We greatly appreciate the thoughtful points made by Dr. Kerman regarding our recently published study evaluating the association of hospitalist continuity on adverse events (AEs).[1] We agree that a 7‐on/7‐off staffing model may limit discontinuity relative to models using shorter rotations lengths. Many hospital medicine programs use a 7‐on/7‐off model to optimize continuity. Longer rotation lengths are uncommon, as they may lead to fatigue and negatively affect physician work‐life balance. Shorter rotation lengths do exist, and we acknowledge that a study in a setting with greater fragmentation may have detected an effect.

We respectfully disagree with Dr. Kerman's concern that our methods for AE detection and confirmation may have been insensitive. We did not rely on incident reports, as these systems suffer from under‐reporting and often represent only a fraction of true AEs. We used a modified version of the classic 2‐stage method to identify and confirm AEs.[2] In the first stage, we used computerized screens, based on criteria from the Harvard Medical Practice Study and Institute for Healthcare Improvement global trigger tool, to identify potential AEs.[3, 4, 5] A research nurse created narrative summaries of potential AEs. A physician researcher then reviewed the narrative summaries to confirm whether an AE was truly present. This time‐consuming method is much more sensitive and specific than other options for patient safety measurement, including administrative data analyses and incident reporting systems.[6, 7]

With respect to other outcomes that may be affected by hospitalist continuity, we recently published a separate study showing that lower inpatient physician continuity was significantly associated with modest increases in hospital costs.[8] We found no association between continuity and patient satisfaction, but were likely underpowered to detect one. Interestingly, some of the models in our study suggested a slightly reduced risk of readmission with lower continuity. We were surprised by this finding and hypothesized that countervailing forces may be at play during handoffs of care from 1 hospitalist to another. Transitions of care introduce the opportunity for critical information to be lost, but they also introduce the potential for patient reassessment. A hospitalist newly taking over care from another may not be anchored to the initial diagnostic impressions and management plan established by the first. Of course, the potential benefit of a reassessment could only occur if the new hospitalist has time to perform one. At extremely high patient volumes, this theoretical benefit is unlikely to exist.

We did not include length of stay (LOS) as an outcome because hospitalist continuity and LOS are interdependent. Although discontinuity may lead to longer LOS, longer LOS definitely increases the probability of discontinuity. Thus, we controlled for LOS in our statistical models to isolate the effect of continuity. The study by Epstein and colleagues did not take into account the interdependence between LOS and hospitalist continuity.[9] Observational studies are not ideal for determining the effect of continuity on LOS. The Combing Incentives and Continuity Leading to Efficiency (CICLE) study by Chandra and colleagues was a pre‐post evaluation of a hospitalist staffing model specifically designed to improve continuity.[10] In the CICLE model, physicians work in a 4‐day rotation. On day 1, physicians exclusively admit patients. On day 2, physicians care for patients admitted on day 1 and accept patients admitted overnight. On days 3 and 4, physicians continue to care for patients received on days 1 and 2, but receive no additional patients. The remaining patients are transitioned to the next physician entering the cycle at the end of day 4. Chandra and colleagues found a 7.5% reduction in LOS and an 8.5% reduction in charges. Interestingly, they also found a 13.5% increase in readmissions that did not achieve statistical significance (P=0.08). The CICLE study suggests continuity does affect LOS, but is limited in that it did not account for a potential preexisting trend toward lower LOS.

Dr. Kerman presents data showing that it takes longer for a physician to care for a patient who is new to him or her than for a patient who is previously known. This finding has face validity. However, as we have suggested, the extra time spent by the oncoming physician may have both advantages and disadvantages. The disadvantages include time‐consuming cognitive work for the physician and the potential for information loss affecting patient care. The potential advantage is a second physician reassessing the diagnosis and management decisions established by the first, potentially correcting errors and optimizing care.

Ultimately, more research is needed to illuminate the effect of hospitalist continuity on patient outcomes. For now, we feel that hospital medicine group leaders need not institute lengthy rotations or staffing models that prioritize continuity above all other factors, as continuity appears to have little impact on patient outcomes.

We greatly appreciate the thoughtful points made by Dr. Kerman regarding our recently published study evaluating the association of hospitalist continuity on adverse events (AEs).[1] We agree that a 7‐on/7‐off staffing model may limit discontinuity relative to models using shorter rotations lengths. Many hospital medicine programs use a 7‐on/7‐off model to optimize continuity. Longer rotation lengths are uncommon, as they may lead to fatigue and negatively affect physician work‐life balance. Shorter rotation lengths do exist, and we acknowledge that a study in a setting with greater fragmentation may have detected an effect.

