Hospital Medicine

The 25 million people in the United States with limited English proficiency (LEP), which is defined as speaking English less than “very well”, are at increased risk for healthcare disparities that result in preventable harm and poor patient experiences compared with English-proficient patients.^1,2 The use of trained professional interpreters is associated with improved communication, healthcare outcomes, safety, and experiences for LEP patients.³ However, underuse of professional interpreters remains common.⁴ Healthcare staff frequently use family members, friends, or minor children as interpreters or try to “get by” with the patient’s limited English skills or staff’s limited non-English skills.⁵ These practices regularly compromise patient safety and quality for LEP patients and their families.

In the article “Inpatient Communication Barriers and Drivers when Caring for Limited English Proficiency Children,” Dr. Choe and colleagues approach the problem of interpreter underuse by studying the barriers and facilitators that exist at their children’s hospital.⁶ The group conducted four sessions using Group Level Assessment, a structured, interactive approach to understanding a problem and identifying potential solutions. Sixty-four pediatric hospitalists and residents, bedside nurses, and staff interpreters participated. Participants identified four primary barriers to communicating effectively with LEP families: difficulty accessing interpreter services, uncertainty in communicating with LEP families, unclear roles and expectations of different team members, and unmet expectations related to family engagement. They also identified four drivers of effective communication: collaborative problem-solving between providers and interpreters, greater attention to cultural context, practicing empathy for patients and families, and using family centered communication strategies.

This study reinforces that myriad challenges remain in accessing and using an interpreter. The barriers identified fall into two major categories: systems for accessing interpretation and communication involving an interpreter. Both ultimately must be addressed to achieve equitable communication for LEP patients/families. As interpreter use is contingent upon access, optimizing delivery systems is an essential foundation. At this study site, key barriers were the opaque scheduling processes and inconsistent access to and unfamiliarity with interpreter-related technology (eg, for telephone or video interpretation). These barriers are likely generalizable to many other hospitals. Priority should be given to developing transparent, consistent, and reliable processes for interpreter access. Interventions to improve interpreter access, such as one-touch interpreter telephones at every hospital bedside, have been more successful in improving interpreter use than provider education or regulatory mandates.⁴

The challenges identified around communicating with LEP families via interpreter are also likely generalizable. In the current study, participants described a clear tension around the interpreters’ optimal role, in which the care team might want the interpreter to intervene or participate in the discussion more, while interpreter standards require that they remain a neutral conduit for information. This neutral-party approach, when taken to the extreme, can limit the bidirectional communication between clinical teams and interpreters necessary to address communication challenges. Fostering collaborative problem-solving between interpreters and clinicians, in both formal and informal settings, is critically needed to improve the quality of communication during encounters. In addition to the proposed presession meeting between the clinician and interpreter, incorporating a debriefing after an interpreter-mediated encounter could offer an opportunity for bidirectional feedback. Unfortunately, interpreter scheduling constraints, fueled by the lack of reimbursement for interpretation in most states, frequently limit the feasibility of such proposals.

Participating providers also reported decreased engagement with LEP families and that they spent less time with them. These observations also merit attention if we are to achieve equitable outcomes for LEP patients. A conversation via interpreter requires more time for the same content, given the time needed to interpret the message. The fact that participants reported spending less time with LEP families means that less communication occurs with those families, compared with others. There are well-established links between good communication and improved clinical outcomes, including everything from decreased glycosylated hemoglobin levels to lower inpatient narcotic use.⁷ Thus, it is not surprising that patients with fewer opportunities to communicate fully have worse clinical outcomes.⁸ Addressing this will require changing hospital culture and provider expectations. Healthcare systems could support this effort with interventions such as decreased nursing assignments, longer allocated rounding times, longer outpatient clinic visits, and additional “points” in resident patient caps, if they exist, for LEP patients. Such steps would be an important investment in improving outcomes and decreasing costs for these vulnerable patients.

For all the barriers identified by Choe and colleagues, solutions are needed. Some may be generalizable, some may be location-specific, and most will be somewhere in between, requiring context-specific tailoring. We recommend a quality improvement (QI) approach, as the evidence-based best practice for communicating with LEP patients and families is well-known, but the gap is in delivering care that meets that standard. Leveraging the growing QI expertise at many institutions to devise approaches that go beyond provider education to change the systems and culture around communicating with LEP patients holds our best promise for improving the safety and effectiveness of care for this population.

Disclosures

The authors have no financial relationships relevant to this article to disclose nor do they have any conflicts of interest relevant to this article to disclose.

Funding

Dr. Lion’s time was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, grant K23 HD078507 (PI Lion).

The 25 million people in the United States with limited English proficiency (LEP), which is defined as speaking English less than “very well”, are at increased risk for healthcare disparities that result in preventable harm and poor patient experiences compared with English-proficient patients.^1,2 The use of trained professional interpreters is associated with improved communication, healthcare outcomes, safety, and experiences for LEP patients.³ However, underuse of professional interpreters remains common.⁴ Healthcare staff frequently use family members, friends, or minor children as interpreters or try to “get by” with the patient’s limited English skills or staff’s limited non-English skills.⁵ These practices regularly compromise patient safety and quality for LEP patients and their families.

In the article “Inpatient Communication Barriers and Drivers when Caring for Limited English Proficiency Children,” Dr. Choe and colleagues approach the problem of interpreter underuse by studying the barriers and facilitators that exist at their children’s hospital.⁶ The group conducted four sessions using Group Level Assessment, a structured, interactive approach to understanding a problem and identifying potential solutions. Sixty-four pediatric hospitalists and residents, bedside nurses, and staff interpreters participated. Participants identified four primary barriers to communicating effectively with LEP families: difficulty accessing interpreter services, uncertainty in communicating with LEP families, unclear roles and expectations of different team members, and unmet expectations related to family engagement. They also identified four drivers of effective communication: collaborative problem-solving between providers and interpreters, greater attention to cultural context, practicing empathy for patients and families, and using family centered communication strategies.

This study reinforces that myriad challenges remain in accessing and using an interpreter. The barriers identified fall into two major categories: systems for accessing interpretation and communication involving an interpreter. Both ultimately must be addressed to achieve equitable communication for LEP patients/families. As interpreter use is contingent upon access, optimizing delivery systems is an essential foundation. At this study site, key barriers were the opaque scheduling processes and inconsistent access to and unfamiliarity with interpreter-related technology (eg, for telephone or video interpretation). These barriers are likely generalizable to many other hospitals. Priority should be given to developing transparent, consistent, and reliable processes for interpreter access. Interventions to improve interpreter access, such as one-touch interpreter telephones at every hospital bedside, have been more successful in improving interpreter use than provider education or regulatory mandates.⁴

The challenges identified around communicating with LEP families via interpreter are also likely generalizable. In the current study, participants described a clear tension around the interpreters’ optimal role, in which the care team might want the interpreter to intervene or participate in the discussion more, while interpreter standards require that they remain a neutral conduit for information. This neutral-party approach, when taken to the extreme, can limit the bidirectional communication between clinical teams and interpreters necessary to address communication challenges. Fostering collaborative problem-solving between interpreters and clinicians, in both formal and informal settings, is critically needed to improve the quality of communication during encounters. In addition to the proposed presession meeting between the clinician and interpreter, incorporating a debriefing after an interpreter-mediated encounter could offer an opportunity for bidirectional feedback. Unfortunately, interpreter scheduling constraints, fueled by the lack of reimbursement for interpretation in most states, frequently limit the feasibility of such proposals.

Participating providers also reported decreased engagement with LEP families and that they spent less time with them. These observations also merit attention if we are to achieve equitable outcomes for LEP patients. A conversation via interpreter requires more time for the same content, given the time needed to interpret the message. The fact that participants reported spending less time with LEP families means that less communication occurs with those families, compared with others. There are well-established links between good communication and improved clinical outcomes, including everything from decreased glycosylated hemoglobin levels to lower inpatient narcotic use.⁷ Thus, it is not surprising that patients with fewer opportunities to communicate fully have worse clinical outcomes.⁸ Addressing this will require changing hospital culture and provider expectations. Healthcare systems could support this effort with interventions such as decreased nursing assignments, longer allocated rounding times, longer outpatient clinic visits, and additional “points” in resident patient caps, if they exist, for LEP patients. Such steps would be an important investment in improving outcomes and decreasing costs for these vulnerable patients.

For all the barriers identified by Choe and colleagues, solutions are needed. Some may be generalizable, some may be location-specific, and most will be somewhere in between, requiring context-specific tailoring. We recommend a quality improvement (QI) approach, as the evidence-based best practice for communicating with LEP patients and families is well-known, but the gap is in delivering care that meets that standard. Leveraging the growing QI expertise at many institutions to devise approaches that go beyond provider education to change the systems and culture around communicating with LEP patients holds our best promise for improving the safety and effectiveness of care for this population.

Disclosures

The authors have no financial relationships relevant to this article to disclose nor do they have any conflicts of interest relevant to this article to disclose.

Funding

Dr. Lion’s time was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, grant K23 HD078507 (PI Lion).

The 25 million people in the United States with limited English proficiency (LEP), which is defined as speaking English less than “very well”, are at increased risk for healthcare disparities that result in preventable harm and poor patient experiences compared with English-proficient patients.^1,2 The use of trained professional interpreters is associated with improved communication, healthcare outcomes, safety, and experiences for LEP patients.³ However, underuse of professional interpreters remains common.⁴ Healthcare staff frequently use family members, friends, or minor children as interpreters or try to “get by” with the patient’s limited English skills or staff’s limited non-English skills.⁵ These practices regularly compromise patient safety and quality for LEP patients and their families.

In the article “Inpatient Communication Barriers and Drivers when Caring for Limited English Proficiency Children,” Dr. Choe and colleagues approach the problem of interpreter underuse by studying the barriers and facilitators that exist at their children’s hospital.⁶ The group conducted four sessions using Group Level Assessment, a structured, interactive approach to understanding a problem and identifying potential solutions. Sixty-four pediatric hospitalists and residents, bedside nurses, and staff interpreters participated. Participants identified four primary barriers to communicating effectively with LEP families: difficulty accessing interpreter services, uncertainty in communicating with LEP families, unclear roles and expectations of different team members, and unmet expectations related to family engagement. They also identified four drivers of effective communication: collaborative problem-solving between providers and interpreters, greater attention to cultural context, practicing empathy for patients and families, and using family centered communication strategies.

This study reinforces that myriad challenges remain in accessing and using an interpreter. The barriers identified fall into two major categories: systems for accessing interpretation and communication involving an interpreter. Both ultimately must be addressed to achieve equitable communication for LEP patients/families. As interpreter use is contingent upon access, optimizing delivery systems is an essential foundation. At this study site, key barriers were the opaque scheduling processes and inconsistent access to and unfamiliarity with interpreter-related technology (eg, for telephone or video interpretation). These barriers are likely generalizable to many other hospitals. Priority should be given to developing transparent, consistent, and reliable processes for interpreter access. Interventions to improve interpreter access, such as one-touch interpreter telephones at every hospital bedside, have been more successful in improving interpreter use than provider education or regulatory mandates.⁴

The challenges identified around communicating with LEP families via interpreter are also likely generalizable. In the current study, participants described a clear tension around the interpreters’ optimal role, in which the care team might want the interpreter to intervene or participate in the discussion more, while interpreter standards require that they remain a neutral conduit for information. This neutral-party approach, when taken to the extreme, can limit the bidirectional communication between clinical teams and interpreters necessary to address communication challenges. Fostering collaborative problem-solving between interpreters and clinicians, in both formal and informal settings, is critically needed to improve the quality of communication during encounters. In addition to the proposed presession meeting between the clinician and interpreter, incorporating a debriefing after an interpreter-mediated encounter could offer an opportunity for bidirectional feedback. Unfortunately, interpreter scheduling constraints, fueled by the lack of reimbursement for interpretation in most states, frequently limit the feasibility of such proposals.

Participating providers also reported decreased engagement with LEP families and that they spent less time with them. These observations also merit attention if we are to achieve equitable outcomes for LEP patients. A conversation via interpreter requires more time for the same content, given the time needed to interpret the message. The fact that participants reported spending less time with LEP families means that less communication occurs with those families, compared with others. There are well-established links between good communication and improved clinical outcomes, including everything from decreased glycosylated hemoglobin levels to lower inpatient narcotic use.⁷ Thus, it is not surprising that patients with fewer opportunities to communicate fully have worse clinical outcomes.⁸ Addressing this will require changing hospital culture and provider expectations. Healthcare systems could support this effort with interventions such as decreased nursing assignments, longer allocated rounding times, longer outpatient clinic visits, and additional “points” in resident patient caps, if they exist, for LEP patients. Such steps would be an important investment in improving outcomes and decreasing costs for these vulnerable patients.

For all the barriers identified by Choe and colleagues, solutions are needed. Some may be generalizable, some may be location-specific, and most will be somewhere in between, requiring context-specific tailoring. We recommend a quality improvement (QI) approach, as the evidence-based best practice for communicating with LEP patients and families is well-known, but the gap is in delivering care that meets that standard. Leveraging the growing QI expertise at many institutions to devise approaches that go beyond provider education to change the systems and culture around communicating with LEP patients holds our best promise for improving the safety and effectiveness of care for this population.

Disclosures

The authors have no financial relationships relevant to this article to disclose nor do they have any conflicts of interest relevant to this article to disclose.

Funding

Dr. Lion’s time was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, grant K23 HD078507 (PI Lion).

In the past five years, it has become increasingly apparent that hospital physiologic monitoring systems are not functioning optimally for children. On pediatric wards, 26%-48% of children are continuously monitored, and these children generate between 42 and 155 alarms per day.¹ Just 1% or fewer are considered actionable or informative, slowing nurses’ response times and placing patients at risk of delayed recognition of life-threatening events.^2,3 While some factors associated with alarm response times have been elucidated,³ in order to design safe and effective monitoring systems, further work is needed to understand the complex decision-making process that nurses face when encountering alarms outside a patient’s room. It is in this area that Schondelmeyer and colleagues strive to enhance our understanding in this issue of the Journal of Hospital Medicine.⁴

Schondelmeyer et al. conducted a single-center, observational study using mixed methods in a general pediatric unit. Trained observers shadowed nine nurses one to four times each, during which nurses were asked to “think aloud” as they managed physiologic monitor alarms, rationalizing their decisions about how and why they might respond for the observer to document. Observers accumulated 61 patient-hours of observation before investigators halted data collection because new insights about alarm responses were no longer emerging from the data (thematic saturation).

Nurses thought aloud about 207 alarms during the study, which the investigators estimated comprised about one third of the alarms that occurred during observation periods. Most of the 207 occurred while the nurse was already in the patient’s room, where a response decision is uncomplicated. More interesting were the 45 alarms heard while outside the patient’s room, where nurses face the complex decision of whether to interrupt their current tasks and respond or delay their response and assume the associated risk of nonresponse to a potentially deteriorating patient. Of the 45 alarms, nurses went into the room to evaluate the patient 15 times and, after doing so, reported that five of the 15 warranted in-person responses to address technical issues with the monitor, clinical issues, or patients’ comfort. Reassuring clinical contexts—such as presence of the medical team or family in the room and recent patient assessments—were the reasons most commonly provided to explain alarm nonresponse.

This study has two key limitations. First, the authors designed the study to observe nurses’ responses until thematic saturation was achieved. However, the small sample size (nine nurses, 45 out-of-room alarms) could raise questions about whether sufficient data were captured to make broadly generalizable conclusions, given the diverse range of patients, families, and clinical scenarios nurses encounter on an inpatient unit. Second, by instructing nurse participants to verbalize their rationale for response or nonresponse, investigators essentially asked nurses to override the “Type 1”, heuristic-based reasoning⁵ that research suggests regulates nursing responses to alarms when adapting to circumstances requiring high cognitive demand or a heavy workload.³ While innovative, it is possible that this approach prevented the investigators from fully achieving their stated objective of describing how bedside nurses think about and act upon alarms.

Nonetheless, the findings by Schondelmeyer and colleagues extend our emerging understanding of why alarm responses are disconcertingly slow. Nursing staff’s dismissal of monitor alarms that are discordant with a reassuring patient evaluation underscores the imperative to reduce nuisance alarms. Furthermore, the explicit statements justifying alarm nonresponse because of the presence of family members build upon prior findings of longer response times when family members are at the bedside³ and invite a provocative question: how would family members feel if they knew that they were being entrusted as a foundational component of safety monitoring in the hospital? In their recently published study conducted at the same hospital,⁶ Schondelmeyer’s team elicited perceptions that families are deeply concerned about staff nonresponse to alarms—as one nurse stated, parents “wonder what’s going on when no one comes in.” While there is a valuable role for integrating families into efforts to overcome threats to patient safety, as has been achieved with family error reporting⁷ and communication on family-centered rounds,⁸ this must occur in a structured, explicit, and deliberate manner, with families engaged as key stakeholders.

In summary, while Schondelmeyer and colleagues may not have exposed the depth of implicit thinking that governs nurses’ responses to alarms, they have highlighted the high-stakes decisions that nurses confront on a daily basis in an environment with exceedingly high alarm rates and low alarm actionability. The authors cite staff education among potential solutions to improve the safety of continuous monitoring, but such an intervention cannot be effective in a system that places impossible burdens on nurses. An openly family centered and multidisciplinary approach to reengineering the system for monitoring hospitalized children is needed to enable nurses to respond quickly and accurately to patients at risk of clinical deterioration.

Disclosures

The authors report no conflicts of interest.

In the past five years, it has become increasingly apparent that hospital physiologic monitoring systems are not functioning optimally for children. On pediatric wards, 26%-48% of children are continuously monitored, and these children generate between 42 and 155 alarms per day.¹ Just 1% or fewer are considered actionable or informative, slowing nurses’ response times and placing patients at risk of delayed recognition of life-threatening events.^2,3 While some factors associated with alarm response times have been elucidated,³ in order to design safe and effective monitoring systems, further work is needed to understand the complex decision-making process that nurses face when encountering alarms outside a patient’s room. It is in this area that Schondelmeyer and colleagues strive to enhance our understanding in this issue of the Journal of Hospital Medicine.⁴

Schondelmeyer et al. conducted a single-center, observational study using mixed methods in a general pediatric unit. Trained observers shadowed nine nurses one to four times each, during which nurses were asked to “think aloud” as they managed physiologic monitor alarms, rationalizing their decisions about how and why they might respond for the observer to document. Observers accumulated 61 patient-hours of observation before investigators halted data collection because new insights about alarm responses were no longer emerging from the data (thematic saturation).

Nurses thought aloud about 207 alarms during the study, which the investigators estimated comprised about one third of the alarms that occurred during observation periods. Most of the 207 occurred while the nurse was already in the patient’s room, where a response decision is uncomplicated. More interesting were the 45 alarms heard while outside the patient’s room, where nurses face the complex decision of whether to interrupt their current tasks and respond or delay their response and assume the associated risk of nonresponse to a potentially deteriorating patient. Of the 45 alarms, nurses went into the room to evaluate the patient 15 times and, after doing so, reported that five of the 15 warranted in-person responses to address technical issues with the monitor, clinical issues, or patients’ comfort. Reassuring clinical contexts—such as presence of the medical team or family in the room and recent patient assessments—were the reasons most commonly provided to explain alarm nonresponse.

This study has two key limitations. First, the authors designed the study to observe nurses’ responses until thematic saturation was achieved. However, the small sample size (nine nurses, 45 out-of-room alarms) could raise questions about whether sufficient data were captured to make broadly generalizable conclusions, given the diverse range of patients, families, and clinical scenarios nurses encounter on an inpatient unit. Second, by instructing nurse participants to verbalize their rationale for response or nonresponse, investigators essentially asked nurses to override the “Type 1”, heuristic-based reasoning⁵ that research suggests regulates nursing responses to alarms when adapting to circumstances requiring high cognitive demand or a heavy workload.³ While innovative, it is possible that this approach prevented the investigators from fully achieving their stated objective of describing how bedside nurses think about and act upon alarms.

Nonetheless, the findings by Schondelmeyer and colleagues extend our emerging understanding of why alarm responses are disconcertingly slow. Nursing staff’s dismissal of monitor alarms that are discordant with a reassuring patient evaluation underscores the imperative to reduce nuisance alarms. Furthermore, the explicit statements justifying alarm nonresponse because of the presence of family members build upon prior findings of longer response times when family members are at the bedside³ and invite a provocative question: how would family members feel if they knew that they were being entrusted as a foundational component of safety monitoring in the hospital? In their recently published study conducted at the same hospital,⁶ Schondelmeyer’s team elicited perceptions that families are deeply concerned about staff nonresponse to alarms—as one nurse stated, parents “wonder what’s going on when no one comes in.” While there is a valuable role for integrating families into efforts to overcome threats to patient safety, as has been achieved with family error reporting⁷ and communication on family-centered rounds,⁸ this must occur in a structured, explicit, and deliberate manner, with families engaged as key stakeholders.

In summary, while Schondelmeyer and colleagues may not have exposed the depth of implicit thinking that governs nurses’ responses to alarms, they have highlighted the high-stakes decisions that nurses confront on a daily basis in an environment with exceedingly high alarm rates and low alarm actionability. The authors cite staff education among potential solutions to improve the safety of continuous monitoring, but such an intervention cannot be effective in a system that places impossible burdens on nurses. An openly family centered and multidisciplinary approach to reengineering the system for monitoring hospitalized children is needed to enable nurses to respond quickly and accurately to patients at risk of clinical deterioration.

Disclosures

The authors report no conflicts of interest.

In the past five years, it has become increasingly apparent that hospital physiologic monitoring systems are not functioning optimally for children. On pediatric wards, 26%-48% of children are continuously monitored, and these children generate between 42 and 155 alarms per day.¹ Just 1% or fewer are considered actionable or informative, slowing nurses’ response times and placing patients at risk of delayed recognition of life-threatening events.^2,3 While some factors associated with alarm response times have been elucidated,³ in order to design safe and effective monitoring systems, further work is needed to understand the complex decision-making process that nurses face when encountering alarms outside a patient’s room. It is in this area that Schondelmeyer and colleagues strive to enhance our understanding in this issue of the Journal of Hospital Medicine.⁴

Schondelmeyer et al. conducted a single-center, observational study using mixed methods in a general pediatric unit. Trained observers shadowed nine nurses one to four times each, during which nurses were asked to “think aloud” as they managed physiologic monitor alarms, rationalizing their decisions about how and why they might respond for the observer to document. Observers accumulated 61 patient-hours of observation before investigators halted data collection because new insights about alarm responses were no longer emerging from the data (thematic saturation).

Nurses thought aloud about 207 alarms during the study, which the investigators estimated comprised about one third of the alarms that occurred during observation periods. Most of the 207 occurred while the nurse was already in the patient’s room, where a response decision is uncomplicated. More interesting were the 45 alarms heard while outside the patient’s room, where nurses face the complex decision of whether to interrupt their current tasks and respond or delay their response and assume the associated risk of nonresponse to a potentially deteriorating patient. Of the 45 alarms, nurses went into the room to evaluate the patient 15 times and, after doing so, reported that five of the 15 warranted in-person responses to address technical issues with the monitor, clinical issues, or patients’ comfort. Reassuring clinical contexts—such as presence of the medical team or family in the room and recent patient assessments—were the reasons most commonly provided to explain alarm nonresponse.

