screening; pelvic and clinical breast examination
• Q0091, Screening Papanicolaou smear; obtaining, preparing, and conveyance of cervical or vaginal smear to laboratory.

It is not necessary to perform both of these services every 2 years (for instance, the patient may not need a Pap smear every 2 years based on her age and history), but the benefit is available if the service is performed. If the woman is at high risk for developing cervical or vaginal cancer, Medicare will cover this portion of the encounter every year so long as the Medicare-defined criteria for high risk have been documented at the time of the exam.

Related article:
GYN coding changes to note for your maximized reimbursement

Ms. Witt is an independent coding and documentation consultant and former program manager, department of coding and nomenclature, American Congress of Obstetricians and Gynecologists.

---

The author reports no financial relationships relevant to this article.

Read the authors’ interpretation of the new USPSTF statement

Interpreting the new USPSTF statement

We understand the USPSTF statement to mean that pelvic exams should not be abandoned, but rather should be individualized to each patient for her specific visit. We agree that for visits focused on counseling and routine screening in asymptomatic, nonpregnant women, pelvic exams likely will not increase the early detection and treatment of disease and more benefit likely would be derived by performing and discussing evidence-based and age-appropriate health services. A classic example would be for initiation or maintenance of oral contraception in an 18-year-old patient for whom an exam could cause unnecessary trauma, pain, or psychological distress leading to future avoidance or barriers to seeking health care. For long-acting reversible contraception placement, however, a pelvic exam clearly would be necessary for insertion of an intrauterine device.

Related article:
Women’s Preventive Services Initiative Guidelines provide consensus for practicing ObGyns

Indications for pelvic examination

Remember that the pelvic examination has 3 distinct parts (and that not all parts need to be routinely conducted)³:

• general inspection of the external genitalia and vulva
• speculum examination and evaluation of the vagina and cervix
• bimanual examination with possible rectovaginal examination in age-appropriate or symptomatic women.

According to the Well-Woman Task Force of the American College of Obstetricians and Gynecologists (ACOG), “For women 21 years and older, external exam may be performed annually and that inclusion of speculum examination, bimanual examination, or both in otherwise healthy women should be a shared, informed decision between patient and provider.”⁴

Indications for performing certain parts of the pelvic exam include⁴:

• routine screening for cervical cancer (Pap test)
• routine screening for gonorrhea, chlamydia infection, and other sexually transmitted infections
• evaluation of abnormal vaginal discharge
• evaluation of abnormal bleeding, pelvic pain, and pelvic floor disorders, such as prolapse, urinary incontinence, and accidental bowel leakage
• evaluation of menopausal symptoms, such as dryness, dyspareunia, and the genitourinary syndrome of menopause
• evaluation of women at increased risk for gynecologic malignancy, such as women with known hereditary breast–ovarian cancer syndromes.

In 2016, ACOG launched the Women’s Preventive Services Initiative (WPSI) in conjunction with the Health Resources and Services Administration (HRSA) of the US Department of Health and Human Services. In this 5-year collaboration, the agencies are endeavoring to review and update the recommendations for women’s preventive health care services, including well-woman visits, human papillomavirus testing, and contraception, among many others.⁵ Once the HRSA adopts these recommendations, women will be able to access comprehensive preventive health services without incurring any out-of-pocket expenses.

The pediatric and adolescent gynecologist perspective

---

Roshanak Mansouri Zinn, MD, and Rebekah L. Williams, MD, MS

No literature addresses the utility of screening pelvic examination in the pediatric and adolescent population. According to the American College of Obstetricians and Gynecologists Committee on Adolescent Health Care opinion on the initial reproductive health visit for screening and preventive reproductive health care (reaffirmed in 2016), a screening internal exam is not necessary, but an external genital exam may be indicated and may vary depending on the patient's concerns and prior clinical encounters.¹ The American Academy of Pediatrics promotes annual screening external genital examination for all female patients as part of routine primary care, with internal examinations only as indicated.²

Age-appropriate pelvic examination for girls and nonsexually active adolescents usually is limited to an external genital exam to evaluate the anatomy and note the sexual maturity rating (Tanner stage), an important indicator of normal pubertal development. As in adults, the potential benefits of screening examination in this population include detection of benign gynecologic conditions (including vulvar skin conditions and abnormalities of hymenal or vaginal development). Additionally, early reproductive health visits are an important time for clinicians to build rapport with younger patients and to provide anticipatory education on menstruation, hygiene, and anatomy. These visits can destigmatize and demystify the pelvic examination and help young women seek care more appropriately and more comfortably if problems do arise.

Even when a pelvic exam is indicated, a patient's young age can give providers pause as to what type of exam to perform. Patients with vulvovaginal symptoms, abnormal vaginal bleeding, vaginal discharge, or pelvic or abdominal pain should receive complete evaluation with external genital examination. If external vaginal examination does not allow for complete assessment of the problem, the patient and provider can assess the likelihood of her tolerating an internal exam in the clinic versus undergoing vaginoscopy under sedation. Limited laboratory evaluation and transabdominal pelvic ultrasonography may provide sufficient information for appropriate clinical decision making and management without internal examination. If symptoms persist or do not respond to first-line treatment, an internal exam should be performed.

Patients of any age may experience anxiety or physical discomfort or may even delay or avoid seeking care because of fear of a pelvic exam. However, providers of reproductive health care for children and adolescents can offer early education, reassurance, and a more comfortable experience when pelvic examination is necessary in this population.

References

1. American College of Obstetricians and Gynecologists Committee on Adolescent Health Care. Committee Opinion No. 598: Committee on Adolescent Health Care: the initial reproductive health visit. Obstet Gynecol. 2014;123(5):1143-1147.
2. Braverman PK, Breech L; Committee on Adolescence. American Academy of Pediatrics. Clinical report: gynecologic examination for adolescents in the pediatric office setting. Pediatrics. 2010;126(3):583-590.

 

Dr. Mansouri Zinn is Assistant Professor, Department of Women's Health, University of Texas at Austin.

Dr. Williams is Assistant Professor, Clinical Pediatrics, Section of Adolescent Medicine, Indiana University School of Medicine, Indianapolis.

Developed in collaboration with the North American Society for Pediatric and Adolescent Gynecology

---

The authors report no financial relationships relevant to this article.

How will the USPSTF statement affect practice?

In an editorial in the Journal of the American Medical Association commenting on the USPSTF statement, McNicholas and Peipert stated, “Based on the recommendation from the task force, clinicians may ask whether the pelvic examination should be abandoned. The answer is not found in this recommendation statement, but instead in a renewed commitment to shared decision making.”⁶ We wholeheartedly agree with this statement. The health care provider and the patient should make the decision, taking into consideration the patient’s risk factors for gynecologic cancers and other conditions, her personal preferences, and her overall values.

This new USPSTF recommendation statement will not change how we currently practice, and the statement’s grade I rating should not impact insurance coverage for pelvic exams. Additionally, further research is needed to better elucidate the role of the pelvic exam at well-woman visits, with hopes of obtaining more precise guidelines from the USPSTF and ACOG.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

More than 44 million pelvic examinations are performed annually in the United States.¹ In March 2017, the United States Preventive Services Task Force (USPSTF) published an updated recommendation statement regarding the need for routine screening pelvic examinations in asymptomatic adult women (18 years and older) receiving primary care: “The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of performing screening pelvic examinations in asymptomatic, nonpregnant adult women.”²

That statement, however, was assigned a grade of I, which means that evidence is lacking, of poor quality, or conflicting, and that the balance of benefits and harms cannot be determined. This USPSTF recommendation statement thus will not change practice for ObGyn providers but likely will renew our commitment to provide individualized well-woman care. There was inadequate or poor quality evidence for benefits related to all-cause mortality, disease-specific morbidity, and quality of life, as well as inadequate evidence on harms related to false-positive findings and anxiety stemming from screening pelvic exams.

Read about coding and billing for a standard pelvic exam

The pelvic examination and insurance coverage

---

Melanie Witt, RN, MA

Coding and billing for the care provided at a well-woman visit can be uncomplicated if you know the right codes for the right program. The information presented here concerns straightforward preventive care and assumes that the patient also has not presented with a significant problem at the same visit.

First, a patient who is not Medicare-eligible might have insurance coverage for an annual preventive care examination every year. Normally, this service would be billed using the Current Procedural Terminology (CPT) preventive medicine codes, but some insurers require the use of special codes for an annual gynecologic exam. These special codes are:

• S0610, Annual gynecological examination, new patient
• S0612, Annual gynecological examination, established patient
• S0613, Annual gynecological examination; clinical breast examination without pelvic evaluation.

Notably, Aetna, Cigna, and UnitedHealthcare require these codes to signify that a pelvic examination has been performed (except for code S0613), but many Blue Cross Blue Shield programs, for whom these codes were originally created, are now reverting to the CPT preventive medicine codes for all preventive care.

CPT outlines the requirements for use of the preventive medicine codes as: an initial or periodic comprehensive preventive medicine evaluation or reevaluation and management (E/M) service, which includes an age- and gender-appropriate history, examination, counseling/anticipatory guidance/risk factor reduction interventions, and the ordering of laboratory/diagnostic procedures. The codes are divided into new or established patient categories by age range as follows:

The Medicare E/M documentation guidelines do not apply to preventive services, and a head-to-toe examination also is not required. CPT recognizes the American College of Obstetricians and Gynecologists (ACOG) as an authoritative body to make recommendations for the expected preventive service for women, and if such a service is provided and documented, the preventive care codes are to be reported. The payers who use the S codes for a gynecologic exam will require that a pelvic examination has been performed, but such an examination would not be required when using the CPT codes or ACOG's guidelines if the physician and patient agreed that such an exam was not warranted every year. The other components of a preventive service applicable to the female patient's age, however, should be documented in order to report the CPT codes for preventive medicine services.

If a pelvic examination is not performed, say because the patient is young and not sexually active, but an examination of other areas is carried out, the diagnosis code would change from Z01.411, Encounter for gynecological examination (general) (routine) with abnormal findings, or Z01.419, Encounter for gynecological examination (general) (routine) without abnormal findings, to a general health exam: Z00.00, Encounter for general adult medical examination without abnormal findings, or Z00.01, Encounter for general adult medical examination with abnormal findings.  

What about Medicare?

Medicare requirements are somewhat different. First, Medicare covers only a small portion of the preventive care service; that is, it covers a physical examination of the genital organs and breasts and the collection and conveyance of a Pap specimen to the laboratory every 2 years for a low-risk patient. Second, the codes required to get reimbursed for the examination are:

• G0101, Cervical or vaginal cancer screening; pelvic and clinical breast examination
• Q0091, Screening Papanicolaou smear; obtaining, preparing, and conveyance of cervical or vaginal smear to laboratory.

It is not necessary to perform both of these services every 2 years (for instance, the patient may not need a Pap smear every 2 years based on her age and history), but the benefit is available if the service is performed. If the woman is at high risk for developing cervical or vaginal cancer, Medicare will cover this portion of the encounter every year so long as the Medicare-defined criteria for high risk have been documented at the time of the exam.

Related article:
GYN coding changes to note for your maximized reimbursement

Ms. Witt is an independent coding and documentation consultant and former program manager, department of coding and nomenclature, American Congress of Obstetricians and Gynecologists.

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The author reports no financial relationships relevant to this article.

Read the authors’ interpretation of the new USPSTF statement

Interpreting the new USPSTF statement

We understand the USPSTF statement to mean that pelvic exams should not be abandoned, but rather should be individualized to each patient for her specific visit. We agree that for visits focused on counseling and routine screening in asymptomatic, nonpregnant women, pelvic exams likely will not increase the early detection and treatment of disease and more benefit likely would be derived by performing and discussing evidence-based and age-appropriate health services. A classic example would be for initiation or maintenance of oral contraception in an 18-year-old patient for whom an exam could cause unnecessary trauma, pain, or psychological distress leading to future avoidance or barriers to seeking health care. For long-acting reversible contraception placement, however, a pelvic exam clearly would be necessary for insertion of an intrauterine device.

Related article:
Women’s Preventive Services Initiative Guidelines provide consensus for practicing ObGyns

Indications for pelvic examination

Remember that the pelvic examination has 3 distinct parts (and that not all parts need to be routinely conducted)³:

• general inspection of the external genitalia and vulva
• speculum examination and evaluation of the vagina and cervix
• bimanual examination with possible rectovaginal examination in age-appropriate or symptomatic women.

According to the Well-Woman Task Force of the American College of Obstetricians and Gynecologists (ACOG), “For women 21 years and older, external exam may be performed annually and that inclusion of speculum examination, bimanual examination, or both in otherwise healthy women should be a shared, informed decision between patient and provider.”⁴

Indications for performing certain parts of the pelvic exam include⁴:

• routine screening for cervical cancer (Pap test)
• routine screening for gonorrhea, chlamydia infection, and other sexually transmitted infections
• evaluation of abnormal vaginal discharge
• evaluation of abnormal bleeding, pelvic pain, and pelvic floor disorders, such as prolapse, urinary incontinence, and accidental bowel leakage
• evaluation of menopausal symptoms, such as dryness, dyspareunia, and the genitourinary syndrome of menopause
• evaluation of women at increased risk for gynecologic malignancy, such as women with known hereditary breast–ovarian cancer syndromes.

In 2016, ACOG launched the Women’s Preventive Services Initiative (WPSI) in conjunction with the Health Resources and Services Administration (HRSA) of the US Department of Health and Human Services. In this 5-year collaboration, the agencies are endeavoring to review and update the recommendations for women’s preventive health care services, including well-woman visits, human papillomavirus testing, and contraception, among many others.⁵ Once the HRSA adopts these recommendations, women will be able to access comprehensive preventive health services without incurring any out-of-pocket expenses.

The pediatric and adolescent gynecologist perspective

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Roshanak Mansouri Zinn, MD, and Rebekah L. Williams, MD, MS

No literature addresses the utility of screening pelvic examination in the pediatric and adolescent population. According to the American College of Obstetricians and Gynecologists Committee on Adolescent Health Care opinion on the initial reproductive health visit for screening and preventive reproductive health care (reaffirmed in 2016), a screening internal exam is not necessary, but an external genital exam may be indicated and may vary depending on the patient's concerns and prior clinical encounters.¹ The American Academy of Pediatrics promotes annual screening external genital examination for all female patients as part of routine primary care, with internal examinations only as indicated.²

Age-appropriate pelvic examination for girls and nonsexually active adolescents usually is limited to an external genital exam to evaluate the anatomy and note the sexual maturity rating (Tanner stage), an important indicator of normal pubertal development. As in adults, the potential benefits of screening examination in this population include detection of benign gynecologic conditions (including vulvar skin conditions and abnormalities of hymenal or vaginal development). Additionally, early reproductive health visits are an important time for clinicians to build rapport with younger patients and to provide anticipatory education on menstruation, hygiene, and anatomy. These visits can destigmatize and demystify the pelvic examination and help young women seek care more appropriately and more comfortably if problems do arise.

Even when a pelvic exam is indicated, a patient's young age can give providers pause as to what type of exam to perform. Patients with vulvovaginal symptoms, abnormal vaginal bleeding, vaginal discharge, or pelvic or abdominal pain should receive complete evaluation with external genital examination. If external vaginal examination does not allow for complete assessment of the problem, the patient and provider can assess the likelihood of her tolerating an internal exam in the clinic versus undergoing vaginoscopy under sedation. Limited laboratory evaluation and transabdominal pelvic ultrasonography may provide sufficient information for appropriate clinical decision making and management without internal examination. If symptoms persist or do not respond to first-line treatment, an internal exam should be performed.

Patients of any age may experience anxiety or physical discomfort or may even delay or avoid seeking care because of fear of a pelvic exam. However, providers of reproductive health care for children and adolescents can offer early education, reassurance, and a more comfortable experience when pelvic examination is necessary in this population.

References

1. American College of Obstetricians and Gynecologists Committee on Adolescent Health Care. Committee Opinion No. 598: Committee on Adolescent Health Care: the initial reproductive health visit. Obstet Gynecol. 2014;123(5):1143-1147.
2. Braverman PK, Breech L; Committee on Adolescence. American Academy of Pediatrics. Clinical report: gynecologic examination for adolescents in the pediatric office setting. Pediatrics. 2010;126(3):583-590.

 

Dr. Mansouri Zinn is Assistant Professor, Department of Women's Health, University of Texas at Austin.

Dr. Williams is Assistant Professor, Clinical Pediatrics, Section of Adolescent Medicine, Indiana University School of Medicine, Indianapolis.

Developed in collaboration with the North American Society for Pediatric and Adolescent Gynecology

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The authors report no financial relationships relevant to this article.

How will the USPSTF statement affect practice?

In an editorial in the Journal of the American Medical Association commenting on the USPSTF statement, McNicholas and Peipert stated, “Based on the recommendation from the task force, clinicians may ask whether the pelvic examination should be abandoned. The answer is not found in this recommendation statement, but instead in a renewed commitment to shared decision making.”⁶ We wholeheartedly agree with this statement. The health care provider and the patient should make the decision, taking into consideration the patient’s risk factors for gynecologic cancers and other conditions, her personal preferences, and her overall values.

This new USPSTF recommendation statement will not change how we currently practice, and the statement’s grade I rating should not impact insurance coverage for pelvic exams. Additionally, further research is needed to better elucidate the role of the pelvic exam at well-woman visits, with hopes of obtaining more precise guidelines from the USPSTF and ACOG.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

© 2017 Society of Hospital Medicine

Strategies are needed to empower frontline clinicians to work with organizational leadership to reduce healthcare costs and improve high-value care. Caring Wisely^® is a program developed by the University of California, San Francisco’s (UCSF) Center for Healthcare Value (CHV), aimed at engaging frontline clinicians and staff, connecting them with implementation experts, and supporting the development of targeted interventions to improve value. Financial savings from the program more than cover program costs. Caring Wisely^® provides an institutional model for implementing robust interventions to address areas of low-value care.

Launched in 2013, the annual Caring Wisely^® program consists of 3 stages for identifying projects that meet the following criteria:

• Potential to measurably reduce UCSF Health’s costs of care without transferring costs to patients, insurers, or other providers
• Plan for ensuring that health outcomes are maintained or improved
• Envision disseminating the intervention within and beyond UCSF
• Demonstrate commitment and engagement of clinical leadership and frontline staff.

The first stage is the Ideas Contest, a UCSF Health-wide call (to learn more about UCSF Health: https://www.ucsf.edu/sites/default/files/052516_About_UCSF.pdf) to identify areas that may be targeted to reduce unnecessary services, inefficiencies, and healthcare costs. We use a crowdsourcing platform—Open Proposals—to solicit the best ideas from frontline clinicians and staff.¹ Open Proposals is a secure, web-based platform for transparent and collaborative proposal development that displays threads of comments, responses, and revisions, and allows submissions to be “liked.” Open Proposals is managed by the UCSF Clinical and Translational Science Institute, funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health. Using institutional e-mail lists for faculty, staff and residents, as well as described at monthly managers and directors meetings, the Ideas Contest is announced each year by the Chief Medical Officer and the CHV leadership. The Caring Wisely^® Executive Steering Committee, which consists of CHV and senior UCSF Health system leaders, selects the top 5-10 ideas based on the above criteria. Each winning idea receives a $100 gift certificate for a popular restaurant in San Francisco, and the list of winners is announced to the entire UCSF community.

The second stage is the Request for Proposals. The Caring Wisely^® program solicits proposals that outline implementation plans to target specific areas identified through the Ideas Contest. Finalists from the Ideas Contest are encouraged to submit proposals that address the problem they identified, but anyone affiliated with UCSF Health may submit a proposal on a winning idea. There is an approximately 4-week open submission period during which applicants submit brief 2-page proposals on the Open Proposal platform. This is followed by a period of optimization that leverages the social media aspect of the Open Proposals platform in which the UCSF Health community asks clarifying questions, make suggestions, and modifications can be made to the proposals. All submissions receive written feedback from at least one Steering Committee member. In addition, the Caring Wisely^® Director directly invites relevant UCSF colleagues, administrators, or program leaders to comment on proposals and make suggestions for improvement. Plans for assessing financial and health care delivery impacts are developed in collaboration with the UCSF Health Finance department. UCSF Health managers and leaders who are stakeholders in project proposal areas are consulted to provide input and finalize proposal plans, including the identification of existing personnel who can support and drive the project forward. Proposers use this feedback to revise their applications throughout this stage.

The third stage is Project Implementation. The Caring Wisely^® Executive Steering Committee selects up to 3 winners from the submitted proposals. Using the program criteria above, each project is scored independently, discussed in committee, and rescored to identify the top proposals. Each selected project receives a maximum budget of $50,000 that can be used for project materials, activities, and salary support for project leaders or staff. In addition to funding, each project team receives input from the implementation science team to co-develop and implement the intervention with a goal of creating a first-test-of-change within 3-6 months. A key feature of Caring Wisely^® is the partnership between project teams and the Caring Wisely^® implementation team, which includes a director, program manager, data analysts, and implementation scientists (Table 1).

LIST OF ABBREVIATIONS

UCSF—University of California, San Francisco; PRECEDE—Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation; PROCEED—Policy, Regulatory and Organizational Constructs in Educational and Environmental Development

Acknowledgments

Other participants in blood transfusion reduction project (D. Johnson, K. Curcione); IV-to-PO Switch (C. Tsourounis, A. Pollock); Surgical Supply Cost Reduction (C. Zygourakis); Perioperative Efficiency (L. Hampson); CT for PE Risk Prediction (E. Weber); ERAS Pathways (L. Chen); Neonatal Sepsis Risk Prediction (T. Newman); Post-Operative Debrief (S. Imershein). Caring Wisely Executive Steering Committee (J. Adler, S. Antrum, A Auerbach, J. Bennan, M. Blum, C. Ritchie, C. Tsourounis). This Center for Healthcare Value is funded in part by a grant from the Grove Foundation. We appreciate additional review and comments to the manuscript provided by George Sawaya and Adams Dudley.

Disclosures 

Christopher Moriates has accepted royalties from McGraw-Hill for textbook, Understanding Value-Based Healthcare. Alvin Rajkomar has received fees as a research adviser from Google, Inc.

© 2017 Society of Hospital Medicine

Strategies are needed to empower frontline clinicians to work with organizational leadership to reduce healthcare costs and improve high-value care. Caring Wisely^® is a program developed by the University of California, San Francisco’s (UCSF) Center for Healthcare Value (CHV), aimed at engaging frontline clinicians and staff, connecting them with implementation experts, and supporting the development of targeted interventions to improve value. Financial savings from the program more than cover program costs. Caring Wisely^® provides an institutional model for implementing robust interventions to address areas of low-value care.

Launched in 2013, the annual Caring Wisely^® program consists of 3 stages for identifying projects that meet the following criteria:

• Potential to measurably reduce UCSF Health’s costs of care without transferring costs to patients, insurers, or other providers
• Plan for ensuring that health outcomes are maintained or improved
• Envision disseminating the intervention within and beyond UCSF
• Demonstrate commitment and engagement of clinical leadership and frontline staff.

The first stage is the Ideas Contest, a UCSF Health-wide call (to learn more about UCSF Health: https://www.ucsf.edu/sites/default/files/052516_About_UCSF.pdf) to identify areas that may be targeted to reduce unnecessary services, inefficiencies, and healthcare costs. We use a crowdsourcing platform—Open Proposals—to solicit the best ideas from frontline clinicians and staff.¹ Open Proposals is a secure, web-based platform for transparent and collaborative proposal development that displays threads of comments, responses, and revisions, and allows submissions to be “liked.” Open Proposals is managed by the UCSF Clinical and Translational Science Institute, funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health. Using institutional e-mail lists for faculty, staff and residents, as well as described at monthly managers and directors meetings, the Ideas Contest is announced each year by the Chief Medical Officer and the CHV leadership. The Caring Wisely^® Executive Steering Committee, which consists of CHV and senior UCSF Health system leaders, selects the top 5-10 ideas based on the above criteria. Each winning idea receives a $100 gift certificate for a popular restaurant in San Francisco, and the list of winners is announced to the entire UCSF community.

The second stage is the Request for Proposals. The Caring Wisely^® program solicits proposals that outline implementation plans to target specific areas identified through the Ideas Contest. Finalists from the Ideas Contest are encouraged to submit proposals that address the problem they identified, but anyone affiliated with UCSF Health may submit a proposal on a winning idea. There is an approximately 4-week open submission period during which applicants submit brief 2-page proposals on the Open Proposal platform. This is followed by a period of optimization that leverages the social media aspect of the Open Proposals platform in which the UCSF Health community asks clarifying questions, make suggestions, and modifications can be made to the proposals. All submissions receive written feedback from at least one Steering Committee member. In addition, the Caring Wisely^® Director directly invites relevant UCSF colleagues, administrators, or program leaders to comment on proposals and make suggestions for improvement. Plans for assessing financial and health care delivery impacts are developed in collaboration with the UCSF Health Finance department. UCSF Health managers and leaders who are stakeholders in project proposal areas are consulted to provide input and finalize proposal plans, including the identification of existing personnel who can support and drive the project forward. Proposers use this feedback to revise their applications throughout this stage.

The third stage is Project Implementation. The Caring Wisely^® Executive Steering Committee selects up to 3 winners from the submitted proposals. Using the program criteria above, each project is scored independently, discussed in committee, and rescored to identify the top proposals. Each selected project receives a maximum budget of $50,000 that can be used for project materials, activities, and salary support for project leaders or staff. In addition to funding, each project team receives input from the implementation science team to co-develop and implement the intervention with a goal of creating a first-test-of-change within 3-6 months. A key feature of Caring Wisely^® is the partnership between project teams and the Caring Wisely^® implementation team, which includes a director, program manager, data analysts, and implementation scientists (Table 1).

LIST OF ABBREVIATIONS

UCSF—University of California, San Francisco; PRECEDE—Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation; PROCEED—Policy, Regulatory and Organizational Constructs in Educational and Environmental Development

Acknowledgments

Other participants in blood transfusion reduction project (D. Johnson, K. Curcione); IV-to-PO Switch (C. Tsourounis, A. Pollock); Surgical Supply Cost Reduction (C. Zygourakis); Perioperative Efficiency (L. Hampson); CT for PE Risk Prediction (E. Weber); ERAS Pathways (L. Chen); Neonatal Sepsis Risk Prediction (T. Newman); Post-Operative Debrief (S. Imershein). Caring Wisely Executive Steering Committee (J. Adler, S. Antrum, A Auerbach, J. Bennan, M. Blum, C. Ritchie, C. Tsourounis). This Center for Healthcare Value is funded in part by a grant from the Grove Foundation. We appreciate additional review and comments to the manuscript provided by George Sawaya and Adams Dudley.

Disclosures 

Christopher Moriates has accepted royalties from McGraw-Hill for textbook, Understanding Value-Based Healthcare. Alvin Rajkomar has received fees as a research adviser from Google, Inc.

© 2017 Society of Hospital Medicine

Strategies are needed to empower frontline clinicians to work with organizational leadership to reduce healthcare costs and improve high-value care. Caring Wisely^® is a program developed by the University of California, San Francisco’s (UCSF) Center for Healthcare Value (CHV), aimed at engaging frontline clinicians and staff, connecting them with implementation experts, and supporting the development of targeted interventions to improve value. Financial savings from the program more than cover program costs. Caring Wisely^® provides an institutional model for implementing robust interventions to address areas of low-value care.

Launched in 2013, the annual Caring Wisely^® program consists of 3 stages for identifying projects that meet the following criteria:

• Potential to measurably reduce UCSF Health’s costs of care without transferring costs to patients, insurers, or other providers
• Plan for ensuring that health outcomes are maintained or improved
• Envision disseminating the intervention within and beyond UCSF
• Demonstrate commitment and engagement of clinical leadership and frontline staff.