We respectfully disagree with Dr. Kerman's concern that our methods for AE detection and confirmation may have been insensitive. We did not rely on incident reports, as these systems suffer from under‐reporting and often represent only a fraction of true AEs. We used a modified version of the classic 2‐stage method to identify and confirm AEs.[2] In the first stage, we used computerized screens, based on criteria from the Harvard Medical Practice Study and Institute for Healthcare Improvement global trigger tool, to identify potential AEs.[3, 4, 5] A research nurse created narrative summaries of potential AEs. A physician researcher then reviewed the narrative summaries to confirm whether an AE was truly present. This time‐consuming method is much more sensitive and specific than other options for patient safety measurement, including administrative data analyses and incident reporting systems.[6, 7]

With respect to other outcomes that may be affected by hospitalist continuity, we recently published a separate study showing that lower inpatient physician continuity was significantly associated with modest increases in hospital costs.[8] We found no association between continuity and patient satisfaction, but were likely underpowered to detect one. Interestingly, some of the models in our study suggested a slightly reduced risk of readmission with lower continuity. We were surprised by this finding and hypothesized that countervailing forces may be at play during handoffs of care from 1 hospitalist to another. Transitions of care introduce the opportunity for critical information to be lost, but they also introduce the potential for patient reassessment. A hospitalist newly taking over care from another may not be anchored to the initial diagnostic impressions and management plan established by the first. Of course, the potential benefit of a reassessment could only occur if the new hospitalist has time to perform one. At extremely high patient volumes, this theoretical benefit is unlikely to exist.

We did not include length of stay (LOS) as an outcome because hospitalist continuity and LOS are interdependent. Although discontinuity may lead to longer LOS, longer LOS definitely increases the probability of discontinuity. Thus, we controlled for LOS in our statistical models to isolate the effect of continuity. The study by Epstein and colleagues did not take into account the interdependence between LOS and hospitalist continuity.[9] Observational studies are not ideal for determining the effect of continuity on LOS. The Combing Incentives and Continuity Leading to Efficiency (CICLE) study by Chandra and colleagues was a pre‐post evaluation of a hospitalist staffing model specifically designed to improve continuity.[10] In the CICLE model, physicians work in a 4‐day rotation. On day 1, physicians exclusively admit patients. On day 2, physicians care for patients admitted on day 1 and accept patients admitted overnight. On days 3 and 4, physicians continue to care for patients received on days 1 and 2, but receive no additional patients. The remaining patients are transitioned to the next physician entering the cycle at the end of day 4. Chandra and colleagues found a 7.5% reduction in LOS and an 8.5% reduction in charges. Interestingly, they also found a 13.5% increase in readmissions that did not achieve statistical significance (P=0.08). The CICLE study suggests continuity does affect LOS, but is limited in that it did not account for a potential preexisting trend toward lower LOS.

Dr. Kerman presents data showing that it takes longer for a physician to care for a patient who is new to him or her than for a patient who is previously known. This finding has face validity. However, as we have suggested, the extra time spent by the oncoming physician may have both advantages and disadvantages. The disadvantages include time‐consuming cognitive work for the physician and the potential for information loss affecting patient care. The potential advantage is a second physician reassessing the diagnosis and management decisions established by the first, potentially correcting errors and optimizing care.

Ultimately, more research is needed to illuminate the effect of hospitalist continuity on patient outcomes. For now, we feel that hospital medicine group leaders need not institute lengthy rotations or staffing models that prioritize continuity above all other factors, as continuity appears to have little impact on patient outcomes.

We greatly appreciate the thoughtful points made by Dr. Kerman regarding our recently published study evaluating the association of hospitalist continuity on adverse events (AEs).[1] We agree that a 7‐on/7‐off staffing model may limit discontinuity relative to models using shorter rotations lengths. Many hospital medicine programs use a 7‐on/7‐off model to optimize continuity. Longer rotation lengths are uncommon, as they may lead to fatigue and negatively affect physician work‐life balance. Shorter rotation lengths do exist, and we acknowledge that a study in a setting with greater fragmentation may have detected an effect.

We respectfully disagree with Dr. Kerman's concern that our methods for AE detection and confirmation may have been insensitive. We did not rely on incident reports, as these systems suffer from under‐reporting and often represent only a fraction of true AEs. We used a modified version of the classic 2‐stage method to identify and confirm AEs.[2] In the first stage, we used computerized screens, based on criteria from the Harvard Medical Practice Study and Institute for Healthcare Improvement global trigger tool, to identify potential AEs.[3, 4, 5] A research nurse created narrative summaries of potential AEs. A physician researcher then reviewed the narrative summaries to confirm whether an AE was truly present. This time‐consuming method is much more sensitive and specific than other options for patient safety measurement, including administrative data analyses and incident reporting systems.[6, 7]

With respect to other outcomes that may be affected by hospitalist continuity, we recently published a separate study showing that lower inpatient physician continuity was significantly associated with modest increases in hospital costs.[8] We found no association between continuity and patient satisfaction, but were likely underpowered to detect one. Interestingly, some of the models in our study suggested a slightly reduced risk of readmission with lower continuity. We were surprised by this finding and hypothesized that countervailing forces may be at play during handoffs of care from 1 hospitalist to another. Transitions of care introduce the opportunity for critical information to be lost, but they also introduce the potential for patient reassessment. A hospitalist newly taking over care from another may not be anchored to the initial diagnostic impressions and management plan established by the first. Of course, the potential benefit of a reassessment could only occur if the new hospitalist has time to perform one. At extremely high patient volumes, this theoretical benefit is unlikely to exist.