This study has two key limitations. First, the authors designed the study to observe nurses’ responses until thematic saturation was achieved. However, the small sample size (nine nurses, 45 out-of-room alarms) could raise questions about whether sufficient data were captured to make broadly generalizable conclusions, given the diverse range of patients, families, and clinical scenarios nurses encounter on an inpatient unit. Second, by instructing nurse participants to verbalize their rationale for response or nonresponse, investigators essentially asked nurses to override the “Type 1”, heuristic-based reasoning⁵ that research suggests regulates nursing responses to alarms when adapting to circumstances requiring high cognitive demand or a heavy workload.³ While innovative, it is possible that this approach prevented the investigators from fully achieving their stated objective of describing how bedside nurses think about and act upon alarms.

Nonetheless, the findings by Schondelmeyer and colleagues extend our emerging understanding of why alarm responses are disconcertingly slow. Nursing staff’s dismissal of monitor alarms that are discordant with a reassuring patient evaluation underscores the imperative to reduce nuisance alarms. Furthermore, the explicit statements justifying alarm nonresponse because of the presence of family members build upon prior findings of longer response times when family members are at the bedside³ and invite a provocative question: how would family members feel if they knew that they were being entrusted as a foundational component of safety monitoring in the hospital? In their recently published study conducted at the same hospital,⁶ Schondelmeyer’s team elicited perceptions that families are deeply concerned about staff nonresponse to alarms—as one nurse stated, parents “wonder what’s going on when no one comes in.” While there is a valuable role for integrating families into efforts to overcome threats to patient safety, as has been achieved with family error reporting⁷ and communication on family-centered rounds,⁸ this must occur in a structured, explicit, and deliberate manner, with families engaged as key stakeholders.

In summary, while Schondelmeyer and colleagues may not have exposed the depth of implicit thinking that governs nurses’ responses to alarms, they have highlighted the high-stakes decisions that nurses confront on a daily basis in an environment with exceedingly high alarm rates and low alarm actionability. The authors cite staff education among potential solutions to improve the safety of continuous monitoring, but such an intervention cannot be effective in a system that places impossible burdens on nurses. An openly family centered and multidisciplinary approach to reengineering the system for monitoring hospitalized children is needed to enable nurses to respond quickly and accurately to patients at risk of clinical deterioration.

Disclosures

The authors report no conflicts of interest.

In 2016, Pediatric Hospital Medicine (PHM) was recognized as a subspecialty under the American Board of Pediatrics (ABP), one of 24 certifying boards of the American Board of Medical Specialties. As with all new ABP subspecialty certification processes, a “practice pathway” with specific eligibility criteria allows individuals with expertise and sufficient practice experience within the discipline to take the certification examination. For PHM, certification via the practice pathway is permissible for the 2019, 2021, and 2023 certifying examinations.¹ In this perspective, we provide an illustration of ABP leadership and the PHM community partnering to mitigate unintentional gender bias that surfaced after the practice pathway eligibility criteria were implemented. We also provide recommendations to revise these criteria to eliminate future gender bias and promote equity in medicine.

In July 2019, individuals within the PHM community began to share stories of being denied eligibility to sit for the 2019 exam.² Some of the reported denials were due to an eligibility criterion related to “practice interruptions”, which stated that practice interruptions cannot exceed three months in the preceding four years or six months in the preceding five years. Notably, some women reported that their applications were denied because of practice interruptions due to maternity leave. These stories raised significant concerns of gender bias in the board certification process and sparked collective action to revise the board certification eligibility criteria. A petition was circulated within the PHM community and received 1,479 signatures in two weeks.

Given the magnitude of concern, leaders within the PHM community, with support from the American Academy of Pediatrics, collaboratively engaged with the ABP and members of the ABP PHM subboard to improve the transparency and equity of the eligibility process. As a result of this activism and effective dialogue, the ABP revised the PHM board certification eligibility criteria and removed the practice interruption criterion.¹ Through this unique experience of advocacy and partnership in medicine, the PHM community and ABP were able to work together to mitigate unintentional gender bias in the board certification process. However, this collaboration must continue as we believe the revised criteria remain unintentionally biased against women.

Gender bias is defined as the unfair difference in the way men and women are treated.³ Maternal bias is further characterized as bias experienced by mothers related to motherhood, often involving discrimination based on pregnancy, maternity leave, or breastfeeding. Both are common in medicine. Two-thirds of physician mothers report experiencing gender bias and more than a third experience maternal bias.⁴ This bias may be explicit, or intentional, but often the bias is unintentional. This bias can occur even with equal representation of women and men on committees determining eligibility, and even when the committee believes it is not biased.⁵ Furthermore, gender or maternal bias negatively affects individuals in medicine in regards to future employment, career advancement, and compensation.^6-11

Given these implications, we celebrate the removal of the practice interruptions criterion as it was unintentionally biased against women. Eligibility criteria that considered practice interruptions would have disproportionately affected women due to leaves related to pregnancy and due to discrepancies in the length of parental leave for mothers versus fathers. Though the ABP’s initial review of cases of denial did not demonstrate a significant difference in the proportion of men and women who were denied, these data may be misleading. Potential reasons why the ABP did not find significant differences in denial rates between women and men include: (1) some women who had recent maternity leaves chose not to apply because of concerns they may be denied; or (2) some women did not disclose maternity leaves on their application because they did not interpret maternity leave to be a practice interruption. This “self-censoring” may have resulted in incomplete data, making it difficult to fully understand the differential impact of this criterion on women versus men. Therefore, it is essential that we as a profession continue to identify any areas where gender bias exists in determining eligibility for certification, employment, or career advancement within medicine and eliminate it.

Despite the improvements made in the revised criteria, further revision is necessary to remove the criterion related to the “start date”, which will differentially affect women. This criterion states that an individual must have started their PHM practice on or before July of the first year of a four-year look-back period (eg, July 2015 for the 2019 cycle). We present three theoretical cases to illustrate gender bias with respect to this criterion (Table). Even though Applicants #2 and #3 accrue far more than the minimum number of hours in their first year—and more hours overall than Applicant #1—both of these women will remain ineligible under the revised criteria. While Applicant #2 could be eligible for the 2021 or 2023 cycle, Applicant #3, who is new to PHM practice in 2019 as a residency graduate, will not be eligible at all under the practice pathway due to delayed graduation from residency.

Disclosures

The authors have nothing to disclose.

In 2016, Pediatric Hospital Medicine (PHM) was recognized as a subspecialty under the American Board of Pediatrics (ABP), one of 24 certifying boards of the American Board of Medical Specialties. As with all new ABP subspecialty certification processes, a “practice pathway” with specific eligibility criteria allows individuals with expertise and sufficient practice experience within the discipline to take the certification examination. For PHM, certification via the practice pathway is permissible for the 2019, 2021, and 2023 certifying examinations.¹ In this perspective, we provide an illustration of ABP leadership and the PHM community partnering to mitigate unintentional gender bias that surfaced after the practice pathway eligibility criteria were implemented. We also provide recommendations to revise these criteria to eliminate future gender bias and promote equity in medicine.

In July 2019, individuals within the PHM community began to share stories of being denied eligibility to sit for the 2019 exam.² Some of the reported denials were due to an eligibility criterion related to “practice interruptions”, which stated that practice interruptions cannot exceed three months in the preceding four years or six months in the preceding five years. Notably, some women reported that their applications were denied because of practice interruptions due to maternity leave. These stories raised significant concerns of gender bias in the board certification process and sparked collective action to revise the board certification eligibility criteria. A petition was circulated within the PHM community and received 1,479 signatures in two weeks.

Given the magnitude of concern, leaders within the PHM community, with support from the American Academy of Pediatrics, collaboratively engaged with the ABP and members of the ABP PHM subboard to improve the transparency and equity of the eligibility process. As a result of this activism and effective dialogue, the ABP revised the PHM board certification eligibility criteria and removed the practice interruption criterion.¹ Through this unique experience of advocacy and partnership in medicine, the PHM community and ABP were able to work together to mitigate unintentional gender bias in the board certification process. However, this collaboration must continue as we believe the revised criteria remain unintentionally biased against women.

Gender bias is defined as the unfair difference in the way men and women are treated.³ Maternal bias is further characterized as bias experienced by mothers related to motherhood, often involving discrimination based on pregnancy, maternity leave, or breastfeeding. Both are common in medicine. Two-thirds of physician mothers report experiencing gender bias and more than a third experience maternal bias.⁴ This bias may be explicit, or intentional, but often the bias is unintentional. This bias can occur even with equal representation of women and men on committees determining eligibility, and even when the committee believes it is not biased.⁵ Furthermore, gender or maternal bias negatively affects individuals in medicine in regards to future employment, career advancement, and compensation.^6-11

Given these implications, we celebrate the removal of the practice interruptions criterion as it was unintentionally biased against women. Eligibility criteria that considered practice interruptions would have disproportionately affected women due to leaves related to pregnancy and due to discrepancies in the length of parental leave for mothers versus fathers. Though the ABP’s initial review of cases of denial did not demonstrate a significant difference in the proportion of men and women who were denied, these data may be misleading. Potential reasons why the ABP did not find significant differences in denial rates between women and men include: (1) some women who had recent maternity leaves chose not to apply because of concerns they may be denied; or (2) some women did not disclose maternity leaves on their application because they did not interpret maternity leave to be a practice interruption. This “self-censoring” may have resulted in incomplete data, making it difficult to fully understand the differential impact of this criterion on women versus men. Therefore, it is essential that we as a profession continue to identify any areas where gender bias exists in determining eligibility for certification, employment, or career advancement within medicine and eliminate it.

Despite the improvements made in the revised criteria, further revision is necessary to remove the criterion related to the “start date”, which will differentially affect women. This criterion states that an individual must have started their PHM practice on or before July of the first year of a four-year look-back period (eg, July 2015 for the 2019 cycle). We present three theoretical cases to illustrate gender bias with respect to this criterion (Table). Even though Applicants #2 and #3 accrue far more than the minimum number of hours in their first year—and more hours overall than Applicant #1—both of these women will remain ineligible under the revised criteria. While Applicant #2 could be eligible for the 2021 or 2023 cycle, Applicant #3, who is new to PHM practice in 2019 as a residency graduate, will not be eligible at all under the practice pathway due to delayed graduation from residency.

Disclosures

The authors have nothing to disclose.

In 2016, Pediatric Hospital Medicine (PHM) was recognized as a subspecialty under the American Board of Pediatrics (ABP), one of 24 certifying boards of the American Board of Medical Specialties. As with all new ABP subspecialty certification processes, a “practice pathway” with specific eligibility criteria allows individuals with expertise and sufficient practice experience within the discipline to take the certification examination. For PHM, certification via the practice pathway is permissible for the 2019, 2021, and 2023 certifying examinations.¹ In this perspective, we provide an illustration of ABP leadership and the PHM community partnering to mitigate unintentional gender bias that surfaced after the practice pathway eligibility criteria were implemented. We also provide recommendations to revise these criteria to eliminate future gender bias and promote equity in medicine.

In July 2019, individuals within the PHM community began to share stories of being denied eligibility to sit for the 2019 exam.² Some of the reported denials were due to an eligibility criterion related to “practice interruptions”, which stated that practice interruptions cannot exceed three months in the preceding four years or six months in the preceding five years. Notably, some women reported that their applications were denied because of practice interruptions due to maternity leave. These stories raised significant concerns of gender bias in the board certification process and sparked collective action to revise the board certification eligibility criteria. A petition was circulated within the PHM community and received 1,479 signatures in two weeks.

Given the magnitude of concern, leaders within the PHM community, with support from the American Academy of Pediatrics, collaboratively engaged with the ABP and members of the ABP PHM subboard to improve the transparency and equity of the eligibility process. As a result of this activism and effective dialogue, the ABP revised the PHM board certification eligibility criteria and removed the practice interruption criterion.¹ Through this unique experience of advocacy and partnership in medicine, the PHM community and ABP were able to work together to mitigate unintentional gender bias in the board certification process. However, this collaboration must continue as we believe the revised criteria remain unintentionally biased against women.

Gender bias is defined as the unfair difference in the way men and women are treated.³ Maternal bias is further characterized as bias experienced by mothers related to motherhood, often involving discrimination based on pregnancy, maternity leave, or breastfeeding. Both are common in medicine. Two-thirds of physician mothers report experiencing gender bias and more than a third experience maternal bias.⁴ This bias may be explicit, or intentional, but often the bias is unintentional. This bias can occur even with equal representation of women and men on committees determining eligibility, and even when the committee believes it is not biased.⁵ Furthermore, gender or maternal bias negatively affects individuals in medicine in regards to future employment, career advancement, and compensation.^6-11

Given these implications, we celebrate the removal of the practice interruptions criterion as it was unintentionally biased against women. Eligibility criteria that considered practice interruptions would have disproportionately affected women due to leaves related to pregnancy and due to discrepancies in the length of parental leave for mothers versus fathers. Though the ABP’s initial review of cases of denial did not demonstrate a significant difference in the proportion of men and women who were denied, these data may be misleading. Potential reasons why the ABP did not find significant differences in denial rates between women and men include: (1) some women who had recent maternity leaves chose not to apply because of concerns they may be denied; or (2) some women did not disclose maternity leaves on their application because they did not interpret maternity leave to be a practice interruption. This “self-censoring” may have resulted in incomplete data, making it difficult to fully understand the differential impact of this criterion on women versus men. Therefore, it is essential that we as a profession continue to identify any areas where gender bias exists in determining eligibility for certification, employment, or career advancement within medicine and eliminate it.

Despite the improvements made in the revised criteria, further revision is necessary to remove the criterion related to the “start date”, which will differentially affect women. This criterion states that an individual must have started their PHM practice on or before July of the first year of a four-year look-back period (eg, July 2015 for the 2019 cycle). We present three theoretical cases to illustrate gender bias with respect to this criterion (Table). Even though Applicants #2 and #3 accrue far more than the minimum number of hours in their first year—and more hours overall than Applicant #1—both of these women will remain ineligible under the revised criteria. While Applicant #2 could be eligible for the 2021 or 2023 cycle, Applicant #3, who is new to PHM practice in 2019 as a residency graduate, will not be eligible at all under the practice pathway due to delayed graduation from residency.

Disclosures

The authors have nothing to disclose.

“Better than a thousand days of diligent study is one day with a great teacher.”
—Japanese proverb

My chairman of medicine in medical school was a looming, intimidating, diagnostic genius—and one of the best teachers I have ever had. As a sub-intern it seemed I learned more in one month with him than in my prior six months of medical school.  After the rotation, I asked him how he became such an effective teacher. “Simple,” he said, “I invest significant time and effort.”

But time is limited and you have to be smart with how you invest it. Here are three pearls that are a wise investment—they will make you a better teacher.

Those who seem to teach effortlessly do so after substantial behind-the-scenes effort. Read on your patients before rounds. Identify key teaching points and useful literature. Get some questions ready to define knowledge gaps and create “Teaching Scripts.”

Teaching Scripts are preplanned summaries of specific topics that can be used on rounds or longer talks and are “triggered” by common scenarios (eg, hypoxia). Great teaching scripts use a “hook” to engage the learner (commonly a thought-provoking question or story), two to five teaching points, and purposeful questions, mnemonics, and visual representations.

You should aim to develop at least five teaching scripts on commonly encountered topics. Eventually, you should have twenty scripts you can easily reference.

Technology significantly enhances the efficiency and impact of your teaching. For example, on rounds use your cell phone to display and teach anatomy, radiographic images, and EKGs. Use an iPad as a mobile whiteboard. Use email to collate and disseminate teaching points or send links to valuable learning resources like procedural videos. At its best, you can develop new programs and recruit team members to create resources, like I did with an online series focused on teaching to teach using graphically-enhanced TED-style talks¹ and animated whiteboard videos.²

Do you easily remember the content from your medical school lectures?  Likely not. But you likely remember moments from your favorite comedian or TED talk. Unlike the many PowerPoint lectures you’ve sat through, I’ll bet you stay engaged in films and documentaries. Why the difference? In short—medical educators often don’t make content engaging, readily understood, or memorable. To be most effective in teaching, learn from experts in other fields. Think how storytelling, film, theater, and graphic design contribute to learning. Don’t be afraid to be different.

All of these disciplines recognize the power of storytelling to make their points more impactful and memorable. Leverage this by mixing lessons with stories to create teaching points that stick. Lessons of character and morals can be highlighted through stories of personal struggles, prior patients, or people you admire. Clinical tips can be reinforced through sharing a “clinical story”—concise retellings of high-yield patient cases with diagnosis or management tips.

These disciplines also recognize the importance of “setting the stage” to create an optimal experience. We too can learn from this by setting the stage for our learners. Build a learning environment that is positive, collaborative, and fun by being open, curious, and enthusiastic. Treat your team to coffee rounds or lunch and get to know each learner as you walk between patients. As Teddy Roosevelt said, “people don’t care how much you know, until they know how much you care.”

My chairman taught me that exceptional teaching is not a talent of the gifted, it is a skill of the diligent. If you invest in your teaching, you can make a tremendous impact in the lives of your learners. Are you ready to be empowered?

Acknowledgments

The author wishes to thank Rana Kabeer and Sally Salari for their assistance in storyboarding, graphic design, and video editing of the MENTOR Video Series.

Disclosures

Dr. Cronin has nothing to disclose.

 

“Better than a thousand days of diligent study is one day with a great teacher.”
—Japanese proverb

My chairman of medicine in medical school was a looming, intimidating, diagnostic genius—and one of the best teachers I have ever had. As a sub-intern it seemed I learned more in one month with him than in my prior six months of medical school.  After the rotation, I asked him how he became such an effective teacher. “Simple,” he said, “I invest significant time and effort.”

But time is limited and you have to be smart with how you invest it. Here are three pearls that are a wise investment—they will make you a better teacher.

Those who seem to teach effortlessly do so after substantial behind-the-scenes effort. Read on your patients before rounds. Identify key teaching points and useful literature. Get some questions ready to define knowledge gaps and create “Teaching Scripts.”

Teaching Scripts are preplanned summaries of specific topics that can be used on rounds or longer talks and are “triggered” by common scenarios (eg, hypoxia). Great teaching scripts use a “hook” to engage the learner (commonly a thought-provoking question or story), two to five teaching points, and purposeful questions, mnemonics, and visual representations.

You should aim to develop at least five teaching scripts on commonly encountered topics. Eventually, you should have twenty scripts you can easily reference.

Technology significantly enhances the efficiency and impact of your teaching. For example, on rounds use your cell phone to display and teach anatomy, radiographic images, and EKGs. Use an iPad as a mobile whiteboard. Use email to collate and disseminate teaching points or send links to valuable learning resources like procedural videos. At its best, you can develop new programs and recruit team members to create resources, like I did with an online series focused on teaching to teach using graphically-enhanced TED-style talks¹ and animated whiteboard videos.²

Do you easily remember the content from your medical school lectures?  Likely not. But you likely remember moments from your favorite comedian or TED talk. Unlike the many PowerPoint lectures you’ve sat through, I’ll bet you stay engaged in films and documentaries. Why the difference? In short—medical educators often don’t make content engaging, readily understood, or memorable. To be most effective in teaching, learn from experts in other fields. Think how storytelling, film, theater, and graphic design contribute to learning. Don’t be afraid to be different.

All of these disciplines recognize the power of storytelling to make their points more impactful and memorable. Leverage this by mixing lessons with stories to create teaching points that stick. Lessons of character and morals can be highlighted through stories of personal struggles, prior patients, or people you admire. Clinical tips can be reinforced through sharing a “clinical story”—concise retellings of high-yield patient cases with diagnosis or management tips.

These disciplines also recognize the importance of “setting the stage” to create an optimal experience. We too can learn from this by setting the stage for our learners. Build a learning environment that is positive, collaborative, and fun by being open, curious, and enthusiastic. Treat your team to coffee rounds or lunch and get to know each learner as you walk between patients. As Teddy Roosevelt said, “people don’t care how much you know, until they know how much you care.”

My chairman taught me that exceptional teaching is not a talent of the gifted, it is a skill of the diligent. If you invest in your teaching, you can make a tremendous impact in the lives of your learners. Are you ready to be empowered?

Acknowledgments

The author wishes to thank Rana Kabeer and Sally Salari for their assistance in storyboarding, graphic design, and video editing of the MENTOR Video Series.

Disclosures

Dr. Cronin has nothing to disclose.

 

“Better than a thousand days of diligent study is one day with a great teacher.”
—Japanese proverb

My chairman of medicine in medical school was a looming, intimidating, diagnostic genius—and one of the best teachers I have ever had. As a sub-intern it seemed I learned more in one month with him than in my prior six months of medical school.  After the rotation, I asked him how he became such an effective teacher. “Simple,” he said, “I invest significant time and effort.”

But time is limited and you have to be smart with how you invest it. Here are three pearls that are a wise investment—they will make you a better teacher.

Those who seem to teach effortlessly do so after substantial behind-the-scenes effort. Read on your patients before rounds. Identify key teaching points and useful literature. Get some questions ready to define knowledge gaps and create “Teaching Scripts.”

Teaching Scripts are preplanned summaries of specific topics that can be used on rounds or longer talks and are “triggered” by common scenarios (eg, hypoxia). Great teaching scripts use a “hook” to engage the learner (commonly a thought-provoking question or story), two to five teaching points, and purposeful questions, mnemonics, and visual representations.

You should aim to develop at least five teaching scripts on commonly encountered topics. Eventually, you should have twenty scripts you can easily reference.

Technology significantly enhances the efficiency and impact of your teaching. For example, on rounds use your cell phone to display and teach anatomy, radiographic images, and EKGs. Use an iPad as a mobile whiteboard. Use email to collate and disseminate teaching points or send links to valuable learning resources like procedural videos. At its best, you can develop new programs and recruit team members to create resources, like I did with an online series focused on teaching to teach using graphically-enhanced TED-style talks¹ and animated whiteboard videos.²

Do you easily remember the content from your medical school lectures?  Likely not. But you likely remember moments from your favorite comedian or TED talk. Unlike the many PowerPoint lectures you’ve sat through, I’ll bet you stay engaged in films and documentaries. Why the difference? In short—medical educators often don’t make content engaging, readily understood, or memorable. To be most effective in teaching, learn from experts in other fields. Think how storytelling, film, theater, and graphic design contribute to learning. Don’t be afraid to be different.

All of these disciplines recognize the power of storytelling to make their points more impactful and memorable. Leverage this by mixing lessons with stories to create teaching points that stick. Lessons of character and morals can be highlighted through stories of personal struggles, prior patients, or people you admire. Clinical tips can be reinforced through sharing a “clinical story”—concise retellings of high-yield patient cases with diagnosis or management tips.