The first stage is the Ideas Contest, a UCSF Health-wide call (to learn more about UCSF Health: https://www.ucsf.edu/sites/default/files/052516_About_UCSF.pdf) to identify areas that may be targeted to reduce unnecessary services, inefficiencies, and healthcare costs. We use a crowdsourcing platform—Open Proposals—to solicit the best ideas from frontline clinicians and staff.¹ Open Proposals is a secure, web-based platform for transparent and collaborative proposal development that displays threads of comments, responses, and revisions, and allows submissions to be “liked.” Open Proposals is managed by the UCSF Clinical and Translational Science Institute, funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health. Using institutional e-mail lists for faculty, staff and residents, as well as described at monthly managers and directors meetings, the Ideas Contest is announced each year by the Chief Medical Officer and the CHV leadership. The Caring Wisely^® Executive Steering Committee, which consists of CHV and senior UCSF Health system leaders, selects the top 5-10 ideas based on the above criteria. Each winning idea receives a $100 gift certificate for a popular restaurant in San Francisco, and the list of winners is announced to the entire UCSF community.

The second stage is the Request for Proposals. The Caring Wisely^® program solicits proposals that outline implementation plans to target specific areas identified through the Ideas Contest. Finalists from the Ideas Contest are encouraged to submit proposals that address the problem they identified, but anyone affiliated with UCSF Health may submit a proposal on a winning idea. There is an approximately 4-week open submission period during which applicants submit brief 2-page proposals on the Open Proposal platform. This is followed by a period of optimization that leverages the social media aspect of the Open Proposals platform in which the UCSF Health community asks clarifying questions, make suggestions, and modifications can be made to the proposals. All submissions receive written feedback from at least one Steering Committee member. In addition, the Caring Wisely^® Director directly invites relevant UCSF colleagues, administrators, or program leaders to comment on proposals and make suggestions for improvement. Plans for assessing financial and health care delivery impacts are developed in collaboration with the UCSF Health Finance department. UCSF Health managers and leaders who are stakeholders in project proposal areas are consulted to provide input and finalize proposal plans, including the identification of existing personnel who can support and drive the project forward. Proposers use this feedback to revise their applications throughout this stage.

The third stage is Project Implementation. The Caring Wisely^® Executive Steering Committee selects up to 3 winners from the submitted proposals. Using the program criteria above, each project is scored independently, discussed in committee, and rescored to identify the top proposals. Each selected project receives a maximum budget of $50,000 that can be used for project materials, activities, and salary support for project leaders or staff. In addition to funding, each project team receives input from the implementation science team to co-develop and implement the intervention with a goal of creating a first-test-of-change within 3-6 months. A key feature of Caring Wisely^® is the partnership between project teams and the Caring Wisely^® implementation team, which includes a director, program manager, data analysts, and implementation scientists (Table 1).

LIST OF ABBREVIATIONS

UCSF—University of California, San Francisco; PRECEDE—Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation; PROCEED—Policy, Regulatory and Organizational Constructs in Educational and Environmental Development

Acknowledgments

Other participants in blood transfusion reduction project (D. Johnson, K. Curcione); IV-to-PO Switch (C. Tsourounis, A. Pollock); Surgical Supply Cost Reduction (C. Zygourakis); Perioperative Efficiency (L. Hampson); CT for PE Risk Prediction (E. Weber); ERAS Pathways (L. Chen); Neonatal Sepsis Risk Prediction (T. Newman); Post-Operative Debrief (S. Imershein). Caring Wisely Executive Steering Committee (J. Adler, S. Antrum, A Auerbach, J. Bennan, M. Blum, C. Ritchie, C. Tsourounis). This Center for Healthcare Value is funded in part by a grant from the Grove Foundation. We appreciate additional review and comments to the manuscript provided by George Sawaya and Adams Dudley.

Disclosures 

Christopher Moriates has accepted royalties from McGraw-Hill for textbook, Understanding Value-Based Healthcare. Alvin Rajkomar has received fees as a research adviser from Google, Inc.

© 2017 Society of Hospital Medicine

The “Things We Do for No Reason” series reviews practices which have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Ammonia is predominantly generated in the gut by intestinal bacteria and enzymes and detoxified primarily in the liver. Since the 1930s, ammonia has been identified as the principal culprit in hepatic encephalopathy (HE). Many physicians utilize serum ammonia to diagnose, assess severity, and determine the resolution of HE in patients with chronic liver disease (CLD) despite research showing that ammonia levels are unhelpful in all of these clinical circumstances. HE in patients with CLD is a clinical diagnosis of exclusion that should not be based on ammonia levels.

A 62-year-old man diagnosed with cirrhosis due to Hepatitis C and alcoholism was brought to the emergency department for alteration in mentation. He had scant melenic stools 5 days preceding his admission and did not exhibit overt signs or symptoms of infection. His systemic examination was normal except for somnolence, disorientation to space and time, asterixis, and ascites. His lab parameters were within normal limits except for an elevated blood urea nitrogen and thrombocytopenia. His blood cultures did not grow any organisms, and paracentesis ruled out spontaneous bacterial peritonitis. During his hospital stay, he underwent esophageal variceal banding and was effectively managed with lactulose and rifaximin. The patient was alert, fully oriented, and without asterixis at the time of discharge 6 days later. Would an elevated venous ammonia level at admission alter management? If the ammonia level was elevated, would serial ammonia measurements affect management?

The colonic microbiome produces ammonia from dietary nitrogen. In health, approximately 85% of it is detoxified by the liver and excreted as urea in urine, while muscle and brain tissue metabolize the remaining 15%. The process of transamination and the urea cycle prevents this metabolic product from accumulating in the body. The elevated levels of nitrogenous toxins, including ammonia, in the systemic circulation of patients with CLD occur due to hepatocellular dysfunction and/or portosystemic shunting. This hyperammonemia is compounded by reduced peripheral metabolism of ammonia by muscle as a consequence of cachexia and muscle atrophy. Astrocytes synthesize glutamine excessively in the setting of hyperammonemia, resulting in astrocyte swelling and the generation of reactive oxygen species. Astrocyte swelling, free radical generation, and increased inhibitory function of gamma-Aminobutyric Acid result in cerebral dysfunction.^1,2 HE manifests as a broad spectrum of neurological or psychiatric abnormalities ranging from subclinical alterations to coma and was commonly graded on the West Haven Criteria (WHC) of 0 to 4 (Table).³ The Grade 0 from the previous WHC, referenced in many trials included in this article, has been replaced with minimal HE in the newly updated WHC by the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver.^4,5

The ammonia hypothesis posits that ammonia is key in the pathogenesis of HE.^6-10 Some of the common precipitants of HE—gastrointestinal bleeding, infection, and renal failure—promote hyperammonemia.¹¹ HE is treated with nonabsorbable disaccharides (lactulose and lactitol) and rifaximin, which reduce the serum concentration of ammonia. Given these associations between HE and ammonia, physicians have for decades tested serum ammonia levels to diagnose HE and chart its resolution. In a study conducted by the Bavarian Society of Gastroenterology,¹² 60% of the respondents to an anonymous questionnaire regularly performed ammonia analysis in all their patients with liver cirrhosis, believing that it efficiently diagnosed HE.

Accuracy of Serum Ammonia

Multiple factors affect the accuracy of ammonia levels. First, fist clenching or the use of a tourniquet during the process of phlebotomy can falsely increase ammonia levels.¹³ Second, some authors have argued that the source of the ammonia sample matters. Kramer et al.¹⁴ reported that partial pressure of ammonia correlated closely with the degree of clinical and electrophysiological abnormalities of HE. However, Nicolao et al.¹⁵ and Ong et al.¹⁶ showed that the blood ammonia levels, whether measured by total venous, total arterial, or partial pressure methods, were equivalent. Third, ammonia levels are dependent on the time to processing of the specimen. Inaccurate results may occur if the blood sample is not immediately placed on ice after collection or if it is not centrifuged within 15 minutes of collection.^17,18

Ammonia Levels and Diagnosis of HE

Even with proper collection and processing, ammonia levels in patients with CLD do not reliably diagnose HE. Gundling et al.¹⁹ determined the sensitivity and specificity of venous ammonia levels ≥ 55 µmol/L to diagnose HE to be 47.2% and 78.3%, respectively, by using a gold standard of the WHC and the critical flicker frequency test (a psychophysiologic test). The positive predictive and negative predictive values of ammonia were 77.3% and 48.6%, with an overall diagnostic accuracy of 59.3%. Approximately 60% of the patients with Grade 3 WHC HE had a normal ammonia level in this study. Ong et al¹⁶ found that only 31% of patients with CLD and no evidence of HE had a normal ammonia level.In other words, CLD patients with normal ammonia levels can have HE, and patients with elevated ammonia levels may have normal cognitive functioning.

Furthermore, ammonia levels are not a valid tool to diagnose HE even with an oral glutamine challenge.²⁰ Most importantly, HE is a clinical diagnosis reached following the exclusion of other likely causes of cerebral dysfunction, independent of the ammonia level.

Ammonia Levels and Staging HE

The grading of HE was introduced to assess the response to an intervention in patients with HE enrolled in clinical trials.²¹ Tools like the WHC (Table) categorize the severity of HE. Nicolao et al.¹⁵ noted significant overlap in the levels of ammonia between patients with HE Grades 1 and 2 when compared with patients with Grades 3 and 4. This considerable overlap in levels of ammonia was more evident among patients with Grades 0 to 2 per Ong’s study.¹⁶ Most importantly, hospitalists do not need ammonia levels to determine that a patient has HE Grade 3 or HE Grade 4 symptoms, as the stage is graded on clinical grounds only. Once other causes for cerebral dysfunction have been ruled out, the ammonia level does not add to the clinical picture.

Serial Ammonia Levels and Resolution of HE

If the ammonia hypothesis is the sole explanation for the pathogenesis of HE, then the resolution of HE symptoms should be associated with normalization of ammonia levels. Physicians have commonly followed ammonia levels serially throughout a hospital stay. Nicolao et al.¹⁵ evaluated the association of ammonia with HE. They noted that some of the CLD patients had unchanged or increasing levels of ammonia despite overt neurological improvement from their HE.¹⁵ Some have argued that the normalization of ammonia levels lag behind the clinical improvement by 48 hours after resolution of symptoms. In the Nicolao et al.¹⁵ study, ammonia levels for almost all of the patients did not normalize 48 hours after resolution of neurologic symptoms. Moreover, 29% of the patients were noted to have higher venous ammonia levels 48 hours after the resolution of neurologic symptoms.¹⁵ These data underscore why serial measurements of ammonia in patients with CLD are not useful. For patients with overt symptoms, clinicians can determine improvement based on serial exams.

• HE is a diagnosis of exclusion and is made on clinical grounds.
• Do not check serum ammonia levels in patients with CLD to diagnose HE, to assess the severity of HE, or to determine whether HE is resolving.
• Use your clinical evaluation to determine the severity and course of HE.
• Treatment should be tailored according to clinical findings, not ammonia levels.

The attraction of the ammonia theory to explain HE continues to lead physicians to check and follow blood ammonia levels in patients with CLD and suspected HE. However, ammonia measurement, as in the clinical vignette, should be replaced by a thorough clinical evaluation to rule out other causes for altered mental status. Serial exams of the patient should guide management, not ammonia levels.

Disclosure 

The authors report no conflicts of interest.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook, and don’t forget to “Like It” on Facebook or retweet it on Twitter.

© 2017 Society of Hospital Medicine

The “Things We Do for No Reason” series reviews practices which have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Ammonia is predominantly generated in the gut by intestinal bacteria and enzymes and detoxified primarily in the liver. Since the 1930s, ammonia has been identified as the principal culprit in hepatic encephalopathy (HE). Many physicians utilize serum ammonia to diagnose, assess severity, and determine the resolution of HE in patients with chronic liver disease (CLD) despite research showing that ammonia levels are unhelpful in all of these clinical circumstances. HE in patients with CLD is a clinical diagnosis of exclusion that should not be based on ammonia levels.

A 62-year-old man diagnosed with cirrhosis due to Hepatitis C and alcoholism was brought to the emergency department for alteration in mentation. He had scant melenic stools 5 days preceding his admission and did not exhibit overt signs or symptoms of infection. His systemic examination was normal except for somnolence, disorientation to space and time, asterixis, and ascites. His lab parameters were within normal limits except for an elevated blood urea nitrogen and thrombocytopenia. His blood cultures did not grow any organisms, and paracentesis ruled out spontaneous bacterial peritonitis. During his hospital stay, he underwent esophageal variceal banding and was effectively managed with lactulose and rifaximin. The patient was alert, fully oriented, and without asterixis at the time of discharge 6 days later. Would an elevated venous ammonia level at admission alter management? If the ammonia level was elevated, would serial ammonia measurements affect management?

The colonic microbiome produces ammonia from dietary nitrogen. In health, approximately 85% of it is detoxified by the liver and excreted as urea in urine, while muscle and brain tissue metabolize the remaining 15%. The process of transamination and the urea cycle prevents this metabolic product from accumulating in the body. The elevated levels of nitrogenous toxins, including ammonia, in the systemic circulation of patients with CLD occur due to hepatocellular dysfunction and/or portosystemic shunting. This hyperammonemia is compounded by reduced peripheral metabolism of ammonia by muscle as a consequence of cachexia and muscle atrophy. Astrocytes synthesize glutamine excessively in the setting of hyperammonemia, resulting in astrocyte swelling and the generation of reactive oxygen species. Astrocyte swelling, free radical generation, and increased inhibitory function of gamma-Aminobutyric Acid result in cerebral dysfunction.^1,2 HE manifests as a broad spectrum of neurological or psychiatric abnormalities ranging from subclinical alterations to coma and was commonly graded on the West Haven Criteria (WHC) of 0 to 4 (Table).³ The Grade 0 from the previous WHC, referenced in many trials included in this article, has been replaced with minimal HE in the newly updated WHC by the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver.^4,5

The ammonia hypothesis posits that ammonia is key in the pathogenesis of HE.^6-10 Some of the common precipitants of HE—gastrointestinal bleeding, infection, and renal failure—promote hyperammonemia.¹¹ HE is treated with nonabsorbable disaccharides (lactulose and lactitol) and rifaximin, which reduce the serum concentration of ammonia. Given these associations between HE and ammonia, physicians have for decades tested serum ammonia levels to diagnose HE and chart its resolution. In a study conducted by the Bavarian Society of Gastroenterology,¹² 60% of the respondents to an anonymous questionnaire regularly performed ammonia analysis in all their patients with liver cirrhosis, believing that it efficiently diagnosed HE.

Accuracy of Serum Ammonia

Multiple factors affect the accuracy of ammonia levels. First, fist clenching or the use of a tourniquet during the process of phlebotomy can falsely increase ammonia levels.¹³ Second, some authors have argued that the source of the ammonia sample matters. Kramer et al.¹⁴ reported that partial pressure of ammonia correlated closely with the degree of clinical and electrophysiological abnormalities of HE. However, Nicolao et al.¹⁵ and Ong et al.¹⁶ showed that the blood ammonia levels, whether measured by total venous, total arterial, or partial pressure methods, were equivalent. Third, ammonia levels are dependent on the time to processing of the specimen. Inaccurate results may occur if the blood sample is not immediately placed on ice after collection or if it is not centrifuged within 15 minutes of collection.^17,18

Ammonia Levels and Diagnosis of HE

Even with proper collection and processing, ammonia levels in patients with CLD do not reliably diagnose HE. Gundling et al.¹⁹ determined the sensitivity and specificity of venous ammonia levels ≥ 55 µmol/L to diagnose HE to be 47.2% and 78.3%, respectively, by using a gold standard of the WHC and the critical flicker frequency test (a psychophysiologic test). The positive predictive and negative predictive values of ammonia were 77.3% and 48.6%, with an overall diagnostic accuracy of 59.3%. Approximately 60% of the patients with Grade 3 WHC HE had a normal ammonia level in this study. Ong et al¹⁶ found that only 31% of patients with CLD and no evidence of HE had a normal ammonia level.In other words, CLD patients with normal ammonia levels can have HE, and patients with elevated ammonia levels may have normal cognitive functioning.

Furthermore, ammonia levels are not a valid tool to diagnose HE even with an oral glutamine challenge.²⁰ Most importantly, HE is a clinical diagnosis reached following the exclusion of other likely causes of cerebral dysfunction, independent of the ammonia level.

Ammonia Levels and Staging HE

The grading of HE was introduced to assess the response to an intervention in patients with HE enrolled in clinical trials.²¹ Tools like the WHC (Table) categorize the severity of HE. Nicolao et al.¹⁵ noted significant overlap in the levels of ammonia between patients with HE Grades 1 and 2 when compared with patients with Grades 3 and 4. This considerable overlap in levels of ammonia was more evident among patients with Grades 0 to 2 per Ong’s study.¹⁶ Most importantly, hospitalists do not need ammonia levels to determine that a patient has HE Grade 3 or HE Grade 4 symptoms, as the stage is graded on clinical grounds only. Once other causes for cerebral dysfunction have been ruled out, the ammonia level does not add to the clinical picture.

Serial Ammonia Levels and Resolution of HE

If the ammonia hypothesis is the sole explanation for the pathogenesis of HE, then the resolution of HE symptoms should be associated with normalization of ammonia levels. Physicians have commonly followed ammonia levels serially throughout a hospital stay. Nicolao et al.¹⁵ evaluated the association of ammonia with HE. They noted that some of the CLD patients had unchanged or increasing levels of ammonia despite overt neurological improvement from their HE.¹⁵ Some have argued that the normalization of ammonia levels lag behind the clinical improvement by 48 hours after resolution of symptoms. In the Nicolao et al.¹⁵ study, ammonia levels for almost all of the patients did not normalize 48 hours after resolution of neurologic symptoms. Moreover, 29% of the patients were noted to have higher venous ammonia levels 48 hours after the resolution of neurologic symptoms.¹⁵ These data underscore why serial measurements of ammonia in patients with CLD are not useful. For patients with overt symptoms, clinicians can determine improvement based on serial exams.

• HE is a diagnosis of exclusion and is made on clinical grounds.
• Do not check serum ammonia levels in patients with CLD to diagnose HE, to assess the severity of HE, or to determine whether HE is resolving.
• Use your clinical evaluation to determine the severity and course of HE.
• Treatment should be tailored according to clinical findings, not ammonia levels.

The attraction of the ammonia theory to explain HE continues to lead physicians to check and follow blood ammonia levels in patients with CLD and suspected HE. However, ammonia measurement, as in the clinical vignette, should be replaced by a thorough clinical evaluation to rule out other causes for altered mental status. Serial exams of the patient should guide management, not ammonia levels.

Disclosure 

The authors report no conflicts of interest.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook, and don’t forget to “Like It” on Facebook or retweet it on Twitter.

© 2017 Society of Hospital Medicine

The “Things We Do for No Reason” series reviews practices which have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Ammonia is predominantly generated in the gut by intestinal bacteria and enzymes and detoxified primarily in the liver. Since the 1930s, ammonia has been identified as the principal culprit in hepatic encephalopathy (HE). Many physicians utilize serum ammonia to diagnose, assess severity, and determine the resolution of HE in patients with chronic liver disease (CLD) despite research showing that ammonia levels are unhelpful in all of these clinical circumstances. HE in patients with CLD is a clinical diagnosis of exclusion that should not be based on ammonia levels.

A 62-year-old man diagnosed with cirrhosis due to Hepatitis C and alcoholism was brought to the emergency department for alteration in mentation. He had scant melenic stools 5 days preceding his admission and did not exhibit overt signs or symptoms of infection. His systemic examination was normal except for somnolence, disorientation to space and time, asterixis, and ascites. His lab parameters were within normal limits except for an elevated blood urea nitrogen and thrombocytopenia. His blood cultures did not grow any organisms, and paracentesis ruled out spontaneous bacterial peritonitis. During his hospital stay, he underwent esophageal variceal banding and was effectively managed with lactulose and rifaximin. The patient was alert, fully oriented, and without asterixis at the time of discharge 6 days later. Would an elevated venous ammonia level at admission alter management? If the ammonia level was elevated, would serial ammonia measurements affect management?

The colonic microbiome produces ammonia from dietary nitrogen. In health, approximately 85% of it is detoxified by the liver and excreted as urea in urine, while muscle and brain tissue metabolize the remaining 15%. The process of transamination and the urea cycle prevents this metabolic product from accumulating in the body. The elevated levels of nitrogenous toxins, including ammonia, in the systemic circulation of patients with CLD occur due to hepatocellular dysfunction and/or portosystemic shunting. This hyperammonemia is compounded by reduced peripheral metabolism of ammonia by muscle as a consequence of cachexia and muscle atrophy. Astrocytes synthesize glutamine excessively in the setting of hyperammonemia, resulting in astrocyte swelling and the generation of reactive oxygen species. Astrocyte swelling, free radical generation, and increased inhibitory function of gamma-Aminobutyric Acid result in cerebral dysfunction.^1,2 HE manifests as a broad spectrum of neurological or psychiatric abnormalities ranging from subclinical alterations to coma and was commonly graded on the West Haven Criteria (WHC) of 0 to 4 (Table).³ The Grade 0 from the previous WHC, referenced in many trials included in this article, has been replaced with minimal HE in the newly updated WHC by the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver.^4,5

The ammonia hypothesis posits that ammonia is key in the pathogenesis of HE.^6-10 Some of the common precipitants of HE—gastrointestinal bleeding, infection, and renal failure—promote hyperammonemia.¹¹ HE is treated with nonabsorbable disaccharides (lactulose and lactitol) and rifaximin, which reduce the serum concentration of ammonia. Given these associations between HE and ammonia, physicians have for decades tested serum ammonia levels to diagnose HE and chart its resolution. In a study conducted by the Bavarian Society of Gastroenterology,¹² 60% of the respondents to an anonymous questionnaire regularly performed ammonia analysis in all their patients with liver cirrhosis, believing that it efficiently diagnosed HE.

Accuracy of Serum Ammonia

Multiple factors affect the accuracy of ammonia levels. First, fist clenching or the use of a tourniquet during the process of phlebotomy can falsely increase ammonia levels.¹³ Second, some authors have argued that the source of the ammonia sample matters. Kramer et al.¹⁴ reported that partial pressure of ammonia correlated closely with the degree of clinical and electrophysiological abnormalities of HE. However, Nicolao et al.¹⁵ and Ong et al.¹⁶ showed that the blood ammonia levels, whether measured by total venous, total arterial, or partial pressure methods, were equivalent. Third, ammonia levels are dependent on the time to processing of the specimen. Inaccurate results may occur if the blood sample is not immediately placed on ice after collection or if it is not centrifuged within 15 minutes of collection.^17,18

Ammonia Levels and Diagnosis of HE

Even with proper collection and processing, ammonia levels in patients with CLD do not reliably diagnose HE. Gundling et al.¹⁹ determined the sensitivity and specificity of venous ammonia levels ≥ 55 µmol/L to diagnose HE to be 47.2% and 78.3%, respectively, by using a gold standard of the WHC and the critical flicker frequency test (a psychophysiologic test). The positive predictive and negative predictive values of ammonia were 77.3% and 48.6%, with an overall diagnostic accuracy of 59.3%. Approximately 60% of the patients with Grade 3 WHC HE had a normal ammonia level in this study. Ong et al¹⁶ found that only 31% of patients with CLD and no evidence of HE had a normal ammonia level.In other words, CLD patients with normal ammonia levels can have HE, and patients with elevated ammonia levels may have normal cognitive functioning.

Furthermore, ammonia levels are not a valid tool to diagnose HE even with an oral glutamine challenge.²⁰ Most importantly, HE is a clinical diagnosis reached following the exclusion of other likely causes of cerebral dysfunction, independent of the ammonia level.

Ammonia Levels and Staging HE

The grading of HE was introduced to assess the response to an intervention in patients with HE enrolled in clinical trials.²¹ Tools like the WHC (Table) categorize the severity of HE. Nicolao et al.¹⁵ noted significant overlap in the levels of ammonia between patients with HE Grades 1 and 2 when compared with patients with Grades 3 and 4. This considerable overlap in levels of ammonia was more evident among patients with Grades 0 to 2 per Ong’s study.¹⁶ Most importantly, hospitalists do not need ammonia levels to determine that a patient has HE Grade 3 or HE Grade 4 symptoms, as the stage is graded on clinical grounds only. Once other causes for cerebral dysfunction have been ruled out, the ammonia level does not add to the clinical picture.

Serial Ammonia Levels and Resolution of HE

If the ammonia hypothesis is the sole explanation for the pathogenesis of HE, then the resolution of HE symptoms should be associated with normalization of ammonia levels. Physicians have commonly followed ammonia levels serially throughout a hospital stay. Nicolao et al.¹⁵ evaluated the association of ammonia with HE. They noted that some of the CLD patients had unchanged or increasing levels of ammonia despite overt neurological improvement from their HE.¹⁵ Some have argued that the normalization of ammonia levels lag behind the clinical improvement by 48 hours after resolution of symptoms. In the Nicolao et al.¹⁵ study, ammonia levels for almost all of the patients did not normalize 48 hours after resolution of neurologic symptoms. Moreover, 29% of the patients were noted to have higher venous ammonia levels 48 hours after the resolution of neurologic symptoms.¹⁵ These data underscore why serial measurements of ammonia in patients with CLD are not useful. For patients with overt symptoms, clinicians can determine improvement based on serial exams.

• HE is a diagnosis of exclusion and is made on clinical grounds.
• Do not check serum ammonia levels in patients with CLD to diagnose HE, to assess the severity of HE, or to determine whether HE is resolving.
• Use your clinical evaluation to determine the severity and course of HE.
• Treatment should be tailored according to clinical findings, not ammonia levels.

The attraction of the ammonia theory to explain HE continues to lead physicians to check and follow blood ammonia levels in patients with CLD and suspected HE. However, ammonia measurement, as in the clinical vignette, should be replaced by a thorough clinical evaluation to rule out other causes for altered mental status. Serial exams of the patient should guide management, not ammonia levels.

Disclosure 

The authors report no conflicts of interest.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook, and don’t forget to “Like It” on Facebook or retweet it on Twitter.

BACKGROUND

Physiologic monitors are intended to prevent cardiac and respiratory arrest by generating alarms to alert clinicians to signs of instability. To minimize the probability that monitors will miss signs of deterioration, alarm algorithms and default parameters are often set to maximize sensitivity while sacrificing specificity.¹ As a result, monitors generate large numbers of nonactionable alarms—alarms that are either invalid and do not accurately represent the physiologic status of the patient or are valid but do not warrant clinical intervention.² Prior research has demonstrated that the pediatric intensive care unit (PICU) is responsible for a higher proportion of alarms than pediatric wards³ and a large proportion of these alarms, 87% - 97%, are nonactionable.^4-8 In national surveys of healthcare staff, respondents report that high alarm rates interrupt patient care and can lead clinicians to disable alarms entirely.⁹ Recent research has supported this, demonstrating that nurses who are exposed to higher numbers of alarms have slower response times to alarms.^4,10 In an attempt to mitigate safety risks, the Joint Commission in 2012 issued recommendations for hospitals to (a) establish guidelines for tailoring alarm settings and limits for individual patients and (b) identify situations in which alarms are not clinically necessary.¹¹

In order to address these recommendations within our PICU, we sought to evaluate the impact of a focused physiologic monitor alarm reduction intervention integrated into safety huddles. Safety huddles are brief, structured discussions among physicians, nurses, and other staff aiming to identify safety concerns.¹² Huddles offer an appropriate forum for reviewing alarm data and identifying patients whose high alarm rates may necessitate safe tailoring of alarm limits. Pilot data demonstrating high alarm rates among low-acuity PICU patients led us to hypothesize that low-acuity, high-alarm PICU patients would be a safe and effective target for an alarm huddle-based intervention.

In this study, we aimed to measure the impact of a structured safety huddle review of low-acuity PICU patients with high rates of priority alarms who were randomized to intervention compared with other low-acuity, high-alarm, concurrent, and historical control patients in the PICU.