We did not include length of stay (LOS) as an outcome because hospitalist continuity and LOS are interdependent. Although discontinuity may lead to longer LOS, longer LOS definitely increases the probability of discontinuity. Thus, we controlled for LOS in our statistical models to isolate the effect of continuity. The study by Epstein and colleagues did not take into account the interdependence between LOS and hospitalist continuity.[9] Observational studies are not ideal for determining the effect of continuity on LOS. The Combing Incentives and Continuity Leading to Efficiency (CICLE) study by Chandra and colleagues was a pre‐post evaluation of a hospitalist staffing model specifically designed to improve continuity.[10] In the CICLE model, physicians work in a 4‐day rotation. On day 1, physicians exclusively admit patients. On day 2, physicians care for patients admitted on day 1 and accept patients admitted overnight. On days 3 and 4, physicians continue to care for patients received on days 1 and 2, but receive no additional patients. The remaining patients are transitioned to the next physician entering the cycle at the end of day 4. Chandra and colleagues found a 7.5% reduction in LOS and an 8.5% reduction in charges. Interestingly, they also found a 13.5% increase in readmissions that did not achieve statistical significance (P=0.08). The CICLE study suggests continuity does affect LOS, but is limited in that it did not account for a potential preexisting trend toward lower LOS.

Dr. Kerman presents data showing that it takes longer for a physician to care for a patient who is new to him or her than for a patient who is previously known. This finding has face validity. However, as we have suggested, the extra time spent by the oncoming physician may have both advantages and disadvantages. The disadvantages include time‐consuming cognitive work for the physician and the potential for information loss affecting patient care. The potential advantage is a second physician reassessing the diagnosis and management decisions established by the first, potentially correcting errors and optimizing care.

Ultimately, more research is needed to illuminate the effect of hospitalist continuity on patient outcomes. For now, we feel that hospital medicine group leaders need not institute lengthy rotations or staffing models that prioritize continuity above all other factors, as continuity appears to have little impact on patient outcomes.

Congratulations once again to Dr. Kevin O'Leary and his team at Northwestern Memorial Hospital for adding yet another thoughtful piece of research to the debate around continuity of care and team dynamics with their study The Effect of Hospitalist Continuity on Adverse Events published in the March 2015 issue of the Journal of Hospital Medicine.[1] However, I believe it would be unfortunate for their current negative study on frequency of adverse events to cause us to lose sight of the potential centrality of continuity of care to overall quality and efficiency and the need for further research.

I would like to add a perspective from our institutions experience, where fragmentation of care is common, with a focus on effects of continuity on work productivity. I would also like to comment in the context of the bigger scheme, where I believe there is a great deal of evidence that continuity of care is highly desirable for multiple reasons. Continuity in the inpatient setting has been shown to have effects on: (1) provider satisfaction, (2) length of stay, (3) efficiency, safety/medical errors, and (4) cost of care, patient satisfaction and readmission rates.[2]

For example, a study by Chandra et al.[3] showed improved continuity using the Creating Incentives and Continuity Leading to Efficiency model, which decreased LOS and reduced mean total charges by 20%. There is also evidence from the outpatient setting that greater continuity has been associated with better hypertensive control, lower risk of hospitalization, fewer emergency department visits, higher patient satisfaction, and higher physician satisfaction.[4, 5, 6, 7, 8]

Not captured in any of the literature I am aware of to date is the effect on work productivity of care fragmentation. According to data from an unpublished time study at our institution, each change of service to a new provider required an average of 12 extra minutes for rounding on each new patient to be evaluated, which included both time spent studying the chart as well as extra time spent reassessing the patient at the bedside. When we restructured our service to improve continuity by increasing the number of patients admitted and followed by the same provider (from 14% to 31%), we reduced the number of providers per stay from 2.4 to 2.1, and reduced the number of annual handoffs by 3600, and found that a total of 900 hours, or 0.45 full‐time equivalents (FTE) per year, were saved for our program of approximately 30 FTEs.