These disciplines also recognize the importance of “setting the stage” to create an optimal experience. We too can learn from this by setting the stage for our learners. Build a learning environment that is positive, collaborative, and fun by being open, curious, and enthusiastic. Treat your team to coffee rounds or lunch and get to know each learner as you walk between patients. As Teddy Roosevelt said, “people don’t care how much you know, until they know how much you care.”

My chairman taught me that exceptional teaching is not a talent of the gifted, it is a skill of the diligent. If you invest in your teaching, you can make a tremendous impact in the lives of your learners. Are you ready to be empowered?

Acknowledgments

The author wishes to thank Rana Kabeer and Sally Salari for their assistance in storyboarding, graphic design, and video editing of the MENTOR Video Series.

Disclosures

Dr. Cronin has nothing to disclose.

 

When an emergency department (ED) closes, neighboring hospitals—“bystander hospitals”—feel the effects, especially if they are already near or at full capacity: The health outcomes for their patients worsen, according to findings from a study funded by the National Heart, Lung, and Blood Institute (NHLBI).

The researchers examined outcomes for more than 1 million patients at nearly 4,000 hospitals in both urban and rural areas who had been affected by the closure or opening of an ED. The primary measures were 30-day, 90-day, and 1-year mortality rates, and 30-day readmission rates for heart attack. The researchers chose heart attacks because of the known benefits of timely treatment.

The researchers used changes in driving time between an ED and the next-closest one as a proxy for a closure or opening. If driving time increased, it meant a nearby ED had closed.

They found that when it took an additional 30 minutes or more to get to another hospital, the 1-year mortality rate in those receiving hospitals increased by 8% and the 30-day readmission rate by 6%. The likelihood of patients receiving an angioplasty or stent dropped by 4%.

However, the researchers also found that when an ED opened, the patients in the bystander hospitals benefited, experiencing a reduction in 1-year mortality by 5%. And the likelihood of their receiving percutaneous coronary intervention improved by 12%.

The study is believed to be the first to evaluate the impact of ED openings and closures on other hospitals. The lead author of the study, Renee Hsia, MD, said, “We now have evidence that hospital closures affect other hospitals, and they do so in different ways. Hospitals that are already crowded will likely be unable to maintain the same quality when a nearby emergency department closes.”

Limited resources at high-occupancy hospitals make them “sensitive to changes” in neighboring communities, the researchers say. “Hospital closures stress the health care infrastructure,” says Nicole Redmond, MD, PhD, MPH, a medical officer at NHLBI, “especially if the hospital is already caring for a socially and medically complex patient population and working at full capacity. As a result, such closures may inadvertently increase the health disparities that we are trying to mitigate.”

When an emergency department (ED) closes, neighboring hospitals—“bystander hospitals”—feel the effects, especially if they are already near or at full capacity: The health outcomes for their patients worsen, according to findings from a study funded by the National Heart, Lung, and Blood Institute (NHLBI).

The researchers examined outcomes for more than 1 million patients at nearly 4,000 hospitals in both urban and rural areas who had been affected by the closure or opening of an ED. The primary measures were 30-day, 90-day, and 1-year mortality rates, and 30-day readmission rates for heart attack. The researchers chose heart attacks because of the known benefits of timely treatment.

The researchers used changes in driving time between an ED and the next-closest one as a proxy for a closure or opening. If driving time increased, it meant a nearby ED had closed.

They found that when it took an additional 30 minutes or more to get to another hospital, the 1-year mortality rate in those receiving hospitals increased by 8% and the 30-day readmission rate by 6%. The likelihood of patients receiving an angioplasty or stent dropped by 4%.

However, the researchers also found that when an ED opened, the patients in the bystander hospitals benefited, experiencing a reduction in 1-year mortality by 5%. And the likelihood of their receiving percutaneous coronary intervention improved by 12%.

The study is believed to be the first to evaluate the impact of ED openings and closures on other hospitals. The lead author of the study, Renee Hsia, MD, said, “We now have evidence that hospital closures affect other hospitals, and they do so in different ways. Hospitals that are already crowded will likely be unable to maintain the same quality when a nearby emergency department closes.”

Limited resources at high-occupancy hospitals make them “sensitive to changes” in neighboring communities, the researchers say. “Hospital closures stress the health care infrastructure,” says Nicole Redmond, MD, PhD, MPH, a medical officer at NHLBI, “especially if the hospital is already caring for a socially and medically complex patient population and working at full capacity. As a result, such closures may inadvertently increase the health disparities that we are trying to mitigate.”

When an emergency department (ED) closes, neighboring hospitals—“bystander hospitals”—feel the effects, especially if they are already near or at full capacity: The health outcomes for their patients worsen, according to findings from a study funded by the National Heart, Lung, and Blood Institute (NHLBI).

The researchers examined outcomes for more than 1 million patients at nearly 4,000 hospitals in both urban and rural areas who had been affected by the closure or opening of an ED. The primary measures were 30-day, 90-day, and 1-year mortality rates, and 30-day readmission rates for heart attack. The researchers chose heart attacks because of the known benefits of timely treatment.

The researchers used changes in driving time between an ED and the next-closest one as a proxy for a closure or opening. If driving time increased, it meant a nearby ED had closed.

They found that when it took an additional 30 minutes or more to get to another hospital, the 1-year mortality rate in those receiving hospitals increased by 8% and the 30-day readmission rate by 6%. The likelihood of patients receiving an angioplasty or stent dropped by 4%.

However, the researchers also found that when an ED opened, the patients in the bystander hospitals benefited, experiencing a reduction in 1-year mortality by 5%. And the likelihood of their receiving percutaneous coronary intervention improved by 12%.

The study is believed to be the first to evaluate the impact of ED openings and closures on other hospitals. The lead author of the study, Renee Hsia, MD, said, “We now have evidence that hospital closures affect other hospitals, and they do so in different ways. Hospitals that are already crowded will likely be unable to maintain the same quality when a nearby emergency department closes.”

Limited resources at high-occupancy hospitals make them “sensitive to changes” in neighboring communities, the researchers say. “Hospital closures stress the health care infrastructure,” says Nicole Redmond, MD, PhD, MPH, a medical officer at NHLBI, “especially if the hospital is already caring for a socially and medically complex patient population and working at full capacity. As a result, such closures may inadvertently increase the health disparities that we are trying to mitigate.”

MINNEAPOLIS -- Picture this: A patient with cancer wants to get better and needs your help. But he or she refuses to hear the prognosis or understand the treatment options. The patient, in essence, has embraced a personal don’t-ask, don’t-tell policy.

What should you do as a medical professional? Get help from a psychologist and consider the ethics of the situation, advised a VA psychologist in a presentation at the September 2019 annual meeting of the Association of VA Hematology/Oncology.

Alyssa Ford, PhD, psychosocial oncology coordinator at VA Pittsburgh Healthcare System in Pennsylvania, said she has faced this situation. “We didn’t know the staging yet, but the veteran did not want to know about their prognosis or the treatment options,” she recalled. “They just wanted to fight this cancer.”

At issue in this case, she said, is this question: “What do we do when a patient opts out about receiving sufficient information to make an informed choice?”

As she explained, the key is to understand the person’s capacity—the ability to make an informed decision. “It can be assessed by any licensed health care provider who understands the components of capacity and is able to assess them.”

Ford evaluates a patient’s capacity by analyzing whether he or she can perform 4 tasks: Make decisions, live independently, manage finances, and grant power of attorney.  “Often,” she said, “they have 1 but not all.” 

Other components of capacity include the ability to understand one’s medical situation, an appreciation of the pros and cons of treatment options, the consistency of choices over time, and the ability to reason. “Can the patient consider the risks and benefits of each option and consider quality of life vs quantity of life in light of their own cultural identity and personal values?”

Keep in mind that levels of capacity can change over time, Ford said, and remember that these judgements are not arbitrary or punitive.

When someone doesn’t have capacity, she said, “it doesn’t necessarily tell us why or whether it will come back. But it does say they can’t provide informed consent.”

What happened to the determinedly reluctant patient who simply wanted to “fight” and not make decisions?

“The oncology provider chose to have the psychology provider in the room while staging information and prognosis was shared,” Ford said in an interview following her presentation. “And the psychology provider assisted with ensuring that the veteran received education in simple terms and in promoting active coping.”

In addition, she said, “the psychologist provider also spent several minutes after the visit giving the veteran an opportunity to discuss their feelings. And the provider physically escorted the veteran to the laboratory to ensure that the impact of receiving difficult news did not impair mood or cognition to the point that the veteran left the medical center instead of engaging in the next step of needed medical care.”

Ford reports no relevant disclosures.

MINNEAPOLIS -- Picture this: A patient with cancer wants to get better and needs your help. But he or she refuses to hear the prognosis or understand the treatment options. The patient, in essence, has embraced a personal don’t-ask, don’t-tell policy.

What should you do as a medical professional? Get help from a psychologist and consider the ethics of the situation, advised a VA psychologist in a presentation at the September 2019 annual meeting of the Association of VA Hematology/Oncology.

Alyssa Ford, PhD, psychosocial oncology coordinator at VA Pittsburgh Healthcare System in Pennsylvania, said she has faced this situation. “We didn’t know the staging yet, but the veteran did not want to know about their prognosis or the treatment options,” she recalled. “They just wanted to fight this cancer.”

At issue in this case, she said, is this question: “What do we do when a patient opts out about receiving sufficient information to make an informed choice?”

As she explained, the key is to understand the person’s capacity—the ability to make an informed decision. “It can be assessed by any licensed health care provider who understands the components of capacity and is able to assess them.”

Ford evaluates a patient’s capacity by analyzing whether he or she can perform 4 tasks: Make decisions, live independently, manage finances, and grant power of attorney.  “Often,” she said, “they have 1 but not all.” 

Other components of capacity include the ability to understand one’s medical situation, an appreciation of the pros and cons of treatment options, the consistency of choices over time, and the ability to reason. “Can the patient consider the risks and benefits of each option and consider quality of life vs quantity of life in light of their own cultural identity and personal values?”

Keep in mind that levels of capacity can change over time, Ford said, and remember that these judgements are not arbitrary or punitive.

When someone doesn’t have capacity, she said, “it doesn’t necessarily tell us why or whether it will come back. But it does say they can’t provide informed consent.”

What happened to the determinedly reluctant patient who simply wanted to “fight” and not make decisions?

“The oncology provider chose to have the psychology provider in the room while staging information and prognosis was shared,” Ford said in an interview following her presentation. “And the psychology provider assisted with ensuring that the veteran received education in simple terms and in promoting active coping.”

In addition, she said, “the psychologist provider also spent several minutes after the visit giving the veteran an opportunity to discuss their feelings. And the provider physically escorted the veteran to the laboratory to ensure that the impact of receiving difficult news did not impair mood or cognition to the point that the veteran left the medical center instead of engaging in the next step of needed medical care.”

Ford reports no relevant disclosures.

MINNEAPOLIS -- Picture this: A patient with cancer wants to get better and needs your help. But he or she refuses to hear the prognosis or understand the treatment options. The patient, in essence, has embraced a personal don’t-ask, don’t-tell policy.

What should you do as a medical professional? Get help from a psychologist and consider the ethics of the situation, advised a VA psychologist in a presentation at the September 2019 annual meeting of the Association of VA Hematology/Oncology.

Alyssa Ford, PhD, psychosocial oncology coordinator at VA Pittsburgh Healthcare System in Pennsylvania, said she has faced this situation. “We didn’t know the staging yet, but the veteran did not want to know about their prognosis or the treatment options,” she recalled. “They just wanted to fight this cancer.”

At issue in this case, she said, is this question: “What do we do when a patient opts out about receiving sufficient information to make an informed choice?”

As she explained, the key is to understand the person’s capacity—the ability to make an informed decision. “It can be assessed by any licensed health care provider who understands the components of capacity and is able to assess them.”

Ford evaluates a patient’s capacity by analyzing whether he or she can perform 4 tasks: Make decisions, live independently, manage finances, and grant power of attorney.  “Often,” she said, “they have 1 but not all.” 

Other components of capacity include the ability to understand one’s medical situation, an appreciation of the pros and cons of treatment options, the consistency of choices over time, and the ability to reason. “Can the patient consider the risks and benefits of each option and consider quality of life vs quantity of life in light of their own cultural identity and personal values?”

Keep in mind that levels of capacity can change over time, Ford said, and remember that these judgements are not arbitrary or punitive.

When someone doesn’t have capacity, she said, “it doesn’t necessarily tell us why or whether it will come back. But it does say they can’t provide informed consent.”

What happened to the determinedly reluctant patient who simply wanted to “fight” and not make decisions?

“The oncology provider chose to have the psychology provider in the room while staging information and prognosis was shared,” Ford said in an interview following her presentation. “And the psychology provider assisted with ensuring that the veteran received education in simple terms and in promoting active coping.”

In addition, she said, “the psychologist provider also spent several minutes after the visit giving the veteran an opportunity to discuss their feelings. And the provider physically escorted the veteran to the laboratory to ensure that the impact of receiving difficult news did not impair mood or cognition to the point that the veteran left the medical center instead of engaging in the next step of needed medical care.”

Ford reports no relevant disclosures.

The first Pediatric Hospital Medicine (PHM) fellowships in the United States were established in 2003;¹ and since then, the field has expanded and matured dramatically. This growth, accompanied by greater definition of the role and recommended competencies of pediatric hospitalists,² culminated in the submission of a petition to the American Board of Pediatrics (ABP) in August 2014 to consider recognition of PHM as a new pediatric subspecialty.³ After an 18-month iterative process requiring extensive input from the Joint Council of Pediatric Hospital Medicine, ABP subcommittees, the Association of Medical School Pediatric Department Chairs, the Association of Pediatric Program Directors, and other prominent pediatric professional societies, the ABP voted in December 2015 to recommend that the American Board of Medical Subspecialties (ABMS) recognize PHM as a new subspecialty.³

The ABP subsequently announced three pathways for board certification in PHM:

• Training pathway for those completing an Accreditation Council for Graduate Medical Education–accredited two-year PHM fellowship program;
• Practice pathway for those satisfying ABP criteria for clinical activity in PHM for four years prior to exam dates (in 2019, 2021, and 2023), initially described as “direct patient care of hospitalized children ≥25% full-time equivalent (FTE) defined as ≥450-500 hours per year every year for the preceding four years”;⁴
• Combined pathway for those completing less than two years of fellowship, who would be required to complete two years of practice experience that satisfy the same criteria as each year of the practice pathway.⁵

While the training pathway met near-uniform acceptance, concerns were raised through the American Academy of Pediatrics Section of Hospital Medicine (AAP SOHM) Listserv regarding the practice pathway, and by extension, the combined pathway. Specifically, language describing the necessary characteristics of acceptable PHM practice was felt to be vague and not transparent. Listserv posts also raised concerns regarding the potential exclusion of “niche” practices such as subspecialty hospitalists and newborn hospitalists. As applicants in the practice pathway began to receive denials, opinions voiced in listserv posts were increasingly critical of the ABP’s lack of transparency regarding the specific criteria adjudicating applications.

A group of hospitalists, led by Dr. David Skey, a pediatric hospitalist at Arnold Palmer Children’s Hospital in Orlando, Florida, created a petition which was submitted to the ABP on August 6, 2019, and raised the following issues:

• “A perception of unfairness/bias in the practice pathway criteria and the way these criteria have been applied.
• Denials based on gaps in employment without reasonable consideration of mitigating factors.
• Lack of transparency, accountability, and responsiveness from the ABP.”⁶

The petition, posted on the AAP SOHM listserv and signed by 1,479 individuals,⁷ raised concerns of anecdotal evidence that the practice pathway criteria disproportionately disadvantaged women, although intentional bias was not suspected by the signers of the letter. The petition’s signers submitted the following demands to the ABP:

• “Facilitate a timely analysis to determine if gender bias is present or perform this analysis internally and release the findings publicly.
• Revise the practice pathway criteria to be more inclusive of applicants with interrupted practice and varied clinical experience, to include clear-cut parameters rather than considering these applications on a closed-door ‘case-by-case basis...at the discretion of the ABP’.
• Clarify the appeals process and improve responsiveness to appeals and inquiries regarding denials.
• Provide a formal response to this petition letter through the PHM ListServ and/or the ABP website within one week of receiving the signed petition.”⁶

A formal response to the petition was released on the AAP SOHM Listserv on August 29, 2019, to address the concerns raised and is published in this issue of the Journal of Hospital Medicine.⁴ In response to the allegation of gender bias, the ABP maintained that the data did not support this, as the denial rate for females (4.0%) was not significantly different than that for males (3.7%). The response acknowledged that once clear-cut criteria were decided upon to augment the general practice pathway criteria published at the outset, these criteria should have been disseminated. The ABP maintained, however, that these criteria, once established, were used consistently in adjudicating all applications. To clarify and simplify the eligibility criteria, the percentage of the full-time equivalent and practice interruption criteria were removed, as the work-hours criteria (direct patient care of hospitalized children ≥450-500 hours per year every year for the preceding four years)⁸ were deemed sufficient to ensure adequate clinical participation.

The Society of Hospital Medicine (SHM), through pediatric hospitalists and pediatricians on its Board, committees, and the Executive Council of the Pediatric Special Interest Group, has followed with great interest the public debate surrounding the PHM certification process and the subsequent PHM petition to the ABP. The ABP responded swiftly and with full transparency to the petition, and SHM supports these efforts by the ABP to provide a timely, honest, data-driven response to the concerns raised by the PHM petition. SHM recognizes that the mission of the ABP is to provide the public with confidence that physicians with ABP board certifications meet appropriate “standards of excellence”. While the revisions implemented by the ABP in its response still may not satisfy the concerns of all members of the PHM community, SHM recognizes that the revised requirements remain true to the mission of the ABP.

SHM applauds the authors and signatories of the PHM petition for bravely raising their concerns of gender bias and lack of transparency. The response of the ABP to this petition by further improving transparency serves as an example of continuous improvement in collaborative practice to all medical specialty boards.

While SHM supports the ABP response to the PHM petition, it is clear that excellent physicians caring for hospitalized children will be unable to achieve PHM board certification for a variety of reasons. For these physicians who are not PHM board certified as pediatric hospitalists by the ABP, SHM supports providing these physicians with recognition as hospitalists. These include “niche” hospitalists, such as newborn hospitalists, subacute hospitalists, and subspecialty hospitalists. SHM will also continue to support and recognize community-based hospitalists, family medicine-trained hospitalists, and Med-Peds hospitalists whose practice may not comply with criteria laid out by the ABP. For these physicians, receiving Fellow designation through SHM, a merit-based distinction requiring demonstration of clinical excellence and commitment to hospital medicine, is another route whereby physicians can achieve designation as a hospitalist.

SHM supports future efforts by the ABP to be vigilant for bias of any sort in the certification process. Other future considerations for the PHM community include the possibility of a focused practice pathway in hospital medicine (FPHM) for pediatrics as is currently jointly offered by the American Board of Internal Medicine (ABIM) and the American Board of Family Medicine (ABFM). This maintenance of certification program is a variation of internal medicine or family medicine recertification, not a subspecialty, but allows physicians practicing primarily in inpatient settings to focus continuing education efforts on skills and attitudes needed for inpatient practice.⁹ While this possibility was discounted by the ABP in the past based on initially low numbers of physicians choosing this pathway, this pathway has grown from initially attracting 150 internal medicine applicants yearly to 265 in 2015.¹⁰ The ABMS approved the ABIM/ABFM FPHM as its first approved designation in March 2017 after more than 2,500 physicians earned this designation.¹¹ Of the >2,800 pediatric residency graduates (not including combined programs) each year, 10% report planning on becoming pediatric hospitalists,¹² and currently only 72-74 fellows graduate from PHM fellowships yearly.¹³ FPHM for pediatric hospital medicine would provide focused maintenance of certification and hospitalist designation for those who cannot match to fellowship programs.

Acknowledgments

The authors would like to acknowledge the input and support from the Executive Council of the Society of Hospital Medicine Pediatric Special Interest Group in writing this statement.

Disclosures

Dr. Chang served as an author of the Pediatric Hospital Medicine Petition to the American Board of Pediatrics for Subspecialty Certification. Drs. Hopkins, Rehm, Gage, and Shen have nothing to disclose.

The first Pediatric Hospital Medicine (PHM) fellowships in the United States were established in 2003;¹ and since then, the field has expanded and matured dramatically. This growth, accompanied by greater definition of the role and recommended competencies of pediatric hospitalists,² culminated in the submission of a petition to the American Board of Pediatrics (ABP) in August 2014 to consider recognition of PHM as a new pediatric subspecialty.³ After an 18-month iterative process requiring extensive input from the Joint Council of Pediatric Hospital Medicine, ABP subcommittees, the Association of Medical School Pediatric Department Chairs, the Association of Pediatric Program Directors, and other prominent pediatric professional societies, the ABP voted in December 2015 to recommend that the American Board of Medical Subspecialties (ABMS) recognize PHM as a new subspecialty.³

The ABP subsequently announced three pathways for board certification in PHM:

• Training pathway for those completing an Accreditation Council for Graduate Medical Education–accredited two-year PHM fellowship program;
• Practice pathway for those satisfying ABP criteria for clinical activity in PHM for four years prior to exam dates (in 2019, 2021, and 2023), initially described as “direct patient care of hospitalized children ≥25% full-time equivalent (FTE) defined as ≥450-500 hours per year every year for the preceding four years”;⁴
• Combined pathway for those completing less than two years of fellowship, who would be required to complete two years of practice experience that satisfy the same criteria as each year of the practice pathway.⁵

While the training pathway met near-uniform acceptance, concerns were raised through the American Academy of Pediatrics Section of Hospital Medicine (AAP SOHM) Listserv regarding the practice pathway, and by extension, the combined pathway. Specifically, language describing the necessary characteristics of acceptable PHM practice was felt to be vague and not transparent. Listserv posts also raised concerns regarding the potential exclusion of “niche” practices such as subspecialty hospitalists and newborn hospitalists. As applicants in the practice pathway began to receive denials, opinions voiced in listserv posts were increasingly critical of the ABP’s lack of transparency regarding the specific criteria adjudicating applications.