Study Definitions

Priority alarm activation rate. We conceptualized priority alarms as any alarm for a clinical condition that requires a timely response to determine if intervention is necessary to save a patient’s life,⁴ yet little empirical data support its existence in the hospital. We operationally defined these alarms on the General Electric Solar physiologic monitoring devices as any potentially life-threatening events including lethal arrhythmias (asystole, ventricular tachycardia, and ventricular fibrillation) and alarms for vital signs (heart rate, respiratory rate, and oxygen saturation) outside of the set parameter limits. These alarms produced audible tones in the patient room and automatically sent text messages to the nurse’s phone and had the potential to contribute to alarm fatigue regardless of the nurse’s location.

High-alarm patients. High-alarm patients were those who had more than 40 priority alarms in the preceding 4 hours, representing the top 20% of alarm rates in the PICU according to prior quality improvement projects completed in our PICU.

Low-acuity patients. Prior to and during this study, patient acuity was determined using the OptiLink Patient Classification System (OptiLink Healthcare Management Systems, Inc.; Tigard, OR; www.optilinkhealthcare.com; see Appendix 1) for the PICU twice daily. Low-acuity patients comprised on average 16% of the PICU patients.

Setting and Subjects

This study was performed in the PICU at The Children’s Hospital of Philadelphia.

The PICU is made up of 3 separate wings: east, south, and west. Bed availability was the only factor determining patient placement on the east, south, or west wing; the physical bed location was not preferentially assigned based on diagnosis or disease severity. The east wing was the intervention unit where the huddles occurred.

The PICU is composed of 3 different geographical teams. Two of the teams are composed of 4 to 5 pediatric or emergency medicine residents, 1 fellow, and 1 attending covering the south and west wings. The third team, located on the east wing, is composed of 1 to 2 pediatric residents, 2 to 3 nurse practitioners, 1 fellow, and 1 attending. Bedside family-centered rounds are held at each patient room, with the bedside nurse participating by reading a nursing rounding script that includes vital signs, vascular access, continuous medications, and additional questions or concerns.

Control subjects were any monitored patients on any of the 3 wings of the PICU between April 1, 2015, and October 31, 2015. The control patients were in 2 categories: historical controls from April 1, 2015, to May 31, 2015, and concurrent controls from June 1, 2015, to October 31, 2015, who were located anywhere in the PICU. On each nonholiday weekday beginning June 1, 2015, we randomly selected up to 2 patients to receive the intervention. These were high-alarm, low-acuity patients on the east wing to be discussed in the daily morning huddle. If more than 2 high-alarm, low-acuity patients were eligible for intervention, they were randomly selected by using the RAND function in Microsoft Excel. The other low-acuity, high-alarm patients in the PICU were included as control patients. Patients were eligible for the study if they were present for the 4 hours prior to huddle and present past noon on the day of huddle. If patients met criteria as high-alarm, low-acuity patients on multiple days, they could be enrolled as intervention or control patients multiple times. Patients’ alarm rates were calculated by dividing the number of alarms by their length of stay to the minute. There was no adjustment made for patients enrolled more than once.

Human Subjects Protection

The Institutional Review Board of The Children’s Hospital of Philadelphia approved this study with a waiver of informed consent.

Alarm Capture

We used BedMasterEx (Excel Medical Electronics; Jupiter, FL, http://excel-medical.com/products/bedmaster-ex) software connected to the General Electric monitor network to measure alarm rates. The software captured, in near real time, every alarm that occurred on every monitor in the PICU. Alarm rates over the preceding 4 hours for all PICU patients were exported and summarized by alarm type and level as set by hospital policy (crisis, warning, advisory, and system warning). Crisis and warning alarms were included as they represented potential life-threatening events meeting the definition of priority alarms. Physicians used an order within the PICU admission order-set to order monitoring based on preset age parameters (see online Appendix 1 for default settings). Physician orders were required for nurses to change alarm parameters. Daily electrode changes to reduce false alarms were standard of care.

Primary Outcome

The primary outcome was the change in priority alarm activation rate (the number of priority alarms per day) from prehuddle period (24 hours before morning huddle) to posthuddle period (the 24 hours following morning huddle) for intervention cases as compared with controls.

Primary Intervention

The intervention consisted of integrating a short script to facilitate the discussion of the alarm data during existing safety huddle and rounding workflows. The discussion and subsequent workflow proceeded as follows: A member of the research team who was not involved in patient care brought an alarm data sheet for each randomly selected intervention patient on the east wing to each safety huddle. The huddles were attended by the outgoing night charge nurse, the day charge nurse, and all bedside nurses working on the east wing that day. The alarm data sheet provided to the charge nurse displayed data on the 1 to 2 alarm parameters (respiratory rate, heart rate, or pulse oximetry) that generated the highest number of alarms. The charge nurse listed the high-alarm patients by room number during huddle, and the alarm data sheet was given to the bedside nurse responsible for the patient to facilitate further scripted discussion during bedside rounds with patient-specific information to reduce the alarm rates of individual patients throughout the adjustment of physiologic monitor parameters (see Appendix 2 for sample data sheet and script).

Data Collection

Intervention patients were high-alarm, low-acuity patients on the east wing from June 1, 2015, through October 31, 2015. Two months of baseline data were gathered prior to intervention on all 3 wings; therefore, control patients were high-alarm, low-acuity patients throughout the PICU from April 1, 2015, to May 31, 2015, as historical controls and from June 1, 2015, to October 31, 2015, as concurrent controls. Alarm rates for the 24 hours prior to huddle and the 24 hours following huddle were collected and analyzed. See Figure 1 for schematic of study design.

We collected data on patient characteristics, including patient location, age, sex, and intervention date. Information regarding changes to monitor alarm parameters for both intervention and control patients during the posthuddle period (the period following morning huddle until noon on intervention day) was also collected. We monitored for code blue events and unexpected changes in acuity until discharge or transfer out of the PICU.

Data Analysis

We compared the priority alarm activation rates of individual patients in the 24 hours before and the 24 hours after the huddle intervention and contrasted the differences in rates between intervention and control patients, both concurrent and historical controls. We also divided the intervention and control groups into 2 additional groups each—those patients whose alarm parameters were changed, compared with those whose parameters did not change. We evaluated for possible contamination by comparing alarm rates of historical and concurrent controls, as well as evaluating alarm rates by location. We used mixed-effects regression models to evaluate the effect of the intervention and control type (historical or concurrent) on alarm rates, adjusted for patient age and sex. Analysis was performed using Stata version 10.3 (StataCorp, LLC, College Station, TX) and SAS version 9.4 (SAS Institute Inc., Cary, NC).

Because patients could be enrolled more than once, we refer to the instances when they were included in the study as “events” (huddle discussions for intervention patients and huddle opportunities for controls) below. We identified 49 historical control events between April 1, 2015, and May 31, 2015. During the intervention period, we identified 88 intervention events and 163 concurrent control events between June 1, 2015, and October 31, 2015 (total n = 300; see Table 1 for event characteristics). A total of 6 patients were enrolled more than once as either intervention or control patients.

The average priority alarm activation rate for intervention patients was 433 alarms (95% confidence interval [CI], 392-472) per day in the 24 hours leading up to the intervention and 223 alarms (95% CI, 182-265) per day in the 24 hours following the intervention, a 48.5% unadjusted decrease (95% CI, 38.1%-58.9%). In contrast, priority alarm activation rates for concurrent control patients averaged 412 alarms (95% CI, 383-442) per day in the 24 hours leading up to the morning huddle and 323 alarms (95% CI, 270-375) per day in the 24 hours following huddle, a 21.6% unadjusted decrease (95% CI, 15.3%-27.9%). For historical controls, priority alarm activation rates averaged 369 alarms (95% CI, 339-399) per day in the 24 hours leading up to the morning huddle and 242 alarms (95% CI, 164-320) per day in the 24 hours following huddle, a 34.4% unadjusted decrease (95% CI, 13.5%-55.0%). When we compared historical versus concurrent controls in the unadjusted analysis, concurrent controls had 37 more alarms per day (95% CI, 59 fewer to 134 more; P = 0.45) than historical controls. There was no significant difference between concurrent and historical controls, demonstrating no evidence of contamination.

Adjusted Analysis of Changes in Alarm Rates

The overall estimate of the effect of the intervention adjusted for age and sex compared with concurrent controls was a reduction of 116 priority alarms per day (95% CI, 37-194; P = 0.004, Table 2). The adjusted percent decrease was 29.0% (95% CI, 12.1%-46.0%). There were no unexpected changes in patient acuity or code blue events related to the intervention.

Fidelity Analysis

We tracked changes in alarm parameter settings for evidence of intervention fidelity to determine if the team carried out the recommendations made. We found that 42% of intervention patients and 24% of combined control patients had alarm parameters changed during the posthuddle period (P = 0.002).

For those intervention patients who had parameters changed during the posthuddle period (N = 37), the mean effect was greater at a 54.9% decrease (95% CI, 38.8%-70.8%) in priority alarms as compared with control patients who had parameters adjusted during the posthuddle period (n = 50), having a mean decrease of only 12.2% (95% CI, –18.1%-42.3%). There was a 43.2% decrease (95% CI, 29.3%-57.0%) for intervention patients who were discussed but did not have parameters adjusted during the time window of observation (n = 51), as compared with combined control patients who did not have parameters adjusted (N = 162) who had a 28.1% decrease (95% CI, 16.8%-39.1%); see Figure 2.

This study is the first to demonstrate a successful and safe intervention to reduce the alarm rates of PICU patients. In addition, we observed a more significant reduction in priority alarm activation rates for intervention patients who had their alarm parameters changed during the monitored time period, leading us to hypothesize that providing patient-specific data regarding types of alarms was a key component of the intervention.

In control patients, we observed a reduction in alarm rates over time as well. There are 2 potential explanations for this. First, it is possible that as patients stabilize in the PICU, their vital signs become less extreme and generate fewer alarms even if the alarm parameters are not changed. The second is that parameters were changed within or outside of the time windows during which we evaluated for alarm parameter changes. Nevertheless, the decline over time observed in the intervention patients was greater than in both control groups. This change was even more noticeable in the intervention patients who had their alarm parameters changed during the posthuddle period as compared with controls who had their alarm parameters changed following the posthuddle period. This may have been due to the data provided during the huddle intervention, pointing the team to the cause of the high alarm rate.

Prior successful research regarding reduction of pediatric alarms has often shown decreased use of physiological monitors as 1 approach to reducing unnecessary alarms. The single prior pediatric alarm intervention study conducted on a pediatric ward involved instituting a cardiac monitor care process that included the ordering of age-based parameters, daily replacement of electrodes, individualized assessment of parameters, and a reliable method to discontinue monitoring.¹³ Because most patients in the PICU are critically ill, the reliance on monitor discontinuation as a main approach to decreasing alarms is not feasible in this setting. Instead, the use of targeted alarm parameter adjustments for low-acuity patients demonstrated a safe and feasible approach to decreasing alarms in PICU patients. The daily electrode change and age-based parameters were already in place at our institution.

There are a few limitations to this study. First, we focused only on low-acuity PICU patients. We believe that focusing on low-acuity patients allows for reduction in nonactionable alarms with limited potential for adverse events; however, this approach excludes many critically ill patients who might be at highest risk for harm from alarm fatigue if important alarms are ignored. Second, many of our patients were not present for the full 24 hours pre- and posthuddle due to their low acuity limiting our ability to follow alarm rates over time. Third, changes in alarm parameters were only monitored for a set period of 5 hours following the huddle to determine the effect of the recommended rounding script on changes to alarms. It is possible the changes to alarm parameters outside of the observed posthuddle period affected the alarm rates of both intervention and control patients. Lastly, the balancing metrics of unexpected changes in OptiLink status and code blue events are rare events, and therefore we may have been underpowered to find them. The effects of the huddle intervention on safety huddle length and rounding length were not measured.

Integrating a data-driven monitor alarm discussion into safety huddles was a safe and effective approach to reduce alarms in low-acuity, high-alarm PICU patients. Innovative approaches to make data-driven alarm decisions using informatics tools integrated into monitoring systems and electronic health records have the potential to facilitate cost-effective spread of this intervention.

Disclosure

This work was supported by a pilot grant from the Center for Pediatric Clinical Effectiveness, The Children’s Hospital of Philadelphia. Dr. Bonafide is supported by a Mentored Patient-Oriented Research Career Development Award from the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K23HL116427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations or employers. The funding organizations had no role in the design, preparation, review, or approval of this paper, nor the decision to submit for publication.

BACKGROUND

Physiologic monitors are intended to prevent cardiac and respiratory arrest by generating alarms to alert clinicians to signs of instability. To minimize the probability that monitors will miss signs of deterioration, alarm algorithms and default parameters are often set to maximize sensitivity while sacrificing specificity.¹ As a result, monitors generate large numbers of nonactionable alarms—alarms that are either invalid and do not accurately represent the physiologic status of the patient or are valid but do not warrant clinical intervention.² Prior research has demonstrated that the pediatric intensive care unit (PICU) is responsible for a higher proportion of alarms than pediatric wards³ and a large proportion of these alarms, 87% - 97%, are nonactionable.^4-8 In national surveys of healthcare staff, respondents report that high alarm rates interrupt patient care and can lead clinicians to disable alarms entirely.⁹ Recent research has supported this, demonstrating that nurses who are exposed to higher numbers of alarms have slower response times to alarms.^4,10 In an attempt to mitigate safety risks, the Joint Commission in 2012 issued recommendations for hospitals to (a) establish guidelines for tailoring alarm settings and limits for individual patients and (b) identify situations in which alarms are not clinically necessary.¹¹

In order to address these recommendations within our PICU, we sought to evaluate the impact of a focused physiologic monitor alarm reduction intervention integrated into safety huddles. Safety huddles are brief, structured discussions among physicians, nurses, and other staff aiming to identify safety concerns.¹² Huddles offer an appropriate forum for reviewing alarm data and identifying patients whose high alarm rates may necessitate safe tailoring of alarm limits. Pilot data demonstrating high alarm rates among low-acuity PICU patients led us to hypothesize that low-acuity, high-alarm PICU patients would be a safe and effective target for an alarm huddle-based intervention.

In this study, we aimed to measure the impact of a structured safety huddle review of low-acuity PICU patients with high rates of priority alarms who were randomized to intervention compared with other low-acuity, high-alarm, concurrent, and historical control patients in the PICU.

Study Definitions

Priority alarm activation rate. We conceptualized priority alarms as any alarm for a clinical condition that requires a timely response to determine if intervention is necessary to save a patient’s life,⁴ yet little empirical data support its existence in the hospital. We operationally defined these alarms on the General Electric Solar physiologic monitoring devices as any potentially life-threatening events including lethal arrhythmias (asystole, ventricular tachycardia, and ventricular fibrillation) and alarms for vital signs (heart rate, respiratory rate, and oxygen saturation) outside of the set parameter limits. These alarms produced audible tones in the patient room and automatically sent text messages to the nurse’s phone and had the potential to contribute to alarm fatigue regardless of the nurse’s location.

High-alarm patients. High-alarm patients were those who had more than 40 priority alarms in the preceding 4 hours, representing the top 20% of alarm rates in the PICU according to prior quality improvement projects completed in our PICU.

Low-acuity patients. Prior to and during this study, patient acuity was determined using the OptiLink Patient Classification System (OptiLink Healthcare Management Systems, Inc.; Tigard, OR; www.optilinkhealthcare.com; see Appendix 1) for the PICU twice daily. Low-acuity patients comprised on average 16% of the PICU patients.

Setting and Subjects

This study was performed in the PICU at The Children’s Hospital of Philadelphia.

The PICU is made up of 3 separate wings: east, south, and west. Bed availability was the only factor determining patient placement on the east, south, or west wing; the physical bed location was not preferentially assigned based on diagnosis or disease severity. The east wing was the intervention unit where the huddles occurred.

The PICU is composed of 3 different geographical teams. Two of the teams are composed of 4 to 5 pediatric or emergency medicine residents, 1 fellow, and 1 attending covering the south and west wings. The third team, located on the east wing, is composed of 1 to 2 pediatric residents, 2 to 3 nurse practitioners, 1 fellow, and 1 attending. Bedside family-centered rounds are held at each patient room, with the bedside nurse participating by reading a nursing rounding script that includes vital signs, vascular access, continuous medications, and additional questions or concerns.

Control subjects were any monitored patients on any of the 3 wings of the PICU between April 1, 2015, and October 31, 2015. The control patients were in 2 categories: historical controls from April 1, 2015, to May 31, 2015, and concurrent controls from June 1, 2015, to October 31, 2015, who were located anywhere in the PICU. On each nonholiday weekday beginning June 1, 2015, we randomly selected up to 2 patients to receive the intervention. These were high-alarm, low-acuity patients on the east wing to be discussed in the daily morning huddle. If more than 2 high-alarm, low-acuity patients were eligible for intervention, they were randomly selected by using the RAND function in Microsoft Excel. The other low-acuity, high-alarm patients in the PICU were included as control patients. Patients were eligible for the study if they were present for the 4 hours prior to huddle and present past noon on the day of huddle. If patients met criteria as high-alarm, low-acuity patients on multiple days, they could be enrolled as intervention or control patients multiple times. Patients’ alarm rates were calculated by dividing the number of alarms by their length of stay to the minute. There was no adjustment made for patients enrolled more than once.

Human Subjects Protection

The Institutional Review Board of The Children’s Hospital of Philadelphia approved this study with a waiver of informed consent.

Alarm Capture

We used BedMasterEx (Excel Medical Electronics; Jupiter, FL, http://excel-medical.com/products/bedmaster-ex) software connected to the General Electric monitor network to measure alarm rates. The software captured, in near real time, every alarm that occurred on every monitor in the PICU. Alarm rates over the preceding 4 hours for all PICU patients were exported and summarized by alarm type and level as set by hospital policy (crisis, warning, advisory, and system warning). Crisis and warning alarms were included as they represented potential life-threatening events meeting the definition of priority alarms. Physicians used an order within the PICU admission order-set to order monitoring based on preset age parameters (see online Appendix 1 for default settings). Physician orders were required for nurses to change alarm parameters. Daily electrode changes to reduce false alarms were standard of care.

Primary Outcome

The primary outcome was the change in priority alarm activation rate (the number of priority alarms per day) from prehuddle period (24 hours before morning huddle) to posthuddle period (the 24 hours following morning huddle) for intervention cases as compared with controls.

Primary Intervention

The intervention consisted of integrating a short script to facilitate the discussion of the alarm data during existing safety huddle and rounding workflows. The discussion and subsequent workflow proceeded as follows: A member of the research team who was not involved in patient care brought an alarm data sheet for each randomly selected intervention patient on the east wing to each safety huddle. The huddles were attended by the outgoing night charge nurse, the day charge nurse, and all bedside nurses working on the east wing that day. The alarm data sheet provided to the charge nurse displayed data on the 1 to 2 alarm parameters (respiratory rate, heart rate, or pulse oximetry) that generated the highest number of alarms. The charge nurse listed the high-alarm patients by room number during huddle, and the alarm data sheet was given to the bedside nurse responsible for the patient to facilitate further scripted discussion during bedside rounds with patient-specific information to reduce the alarm rates of individual patients throughout the adjustment of physiologic monitor parameters (see Appendix 2 for sample data sheet and script).

Data Collection

Intervention patients were high-alarm, low-acuity patients on the east wing from June 1, 2015, through October 31, 2015. Two months of baseline data were gathered prior to intervention on all 3 wings; therefore, control patients were high-alarm, low-acuity patients throughout the PICU from April 1, 2015, to May 31, 2015, as historical controls and from June 1, 2015, to October 31, 2015, as concurrent controls. Alarm rates for the 24 hours prior to huddle and the 24 hours following huddle were collected and analyzed. See Figure 1 for schematic of study design.

We collected data on patient characteristics, including patient location, age, sex, and intervention date. Information regarding changes to monitor alarm parameters for both intervention and control patients during the posthuddle period (the period following morning huddle until noon on intervention day) was also collected. We monitored for code blue events and unexpected changes in acuity until discharge or transfer out of the PICU.

Data Analysis

We compared the priority alarm activation rates of individual patients in the 24 hours before and the 24 hours after the huddle intervention and contrasted the differences in rates between intervention and control patients, both concurrent and historical controls. We also divided the intervention and control groups into 2 additional groups each—those patients whose alarm parameters were changed, compared with those whose parameters did not change. We evaluated for possible contamination by comparing alarm rates of historical and concurrent controls, as well as evaluating alarm rates by location. We used mixed-effects regression models to evaluate the effect of the intervention and control type (historical or concurrent) on alarm rates, adjusted for patient age and sex. Analysis was performed using Stata version 10.3 (StataCorp, LLC, College Station, TX) and SAS version 9.4 (SAS Institute Inc., Cary, NC).

Because patients could be enrolled more than once, we refer to the instances when they were included in the study as “events” (huddle discussions for intervention patients and huddle opportunities for controls) below. We identified 49 historical control events between April 1, 2015, and May 31, 2015. During the intervention period, we identified 88 intervention events and 163 concurrent control events between June 1, 2015, and October 31, 2015 (total n = 300; see Table 1 for event characteristics). A total of 6 patients were enrolled more than once as either intervention or control patients.

The average priority alarm activation rate for intervention patients was 433 alarms (95% confidence interval [CI], 392-472) per day in the 24 hours leading up to the intervention and 223 alarms (95% CI, 182-265) per day in the 24 hours following the intervention, a 48.5% unadjusted decrease (95% CI, 38.1%-58.9%). In contrast, priority alarm activation rates for concurrent control patients averaged 412 alarms (95% CI, 383-442) per day in the 24 hours leading up to the morning huddle and 323 alarms (95% CI, 270-375) per day in the 24 hours following huddle, a 21.6% unadjusted decrease (95% CI, 15.3%-27.9%). For historical controls, priority alarm activation rates averaged 369 alarms (95% CI, 339-399) per day in the 24 hours leading up to the morning huddle and 242 alarms (95% CI, 164-320) per day in the 24 hours following huddle, a 34.4% unadjusted decrease (95% CI, 13.5%-55.0%). When we compared historical versus concurrent controls in the unadjusted analysis, concurrent controls had 37 more alarms per day (95% CI, 59 fewer to 134 more; P = 0.45) than historical controls. There was no significant difference between concurrent and historical controls, demonstrating no evidence of contamination.

Adjusted Analysis of Changes in Alarm Rates

The overall estimate of the effect of the intervention adjusted for age and sex compared with concurrent controls was a reduction of 116 priority alarms per day (95% CI, 37-194; P = 0.004, Table 2). The adjusted percent decrease was 29.0% (95% CI, 12.1%-46.0%). There were no unexpected changes in patient acuity or code blue events related to the intervention.

Fidelity Analysis

We tracked changes in alarm parameter settings for evidence of intervention fidelity to determine if the team carried out the recommendations made. We found that 42% of intervention patients and 24% of combined control patients had alarm parameters changed during the posthuddle period (P = 0.002).

For those intervention patients who had parameters changed during the posthuddle period (N = 37), the mean effect was greater at a 54.9% decrease (95% CI, 38.8%-70.8%) in priority alarms as compared with control patients who had parameters adjusted during the posthuddle period (n = 50), having a mean decrease of only 12.2% (95% CI, –18.1%-42.3%). There was a 43.2% decrease (95% CI, 29.3%-57.0%) for intervention patients who were discussed but did not have parameters adjusted during the time window of observation (n = 51), as compared with combined control patients who did not have parameters adjusted (N = 162) who had a 28.1% decrease (95% CI, 16.8%-39.1%); see Figure 2.

This study is the first to demonstrate a successful and safe intervention to reduce the alarm rates of PICU patients. In addition, we observed a more significant reduction in priority alarm activation rates for intervention patients who had their alarm parameters changed during the monitored time period, leading us to hypothesize that providing patient-specific data regarding types of alarms was a key component of the intervention.

In control patients, we observed a reduction in alarm rates over time as well. There are 2 potential explanations for this. First, it is possible that as patients stabilize in the PICU, their vital signs become less extreme and generate fewer alarms even if the alarm parameters are not changed. The second is that parameters were changed within or outside of the time windows during which we evaluated for alarm parameter changes. Nevertheless, the decline over time observed in the intervention patients was greater than in both control groups. This change was even more noticeable in the intervention patients who had their alarm parameters changed during the posthuddle period as compared with controls who had their alarm parameters changed following the posthuddle period. This may have been due to the data provided during the huddle intervention, pointing the team to the cause of the high alarm rate.

Prior successful research regarding reduction of pediatric alarms has often shown decreased use of physiological monitors as 1 approach to reducing unnecessary alarms. The single prior pediatric alarm intervention study conducted on a pediatric ward involved instituting a cardiac monitor care process that included the ordering of age-based parameters, daily replacement of electrodes, individualized assessment of parameters, and a reliable method to discontinue monitoring.¹³ Because most patients in the PICU are critically ill, the reliance on monitor discontinuation as a main approach to decreasing alarms is not feasible in this setting. Instead, the use of targeted alarm parameter adjustments for low-acuity patients demonstrated a safe and feasible approach to decreasing alarms in PICU patients. The daily electrode change and age-based parameters were already in place at our institution.

There are a few limitations to this study. First, we focused only on low-acuity PICU patients. We believe that focusing on low-acuity patients allows for reduction in nonactionable alarms with limited potential for adverse events; however, this approach excludes many critically ill patients who might be at highest risk for harm from alarm fatigue if important alarms are ignored. Second, many of our patients were not present for the full 24 hours pre- and posthuddle due to their low acuity limiting our ability to follow alarm rates over time. Third, changes in alarm parameters were only monitored for a set period of 5 hours following the huddle to determine the effect of the recommended rounding script on changes to alarms. It is possible the changes to alarm parameters outside of the observed posthuddle period affected the alarm rates of both intervention and control patients. Lastly, the balancing metrics of unexpected changes in OptiLink status and code blue events are rare events, and therefore we may have been underpowered to find them. The effects of the huddle intervention on safety huddle length and rounding length were not measured.

Integrating a data-driven monitor alarm discussion into safety huddles was a safe and effective approach to reduce alarms in low-acuity, high-alarm PICU patients. Innovative approaches to make data-driven alarm decisions using informatics tools integrated into monitoring systems and electronic health records have the potential to facilitate cost-effective spread of this intervention.

Disclosure

This work was supported by a pilot grant from the Center for Pediatric Clinical Effectiveness, The Children’s Hospital of Philadelphia. Dr. Bonafide is supported by a Mentored Patient-Oriented Research Career Development Award from the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K23HL116427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations or employers. The funding organizations had no role in the design, preparation, review, or approval of this paper, nor the decision to submit for publication.

BACKGROUND

Physiologic monitors are intended to prevent cardiac and respiratory arrest by generating alarms to alert clinicians to signs of instability. To minimize the probability that monitors will miss signs of deterioration, alarm algorithms and default parameters are often set to maximize sensitivity while sacrificing specificity.¹ As a result, monitors generate large numbers of nonactionable alarms—alarms that are either invalid and do not accurately represent the physiologic status of the patient or are valid but do not warrant clinical intervention.² Prior research has demonstrated that the pediatric intensive care unit (PICU) is responsible for a higher proportion of alarms than pediatric wards³ and a large proportion of these alarms, 87% - 97%, are nonactionable.^4-8 In national surveys of healthcare staff, respondents report that high alarm rates interrupt patient care and can lead clinicians to disable alarms entirely.⁹ Recent research has supported this, demonstrating that nurses who are exposed to higher numbers of alarms have slower response times to alarms.^4,10 In an attempt to mitigate safety risks, the Joint Commission in 2012 issued recommendations for hospitals to (a) establish guidelines for tailoring alarm settings and limits for individual patients and (b) identify situations in which alarms are not clinically necessary.¹¹

In order to address these recommendations within our PICU, we sought to evaluate the impact of a focused physiologic monitor alarm reduction intervention integrated into safety huddles. Safety huddles are brief, structured discussions among physicians, nurses, and other staff aiming to identify safety concerns.¹² Huddles offer an appropriate forum for reviewing alarm data and identifying patients whose high alarm rates may necessitate safe tailoring of alarm limits. Pilot data demonstrating high alarm rates among low-acuity PICU patients led us to hypothesize that low-acuity, high-alarm PICU patients would be a safe and effective target for an alarm huddle-based intervention.