Although the study by O'Leary et al. is an important contribution to the literature, more research needs to be done on the effects of fragmentation and the benefits of continuity. Although safety and adverse events are among the most important indicators to look at, they also may represent a weak and hard to pick up signal without a multicentered and statistically high‐powered study. Extending the study of continuity's effects, including efficiency, safety, costs, provider and patient satisfaction, and readmissions, is well worth further effort.

Congratulations once again to Dr. Kevin O'Leary and his team at Northwestern Memorial Hospital for adding yet another thoughtful piece of research to the debate around continuity of care and team dynamics with their study The Effect of Hospitalist Continuity on Adverse Events published in the March 2015 issue of the Journal of Hospital Medicine.[1] However, I believe it would be unfortunate for their current negative study on frequency of adverse events to cause us to lose sight of the potential centrality of continuity of care to overall quality and efficiency and the need for further research.

I would like to add a perspective from our institutions experience, where fragmentation of care is common, with a focus on effects of continuity on work productivity. I would also like to comment in the context of the bigger scheme, where I believe there is a great deal of evidence that continuity of care is highly desirable for multiple reasons. Continuity in the inpatient setting has been shown to have effects on: (1) provider satisfaction, (2) length of stay, (3) efficiency, safety/medical errors, and (4) cost of care, patient satisfaction and readmission rates.[2]

For example, a study by Chandra et al.[3] showed improved continuity using the Creating Incentives and Continuity Leading to Efficiency model, which decreased LOS and reduced mean total charges by 20%. There is also evidence from the outpatient setting that greater continuity has been associated with better hypertensive control, lower risk of hospitalization, fewer emergency department visits, higher patient satisfaction, and higher physician satisfaction.[4, 5, 6, 7, 8]

Not captured in any of the literature I am aware of to date is the effect on work productivity of care fragmentation. According to data from an unpublished time study at our institution, each change of service to a new provider required an average of 12 extra minutes for rounding on each new patient to be evaluated, which included both time spent studying the chart as well as extra time spent reassessing the patient at the bedside. When we restructured our service to improve continuity by increasing the number of patients admitted and followed by the same provider (from 14% to 31%), we reduced the number of providers per stay from 2.4 to 2.1, and reduced the number of annual handoffs by 3600, and found that a total of 900 hours, or 0.45 full‐time equivalents (FTE) per year, were saved for our program of approximately 30 FTEs.

Although the study by O'Leary et al. is an important contribution to the literature, more research needs to be done on the effects of fragmentation and the benefits of continuity. Although safety and adverse events are among the most important indicators to look at, they also may represent a weak and hard to pick up signal without a multicentered and statistically high‐powered study. Extending the study of continuity's effects, including efficiency, safety, costs, provider and patient satisfaction, and readmissions, is well worth further effort.

Congratulations once again to Dr. Kevin O'Leary and his team at Northwestern Memorial Hospital for adding yet another thoughtful piece of research to the debate around continuity of care and team dynamics with their study The Effect of Hospitalist Continuity on Adverse Events published in the March 2015 issue of the Journal of Hospital Medicine.[1] However, I believe it would be unfortunate for their current negative study on frequency of adverse events to cause us to lose sight of the potential centrality of continuity of care to overall quality and efficiency and the need for further research.

I would like to add a perspective from our institutions experience, where fragmentation of care is common, with a focus on effects of continuity on work productivity. I would also like to comment in the context of the bigger scheme, where I believe there is a great deal of evidence that continuity of care is highly desirable for multiple reasons. Continuity in the inpatient setting has been shown to have effects on: (1) provider satisfaction, (2) length of stay, (3) efficiency, safety/medical errors, and (4) cost of care, patient satisfaction and readmission rates.[2]

For example, a study by Chandra et al.[3] showed improved continuity using the Creating Incentives and Continuity Leading to Efficiency model, which decreased LOS and reduced mean total charges by 20%. There is also evidence from the outpatient setting that greater continuity has been associated with better hypertensive control, lower risk of hospitalization, fewer emergency department visits, higher patient satisfaction, and higher physician satisfaction.[4, 5, 6, 7, 8]

Not captured in any of the literature I am aware of to date is the effect on work productivity of care fragmentation. According to data from an unpublished time study at our institution, each change of service to a new provider required an average of 12 extra minutes for rounding on each new patient to be evaluated, which included both time spent studying the chart as well as extra time spent reassessing the patient at the bedside. When we restructured our service to improve continuity by increasing the number of patients admitted and followed by the same provider (from 14% to 31%), we reduced the number of providers per stay from 2.4 to 2.1, and reduced the number of annual handoffs by 3600, and found that a total of 900 hours, or 0.45 full‐time equivalents (FTE) per year, were saved for our program of approximately 30 FTEs.

Although the study by O'Leary et al. is an important contribution to the literature, more research needs to be done on the effects of fragmentation and the benefits of continuity. Although safety and adverse events are among the most important indicators to look at, they also may represent a weak and hard to pick up signal without a multicentered and statistically high‐powered study. Extending the study of continuity's effects, including efficiency, safety, costs, provider and patient satisfaction, and readmissions, is well worth further effort.