A group of hospitalists, led by Dr. David Skey, a pediatric hospitalist at Arnold Palmer Children’s Hospital in Orlando, Florida, created a petition which was submitted to the ABP on August 6, 2019, and raised the following issues:

• “A perception of unfairness/bias in the practice pathway criteria and the way these criteria have been applied.
• Denials based on gaps in employment without reasonable consideration of mitigating factors.
• Lack of transparency, accountability, and responsiveness from the ABP.”⁶

The petition, posted on the AAP SOHM listserv and signed by 1,479 individuals,⁷ raised concerns of anecdotal evidence that the practice pathway criteria disproportionately disadvantaged women, although intentional bias was not suspected by the signers of the letter. The petition’s signers submitted the following demands to the ABP:

• “Facilitate a timely analysis to determine if gender bias is present or perform this analysis internally and release the findings publicly.
• Revise the practice pathway criteria to be more inclusive of applicants with interrupted practice and varied clinical experience, to include clear-cut parameters rather than considering these applications on a closed-door ‘case-by-case basis...at the discretion of the ABP’.
• Clarify the appeals process and improve responsiveness to appeals and inquiries regarding denials.
• Provide a formal response to this petition letter through the PHM ListServ and/or the ABP website within one week of receiving the signed petition.”⁶

A formal response to the petition was released on the AAP SOHM Listserv on August 29, 2019, to address the concerns raised and is published in this issue of the Journal of Hospital Medicine.⁴ In response to the allegation of gender bias, the ABP maintained that the data did not support this, as the denial rate for females (4.0%) was not significantly different than that for males (3.7%). The response acknowledged that once clear-cut criteria were decided upon to augment the general practice pathway criteria published at the outset, these criteria should have been disseminated. The ABP maintained, however, that these criteria, once established, were used consistently in adjudicating all applications. To clarify and simplify the eligibility criteria, the percentage of the full-time equivalent and practice interruption criteria were removed, as the work-hours criteria (direct patient care of hospitalized children ≥450-500 hours per year every year for the preceding four years)⁸ were deemed sufficient to ensure adequate clinical participation.

The Society of Hospital Medicine (SHM), through pediatric hospitalists and pediatricians on its Board, committees, and the Executive Council of the Pediatric Special Interest Group, has followed with great interest the public debate surrounding the PHM certification process and the subsequent PHM petition to the ABP. The ABP responded swiftly and with full transparency to the petition, and SHM supports these efforts by the ABP to provide a timely, honest, data-driven response to the concerns raised by the PHM petition. SHM recognizes that the mission of the ABP is to provide the public with confidence that physicians with ABP board certifications meet appropriate “standards of excellence”. While the revisions implemented by the ABP in its response still may not satisfy the concerns of all members of the PHM community, SHM recognizes that the revised requirements remain true to the mission of the ABP.

SHM applauds the authors and signatories of the PHM petition for bravely raising their concerns of gender bias and lack of transparency. The response of the ABP to this petition by further improving transparency serves as an example of continuous improvement in collaborative practice to all medical specialty boards.

While SHM supports the ABP response to the PHM petition, it is clear that excellent physicians caring for hospitalized children will be unable to achieve PHM board certification for a variety of reasons. For these physicians who are not PHM board certified as pediatric hospitalists by the ABP, SHM supports providing these physicians with recognition as hospitalists. These include “niche” hospitalists, such as newborn hospitalists, subacute hospitalists, and subspecialty hospitalists. SHM will also continue to support and recognize community-based hospitalists, family medicine-trained hospitalists, and Med-Peds hospitalists whose practice may not comply with criteria laid out by the ABP. For these physicians, receiving Fellow designation through SHM, a merit-based distinction requiring demonstration of clinical excellence and commitment to hospital medicine, is another route whereby physicians can achieve designation as a hospitalist.

SHM supports future efforts by the ABP to be vigilant for bias of any sort in the certification process. Other future considerations for the PHM community include the possibility of a focused practice pathway in hospital medicine (FPHM) for pediatrics as is currently jointly offered by the American Board of Internal Medicine (ABIM) and the American Board of Family Medicine (ABFM). This maintenance of certification program is a variation of internal medicine or family medicine recertification, not a subspecialty, but allows physicians practicing primarily in inpatient settings to focus continuing education efforts on skills and attitudes needed for inpatient practice.⁹ While this possibility was discounted by the ABP in the past based on initially low numbers of physicians choosing this pathway, this pathway has grown from initially attracting 150 internal medicine applicants yearly to 265 in 2015.¹⁰ The ABMS approved the ABIM/ABFM FPHM as its first approved designation in March 2017 after more than 2,500 physicians earned this designation.¹¹ Of the >2,800 pediatric residency graduates (not including combined programs) each year, 10% report planning on becoming pediatric hospitalists,¹² and currently only 72-74 fellows graduate from PHM fellowships yearly.¹³ FPHM for pediatric hospital medicine would provide focused maintenance of certification and hospitalist designation for those who cannot match to fellowship programs.

Acknowledgments

The authors would like to acknowledge the input and support from the Executive Council of the Society of Hospital Medicine Pediatric Special Interest Group in writing this statement.

Disclosures

Dr. Chang served as an author of the Pediatric Hospital Medicine Petition to the American Board of Pediatrics for Subspecialty Certification. Drs. Hopkins, Rehm, Gage, and Shen have nothing to disclose.

The first Pediatric Hospital Medicine (PHM) fellowships in the United States were established in 2003;¹ and since then, the field has expanded and matured dramatically. This growth, accompanied by greater definition of the role and recommended competencies of pediatric hospitalists,² culminated in the submission of a petition to the American Board of Pediatrics (ABP) in August 2014 to consider recognition of PHM as a new pediatric subspecialty.³ After an 18-month iterative process requiring extensive input from the Joint Council of Pediatric Hospital Medicine, ABP subcommittees, the Association of Medical School Pediatric Department Chairs, the Association of Pediatric Program Directors, and other prominent pediatric professional societies, the ABP voted in December 2015 to recommend that the American Board of Medical Subspecialties (ABMS) recognize PHM as a new subspecialty.³

The ABP subsequently announced three pathways for board certification in PHM:

• Training pathway for those completing an Accreditation Council for Graduate Medical Education–accredited two-year PHM fellowship program;
• Practice pathway for those satisfying ABP criteria for clinical activity in PHM for four years prior to exam dates (in 2019, 2021, and 2023), initially described as “direct patient care of hospitalized children ≥25% full-time equivalent (FTE) defined as ≥450-500 hours per year every year for the preceding four years”;⁴
• Combined pathway for those completing less than two years of fellowship, who would be required to complete two years of practice experience that satisfy the same criteria as each year of the practice pathway.⁵

While the training pathway met near-uniform acceptance, concerns were raised through the American Academy of Pediatrics Section of Hospital Medicine (AAP SOHM) Listserv regarding the practice pathway, and by extension, the combined pathway. Specifically, language describing the necessary characteristics of acceptable PHM practice was felt to be vague and not transparent. Listserv posts also raised concerns regarding the potential exclusion of “niche” practices such as subspecialty hospitalists and newborn hospitalists. As applicants in the practice pathway began to receive denials, opinions voiced in listserv posts were increasingly critical of the ABP’s lack of transparency regarding the specific criteria adjudicating applications.

A group of hospitalists, led by Dr. David Skey, a pediatric hospitalist at Arnold Palmer Children’s Hospital in Orlando, Florida, created a petition which was submitted to the ABP on August 6, 2019, and raised the following issues:

• “A perception of unfairness/bias in the practice pathway criteria and the way these criteria have been applied.
• Denials based on gaps in employment without reasonable consideration of mitigating factors.
• Lack of transparency, accountability, and responsiveness from the ABP.”⁶

The petition, posted on the AAP SOHM listserv and signed by 1,479 individuals,⁷ raised concerns of anecdotal evidence that the practice pathway criteria disproportionately disadvantaged women, although intentional bias was not suspected by the signers of the letter. The petition’s signers submitted the following demands to the ABP:

• “Facilitate a timely analysis to determine if gender bias is present or perform this analysis internally and release the findings publicly.
• Revise the practice pathway criteria to be more inclusive of applicants with interrupted practice and varied clinical experience, to include clear-cut parameters rather than considering these applications on a closed-door ‘case-by-case basis...at the discretion of the ABP’.
• Clarify the appeals process and improve responsiveness to appeals and inquiries regarding denials.
• Provide a formal response to this petition letter through the PHM ListServ and/or the ABP website within one week of receiving the signed petition.”⁶

A formal response to the petition was released on the AAP SOHM Listserv on August 29, 2019, to address the concerns raised and is published in this issue of the Journal of Hospital Medicine.⁴ In response to the allegation of gender bias, the ABP maintained that the data did not support this, as the denial rate for females (4.0%) was not significantly different than that for males (3.7%). The response acknowledged that once clear-cut criteria were decided upon to augment the general practice pathway criteria published at the outset, these criteria should have been disseminated. The ABP maintained, however, that these criteria, once established, were used consistently in adjudicating all applications. To clarify and simplify the eligibility criteria, the percentage of the full-time equivalent and practice interruption criteria were removed, as the work-hours criteria (direct patient care of hospitalized children ≥450-500 hours per year every year for the preceding four years)⁸ were deemed sufficient to ensure adequate clinical participation.

The Society of Hospital Medicine (SHM), through pediatric hospitalists and pediatricians on its Board, committees, and the Executive Council of the Pediatric Special Interest Group, has followed with great interest the public debate surrounding the PHM certification process and the subsequent PHM petition to the ABP. The ABP responded swiftly and with full transparency to the petition, and SHM supports these efforts by the ABP to provide a timely, honest, data-driven response to the concerns raised by the PHM petition. SHM recognizes that the mission of the ABP is to provide the public with confidence that physicians with ABP board certifications meet appropriate “standards of excellence”. While the revisions implemented by the ABP in its response still may not satisfy the concerns of all members of the PHM community, SHM recognizes that the revised requirements remain true to the mission of the ABP.

SHM applauds the authors and signatories of the PHM petition for bravely raising their concerns of gender bias and lack of transparency. The response of the ABP to this petition by further improving transparency serves as an example of continuous improvement in collaborative practice to all medical specialty boards.

While SHM supports the ABP response to the PHM petition, it is clear that excellent physicians caring for hospitalized children will be unable to achieve PHM board certification for a variety of reasons. For these physicians who are not PHM board certified as pediatric hospitalists by the ABP, SHM supports providing these physicians with recognition as hospitalists. These include “niche” hospitalists, such as newborn hospitalists, subacute hospitalists, and subspecialty hospitalists. SHM will also continue to support and recognize community-based hospitalists, family medicine-trained hospitalists, and Med-Peds hospitalists whose practice may not comply with criteria laid out by the ABP. For these physicians, receiving Fellow designation through SHM, a merit-based distinction requiring demonstration of clinical excellence and commitment to hospital medicine, is another route whereby physicians can achieve designation as a hospitalist.

SHM supports future efforts by the ABP to be vigilant for bias of any sort in the certification process. Other future considerations for the PHM community include the possibility of a focused practice pathway in hospital medicine (FPHM) for pediatrics as is currently jointly offered by the American Board of Internal Medicine (ABIM) and the American Board of Family Medicine (ABFM). This maintenance of certification program is a variation of internal medicine or family medicine recertification, not a subspecialty, but allows physicians practicing primarily in inpatient settings to focus continuing education efforts on skills and attitudes needed for inpatient practice.⁹ While this possibility was discounted by the ABP in the past based on initially low numbers of physicians choosing this pathway, this pathway has grown from initially attracting 150 internal medicine applicants yearly to 265 in 2015.¹⁰ The ABMS approved the ABIM/ABFM FPHM as its first approved designation in March 2017 after more than 2,500 physicians earned this designation.¹¹ Of the >2,800 pediatric residency graduates (not including combined programs) each year, 10% report planning on becoming pediatric hospitalists,¹² and currently only 72-74 fellows graduate from PHM fellowships yearly.¹³ FPHM for pediatric hospital medicine would provide focused maintenance of certification and hospitalist designation for those who cannot match to fellowship programs.

Acknowledgments

The authors would like to acknowledge the input and support from the Executive Council of the Society of Hospital Medicine Pediatric Special Interest Group in writing this statement.

Disclosures

Dr. Chang served as an author of the Pediatric Hospital Medicine Petition to the American Board of Pediatrics for Subspecialty Certification. Drs. Hopkins, Rehm, Gage, and Shen have nothing to disclose.

Bronchiolitis is the most common cause of nonbirth hospitalization in children in the United States less than one year of age.¹ For children with severe bronchiolitis, high-flow nasal cannula (HFNC) is increasingly used^2-4 to reduce work of breathing and prevent the need for further escalation of ventilatory support.^5,6 Although previous studies suggest that enteral feeding is recommended in the management of patients hospitalized with bronchiolitis,^7-9 limited evidence exists to guide feeding practices for patients receiving HFNC support.^5,10,11

Respiratory support with HFNC has been associated with prolonged periods without enteral hydration/nutrition (ie, nil per os [NPO])¹² primarily due to anticipation of further escalation of respiratory support or concern for increased risk of aspiration. The majority of patients with bronchiolitis managed with HFNC, however, do not require escalation of care.^5,13 When feeding is attempted during HFNC support, it is frequently interrupted.⁵ Moreover, keeping all children NPO when receiving HFNC may be associated with weight loss and longer length of stay (LOS).^12,14 Two small studies found that children admitted to the intensive care unit who received HFNC support for bronchiolitis did not have increased rates of emesis, worsening respiratory distress or aspiration pneumonia when enterally fed.^10,11 However, no comparison of adverse events or LOS has been made between patients who were fed and those who were not fed during HFNC therapy, and previous studies have included only patients who have received HFNC in the intensive care setting.

Supporting safe feeding early in hospitalizations for bronchiolitis may facilitate expedited clinical improvement and discharge. As part of an ongoing bronchiolitis quality improvement initiative at our hospital, we sought to characterize feeding practices during HFNC therapy and assess whether feeding exposure was associated with (1) time to discharge after HFNC or (2) feeding-related adverse events. We hypothesized that feeding during HFNC therapy would be associated with a shorter time to discharge after HFNC cessation.

Study Design, Setting, Participants

This was a retrospective cohort study of patients aged 1-24 months receiving HFNC support for respiratory failure due to bronchiolitis at an academic children’s hospital between January 1, 2015 and March 1, 2017. Our institution has had a clinical practice guideline, associated order set, and respiratory therapy protocol for general care patients with bronchiolitis since 2009. Patients with bronchiolitis who were weaning HFNC have been cared for in both the intensive and general care settings since 2013. A formal process for initiation of HFNC on general care units was instituted in January of 2017. During the study period, no patients with HFNC support for bronchiolitis had all their care entirely outside the intensive care unit at our institution. However, initiation and subsequent use of HFNC may have occurred in either the intensive care or general care setting. No specific guidance for feeding during HFNC existed during this period.

Patients were identified using the Virtual PICU Systems database, (VPS LLC, myvps.org, Los Angeles, California) and, by definition, all patients received at least some of their care in the intensive care unit. Patients with comorbid conditions of prematurity (<35 weeks) and those with cardiopulmonary, neuromuscular, and genetic diseases were included. Patients with preexisting dysphagia, defined as ongoing outpatient speech therapy for swallowing concerns, an admission diagnosis of aspiration pneumonia or on home respiratory support, were excluded. Children (n = 7) were excluded if they had more than one period of HFNC during admission. This study was determined to be exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Data Collection and Study Variables

The following variables were collected from VPS administrative data: patient gender, age, admission and discharge date and time, type and total hours of respiratory support, intensive care admission, and LOS (in hours). Additional demographic, clinical, and feeding exposure variables were abstracted manually from the electronic medical record (Epic, Verona, Wisconsin) using a structured data collection tool and stored in REDCap (Research Electronic Data Capture)¹⁵ including prematurity, race/ethnicity, insurance status, primary language, and passive tobacco smoke exposure. Clinical variables included duration of illness (days) at the time of admission, unit of HFNC initiation (emergency department, general care, intensive care, respiratory rate and oxygen saturation at HFNC initiation (<90%, 91%-92%, or >92%), acquisition of blood gas at HFNC admission, duration of time on HFNC (hours) and need for intubation or noninvasive ventilation prior to HFNC. The Pediatric Index of Mortality 2 Risk of Mortality (PIM 2 ROM)¹⁶ was used to estimate the severity of illness. The PIM2ROM uses clinical variables (systolic blood pressure, fixed pupils, measure of hypoxia using PaO₂/FiO₂ ratio, base excess, mechanical ventilation, elective admission, recovery from surgery, cardiac bypass, high-risk diagnosis, low-risk diagnosis) collected at the time of intensive care admission to generate a score that predicts the risk of mortality for an individual patient.¹⁷

Feeding exposures were documented in three-hour intervals from HFNC initiation to completion using a structured protocol. At each interval the following feeding information was abstracted from a review of nursing and physician documentation and relevant clinical flowsheets: presence or absence of feeding during the interval, route of feeding (oral, nasogastric [NG] or nasojejunal [NJ]). Feeding exposure was categorized a priori as fed at any point during HFNC (vs not fed at any point). Fed children were further characterized as (1) mixed feeding consisting of oral and tube feeds (NG or NJ) or (2) exclusive oral feeding throughout HFNC support (Appendix 1).

The primary outcome was the number of hours to discharge from HFNC cessation. Secondary outcomes were time to discharge from HFNC initiation, all-cause readmissions within seven days of discharge, and potential feeding-related adverse events. Potential adverse events included: (1) aspiration, defined as initiation of antibiotic AND either chest radiograph official interpreted as evidence for aspiration and/or documented concern for aspiration from the treating physician, or (2) intubation after feeding during HFNC.

Statistical Analysis

Descriptive statistics evaluated differences in demographics and clinical variables for feeding exposure groups. We used chi-squared tests for differences in proportions and t-tests or Wilcoxon Rank-Sum tests for differences in means or medians for continuous variables, respectively. Associations between feeding exposure during HFNC and time to discharge (measured in hours) after HFNC completion were modeled with Cox proportional hazards regression. Using this approach, hazard ratios (HR)>1 indicate a higher hazard (rate) of discharge for children with a feeding exposure than for children without the exposure. For example, a hazard ratio equal to two indicates that the exposed population is discharged at twice the rate per unit time as the nonexposed population. Death or censoring events did not occur. Feeding exposure was first modeled dichotomously as not fed or fed. To further explore associations between feeding modality and our outcome, we then modeled feeding exposure categorically as not fed (reference), mixed (oral and tube) feeding, or exclusive oral feeding throughout HFNC.

After constructing a set of unadjusted models, we then adjusted the models for variables having independent (bivariate P < .10) associations with time to discharge: age, unit of HFNC initiation, highest respiratory support required before HFNC, and HFNC duration. Finally, to attempt to account for residual confounding from latent constructs, we also created a set of propensity-weighted Cox proportional hazards models. Propensity weights¹⁸ reflecting the probability of being fed or never being fed during HFNC were created using logistic regression with predictors we hypothesized a priori that may have influenced the clinical decision to feed during HFNC: age, day of illness on admission, prematurity, PIM2 ROM score, respiratory rate, oxygen saturation and blood gas acquisition at HFNC initiation, and highest respiratory support required before HFNC. All analyses were conducted using STATA 14.0 (StataCorp, College Station, Texas), and adjusted hazard ratios (aHR) with 95% confidence intervals (95% CIs) were reported.

Patients (n = 123) had a mean age of 7.3 months (standard deviation [SD] 7.1) and presented on day of illness 4.8 (SD 2.3). Prior to HFNC, 10% required higher respiratory support (3% mechanical ventilation). Former preterm children were 12% of the overall sample.

During HFNC, 37% of patients were never fed, 41% were exclusively orally fed, and 23% had tube or mixed oral and tube feedings (Table 1 and Appendix 2). Children who were not fed were older, but groups were otherwise similar in terms of gender, race/ethnicity, passive smoke exposure, day of illness, unit of HFNC initiation, respiratory support required prior to HFNC, and respiratory rate at HFNC initiation.

This observational study found that being fed during HFNC was associated with shorter time to discharge after HFNC support was completed. Exclusive oral feeding was associated with the shortest time to discharge, and these results were consistent across a variety of analytical approaches. Adverse events were rare and occurred in both fed and unfed children.

These findings advance research on relationships between nutrition and bronchiolitis outcomes. Studies of general care patients with bronchiolitis without HFNC have observed associations between poor nutrition and prolonged LOS.^19,20 Two previous studies of patients receiving HFNC therapy for bronchiolitis concluded that frequent interruption¹¹ and later initiation of enteral nutrition¹⁰ during ICU stay was associated with longer time to discharge.¹¹ To our knowledge, this is the first study of patients with bronchiolitis treated with HFNC in both general care and ICU settings that compared outcomes according to whether children were fed during HFNC therapy. Our results extend previous work demonstrating that delays in feeding may be associated with longer LOS.

Children fed while receiving HFNC for bronchiolitis may have shorter time to discharge than those who are not fed. Feeding-related adverse events were rare regardless of the feeding method. Controlled prospective studies addressing residual confounding are needed to justify a change in the current practice.

Acknowledgments

The authors would like to acknowledge the valuable feedback on earlier drafts from members of the University of Wisconsin Division of Pediatric Hospital Medicine CREATE writing group.

Bronchiolitis is the most common cause of nonbirth hospitalization in children in the United States less than one year of age.¹ For children with severe bronchiolitis, high-flow nasal cannula (HFNC) is increasingly used^2-4 to reduce work of breathing and prevent the need for further escalation of ventilatory support.^5,6 Although previous studies suggest that enteral feeding is recommended in the management of patients hospitalized with bronchiolitis,^7-9 limited evidence exists to guide feeding practices for patients receiving HFNC support.^5,10,11

Respiratory support with HFNC has been associated with prolonged periods without enteral hydration/nutrition (ie, nil per os [NPO])¹² primarily due to anticipation of further escalation of respiratory support or concern for increased risk of aspiration. The majority of patients with bronchiolitis managed with HFNC, however, do not require escalation of care.^5,13 When feeding is attempted during HFNC support, it is frequently interrupted.⁵ Moreover, keeping all children NPO when receiving HFNC may be associated with weight loss and longer length of stay (LOS).^12,14 Two small studies found that children admitted to the intensive care unit who received HFNC support for bronchiolitis did not have increased rates of emesis, worsening respiratory distress or aspiration pneumonia when enterally fed.^10,11 However, no comparison of adverse events or LOS has been made between patients who were fed and those who were not fed during HFNC therapy, and previous studies have included only patients who have received HFNC in the intensive care setting.

Supporting safe feeding early in hospitalizations for bronchiolitis may facilitate expedited clinical improvement and discharge. As part of an ongoing bronchiolitis quality improvement initiative at our hospital, we sought to characterize feeding practices during HFNC therapy and assess whether feeding exposure was associated with (1) time to discharge after HFNC or (2) feeding-related adverse events. We hypothesized that feeding during HFNC therapy would be associated with a shorter time to discharge after HFNC cessation.

Study Design, Setting, Participants

This was a retrospective cohort study of patients aged 1-24 months receiving HFNC support for respiratory failure due to bronchiolitis at an academic children’s hospital between January 1, 2015 and March 1, 2017. Our institution has had a clinical practice guideline, associated order set, and respiratory therapy protocol for general care patients with bronchiolitis since 2009. Patients with bronchiolitis who were weaning HFNC have been cared for in both the intensive and general care settings since 2013. A formal process for initiation of HFNC on general care units was instituted in January of 2017. During the study period, no patients with HFNC support for bronchiolitis had all their care entirely outside the intensive care unit at our institution. However, initiation and subsequent use of HFNC may have occurred in either the intensive care or general care setting. No specific guidance for feeding during HFNC existed during this period.