In this study, we aimed to measure the impact of a structured safety huddle review of low-acuity PICU patients with high rates of priority alarms who were randomized to intervention compared with other low-acuity, high-alarm, concurrent, and historical control patients in the PICU.

Study Definitions

Priority alarm activation rate. We conceptualized priority alarms as any alarm for a clinical condition that requires a timely response to determine if intervention is necessary to save a patient’s life,⁴ yet little empirical data support its existence in the hospital. We operationally defined these alarms on the General Electric Solar physiologic monitoring devices as any potentially life-threatening events including lethal arrhythmias (asystole, ventricular tachycardia, and ventricular fibrillation) and alarms for vital signs (heart rate, respiratory rate, and oxygen saturation) outside of the set parameter limits. These alarms produced audible tones in the patient room and automatically sent text messages to the nurse’s phone and had the potential to contribute to alarm fatigue regardless of the nurse’s location.

High-alarm patients. High-alarm patients were those who had more than 40 priority alarms in the preceding 4 hours, representing the top 20% of alarm rates in the PICU according to prior quality improvement projects completed in our PICU.

Low-acuity patients. Prior to and during this study, patient acuity was determined using the OptiLink Patient Classification System (OptiLink Healthcare Management Systems, Inc.; Tigard, OR; www.optilinkhealthcare.com; see Appendix 1) for the PICU twice daily. Low-acuity patients comprised on average 16% of the PICU patients.

Setting and Subjects

This study was performed in the PICU at The Children’s Hospital of Philadelphia.

The PICU is made up of 3 separate wings: east, south, and west. Bed availability was the only factor determining patient placement on the east, south, or west wing; the physical bed location was not preferentially assigned based on diagnosis or disease severity. The east wing was the intervention unit where the huddles occurred.

The PICU is composed of 3 different geographical teams. Two of the teams are composed of 4 to 5 pediatric or emergency medicine residents, 1 fellow, and 1 attending covering the south and west wings. The third team, located on the east wing, is composed of 1 to 2 pediatric residents, 2 to 3 nurse practitioners, 1 fellow, and 1 attending. Bedside family-centered rounds are held at each patient room, with the bedside nurse participating by reading a nursing rounding script that includes vital signs, vascular access, continuous medications, and additional questions or concerns.

Control subjects were any monitored patients on any of the 3 wings of the PICU between April 1, 2015, and October 31, 2015. The control patients were in 2 categories: historical controls from April 1, 2015, to May 31, 2015, and concurrent controls from June 1, 2015, to October 31, 2015, who were located anywhere in the PICU. On each nonholiday weekday beginning June 1, 2015, we randomly selected up to 2 patients to receive the intervention. These were high-alarm, low-acuity patients on the east wing to be discussed in the daily morning huddle. If more than 2 high-alarm, low-acuity patients were eligible for intervention, they were randomly selected by using the RAND function in Microsoft Excel. The other low-acuity, high-alarm patients in the PICU were included as control patients. Patients were eligible for the study if they were present for the 4 hours prior to huddle and present past noon on the day of huddle. If patients met criteria as high-alarm, low-acuity patients on multiple days, they could be enrolled as intervention or control patients multiple times. Patients’ alarm rates were calculated by dividing the number of alarms by their length of stay to the minute. There was no adjustment made for patients enrolled more than once.

Human Subjects Protection

The Institutional Review Board of The Children’s Hospital of Philadelphia approved this study with a waiver of informed consent.

Alarm Capture

We used BedMasterEx (Excel Medical Electronics; Jupiter, FL, http://excel-medical.com/products/bedmaster-ex) software connected to the General Electric monitor network to measure alarm rates. The software captured, in near real time, every alarm that occurred on every monitor in the PICU. Alarm rates over the preceding 4 hours for all PICU patients were exported and summarized by alarm type and level as set by hospital policy (crisis, warning, advisory, and system warning). Crisis and warning alarms were included as they represented potential life-threatening events meeting the definition of priority alarms. Physicians used an order within the PICU admission order-set to order monitoring based on preset age parameters (see online Appendix 1 for default settings). Physician orders were required for nurses to change alarm parameters. Daily electrode changes to reduce false alarms were standard of care.

Primary Outcome

The primary outcome was the change in priority alarm activation rate (the number of priority alarms per day) from prehuddle period (24 hours before morning huddle) to posthuddle period (the 24 hours following morning huddle) for intervention cases as compared with controls.

Primary Intervention

The intervention consisted of integrating a short script to facilitate the discussion of the alarm data during existing safety huddle and rounding workflows. The discussion and subsequent workflow proceeded as follows: A member of the research team who was not involved in patient care brought an alarm data sheet for each randomly selected intervention patient on the east wing to each safety huddle. The huddles were attended by the outgoing night charge nurse, the day charge nurse, and all bedside nurses working on the east wing that day. The alarm data sheet provided to the charge nurse displayed data on the 1 to 2 alarm parameters (respiratory rate, heart rate, or pulse oximetry) that generated the highest number of alarms. The charge nurse listed the high-alarm patients by room number during huddle, and the alarm data sheet was given to the bedside nurse responsible for the patient to facilitate further scripted discussion during bedside rounds with patient-specific information to reduce the alarm rates of individual patients throughout the adjustment of physiologic monitor parameters (see Appendix 2 for sample data sheet and script).

Data Collection

Intervention patients were high-alarm, low-acuity patients on the east wing from June 1, 2015, through October 31, 2015. Two months of baseline data were gathered prior to intervention on all 3 wings; therefore, control patients were high-alarm, low-acuity patients throughout the PICU from April 1, 2015, to May 31, 2015, as historical controls and from June 1, 2015, to October 31, 2015, as concurrent controls. Alarm rates for the 24 hours prior to huddle and the 24 hours following huddle were collected and analyzed. See Figure 1 for schematic of study design.

We collected data on patient characteristics, including patient location, age, sex, and intervention date. Information regarding changes to monitor alarm parameters for both intervention and control patients during the posthuddle period (the period following morning huddle until noon on intervention day) was also collected. We monitored for code blue events and unexpected changes in acuity until discharge or transfer out of the PICU.

Data Analysis

We compared the priority alarm activation rates of individual patients in the 24 hours before and the 24 hours after the huddle intervention and contrasted the differences in rates between intervention and control patients, both concurrent and historical controls. We also divided the intervention and control groups into 2 additional groups each—those patients whose alarm parameters were changed, compared with those whose parameters did not change. We evaluated for possible contamination by comparing alarm rates of historical and concurrent controls, as well as evaluating alarm rates by location. We used mixed-effects regression models to evaluate the effect of the intervention and control type (historical or concurrent) on alarm rates, adjusted for patient age and sex. Analysis was performed using Stata version 10.3 (StataCorp, LLC, College Station, TX) and SAS version 9.4 (SAS Institute Inc., Cary, NC).

Because patients could be enrolled more than once, we refer to the instances when they were included in the study as “events” (huddle discussions for intervention patients and huddle opportunities for controls) below. We identified 49 historical control events between April 1, 2015, and May 31, 2015. During the intervention period, we identified 88 intervention events and 163 concurrent control events between June 1, 2015, and October 31, 2015 (total n = 300; see Table 1 for event characteristics). A total of 6 patients were enrolled more than once as either intervention or control patients.

The average priority alarm activation rate for intervention patients was 433 alarms (95% confidence interval [CI], 392-472) per day in the 24 hours leading up to the intervention and 223 alarms (95% CI, 182-265) per day in the 24 hours following the intervention, a 48.5% unadjusted decrease (95% CI, 38.1%-58.9%). In contrast, priority alarm activation rates for concurrent control patients averaged 412 alarms (95% CI, 383-442) per day in the 24 hours leading up to the morning huddle and 323 alarms (95% CI, 270-375) per day in the 24 hours following huddle, a 21.6% unadjusted decrease (95% CI, 15.3%-27.9%). For historical controls, priority alarm activation rates averaged 369 alarms (95% CI, 339-399) per day in the 24 hours leading up to the morning huddle and 242 alarms (95% CI, 164-320) per day in the 24 hours following huddle, a 34.4% unadjusted decrease (95% CI, 13.5%-55.0%). When we compared historical versus concurrent controls in the unadjusted analysis, concurrent controls had 37 more alarms per day (95% CI, 59 fewer to 134 more; P = 0.45) than historical controls. There was no significant difference between concurrent and historical controls, demonstrating no evidence of contamination.

Adjusted Analysis of Changes in Alarm Rates

The overall estimate of the effect of the intervention adjusted for age and sex compared with concurrent controls was a reduction of 116 priority alarms per day (95% CI, 37-194; P = 0.004, Table 2). The adjusted percent decrease was 29.0% (95% CI, 12.1%-46.0%). There were no unexpected changes in patient acuity or code blue events related to the intervention.

Fidelity Analysis

We tracked changes in alarm parameter settings for evidence of intervention fidelity to determine if the team carried out the recommendations made. We found that 42% of intervention patients and 24% of combined control patients had alarm parameters changed during the posthuddle period (P = 0.002).

For those intervention patients who had parameters changed during the posthuddle period (N = 37), the mean effect was greater at a 54.9% decrease (95% CI, 38.8%-70.8%) in priority alarms as compared with control patients who had parameters adjusted during the posthuddle period (n = 50), having a mean decrease of only 12.2% (95% CI, –18.1%-42.3%). There was a 43.2% decrease (95% CI, 29.3%-57.0%) for intervention patients who were discussed but did not have parameters adjusted during the time window of observation (n = 51), as compared with combined control patients who did not have parameters adjusted (N = 162) who had a 28.1% decrease (95% CI, 16.8%-39.1%); see Figure 2.

This study is the first to demonstrate a successful and safe intervention to reduce the alarm rates of PICU patients. In addition, we observed a more significant reduction in priority alarm activation rates for intervention patients who had their alarm parameters changed during the monitored time period, leading us to hypothesize that providing patient-specific data regarding types of alarms was a key component of the intervention.

In control patients, we observed a reduction in alarm rates over time as well. There are 2 potential explanations for this. First, it is possible that as patients stabilize in the PICU, their vital signs become less extreme and generate fewer alarms even if the alarm parameters are not changed. The second is that parameters were changed within or outside of the time windows during which we evaluated for alarm parameter changes. Nevertheless, the decline over time observed in the intervention patients was greater than in both control groups. This change was even more noticeable in the intervention patients who had their alarm parameters changed during the posthuddle period as compared with controls who had their alarm parameters changed following the posthuddle period. This may have been due to the data provided during the huddle intervention, pointing the team to the cause of the high alarm rate.

Prior successful research regarding reduction of pediatric alarms has often shown decreased use of physiological monitors as 1 approach to reducing unnecessary alarms. The single prior pediatric alarm intervention study conducted on a pediatric ward involved instituting a cardiac monitor care process that included the ordering of age-based parameters, daily replacement of electrodes, individualized assessment of parameters, and a reliable method to discontinue monitoring.¹³ Because most patients in the PICU are critically ill, the reliance on monitor discontinuation as a main approach to decreasing alarms is not feasible in this setting. Instead, the use of targeted alarm parameter adjustments for low-acuity patients demonstrated a safe and feasible approach to decreasing alarms in PICU patients. The daily electrode change and age-based parameters were already in place at our institution.

There are a few limitations to this study. First, we focused only on low-acuity PICU patients. We believe that focusing on low-acuity patients allows for reduction in nonactionable alarms with limited potential for adverse events; however, this approach excludes many critically ill patients who might be at highest risk for harm from alarm fatigue if important alarms are ignored. Second, many of our patients were not present for the full 24 hours pre- and posthuddle due to their low acuity limiting our ability to follow alarm rates over time. Third, changes in alarm parameters were only monitored for a set period of 5 hours following the huddle to determine the effect of the recommended rounding script on changes to alarms. It is possible the changes to alarm parameters outside of the observed posthuddle period affected the alarm rates of both intervention and control patients. Lastly, the balancing metrics of unexpected changes in OptiLink status and code blue events are rare events, and therefore we may have been underpowered to find them. The effects of the huddle intervention on safety huddle length and rounding length were not measured.

Integrating a data-driven monitor alarm discussion into safety huddles was a safe and effective approach to reduce alarms in low-acuity, high-alarm PICU patients. Innovative approaches to make data-driven alarm decisions using informatics tools integrated into monitoring systems and electronic health records have the potential to facilitate cost-effective spread of this intervention.

Disclosure

This work was supported by a pilot grant from the Center for Pediatric Clinical Effectiveness, The Children’s Hospital of Philadelphia. Dr. Bonafide is supported by a Mentored Patient-Oriented Research Career Development Award from the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K23HL116427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations or employers. The funding organizations had no role in the design, preparation, review, or approval of this paper, nor the decision to submit for publication.

Central venous catheter (CVC) placement is commonly performed in emergency and critical care settings for parenteral access, central monitoring, and hemodialysis. Although potentially lifesaving CVC insertion is associated with immediate risks including injury to nerves, vessels, and lungs.^1-3 These “insertion-related complications” are of particular interest for several reasons. First, the frequency of such complications varies widely, with published rates between 1.4% and 33.2%.^2-7 Reasons for such variation include differences in study definitions of complications (eg, pneumothorax and tip position),^2,5 setting of CVC placement (eg, intensive care unit [ICU] vs emergency room), timing of placement (eg, elective vs emergent), differences in technique, and type of operator (eg, experienced vs learner). Thus, the precise incidence of such events in modern-day training settings with use of ultrasound guidance remains uncertain. Second, mechanical complications might be preventable with adequate training and supervision. Indeed, studies using simulation-based mastery techniques have demonstrated a reduction in rates of complications following intensive training.⁸ Finally, understanding risk factors associated with insertion complications might inform preventative strategies and improve patient safety.^9-11

Few studies to date have examined trainees’ perceptions on CVC training, experience, supervision, and ability to recognize and prevent mechanical complications. While research investigating effects of simulation training has accumulated, most focus on successful completion of the procedure or individual procedural steps with little emphasis on operator perceptions.^12-14 In addition, while multiple studies have shown that unsuccessful line attempts are a risk factor for CVC complications,^3,4,7,15 there is very little known about trainee behavior and perceptions regarding unsuccessful line placement. CVC simulation trainings often assume successful completion of the procedure and do not address the crucial postprocedure steps that should be undertaken if a procedure is unsuccessful. For these reasons, we developed a survey to specifically examine trainee experience with CVC placement, supervision, postprocedural behavior, and attitudes regarding unsuccessful line placement.

Therefore, we designed a study with 2 specific goals: The first is to perform a contemporary analysis of CVC mechanical complication rate at an academic teaching institution and identify potential risk factors associated with these complications. Second, we sought to determine trainee perceptions regarding CVC complication experience, prevention, procedural supervision, and perceptions surrounding unsuccessful line placement.

Design and Setting

We conducted a single-center retrospective review of nontunneled acute CVC procedures between June 1, 2014, and May 1, 2015, at the University of Michigan Health System (UMHS). UMHS is a tertiary care referral center with over 900 inpatient beds, including 99 ICU beds.

All residents in internal medicine, surgery, anesthesia, and emergency medicine receive mandatory education in CVC placement that includes an online training module and simulation-based training with competency assessment. Use of real-time ultrasound guidance is considered the standard of care for CVC placement.

Data Collection

Inpatient procedure notes were electronically searched for terms indicating CVC placement. This was performed by using our hospital’s Data Office for Clinical and Translational Research using the Electronic Medical Record Search Engine tool. Please see the supplemental materials for the full list of search terms. We electronically extracted data, including date of procedure, gender, and most recent body mass index (BMI), within 1 year prior to note. Acute Physiology and Chronic Health Evaluation III (APACHE III) data are tracked for all patients on admission to ICU; this was collected when available. Charts were then manually reviewed to collect additional data, including international normalized ratio (INR), platelet count, lactate level on the day of CVC placement, anticoagulant use (actively prescribed coumadin, therapeutic enoxaparin, therapeutic unfractionated heparin, or direct oral anticoagulant), ventilator or noninvasive positive pressure ventilation (NIPPV) at time of CVC placement, and vasopressor requirement within 24 hours of CVC placement. The procedure note was reviewed to gather information about site of CVC placement, size and type of catheter, number of attempts, procedural success, training level of the operator, and attending presence. Small bore CVCs were defined as 7 French (Fr) or lower. Large bore CVCs were defined as >7 Fr; this includes dialysis catheters, Cordis catheters (Cordis, Fremont, CA), and cooling catheters. The times of the procedure note and postprocedure chest x-ray (CXR) were recorded, including whether the CVC was placed on a weekend (Friday 7 pm to Sunday at midnight) or weekday.

Primary Outcome

The primary outcome was the rate of severe mechanical complications related to CVC placement. Similar to prior studies,² we defined severe mechanical complications as arterial placement of dilator or catheter, hemothorax, pneumothorax, cerebral ischemia, patient death (related to procedure), significant hematoma, or vascular injury (defined as complication requiring expert consultation or blood product transfusion). We did not require a lower limit on blood transfusion. We considered pneumothorax a complication regardless of whether chest tube intervention was performed, as pneumothorax subjects the patient to additional tests (eg, serial CXRs) and sometimes symptoms (shortness of breath, pain, anxiety) regardless of whether or not a chest tube was required. Complications were confirmed by a direct review of procedure notes, progress notes, discharge summaries, and imaging studies.

Trainee Survey

A survey was electronically disseminated to all internal medicine and medicine-pediatric residents to inquire about CVC experiences, including time spent in the medical ICU, number of CVCs performed, postprocedure behavior for both failed and successful CVCs, and supervision experience and attitudes. Please see supplemental materials for full survey contents.

Statistical Methods

Descriptive statistics (percentage) were used to summarize data. Continuous and categorical variables were compared using Student t tests and chi-square tests, respectively. All analyses were performed using SAS 9.3 (SAS Institute, Cary, NC).

Ethical and Regulatory Oversight

The study was deemed exempt by the University of Michigan Institutional Review Board (HUM00100549) as data collection was part of a quality improvement effort.

Demographics and Characteristics of Device Insertion

Between June 1, 2014, and May 1, 2015, 730 CVC procedure notes were reviewed (Table 1). The mean age of the study population was 58.9 years, and 41.6% (n = 304) were female. BMI data were available in 400 patients without complications and 5 patients with complications; the average BMI was 31.5 kg/m². The APACHE III score was available for 442 patients without complications and 10 patients with complications; the average score was 86 (range 19-200). Most of the CVCs placed (n= 504, 69%) were small bore (<7 Fr). The majority of catheters were placed in the internal jugular (IJ) position (n = 525, 71.9%), followed by femoral (n = 144, 19.7%), subclavian (N = 57, 7.8%), and undocumented (n = 4, 0.6%). Ninety-six percent (n = 699) of CVCs were successfully placed. Seventy-six percent (n = 558) of procedure notes included documentation of the number of CVC attempts; of these, 85% documented 2 or fewer attempts. The majority of CVCs were placed by residents (n = 537, 73.9%), followed by fellows (N = 127, 17.5%) and attendings (n = 27, 3.7%). Attending supervision for all or key portions of CVC placement occurred 34.7% (n = 244) of the time overall and was lower for internal medicine trainees (n = 98/463, 21.2%) compared with surgical trainees (n = 73/127, 57.4%) or emergency medicine trainees (n = 62/96, 64.6%; P < 0.001). All successful IJ and subclavian CVCs except for 2 insertions (0.3%) had a postprocedure CXR. A minority of notes documented pressure transduction (4.5%) or blood gas analysis (0.2%) to confirm venous placement.

Mechanical Complications

The mechanical complications identified included pneumothorax (n = 5), bleeding requiring transfusion (n = 3), vascular injury requiring expert consultation or intervention (n = 3), stroke (n = 1), and death (n = 2). Vascular injuries included 1 neck hematoma with superinfection requiring antibiotics, 1 neck hematoma requiring otolaryngology and vascular surgery consultation, and 1 venous dissection of IJ vein requiring vascular surgery consultation. None of these cases required operative intervention. The stroke was caused by inadvertent CVC placement into the carotid artery. One patient experienced tension pneumothorax and died due to this complication; this death occurred after 3 failed left subclavian CVC attempts and an ultimately successful CVC placement into left IJ vein. Another death occurred immediately following unsuccessful Cordis placement. As no autopsy was performed, it is impossible to know if the cause of death was the line placement. However, it would be prudent to consider this as a CVC complication given the temporal relationship to line placement. Thus, the total number of patients who experienced severe mechanical complications was 14 out of 730 (1.92%).

Risk Factors for Mechanical Complications

Certain patient factors were more commonly associated with complications. For example, BMI was significantly lower in the group that experienced complications vs those that did not (25.7 vs 31.0 kg/m², P = 0.001). No other associations between demographic factors, including age (61.4 years vs 58.9 years, P = 0.57) or sex (57.1% male vs 41.3% female, P = 0.24), or admission APACHE III score (96 vs 86, P = 0.397) were noted. The mean INR, platelets, and lactate did not differ between the 2 groups. There was no difference between the use of vasopressors. Ventilator use (including endotracheal tube or NIPPV) was found to be significantly higher in the group that experienced mechanical complications (78.5% vs 65.9%, P = 0.001). Anticoagulation use was also associated with mechanical complications (28.6% vs 20.6%, P = 0.05); 3 patients on anticoagulation experienced significant hematomas. Mechanical complications were more common with subclavian location (21.4% vs 7.8%, P = 0.001); in all 3 cases involving subclavian CVC placement, the complication experienced was pneumothorax. The number of attempts significantly differed between the 2 groups, with an average of 1.5 attempts in the group without complications and 2.2 attempts in the group that experienced complications (P = 0.02). Additionally, rates of successful placement were lower among patients who experienced complications (78.6% vs 95.7%, P = 0.001).

With respect to operator characteristics, no significant difference between the levels of training was noted among those who experienced complications vs those who did not. Attending supervision was more frequent for the group that experienced complications (61.5% vs 34.2%, P = 0.04). There was no significant difference in complication rate according to the first vs the second half of the academic year (0.4% vs 0.3% per month, P = 0.30) or CVC placement during the day vs night (1.9% vs 2.0%, P = 0.97). A trend toward more complications in CVCs placed over the weekend compared to a weekday was observed (2.80% vs 1.23%, P = 0.125).

Unsuccessful CVCs

There were 30 documented unsuccessful CVC procedures, representing 4.1% of all procedures. Of these, 3 procedures had complications; these included 2 pneumothoraxes (1 leading to death) and 1 unexplained death. Twenty-four of the unsuccessful CVC attempts were in either the subclavian or IJ location; of these, 5 (21%) did not have a postprocedure CXR obtained.

Survey Results

The survey was completed by 103 out of 166 internal medicine residents (62% response rate). Of these, 55% (n = 57) reported having performed 5 or more CVCs, and 14% (n = 14) had performed more than 15 CVCs.

All respondents who had performed at least 1 CVC (n = 80) were asked about their perceptions regarding attending supervision. Eighty-one percent (n = 65/80) responded that they have never been directly supervised by an attending during CVC placement, while 16% (n = 13/80) reported being supervised less than 25% of the time. Most (n = 53/75, 71%) did not feel that attending supervision affected their performance, while 21% (n = 16/75) felt it affected performance negatively, and only 8% (n = 6/75) stated it affected performance positively. Nineteen percent (n = 15/80) indicated that they prefer more supervision by attendings, while 35% (n = 28/80) did not wish for more attending supervision, and 46% (n = 37/80) were indifferent.

We performed a contemporary analysis of CVC placement at an academic tertiary care center and observed a rate of severe mechanical complications of 1.9%. This rate is within previously described acceptable thresholds.¹⁶ Our study adds to the literature by identifying several important risk factors for development of mechanical complications. We confirm many risk factors that have been noted historically, such as subclavian line location,^2,3 attending supervision,³ low BMI,⁴ number of CVC attempts, and unsuccessful CVC placement.^3,4,7,15 We identified several unique risk factors, including systemic anticoagulation as well as ventilator use. Lastly, we identified unexpected deficits in trainee knowledge surrounding management of failed CVCs and negative attitudes regarding attending supervision.

Most existing literature evaluated risk factors for CVC complication prior to routine ultrasound use;^3-5,7,15 surprisingly, it appears that severe mechanical complications do not differ dramatically in the real-time ultrasound era. Eisen et al.³ prospectively studied CVC placement at an academic medical center and found a severe mechanical complication rate (as defined in our paper) of 1.9% due to pneumothorax (1.3%), hemothorax (0.3%), and death (0.3%).We would expect the number of complications to decrease in the postultrasound era, and indeed it appears that pneumothoraces have decreased likely due to ultrasound guidance and decrease in subclavian location. However, in contrast, rates of significant hematomas and bleeding are higher in our study. Although we are unable to state why this may be the case, increasing use of anticoagulation in the general population might explain this finding.¹⁷ For instance, of the 6 patients who experienced hematomas or vascular injuries in our study, 3 were on anticoagulation at the time of CVC placement.

Interestingly, time of academic year of CVC placement and level of training were not correlated with an increased risk of complications, nor was time of day of CVC placement. In contrast, Merrer et al.showed that CVC insertion during nighttime was significantly associated with increased mechanical complications (odds ratio 2.06, 95% confidence interval, 1.04-4.08;,P = 0.03).⁵ This difference may be attributable to the fact that most of our ICUs now have a night float system rather than a more traditional 24-hour call model; therefore, trainees are less likely to be sleep deprived during CVC placement at night.

Severity of illness did not appear to significantly affect mechanical complication rates based on similar APACHE scores between the 2 groups. In addition, other indicators of illness severity (vasopressor use or lactate level) did not suggest that sicker patients may be more likely to experience mechanical complications than others. One could conjecture that perhaps sicker patients were more likely to have lines placed by more experienced trainees, although the present study design does not allow us to answer this question. Interestingly, ventilator use was associated with higher rates of complications. We cannot say definitively why this was the case; however, 1 contributing factor may be the physical constraints of placing the CVC around ventilator tubing.

Several unexpected findings surrounding attending supervision were noted: first, attending supervision appears to be significantly associated with increased complication rate, and second, trainees have negative perceptions regarding attending supervision. Eisen et al.showed a similar association between attending supervision and complication rate.³ It is possible that the increased complication rate is because sicker patients are more likely to have procedural supervision by attendings, attending physicians may be called to supervise when a CVC placement is not going as planned, or attendings may supervise more inexperienced operators. Reasons behind negative trainee attitudes surrounding supervision are unclear and literature on this topic is limited. This is an area that warrants further exploration in future studies.

Another unexpected finding is trainee practices regarding unsuccessful CVC placement; most trainees do not document failed procedures or order follow-up CXRs after unsuccessful CVC attempts. Given the higher risk of complications after unsuccessful CVCs, it is paramount that all physicians are trained to order postprocedure CXR to rule out pneumothorax or hemothorax. Furthermore, documentation of failed procedures is important for medical accuracy, transparency, and also hospital billing. It is unknown if these practices surrounding unsuccessful CVCs are institution-specific or more widespread. As far as we know, this is the first time that trainee practices regarding failed CVC placement have been published. Interestingly, while many current guidelines call attention to prevention, recognition, and management of central line-associated mechanical complications, specific recommendations about postprocedure behavior after failed CVC placement are not published.^9-11 We feel it is critical that institutions reflect on their own practices, especially given that unsuccessful CVCs are shown to be correlated with a significant increase in complication rate. At our own institution, we have initiated an educational component of central line training for medicine trainees specifically addressing failed central line attempts.