Anticoagulants are among the prescriptions with the highest risk of an adverse drug event (ADE) after discharge, and many of these events are preventable.[1, 2, 3] In recognition of the high risk for adverse events, the Institute for Healthcare Improvement Map details several key features of a safe anticoagulation management program, including the recommendation during the transition period that clinicians ensure proper lab monitoring and establish capacity for follow‐up testing.[4]

Despite the potential for harm, most hospitals do not have a structured process for the transmission of vital information related to warfarin management from the inpatient to the ambulatory setting. Our aim was to develop a concise report containing the essential information regarding the patient's anticoagulation regimen, the Safe Transitions Anticoagulation Report (STAR), and a process to ensure the report can be readily accessed and utilized by ambulatory clinicians.

METHODS

We assembled an interdisciplinary team to develop a new report and workflow to ensure that information on inpatient warfarin management was transmitted to outpatient providers in a reliable and structured manner. Explicit goals were to maximize use of the electronic medical record (EMR) to autopopulate aspects of the new report and create a process that worked seamlessly into the workflow. The final items were selected based on the risk of harm if not conveyed and feasibility of incorporation through the EMR:

• Warfarin doses: the 7 warfarin doses immediately prior to discharge
• International normalized ratio (INR) values: the 7 INR values immediately prior to discharge
• Bridging anticoagulation: the low‐molecular‐weight heparin (LMWH) prescribed as a bridging anticoagulant, if any

The STAR resides in both the discharge summary and the after visit summary (AVS) for patients discharged on warfarin. At our institution, the AVS contains a medication list, discharge instructions, and appointments, and is automatically produced through our EMR. Our institution utilizes the Epic EMR (Epic Systems Corp., Verona, WI) in the hospital, ambulatory clinics, and faculty practices.

A process was developed where a structured table is automatically created (Figure 1). A field was added to our EMR's discharge summary template asking whether the patient is being discharged on warfarin. Answering yes produces a second question asking whether the patient is also being discharged on bridging anticoagulation with LMWH. The STAR is not inserted into the discharge summary if the clinician completing the discharge summary deletes the anticoagulation question. The STAR is automatically created for patients discharged on warfarin and inserted into the AVS by the EMR regardless of whether the discharge summary has been completed. Patients are instructed by their nurse to bring their AVS to their follow‐up appointments.

Figure 1

The STAR project team utilized plan‐do‐study‐act cycles to test small changes and make revisions. The workflow was piloted on 2 medical/surgical units from January 2014 through March 2014, and revised based on feedback from clinicians and nursing staff. The STAR initiative was fully implemented across our institution in April 2014.

The study was evaluated by the institutional review board of the Icahn School of Medicine at Mount Sinai, and full review was waived.

RESULTS

The STAR was embedded in the discharge summary for 1370 (78.6%) discharges during the intervention period. A total of 505 patients in the preintervention period and 292 patients in the intervention period were established patients at an affiliated practice and comprised the study population. Demographics and indications for warfarin for the preintervention and intervention groups are listed in the Table 1.

The frequency of INR testing within 10 days of discharge was similar for the preintervention and intervention periods (41.4% and 47.6%, respectively, P=0.09). Similarly, the likelihood of having at least 1 therapeutic INR value within 10 days of discharge was not statistically different for the groups (17.0% and 21.2%, P=0.14). The pattern was similar for the 3‐, 7‐, and 30‐day periods; a higher percentage of the intervention group had INR testing and attained a therapeutic INR value, though for no time period did this reach statistical significance. This pattern was also found when limiting the analysis to patients discharged home rather than to a facility, patients on warfarin, prior to admission, and patients started on warfarin during the hospitalization.

A total of 87of 207 (42.0%) clinicians responded to the survey. Of respondents, 75% reported that they had seen 1 patient who had been discharged on warfarin since the STAR initiative had begun, 58% of whom reported having viewed the STAR. Most respondents who viewed the STAR found it to be helpful or very helpful in guiding warfarin management (67%), improving their workflow and efficiency (58%), and improving patient safety (77%). Approximately one‐third of respondents who had viewed the STAR (34%) reported that they selected a different dose than they would have chosen had the STAR not been available.

DISCUSSION

We developed a concise report that is seamlessly created and inserted into the discharge summary. Though the STAR was perceived as improving patient safety by ambulatory care providers, there was no impact on attaining a therapeutic INR after discharge. There are several possible explanations for a lack of benefit. Most notably, our intervention was comprised of a stand‐alone EMR‐based tool and focused on 1 component of the transitions process. Given the complexity of healthcare delivery and anticoagulant management, it is likely that broader interventions are required to improve clinical outcomes over the transition period. Potential targets of multifaceted approaches may include improving access to care, providing greater access to anticoagulation clinics, enhancing patient education, and promoting direct physician‐physician communication. Bundled interventions will likely need to include involvement of an interdisciplinary team, such as pharmacist involvement in the medication reconciliation process.