Patients were identified using the Virtual PICU Systems database, (VPS LLC, myvps.org, Los Angeles, California) and, by definition, all patients received at least some of their care in the intensive care unit. Patients with comorbid conditions of prematurity (<35 weeks) and those with cardiopulmonary, neuromuscular, and genetic diseases were included. Patients with preexisting dysphagia, defined as ongoing outpatient speech therapy for swallowing concerns, an admission diagnosis of aspiration pneumonia or on home respiratory support, were excluded. Children (n = 7) were excluded if they had more than one period of HFNC during admission. This study was determined to be exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Data Collection and Study Variables

The following variables were collected from VPS administrative data: patient gender, age, admission and discharge date and time, type and total hours of respiratory support, intensive care admission, and LOS (in hours). Additional demographic, clinical, and feeding exposure variables were abstracted manually from the electronic medical record (Epic, Verona, Wisconsin) using a structured data collection tool and stored in REDCap (Research Electronic Data Capture)¹⁵ including prematurity, race/ethnicity, insurance status, primary language, and passive tobacco smoke exposure. Clinical variables included duration of illness (days) at the time of admission, unit of HFNC initiation (emergency department, general care, intensive care, respiratory rate and oxygen saturation at HFNC initiation (<90%, 91%-92%, or >92%), acquisition of blood gas at HFNC admission, duration of time on HFNC (hours) and need for intubation or noninvasive ventilation prior to HFNC. The Pediatric Index of Mortality 2 Risk of Mortality (PIM 2 ROM)¹⁶ was used to estimate the severity of illness. The PIM2ROM uses clinical variables (systolic blood pressure, fixed pupils, measure of hypoxia using PaO₂/FiO₂ ratio, base excess, mechanical ventilation, elective admission, recovery from surgery, cardiac bypass, high-risk diagnosis, low-risk diagnosis) collected at the time of intensive care admission to generate a score that predicts the risk of mortality for an individual patient.¹⁷

Feeding exposures were documented in three-hour intervals from HFNC initiation to completion using a structured protocol. At each interval the following feeding information was abstracted from a review of nursing and physician documentation and relevant clinical flowsheets: presence or absence of feeding during the interval, route of feeding (oral, nasogastric [NG] or nasojejunal [NJ]). Feeding exposure was categorized a priori as fed at any point during HFNC (vs not fed at any point). Fed children were further characterized as (1) mixed feeding consisting of oral and tube feeds (NG or NJ) or (2) exclusive oral feeding throughout HFNC support (Appendix 1).

The primary outcome was the number of hours to discharge from HFNC cessation. Secondary outcomes were time to discharge from HFNC initiation, all-cause readmissions within seven days of discharge, and potential feeding-related adverse events. Potential adverse events included: (1) aspiration, defined as initiation of antibiotic AND either chest radiograph official interpreted as evidence for aspiration and/or documented concern for aspiration from the treating physician, or (2) intubation after feeding during HFNC.

Statistical Analysis

Descriptive statistics evaluated differences in demographics and clinical variables for feeding exposure groups. We used chi-squared tests for differences in proportions and t-tests or Wilcoxon Rank-Sum tests for differences in means or medians for continuous variables, respectively. Associations between feeding exposure during HFNC and time to discharge (measured in hours) after HFNC completion were modeled with Cox proportional hazards regression. Using this approach, hazard ratios (HR)>1 indicate a higher hazard (rate) of discharge for children with a feeding exposure than for children without the exposure. For example, a hazard ratio equal to two indicates that the exposed population is discharged at twice the rate per unit time as the nonexposed population. Death or censoring events did not occur. Feeding exposure was first modeled dichotomously as not fed or fed. To further explore associations between feeding modality and our outcome, we then modeled feeding exposure categorically as not fed (reference), mixed (oral and tube) feeding, or exclusive oral feeding throughout HFNC.

After constructing a set of unadjusted models, we then adjusted the models for variables having independent (bivariate P < .10) associations with time to discharge: age, unit of HFNC initiation, highest respiratory support required before HFNC, and HFNC duration. Finally, to attempt to account for residual confounding from latent constructs, we also created a set of propensity-weighted Cox proportional hazards models. Propensity weights¹⁸ reflecting the probability of being fed or never being fed during HFNC were created using logistic regression with predictors we hypothesized a priori that may have influenced the clinical decision to feed during HFNC: age, day of illness on admission, prematurity, PIM2 ROM score, respiratory rate, oxygen saturation and blood gas acquisition at HFNC initiation, and highest respiratory support required before HFNC. All analyses were conducted using STATA 14.0 (StataCorp, College Station, Texas), and adjusted hazard ratios (aHR) with 95% confidence intervals (95% CIs) were reported.

Patients (n = 123) had a mean age of 7.3 months (standard deviation [SD] 7.1) and presented on day of illness 4.8 (SD 2.3). Prior to HFNC, 10% required higher respiratory support (3% mechanical ventilation). Former preterm children were 12% of the overall sample.

During HFNC, 37% of patients were never fed, 41% were exclusively orally fed, and 23% had tube or mixed oral and tube feedings (Table 1 and Appendix 2). Children who were not fed were older, but groups were otherwise similar in terms of gender, race/ethnicity, passive smoke exposure, day of illness, unit of HFNC initiation, respiratory support required prior to HFNC, and respiratory rate at HFNC initiation.

This observational study found that being fed during HFNC was associated with shorter time to discharge after HFNC support was completed. Exclusive oral feeding was associated with the shortest time to discharge, and these results were consistent across a variety of analytical approaches. Adverse events were rare and occurred in both fed and unfed children.

These findings advance research on relationships between nutrition and bronchiolitis outcomes. Studies of general care patients with bronchiolitis without HFNC have observed associations between poor nutrition and prolonged LOS.^19,20 Two previous studies of patients receiving HFNC therapy for bronchiolitis concluded that frequent interruption¹¹ and later initiation of enteral nutrition¹⁰ during ICU stay was associated with longer time to discharge.¹¹ To our knowledge, this is the first study of patients with bronchiolitis treated with HFNC in both general care and ICU settings that compared outcomes according to whether children were fed during HFNC therapy. Our results extend previous work demonstrating that delays in feeding may be associated with longer LOS.

Children fed while receiving HFNC for bronchiolitis may have shorter time to discharge than those who are not fed. Feeding-related adverse events were rare regardless of the feeding method. Controlled prospective studies addressing residual confounding are needed to justify a change in the current practice.

Acknowledgments

The authors would like to acknowledge the valuable feedback on earlier drafts from members of the University of Wisconsin Division of Pediatric Hospital Medicine CREATE writing group.

Bronchiolitis is the most common cause of nonbirth hospitalization in children in the United States less than one year of age.¹ For children with severe bronchiolitis, high-flow nasal cannula (HFNC) is increasingly used^2-4 to reduce work of breathing and prevent the need for further escalation of ventilatory support.^5,6 Although previous studies suggest that enteral feeding is recommended in the management of patients hospitalized with bronchiolitis,^7-9 limited evidence exists to guide feeding practices for patients receiving HFNC support.^5,10,11

Respiratory support with HFNC has been associated with prolonged periods without enteral hydration/nutrition (ie, nil per os [NPO])¹² primarily due to anticipation of further escalation of respiratory support or concern for increased risk of aspiration. The majority of patients with bronchiolitis managed with HFNC, however, do not require escalation of care.^5,13 When feeding is attempted during HFNC support, it is frequently interrupted.⁵ Moreover, keeping all children NPO when receiving HFNC may be associated with weight loss and longer length of stay (LOS).^12,14 Two small studies found that children admitted to the intensive care unit who received HFNC support for bronchiolitis did not have increased rates of emesis, worsening respiratory distress or aspiration pneumonia when enterally fed.^10,11 However, no comparison of adverse events or LOS has been made between patients who were fed and those who were not fed during HFNC therapy, and previous studies have included only patients who have received HFNC in the intensive care setting.

Supporting safe feeding early in hospitalizations for bronchiolitis may facilitate expedited clinical improvement and discharge. As part of an ongoing bronchiolitis quality improvement initiative at our hospital, we sought to characterize feeding practices during HFNC therapy and assess whether feeding exposure was associated with (1) time to discharge after HFNC or (2) feeding-related adverse events. We hypothesized that feeding during HFNC therapy would be associated with a shorter time to discharge after HFNC cessation.

Study Design, Setting, Participants

This was a retrospective cohort study of patients aged 1-24 months receiving HFNC support for respiratory failure due to bronchiolitis at an academic children’s hospital between January 1, 2015 and March 1, 2017. Our institution has had a clinical practice guideline, associated order set, and respiratory therapy protocol for general care patients with bronchiolitis since 2009. Patients with bronchiolitis who were weaning HFNC have been cared for in both the intensive and general care settings since 2013. A formal process for initiation of HFNC on general care units was instituted in January of 2017. During the study period, no patients with HFNC support for bronchiolitis had all their care entirely outside the intensive care unit at our institution. However, initiation and subsequent use of HFNC may have occurred in either the intensive care or general care setting. No specific guidance for feeding during HFNC existed during this period.

Patients were identified using the Virtual PICU Systems database, (VPS LLC, myvps.org, Los Angeles, California) and, by definition, all patients received at least some of their care in the intensive care unit. Patients with comorbid conditions of prematurity (<35 weeks) and those with cardiopulmonary, neuromuscular, and genetic diseases were included. Patients with preexisting dysphagia, defined as ongoing outpatient speech therapy for swallowing concerns, an admission diagnosis of aspiration pneumonia or on home respiratory support, were excluded. Children (n = 7) were excluded if they had more than one period of HFNC during admission. This study was determined to be exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Data Collection and Study Variables

The following variables were collected from VPS administrative data: patient gender, age, admission and discharge date and time, type and total hours of respiratory support, intensive care admission, and LOS (in hours). Additional demographic, clinical, and feeding exposure variables were abstracted manually from the electronic medical record (Epic, Verona, Wisconsin) using a structured data collection tool and stored in REDCap (Research Electronic Data Capture)¹⁵ including prematurity, race/ethnicity, insurance status, primary language, and passive tobacco smoke exposure. Clinical variables included duration of illness (days) at the time of admission, unit of HFNC initiation (emergency department, general care, intensive care, respiratory rate and oxygen saturation at HFNC initiation (<90%, 91%-92%, or >92%), acquisition of blood gas at HFNC admission, duration of time on HFNC (hours) and need for intubation or noninvasive ventilation prior to HFNC. The Pediatric Index of Mortality 2 Risk of Mortality (PIM 2 ROM)¹⁶ was used to estimate the severity of illness. The PIM2ROM uses clinical variables (systolic blood pressure, fixed pupils, measure of hypoxia using PaO₂/FiO₂ ratio, base excess, mechanical ventilation, elective admission, recovery from surgery, cardiac bypass, high-risk diagnosis, low-risk diagnosis) collected at the time of intensive care admission to generate a score that predicts the risk of mortality for an individual patient.¹⁷

Feeding exposures were documented in three-hour intervals from HFNC initiation to completion using a structured protocol. At each interval the following feeding information was abstracted from a review of nursing and physician documentation and relevant clinical flowsheets: presence or absence of feeding during the interval, route of feeding (oral, nasogastric [NG] or nasojejunal [NJ]). Feeding exposure was categorized a priori as fed at any point during HFNC (vs not fed at any point). Fed children were further characterized as (1) mixed feeding consisting of oral and tube feeds (NG or NJ) or (2) exclusive oral feeding throughout HFNC support (Appendix 1).

The primary outcome was the number of hours to discharge from HFNC cessation. Secondary outcomes were time to discharge from HFNC initiation, all-cause readmissions within seven days of discharge, and potential feeding-related adverse events. Potential adverse events included: (1) aspiration, defined as initiation of antibiotic AND either chest radiograph official interpreted as evidence for aspiration and/or documented concern for aspiration from the treating physician, or (2) intubation after feeding during HFNC.

Statistical Analysis

Descriptive statistics evaluated differences in demographics and clinical variables for feeding exposure groups. We used chi-squared tests for differences in proportions and t-tests or Wilcoxon Rank-Sum tests for differences in means or medians for continuous variables, respectively. Associations between feeding exposure during HFNC and time to discharge (measured in hours) after HFNC completion were modeled with Cox proportional hazards regression. Using this approach, hazard ratios (HR)>1 indicate a higher hazard (rate) of discharge for children with a feeding exposure than for children without the exposure. For example, a hazard ratio equal to two indicates that the exposed population is discharged at twice the rate per unit time as the nonexposed population. Death or censoring events did not occur. Feeding exposure was first modeled dichotomously as not fed or fed. To further explore associations between feeding modality and our outcome, we then modeled feeding exposure categorically as not fed (reference), mixed (oral and tube) feeding, or exclusive oral feeding throughout HFNC.

After constructing a set of unadjusted models, we then adjusted the models for variables having independent (bivariate P < .10) associations with time to discharge: age, unit of HFNC initiation, highest respiratory support required before HFNC, and HFNC duration. Finally, to attempt to account for residual confounding from latent constructs, we also created a set of propensity-weighted Cox proportional hazards models. Propensity weights¹⁸ reflecting the probability of being fed or never being fed during HFNC were created using logistic regression with predictors we hypothesized a priori that may have influenced the clinical decision to feed during HFNC: age, day of illness on admission, prematurity, PIM2 ROM score, respiratory rate, oxygen saturation and blood gas acquisition at HFNC initiation, and highest respiratory support required before HFNC. All analyses were conducted using STATA 14.0 (StataCorp, College Station, Texas), and adjusted hazard ratios (aHR) with 95% confidence intervals (95% CIs) were reported.

Patients (n = 123) had a mean age of 7.3 months (standard deviation [SD] 7.1) and presented on day of illness 4.8 (SD 2.3). Prior to HFNC, 10% required higher respiratory support (3% mechanical ventilation). Former preterm children were 12% of the overall sample.

During HFNC, 37% of patients were never fed, 41% were exclusively orally fed, and 23% had tube or mixed oral and tube feedings (Table 1 and Appendix 2). Children who were not fed were older, but groups were otherwise similar in terms of gender, race/ethnicity, passive smoke exposure, day of illness, unit of HFNC initiation, respiratory support required prior to HFNC, and respiratory rate at HFNC initiation.

This observational study found that being fed during HFNC was associated with shorter time to discharge after HFNC support was completed. Exclusive oral feeding was associated with the shortest time to discharge, and these results were consistent across a variety of analytical approaches. Adverse events were rare and occurred in both fed and unfed children.

These findings advance research on relationships between nutrition and bronchiolitis outcomes. Studies of general care patients with bronchiolitis without HFNC have observed associations between poor nutrition and prolonged LOS.^19,20 Two previous studies of patients receiving HFNC therapy for bronchiolitis concluded that frequent interruption¹¹ and later initiation of enteral nutrition¹⁰ during ICU stay was associated with longer time to discharge.¹¹ To our knowledge, this is the first study of patients with bronchiolitis treated with HFNC in both general care and ICU settings that compared outcomes according to whether children were fed during HFNC therapy. Our results extend previous work demonstrating that delays in feeding may be associated with longer LOS.

Children fed while receiving HFNC for bronchiolitis may have shorter time to discharge than those who are not fed. Feeding-related adverse events were rare regardless of the feeding method. Controlled prospective studies addressing residual confounding are needed to justify a change in the current practice.

Acknowledgments

The authors would like to acknowledge the valuable feedback on earlier drafts from members of the University of Wisconsin Division of Pediatric Hospital Medicine CREATE writing group.

Under the Hospital Readmission Reduction Program (HRRP), hospitals with higher than expected readmissions for select conditions receive a financial penalty. In 2017, hospitals were penalized a total of $528 million.^1,2 In an effort to deter readmissions, hospitals have focused on the transition from inpatient to outpatient care with particular emphasis on timely follow-up with a primary care physician (PCP).^3-7 Medicare has also introduced transitional care codes, which reimburse physicians for follow-up care after a hospitalization.

Most observational studies have found an association among patients discharged from the hospital between early follow-up with a PCP and fewer readmissions. One study found that patients without timely PCP follow-up after hospitalization on medical wards had a 10-fold increase in the likelihood of readmission.⁵ This association between early PCP follow-up and readmissions has been echoed in studies of all general admissions,⁵ as well as hospitalizations specific to heart failure,^7,8 chronic obstructive pulmonary disease,³ high-risk surgery,⁹ and sickle cell disease.¹⁰ One potential concern with this prior literature is that unmeasured patient characteristics might be confounders; for example, patients with more social support may be both more likely to have follow-up visits and less likely to have readmissions. Also, there are several studies showing no association between early PCP appointments and readmission rates.^6,9,11-13

Several prior interventional studies to improve the care transition from hospital to outpatient care have successfully deterred readmissions.¹⁴ In these trials, facilitating early PCP follow-up is just one component of a larger intervention,¹⁵ and a systemic review noted that the interventions were heterogeneous and often consisted of multiple complex steps.⁶ It is less clear whether interventions to facilitate early PCP follow-up alone are successful.

In this study, we evaluated the impact of an intervention that focused on facilitating early follow-up of PCPs. We assessed the impact of this intervention on the likelihood of having a PCP appointment within seven days of discharge and being readmitted within 30 days of discharge.

Postdischarge Appointment Service

In the fall of 2009, Beth Israel Deaconess introduced a postdischarge appointment intervention to facilitate follow-up with PCPs and specialty physicians after discharge from the hospital. Within the provider order entry system, attending and resident physicians enter a discharge appointment request for specified providers within and outside of the medical center and a specified time period. For example, a physician may enter a request to schedule a PCP appointment within 2-3, 4-8, 9-15, 16-30, or >30 days of discharge. Physicians are asked to submit this request on the day of discharge. The request is transmitted to dedicated staff (four full-time administrative staff and four part-time registered nurses) who verify the PCP, process the orders, and call the relevant practices to book the appointments. The date and time of the follow-up appointments are set without input from the patient. The details of the appointment, location, phone number of the clinic, and any other relevant instructions are automatically entered into the discharge instructions and discharge summary. The service is available Monday through Friday, and the turnaround on appointment creation is typically within one to three hours of the request. For patients who do not have a PCP or want to switch their PCP, the discharging physician can request a new PCP within the health system, and the service will schedule an appointment in this new PCP’s practice. Anecdotally, physicians are more likely to order the postdischarge appointment service for patients with more complex illnesses and longer lengths of stay and for those who come from underserved populations, as they perceive that it is more important for the patient to have this follow-up appointment, and/or the patient may have a harder time navigating the system and scheduling an appointment. Because of funding limitations, the hospital limited the intervention to hospitalizations on the general medicine and cardiology services. It was expanded in late 2011 to include the trauma surgery service.

Study Population

We conducted a retrospective, cohort study at Beth Israel Deaconess Medical Center, a tertiary care hospital, using data derived from electronic health records for all hospitalizations from September 2008 to October 2015. At this medical center, the vast majority of patients on the general medicine service are cared for by hospitalists and not their PCPs. We focused on patients 18 years of age or older discharged home and excluded hospitalizations where the patient died, was transferred to another hospital, or was discharged to a skilled nursing facility or inpatient rehabilitation hospital. We excluded patients who were kept under observation in the emergency department (ED), but our data did include patients cared for on a hospital ward under observation. To measure whether patients attended a follow-up visit, we used internal scheduling data and therefore only included hospitalizations for patients with a PCP affiliated with the Beth Israel Deaconess medical system. This includes patients previously without a PCP whose first PCP appointment was after discharge. Finally, we limited our sample to hospitalizations on the general medicine and cardiology services because, as previously discussed, these are the services where the intervention was available. To address transfers within the hospital from one service to another, we categorized hospitalizations by the service on the date of discharge.

Outcomes

The primary outcomes of this study were kept PCP follow-up visits within seven days and readmission within 30 days of discharge. We focused on PCP visits within seven days, as this has been the measure used in prior research,^5,7 but conducted a sensitivity analysis of PCP follow-up within 14 days. No-shows for the scheduled follow-up PCP appointments were not included. We focused on readmissions within 30 days of discharge, given this is the measure used in the HRRP,¹⁶ but conducted a sensitivity analysis of 14 days. Secondary outcomes included ED revisit within the 30 days. Given the data available, we only observed physician visits and hospitalizations that occurred within the Beth Israel Deaconess system.

Analyses

We conducted two analyses to assess whether the implementation of the postdischarge appointment service was associated with an increase in PCP follow-up and a decrease in the readmission rate.

In the first analysis, we focused only on hospitalizations from the medical and cardiology services during the postintervention period between January 2011 and September 2015 (n = 17,582). We compared the PCP follow-up rate and the readmission rate among hospitalizations where the postdischarge appointment service was used versus those where it was not used. We used a multivariable logistic regression, and the covariates included in the model were age, gender, hospital length of stay, and diagnosis-related group (DRG) cost weight. The DRG cost weight captures the average resources used to treat Medicare patients’ hospitalizations within a given DRG category and was used as a surrogate marker for the complexity of hospitalization.¹⁷ Instead of presenting odds ratios, we used predictive margins to generate adjusted percentage point estimates of the differences in our outcomes associated with the use of the postdischarge appointment service.¹⁸

In our second analysis, we examined the period before and after the introduction of the postdischarge appointment service (September 2008 through October 2015, n = 20,918). Among these hospitalizations, we conducted an instrumental variable analysis to address the concern that there are unmeasured differences between those patients who receive the discharge appointment service and those who do not. Instrumental variable analyses are used to estimate causal relationships in observational studies.¹⁹ A valid instrument is associated with the explanatory variable (use of the postdischarge appointment service) but has no independent effect on the outcomes (follow-up visits, readmissions). In this analysis, our set of instruments was the day of the week of admission (indicator variable for each day) interacted with the time period (pre- vs postintervention period).

This instrumental variable exploits the fact that the postdischarge appointment service was only available on weekdays and that physicians are asked to only submit the order for follow-up appointments on the day of discharge. We focused on the day of the week of admission (versus discharge) because of concerns that patients with more complicated hospital courses might be kept in the hospital over the weekend (eg, to facilitate testing available only on weekdays or to consult with regular physicians only available on weekdays). This would create a relationship between the day of discharge and the outcomes (follow-up visits, readmissions). The day of admission is less likely to be impacted by this bias. Given concerns that admissions on different days of the week might be different, our instrument is the day of the week interacted with the time period. Therefore, to create bias, there must be a systematic change in the nature of admissions on a given day of the week during this time period. We provide more details on this analysis, testing of the instrument, and results in the Appendix.

Analyses were conducted in Stata, version 14.2 (StataCorp LP, College Station, Texas). Statistical testing was two-sided, with a significance level of 0.05, and the project was judged exempt by the Committee on Clinical Investigations for Beth Israel Deaconess Medical Center.

Overall, there were 17,582 hospitalizations on the medicine and cardiology services following implementation of the postdischarge appointment service. The use of the postdischarge appointment service rose rapidly after it was introduced (Figure) and then plateaued at roughly 50%. Of the hospitalizations where the postdischarge appointment service was used, the physician requested a new PCP for 1.2% of the patients. Among hospitalizations where the intervention was used, the average age was 65.5 years, 55.7% were female, the length of stay was 3.52 days, the DRG cost weight was 1.26 and 20.4% were patients on the cardiology service. Characteristics were similar in hospitalizations where the services were not used (Table 1).