This study has several limitations, including a retrospective study design at a single institution. There was a low overall number of complications, which reduced our ability to detect risk factors for complications and did not allow us to perform multivariable adjustment. Other limitations are that only documented CVC attempts were recorded and only those that met our search criteria. Lastly, not all notes contain information such as the number of attempts or peer supervision. Furthermore, the definition of CVC “attempt” is left to the operator’s discretion.

In conclusion, we observed a modern CVC mechanical complication rate of 1.9%. While the complication rate is similar to previous studies, there appear to be lower rates of pneumothorax and higher rates of bleeding complications. We also identified a deficit in trainee education regarding unsuccessful CVC placement; this is a novel finding and requires further investigation at other centers.

Disclosure: The authors have no conflicts of interest to report.

Central venous catheter (CVC) placement is commonly performed in emergency and critical care settings for parenteral access, central monitoring, and hemodialysis. Although potentially lifesaving CVC insertion is associated with immediate risks including injury to nerves, vessels, and lungs.^1-3 These “insertion-related complications” are of particular interest for several reasons. First, the frequency of such complications varies widely, with published rates between 1.4% and 33.2%.^2-7 Reasons for such variation include differences in study definitions of complications (eg, pneumothorax and tip position),^2,5 setting of CVC placement (eg, intensive care unit [ICU] vs emergency room), timing of placement (eg, elective vs emergent), differences in technique, and type of operator (eg, experienced vs learner). Thus, the precise incidence of such events in modern-day training settings with use of ultrasound guidance remains uncertain. Second, mechanical complications might be preventable with adequate training and supervision. Indeed, studies using simulation-based mastery techniques have demonstrated a reduction in rates of complications following intensive training.⁸ Finally, understanding risk factors associated with insertion complications might inform preventative strategies and improve patient safety.^9-11

Few studies to date have examined trainees’ perceptions on CVC training, experience, supervision, and ability to recognize and prevent mechanical complications. While research investigating effects of simulation training has accumulated, most focus on successful completion of the procedure or individual procedural steps with little emphasis on operator perceptions.^12-14 In addition, while multiple studies have shown that unsuccessful line attempts are a risk factor for CVC complications,^3,4,7,15 there is very little known about trainee behavior and perceptions regarding unsuccessful line placement. CVC simulation trainings often assume successful completion of the procedure and do not address the crucial postprocedure steps that should be undertaken if a procedure is unsuccessful. For these reasons, we developed a survey to specifically examine trainee experience with CVC placement, supervision, postprocedural behavior, and attitudes regarding unsuccessful line placement.

Therefore, we designed a study with 2 specific goals: The first is to perform a contemporary analysis of CVC mechanical complication rate at an academic teaching institution and identify potential risk factors associated with these complications. Second, we sought to determine trainee perceptions regarding CVC complication experience, prevention, procedural supervision, and perceptions surrounding unsuccessful line placement.

Design and Setting

We conducted a single-center retrospective review of nontunneled acute CVC procedures between June 1, 2014, and May 1, 2015, at the University of Michigan Health System (UMHS). UMHS is a tertiary care referral center with over 900 inpatient beds, including 99 ICU beds.

All residents in internal medicine, surgery, anesthesia, and emergency medicine receive mandatory education in CVC placement that includes an online training module and simulation-based training with competency assessment. Use of real-time ultrasound guidance is considered the standard of care for CVC placement.

Data Collection

Inpatient procedure notes were electronically searched for terms indicating CVC placement. This was performed by using our hospital’s Data Office for Clinical and Translational Research using the Electronic Medical Record Search Engine tool. Please see the supplemental materials for the full list of search terms. We electronically extracted data, including date of procedure, gender, and most recent body mass index (BMI), within 1 year prior to note. Acute Physiology and Chronic Health Evaluation III (APACHE III) data are tracked for all patients on admission to ICU; this was collected when available. Charts were then manually reviewed to collect additional data, including international normalized ratio (INR), platelet count, lactate level on the day of CVC placement, anticoagulant use (actively prescribed coumadin, therapeutic enoxaparin, therapeutic unfractionated heparin, or direct oral anticoagulant), ventilator or noninvasive positive pressure ventilation (NIPPV) at time of CVC placement, and vasopressor requirement within 24 hours of CVC placement. The procedure note was reviewed to gather information about site of CVC placement, size and type of catheter, number of attempts, procedural success, training level of the operator, and attending presence. Small bore CVCs were defined as 7 French (Fr) or lower. Large bore CVCs were defined as >7 Fr; this includes dialysis catheters, Cordis catheters (Cordis, Fremont, CA), and cooling catheters. The times of the procedure note and postprocedure chest x-ray (CXR) were recorded, including whether the CVC was placed on a weekend (Friday 7 pm to Sunday at midnight) or weekday.

Primary Outcome

The primary outcome was the rate of severe mechanical complications related to CVC placement. Similar to prior studies,² we defined severe mechanical complications as arterial placement of dilator or catheter, hemothorax, pneumothorax, cerebral ischemia, patient death (related to procedure), significant hematoma, or vascular injury (defined as complication requiring expert consultation or blood product transfusion). We did not require a lower limit on blood transfusion. We considered pneumothorax a complication regardless of whether chest tube intervention was performed, as pneumothorax subjects the patient to additional tests (eg, serial CXRs) and sometimes symptoms (shortness of breath, pain, anxiety) regardless of whether or not a chest tube was required. Complications were confirmed by a direct review of procedure notes, progress notes, discharge summaries, and imaging studies.

Trainee Survey

A survey was electronically disseminated to all internal medicine and medicine-pediatric residents to inquire about CVC experiences, including time spent in the medical ICU, number of CVCs performed, postprocedure behavior for both failed and successful CVCs, and supervision experience and attitudes. Please see supplemental materials for full survey contents.

Statistical Methods

Descriptive statistics (percentage) were used to summarize data. Continuous and categorical variables were compared using Student t tests and chi-square tests, respectively. All analyses were performed using SAS 9.3 (SAS Institute, Cary, NC).

Ethical and Regulatory Oversight

The study was deemed exempt by the University of Michigan Institutional Review Board (HUM00100549) as data collection was part of a quality improvement effort.

Demographics and Characteristics of Device Insertion

Between June 1, 2014, and May 1, 2015, 730 CVC procedure notes were reviewed (Table 1). The mean age of the study population was 58.9 years, and 41.6% (n = 304) were female. BMI data were available in 400 patients without complications and 5 patients with complications; the average BMI was 31.5 kg/m². The APACHE III score was available for 442 patients without complications and 10 patients with complications; the average score was 86 (range 19-200). Most of the CVCs placed (n= 504, 69%) were small bore (<7 Fr). The majority of catheters were placed in the internal jugular (IJ) position (n = 525, 71.9%), followed by femoral (n = 144, 19.7%), subclavian (N = 57, 7.8%), and undocumented (n = 4, 0.6%). Ninety-six percent (n = 699) of CVCs were successfully placed. Seventy-six percent (n = 558) of procedure notes included documentation of the number of CVC attempts; of these, 85% documented 2 or fewer attempts. The majority of CVCs were placed by residents (n = 537, 73.9%), followed by fellows (N = 127, 17.5%) and attendings (n = 27, 3.7%). Attending supervision for all or key portions of CVC placement occurred 34.7% (n = 244) of the time overall and was lower for internal medicine trainees (n = 98/463, 21.2%) compared with surgical trainees (n = 73/127, 57.4%) or emergency medicine trainees (n = 62/96, 64.6%; P < 0.001). All successful IJ and subclavian CVCs except for 2 insertions (0.3%) had a postprocedure CXR. A minority of notes documented pressure transduction (4.5%) or blood gas analysis (0.2%) to confirm venous placement.

Mechanical Complications

The mechanical complications identified included pneumothorax (n = 5), bleeding requiring transfusion (n = 3), vascular injury requiring expert consultation or intervention (n = 3), stroke (n = 1), and death (n = 2). Vascular injuries included 1 neck hematoma with superinfection requiring antibiotics, 1 neck hematoma requiring otolaryngology and vascular surgery consultation, and 1 venous dissection of IJ vein requiring vascular surgery consultation. None of these cases required operative intervention. The stroke was caused by inadvertent CVC placement into the carotid artery. One patient experienced tension pneumothorax and died due to this complication; this death occurred after 3 failed left subclavian CVC attempts and an ultimately successful CVC placement into left IJ vein. Another death occurred immediately following unsuccessful Cordis placement. As no autopsy was performed, it is impossible to know if the cause of death was the line placement. However, it would be prudent to consider this as a CVC complication given the temporal relationship to line placement. Thus, the total number of patients who experienced severe mechanical complications was 14 out of 730 (1.92%).

Risk Factors for Mechanical Complications

Certain patient factors were more commonly associated with complications. For example, BMI was significantly lower in the group that experienced complications vs those that did not (25.7 vs 31.0 kg/m², P = 0.001). No other associations between demographic factors, including age (61.4 years vs 58.9 years, P = 0.57) or sex (57.1% male vs 41.3% female, P = 0.24), or admission APACHE III score (96 vs 86, P = 0.397) were noted. The mean INR, platelets, and lactate did not differ between the 2 groups. There was no difference between the use of vasopressors. Ventilator use (including endotracheal tube or NIPPV) was found to be significantly higher in the group that experienced mechanical complications (78.5% vs 65.9%, P = 0.001). Anticoagulation use was also associated with mechanical complications (28.6% vs 20.6%, P = 0.05); 3 patients on anticoagulation experienced significant hematomas. Mechanical complications were more common with subclavian location (21.4% vs 7.8%, P = 0.001); in all 3 cases involving subclavian CVC placement, the complication experienced was pneumothorax. The number of attempts significantly differed between the 2 groups, with an average of 1.5 attempts in the group without complications and 2.2 attempts in the group that experienced complications (P = 0.02). Additionally, rates of successful placement were lower among patients who experienced complications (78.6% vs 95.7%, P = 0.001).

With respect to operator characteristics, no significant difference between the levels of training was noted among those who experienced complications vs those who did not. Attending supervision was more frequent for the group that experienced complications (61.5% vs 34.2%, P = 0.04). There was no significant difference in complication rate according to the first vs the second half of the academic year (0.4% vs 0.3% per month, P = 0.30) or CVC placement during the day vs night (1.9% vs 2.0%, P = 0.97). A trend toward more complications in CVCs placed over the weekend compared to a weekday was observed (2.80% vs 1.23%, P = 0.125).

Unsuccessful CVCs

There were 30 documented unsuccessful CVC procedures, representing 4.1% of all procedures. Of these, 3 procedures had complications; these included 2 pneumothoraxes (1 leading to death) and 1 unexplained death. Twenty-four of the unsuccessful CVC attempts were in either the subclavian or IJ location; of these, 5 (21%) did not have a postprocedure CXR obtained.

Survey Results

The survey was completed by 103 out of 166 internal medicine residents (62% response rate). Of these, 55% (n = 57) reported having performed 5 or more CVCs, and 14% (n = 14) had performed more than 15 CVCs.

All respondents who had performed at least 1 CVC (n = 80) were asked about their perceptions regarding attending supervision. Eighty-one percent (n = 65/80) responded that they have never been directly supervised by an attending during CVC placement, while 16% (n = 13/80) reported being supervised less than 25% of the time. Most (n = 53/75, 71%) did not feel that attending supervision affected their performance, while 21% (n = 16/75) felt it affected performance negatively, and only 8% (n = 6/75) stated it affected performance positively. Nineteen percent (n = 15/80) indicated that they prefer more supervision by attendings, while 35% (n = 28/80) did not wish for more attending supervision, and 46% (n = 37/80) were indifferent.

We performed a contemporary analysis of CVC placement at an academic tertiary care center and observed a rate of severe mechanical complications of 1.9%. This rate is within previously described acceptable thresholds.¹⁶ Our study adds to the literature by identifying several important risk factors for development of mechanical complications. We confirm many risk factors that have been noted historically, such as subclavian line location,^2,3 attending supervision,³ low BMI,⁴ number of CVC attempts, and unsuccessful CVC placement.^3,4,7,15 We identified several unique risk factors, including systemic anticoagulation as well as ventilator use. Lastly, we identified unexpected deficits in trainee knowledge surrounding management of failed CVCs and negative attitudes regarding attending supervision.

Most existing literature evaluated risk factors for CVC complication prior to routine ultrasound use;^3-5,7,15 surprisingly, it appears that severe mechanical complications do not differ dramatically in the real-time ultrasound era. Eisen et al.³ prospectively studied CVC placement at an academic medical center and found a severe mechanical complication rate (as defined in our paper) of 1.9% due to pneumothorax (1.3%), hemothorax (0.3%), and death (0.3%).We would expect the number of complications to decrease in the postultrasound era, and indeed it appears that pneumothoraces have decreased likely due to ultrasound guidance and decrease in subclavian location. However, in contrast, rates of significant hematomas and bleeding are higher in our study. Although we are unable to state why this may be the case, increasing use of anticoagulation in the general population might explain this finding.¹⁷ For instance, of the 6 patients who experienced hematomas or vascular injuries in our study, 3 were on anticoagulation at the time of CVC placement.

Interestingly, time of academic year of CVC placement and level of training were not correlated with an increased risk of complications, nor was time of day of CVC placement. In contrast, Merrer et al.showed that CVC insertion during nighttime was significantly associated with increased mechanical complications (odds ratio 2.06, 95% confidence interval, 1.04-4.08;,P = 0.03).⁵ This difference may be attributable to the fact that most of our ICUs now have a night float system rather than a more traditional 24-hour call model; therefore, trainees are less likely to be sleep deprived during CVC placement at night.

Severity of illness did not appear to significantly affect mechanical complication rates based on similar APACHE scores between the 2 groups. In addition, other indicators of illness severity (vasopressor use or lactate level) did not suggest that sicker patients may be more likely to experience mechanical complications than others. One could conjecture that perhaps sicker patients were more likely to have lines placed by more experienced trainees, although the present study design does not allow us to answer this question. Interestingly, ventilator use was associated with higher rates of complications. We cannot say definitively why this was the case; however, 1 contributing factor may be the physical constraints of placing the CVC around ventilator tubing.

Several unexpected findings surrounding attending supervision were noted: first, attending supervision appears to be significantly associated with increased complication rate, and second, trainees have negative perceptions regarding attending supervision. Eisen et al.showed a similar association between attending supervision and complication rate.³ It is possible that the increased complication rate is because sicker patients are more likely to have procedural supervision by attendings, attending physicians may be called to supervise when a CVC placement is not going as planned, or attendings may supervise more inexperienced operators. Reasons behind negative trainee attitudes surrounding supervision are unclear and literature on this topic is limited. This is an area that warrants further exploration in future studies.

Another unexpected finding is trainee practices regarding unsuccessful CVC placement; most trainees do not document failed procedures or order follow-up CXRs after unsuccessful CVC attempts. Given the higher risk of complications after unsuccessful CVCs, it is paramount that all physicians are trained to order postprocedure CXR to rule out pneumothorax or hemothorax. Furthermore, documentation of failed procedures is important for medical accuracy, transparency, and also hospital billing. It is unknown if these practices surrounding unsuccessful CVCs are institution-specific or more widespread. As far as we know, this is the first time that trainee practices regarding failed CVC placement have been published. Interestingly, while many current guidelines call attention to prevention, recognition, and management of central line-associated mechanical complications, specific recommendations about postprocedure behavior after failed CVC placement are not published.^9-11 We feel it is critical that institutions reflect on their own practices, especially given that unsuccessful CVCs are shown to be correlated with a significant increase in complication rate. At our own institution, we have initiated an educational component of central line training for medicine trainees specifically addressing failed central line attempts.

This study has several limitations, including a retrospective study design at a single institution. There was a low overall number of complications, which reduced our ability to detect risk factors for complications and did not allow us to perform multivariable adjustment. Other limitations are that only documented CVC attempts were recorded and only those that met our search criteria. Lastly, not all notes contain information such as the number of attempts or peer supervision. Furthermore, the definition of CVC “attempt” is left to the operator’s discretion.

In conclusion, we observed a modern CVC mechanical complication rate of 1.9%. While the complication rate is similar to previous studies, there appear to be lower rates of pneumothorax and higher rates of bleeding complications. We also identified a deficit in trainee education regarding unsuccessful CVC placement; this is a novel finding and requires further investigation at other centers.

Disclosure: The authors have no conflicts of interest to report.

Central venous catheter (CVC) placement is commonly performed in emergency and critical care settings for parenteral access, central monitoring, and hemodialysis. Although potentially lifesaving CVC insertion is associated with immediate risks including injury to nerves, vessels, and lungs.^1-3 These “insertion-related complications” are of particular interest for several reasons. First, the frequency of such complications varies widely, with published rates between 1.4% and 33.2%.^2-7 Reasons for such variation include differences in study definitions of complications (eg, pneumothorax and tip position),^2,5 setting of CVC placement (eg, intensive care unit [ICU] vs emergency room), timing of placement (eg, elective vs emergent), differences in technique, and type of operator (eg, experienced vs learner). Thus, the precise incidence of such events in modern-day training settings with use of ultrasound guidance remains uncertain. Second, mechanical complications might be preventable with adequate training and supervision. Indeed, studies using simulation-based mastery techniques have demonstrated a reduction in rates of complications following intensive training.⁸ Finally, understanding risk factors associated with insertion complications might inform preventative strategies and improve patient safety.^9-11

Few studies to date have examined trainees’ perceptions on CVC training, experience, supervision, and ability to recognize and prevent mechanical complications. While research investigating effects of simulation training has accumulated, most focus on successful completion of the procedure or individual procedural steps with little emphasis on operator perceptions.^12-14 In addition, while multiple studies have shown that unsuccessful line attempts are a risk factor for CVC complications,^3,4,7,15 there is very little known about trainee behavior and perceptions regarding unsuccessful line placement. CVC simulation trainings often assume successful completion of the procedure and do not address the crucial postprocedure steps that should be undertaken if a procedure is unsuccessful. For these reasons, we developed a survey to specifically examine trainee experience with CVC placement, supervision, postprocedural behavior, and attitudes regarding unsuccessful line placement.

Therefore, we designed a study with 2 specific goals: The first is to perform a contemporary analysis of CVC mechanical complication rate at an academic teaching institution and identify potential risk factors associated with these complications. Second, we sought to determine trainee perceptions regarding CVC complication experience, prevention, procedural supervision, and perceptions surrounding unsuccessful line placement.

Design and Setting

We conducted a single-center retrospective review of nontunneled acute CVC procedures between June 1, 2014, and May 1, 2015, at the University of Michigan Health System (UMHS). UMHS is a tertiary care referral center with over 900 inpatient beds, including 99 ICU beds.

All residents in internal medicine, surgery, anesthesia, and emergency medicine receive mandatory education in CVC placement that includes an online training module and simulation-based training with competency assessment. Use of real-time ultrasound guidance is considered the standard of care for CVC placement.

Data Collection

Inpatient procedure notes were electronically searched for terms indicating CVC placement. This was performed by using our hospital’s Data Office for Clinical and Translational Research using the Electronic Medical Record Search Engine tool. Please see the supplemental materials for the full list of search terms. We electronically extracted data, including date of procedure, gender, and most recent body mass index (BMI), within 1 year prior to note. Acute Physiology and Chronic Health Evaluation III (APACHE III) data are tracked for all patients on admission to ICU; this was collected when available. Charts were then manually reviewed to collect additional data, including international normalized ratio (INR), platelet count, lactate level on the day of CVC placement, anticoagulant use (actively prescribed coumadin, therapeutic enoxaparin, therapeutic unfractionated heparin, or direct oral anticoagulant), ventilator or noninvasive positive pressure ventilation (NIPPV) at time of CVC placement, and vasopressor requirement within 24 hours of CVC placement. The procedure note was reviewed to gather information about site of CVC placement, size and type of catheter, number of attempts, procedural success, training level of the operator, and attending presence. Small bore CVCs were defined as 7 French (Fr) or lower. Large bore CVCs were defined as >7 Fr; this includes dialysis catheters, Cordis catheters (Cordis, Fremont, CA), and cooling catheters. The times of the procedure note and postprocedure chest x-ray (CXR) were recorded, including whether the CVC was placed on a weekend (Friday 7 pm to Sunday at midnight) or weekday.

Primary Outcome

The primary outcome was the rate of severe mechanical complications related to CVC placement. Similar to prior studies,² we defined severe mechanical complications as arterial placement of dilator or catheter, hemothorax, pneumothorax, cerebral ischemia, patient death (related to procedure), significant hematoma, or vascular injury (defined as complication requiring expert consultation or blood product transfusion). We did not require a lower limit on blood transfusion. We considered pneumothorax a complication regardless of whether chest tube intervention was performed, as pneumothorax subjects the patient to additional tests (eg, serial CXRs) and sometimes symptoms (shortness of breath, pain, anxiety) regardless of whether or not a chest tube was required. Complications were confirmed by a direct review of procedure notes, progress notes, discharge summaries, and imaging studies.

Trainee Survey

A survey was electronically disseminated to all internal medicine and medicine-pediatric residents to inquire about CVC experiences, including time spent in the medical ICU, number of CVCs performed, postprocedure behavior for both failed and successful CVCs, and supervision experience and attitudes. Please see supplemental materials for full survey contents.

Statistical Methods

Descriptive statistics (percentage) were used to summarize data. Continuous and categorical variables were compared using Student t tests and chi-square tests, respectively. All analyses were performed using SAS 9.3 (SAS Institute, Cary, NC).

Ethical and Regulatory Oversight

The study was deemed exempt by the University of Michigan Institutional Review Board (HUM00100549) as data collection was part of a quality improvement effort.

Demographics and Characteristics of Device Insertion

Between June 1, 2014, and May 1, 2015, 730 CVC procedure notes were reviewed (Table 1). The mean age of the study population was 58.9 years, and 41.6% (n = 304) were female. BMI data were available in 400 patients without complications and 5 patients with complications; the average BMI was 31.5 kg/m². The APACHE III score was available for 442 patients without complications and 10 patients with complications; the average score was 86 (range 19-200). Most of the CVCs placed (n= 504, 69%) were small bore (<7 Fr). The majority of catheters were placed in the internal jugular (IJ) position (n = 525, 71.9%), followed by femoral (n = 144, 19.7%), subclavian (N = 57, 7.8%), and undocumented (n = 4, 0.6%). Ninety-six percent (n = 699) of CVCs were successfully placed. Seventy-six percent (n = 558) of procedure notes included documentation of the number of CVC attempts; of these, 85% documented 2 or fewer attempts. The majority of CVCs were placed by residents (n = 537, 73.9%), followed by fellows (N = 127, 17.5%) and attendings (n = 27, 3.7%). Attending supervision for all or key portions of CVC placement occurred 34.7% (n = 244) of the time overall and was lower for internal medicine trainees (n = 98/463, 21.2%) compared with surgical trainees (n = 73/127, 57.4%) or emergency medicine trainees (n = 62/96, 64.6%; P < 0.001). All successful IJ and subclavian CVCs except for 2 insertions (0.3%) had a postprocedure CXR. A minority of notes documented pressure transduction (4.5%) or blood gas analysis (0.2%) to confirm venous placement.

Mechanical Complications

The mechanical complications identified included pneumothorax (n = 5), bleeding requiring transfusion (n = 3), vascular injury requiring expert consultation or intervention (n = 3), stroke (n = 1), and death (n = 2). Vascular injuries included 1 neck hematoma with superinfection requiring antibiotics, 1 neck hematoma requiring otolaryngology and vascular surgery consultation, and 1 venous dissection of IJ vein requiring vascular surgery consultation. None of these cases required operative intervention. The stroke was caused by inadvertent CVC placement into the carotid artery. One patient experienced tension pneumothorax and died due to this complication; this death occurred after 3 failed left subclavian CVC attempts and an ultimately successful CVC placement into left IJ vein. Another death occurred immediately following unsuccessful Cordis placement. As no autopsy was performed, it is impossible to know if the cause of death was the line placement. However, it would be prudent to consider this as a CVC complication given the temporal relationship to line placement. Thus, the total number of patients who experienced severe mechanical complications was 14 out of 730 (1.92%).

Risk Factors for Mechanical Complications

Certain patient factors were more commonly associated with complications. For example, BMI was significantly lower in the group that experienced complications vs those that did not (25.7 vs 31.0 kg/m², P = 0.001). No other associations between demographic factors, including age (61.4 years vs 58.9 years, P = 0.57) or sex (57.1% male vs 41.3% female, P = 0.24), or admission APACHE III score (96 vs 86, P = 0.397) were noted. The mean INR, platelets, and lactate did not differ between the 2 groups. There was no difference between the use of vasopressors. Ventilator use (including endotracheal tube or NIPPV) was found to be significantly higher in the group that experienced mechanical complications (78.5% vs 65.9%, P = 0.001). Anticoagulation use was also associated with mechanical complications (28.6% vs 20.6%, P = 0.05); 3 patients on anticoagulation experienced significant hematomas. Mechanical complications were more common with subclavian location (21.4% vs 7.8%, P = 0.001); in all 3 cases involving subclavian CVC placement, the complication experienced was pneumothorax. The number of attempts significantly differed between the 2 groups, with an average of 1.5 attempts in the group without complications and 2.2 attempts in the group that experienced complications (P = 0.02). Additionally, rates of successful placement were lower among patients who experienced complications (78.6% vs 95.7%, P = 0.001).

With respect to operator characteristics, no significant difference between the levels of training was noted among those who experienced complications vs those who did not. Attending supervision was more frequent for the group that experienced complications (61.5% vs 34.2%, P = 0.04). There was no significant difference in complication rate according to the first vs the second half of the academic year (0.4% vs 0.3% per month, P = 0.30) or CVC placement during the day vs night (1.9% vs 2.0%, P = 0.97). A trend toward more complications in CVCs placed over the weekend compared to a weekday was observed (2.80% vs 1.23%, P = 0.125).

Unsuccessful CVCs

There were 30 documented unsuccessful CVC procedures, representing 4.1% of all procedures. Of these, 3 procedures had complications; these included 2 pneumothoraxes (1 leading to death) and 1 unexplained death. Twenty-four of the unsuccessful CVC attempts were in either the subclavian or IJ location; of these, 5 (21%) did not have a postprocedure CXR obtained.

Survey Results

The survey was completed by 103 out of 166 internal medicine residents (62% response rate). Of these, 55% (n = 57) reported having performed 5 or more CVCs, and 14% (n = 14) had performed more than 15 CVCs.

All respondents who had performed at least 1 CVC (n = 80) were asked about their perceptions regarding attending supervision. Eighty-one percent (n = 65/80) responded that they have never been directly supervised by an attending during CVC placement, while 16% (n = 13/80) reported being supervised less than 25% of the time. Most (n = 53/75, 71%) did not feel that attending supervision affected their performance, while 21% (n = 16/75) felt it affected performance negatively, and only 8% (n = 6/75) stated it affected performance positively. Nineteen percent (n = 15/80) indicated that they prefer more supervision by attendings, while 35% (n = 28/80) did not wish for more attending supervision, and 46% (n = 37/80) were indifferent.

We performed a contemporary analysis of CVC placement at an academic tertiary care center and observed a rate of severe mechanical complications of 1.9%. This rate is within previously described acceptable thresholds.¹⁶ Our study adds to the literature by identifying several important risk factors for development of mechanical complications. We confirm many risk factors that have been noted historically, such as subclavian line location,^2,3 attending supervision,³ low BMI,⁴ number of CVC attempts, and unsuccessful CVC placement.^3,4,7,15 We identified several unique risk factors, including systemic anticoagulation as well as ventilator use. Lastly, we identified unexpected deficits in trainee knowledge surrounding management of failed CVCs and negative attitudes regarding attending supervision.