The transition period from the hospital to the outpatient setting has the potential to jeopardize patient safety if vital information is not reliably transmitted across venues.[5, 6, 7, 8] Forster and colleagues noted an 11% incidence of ADEs in the posthospitalization period, of which 60% were either preventable or ameliorable.[3] To decrease the risk to patient safety during the transitions period, the Transitions of Care Consensus policy statement by the Society of Hospital Medicine and other medical organizations called for incorporation of standard data transfer forms (templates and transmission protocols).[9] Despite the high risk and preventable nature of many of the events, few specific tools have been developed. As part of broader initiatives to improve the transitions process, the STAR may have the potential to be a means for health systems to improve the quality of the transition of care for patients on anticoagulants.

Our study has several limitations. First, it was performed at a single health system. It is unknown whether the EMR‐based report could be similarly employed at other systems. Second, our study was unable to assess clinical endpoints. Given the lack of effect on attaining a therapeutic INR, it is unlikely that downstream outcomes, such as thromboembolism, were impacted. Lastly, we were unable to examine whether our intervention improved the care of patients whose outpatient provider was external to our system.

The STAR is a concise tool developed to provide essential anticoagulant‐related information to ambulatory providers. Though the report was perceived as improving patient safety, our finding of a lack of impact on attaining a therapeutic INR after discharge suggests that the tool would need to be a component of a broader multifaceted intervention to impact clinical outcomes.

Anticoagulants are among the prescriptions with the highest risk of an adverse drug event (ADE) after discharge, and many of these events are preventable.[1, 2, 3] In recognition of the high risk for adverse events, the Institute for Healthcare Improvement Map details several key features of a safe anticoagulation management program, including the recommendation during the transition period that clinicians ensure proper lab monitoring and establish capacity for follow‐up testing.[4]

Despite the potential for harm, most hospitals do not have a structured process for the transmission of vital information related to warfarin management from the inpatient to the ambulatory setting. Our aim was to develop a concise report containing the essential information regarding the patient's anticoagulation regimen, the Safe Transitions Anticoagulation Report (STAR), and a process to ensure the report can be readily accessed and utilized by ambulatory clinicians.

METHODS

We assembled an interdisciplinary team to develop a new report and workflow to ensure that information on inpatient warfarin management was transmitted to outpatient providers in a reliable and structured manner. Explicit goals were to maximize use of the electronic medical record (EMR) to autopopulate aspects of the new report and create a process that worked seamlessly into the workflow. The final items were selected based on the risk of harm if not conveyed and feasibility of incorporation through the EMR:

• Warfarin doses: the 7 warfarin doses immediately prior to discharge
• International normalized ratio (INR) values: the 7 INR values immediately prior to discharge
• Bridging anticoagulation: the low‐molecular‐weight heparin (LMWH) prescribed as a bridging anticoagulant, if any

The STAR resides in both the discharge summary and the after visit summary (AVS) for patients discharged on warfarin. At our institution, the AVS contains a medication list, discharge instructions, and appointments, and is automatically produced through our EMR. Our institution utilizes the Epic EMR (Epic Systems Corp., Verona, WI) in the hospital, ambulatory clinics, and faculty practices.

A process was developed where a structured table is automatically created (Figure 1). A field was added to our EMR's discharge summary template asking whether the patient is being discharged on warfarin. Answering yes produces a second question asking whether the patient is also being discharged on bridging anticoagulation with LMWH. The STAR is not inserted into the discharge summary if the clinician completing the discharge summary deletes the anticoagulation question. The STAR is automatically created for patients discharged on warfarin and inserted into the AVS by the EMR regardless of whether the discharge summary has been completed. Patients are instructed by their nurse to bring their AVS to their follow‐up appointments.

Figure 1

The STAR project team utilized plan‐do‐study‐act cycles to test small changes and make revisions. The workflow was piloted on 2 medical/surgical units from January 2014 through March 2014, and revised based on feedback from clinicians and nursing staff. The STAR initiative was fully implemented across our institution in April 2014.

The study was evaluated by the institutional review board of the Icahn School of Medicine at Mount Sinai, and full review was waived.

RESULTS

The STAR was embedded in the discharge summary for 1370 (78.6%) discharges during the intervention period. A total of 505 patients in the preintervention period and 292 patients in the intervention period were established patients at an affiliated practice and comprised the study population. Demographics and indications for warfarin for the preintervention and intervention groups are listed in the Table 1.