Multivariable Logistic Regression

In this analysis, we focused on the 17,582 hospitalizations from January 2011 to September 2015 on the general medicine and cardiology services that occurred after the postdischarge appointment service was introduced. Among these hospitalizations, the postdischarge appointment service was used in 51.8% of discharges.

In an unadjusted analysis, patients discharged using the tool had higher rates of seven-day PCP follow-up (60.2% vs 29.2%, P < .001) and lower 30-day readmission rates (14.7% vs 16.7%; P < .001) than those who were not (Table 2). There was no significant difference in 30-day ED revisit between hospitalizations with and without use of the postdischarge appointment service (22.3% vs 23.1%; P = .23).

Instrumental Variable Analysis

In our instrumental variable analysis, we used all hospitalizations both before and after the introduction of the intervention. In this analysis, we estimate that use of the postdischarge appointment service increases the probability of visiting a PCP within seven days by 33.4 percentage points (95% CI: 7.9%, 58.9%; P = .01) (Table 3). The use of the postdischarge appointment was associated with a 2.5 percentage point (95% CI: −22.0%, 17.1%; P = .80) reduction in readmissions and a 4.8 percentage point (95% CI; −27.5%, 17.9%; P = .68) reduction in an ED visit within 30 days (Table 3). Neither of these differences were statistically significant with wide confidence intervals.

In sensitivity analyses, we obtained similar results when we considered PCP visits and readmissions within 14 days.

The hospital introduced the postdischarge appointment service to facilitate postdischarge appointments and to deter readmissions. In our analyses the use of the postdischarge appointment service was associated with a substantial 30 percentage point increase in the likelihood of a PCP follow-up visit within seven days after hospital discharge. There was a roughly 2% reduction in 30-day readmissions, but this difference was not consistently statistically significant across our analyses. Together, our evaluation implies that this type of intervention may make it much easier for patients to attend a PCP appointment, but scheduling an appointment alone may have a modest impact on deterring a readmission.

Our findings are inconsistent with prior studies that described a strong association between early PCP follow-up and readmissions. However, our results were consistent with research where follow-up visits were not clearly protective against readmissions.²⁰ One potential explanation of the discrepant findings is that there are unmeasured socioeconomic differences between patients who have a PCP follow-up appointment and those who do not.

We advance the literature by studying an intervention focused only on increasing early PCP follow-up. Most successful readmission programs that have been studied in randomized, controlled trials take a multipronged approach, including transitional care management with dedicated staff and medication reconciliation.^{3-7,9,15,21-23} For example, Coleman and colleagues randomized 750 hospitalized patients to a care-transitions intervention, which led to a substantial decrease in readmissions.¹⁵ Their care-transitions intervention included four components: (1) timely PCP or specialist follow-up, (2) educating patients on how best to take their medications, (3) a patient-centered record that allowed them to track their own disease and care, and (4) disease-specific patient education. The relative importance of each of these components in deterring readmissions is unclear. Instead of this multipronged strategy, we focused on a single component—timely follow-up. Together, our study and these prior studies are broadly consistent with a meta-analysis that suggests that transitional care programs with a narrow focus are less successful at reducing readmissions.²⁴ Facilitating early PCP follow-up alone is not a panacea and can be undermined by the incomplete or inexistent transmission of the discharge paperwork.^{25, 26} Moreover, the impact of interventions may only be seen among the highest-risk populations, and ongoing work by others seeks to identify these patients.²⁷

Regardless of the impact on readmissions, it is important to acknowledge that early PCP follow-up offers many potential benefits. Continuing to evaluate and treat new diagnoses, adjusting and reconciling medications, reconnecting with outpatient providers, capturing new incidental findings, and ensuring stability through regular follow-up are just a few of the potential benefits. We believe the dramatic increase observed in PCP follow-up reflects the administrative complexity required for a patient to call their PCP’s office and to schedule a follow-up appointment soon after they are discharged from the hospital. Our study implies that simply requesting that a patient call their PCP to schedule a timely appointment is often impossible, and this may be particularly true for those who need to obtain a new PCP.

Our study has many limitations. The study was limited to a single academic center, and the intervention was limited to patients cared for by the general medicine and cardiology services. Our multivariable regression analysis comparing outcomes among patients where the postdischarge appointment service was used and not used may be biased by unmeasured differences in these patients. We attempted to address this limitation by exploiting the fact that the intervention was only available on weekdays through an instrumental variable analysis, but the instrument we used itself is subject to bias. Also, in the instrumental variable analysis, our estimates were imprecise and therefore not powered to identify smaller but still clinically important reductions in readmissions. Given the data limitations, we could not compare the no-show rates among appointments made by the discharge appointment service versus those made by patients. Finally, we were only able to observe follow-up visits and hospitalizations within the health system, and it is possible that our results were biased by patients preferentially going to other hospitals for readmission.

In summary, we found that the introduction of a postdischarge appointment service resulted in substantially increased rates of early PCP follow-up but less clear benefits in preventing readmissions.

Under the Hospital Readmission Reduction Program (HRRP), hospitals with higher than expected readmissions for select conditions receive a financial penalty. In 2017, hospitals were penalized a total of $528 million.^1,2 In an effort to deter readmissions, hospitals have focused on the transition from inpatient to outpatient care with particular emphasis on timely follow-up with a primary care physician (PCP).^3-7 Medicare has also introduced transitional care codes, which reimburse physicians for follow-up care after a hospitalization.

Most observational studies have found an association among patients discharged from the hospital between early follow-up with a PCP and fewer readmissions. One study found that patients without timely PCP follow-up after hospitalization on medical wards had a 10-fold increase in the likelihood of readmission.⁵ This association between early PCP follow-up and readmissions has been echoed in studies of all general admissions,⁵ as well as hospitalizations specific to heart failure,^7,8 chronic obstructive pulmonary disease,³ high-risk surgery,⁹ and sickle cell disease.¹⁰ One potential concern with this prior literature is that unmeasured patient characteristics might be confounders; for example, patients with more social support may be both more likely to have follow-up visits and less likely to have readmissions. Also, there are several studies showing no association between early PCP appointments and readmission rates.^6,9,11-13

Several prior interventional studies to improve the care transition from hospital to outpatient care have successfully deterred readmissions.¹⁴ In these trials, facilitating early PCP follow-up is just one component of a larger intervention,¹⁵ and a systemic review noted that the interventions were heterogeneous and often consisted of multiple complex steps.⁶ It is less clear whether interventions to facilitate early PCP follow-up alone are successful.

In this study, we evaluated the impact of an intervention that focused on facilitating early follow-up of PCPs. We assessed the impact of this intervention on the likelihood of having a PCP appointment within seven days of discharge and being readmitted within 30 days of discharge.

Postdischarge Appointment Service

In the fall of 2009, Beth Israel Deaconess introduced a postdischarge appointment intervention to facilitate follow-up with PCPs and specialty physicians after discharge from the hospital. Within the provider order entry system, attending and resident physicians enter a discharge appointment request for specified providers within and outside of the medical center and a specified time period. For example, a physician may enter a request to schedule a PCP appointment within 2-3, 4-8, 9-15, 16-30, or >30 days of discharge. Physicians are asked to submit this request on the day of discharge. The request is transmitted to dedicated staff (four full-time administrative staff and four part-time registered nurses) who verify the PCP, process the orders, and call the relevant practices to book the appointments. The date and time of the follow-up appointments are set without input from the patient. The details of the appointment, location, phone number of the clinic, and any other relevant instructions are automatically entered into the discharge instructions and discharge summary. The service is available Monday through Friday, and the turnaround on appointment creation is typically within one to three hours of the request. For patients who do not have a PCP or want to switch their PCP, the discharging physician can request a new PCP within the health system, and the service will schedule an appointment in this new PCP’s practice. Anecdotally, physicians are more likely to order the postdischarge appointment service for patients with more complex illnesses and longer lengths of stay and for those who come from underserved populations, as they perceive that it is more important for the patient to have this follow-up appointment, and/or the patient may have a harder time navigating the system and scheduling an appointment. Because of funding limitations, the hospital limited the intervention to hospitalizations on the general medicine and cardiology services. It was expanded in late 2011 to include the trauma surgery service.

Study Population

We conducted a retrospective, cohort study at Beth Israel Deaconess Medical Center, a tertiary care hospital, using data derived from electronic health records for all hospitalizations from September 2008 to October 2015. At this medical center, the vast majority of patients on the general medicine service are cared for by hospitalists and not their PCPs. We focused on patients 18 years of age or older discharged home and excluded hospitalizations where the patient died, was transferred to another hospital, or was discharged to a skilled nursing facility or inpatient rehabilitation hospital. We excluded patients who were kept under observation in the emergency department (ED), but our data did include patients cared for on a hospital ward under observation. To measure whether patients attended a follow-up visit, we used internal scheduling data and therefore only included hospitalizations for patients with a PCP affiliated with the Beth Israel Deaconess medical system. This includes patients previously without a PCP whose first PCP appointment was after discharge. Finally, we limited our sample to hospitalizations on the general medicine and cardiology services because, as previously discussed, these are the services where the intervention was available. To address transfers within the hospital from one service to another, we categorized hospitalizations by the service on the date of discharge.

Outcomes

The primary outcomes of this study were kept PCP follow-up visits within seven days and readmission within 30 days of discharge. We focused on PCP visits within seven days, as this has been the measure used in prior research,^5,7 but conducted a sensitivity analysis of PCP follow-up within 14 days. No-shows for the scheduled follow-up PCP appointments were not included. We focused on readmissions within 30 days of discharge, given this is the measure used in the HRRP,¹⁶ but conducted a sensitivity analysis of 14 days. Secondary outcomes included ED revisit within the 30 days. Given the data available, we only observed physician visits and hospitalizations that occurred within the Beth Israel Deaconess system.

Analyses

We conducted two analyses to assess whether the implementation of the postdischarge appointment service was associated with an increase in PCP follow-up and a decrease in the readmission rate.

In the first analysis, we focused only on hospitalizations from the medical and cardiology services during the postintervention period between January 2011 and September 2015 (n = 17,582). We compared the PCP follow-up rate and the readmission rate among hospitalizations where the postdischarge appointment service was used versus those where it was not used. We used a multivariable logistic regression, and the covariates included in the model were age, gender, hospital length of stay, and diagnosis-related group (DRG) cost weight. The DRG cost weight captures the average resources used to treat Medicare patients’ hospitalizations within a given DRG category and was used as a surrogate marker for the complexity of hospitalization.¹⁷ Instead of presenting odds ratios, we used predictive margins to generate adjusted percentage point estimates of the differences in our outcomes associated with the use of the postdischarge appointment service.¹⁸

In our second analysis, we examined the period before and after the introduction of the postdischarge appointment service (September 2008 through October 2015, n = 20,918). Among these hospitalizations, we conducted an instrumental variable analysis to address the concern that there are unmeasured differences between those patients who receive the discharge appointment service and those who do not. Instrumental variable analyses are used to estimate causal relationships in observational studies.¹⁹ A valid instrument is associated with the explanatory variable (use of the postdischarge appointment service) but has no independent effect on the outcomes (follow-up visits, readmissions). In this analysis, our set of instruments was the day of the week of admission (indicator variable for each day) interacted with the time period (pre- vs postintervention period).

This instrumental variable exploits the fact that the postdischarge appointment service was only available on weekdays and that physicians are asked to only submit the order for follow-up appointments on the day of discharge. We focused on the day of the week of admission (versus discharge) because of concerns that patients with more complicated hospital courses might be kept in the hospital over the weekend (eg, to facilitate testing available only on weekdays or to consult with regular physicians only available on weekdays). This would create a relationship between the day of discharge and the outcomes (follow-up visits, readmissions). The day of admission is less likely to be impacted by this bias. Given concerns that admissions on different days of the week might be different, our instrument is the day of the week interacted with the time period. Therefore, to create bias, there must be a systematic change in the nature of admissions on a given day of the week during this time period. We provide more details on this analysis, testing of the instrument, and results in the Appendix.

Analyses were conducted in Stata, version 14.2 (StataCorp LP, College Station, Texas). Statistical testing was two-sided, with a significance level of 0.05, and the project was judged exempt by the Committee on Clinical Investigations for Beth Israel Deaconess Medical Center.

Overall, there were 17,582 hospitalizations on the medicine and cardiology services following implementation of the postdischarge appointment service. The use of the postdischarge appointment service rose rapidly after it was introduced (Figure) and then plateaued at roughly 50%. Of the hospitalizations where the postdischarge appointment service was used, the physician requested a new PCP for 1.2% of the patients. Among hospitalizations where the intervention was used, the average age was 65.5 years, 55.7% were female, the length of stay was 3.52 days, the DRG cost weight was 1.26 and 20.4% were patients on the cardiology service. Characteristics were similar in hospitalizations where the services were not used (Table 1).

Multivariable Logistic Regression

In this analysis, we focused on the 17,582 hospitalizations from January 2011 to September 2015 on the general medicine and cardiology services that occurred after the postdischarge appointment service was introduced. Among these hospitalizations, the postdischarge appointment service was used in 51.8% of discharges.

In an unadjusted analysis, patients discharged using the tool had higher rates of seven-day PCP follow-up (60.2% vs 29.2%, P < .001) and lower 30-day readmission rates (14.7% vs 16.7%; P < .001) than those who were not (Table 2). There was no significant difference in 30-day ED revisit between hospitalizations with and without use of the postdischarge appointment service (22.3% vs 23.1%; P = .23).

Instrumental Variable Analysis

In our instrumental variable analysis, we used all hospitalizations both before and after the introduction of the intervention. In this analysis, we estimate that use of the postdischarge appointment service increases the probability of visiting a PCP within seven days by 33.4 percentage points (95% CI: 7.9%, 58.9%; P = .01) (Table 3). The use of the postdischarge appointment was associated with a 2.5 percentage point (95% CI: −22.0%, 17.1%; P = .80) reduction in readmissions and a 4.8 percentage point (95% CI; −27.5%, 17.9%; P = .68) reduction in an ED visit within 30 days (Table 3). Neither of these differences were statistically significant with wide confidence intervals.

In sensitivity analyses, we obtained similar results when we considered PCP visits and readmissions within 14 days.

The hospital introduced the postdischarge appointment service to facilitate postdischarge appointments and to deter readmissions. In our analyses the use of the postdischarge appointment service was associated with a substantial 30 percentage point increase in the likelihood of a PCP follow-up visit within seven days after hospital discharge. There was a roughly 2% reduction in 30-day readmissions, but this difference was not consistently statistically significant across our analyses. Together, our evaluation implies that this type of intervention may make it much easier for patients to attend a PCP appointment, but scheduling an appointment alone may have a modest impact on deterring a readmission.

Our findings are inconsistent with prior studies that described a strong association between early PCP follow-up and readmissions. However, our results were consistent with research where follow-up visits were not clearly protective against readmissions.²⁰ One potential explanation of the discrepant findings is that there are unmeasured socioeconomic differences between patients who have a PCP follow-up appointment and those who do not.

We advance the literature by studying an intervention focused only on increasing early PCP follow-up. Most successful readmission programs that have been studied in randomized, controlled trials take a multipronged approach, including transitional care management with dedicated staff and medication reconciliation.^{3-7,9,15,21-23} For example, Coleman and colleagues randomized 750 hospitalized patients to a care-transitions intervention, which led to a substantial decrease in readmissions.¹⁵ Their care-transitions intervention included four components: (1) timely PCP or specialist follow-up, (2) educating patients on how best to take their medications, (3) a patient-centered record that allowed them to track their own disease and care, and (4) disease-specific patient education. The relative importance of each of these components in deterring readmissions is unclear. Instead of this multipronged strategy, we focused on a single component—timely follow-up. Together, our study and these prior studies are broadly consistent with a meta-analysis that suggests that transitional care programs with a narrow focus are less successful at reducing readmissions.²⁴ Facilitating early PCP follow-up alone is not a panacea and can be undermined by the incomplete or inexistent transmission of the discharge paperwork.^{25, 26} Moreover, the impact of interventions may only be seen among the highest-risk populations, and ongoing work by others seeks to identify these patients.²⁷

Regardless of the impact on readmissions, it is important to acknowledge that early PCP follow-up offers many potential benefits. Continuing to evaluate and treat new diagnoses, adjusting and reconciling medications, reconnecting with outpatient providers, capturing new incidental findings, and ensuring stability through regular follow-up are just a few of the potential benefits. We believe the dramatic increase observed in PCP follow-up reflects the administrative complexity required for a patient to call their PCP’s office and to schedule a follow-up appointment soon after they are discharged from the hospital. Our study implies that simply requesting that a patient call their PCP to schedule a timely appointment is often impossible, and this may be particularly true for those who need to obtain a new PCP.

Our study has many limitations. The study was limited to a single academic center, and the intervention was limited to patients cared for by the general medicine and cardiology services. Our multivariable regression analysis comparing outcomes among patients where the postdischarge appointment service was used and not used may be biased by unmeasured differences in these patients. We attempted to address this limitation by exploiting the fact that the intervention was only available on weekdays through an instrumental variable analysis, but the instrument we used itself is subject to bias. Also, in the instrumental variable analysis, our estimates were imprecise and therefore not powered to identify smaller but still clinically important reductions in readmissions. Given the data limitations, we could not compare the no-show rates among appointments made by the discharge appointment service versus those made by patients. Finally, we were only able to observe follow-up visits and hospitalizations within the health system, and it is possible that our results were biased by patients preferentially going to other hospitals for readmission.

In summary, we found that the introduction of a postdischarge appointment service resulted in substantially increased rates of early PCP follow-up but less clear benefits in preventing readmissions.

Under the Hospital Readmission Reduction Program (HRRP), hospitals with higher than expected readmissions for select conditions receive a financial penalty. In 2017, hospitals were penalized a total of $528 million.^1,2 In an effort to deter readmissions, hospitals have focused on the transition from inpatient to outpatient care with particular emphasis on timely follow-up with a primary care physician (PCP).^3-7 Medicare has also introduced transitional care codes, which reimburse physicians for follow-up care after a hospitalization.

Most observational studies have found an association among patients discharged from the hospital between early follow-up with a PCP and fewer readmissions. One study found that patients without timely PCP follow-up after hospitalization on medical wards had a 10-fold increase in the likelihood of readmission.⁵ This association between early PCP follow-up and readmissions has been echoed in studies of all general admissions,⁵ as well as hospitalizations specific to heart failure,^7,8 chronic obstructive pulmonary disease,³ high-risk surgery,⁹ and sickle cell disease.¹⁰ One potential concern with this prior literature is that unmeasured patient characteristics might be confounders; for example, patients with more social support may be both more likely to have follow-up visits and less likely to have readmissions. Also, there are several studies showing no association between early PCP appointments and readmission rates.^6,9,11-13

Several prior interventional studies to improve the care transition from hospital to outpatient care have successfully deterred readmissions.¹⁴ In these trials, facilitating early PCP follow-up is just one component of a larger intervention,¹⁵ and a systemic review noted that the interventions were heterogeneous and often consisted of multiple complex steps.⁶ It is less clear whether interventions to facilitate early PCP follow-up alone are successful.

In this study, we evaluated the impact of an intervention that focused on facilitating early follow-up of PCPs. We assessed the impact of this intervention on the likelihood of having a PCP appointment within seven days of discharge and being readmitted within 30 days of discharge.

Postdischarge Appointment Service

In the fall of 2009, Beth Israel Deaconess introduced a postdischarge appointment intervention to facilitate follow-up with PCPs and specialty physicians after discharge from the hospital. Within the provider order entry system, attending and resident physicians enter a discharge appointment request for specified providers within and outside of the medical center and a specified time period. For example, a physician may enter a request to schedule a PCP appointment within 2-3, 4-8, 9-15, 16-30, or >30 days of discharge. Physicians are asked to submit this request on the day of discharge. The request is transmitted to dedicated staff (four full-time administrative staff and four part-time registered nurses) who verify the PCP, process the orders, and call the relevant practices to book the appointments. The date and time of the follow-up appointments are set without input from the patient. The details of the appointment, location, phone number of the clinic, and any other relevant instructions are automatically entered into the discharge instructions and discharge summary. The service is available Monday through Friday, and the turnaround on appointment creation is typically within one to three hours of the request. For patients who do not have a PCP or want to switch their PCP, the discharging physician can request a new PCP within the health system, and the service will schedule an appointment in this new PCP’s practice. Anecdotally, physicians are more likely to order the postdischarge appointment service for patients with more complex illnesses and longer lengths of stay and for those who come from underserved populations, as they perceive that it is more important for the patient to have this follow-up appointment, and/or the patient may have a harder time navigating the system and scheduling an appointment. Because of funding limitations, the hospital limited the intervention to hospitalizations on the general medicine and cardiology services. It was expanded in late 2011 to include the trauma surgery service.

Study Population

We conducted a retrospective, cohort study at Beth Israel Deaconess Medical Center, a tertiary care hospital, using data derived from electronic health records for all hospitalizations from September 2008 to October 2015. At this medical center, the vast majority of patients on the general medicine service are cared for by hospitalists and not their PCPs. We focused on patients 18 years of age or older discharged home and excluded hospitalizations where the patient died, was transferred to another hospital, or was discharged to a skilled nursing facility or inpatient rehabilitation hospital. We excluded patients who were kept under observation in the emergency department (ED), but our data did include patients cared for on a hospital ward under observation. To measure whether patients attended a follow-up visit, we used internal scheduling data and therefore only included hospitalizations for patients with a PCP affiliated with the Beth Israel Deaconess medical system. This includes patients previously without a PCP whose first PCP appointment was after discharge. Finally, we limited our sample to hospitalizations on the general medicine and cardiology services because, as previously discussed, these are the services where the intervention was available. To address transfers within the hospital from one service to another, we categorized hospitalizations by the service on the date of discharge.

Outcomes

The primary outcomes of this study were kept PCP follow-up visits within seven days and readmission within 30 days of discharge. We focused on PCP visits within seven days, as this has been the measure used in prior research,^5,7 but conducted a sensitivity analysis of PCP follow-up within 14 days. No-shows for the scheduled follow-up PCP appointments were not included. We focused on readmissions within 30 days of discharge, given this is the measure used in the HRRP,¹⁶ but conducted a sensitivity analysis of 14 days. Secondary outcomes included ED revisit within the 30 days. Given the data available, we only observed physician visits and hospitalizations that occurred within the Beth Israel Deaconess system.

Analyses

We conducted two analyses to assess whether the implementation of the postdischarge appointment service was associated with an increase in PCP follow-up and a decrease in the readmission rate.