Most existing literature evaluated risk factors for CVC complication prior to routine ultrasound use;^3-5,7,15 surprisingly, it appears that severe mechanical complications do not differ dramatically in the real-time ultrasound era. Eisen et al.³ prospectively studied CVC placement at an academic medical center and found a severe mechanical complication rate (as defined in our paper) of 1.9% due to pneumothorax (1.3%), hemothorax (0.3%), and death (0.3%).We would expect the number of complications to decrease in the postultrasound era, and indeed it appears that pneumothoraces have decreased likely due to ultrasound guidance and decrease in subclavian location. However, in contrast, rates of significant hematomas and bleeding are higher in our study. Although we are unable to state why this may be the case, increasing use of anticoagulation in the general population might explain this finding.¹⁷ For instance, of the 6 patients who experienced hematomas or vascular injuries in our study, 3 were on anticoagulation at the time of CVC placement.

Interestingly, time of academic year of CVC placement and level of training were not correlated with an increased risk of complications, nor was time of day of CVC placement. In contrast, Merrer et al.showed that CVC insertion during nighttime was significantly associated with increased mechanical complications (odds ratio 2.06, 95% confidence interval, 1.04-4.08;,P = 0.03).⁵ This difference may be attributable to the fact that most of our ICUs now have a night float system rather than a more traditional 24-hour call model; therefore, trainees are less likely to be sleep deprived during CVC placement at night.

Severity of illness did not appear to significantly affect mechanical complication rates based on similar APACHE scores between the 2 groups. In addition, other indicators of illness severity (vasopressor use or lactate level) did not suggest that sicker patients may be more likely to experience mechanical complications than others. One could conjecture that perhaps sicker patients were more likely to have lines placed by more experienced trainees, although the present study design does not allow us to answer this question. Interestingly, ventilator use was associated with higher rates of complications. We cannot say definitively why this was the case; however, 1 contributing factor may be the physical constraints of placing the CVC around ventilator tubing.

Several unexpected findings surrounding attending supervision were noted: first, attending supervision appears to be significantly associated with increased complication rate, and second, trainees have negative perceptions regarding attending supervision. Eisen et al.showed a similar association between attending supervision and complication rate.³ It is possible that the increased complication rate is because sicker patients are more likely to have procedural supervision by attendings, attending physicians may be called to supervise when a CVC placement is not going as planned, or attendings may supervise more inexperienced operators. Reasons behind negative trainee attitudes surrounding supervision are unclear and literature on this topic is limited. This is an area that warrants further exploration in future studies.

Another unexpected finding is trainee practices regarding unsuccessful CVC placement; most trainees do not document failed procedures or order follow-up CXRs after unsuccessful CVC attempts. Given the higher risk of complications after unsuccessful CVCs, it is paramount that all physicians are trained to order postprocedure CXR to rule out pneumothorax or hemothorax. Furthermore, documentation of failed procedures is important for medical accuracy, transparency, and also hospital billing. It is unknown if these practices surrounding unsuccessful CVCs are institution-specific or more widespread. As far as we know, this is the first time that trainee practices regarding failed CVC placement have been published. Interestingly, while many current guidelines call attention to prevention, recognition, and management of central line-associated mechanical complications, specific recommendations about postprocedure behavior after failed CVC placement are not published.^9-11 We feel it is critical that institutions reflect on their own practices, especially given that unsuccessful CVCs are shown to be correlated with a significant increase in complication rate. At our own institution, we have initiated an educational component of central line training for medicine trainees specifically addressing failed central line attempts.

This study has several limitations, including a retrospective study design at a single institution. There was a low overall number of complications, which reduced our ability to detect risk factors for complications and did not allow us to perform multivariable adjustment. Other limitations are that only documented CVC attempts were recorded and only those that met our search criteria. Lastly, not all notes contain information such as the number of attempts or peer supervision. Furthermore, the definition of CVC “attempt” is left to the operator’s discretion.

In conclusion, we observed a modern CVC mechanical complication rate of 1.9%. While the complication rate is similar to previous studies, there appear to be lower rates of pneumothorax and higher rates of bleeding complications. We also identified a deficit in trainee education regarding unsuccessful CVC placement; this is a novel finding and requires further investigation at other centers.

Disclosure: The authors have no conflicts of interest to report.

ObGyns are mindfully choosing their practice environments. The trend, as reported by the American College of Obstetricians and Gynecologists (ACOG),¹ shows movement from private practice to employment: an increasing number of ObGyns have joined large practices and are employed. Overall, fewer than half of US physicians owned their medical practice in 2016, reported the American Medical Association (AMA).² This is the first time that the majority of physicians are not practice owners.

Although employed ObGyns earn 9% less than self-employed ObGyns, ($276,000 vs $300,000, respectively), trading a higher salary for less time spent on administrative tasks seems to be worth the pay cut, reports Medscape. Employed ObGyns reported receiving additional benefits that might not have been available to self-employed ObGyns: professional liability coverage, employer-subsidized health and dental insurance, paid time off, and a retirement plan with employer match.³

What matters to ObGyns when choosing a practice setting?

Several decisions about practice setting need to be made at the beginning and throughout a career, among them the type of practice, desired salary, work-life balance, (the latter 2 may be influenced by practice type), and location.

Type of practice

“Patients benefit when physicians practice in settings they find professionally and personally rewarding,” said AMA President Andrew W. Gurman, MD. “The AMA is committed to helping physicians navigate their practice options and offers innovative strategies and resources to ensure physicians in all practice sizes and setting can thrive in the changing health environment.”²

More and more, that environment is a practice wholly owned by physicians. The AMA reports that in 2016, 55.8% of physicians worked in such a practice (including physicians who have an ownership stake in the practice, those who are employed by the practice, and those who are independent contractors).² An approximate 13.8% of physicians worked at practices with more than 50 physicians in 2016. The majority (57.8%), however, practiced in groups with 10 or fewer physicians. The most common practice type was the single-specialty group (42.8%), followed by the multispecialty group practice (24.6%).²

Paying physicians a salary instead of compensating them based on volume may improve physician satisfaction—it removes the need to deal with complex fee-for-service systems, say Ian Larkin, PhD, and George Loewenstein, PhD. In fee-for-service payment arrangements, physicians may be encouraged to order more tests and procedures because doing so may increase income. A better strategy, say Larkin and Loewenstein, is to switch to a straight salary system. Known for their quality of care and comparatively low costs, the Mayo Clinic, Cleveland Clinic, and Kaiser Permanente have successfully implemented this payment system.⁴

Related article:
ObGyn salaries jumped in the last year

Desired salary

The mean income for ObGyns rose by 3% in 2016 over 2015 ($286,000 compared with $277,000), according to Medscape.⁵ This jump follows a gradual increase over the last few years ($249,000 in 2014; $243,000 in 2013; $242,000 in 2012; $220,000 in 2011).^1,5,6

The highest earnings among all physicians were orthopedists ($489,000), plastic surgeons ($440,000), and cardiologists ($410,000). Pediatricians were the lowest paid physicians at $202,000.³

Fair compensation. Fewer than half (48%) of ObGyns who completed the Medscape survey felt they were fairly compensated in 2016, and 41% of those who were dissatisfied with their compensation believed they deserved to be earning between 11% and 25% more. When asked if they would still choose medicine, 72% of ObGyns answered affirmatively. Of those who would choose medicine again, 76% would choose obstetrics and gynecology once more.³

Gender differences. As in years past, full-time male ObGyns reported higher earnings (13%) than female ObGyns ($306,000 vs $270,000, respectively; (FIGURE 1).^3,5,7,8

Among ObGyns who responded to the 2017 Medscape survey, 14% of women and 10% of men indicated that they work part-time.³ Last year, 13% of female ObGyns reported part-time employment versus 16% of male ObGyns.⁶

Among the ObGyns who answered the 2017 survey, there was a gender gap in participation related to race. Although more men than women responded to the survey, more women than men ObGyns among black/African American (women, 78%), Asian (women, 69%), and white/Caucasian (women, 53%) groups responded. Men outweighed women only among Hispanic/Latino ObGyns (60%) who answered the survey.³

Read about work-life balance, job satisfaction, and burnout

Work-life balance

ACOG predicts that mid-career and younger ObGyns will focus on work-life balance issues. Practice sites (ambulatory, hospital, or a combination) that offer part-time schedules or extra time for nonprofessional matters are becoming the most desirable to these practitioners.¹

What satisfies and dissatisfies ObGyns? ObGyns reported to Medscape that their relationships with patients (41% of respondents) was the most rewarding part of their job (FIGURE 2).³

There are many job aspects that dissatisfy ObGyns, including^1,3,9:

• too many bureaucratic tasks
• the short time allotted for each patient office visit
• electronic health records (EHR) and increased computerization
• not feeling appreciated or properly compensated
• spending too many hours at work
• the impact of regulatory changes on clinical practice.

Bureaucratic tasks remain a primary cause for burnout among all physicians.¹⁰ This year, 56% of all physicians reported spending 10 hours or more per week on paperwork and administrative tasks, up from 35% in the 2014 report. More than half (54%) of ObGyns reported spending 10 hours or more on paperwork.³ For every hour of face-to-face patient time, physicians spent nearly 2 additional hours on their EHR and administration tasks.⁹

Time with patients. Medscape reported that 38% of ObGyns spent more than 45 hours per week with patients (FIGURE 3).

ACOG notes that ObGyns are increasingly referring patients to subspecialists, which frustrates patients and increases their costs.¹

ObGyns rank high in burnout rates. Burnout rates for physicians are twice that of other working adults.¹ ObGyns rank second (56%) in burn out (Emergency Medicine, 59%).¹⁰ When Medscape survey respondents were asked to grade their burnout level from 1 to 7 (1 = “It does not interfere with my life;” 7 = “It is so severe that I am thinking of leaving medicine altogether”), ObGyns ranked their burnout level at 4.3.¹⁰ Female physicians reported a higher percentage of burnout than their male colleagues (55% vs 45%, respectively).¹⁰ An estimated 40% to 75% of ObGyns experienced some level of burnout.¹

According to ACOG, the specialty is included among the “noncontrollable” lifestyle specialties, especially for those aged 50 years or younger. Many Millennials (born 1980 to 2000) do not view their work and professional achievement as central to their lives; ObGyns aged younger than 35 years want to work fewer hours per week compared with their older colleagues, says ACOG. However, when this option is unavailable, an increasing number of Millennials report lowered job satisfaction.¹

Related article:
What can administrators and ObGyns do together to reduce physician burnout?
 

Mindfulness about quality of life. The relationship of burnout to quality of life issues is gaining in awareness. In a recent OBG Management article, Lucia DiVenere, MA, noted that, “Being mindful of wellness strategies and practice efficiencies can help ObGyns avoid burnout’s deleterious effects—and thrive both personally and professionally.”¹¹

“We need to stop blaming individuals and treat physician burnout as a system issue…If it affects half our physicians, it is indirectly affecting half our patients,” notes Tait Shanafelt, MD, a hematologist and physician-burnout researcher at the Mayo Clinic.⁹ He says that burnout relates to a physician’s “professional spirit of life, and it primarily affects individuals whose work involves an intense interaction with people.”⁹

The Mayo Clinic in Minneapolis, Minnesota, has taken a lead in developing a space for their physicians to “reset” by offering a room where health professionals can retreat if they need a moment to recover from a traumatic event.⁹

Read about what factors attract ObGyns to specific locations

Location, location, location

Specific areas of the country are more attractive for their higher compensation rates. The highest average compensation was reported by ObGyns in the North Central area ($339,000), West ($301,000), and Great Lakes ($297,000) regions, while the lowest compensation rates were found in the Northwest ($260,000), Southwest ($268,000), and South Central ($275,000) areas.³

Key factors, such as healthy patient populations, higher rates of health insurance coverage, and lower stress levels attract physicians (FIGURE 4). Minnesota ranked the #1 best place to practice because it has the 4th healthiest population, 2nd highest rate of employer-sponsored health insurance, the 17th lowest number of malpractice lawsuits, and a medical board that is the 3rd least harsh in the nation.¹² Unfortunate situations such as the highest malpractice rates per capita, least healthy population, 8th lowest rate of employer-sponsored health insurance, and the 9th lowest compensation rate for physicians make Louisiana the worst place to practice in 2017.¹²

Supply and demand creates substantial geographic imbalances in the number of ObGyns in the United States. ACOG pro-jects that the need for ObGyns will increase nationally by 6% in the next 10 years, although demand will vary geographically from a 27% increase in Nevada to an 11% decrease in West Virginia.¹ Especially vulnerable states (Arizona, Washington, Utah, Idaho) currently have an insufficient supply of ObGyns and are projected to see an increased future demand. Florida, Texas, North Carolina, and Nevada will be at risk, according to ACOG, because the adult female population is expected to increase.¹

2017 Medscape survey demographics

The Medscape Compensation Report 2017 is a based on the responses of 19,270 physicians across 27+ specialties, 5% of whom were ObGyns. Data were collected in an online survey conducted from December 20, 2016, to March 7, 2017.³

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

ObGyns are mindfully choosing their practice environments. The trend, as reported by the American College of Obstetricians and Gynecologists (ACOG),¹ shows movement from private practice to employment: an increasing number of ObGyns have joined large practices and are employed. Overall, fewer than half of US physicians owned their medical practice in 2016, reported the American Medical Association (AMA).² This is the first time that the majority of physicians are not practice owners.

Although employed ObGyns earn 9% less than self-employed ObGyns, ($276,000 vs $300,000, respectively), trading a higher salary for less time spent on administrative tasks seems to be worth the pay cut, reports Medscape. Employed ObGyns reported receiving additional benefits that might not have been available to self-employed ObGyns: professional liability coverage, employer-subsidized health and dental insurance, paid time off, and a retirement plan with employer match.³

What matters to ObGyns when choosing a practice setting?

Several decisions about practice setting need to be made at the beginning and throughout a career, among them the type of practice, desired salary, work-life balance, (the latter 2 may be influenced by practice type), and location.

Type of practice

“Patients benefit when physicians practice in settings they find professionally and personally rewarding,” said AMA President Andrew W. Gurman, MD. “The AMA is committed to helping physicians navigate their practice options and offers innovative strategies and resources to ensure physicians in all practice sizes and setting can thrive in the changing health environment.”²

More and more, that environment is a practice wholly owned by physicians. The AMA reports that in 2016, 55.8% of physicians worked in such a practice (including physicians who have an ownership stake in the practice, those who are employed by the practice, and those who are independent contractors).² An approximate 13.8% of physicians worked at practices with more than 50 physicians in 2016. The majority (57.8%), however, practiced in groups with 10 or fewer physicians. The most common practice type was the single-specialty group (42.8%), followed by the multispecialty group practice (24.6%).²

Paying physicians a salary instead of compensating them based on volume may improve physician satisfaction—it removes the need to deal with complex fee-for-service systems, say Ian Larkin, PhD, and George Loewenstein, PhD. In fee-for-service payment arrangements, physicians may be encouraged to order more tests and procedures because doing so may increase income. A better strategy, say Larkin and Loewenstein, is to switch to a straight salary system. Known for their quality of care and comparatively low costs, the Mayo Clinic, Cleveland Clinic, and Kaiser Permanente have successfully implemented this payment system.⁴

Related article:
ObGyn salaries jumped in the last year

Desired salary

The mean income for ObGyns rose by 3% in 2016 over 2015 ($286,000 compared with $277,000), according to Medscape.⁵ This jump follows a gradual increase over the last few years ($249,000 in 2014; $243,000 in 2013; $242,000 in 2012; $220,000 in 2011).^1,5,6

The highest earnings among all physicians were orthopedists ($489,000), plastic surgeons ($440,000), and cardiologists ($410,000). Pediatricians were the lowest paid physicians at $202,000.³

Fair compensation. Fewer than half (48%) of ObGyns who completed the Medscape survey felt they were fairly compensated in 2016, and 41% of those who were dissatisfied with their compensation believed they deserved to be earning between 11% and 25% more. When asked if they would still choose medicine, 72% of ObGyns answered affirmatively. Of those who would choose medicine again, 76% would choose obstetrics and gynecology once more.³

Gender differences. As in years past, full-time male ObGyns reported higher earnings (13%) than female ObGyns ($306,000 vs $270,000, respectively; (FIGURE 1).^3,5,7,8

Among ObGyns who responded to the 2017 Medscape survey, 14% of women and 10% of men indicated that they work part-time.³ Last year, 13% of female ObGyns reported part-time employment versus 16% of male ObGyns.⁶

Among the ObGyns who answered the 2017 survey, there was a gender gap in participation related to race. Although more men than women responded to the survey, more women than men ObGyns among black/African American (women, 78%), Asian (women, 69%), and white/Caucasian (women, 53%) groups responded. Men outweighed women only among Hispanic/Latino ObGyns (60%) who answered the survey.³

Read about work-life balance, job satisfaction, and burnout

Work-life balance

ACOG predicts that mid-career and younger ObGyns will focus on work-life balance issues. Practice sites (ambulatory, hospital, or a combination) that offer part-time schedules or extra time for nonprofessional matters are becoming the most desirable to these practitioners.¹

What satisfies and dissatisfies ObGyns? ObGyns reported to Medscape that their relationships with patients (41% of respondents) was the most rewarding part of their job (FIGURE 2).³

There are many job aspects that dissatisfy ObGyns, including^1,3,9:

• too many bureaucratic tasks
• the short time allotted for each patient office visit
• electronic health records (EHR) and increased computerization
• not feeling appreciated or properly compensated
• spending too many hours at work
• the impact of regulatory changes on clinical practice.

Bureaucratic tasks remain a primary cause for burnout among all physicians.¹⁰ This year, 56% of all physicians reported spending 10 hours or more per week on paperwork and administrative tasks, up from 35% in the 2014 report. More than half (54%) of ObGyns reported spending 10 hours or more on paperwork.³ For every hour of face-to-face patient time, physicians spent nearly 2 additional hours on their EHR and administration tasks.⁹

Time with patients. Medscape reported that 38% of ObGyns spent more than 45 hours per week with patients (FIGURE 3).

ACOG notes that ObGyns are increasingly referring patients to subspecialists, which frustrates patients and increases their costs.¹

ObGyns rank high in burnout rates. Burnout rates for physicians are twice that of other working adults.¹ ObGyns rank second (56%) in burn out (Emergency Medicine, 59%).¹⁰ When Medscape survey respondents were asked to grade their burnout level from 1 to 7 (1 = “It does not interfere with my life;” 7 = “It is so severe that I am thinking of leaving medicine altogether”), ObGyns ranked their burnout level at 4.3.¹⁰ Female physicians reported a higher percentage of burnout than their male colleagues (55% vs 45%, respectively).¹⁰ An estimated 40% to 75% of ObGyns experienced some level of burnout.¹

According to ACOG, the specialty is included among the “noncontrollable” lifestyle specialties, especially for those aged 50 years or younger. Many Millennials (born 1980 to 2000) do not view their work and professional achievement as central to their lives; ObGyns aged younger than 35 years want to work fewer hours per week compared with their older colleagues, says ACOG. However, when this option is unavailable, an increasing number of Millennials report lowered job satisfaction.¹

Related article:
What can administrators and ObGyns do together to reduce physician burnout?
 

Mindfulness about quality of life. The relationship of burnout to quality of life issues is gaining in awareness. In a recent OBG Management article, Lucia DiVenere, MA, noted that, “Being mindful of wellness strategies and practice efficiencies can help ObGyns avoid burnout’s deleterious effects—and thrive both personally and professionally.”¹¹

“We need to stop blaming individuals and treat physician burnout as a system issue…If it affects half our physicians, it is indirectly affecting half our patients,” notes Tait Shanafelt, MD, a hematologist and physician-burnout researcher at the Mayo Clinic.⁹ He says that burnout relates to a physician’s “professional spirit of life, and it primarily affects individuals whose work involves an intense interaction with people.”⁹

The Mayo Clinic in Minneapolis, Minnesota, has taken a lead in developing a space for their physicians to “reset” by offering a room where health professionals can retreat if they need a moment to recover from a traumatic event.⁹

Read about what factors attract ObGyns to specific locations

Location, location, location

Specific areas of the country are more attractive for their higher compensation rates. The highest average compensation was reported by ObGyns in the North Central area ($339,000), West ($301,000), and Great Lakes ($297,000) regions, while the lowest compensation rates were found in the Northwest ($260,000), Southwest ($268,000), and South Central ($275,000) areas.³

Key factors, such as healthy patient populations, higher rates of health insurance coverage, and lower stress levels attract physicians (FIGURE 4). Minnesota ranked the #1 best place to practice because it has the 4th healthiest population, 2nd highest rate of employer-sponsored health insurance, the 17th lowest number of malpractice lawsuits, and a medical board that is the 3rd least harsh in the nation.¹² Unfortunate situations such as the highest malpractice rates per capita, least healthy population, 8th lowest rate of employer-sponsored health insurance, and the 9th lowest compensation rate for physicians make Louisiana the worst place to practice in 2017.¹²

Supply and demand creates substantial geographic imbalances in the number of ObGyns in the United States. ACOG pro-jects that the need for ObGyns will increase nationally by 6% in the next 10 years, although demand will vary geographically from a 27% increase in Nevada to an 11% decrease in West Virginia.¹ Especially vulnerable states (Arizona, Washington, Utah, Idaho) currently have an insufficient supply of ObGyns and are projected to see an increased future demand. Florida, Texas, North Carolina, and Nevada will be at risk, according to ACOG, because the adult female population is expected to increase.¹

2017 Medscape survey demographics

The Medscape Compensation Report 2017 is a based on the responses of 19,270 physicians across 27+ specialties, 5% of whom were ObGyns. Data were collected in an online survey conducted from December 20, 2016, to March 7, 2017.³

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

ObGyns are mindfully choosing their practice environments. The trend, as reported by the American College of Obstetricians and Gynecologists (ACOG),¹ shows movement from private practice to employment: an increasing number of ObGyns have joined large practices and are employed. Overall, fewer than half of US physicians owned their medical practice in 2016, reported the American Medical Association (AMA).² This is the first time that the majority of physicians are not practice owners.

Although employed ObGyns earn 9% less than self-employed ObGyns, ($276,000 vs $300,000, respectively), trading a higher salary for less time spent on administrative tasks seems to be worth the pay cut, reports Medscape. Employed ObGyns reported receiving additional benefits that might not have been available to self-employed ObGyns: professional liability coverage, employer-subsidized health and dental insurance, paid time off, and a retirement plan with employer match.³

What matters to ObGyns when choosing a practice setting?

Several decisions about practice setting need to be made at the beginning and throughout a career, among them the type of practice, desired salary, work-life balance, (the latter 2 may be influenced by practice type), and location.

Type of practice

“Patients benefit when physicians practice in settings they find professionally and personally rewarding,” said AMA President Andrew W. Gurman, MD. “The AMA is committed to helping physicians navigate their practice options and offers innovative strategies and resources to ensure physicians in all practice sizes and setting can thrive in the changing health environment.”²

More and more, that environment is a practice wholly owned by physicians. The AMA reports that in 2016, 55.8% of physicians worked in such a practice (including physicians who have an ownership stake in the practice, those who are employed by the practice, and those who are independent contractors).² An approximate 13.8% of physicians worked at practices with more than 50 physicians in 2016. The majority (57.8%), however, practiced in groups with 10 or fewer physicians. The most common practice type was the single-specialty group (42.8%), followed by the multispecialty group practice (24.6%).²

Paying physicians a salary instead of compensating them based on volume may improve physician satisfaction—it removes the need to deal with complex fee-for-service systems, say Ian Larkin, PhD, and George Loewenstein, PhD. In fee-for-service payment arrangements, physicians may be encouraged to order more tests and procedures because doing so may increase income. A better strategy, say Larkin and Loewenstein, is to switch to a straight salary system. Known for their quality of care and comparatively low costs, the Mayo Clinic, Cleveland Clinic, and Kaiser Permanente have successfully implemented this payment system.⁴

Related article:
ObGyn salaries jumped in the last year

Desired salary

The mean income for ObGyns rose by 3% in 2016 over 2015 ($286,000 compared with $277,000), according to Medscape.⁵ This jump follows a gradual increase over the last few years ($249,000 in 2014; $243,000 in 2013; $242,000 in 2012; $220,000 in 2011).^1,5,6

The highest earnings among all physicians were orthopedists ($489,000), plastic surgeons ($440,000), and cardiologists ($410,000). Pediatricians were the lowest paid physicians at $202,000.³

Fair compensation. Fewer than half (48%) of ObGyns who completed the Medscape survey felt they were fairly compensated in 2016, and 41% of those who were dissatisfied with their compensation believed they deserved to be earning between 11% and 25% more. When asked if they would still choose medicine, 72% of ObGyns answered affirmatively. Of those who would choose medicine again, 76% would choose obstetrics and gynecology once more.³

Gender differences. As in years past, full-time male ObGyns reported higher earnings (13%) than female ObGyns ($306,000 vs $270,000, respectively; (FIGURE 1).^3,5,7,8

Among ObGyns who responded to the 2017 Medscape survey, 14% of women and 10% of men indicated that they work part-time.³ Last year, 13% of female ObGyns reported part-time employment versus 16% of male ObGyns.⁶

Among the ObGyns who answered the 2017 survey, there was a gender gap in participation related to race. Although more men than women responded to the survey, more women than men ObGyns among black/African American (women, 78%), Asian (women, 69%), and white/Caucasian (women, 53%) groups responded. Men outweighed women only among Hispanic/Latino ObGyns (60%) who answered the survey.³

Read about work-life balance, job satisfaction, and burnout

Work-life balance

ACOG predicts that mid-career and younger ObGyns will focus on work-life balance issues. Practice sites (ambulatory, hospital, or a combination) that offer part-time schedules or extra time for nonprofessional matters are becoming the most desirable to these practitioners.¹

What satisfies and dissatisfies ObGyns? ObGyns reported to Medscape that their relationships with patients (41% of respondents) was the most rewarding part of their job (FIGURE 2).³

There are many job aspects that dissatisfy ObGyns, including^1,3,9:

• too many bureaucratic tasks
• the short time allotted for each patient office visit
• electronic health records (EHR) and increased computerization
• not feeling appreciated or properly compensated
• spending too many hours at work
• the impact of regulatory changes on clinical practice.

Bureaucratic tasks remain a primary cause for burnout among all physicians.¹⁰ This year, 56% of all physicians reported spending 10 hours or more per week on paperwork and administrative tasks, up from 35% in the 2014 report. More than half (54%) of ObGyns reported spending 10 hours or more on paperwork.³ For every hour of face-to-face patient time, physicians spent nearly 2 additional hours on their EHR and administration tasks.⁹

Time with patients. Medscape reported that 38% of ObGyns spent more than 45 hours per week with patients (FIGURE 3).

ACOG notes that ObGyns are increasingly referring patients to subspecialists, which frustrates patients and increases their costs.¹

ObGyns rank high in burnout rates. Burnout rates for physicians are twice that of other working adults.¹ ObGyns rank second (56%) in burn out (Emergency Medicine, 59%).¹⁰ When Medscape survey respondents were asked to grade their burnout level from 1 to 7 (1 = “It does not interfere with my life;” 7 = “It is so severe that I am thinking of leaving medicine altogether”), ObGyns ranked their burnout level at 4.3.¹⁰ Female physicians reported a higher percentage of burnout than their male colleagues (55% vs 45%, respectively).¹⁰ An estimated 40% to 75% of ObGyns experienced some level of burnout.¹

According to ACOG, the specialty is included among the “noncontrollable” lifestyle specialties, especially for those aged 50 years or younger. Many Millennials (born 1980 to 2000) do not view their work and professional achievement as central to their lives; ObGyns aged younger than 35 years want to work fewer hours per week compared with their older colleagues, says ACOG. However, when this option is unavailable, an increasing number of Millennials report lowered job satisfaction.¹

Related article:
What can administrators and ObGyns do together to reduce physician burnout?
 