The frequency of INR testing within 10 days of discharge was similar for the preintervention and intervention periods (41.4% and 47.6%, respectively, P=0.09). Similarly, the likelihood of having at least 1 therapeutic INR value within 10 days of discharge was not statistically different for the groups (17.0% and 21.2%, P=0.14). The pattern was similar for the 3‐, 7‐, and 30‐day periods; a higher percentage of the intervention group had INR testing and attained a therapeutic INR value, though for no time period did this reach statistical significance. This pattern was also found when limiting the analysis to patients discharged home rather than to a facility, patients on warfarin, prior to admission, and patients started on warfarin during the hospitalization.

A total of 87of 207 (42.0%) clinicians responded to the survey. Of respondents, 75% reported that they had seen 1 patient who had been discharged on warfarin since the STAR initiative had begun, 58% of whom reported having viewed the STAR. Most respondents who viewed the STAR found it to be helpful or very helpful in guiding warfarin management (67%), improving their workflow and efficiency (58%), and improving patient safety (77%). Approximately one‐third of respondents who had viewed the STAR (34%) reported that they selected a different dose than they would have chosen had the STAR not been available.

DISCUSSION

We developed a concise report that is seamlessly created and inserted into the discharge summary. Though the STAR was perceived as improving patient safety by ambulatory care providers, there was no impact on attaining a therapeutic INR after discharge. There are several possible explanations for a lack of benefit. Most notably, our intervention was comprised of a stand‐alone EMR‐based tool and focused on 1 component of the transitions process. Given the complexity of healthcare delivery and anticoagulant management, it is likely that broader interventions are required to improve clinical outcomes over the transition period. Potential targets of multifaceted approaches may include improving access to care, providing greater access to anticoagulation clinics, enhancing patient education, and promoting direct physician‐physician communication. Bundled interventions will likely need to include involvement of an interdisciplinary team, such as pharmacist involvement in the medication reconciliation process.

The transition period from the hospital to the outpatient setting has the potential to jeopardize patient safety if vital information is not reliably transmitted across venues.[5, 6, 7, 8] Forster and colleagues noted an 11% incidence of ADEs in the posthospitalization period, of which 60% were either preventable or ameliorable.[3] To decrease the risk to patient safety during the transitions period, the Transitions of Care Consensus policy statement by the Society of Hospital Medicine and other medical organizations called for incorporation of standard data transfer forms (templates and transmission protocols).[9] Despite the high risk and preventable nature of many of the events, few specific tools have been developed. As part of broader initiatives to improve the transitions process, the STAR may have the potential to be a means for health systems to improve the quality of the transition of care for patients on anticoagulants.

Our study has several limitations. First, it was performed at a single health system. It is unknown whether the EMR‐based report could be similarly employed at other systems. Second, our study was unable to assess clinical endpoints. Given the lack of effect on attaining a therapeutic INR, it is unlikely that downstream outcomes, such as thromboembolism, were impacted. Lastly, we were unable to examine whether our intervention improved the care of patients whose outpatient provider was external to our system.

The STAR is a concise tool developed to provide essential anticoagulant‐related information to ambulatory providers. Though the report was perceived as improving patient safety, our finding of a lack of impact on attaining a therapeutic INR after discharge suggests that the tool would need to be a component of a broader multifaceted intervention to impact clinical outcomes.

Anticoagulants are among the prescriptions with the highest risk of an adverse drug event (ADE) after discharge, and many of these events are preventable.[1, 2, 3] In recognition of the high risk for adverse events, the Institute for Healthcare Improvement Map details several key features of a safe anticoagulation management program, including the recommendation during the transition period that clinicians ensure proper lab monitoring and establish capacity for follow‐up testing.[4]

Despite the potential for harm, most hospitals do not have a structured process for the transmission of vital information related to warfarin management from the inpatient to the ambulatory setting. Our aim was to develop a concise report containing the essential information regarding the patient's anticoagulation regimen, the Safe Transitions Anticoagulation Report (STAR), and a process to ensure the report can be readily accessed and utilized by ambulatory clinicians.

METHODS

We assembled an interdisciplinary team to develop a new report and workflow to ensure that information on inpatient warfarin management was transmitted to outpatient providers in a reliable and structured manner. Explicit goals were to maximize use of the electronic medical record (EMR) to autopopulate aspects of the new report and create a process that worked seamlessly into the workflow. The final items were selected based on the risk of harm if not conveyed and feasibility of incorporation through the EMR:

• Warfarin doses: the 7 warfarin doses immediately prior to discharge
• International normalized ratio (INR) values: the 7 INR values immediately prior to discharge
• Bridging anticoagulation: the low‐molecular‐weight heparin (LMWH) prescribed as a bridging anticoagulant, if any

The STAR resides in both the discharge summary and the after visit summary (AVS) for patients discharged on warfarin. At our institution, the AVS contains a medication list, discharge instructions, and appointments, and is automatically produced through our EMR. Our institution utilizes the Epic EMR (Epic Systems Corp., Verona, WI) in the hospital, ambulatory clinics, and faculty practices.