In the first analysis, we focused only on hospitalizations from the medical and cardiology services during the postintervention period between January 2011 and September 2015 (n = 17,582). We compared the PCP follow-up rate and the readmission rate among hospitalizations where the postdischarge appointment service was used versus those where it was not used. We used a multivariable logistic regression, and the covariates included in the model were age, gender, hospital length of stay, and diagnosis-related group (DRG) cost weight. The DRG cost weight captures the average resources used to treat Medicare patients’ hospitalizations within a given DRG category and was used as a surrogate marker for the complexity of hospitalization.¹⁷ Instead of presenting odds ratios, we used predictive margins to generate adjusted percentage point estimates of the differences in our outcomes associated with the use of the postdischarge appointment service.¹⁸

In our second analysis, we examined the period before and after the introduction of the postdischarge appointment service (September 2008 through October 2015, n = 20,918). Among these hospitalizations, we conducted an instrumental variable analysis to address the concern that there are unmeasured differences between those patients who receive the discharge appointment service and those who do not. Instrumental variable analyses are used to estimate causal relationships in observational studies.¹⁹ A valid instrument is associated with the explanatory variable (use of the postdischarge appointment service) but has no independent effect on the outcomes (follow-up visits, readmissions). In this analysis, our set of instruments was the day of the week of admission (indicator variable for each day) interacted with the time period (pre- vs postintervention period).

This instrumental variable exploits the fact that the postdischarge appointment service was only available on weekdays and that physicians are asked to only submit the order for follow-up appointments on the day of discharge. We focused on the day of the week of admission (versus discharge) because of concerns that patients with more complicated hospital courses might be kept in the hospital over the weekend (eg, to facilitate testing available only on weekdays or to consult with regular physicians only available on weekdays). This would create a relationship between the day of discharge and the outcomes (follow-up visits, readmissions). The day of admission is less likely to be impacted by this bias. Given concerns that admissions on different days of the week might be different, our instrument is the day of the week interacted with the time period. Therefore, to create bias, there must be a systematic change in the nature of admissions on a given day of the week during this time period. We provide more details on this analysis, testing of the instrument, and results in the Appendix.

Analyses were conducted in Stata, version 14.2 (StataCorp LP, College Station, Texas). Statistical testing was two-sided, with a significance level of 0.05, and the project was judged exempt by the Committee on Clinical Investigations for Beth Israel Deaconess Medical Center.

Overall, there were 17,582 hospitalizations on the medicine and cardiology services following implementation of the postdischarge appointment service. The use of the postdischarge appointment service rose rapidly after it was introduced (Figure) and then plateaued at roughly 50%. Of the hospitalizations where the postdischarge appointment service was used, the physician requested a new PCP for 1.2% of the patients. Among hospitalizations where the intervention was used, the average age was 65.5 years, 55.7% were female, the length of stay was 3.52 days, the DRG cost weight was 1.26 and 20.4% were patients on the cardiology service. Characteristics were similar in hospitalizations where the services were not used (Table 1).

Multivariable Logistic Regression

In this analysis, we focused on the 17,582 hospitalizations from January 2011 to September 2015 on the general medicine and cardiology services that occurred after the postdischarge appointment service was introduced. Among these hospitalizations, the postdischarge appointment service was used in 51.8% of discharges.

In an unadjusted analysis, patients discharged using the tool had higher rates of seven-day PCP follow-up (60.2% vs 29.2%, P < .001) and lower 30-day readmission rates (14.7% vs 16.7%; P < .001) than those who were not (Table 2). There was no significant difference in 30-day ED revisit between hospitalizations with and without use of the postdischarge appointment service (22.3% vs 23.1%; P = .23).

Instrumental Variable Analysis

In our instrumental variable analysis, we used all hospitalizations both before and after the introduction of the intervention. In this analysis, we estimate that use of the postdischarge appointment service increases the probability of visiting a PCP within seven days by 33.4 percentage points (95% CI: 7.9%, 58.9%; P = .01) (Table 3). The use of the postdischarge appointment was associated with a 2.5 percentage point (95% CI: −22.0%, 17.1%; P = .80) reduction in readmissions and a 4.8 percentage point (95% CI; −27.5%, 17.9%; P = .68) reduction in an ED visit within 30 days (Table 3). Neither of these differences were statistically significant with wide confidence intervals.

In sensitivity analyses, we obtained similar results when we considered PCP visits and readmissions within 14 days.

The hospital introduced the postdischarge appointment service to facilitate postdischarge appointments and to deter readmissions. In our analyses the use of the postdischarge appointment service was associated with a substantial 30 percentage point increase in the likelihood of a PCP follow-up visit within seven days after hospital discharge. There was a roughly 2% reduction in 30-day readmissions, but this difference was not consistently statistically significant across our analyses. Together, our evaluation implies that this type of intervention may make it much easier for patients to attend a PCP appointment, but scheduling an appointment alone may have a modest impact on deterring a readmission.

Our findings are inconsistent with prior studies that described a strong association between early PCP follow-up and readmissions. However, our results were consistent with research where follow-up visits were not clearly protective against readmissions.²⁰ One potential explanation of the discrepant findings is that there are unmeasured socioeconomic differences between patients who have a PCP follow-up appointment and those who do not.

We advance the literature by studying an intervention focused only on increasing early PCP follow-up. Most successful readmission programs that have been studied in randomized, controlled trials take a multipronged approach, including transitional care management with dedicated staff and medication reconciliation.^{3-7,9,15,21-23} For example, Coleman and colleagues randomized 750 hospitalized patients to a care-transitions intervention, which led to a substantial decrease in readmissions.¹⁵ Their care-transitions intervention included four components: (1) timely PCP or specialist follow-up, (2) educating patients on how best to take their medications, (3) a patient-centered record that allowed them to track their own disease and care, and (4) disease-specific patient education. The relative importance of each of these components in deterring readmissions is unclear. Instead of this multipronged strategy, we focused on a single component—timely follow-up. Together, our study and these prior studies are broadly consistent with a meta-analysis that suggests that transitional care programs with a narrow focus are less successful at reducing readmissions.²⁴ Facilitating early PCP follow-up alone is not a panacea and can be undermined by the incomplete or inexistent transmission of the discharge paperwork.^{25, 26} Moreover, the impact of interventions may only be seen among the highest-risk populations, and ongoing work by others seeks to identify these patients.²⁷

Regardless of the impact on readmissions, it is important to acknowledge that early PCP follow-up offers many potential benefits. Continuing to evaluate and treat new diagnoses, adjusting and reconciling medications, reconnecting with outpatient providers, capturing new incidental findings, and ensuring stability through regular follow-up are just a few of the potential benefits. We believe the dramatic increase observed in PCP follow-up reflects the administrative complexity required for a patient to call their PCP’s office and to schedule a follow-up appointment soon after they are discharged from the hospital. Our study implies that simply requesting that a patient call their PCP to schedule a timely appointment is often impossible, and this may be particularly true for those who need to obtain a new PCP.

Our study has many limitations. The study was limited to a single academic center, and the intervention was limited to patients cared for by the general medicine and cardiology services. Our multivariable regression analysis comparing outcomes among patients where the postdischarge appointment service was used and not used may be biased by unmeasured differences in these patients. We attempted to address this limitation by exploiting the fact that the intervention was only available on weekdays through an instrumental variable analysis, but the instrument we used itself is subject to bias. Also, in the instrumental variable analysis, our estimates were imprecise and therefore not powered to identify smaller but still clinically important reductions in readmissions. Given the data limitations, we could not compare the no-show rates among appointments made by the discharge appointment service versus those made by patients. Finally, we were only able to observe follow-up visits and hospitalizations within the health system, and it is possible that our results were biased by patients preferentially going to other hospitals for readmission.

In summary, we found that the introduction of a postdischarge appointment service resulted in substantially increased rates of early PCP follow-up but less clear benefits in preventing readmissions.

Accidental falls are among the most common incidents reported in hospitals, complicating approximately 2% of hospital stays.^1-3 Approximately 25% of falls in hospitalized patients result in injury, and 2% involve fractures.⁴ Substantial costs are associated with falls, including patient care costs associated with increased length of stay and liability.^5-7

Beginning October 1, 2008, the Centers for Medicare & Medicaid Services (CMS) stopped reimbursing hospitals for the additional care associated with eight hospital-acquired conditions (HACs), including serious fall-related injury, which were believed to be “reasonably preventable.”^8,9 Before this change, hospitals recovered the costs of these “never events” by assigning a higher level MS-DRG (Medicare Severity Diagnosis-Related Group) code for patients experiencing such an event. This is no longer allowed under the revised CMS Prospective Payment System rules.

Although the financial penalty for iatrogenic injury was modest, the payment change placed pressure on hospital staff to decrease falls, and some nurses reported changing practice to be more restrictive of patient mobility.¹⁰ Increased use of physical restraints is a potential unintended consequence of this rule change.¹¹ Restraints are known to cause agitation, delirium, decubiti, deconditioning, strangulation, and death.¹² Not surprisingly, use of restraints is discouraged in hospitals and is a CMS quality of care indicator.^13,14 Although there is no evidence that restraint use prevents patients from falling,^15,16 there is a perception among both health professionals and patients that restraints reduce the risk of falling, and they are often used as a “last resort” method of fall prevention.^17-19

The aim of this longitudinal study was to determine whether this payment change was associated with changes in short-, intermediate-, and long-term rates of falls, injurious falls, and physical restraint use in acute care hospitals. The CMS has included fall-related hip fracture in newer value-based purchasing programs by adding Patient Safety Indictor (PSI) 90 to both the HACs Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ in FY2015. However, the HACs Initiative remains the only Medicare value program that directly penalizes all injurious inpatient falls.

Study Units

As previously described,²² the National Database of Nursing Quality Indicators (NDNQI) is a data collection project initiated by the American Nurses Association (ANA). The NDNQI provides national comparative data at the unit and facility levels on nursing-sensitive indicators endorsed by the National Quality Forum. More than 2,000 hospitals voluntarily participate in the NDNQI, including virtually all ANA Magnet-recognized hospitals, and more than 90% of nursing units participate in the fall measures (NDNQI, personal communication). At the start of study data collection, the project was administered by the School of Nursing at the University of Kansas Medical Center. In 2014, the ownership of the NDNQI was transferred from the ANA to Press Ganey Associates, Inc. In addition to standardized data on unit, facility, and staffing characteristics, the NDNQI member hospitals can elect to submit monthly data on falls and quarterly data on physical restraint use prevalence.

We examined the data collected from adult medical, medical-surgical, and surgical units in United States acute care hospitals that elected to participate in the fall and physical restraint use data collection within the NDNQI for the 27 months before and the 87 months after the implementation of the CMS rule change. Eligible units contributed at least one fall and physical restraint use data point during both the 27 months preceding October 1, 2008, and the 87 months immediately after. The Institutional Review Board at the University of Kansas Medical Center reviewed and approved the study before its implementation.

Endpoints

Fall Events

The NDNQI defines a patient fall as an unplanned descent to the floor, regardless of whether the fall results in injury and regardless of whether the patient was assisted to the floor by a member of the hospital staff. Events in which a patient lands on a surface where one would not expect to find a patient (eg, on a mat next to a low bed) are also counted as falls.

Using internal data sources (eg, medical records, incident reports), participating hospitals report the number of inpatient falls each month to the NDNQI. We analyzed the falls data for the period July 1, 2006, through December 31, 2015. Thus, each unit could contribute 114 months (27 months before the rule change and 87 months after the rule change) of falls data.

Hospitals classify the injury level of each fall as none, minor (resulting in bruise, pain, abrasion, wound cleaning, or limb elevation, or in the use of ice, dressing, or topical medication), moderate (resulting in suturing, splinting, muscle or joint strain, or application of steri-strips or skin glue), major (resulting in surgery, casting, traction, any type of fracture, consultation for neurological or internal injury, or receipt of blood products for patients with coagulopathy), or death (resulting from injuries sustained from falling). For this study, a fall resulting in any injury (including minor) was considered as an injurious fall. The NDNQI data have been validated for falls and fall injury.^23,24

Based on patient counts from unit censuses and/or internal data on actual patient hours on the unit, hospitals also report to the NDNQI the monthly number of patient days for each unit for which falls data are reported. The NDNQI uses these data to calculate each unit’s total and injurious fall rate per 1,000 patient days.

Physical Restraint Use

The NDNQI follows the CMS definition of restraint, which is “any manual method, physical or mechanical device, material, or equipment that immobilizes or reduces the ability of a patient to move his or her arms, legs, body, or head freely”.¹³ The NDNQI restraint use data are collected quarterly. Participating hospitals choose one day each quarter to conduct a restraint use prevalence survey on participating units. On the selected day, designated RNs within these hospitals visually assess each patient on the unit for restraint use. Based on this survey, hospitals report to the NDNQI the total count of patients surveyed and whether each was restrained. For restrained patients, hospitals also report the type of restraint as limb, vest, or other (eg, four side rails, net beds, mitts not attached to the bed).

We analyzed the restraint use data for the period October 1, 2006, through December 31, 2015. Thus, 37 quarters of data (eight pre- and 29 postrule change) were available. For this study, we computed for each unit the quarterly proportion of surveyed patients who were physically restrained by dividing the total count of restrained patients (regardless of the type of restraint) on the day of the survey by the total count of surveyed patients.

Covariates

Unit- and facility-level covariates were included in several model specifications to determine whether patient or facility characteristics affected the results. The unit-level covariates included the type of nursing unit (medical, medical and surgical, or surgical), monthly rates of total nursing hours per patient day, and nursing skill mix (percent registered nurses/total nursing personnel). The three facility-level variables included urban–rural location (defined as metropolitan [located in an area containing an urban core with a population of at least 50,000], micropolitan [located in an area containing an urban core with a population of 10,000-49,999], or neither), bed size (<300 beds or ≥300 beds), and teaching status (academic health center, major teaching hospital, or nonteaching hospital).

Because larger, academically affiliated hospitals are overrepresented in the NDNQI, we conducted stratified analyses of these variables to explore how change in the rates of falls and restraint use in the entire sample might differ between hospitals according to bed size (<300 beds, ≥300 beds) and teaching status (nonteaching versus teaching and academic health center).

Statistical Methods

We compared the mean annual rates of change in falls, injurious falls, and physical restraint use prevalence during the two years before the HACs Initiative went into effect (October 2006-September 2008) with the mean annual rates of change following the implementation of the payment rule. Short-term (one-year) change was the slope from October 2008 to September 2009, intermediate-term (four-year) change was the slope from October 2008 to September 2012, and long-term (seven-year) change was the slope from October 2008 to September 2015.

Monthly rates of falls and injurious falls over the 114-month period were modeled using negative binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). We also fit the models that included the terms in the base model and facility-level covariates, unit-level covariates, both individually and combined. All models included terms for seasonality.

Quarterly prevalence rates of restraint use over the 37 quarters were modeled using beta-binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). Similar to the one specified for falls, models were also fitted that included facility- and unit-level covariates as described above.

To adjust for multiple comparisons of the three postimplementation slopes, all confidence intervals were Bonferroni corrected.

Nursing Units

We included nursing units with one or more months of falls data and one or more quarters of restraint use data before and after the rule change. Of the 11,117 nursing units that submitted data to the NDNQI, 2,862 units (983 medical, 1,219 medical-surgical, and 660 surgical) with the requisite demographic, falls, and restraint use data were considered for inclusion in the study. The characteristics of the nursing units (ie, the type of unit, total nursing hours per patient day, and nursing skill mix) and hospitals (ie, location, bed size, teaching status) included in the study were similar to those of the overall NDNQI member units.

Baseline Characteristics

In the first study month (July 2006), 1,941 sample nursing units reported 5,101 falls during 1,401,652 patient-days of observation. Of these, 1,502 (29%) resulted in injury (1,281 minor, 144 moderate, 75 major, and two deaths). Across falls, the median (interquartile range [IQR]) patient age was 70 (55-80) years, with males accounting for 51% of falls. Most of the falls, 4,328 (85%), were documented as unassisted. A total of 209 (4%) falls occurred while physical restraints were in use.

In the first quarterly restraint use prevalence survey (October 2006), the 829 participating nursing units surveyed 19,979 patients (median [IQR] = 23 [20-23] patients per nursing unit). The median (IQR) age was 66 (51-78) years, and 54% of them were females. At the time of the survey, restraints were in use for 326 (1.6%) patients. Restrained patients were older than unrestrained patients (median age: 78 vs 65 years) and more likely to be male (56% vs 46%). Limb restraints were used for 139 patients, vest restraints for 66, both limb and vest restraints for 24, and other restraint types were used for 113 patients (including 11 in limb restraints and 5 in limb and vest restraints).

Change in Endpoints after Implementation of the HACs Initiative

Stratified Analysis

At baseline, fall rates and restraint use prevalence were slightly higher, whereas the rate of injurious falls was slightly lower, among teaching and academic medical centers compared to those in nonteaching hospitals. Declines in falls rate and restraint use prevalence were higher in teaching hospitals than in nonteaching hospitals (data not included).

We examined the rates of falls and fall injuries among 2,862 hospital units before and after the implementation of the HACs Initiative. Implementation of the CMS payment change was associated with a modest improvement in the rate of decrease for falls; a statistically significant effect on the rate of decrease for injurious falls was detectable only at seven years postchange. Fall reductions were the greatest among teaching and larger hospitals. These findings are consistent with our previous analysis of NDNQI data that found no short-term effect of the rule change on the rate of injurious falls.²⁵

We found no evidence indicating that restraint use prevalence increased because of this payment change. Physical -restraint use prevalence showed a rapidly decreasing trend before 2008, and although the rate of decline was attenuated seven years after the rule change, the overall physical restraint use prevalence in these units in 2015 was less than half of that in 2006. Unlike falls, the steepest declines in restraint use prevalence occurred in smaller hospitals.

The CMS decision to include falls with injury among the “reasonably preventable” HACs was controversial.¹¹ Inpatient falls are largely attributable to individual patient risk factors and are unusual among HACs in the extent to which patient behavior plays a role in their occurrence. Although hospital fall prevention guidelines have been published, only a few controlled trials have been conducted, with little evidence supporting the recommendations.^1,26 A quantitative review found no evidence of benefit in published hospital fall prevention studies using concurrent controls (internal rate of return = 0.92; 95% CI: 0.65-1.30),²⁶ and a recent, well-executed, cluster randomized trial of multifactorial fall prevention interventions found no change in fall rates compared with controls.²⁷ Current hospital fall prevention guidelines are limited to unproven and time-consuming nursing-level (eg, toileting schedules and use of alarms) or organizational-level strategies (eg, changing staff attitudes regarding the inevitability of falls or “leadership support”).^1,28

Despite the large sample size and the use of nurse-reported data that include patient falls from all age groups and not subject to bias due to the regulation itself (eg, ICD coding changes), our findings should be interpreted taking into account several limitations.

First, hospitals participating in the NDNQI self-select to participate and are larger and disproportionately urban compared with nonparticipating hospitals.²⁹ Although our findings were unchanged when hospital-level covariates were included in modeling, analyses stratified by teaching status and bed size demonstrated important differences. Larger teaching hospitals experienced greater fall reductions, whereas restraint use prevalence decreased more rapidly in smaller hospitals.

Second, the absence of a control group prevents us from conclusively attributing changes in falls rate and restraint use prevalence to the 2008 CMS payment change.³⁰ Our findings may have been influenced by other policy changes. For example, in October 2014, the CMS implemented the Hospital-Acquired Condition Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ Program. Under these programs, falls with hip fractures were an indicator that could alter hospital payment.

Third, we did not ascertain the use of all available fall prevention measures such as companions, bed rails, very low beds, bed alarms, and restricted activity.³¹ Nor could the study address changes in patient functional status or discharge location. In a before- and after-study of four hospitals in a single hospital system, we found that bed alarm use increased, restraint orders decreased, and the use of room change or sitters remained stable after the implementation of the CMS payment.³²

Nevertheless, we believe that these findings are consistent with the hypothesis that the HACs Initiative increased the cost of patient falls to hospitals, and, in response, some hospitals were able to modestly reduce the rate of falls. We found no evidence that physical restraint use prevalence increased.

In summary, our findings suggest modest impact of the HACs Initiative on falls and injurious falls, but no unintended impact on restraint use. These results highlight the importance of ensuring that pay-for-performance initiatives target outcomes where there are evidence-based approaches to prevention. The creation or identification of prevention tools and guidelines does not make an outcome preventable. Despite interval improvement in these self-selected hospital units in fall rates and physical restraint use prevalence, falls remain a difficult patient safety problem for hospitals, and further research is required to develop cost-effective, generalizable strategies for their prevention.

Accidental falls are among the most common incidents reported in hospitals, complicating approximately 2% of hospital stays.^1-3 Approximately 25% of falls in hospitalized patients result in injury, and 2% involve fractures.⁴ Substantial costs are associated with falls, including patient care costs associated with increased length of stay and liability.^5-7

Beginning October 1, 2008, the Centers for Medicare & Medicaid Services (CMS) stopped reimbursing hospitals for the additional care associated with eight hospital-acquired conditions (HACs), including serious fall-related injury, which were believed to be “reasonably preventable.”^8,9 Before this change, hospitals recovered the costs of these “never events” by assigning a higher level MS-DRG (Medicare Severity Diagnosis-Related Group) code for patients experiencing such an event. This is no longer allowed under the revised CMS Prospective Payment System rules.

Although the financial penalty for iatrogenic injury was modest, the payment change placed pressure on hospital staff to decrease falls, and some nurses reported changing practice to be more restrictive of patient mobility.¹⁰ Increased use of physical restraints is a potential unintended consequence of this rule change.¹¹ Restraints are known to cause agitation, delirium, decubiti, deconditioning, strangulation, and death.¹² Not surprisingly, use of restraints is discouraged in hospitals and is a CMS quality of care indicator.^13,14 Although there is no evidence that restraint use prevents patients from falling,^15,16 there is a perception among both health professionals and patients that restraints reduce the risk of falling, and they are often used as a “last resort” method of fall prevention.^17-19

The aim of this longitudinal study was to determine whether this payment change was associated with changes in short-, intermediate-, and long-term rates of falls, injurious falls, and physical restraint use in acute care hospitals. The CMS has included fall-related hip fracture in newer value-based purchasing programs by adding Patient Safety Indictor (PSI) 90 to both the HACs Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ in FY2015. However, the HACs Initiative remains the only Medicare value program that directly penalizes all injurious inpatient falls.

Study Units

As previously described,²² the National Database of Nursing Quality Indicators (NDNQI) is a data collection project initiated by the American Nurses Association (ANA). The NDNQI provides national comparative data at the unit and facility levels on nursing-sensitive indicators endorsed by the National Quality Forum. More than 2,000 hospitals voluntarily participate in the NDNQI, including virtually all ANA Magnet-recognized hospitals, and more than 90% of nursing units participate in the fall measures (NDNQI, personal communication). At the start of study data collection, the project was administered by the School of Nursing at the University of Kansas Medical Center. In 2014, the ownership of the NDNQI was transferred from the ANA to Press Ganey Associates, Inc. In addition to standardized data on unit, facility, and staffing characteristics, the NDNQI member hospitals can elect to submit monthly data on falls and quarterly data on physical restraint use prevalence.