Mindfulness about quality of life. The relationship of burnout to quality of life issues is gaining in awareness. In a recent OBG Management article, Lucia DiVenere, MA, noted that, “Being mindful of wellness strategies and practice efficiencies can help ObGyns avoid burnout’s deleterious effects—and thrive both personally and professionally.”¹¹

“We need to stop blaming individuals and treat physician burnout as a system issue…If it affects half our physicians, it is indirectly affecting half our patients,” notes Tait Shanafelt, MD, a hematologist and physician-burnout researcher at the Mayo Clinic.⁹ He says that burnout relates to a physician’s “professional spirit of life, and it primarily affects individuals whose work involves an intense interaction with people.”⁹

The Mayo Clinic in Minneapolis, Minnesota, has taken a lead in developing a space for their physicians to “reset” by offering a room where health professionals can retreat if they need a moment to recover from a traumatic event.⁹

Read about what factors attract ObGyns to specific locations

Location, location, location

Specific areas of the country are more attractive for their higher compensation rates. The highest average compensation was reported by ObGyns in the North Central area ($339,000), West ($301,000), and Great Lakes ($297,000) regions, while the lowest compensation rates were found in the Northwest ($260,000), Southwest ($268,000), and South Central ($275,000) areas.³

Key factors, such as healthy patient populations, higher rates of health insurance coverage, and lower stress levels attract physicians (FIGURE 4). Minnesota ranked the #1 best place to practice because it has the 4th healthiest population, 2nd highest rate of employer-sponsored health insurance, the 17th lowest number of malpractice lawsuits, and a medical board that is the 3rd least harsh in the nation.¹² Unfortunate situations such as the highest malpractice rates per capita, least healthy population, 8th lowest rate of employer-sponsored health insurance, and the 9th lowest compensation rate for physicians make Louisiana the worst place to practice in 2017.¹²

Supply and demand creates substantial geographic imbalances in the number of ObGyns in the United States. ACOG pro-jects that the need for ObGyns will increase nationally by 6% in the next 10 years, although demand will vary geographically from a 27% increase in Nevada to an 11% decrease in West Virginia.¹ Especially vulnerable states (Arizona, Washington, Utah, Idaho) currently have an insufficient supply of ObGyns and are projected to see an increased future demand. Florida, Texas, North Carolina, and Nevada will be at risk, according to ACOG, because the adult female population is expected to increase.¹

2017 Medscape survey demographics

The Medscape Compensation Report 2017 is a based on the responses of 19,270 physicians across 27+ specialties, 5% of whom were ObGyns. Data were collected in an online survey conducted from December 20, 2016, to March 7, 2017.³

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

Skilled nursing facilities (SNFs) play a crucial role in the hospital readmission process.Approximately 1 in 4 Medicare beneficiaries discharged from an acute care hospital is admitted to a SNF instead of returning directly home. Of these patients, 1 in 4 will be readmitted within 30 days,¹ a rate significantly higher than the readmission rate of the inpatient population as a whole.² The 2014 Protecting Access to Medicare Act created a value-based purchasing program that will use quality measures to steer funds to, or away from, individual SNFs. When the program takes effect in 2018, the Centers for Medicare & Medicaid Services will use SNFs’ 30-day all-cause readmission rate to determine which SNFs receive payments and which receive penalties.³ The Affordable Care Act, passed in 2010, has also established penalties for hospitals with higher than expected readmission rates for Medicare patients.⁴

Despite this intensifying regulatory focus, relatively little is known about the factors that drive readmissions from SNFs. A prospective review of data from SNFs in 4 states has shown that SNFs staffed by nurse practitioners or physician assistants and those equipped to provide intravenous therapy were less likely to transfer patients to the hospital for ambulatory care-sensitive diagnoses.⁵ Qualitative studies have provided useful insight into the causes of SNF-to-hospital transfers but have not focused on 30-day readmissions.^6,7 A single survey-based study has examined the causes of SNF-to-hospital readmissions.⁸ However, survey-based methodologies have limited ability to capture the complex perspectives of SNF clinicians, who play a critical role in determining which SNF patients require evaluation or treatment in an acute care setting.

To address this gap in knowledge about factors contributing to SNF readmissions, we conducted a qualitative study examining SNF clinicians’ perspectives on patients readmitted to the hospital within 30 days of discharge. We used a structured interview tool to explore the root causes of readmission with frontline SNF staff, with the goal of using this knowledge to inform future hospital quality improvement (QI) efforts.

Case Identification

Hospital data-tracking software (Allscripts) was used to identify patients who experienced a 30-day, unplanned readmission from SNFs to an academic medical center. We restricted our search to patients whose index admission and readmission were to the medical center’s inpatient general medicine service. A study team member (BWC) monitored the dataset on a weekly basis and contacted SNF clinicians by e-mail and telephone to arrange interviews at times of mutual convenience. To mitigate against recall bias, interviews were conducted within 30 days of the readmission in question. A total of 32 cases were identified. No SNF clinicians refused a request for interview. For 8 of these cases, it was not possible to find a time of mutual convenience within the specified 30-day window. The remaining 24 cases involved patients from 15 SNFs across Connecticut. Interviews were conducted from August 2015 to November 2015.

The project was reviewed by our institution’s Human Investigation Committee and was exempted from Institutional Review Board review.

Study Participants

Interviews were conducted on-site at SNFs with groups of 1 to 4 SNF clinicians and administrators. SNF participants were informed of interviewer credentials and the study’s QI goals prior to participation. Participation was voluntary and did not affect the clinician’s relationship with the hospital or the SNF. Participants were not paid.

Interventions to Reduce Acute Care Transfers (INTERACT) is a QI program that includes training for clinicians, communication tools, and advance care planning tools.⁹ INTERACT is currently used in 138 Connecticut SNFs as part of a statewide QI effort funded by the Connecticut State Department of Public Health. In prospective QI studies,^10,11 implementation of INTERACT has been associated with decreased transfers from SNFs to acute care hospitals. The INTERACT Quality Improvement Tool, one part of the INTERACT bundle of interventions, is a 26-item questionnaire used to identify root causes of transfers from SNFs to acute care hospitals. It includes both checklists and open-ended questions about patient factors, SNF procedures, and SNF clinical decision-making.

We used the INTERACT QI Tool¹² to conduct structured interviews with nurses and administrators at SNFs. Interviewers used a hard copy of the tool to maintain field notes, and all parts of the questionnaire were completed in each interview. Although the questionnaire elicits baseline demographic and medical information, such as the patient’s age and vital signs prior to readmission, the majority of each interview was dedicated to discussion of the open-ended questions in Table 1. Upon completion of the INTERACT QI Tool, the interviewer asked 2 open-ended questions about reducing readmissions and 4 closed-ended questions regarding SNF admission procedures. (Table 1) The supplemental questions were added after preliminary interviews with SNF clinicians revealed concerns about the SNF referral process and about communication between the hospital, emergency department (ED) and SNFs—issues not included in the INTERACT questionnaire. Interviewers used phatic communication, probing questions, and follow-up questions to elicit detailed information from participants, and participant responses were not limited to topics in the questionnaire and the list of supplemental questions.

Interviews were conducted by a hospital clinical integration coordinator, social worker, and a physician (KB, MCB, BWC). All interviewers received formal training in qualitative research methods prior to the study.

All interviews were audio recorded, with permission from the participants, and were professionally transcribed. Field notes were maintained to ensure accuracy of INTERACT QI Tool data. Participant interviews covered no more than two cases per session and lasted from 18 to 71 minutes (mean duration, 38 minutes).

Analysis

Analysis of transcripts was inductive and informed by grounded theory methodology, in which data is reviewed for repeating ideas, which are then analyzed and grouped to develop a theoretical understanding of the phenomenon under investigation.^13,14

A preliminary codebook was developed using transcripts of the first 11 interviews. All statements relevant to the readmission process were extracted from the raw interview transcript and collected into a single list. This list was then reviewed for statements sharing a particular idea or concern. Such statements were grouped together under the heading of a repeating idea, and each repeating idea was assigned a code. Using this codebook, each transcript was independently reviewed and coded by three study team members with formal training in inductive qualitative analysis (KB, KTM, BWC). Reviewers assigned codes to sections of relevant text. Discrepancies in code assignment were discussed among the 3 analysts until consensus was reached. Using the method of constant comparison described in grounded theory,the codebook was updated continuously as the process of coding transcripts proceeded.¹² Changes to the codebook were discussed among the coding team until consensus was achieved. The process of data acquisition and coding continued until theoretical saturation was reached. Themes relating to underlying factors associated with readmissions were then identified based on shared properties among repeating ideas. ATLAS.ti (Scientific Software, Berlin, Germany, Version 7) was used to facilitate data organization and retrieval.

The SNFs in our study included 12 for-profit and 3 non-profit facilities. The number of licensed beds in each facility ranged from 73 to 360, with a mean of 148 beds. The SNFs had CMS Nursing Home Compare ratings ranging from 1 star, the lowest possible rating, to 5 stars, the highest possible,¹⁵ with a mean rating of 2.9 stars. Our analysis did not reveal differences in perceived contributions to readmissions from large vs. small or highly rated vs poorly rated SNFs.

Emergent transitions: Lack of coordination between ED and SNF

SNF clinicians frequently encountered situations in which a relatively stable patient was readmitted to the hospital after being transferred to the ED, despite the fact that SNF clinicians believed the patient should have returned to the SNF once a specific test was performed or service rendered at the ED. Commonly cited clinical scenarios that resulted in such readmissions included placement of urinary catheters and evaluation for cystitis. An assistant director of nursing reported that “the ER doesn’t want to hear my side of the story,” making it difficult for her to provide information that would prevent such readmissions. Other SNF clinicians reported similar difficulties in communicating with ED clinicians.

Code status: Incompletely addressed goals of care

The SNF clinicians in our study described cases in which patients with end-stage lung disease and disseminated cancer were readmitted to the hospital, despite SNF efforts to prevent readmission and provide palliative care within the SNF. For example, a SNF advanced practice nurse described a case in which a patient with widely metastatic cancer requested readmission to the hospital for treatment of deep vein thrombosis, despite longstanding recommendations from SNF staff that the patient forego hospitalization and enroll in hospice care. After discussion of code status and goals of care with hospital clinicians, the patient chose to enroll in hospice care and not to continue anticoagulation. SNF clinicians often perceived that, in the words of one administrator, “the palliative talks in the hospital outweigh our talks by a lot.” Numerous SNF clinicians believed that in-depth clarification of goals of care prior to discharge could prevent some readmissions.

Wrong patient, wrong place: Mismatch between clinical needs and SNF capabilities

One director of nursing stated that “[when] you read a referral, there’s a huge difference sometimes between what you read and what you see.” SNF clinicians reported that this discrepancy between clinical report and clinical reality often leads to patients being placed in SNFs that are unequipped to care for them. Many patients were perceived as being too ill for discharge from the acute-care setting in the first place. A nurse manager described this as a pattern of “pushing patients out of the hospital.” However, mismatches in clinical disposition were also seen as contributing to readmissions for medically stable patients, such as those with dementia, for whom SNFs frequently lack adequate staffing and physical safeguards.

Missing links: Important clinical information not effectively communicated by hospital

SNF clinicians described numerous challenges in formulating plans of care based on hospital discharge documentation. Discrepancies between discharge summaries and patient instructions were perceived as common and potential causes of readmissions. For patients discharged from the academic medical center in this study, medication instructions are included in both the discharge summary sent to the SNF and in a patient instruction packet. Several SNF clinicians said that it was common for a course of antibiotics to be listed on the discharge summary but not the patient instruction packet, or vice versa. SNF clinicians, who usually lack access to the hospital’s electronic medical record, have limited means for determining the correct document. Other important clinical data points, such as intermittent intravenous (IV) furosemide dosing and suppressive antibiotic regimens, were omitted from discharge paperwork altogether. SNF clinicians had difficulty reaching hospital clinicians who could clarify these clinical questions. “Good luck finding the person that took care of [the patient] three days before,” said one director of nursing.

Change starts at home: Challenges in SNF processes and culture

Many clinicians in our study reported that their facilities had recently added clinical capabilities in an effort to care for patients with complex medical problems. For example, to prevent transfers of patients with decompensated heart failure, several facilities in our study had recently obtained certification to give IV diuretics. However, as one director of nursing stated, these efforts require “buy-in” from doctors to decrease readmissions. That buy-in has not always been forthcoming. SNF clinicians also reported difficulty convincing patients and families that their facilities are capable of providing care that, in the past, might only have been available in acute-care settings.

These themes, along with associated sub-themes and representative quotations, are shown above (Table 4).

Our study suggests that the interaction between EDs and SNFs is an important and understudied domain in the spectrum of events leading to readmission. Prior studies have documented inadequacies in patient information provided by SNFs to EDs.^16,17 Efforts to improve SNF-to-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^18,19 However, many of the clinicians in our study reported taking proactive steps to communicate with ED clinicians. These efforts encountered logistical and cultural barriers, with information that might have prevented readmission failing to reach ED providers. Many of the SNF clinicians in our study perceived this failure as a common cause of readmission, especially for relatively stable SNF patients.

Previous studies have pointed to a role for goals of care discussions in reducing hospital readmissions.²⁰ Our data underscore an important qualification to these findings: Location matters. The SNF clinicians in our study reported frequent and detailed goals of care discussions with their patients. However, they also reported that goals of care discussions held in the subacute setting carried less weight with patients and families than discussions held in the hospital. SNF clinicians described a number of cases in which patients were willing to adjust code status or goals of care only after being readmitted to the hospital.

Our study also points to the implications of existing research showing that patients are discharged from acute care hospitals “quicker and sicker” than they had been prior to the 1983 adoption of Medicare’s prospective payment system.²¹ Specifically, the SNF clinicians we interviewed perceived a strong link between patient acuity at the time of transfer and SNFs’ persistently high readmission rates. As SNFs have worked to expand their clinical capabilities, they struggle to win buy-in from physicians and families, many of whom view SNFs as incapable of managing acute illness. Many SNF clinicians also pointed to deficiencies in their own referral and admission processes as a recurring cause of readmissions. For example, several patients in our analysis suffered from dementia. Although these patients were stable enough to leave the acute care setting, the SNF clinicians responsible for their readmissions felt that their SNFs were not well-equipped to care for patients with dementia and that the patients should instead have been transferred to facilities with more robust resources for dementia care.

Finally, our findings highlight a fundamental tension between hospitals and SNFs: Which facility ought to shoulder the responsibility and cost for services that may prevent a readmission—the hospital or the SNF? For example, does responsibility for coordinating subspecialist evaluation of a patient’s chronic condition fall to the hospital or to the SNF? If such an evaluation is undertaken during a hospitalization, it prolongs the patient’s hospital stay and happens at the hospital’s expense. If the patient is discharged to a SNF and sees the subspecialist in clinic, then the SNF must pay for transportation to and from the clinic appointment. SNF clinicians expressed near unanimity that fragmented models of care and high barriers to communication made it difficult to design solutions to these dilemmas.

Strengths and limitations

To our knowledge, this is the first interview-based study examining SNF clinicians’ perspectives on unplanned, 30-day hospital readmissions. We gathered information from clinicians with a range of clinical experience, all of whom had cared directly for the patient who had been readmitted. Our data came from clinicians at 15 SNFs of varying sizes and quality ratings, allowing us to identify a broad range of factors contributing to readmissions.

Because this study relied on qualitative methods, it should be viewed as hypothesis-generating rather than hypothesis-confirming. Further research is needed to determine whether variables related to the themes above are causally linked to SNF readmissions. We identified cases for review using convenience sampling of a cohort of readmitted patients at a single tertiary-care hospital, and all participating SNFs were located in Connecticut. These factors may limit the generalizability of our findings. Although the clinicians we interviewed occupied diverse roles within their respective SNFs, our sample did not include direct-care staff without managerial responsibility, such as certified nursing assistants or licensed practical nurses. This prevented our study from identifying themes into which managers would have limited insight, especially those involving cultural and management practices leading to poor communication between them and their staff. Because our study examines cases in which discharge and readmission were to a general medicine service, it may not describe factors relevant to patients discharged from subspecialist or surgical services.

Implications for future QI efforts and research

Several clinicians we interviewed suggested that readmissions might be reduced by dedicating the services of a hospital professional, such as a nurse or case manager, to monitoring the clinical course of medically complex patients after discharge. A dedicated “transition coach” could clarify deficiencies in discharge paperwork, facilitate necessary follow-up appointments, liaise with staff at both the hospital and the SNF, or coordinate acquisition of necessary equipment. Prospective trials have demonstrated that such interventions can decrease readmission rates among hospitalized patients,^22,23 but formal studies have not been carried out among cohorts of SNF patients.

Prior efforts to improve SNF-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^24,25 The experiences of SNF clinicians in our study suggest that important information also fails to make its way from ED providers to SNFs and that this failure results in unnecessary readmissions of relatively stable SNF patients. Thus, hospitals may be able to prevent SNF readmissions by creating lines of communication between EDs and SNFs and by ensuring that ED physicians and mid-level providers are familiar with the clinical capabilities of local SNFs.

Future research and QI work should also investigate approaches to care coordination that ensure that complex patients are placed in SNFs with resources adequate to address their comorbidities. Potential interventions might include increased use of SNF “liaisons,” who would evaluate patients in-person prior to approving transfer to a given SNF. As has been previously suggested,²⁶ hospitals might also reduce readmissions by narrowing the pool of facilities to which they transfer patients, thereby building more robust, interconnected relationships with a smaller number of SNFs.

SNF clinicians identified areas for improvement at almost every point in the chain of events spanning hospitalization, discharge, and transfer. Among the most frequently cited contributors to readmissions were clinical instability at the time of discharge and omission of clinically important information from discharge documentation. Improved communication between hospitals, ED clinicians, and SNFs, as well as more thoroughly defined goals of care at the time of discharge, were seen as promising ways of decreasing readmissions. Successful interventions for reducing readmissions from SNFs will likely require multifaceted approaches to these problems.

Disclosure: This research was supported by a grant (#P30HS023554-01) from the Agency for Healthcare Research and Quality (AHRQ) and received support from Yale New Haven Hospital and the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA).

Skilled nursing facilities (SNFs) play a crucial role in the hospital readmission process.Approximately 1 in 4 Medicare beneficiaries discharged from an acute care hospital is admitted to a SNF instead of returning directly home. Of these patients, 1 in 4 will be readmitted within 30 days,¹ a rate significantly higher than the readmission rate of the inpatient population as a whole.² The 2014 Protecting Access to Medicare Act created a value-based purchasing program that will use quality measures to steer funds to, or away from, individual SNFs. When the program takes effect in 2018, the Centers for Medicare & Medicaid Services will use SNFs’ 30-day all-cause readmission rate to determine which SNFs receive payments and which receive penalties.³ The Affordable Care Act, passed in 2010, has also established penalties for hospitals with higher than expected readmission rates for Medicare patients.⁴

Despite this intensifying regulatory focus, relatively little is known about the factors that drive readmissions from SNFs. A prospective review of data from SNFs in 4 states has shown that SNFs staffed by nurse practitioners or physician assistants and those equipped to provide intravenous therapy were less likely to transfer patients to the hospital for ambulatory care-sensitive diagnoses.⁵ Qualitative studies have provided useful insight into the causes of SNF-to-hospital transfers but have not focused on 30-day readmissions.^6,7 A single survey-based study has examined the causes of SNF-to-hospital readmissions.⁸ However, survey-based methodologies have limited ability to capture the complex perspectives of SNF clinicians, who play a critical role in determining which SNF patients require evaluation or treatment in an acute care setting.

To address this gap in knowledge about factors contributing to SNF readmissions, we conducted a qualitative study examining SNF clinicians’ perspectives on patients readmitted to the hospital within 30 days of discharge. We used a structured interview tool to explore the root causes of readmission with frontline SNF staff, with the goal of using this knowledge to inform future hospital quality improvement (QI) efforts.

Case Identification

Hospital data-tracking software (Allscripts) was used to identify patients who experienced a 30-day, unplanned readmission from SNFs to an academic medical center. We restricted our search to patients whose index admission and readmission were to the medical center’s inpatient general medicine service. A study team member (BWC) monitored the dataset on a weekly basis and contacted SNF clinicians by e-mail and telephone to arrange interviews at times of mutual convenience. To mitigate against recall bias, interviews were conducted within 30 days of the readmission in question. A total of 32 cases were identified. No SNF clinicians refused a request for interview. For 8 of these cases, it was not possible to find a time of mutual convenience within the specified 30-day window. The remaining 24 cases involved patients from 15 SNFs across Connecticut. Interviews were conducted from August 2015 to November 2015.

The project was reviewed by our institution’s Human Investigation Committee and was exempted from Institutional Review Board review.

Study Participants

Interviews were conducted on-site at SNFs with groups of 1 to 4 SNF clinicians and administrators. SNF participants were informed of interviewer credentials and the study’s QI goals prior to participation. Participation was voluntary and did not affect the clinician’s relationship with the hospital or the SNF. Participants were not paid.

Interventions to Reduce Acute Care Transfers (INTERACT) is a QI program that includes training for clinicians, communication tools, and advance care planning tools.⁹ INTERACT is currently used in 138 Connecticut SNFs as part of a statewide QI effort funded by the Connecticut State Department of Public Health. In prospective QI studies,^10,11 implementation of INTERACT has been associated with decreased transfers from SNFs to acute care hospitals. The INTERACT Quality Improvement Tool, one part of the INTERACT bundle of interventions, is a 26-item questionnaire used to identify root causes of transfers from SNFs to acute care hospitals. It includes both checklists and open-ended questions about patient factors, SNF procedures, and SNF clinical decision-making.

We used the INTERACT QI Tool¹² to conduct structured interviews with nurses and administrators at SNFs. Interviewers used a hard copy of the tool to maintain field notes, and all parts of the questionnaire were completed in each interview. Although the questionnaire elicits baseline demographic and medical information, such as the patient’s age and vital signs prior to readmission, the majority of each interview was dedicated to discussion of the open-ended questions in Table 1. Upon completion of the INTERACT QI Tool, the interviewer asked 2 open-ended questions about reducing readmissions and 4 closed-ended questions regarding SNF admission procedures. (Table 1) The supplemental questions were added after preliminary interviews with SNF clinicians revealed concerns about the SNF referral process and about communication between the hospital, emergency department (ED) and SNFs—issues not included in the INTERACT questionnaire. Interviewers used phatic communication, probing questions, and follow-up questions to elicit detailed information from participants, and participant responses were not limited to topics in the questionnaire and the list of supplemental questions.

Interviews were conducted by a hospital clinical integration coordinator, social worker, and a physician (KB, MCB, BWC). All interviewers received formal training in qualitative research methods prior to the study.

All interviews were audio recorded, with permission from the participants, and were professionally transcribed. Field notes were maintained to ensure accuracy of INTERACT QI Tool data. Participant interviews covered no more than two cases per session and lasted from 18 to 71 minutes (mean duration, 38 minutes).

Analysis

Analysis of transcripts was inductive and informed by grounded theory methodology, in which data is reviewed for repeating ideas, which are then analyzed and grouped to develop a theoretical understanding of the phenomenon under investigation.^13,14

A preliminary codebook was developed using transcripts of the first 11 interviews. All statements relevant to the readmission process were extracted from the raw interview transcript and collected into a single list. This list was then reviewed for statements sharing a particular idea or concern. Such statements were grouped together under the heading of a repeating idea, and each repeating idea was assigned a code. Using this codebook, each transcript was independently reviewed and coded by three study team members with formal training in inductive qualitative analysis (KB, KTM, BWC). Reviewers assigned codes to sections of relevant text. Discrepancies in code assignment were discussed among the 3 analysts until consensus was reached. Using the method of constant comparison described in grounded theory,the codebook was updated continuously as the process of coding transcripts proceeded.¹² Changes to the codebook were discussed among the coding team until consensus was achieved. The process of data acquisition and coding continued until theoretical saturation was reached. Themes relating to underlying factors associated with readmissions were then identified based on shared properties among repeating ideas. ATLAS.ti (Scientific Software, Berlin, Germany, Version 7) was used to facilitate data organization and retrieval.

The SNFs in our study included 12 for-profit and 3 non-profit facilities. The number of licensed beds in each facility ranged from 73 to 360, with a mean of 148 beds. The SNFs had CMS Nursing Home Compare ratings ranging from 1 star, the lowest possible rating, to 5 stars, the highest possible,¹⁵ with a mean rating of 2.9 stars. Our analysis did not reveal differences in perceived contributions to readmissions from large vs. small or highly rated vs poorly rated SNFs.

Emergent transitions: Lack of coordination between ED and SNF

SNF clinicians frequently encountered situations in which a relatively stable patient was readmitted to the hospital after being transferred to the ED, despite the fact that SNF clinicians believed the patient should have returned to the SNF once a specific test was performed or service rendered at the ED. Commonly cited clinical scenarios that resulted in such readmissions included placement of urinary catheters and evaluation for cystitis. An assistant director of nursing reported that “the ER doesn’t want to hear my side of the story,” making it difficult for her to provide information that would prevent such readmissions. Other SNF clinicians reported similar difficulties in communicating with ED clinicians.

Code status: Incompletely addressed goals of care

The SNF clinicians in our study described cases in which patients with end-stage lung disease and disseminated cancer were readmitted to the hospital, despite SNF efforts to prevent readmission and provide palliative care within the SNF. For example, a SNF advanced practice nurse described a case in which a patient with widely metastatic cancer requested readmission to the hospital for treatment of deep vein thrombosis, despite longstanding recommendations from SNF staff that the patient forego hospitalization and enroll in hospice care. After discussion of code status and goals of care with hospital clinicians, the patient chose to enroll in hospice care and not to continue anticoagulation. SNF clinicians often perceived that, in the words of one administrator, “the palliative talks in the hospital outweigh our talks by a lot.” Numerous SNF clinicians believed that in-depth clarification of goals of care prior to discharge could prevent some readmissions.

Wrong patient, wrong place: Mismatch between clinical needs and SNF capabilities

One director of nursing stated that “[when] you read a referral, there’s a huge difference sometimes between what you read and what you see.” SNF clinicians reported that this discrepancy between clinical report and clinical reality often leads to patients being placed in SNFs that are unequipped to care for them. Many patients were perceived as being too ill for discharge from the acute-care setting in the first place. A nurse manager described this as a pattern of “pushing patients out of the hospital.” However, mismatches in clinical disposition were also seen as contributing to readmissions for medically stable patients, such as those with dementia, for whom SNFs frequently lack adequate staffing and physical safeguards.

Missing links: Important clinical information not effectively communicated by hospital

SNF clinicians described numerous challenges in formulating plans of care based on hospital discharge documentation. Discrepancies between discharge summaries and patient instructions were perceived as common and potential causes of readmissions. For patients discharged from the academic medical center in this study, medication instructions are included in both the discharge summary sent to the SNF and in a patient instruction packet. Several SNF clinicians said that it was common for a course of antibiotics to be listed on the discharge summary but not the patient instruction packet, or vice versa. SNF clinicians, who usually lack access to the hospital’s electronic medical record, have limited means for determining the correct document. Other important clinical data points, such as intermittent intravenous (IV) furosemide dosing and suppressive antibiotic regimens, were omitted from discharge paperwork altogether. SNF clinicians had difficulty reaching hospital clinicians who could clarify these clinical questions. “Good luck finding the person that took care of [the patient] three days before,” said one director of nursing.

Change starts at home: Challenges in SNF processes and culture

Many clinicians in our study reported that their facilities had recently added clinical capabilities in an effort to care for patients with complex medical problems. For example, to prevent transfers of patients with decompensated heart failure, several facilities in our study had recently obtained certification to give IV diuretics. However, as one director of nursing stated, these efforts require “buy-in” from doctors to decrease readmissions. That buy-in has not always been forthcoming. SNF clinicians also reported difficulty convincing patients and families that their facilities are capable of providing care that, in the past, might only have been available in acute-care settings.