A process was developed where a structured table is automatically created (Figure 1). A field was added to our EMR's discharge summary template asking whether the patient is being discharged on warfarin. Answering yes produces a second question asking whether the patient is also being discharged on bridging anticoagulation with LMWH. The STAR is not inserted into the discharge summary if the clinician completing the discharge summary deletes the anticoagulation question. The STAR is automatically created for patients discharged on warfarin and inserted into the AVS by the EMR regardless of whether the discharge summary has been completed. Patients are instructed by their nurse to bring their AVS to their follow‐up appointments.

Figure 1

The STAR project team utilized plan‐do‐study‐act cycles to test small changes and make revisions. The workflow was piloted on 2 medical/surgical units from January 2014 through March 2014, and revised based on feedback from clinicians and nursing staff. The STAR initiative was fully implemented across our institution in April 2014.

The study was evaluated by the institutional review board of the Icahn School of Medicine at Mount Sinai, and full review was waived.

RESULTS

The STAR was embedded in the discharge summary for 1370 (78.6%) discharges during the intervention period. A total of 505 patients in the preintervention period and 292 patients in the intervention period were established patients at an affiliated practice and comprised the study population. Demographics and indications for warfarin for the preintervention and intervention groups are listed in the Table 1.

The frequency of INR testing within 10 days of discharge was similar for the preintervention and intervention periods (41.4% and 47.6%, respectively, P=0.09). Similarly, the likelihood of having at least 1 therapeutic INR value within 10 days of discharge was not statistically different for the groups (17.0% and 21.2%, P=0.14). The pattern was similar for the 3‐, 7‐, and 30‐day periods; a higher percentage of the intervention group had INR testing and attained a therapeutic INR value, though for no time period did this reach statistical significance. This pattern was also found when limiting the analysis to patients discharged home rather than to a facility, patients on warfarin, prior to admission, and patients started on warfarin during the hospitalization.

A total of 87of 207 (42.0%) clinicians responded to the survey. Of respondents, 75% reported that they had seen 1 patient who had been discharged on warfarin since the STAR initiative had begun, 58% of whom reported having viewed the STAR. Most respondents who viewed the STAR found it to be helpful or very helpful in guiding warfarin management (67%), improving their workflow and efficiency (58%), and improving patient safety (77%). Approximately one‐third of respondents who had viewed the STAR (34%) reported that they selected a different dose than they would have chosen had the STAR not been available.

DISCUSSION

We developed a concise report that is seamlessly created and inserted into the discharge summary. Though the STAR was perceived as improving patient safety by ambulatory care providers, there was no impact on attaining a therapeutic INR after discharge. There are several possible explanations for a lack of benefit. Most notably, our intervention was comprised of a stand‐alone EMR‐based tool and focused on 1 component of the transitions process. Given the complexity of healthcare delivery and anticoagulant management, it is likely that broader interventions are required to improve clinical outcomes over the transition period. Potential targets of multifaceted approaches may include improving access to care, providing greater access to anticoagulation clinics, enhancing patient education, and promoting direct physician‐physician communication. Bundled interventions will likely need to include involvement of an interdisciplinary team, such as pharmacist involvement in the medication reconciliation process.

The transition period from the hospital to the outpatient setting has the potential to jeopardize patient safety if vital information is not reliably transmitted across venues.[5, 6, 7, 8] Forster and colleagues noted an 11% incidence of ADEs in the posthospitalization period, of which 60% were either preventable or ameliorable.[3] To decrease the risk to patient safety during the transitions period, the Transitions of Care Consensus policy statement by the Society of Hospital Medicine and other medical organizations called for incorporation of standard data transfer forms (templates and transmission protocols).[9] Despite the high risk and preventable nature of many of the events, few specific tools have been developed. As part of broader initiatives to improve the transitions process, the STAR may have the potential to be a means for health systems to improve the quality of the transition of care for patients on anticoagulants.

Our study has several limitations. First, it was performed at a single health system. It is unknown whether the EMR‐based report could be similarly employed at other systems. Second, our study was unable to assess clinical endpoints. Given the lack of effect on attaining a therapeutic INR, it is unlikely that downstream outcomes, such as thromboembolism, were impacted. Lastly, we were unable to examine whether our intervention improved the care of patients whose outpatient provider was external to our system.

The STAR is a concise tool developed to provide essential anticoagulant‐related information to ambulatory providers. Though the report was perceived as improving patient safety, our finding of a lack of impact on attaining a therapeutic INR after discharge suggests that the tool would need to be a component of a broader multifaceted intervention to impact clinical outcomes.