We examined the data collected from adult medical, medical-surgical, and surgical units in United States acute care hospitals that elected to participate in the fall and physical restraint use data collection within the NDNQI for the 27 months before and the 87 months after the implementation of the CMS rule change. Eligible units contributed at least one fall and physical restraint use data point during both the 27 months preceding October 1, 2008, and the 87 months immediately after. The Institutional Review Board at the University of Kansas Medical Center reviewed and approved the study before its implementation.

Endpoints

Fall Events

The NDNQI defines a patient fall as an unplanned descent to the floor, regardless of whether the fall results in injury and regardless of whether the patient was assisted to the floor by a member of the hospital staff. Events in which a patient lands on a surface where one would not expect to find a patient (eg, on a mat next to a low bed) are also counted as falls.

Using internal data sources (eg, medical records, incident reports), participating hospitals report the number of inpatient falls each month to the NDNQI. We analyzed the falls data for the period July 1, 2006, through December 31, 2015. Thus, each unit could contribute 114 months (27 months before the rule change and 87 months after the rule change) of falls data.

Hospitals classify the injury level of each fall as none, minor (resulting in bruise, pain, abrasion, wound cleaning, or limb elevation, or in the use of ice, dressing, or topical medication), moderate (resulting in suturing, splinting, muscle or joint strain, or application of steri-strips or skin glue), major (resulting in surgery, casting, traction, any type of fracture, consultation for neurological or internal injury, or receipt of blood products for patients with coagulopathy), or death (resulting from injuries sustained from falling). For this study, a fall resulting in any injury (including minor) was considered as an injurious fall. The NDNQI data have been validated for falls and fall injury.^23,24

Based on patient counts from unit censuses and/or internal data on actual patient hours on the unit, hospitals also report to the NDNQI the monthly number of patient days for each unit for which falls data are reported. The NDNQI uses these data to calculate each unit’s total and injurious fall rate per 1,000 patient days.

Physical Restraint Use

The NDNQI follows the CMS definition of restraint, which is “any manual method, physical or mechanical device, material, or equipment that immobilizes or reduces the ability of a patient to move his or her arms, legs, body, or head freely”.¹³ The NDNQI restraint use data are collected quarterly. Participating hospitals choose one day each quarter to conduct a restraint use prevalence survey on participating units. On the selected day, designated RNs within these hospitals visually assess each patient on the unit for restraint use. Based on this survey, hospitals report to the NDNQI the total count of patients surveyed and whether each was restrained. For restrained patients, hospitals also report the type of restraint as limb, vest, or other (eg, four side rails, net beds, mitts not attached to the bed).

We analyzed the restraint use data for the period October 1, 2006, through December 31, 2015. Thus, 37 quarters of data (eight pre- and 29 postrule change) were available. For this study, we computed for each unit the quarterly proportion of surveyed patients who were physically restrained by dividing the total count of restrained patients (regardless of the type of restraint) on the day of the survey by the total count of surveyed patients.

Covariates

Unit- and facility-level covariates were included in several model specifications to determine whether patient or facility characteristics affected the results. The unit-level covariates included the type of nursing unit (medical, medical and surgical, or surgical), monthly rates of total nursing hours per patient day, and nursing skill mix (percent registered nurses/total nursing personnel). The three facility-level variables included urban–rural location (defined as metropolitan [located in an area containing an urban core with a population of at least 50,000], micropolitan [located in an area containing an urban core with a population of 10,000-49,999], or neither), bed size (<300 beds or ≥300 beds), and teaching status (academic health center, major teaching hospital, or nonteaching hospital).

Because larger, academically affiliated hospitals are overrepresented in the NDNQI, we conducted stratified analyses of these variables to explore how change in the rates of falls and restraint use in the entire sample might differ between hospitals according to bed size (<300 beds, ≥300 beds) and teaching status (nonteaching versus teaching and academic health center).

Statistical Methods

We compared the mean annual rates of change in falls, injurious falls, and physical restraint use prevalence during the two years before the HACs Initiative went into effect (October 2006-September 2008) with the mean annual rates of change following the implementation of the payment rule. Short-term (one-year) change was the slope from October 2008 to September 2009, intermediate-term (four-year) change was the slope from October 2008 to September 2012, and long-term (seven-year) change was the slope from October 2008 to September 2015.

Monthly rates of falls and injurious falls over the 114-month period were modeled using negative binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). We also fit the models that included the terms in the base model and facility-level covariates, unit-level covariates, both individually and combined. All models included terms for seasonality.

Quarterly prevalence rates of restraint use over the 37 quarters were modeled using beta-binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). Similar to the one specified for falls, models were also fitted that included facility- and unit-level covariates as described above.

To adjust for multiple comparisons of the three postimplementation slopes, all confidence intervals were Bonferroni corrected.

Nursing Units

We included nursing units with one or more months of falls data and one or more quarters of restraint use data before and after the rule change. Of the 11,117 nursing units that submitted data to the NDNQI, 2,862 units (983 medical, 1,219 medical-surgical, and 660 surgical) with the requisite demographic, falls, and restraint use data were considered for inclusion in the study. The characteristics of the nursing units (ie, the type of unit, total nursing hours per patient day, and nursing skill mix) and hospitals (ie, location, bed size, teaching status) included in the study were similar to those of the overall NDNQI member units.

Baseline Characteristics

In the first study month (July 2006), 1,941 sample nursing units reported 5,101 falls during 1,401,652 patient-days of observation. Of these, 1,502 (29%) resulted in injury (1,281 minor, 144 moderate, 75 major, and two deaths). Across falls, the median (interquartile range [IQR]) patient age was 70 (55-80) years, with males accounting for 51% of falls. Most of the falls, 4,328 (85%), were documented as unassisted. A total of 209 (4%) falls occurred while physical restraints were in use.

In the first quarterly restraint use prevalence survey (October 2006), the 829 participating nursing units surveyed 19,979 patients (median [IQR] = 23 [20-23] patients per nursing unit). The median (IQR) age was 66 (51-78) years, and 54% of them were females. At the time of the survey, restraints were in use for 326 (1.6%) patients. Restrained patients were older than unrestrained patients (median age: 78 vs 65 years) and more likely to be male (56% vs 46%). Limb restraints were used for 139 patients, vest restraints for 66, both limb and vest restraints for 24, and other restraint types were used for 113 patients (including 11 in limb restraints and 5 in limb and vest restraints).

Change in Endpoints after Implementation of the HACs Initiative

Stratified Analysis

At baseline, fall rates and restraint use prevalence were slightly higher, whereas the rate of injurious falls was slightly lower, among teaching and academic medical centers compared to those in nonteaching hospitals. Declines in falls rate and restraint use prevalence were higher in teaching hospitals than in nonteaching hospitals (data not included).

We examined the rates of falls and fall injuries among 2,862 hospital units before and after the implementation of the HACs Initiative. Implementation of the CMS payment change was associated with a modest improvement in the rate of decrease for falls; a statistically significant effect on the rate of decrease for injurious falls was detectable only at seven years postchange. Fall reductions were the greatest among teaching and larger hospitals. These findings are consistent with our previous analysis of NDNQI data that found no short-term effect of the rule change on the rate of injurious falls.²⁵

We found no evidence indicating that restraint use prevalence increased because of this payment change. Physical -restraint use prevalence showed a rapidly decreasing trend before 2008, and although the rate of decline was attenuated seven years after the rule change, the overall physical restraint use prevalence in these units in 2015 was less than half of that in 2006. Unlike falls, the steepest declines in restraint use prevalence occurred in smaller hospitals.

The CMS decision to include falls with injury among the “reasonably preventable” HACs was controversial.¹¹ Inpatient falls are largely attributable to individual patient risk factors and are unusual among HACs in the extent to which patient behavior plays a role in their occurrence. Although hospital fall prevention guidelines have been published, only a few controlled trials have been conducted, with little evidence supporting the recommendations.^1,26 A quantitative review found no evidence of benefit in published hospital fall prevention studies using concurrent controls (internal rate of return = 0.92; 95% CI: 0.65-1.30),²⁶ and a recent, well-executed, cluster randomized trial of multifactorial fall prevention interventions found no change in fall rates compared with controls.²⁷ Current hospital fall prevention guidelines are limited to unproven and time-consuming nursing-level (eg, toileting schedules and use of alarms) or organizational-level strategies (eg, changing staff attitudes regarding the inevitability of falls or “leadership support”).^1,28

Despite the large sample size and the use of nurse-reported data that include patient falls from all age groups and not subject to bias due to the regulation itself (eg, ICD coding changes), our findings should be interpreted taking into account several limitations.

First, hospitals participating in the NDNQI self-select to participate and are larger and disproportionately urban compared with nonparticipating hospitals.²⁹ Although our findings were unchanged when hospital-level covariates were included in modeling, analyses stratified by teaching status and bed size demonstrated important differences. Larger teaching hospitals experienced greater fall reductions, whereas restraint use prevalence decreased more rapidly in smaller hospitals.

Second, the absence of a control group prevents us from conclusively attributing changes in falls rate and restraint use prevalence to the 2008 CMS payment change.³⁰ Our findings may have been influenced by other policy changes. For example, in October 2014, the CMS implemented the Hospital-Acquired Condition Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ Program. Under these programs, falls with hip fractures were an indicator that could alter hospital payment.

Third, we did not ascertain the use of all available fall prevention measures such as companions, bed rails, very low beds, bed alarms, and restricted activity.³¹ Nor could the study address changes in patient functional status or discharge location. In a before- and after-study of four hospitals in a single hospital system, we found that bed alarm use increased, restraint orders decreased, and the use of room change or sitters remained stable after the implementation of the CMS payment.³²

Nevertheless, we believe that these findings are consistent with the hypothesis that the HACs Initiative increased the cost of patient falls to hospitals, and, in response, some hospitals were able to modestly reduce the rate of falls. We found no evidence that physical restraint use prevalence increased.

In summary, our findings suggest modest impact of the HACs Initiative on falls and injurious falls, but no unintended impact on restraint use. These results highlight the importance of ensuring that pay-for-performance initiatives target outcomes where there are evidence-based approaches to prevention. The creation or identification of prevention tools and guidelines does not make an outcome preventable. Despite interval improvement in these self-selected hospital units in fall rates and physical restraint use prevalence, falls remain a difficult patient safety problem for hospitals, and further research is required to develop cost-effective, generalizable strategies for their prevention.

Accidental falls are among the most common incidents reported in hospitals, complicating approximately 2% of hospital stays.^1-3 Approximately 25% of falls in hospitalized patients result in injury, and 2% involve fractures.⁴ Substantial costs are associated with falls, including patient care costs associated with increased length of stay and liability.^5-7

Beginning October 1, 2008, the Centers for Medicare & Medicaid Services (CMS) stopped reimbursing hospitals for the additional care associated with eight hospital-acquired conditions (HACs), including serious fall-related injury, which were believed to be “reasonably preventable.”^8,9 Before this change, hospitals recovered the costs of these “never events” by assigning a higher level MS-DRG (Medicare Severity Diagnosis-Related Group) code for patients experiencing such an event. This is no longer allowed under the revised CMS Prospective Payment System rules.

Although the financial penalty for iatrogenic injury was modest, the payment change placed pressure on hospital staff to decrease falls, and some nurses reported changing practice to be more restrictive of patient mobility.¹⁰ Increased use of physical restraints is a potential unintended consequence of this rule change.¹¹ Restraints are known to cause agitation, delirium, decubiti, deconditioning, strangulation, and death.¹² Not surprisingly, use of restraints is discouraged in hospitals and is a CMS quality of care indicator.^13,14 Although there is no evidence that restraint use prevents patients from falling,^15,16 there is a perception among both health professionals and patients that restraints reduce the risk of falling, and they are often used as a “last resort” method of fall prevention.^17-19

The aim of this longitudinal study was to determine whether this payment change was associated with changes in short-, intermediate-, and long-term rates of falls, injurious falls, and physical restraint use in acute care hospitals. The CMS has included fall-related hip fracture in newer value-based purchasing programs by adding Patient Safety Indictor (PSI) 90 to both the HACs Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ in FY2015. However, the HACs Initiative remains the only Medicare value program that directly penalizes all injurious inpatient falls.

Study Units

As previously described,²² the National Database of Nursing Quality Indicators (NDNQI) is a data collection project initiated by the American Nurses Association (ANA). The NDNQI provides national comparative data at the unit and facility levels on nursing-sensitive indicators endorsed by the National Quality Forum. More than 2,000 hospitals voluntarily participate in the NDNQI, including virtually all ANA Magnet-recognized hospitals, and more than 90% of nursing units participate in the fall measures (NDNQI, personal communication). At the start of study data collection, the project was administered by the School of Nursing at the University of Kansas Medical Center. In 2014, the ownership of the NDNQI was transferred from the ANA to Press Ganey Associates, Inc. In addition to standardized data on unit, facility, and staffing characteristics, the NDNQI member hospitals can elect to submit monthly data on falls and quarterly data on physical restraint use prevalence.

We examined the data collected from adult medical, medical-surgical, and surgical units in United States acute care hospitals that elected to participate in the fall and physical restraint use data collection within the NDNQI for the 27 months before and the 87 months after the implementation of the CMS rule change. Eligible units contributed at least one fall and physical restraint use data point during both the 27 months preceding October 1, 2008, and the 87 months immediately after. The Institutional Review Board at the University of Kansas Medical Center reviewed and approved the study before its implementation.

Endpoints

Fall Events

The NDNQI defines a patient fall as an unplanned descent to the floor, regardless of whether the fall results in injury and regardless of whether the patient was assisted to the floor by a member of the hospital staff. Events in which a patient lands on a surface where one would not expect to find a patient (eg, on a mat next to a low bed) are also counted as falls.

Using internal data sources (eg, medical records, incident reports), participating hospitals report the number of inpatient falls each month to the NDNQI. We analyzed the falls data for the period July 1, 2006, through December 31, 2015. Thus, each unit could contribute 114 months (27 months before the rule change and 87 months after the rule change) of falls data.

Hospitals classify the injury level of each fall as none, minor (resulting in bruise, pain, abrasion, wound cleaning, or limb elevation, or in the use of ice, dressing, or topical medication), moderate (resulting in suturing, splinting, muscle or joint strain, or application of steri-strips or skin glue), major (resulting in surgery, casting, traction, any type of fracture, consultation for neurological or internal injury, or receipt of blood products for patients with coagulopathy), or death (resulting from injuries sustained from falling). For this study, a fall resulting in any injury (including minor) was considered as an injurious fall. The NDNQI data have been validated for falls and fall injury.^23,24

Based on patient counts from unit censuses and/or internal data on actual patient hours on the unit, hospitals also report to the NDNQI the monthly number of patient days for each unit for which falls data are reported. The NDNQI uses these data to calculate each unit’s total and injurious fall rate per 1,000 patient days.

Physical Restraint Use

The NDNQI follows the CMS definition of restraint, which is “any manual method, physical or mechanical device, material, or equipment that immobilizes or reduces the ability of a patient to move his or her arms, legs, body, or head freely”.¹³ The NDNQI restraint use data are collected quarterly. Participating hospitals choose one day each quarter to conduct a restraint use prevalence survey on participating units. On the selected day, designated RNs within these hospitals visually assess each patient on the unit for restraint use. Based on this survey, hospitals report to the NDNQI the total count of patients surveyed and whether each was restrained. For restrained patients, hospitals also report the type of restraint as limb, vest, or other (eg, four side rails, net beds, mitts not attached to the bed).

We analyzed the restraint use data for the period October 1, 2006, through December 31, 2015. Thus, 37 quarters of data (eight pre- and 29 postrule change) were available. For this study, we computed for each unit the quarterly proportion of surveyed patients who were physically restrained by dividing the total count of restrained patients (regardless of the type of restraint) on the day of the survey by the total count of surveyed patients.

Covariates

Unit- and facility-level covariates were included in several model specifications to determine whether patient or facility characteristics affected the results. The unit-level covariates included the type of nursing unit (medical, medical and surgical, or surgical), monthly rates of total nursing hours per patient day, and nursing skill mix (percent registered nurses/total nursing personnel). The three facility-level variables included urban–rural location (defined as metropolitan [located in an area containing an urban core with a population of at least 50,000], micropolitan [located in an area containing an urban core with a population of 10,000-49,999], or neither), bed size (<300 beds or ≥300 beds), and teaching status (academic health center, major teaching hospital, or nonteaching hospital).

Because larger, academically affiliated hospitals are overrepresented in the NDNQI, we conducted stratified analyses of these variables to explore how change in the rates of falls and restraint use in the entire sample might differ between hospitals according to bed size (<300 beds, ≥300 beds) and teaching status (nonteaching versus teaching and academic health center).

Statistical Methods

We compared the mean annual rates of change in falls, injurious falls, and physical restraint use prevalence during the two years before the HACs Initiative went into effect (October 2006-September 2008) with the mean annual rates of change following the implementation of the payment rule. Short-term (one-year) change was the slope from October 2008 to September 2009, intermediate-term (four-year) change was the slope from October 2008 to September 2012, and long-term (seven-year) change was the slope from October 2008 to September 2015.

Monthly rates of falls and injurious falls over the 114-month period were modeled using negative binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). We also fit the models that included the terms in the base model and facility-level covariates, unit-level covariates, both individually and combined. All models included terms for seasonality.

Quarterly prevalence rates of restraint use over the 37 quarters were modeled using beta-binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). Similar to the one specified for falls, models were also fitted that included facility- and unit-level covariates as described above.

To adjust for multiple comparisons of the three postimplementation slopes, all confidence intervals were Bonferroni corrected.

Nursing Units

We included nursing units with one or more months of falls data and one or more quarters of restraint use data before and after the rule change. Of the 11,117 nursing units that submitted data to the NDNQI, 2,862 units (983 medical, 1,219 medical-surgical, and 660 surgical) with the requisite demographic, falls, and restraint use data were considered for inclusion in the study. The characteristics of the nursing units (ie, the type of unit, total nursing hours per patient day, and nursing skill mix) and hospitals (ie, location, bed size, teaching status) included in the study were similar to those of the overall NDNQI member units.

Baseline Characteristics

In the first study month (July 2006), 1,941 sample nursing units reported 5,101 falls during 1,401,652 patient-days of observation. Of these, 1,502 (29%) resulted in injury (1,281 minor, 144 moderate, 75 major, and two deaths). Across falls, the median (interquartile range [IQR]) patient age was 70 (55-80) years, with males accounting for 51% of falls. Most of the falls, 4,328 (85%), were documented as unassisted. A total of 209 (4%) falls occurred while physical restraints were in use.

In the first quarterly restraint use prevalence survey (October 2006), the 829 participating nursing units surveyed 19,979 patients (median [IQR] = 23 [20-23] patients per nursing unit). The median (IQR) age was 66 (51-78) years, and 54% of them were females. At the time of the survey, restraints were in use for 326 (1.6%) patients. Restrained patients were older than unrestrained patients (median age: 78 vs 65 years) and more likely to be male (56% vs 46%). Limb restraints were used for 139 patients, vest restraints for 66, both limb and vest restraints for 24, and other restraint types were used for 113 patients (including 11 in limb restraints and 5 in limb and vest restraints).

Change in Endpoints after Implementation of the HACs Initiative

Stratified Analysis

At baseline, fall rates and restraint use prevalence were slightly higher, whereas the rate of injurious falls was slightly lower, among teaching and academic medical centers compared to those in nonteaching hospitals. Declines in falls rate and restraint use prevalence were higher in teaching hospitals than in nonteaching hospitals (data not included).

We examined the rates of falls and fall injuries among 2,862 hospital units before and after the implementation of the HACs Initiative. Implementation of the CMS payment change was associated with a modest improvement in the rate of decrease for falls; a statistically significant effect on the rate of decrease for injurious falls was detectable only at seven years postchange. Fall reductions were the greatest among teaching and larger hospitals. These findings are consistent with our previous analysis of NDNQI data that found no short-term effect of the rule change on the rate of injurious falls.²⁵

We found no evidence indicating that restraint use prevalence increased because of this payment change. Physical -restraint use prevalence showed a rapidly decreasing trend before 2008, and although the rate of decline was attenuated seven years after the rule change, the overall physical restraint use prevalence in these units in 2015 was less than half of that in 2006. Unlike falls, the steepest declines in restraint use prevalence occurred in smaller hospitals.

The CMS decision to include falls with injury among the “reasonably preventable” HACs was controversial.¹¹ Inpatient falls are largely attributable to individual patient risk factors and are unusual among HACs in the extent to which patient behavior plays a role in their occurrence. Although hospital fall prevention guidelines have been published, only a few controlled trials have been conducted, with little evidence supporting the recommendations.^1,26 A quantitative review found no evidence of benefit in published hospital fall prevention studies using concurrent controls (internal rate of return = 0.92; 95% CI: 0.65-1.30),²⁶ and a recent, well-executed, cluster randomized trial of multifactorial fall prevention interventions found no change in fall rates compared with controls.²⁷ Current hospital fall prevention guidelines are limited to unproven and time-consuming nursing-level (eg, toileting schedules and use of alarms) or organizational-level strategies (eg, changing staff attitudes regarding the inevitability of falls or “leadership support”).^1,28

Despite the large sample size and the use of nurse-reported data that include patient falls from all age groups and not subject to bias due to the regulation itself (eg, ICD coding changes), our findings should be interpreted taking into account several limitations.

First, hospitals participating in the NDNQI self-select to participate and are larger and disproportionately urban compared with nonparticipating hospitals.²⁹ Although our findings were unchanged when hospital-level covariates were included in modeling, analyses stratified by teaching status and bed size demonstrated important differences. Larger teaching hospitals experienced greater fall reductions, whereas restraint use prevalence decreased more rapidly in smaller hospitals.

Second, the absence of a control group prevents us from conclusively attributing changes in falls rate and restraint use prevalence to the 2008 CMS payment change.³⁰ Our findings may have been influenced by other policy changes. For example, in October 2014, the CMS implemented the Hospital-Acquired Condition Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ Program. Under these programs, falls with hip fractures were an indicator that could alter hospital payment.

Third, we did not ascertain the use of all available fall prevention measures such as companions, bed rails, very low beds, bed alarms, and restricted activity.³¹ Nor could the study address changes in patient functional status or discharge location. In a before- and after-study of four hospitals in a single hospital system, we found that bed alarm use increased, restraint orders decreased, and the use of room change or sitters remained stable after the implementation of the CMS payment.³²

Nevertheless, we believe that these findings are consistent with the hypothesis that the HACs Initiative increased the cost of patient falls to hospitals, and, in response, some hospitals were able to modestly reduce the rate of falls. We found no evidence that physical restraint use prevalence increased.

In summary, our findings suggest modest impact of the HACs Initiative on falls and injurious falls, but no unintended impact on restraint use. These results highlight the importance of ensuring that pay-for-performance initiatives target outcomes where there are evidence-based approaches to prevention. The creation or identification of prevention tools and guidelines does not make an outcome preventable. Despite interval improvement in these self-selected hospital units in fall rates and physical restraint use prevalence, falls remain a difficult patient safety problem for hospitals, and further research is required to develop cost-effective, generalizable strategies for their prevention.