These themes, along with associated sub-themes and representative quotations, are shown above (Table 4).

Our study suggests that the interaction between EDs and SNFs is an important and understudied domain in the spectrum of events leading to readmission. Prior studies have documented inadequacies in patient information provided by SNFs to EDs.^16,17 Efforts to improve SNF-to-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^18,19 However, many of the clinicians in our study reported taking proactive steps to communicate with ED clinicians. These efforts encountered logistical and cultural barriers, with information that might have prevented readmission failing to reach ED providers. Many of the SNF clinicians in our study perceived this failure as a common cause of readmission, especially for relatively stable SNF patients.

Previous studies have pointed to a role for goals of care discussions in reducing hospital readmissions.²⁰ Our data underscore an important qualification to these findings: Location matters. The SNF clinicians in our study reported frequent and detailed goals of care discussions with their patients. However, they also reported that goals of care discussions held in the subacute setting carried less weight with patients and families than discussions held in the hospital. SNF clinicians described a number of cases in which patients were willing to adjust code status or goals of care only after being readmitted to the hospital.

Our study also points to the implications of existing research showing that patients are discharged from acute care hospitals “quicker and sicker” than they had been prior to the 1983 adoption of Medicare’s prospective payment system.²¹ Specifically, the SNF clinicians we interviewed perceived a strong link between patient acuity at the time of transfer and SNFs’ persistently high readmission rates. As SNFs have worked to expand their clinical capabilities, they struggle to win buy-in from physicians and families, many of whom view SNFs as incapable of managing acute illness. Many SNF clinicians also pointed to deficiencies in their own referral and admission processes as a recurring cause of readmissions. For example, several patients in our analysis suffered from dementia. Although these patients were stable enough to leave the acute care setting, the SNF clinicians responsible for their readmissions felt that their SNFs were not well-equipped to care for patients with dementia and that the patients should instead have been transferred to facilities with more robust resources for dementia care.

Finally, our findings highlight a fundamental tension between hospitals and SNFs: Which facility ought to shoulder the responsibility and cost for services that may prevent a readmission—the hospital or the SNF? For example, does responsibility for coordinating subspecialist evaluation of a patient’s chronic condition fall to the hospital or to the SNF? If such an evaluation is undertaken during a hospitalization, it prolongs the patient’s hospital stay and happens at the hospital’s expense. If the patient is discharged to a SNF and sees the subspecialist in clinic, then the SNF must pay for transportation to and from the clinic appointment. SNF clinicians expressed near unanimity that fragmented models of care and high barriers to communication made it difficult to design solutions to these dilemmas.

Strengths and limitations

To our knowledge, this is the first interview-based study examining SNF clinicians’ perspectives on unplanned, 30-day hospital readmissions. We gathered information from clinicians with a range of clinical experience, all of whom had cared directly for the patient who had been readmitted. Our data came from clinicians at 15 SNFs of varying sizes and quality ratings, allowing us to identify a broad range of factors contributing to readmissions.

Because this study relied on qualitative methods, it should be viewed as hypothesis-generating rather than hypothesis-confirming. Further research is needed to determine whether variables related to the themes above are causally linked to SNF readmissions. We identified cases for review using convenience sampling of a cohort of readmitted patients at a single tertiary-care hospital, and all participating SNFs were located in Connecticut. These factors may limit the generalizability of our findings. Although the clinicians we interviewed occupied diverse roles within their respective SNFs, our sample did not include direct-care staff without managerial responsibility, such as certified nursing assistants or licensed practical nurses. This prevented our study from identifying themes into which managers would have limited insight, especially those involving cultural and management practices leading to poor communication between them and their staff. Because our study examines cases in which discharge and readmission were to a general medicine service, it may not describe factors relevant to patients discharged from subspecialist or surgical services.

Implications for future QI efforts and research

Several clinicians we interviewed suggested that readmissions might be reduced by dedicating the services of a hospital professional, such as a nurse or case manager, to monitoring the clinical course of medically complex patients after discharge. A dedicated “transition coach” could clarify deficiencies in discharge paperwork, facilitate necessary follow-up appointments, liaise with staff at both the hospital and the SNF, or coordinate acquisition of necessary equipment. Prospective trials have demonstrated that such interventions can decrease readmission rates among hospitalized patients,^22,23 but formal studies have not been carried out among cohorts of SNF patients.

Prior efforts to improve SNF-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^24,25 The experiences of SNF clinicians in our study suggest that important information also fails to make its way from ED providers to SNFs and that this failure results in unnecessary readmissions of relatively stable SNF patients. Thus, hospitals may be able to prevent SNF readmissions by creating lines of communication between EDs and SNFs and by ensuring that ED physicians and mid-level providers are familiar with the clinical capabilities of local SNFs.

Future research and QI work should also investigate approaches to care coordination that ensure that complex patients are placed in SNFs with resources adequate to address their comorbidities. Potential interventions might include increased use of SNF “liaisons,” who would evaluate patients in-person prior to approving transfer to a given SNF. As has been previously suggested,²⁶ hospitals might also reduce readmissions by narrowing the pool of facilities to which they transfer patients, thereby building more robust, interconnected relationships with a smaller number of SNFs.

SNF clinicians identified areas for improvement at almost every point in the chain of events spanning hospitalization, discharge, and transfer. Among the most frequently cited contributors to readmissions were clinical instability at the time of discharge and omission of clinically important information from discharge documentation. Improved communication between hospitals, ED clinicians, and SNFs, as well as more thoroughly defined goals of care at the time of discharge, were seen as promising ways of decreasing readmissions. Successful interventions for reducing readmissions from SNFs will likely require multifaceted approaches to these problems.

Disclosure: This research was supported by a grant (#P30HS023554-01) from the Agency for Healthcare Research and Quality (AHRQ) and received support from Yale New Haven Hospital and the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA).

Skilled nursing facilities (SNFs) play a crucial role in the hospital readmission process.Approximately 1 in 4 Medicare beneficiaries discharged from an acute care hospital is admitted to a SNF instead of returning directly home. Of these patients, 1 in 4 will be readmitted within 30 days,¹ a rate significantly higher than the readmission rate of the inpatient population as a whole.² The 2014 Protecting Access to Medicare Act created a value-based purchasing program that will use quality measures to steer funds to, or away from, individual SNFs. When the program takes effect in 2018, the Centers for Medicare & Medicaid Services will use SNFs’ 30-day all-cause readmission rate to determine which SNFs receive payments and which receive penalties.³ The Affordable Care Act, passed in 2010, has also established penalties for hospitals with higher than expected readmission rates for Medicare patients.⁴

Despite this intensifying regulatory focus, relatively little is known about the factors that drive readmissions from SNFs. A prospective review of data from SNFs in 4 states has shown that SNFs staffed by nurse practitioners or physician assistants and those equipped to provide intravenous therapy were less likely to transfer patients to the hospital for ambulatory care-sensitive diagnoses.⁵ Qualitative studies have provided useful insight into the causes of SNF-to-hospital transfers but have not focused on 30-day readmissions.^6,7 A single survey-based study has examined the causes of SNF-to-hospital readmissions.⁸ However, survey-based methodologies have limited ability to capture the complex perspectives of SNF clinicians, who play a critical role in determining which SNF patients require evaluation or treatment in an acute care setting.

To address this gap in knowledge about factors contributing to SNF readmissions, we conducted a qualitative study examining SNF clinicians’ perspectives on patients readmitted to the hospital within 30 days of discharge. We used a structured interview tool to explore the root causes of readmission with frontline SNF staff, with the goal of using this knowledge to inform future hospital quality improvement (QI) efforts.

Case Identification

Hospital data-tracking software (Allscripts) was used to identify patients who experienced a 30-day, unplanned readmission from SNFs to an academic medical center. We restricted our search to patients whose index admission and readmission were to the medical center’s inpatient general medicine service. A study team member (BWC) monitored the dataset on a weekly basis and contacted SNF clinicians by e-mail and telephone to arrange interviews at times of mutual convenience. To mitigate against recall bias, interviews were conducted within 30 days of the readmission in question. A total of 32 cases were identified. No SNF clinicians refused a request for interview. For 8 of these cases, it was not possible to find a time of mutual convenience within the specified 30-day window. The remaining 24 cases involved patients from 15 SNFs across Connecticut. Interviews were conducted from August 2015 to November 2015.

The project was reviewed by our institution’s Human Investigation Committee and was exempted from Institutional Review Board review.

Study Participants

Interviews were conducted on-site at SNFs with groups of 1 to 4 SNF clinicians and administrators. SNF participants were informed of interviewer credentials and the study’s QI goals prior to participation. Participation was voluntary and did not affect the clinician’s relationship with the hospital or the SNF. Participants were not paid.

Interventions to Reduce Acute Care Transfers (INTERACT) is a QI program that includes training for clinicians, communication tools, and advance care planning tools.⁹ INTERACT is currently used in 138 Connecticut SNFs as part of a statewide QI effort funded by the Connecticut State Department of Public Health. In prospective QI studies,^10,11 implementation of INTERACT has been associated with decreased transfers from SNFs to acute care hospitals. The INTERACT Quality Improvement Tool, one part of the INTERACT bundle of interventions, is a 26-item questionnaire used to identify root causes of transfers from SNFs to acute care hospitals. It includes both checklists and open-ended questions about patient factors, SNF procedures, and SNF clinical decision-making.

We used the INTERACT QI Tool¹² to conduct structured interviews with nurses and administrators at SNFs. Interviewers used a hard copy of the tool to maintain field notes, and all parts of the questionnaire were completed in each interview. Although the questionnaire elicits baseline demographic and medical information, such as the patient’s age and vital signs prior to readmission, the majority of each interview was dedicated to discussion of the open-ended questions in Table 1. Upon completion of the INTERACT QI Tool, the interviewer asked 2 open-ended questions about reducing readmissions and 4 closed-ended questions regarding SNF admission procedures. (Table 1) The supplemental questions were added after preliminary interviews with SNF clinicians revealed concerns about the SNF referral process and about communication between the hospital, emergency department (ED) and SNFs—issues not included in the INTERACT questionnaire. Interviewers used phatic communication, probing questions, and follow-up questions to elicit detailed information from participants, and participant responses were not limited to topics in the questionnaire and the list of supplemental questions.

Interviews were conducted by a hospital clinical integration coordinator, social worker, and a physician (KB, MCB, BWC). All interviewers received formal training in qualitative research methods prior to the study.

All interviews were audio recorded, with permission from the participants, and were professionally transcribed. Field notes were maintained to ensure accuracy of INTERACT QI Tool data. Participant interviews covered no more than two cases per session and lasted from 18 to 71 minutes (mean duration, 38 minutes).

Analysis

Analysis of transcripts was inductive and informed by grounded theory methodology, in which data is reviewed for repeating ideas, which are then analyzed and grouped to develop a theoretical understanding of the phenomenon under investigation.^13,14

A preliminary codebook was developed using transcripts of the first 11 interviews. All statements relevant to the readmission process were extracted from the raw interview transcript and collected into a single list. This list was then reviewed for statements sharing a particular idea or concern. Such statements were grouped together under the heading of a repeating idea, and each repeating idea was assigned a code. Using this codebook, each transcript was independently reviewed and coded by three study team members with formal training in inductive qualitative analysis (KB, KTM, BWC). Reviewers assigned codes to sections of relevant text. Discrepancies in code assignment were discussed among the 3 analysts until consensus was reached. Using the method of constant comparison described in grounded theory,the codebook was updated continuously as the process of coding transcripts proceeded.¹² Changes to the codebook were discussed among the coding team until consensus was achieved. The process of data acquisition and coding continued until theoretical saturation was reached. Themes relating to underlying factors associated with readmissions were then identified based on shared properties among repeating ideas. ATLAS.ti (Scientific Software, Berlin, Germany, Version 7) was used to facilitate data organization and retrieval.

The SNFs in our study included 12 for-profit and 3 non-profit facilities. The number of licensed beds in each facility ranged from 73 to 360, with a mean of 148 beds. The SNFs had CMS Nursing Home Compare ratings ranging from 1 star, the lowest possible rating, to 5 stars, the highest possible,¹⁵ with a mean rating of 2.9 stars. Our analysis did not reveal differences in perceived contributions to readmissions from large vs. small or highly rated vs poorly rated SNFs.

Emergent transitions: Lack of coordination between ED and SNF

SNF clinicians frequently encountered situations in which a relatively stable patient was readmitted to the hospital after being transferred to the ED, despite the fact that SNF clinicians believed the patient should have returned to the SNF once a specific test was performed or service rendered at the ED. Commonly cited clinical scenarios that resulted in such readmissions included placement of urinary catheters and evaluation for cystitis. An assistant director of nursing reported that “the ER doesn’t want to hear my side of the story,” making it difficult for her to provide information that would prevent such readmissions. Other SNF clinicians reported similar difficulties in communicating with ED clinicians.

Code status: Incompletely addressed goals of care

The SNF clinicians in our study described cases in which patients with end-stage lung disease and disseminated cancer were readmitted to the hospital, despite SNF efforts to prevent readmission and provide palliative care within the SNF. For example, a SNF advanced practice nurse described a case in which a patient with widely metastatic cancer requested readmission to the hospital for treatment of deep vein thrombosis, despite longstanding recommendations from SNF staff that the patient forego hospitalization and enroll in hospice care. After discussion of code status and goals of care with hospital clinicians, the patient chose to enroll in hospice care and not to continue anticoagulation. SNF clinicians often perceived that, in the words of one administrator, “the palliative talks in the hospital outweigh our talks by a lot.” Numerous SNF clinicians believed that in-depth clarification of goals of care prior to discharge could prevent some readmissions.

Wrong patient, wrong place: Mismatch between clinical needs and SNF capabilities

One director of nursing stated that “[when] you read a referral, there’s a huge difference sometimes between what you read and what you see.” SNF clinicians reported that this discrepancy between clinical report and clinical reality often leads to patients being placed in SNFs that are unequipped to care for them. Many patients were perceived as being too ill for discharge from the acute-care setting in the first place. A nurse manager described this as a pattern of “pushing patients out of the hospital.” However, mismatches in clinical disposition were also seen as contributing to readmissions for medically stable patients, such as those with dementia, for whom SNFs frequently lack adequate staffing and physical safeguards.

Missing links: Important clinical information not effectively communicated by hospital

SNF clinicians described numerous challenges in formulating plans of care based on hospital discharge documentation. Discrepancies between discharge summaries and patient instructions were perceived as common and potential causes of readmissions. For patients discharged from the academic medical center in this study, medication instructions are included in both the discharge summary sent to the SNF and in a patient instruction packet. Several SNF clinicians said that it was common for a course of antibiotics to be listed on the discharge summary but not the patient instruction packet, or vice versa. SNF clinicians, who usually lack access to the hospital’s electronic medical record, have limited means for determining the correct document. Other important clinical data points, such as intermittent intravenous (IV) furosemide dosing and suppressive antibiotic regimens, were omitted from discharge paperwork altogether. SNF clinicians had difficulty reaching hospital clinicians who could clarify these clinical questions. “Good luck finding the person that took care of [the patient] three days before,” said one director of nursing.

Change starts at home: Challenges in SNF processes and culture

Many clinicians in our study reported that their facilities had recently added clinical capabilities in an effort to care for patients with complex medical problems. For example, to prevent transfers of patients with decompensated heart failure, several facilities in our study had recently obtained certification to give IV diuretics. However, as one director of nursing stated, these efforts require “buy-in” from doctors to decrease readmissions. That buy-in has not always been forthcoming. SNF clinicians also reported difficulty convincing patients and families that their facilities are capable of providing care that, in the past, might only have been available in acute-care settings.

These themes, along with associated sub-themes and representative quotations, are shown above (Table 4).

Our study suggests that the interaction between EDs and SNFs is an important and understudied domain in the spectrum of events leading to readmission. Prior studies have documented inadequacies in patient information provided by SNFs to EDs.^16,17 Efforts to improve SNF-to-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^18,19 However, many of the clinicians in our study reported taking proactive steps to communicate with ED clinicians. These efforts encountered logistical and cultural barriers, with information that might have prevented readmission failing to reach ED providers. Many of the SNF clinicians in our study perceived this failure as a common cause of readmission, especially for relatively stable SNF patients.

Previous studies have pointed to a role for goals of care discussions in reducing hospital readmissions.²⁰ Our data underscore an important qualification to these findings: Location matters. The SNF clinicians in our study reported frequent and detailed goals of care discussions with their patients. However, they also reported that goals of care discussions held in the subacute setting carried less weight with patients and families than discussions held in the hospital. SNF clinicians described a number of cases in which patients were willing to adjust code status or goals of care only after being readmitted to the hospital.

Our study also points to the implications of existing research showing that patients are discharged from acute care hospitals “quicker and sicker” than they had been prior to the 1983 adoption of Medicare’s prospective payment system.²¹ Specifically, the SNF clinicians we interviewed perceived a strong link between patient acuity at the time of transfer and SNFs’ persistently high readmission rates. As SNFs have worked to expand their clinical capabilities, they struggle to win buy-in from physicians and families, many of whom view SNFs as incapable of managing acute illness. Many SNF clinicians also pointed to deficiencies in their own referral and admission processes as a recurring cause of readmissions. For example, several patients in our analysis suffered from dementia. Although these patients were stable enough to leave the acute care setting, the SNF clinicians responsible for their readmissions felt that their SNFs were not well-equipped to care for patients with dementia and that the patients should instead have been transferred to facilities with more robust resources for dementia care.

Finally, our findings highlight a fundamental tension between hospitals and SNFs: Which facility ought to shoulder the responsibility and cost for services that may prevent a readmission—the hospital or the SNF? For example, does responsibility for coordinating subspecialist evaluation of a patient’s chronic condition fall to the hospital or to the SNF? If such an evaluation is undertaken during a hospitalization, it prolongs the patient’s hospital stay and happens at the hospital’s expense. If the patient is discharged to a SNF and sees the subspecialist in clinic, then the SNF must pay for transportation to and from the clinic appointment. SNF clinicians expressed near unanimity that fragmented models of care and high barriers to communication made it difficult to design solutions to these dilemmas.

Strengths and limitations

To our knowledge, this is the first interview-based study examining SNF clinicians’ perspectives on unplanned, 30-day hospital readmissions. We gathered information from clinicians with a range of clinical experience, all of whom had cared directly for the patient who had been readmitted. Our data came from clinicians at 15 SNFs of varying sizes and quality ratings, allowing us to identify a broad range of factors contributing to readmissions.

Because this study relied on qualitative methods, it should be viewed as hypothesis-generating rather than hypothesis-confirming. Further research is needed to determine whether variables related to the themes above are causally linked to SNF readmissions. We identified cases for review using convenience sampling of a cohort of readmitted patients at a single tertiary-care hospital, and all participating SNFs were located in Connecticut. These factors may limit the generalizability of our findings. Although the clinicians we interviewed occupied diverse roles within their respective SNFs, our sample did not include direct-care staff without managerial responsibility, such as certified nursing assistants or licensed practical nurses. This prevented our study from identifying themes into which managers would have limited insight, especially those involving cultural and management practices leading to poor communication between them and their staff. Because our study examines cases in which discharge and readmission were to a general medicine service, it may not describe factors relevant to patients discharged from subspecialist or surgical services.

Implications for future QI efforts and research

Several clinicians we interviewed suggested that readmissions might be reduced by dedicating the services of a hospital professional, such as a nurse or case manager, to monitoring the clinical course of medically complex patients after discharge. A dedicated “transition coach” could clarify deficiencies in discharge paperwork, facilitate necessary follow-up appointments, liaise with staff at both the hospital and the SNF, or coordinate acquisition of necessary equipment. Prospective trials have demonstrated that such interventions can decrease readmission rates among hospitalized patients,^22,23 but formal studies have not been carried out among cohorts of SNF patients.

Prior efforts to improve SNF-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^24,25 The experiences of SNF clinicians in our study suggest that important information also fails to make its way from ED providers to SNFs and that this failure results in unnecessary readmissions of relatively stable SNF patients. Thus, hospitals may be able to prevent SNF readmissions by creating lines of communication between EDs and SNFs and by ensuring that ED physicians and mid-level providers are familiar with the clinical capabilities of local SNFs.

Future research and QI work should also investigate approaches to care coordination that ensure that complex patients are placed in SNFs with resources adequate to address their comorbidities. Potential interventions might include increased use of SNF “liaisons,” who would evaluate patients in-person prior to approving transfer to a given SNF. As has been previously suggested,²⁶ hospitals might also reduce readmissions by narrowing the pool of facilities to which they transfer patients, thereby building more robust, interconnected relationships with a smaller number of SNFs.

SNF clinicians identified areas for improvement at almost every point in the chain of events spanning hospitalization, discharge, and transfer. Among the most frequently cited contributors to readmissions were clinical instability at the time of discharge and omission of clinically important information from discharge documentation. Improved communication between hospitals, ED clinicians, and SNFs, as well as more thoroughly defined goals of care at the time of discharge, were seen as promising ways of decreasing readmissions. Successful interventions for reducing readmissions from SNFs will likely require multifaceted approaches to these problems.

Disclosure: This research was supported by a grant (#P30HS023554-01) from the Agency for Healthcare Research and Quality (AHRQ) and received support from Yale New Haven Hospital and the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA).

BOSTON—At Island Neurological Associates in Plainview, New York, researchers uncovered a prevalence of chronic migraine among their population of patients with multiple sclerosis (MS) that was higher than would be expected in the general population. They reported their results at the 59th Annual Scientific Meeting of the American Headache Society. “Since migraine as a whole is generally accepted to occur in about 12% of the population, it appears that our MS patient prevalence of 21% significantly exceeds this [prevalence],” said Ira Turner, MD, a headache subspecialist at the Long Island facility. Similarly, “chronic migraine is thought to occur in 1% to 2% of the general population, but [it occurs] in 7% of our MS population,” Dr. Turner said.

Observing that MS and migraine are both chronic neurologic conditions in which inflammatory processes play an important role, Dr. Turner and colleagues sought evidence for increased migraine prevalence in the MS population. “Anecdotally, it has been our experience that there is a comorbidity of headache disorders in our MS patient population,” Dr. Turner said.

The investigators conducted a retrospective review of the electronic medical record (EMR) system at their community-based Comprehensive MS Center and Center for Headache Care and Research. They reviewed the EMR for all patients with a diagnosis of any form of MS. The EMR was then queried to determine which of the patients with MS had any headache diagnosis listed as a comorbidity. Those headache diagnoses were then reviewed and separated into those that met ICHD-3 beta criteria for chronic migraine, episodic migraine with aura, episodic migraine without aura, episodic cluster headache, chronic cluster headache, tension-type headache, or a nonspecific diagnosis of headache.

The researchers found 610 active patients with a diagnosis of MS. Of these, 139 (23%) also had a headache diagnosis listed in the EMR as a comorbidity. Migraine without aura was coded in 62 patients (10%), migraine with aura in 26 (4%), and chronic migraine in 45 (7%). Combining these diagnoses yielded a prevalence of comorbid migraine of 21% in the MS population studied. Episodic cluster headache was diagnosed in one patient, tension-type headache in two patients, and nonspecific headache in four patients. The prevalence of these three diagnoses was less than 1% each.

“While there is a potential bias caused by our practice having both an MS center and a headache center, this increased prevalence seems to be of great interest and would appear to warrant further investigation,” Dr. Turner said.

—Glenn S. Williams

BOSTON—At Island Neurological Associates in Plainview, New York, researchers uncovered a prevalence of chronic migraine among their population of patients with multiple sclerosis (MS) that was higher than would be expected in the general population. They reported their results at the 59th Annual Scientific Meeting of the American Headache Society. “Since migraine as a whole is generally accepted to occur in about 12% of the population, it appears that our MS patient prevalence of 21% significantly exceeds this [prevalence],” said Ira Turner, MD, a headache subspecialist at the Long Island facility. Similarly, “chronic migraine is thought to occur in 1% to 2% of the general population, but [it occurs] in 7% of our MS population,” Dr. Turner said.

Observing that MS and migraine are both chronic neurologic conditions in which inflammatory processes play an important role, Dr. Turner and colleagues sought evidence for increased migraine prevalence in the MS population. “Anecdotally, it has been our experience that there is a comorbidity of headache disorders in our MS patient population,” Dr. Turner said.

The investigators conducted a retrospective review of the electronic medical record (EMR) system at their community-based Comprehensive MS Center and Center for Headache Care and Research. They reviewed the EMR for all patients with a diagnosis of any form of MS. The EMR was then queried to determine which of the patients with MS had any headache diagnosis listed as a comorbidity. Those headache diagnoses were then reviewed and separated into those that met ICHD-3 beta criteria for chronic migraine, episodic migraine with aura, episodic migraine without aura, episodic cluster headache, chronic cluster headache, tension-type headache, or a nonspecific diagnosis of headache.

The researchers found 610 active patients with a diagnosis of MS. Of these, 139 (23%) also had a headache diagnosis listed in the EMR as a comorbidity. Migraine without aura was coded in 62 patients (10%), migraine with aura in 26 (4%), and chronic migraine in 45 (7%). Combining these diagnoses yielded a prevalence of comorbid migraine of 21% in the MS population studied. Episodic cluster headache was diagnosed in one patient, tension-type headache in two patients, and nonspecific headache in four patients. The prevalence of these three diagnoses was less than 1% each.

“While there is a potential bias caused by our practice having both an MS center and a headache center, this increased prevalence seems to be of great interest and would appear to warrant further investigation,” Dr. Turner said.

—Glenn S. Williams

BOSTON—At Island Neurological Associates in Plainview, New York, researchers uncovered a prevalence of chronic migraine among their population of patients with multiple sclerosis (MS) that was higher than would be expected in the general population. They reported their results at the 59th Annual Scientific Meeting of the American Headache Society. “Since migraine as a whole is generally accepted to occur in about 12% of the population, it appears that our MS patient prevalence of 21% significantly exceeds this [prevalence],” said Ira Turner, MD, a headache subspecialist at the Long Island facility. Similarly, “chronic migraine is thought to occur in 1% to 2% of the general population, but [it occurs] in 7% of our MS population,” Dr. Turner said.

Observing that MS and migraine are both chronic neurologic conditions in which inflammatory processes play an important role, Dr. Turner and colleagues sought evidence for increased migraine prevalence in the MS population. “Anecdotally, it has been our experience that there is a comorbidity of headache disorders in our MS patient population,” Dr. Turner said.

The investigators conducted a retrospective review of the electronic medical record (EMR) system at their community-based Comprehensive MS Center and Center for Headache Care and Research. They reviewed the EMR for all patients with a diagnosis of any form of MS. The EMR was then queried to determine which of the patients with MS had any headache diagnosis listed as a comorbidity. Those headache diagnoses were then reviewed and separated into those that met ICHD-3 beta criteria for chronic migraine, episodic migraine with aura, episodic migraine without aura, episodic cluster headache, chronic cluster headache, tension-type headache, or a nonspecific diagnosis of headache.

The researchers found 610 active patients with a diagnosis of MS. Of these, 139 (23%) also had a headache diagnosis listed in the EMR as a comorbidity. Migraine without aura was coded in 62 patients (10%), migraine with aura in 26 (4%), and chronic migraine in 45 (7%). Combining these diagnoses yielded a prevalence of comorbid migraine of 21% in the MS population studied. Episodic cluster headache was diagnosed in one patient, tension-type headache in two patients, and nonspecific headache in four patients. The prevalence of these three diagnoses was less than 1% each.

“While there is a potential bias caused by our practice having both an MS center and a headache center, this increased prevalence seems to be of great interest and would appear to warrant further investigation,” Dr. Turner said.

—Glenn S. Williams

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg, a tracheostomy tube for breathing).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg, a tracheostomy tube for breathing).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg, a tracheostomy tube for breathing).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.