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A Mixed-Method Study of Practitioners' Perspectives on Issues Related to EHR Medication Reconciliation at a Health System

Rangachari, Pavani, PhD; Dellsperger, Kevin C., MD, PhD; Fallaw, David, MD; Davis, Ian, MD; Sumner, Michael, MD; Ray, Walter, CISSP; Fiedler, Shashana, MPH; Nguyen, Tran, MPH; Rethemeyer, R. Karl, PhD

Quality Management in Healthcare: April/June 2019 - Volume 28 - Issue 2 - p 84–95
doi: 10.1097/QMH.0000000000000208
Original Research
Open
SDC

Background: In an effort to reduce medication discrepancies during transitions of care and improve accuracy of the patient's medication list, AU Health conducted a study to identify a comprehensive set of issues related to electronic health record (EHR) medication reconciliation (MedRec) from the perspective of practitioners directly involved in the EHR MedRec process.

Methods: An exploratory mixed-method design was used. The 2-round study included 15 individual interviews, followed by a survey of 200 practitioners (ie, physicians, nurses, and pharmacists) based in the outpatient and inpatient medicine service at AU Health.

Results: Thematic analysis of interview data identified 55 issue items related to EHR MedRec under 9 issue categories. The survey sought practitioners' importance rating of all issue items identified from interviews. A total of 127 (63%) survey responses were received. Factor analysis served to validate the following 6 of the 9 issue categories, all of which were rated “important” or higher (on average), by over 70% of all respondents: (1) care coordination (CCI); (2) patient education (PEI); (3) ownership and accountability (OAI); (4) processes-of-care (PCI); (5) IT-related (ITRI); and (6) workforce training (WTI). Significance testing of importance rating by professional affiliation revealed no statistically significant differences for CCI and PEI, and some statistically significant differences for OAI, PCI, ITRI, and WTI.

Conclusion: There were 2 key gleanings from the issues related to EHR MedRec unearthed by this study: (1) there was an absence of shared understanding among practitioners, of the value of EHR MedRec in promoting patient safety, which contributed to workarounds, and suboptimal use of the EHR MedRec system; and (2) there was a sociotechnical dimension to many of the issues, creating an added layer of complexity. These gleanings, in turn, provide insights into best practices for managing both (1) clinical transitions of care in the EHR MedRec process and (2) sociotechnical challenges encountered in EHR MedRec implementation.

College of Allied Health Sciences, Augusta University, Georgia (Dr Rangachari and Mss Fiedler and Nguyen); AU Health, Department of Medicine, Medical College of Georgia, Augusta University, Georgia (Drs Dellsperger, Fallaw, Davis, and Sumner); Health IT Division, Augusta University, Georgia (Mr Ray); and Rockefeller College of Public Affairs & Policy, University at Albany, State University of New York (Dr Rethemeyer).

Correspondence: Pavani Rangachari, PhD, College of Allied Health Sciences, Augusta University, 987 St Sebastian Way, Augusta, GA 30912 (prangachari@augusta.edu).

This study was supported by grant number R21HS024335 from the Agency for Healthcare Research and Quality (AHRQ).

The authors wish to thank the senior leadership, staff, and clinicians at AU Health for their support and collaboration in enabling the research team to conduct this study. We are also grateful to the AHRQ for providing funding support for this project.

The study was approved by the Institutional Review Board (IRB) at Augusta University. IRB approval was obtained prior to data collection.

All authors listed in this article have contributed sufficiently to the project to be included as authors. Pavani Rangachari is the primary author of this article and principal investigator (PI) on this project; Kevin C. Dellsperger serves as a project coinvestigator and contributed substantially to the development of the manuscript; David Fallaw serves as a project coinvestigator and provided analytic support for the manuscript; Ian Davis serves as a project coinvestigator and provided analytic support for the manuscript; Michael Sumner serves as a project coinvestigator and provided analytic support for the manuscript; Walter Ray serves as a health IT/EHR expert for the project and provided analytic support for the manuscript; Shashana Fiedler serves as a Graduate Research Assistant for the project and provided analytic support for the manuscript; Tran Nguyen serves as a graduate research assistant for the project and provided analytic support for the manuscript; and R. Karl Rethemeyer serves as a primary statistical consultant for the project and contributed substantially to the development of the manuscript.

The authors declare no conflicts of interest.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Health care delivery and payment reform efforts are increasingly focused on improving quality and safety during transitions of care, when patients are most vulnerable to medical errors.1 Medication errors, in particular, are common at hospital admission and discharge, and are a major contributor to adverse patient outcomes and increased spending associated with transitions of care.2,3 The risk of medication errors is heightened during care transitions because clinicians, and in some cases patients, do not have access to accurate up-to-date medication lists. This can result in the inadvertent addition, omission, or duplication of medications, resulting in “unintended discrepancies” between what patients should be prescribed and what they are actually prescribed.4–6

To help prevent medication errors and discrepancies during transitions of care, patient safety advocates have promoted the use of medication reconciliation or “MedRec.”7,8 MedRec refers to the process of creating the most complete and accurate list of a patient's current medications, comparing the list to those in the patient's records, and communicating the final up-to-date list to the patient, family, caregivers, and the next providers of care. Since 2005, MedRec has been part of the Joint Commission's hospital accreditation program, and more recently, it has become part of the “electronic health record (EHR) meaningful use” requirements.9,10

Despite the regulatory impetus toward EHR MedRec, hospital adherence has lagged for one chief reason—low physician engagement, which, in part, stems from lack of professional consensus about which physician (eg, hospital vs community physician) is responsible for managing a patient's medication list, and the value of MedRec as a clinical tool for improving quality of care.11–15 Moreover, within the hospital context, the assignment of MedRec responsibilities among provider subgroups—multiple physicians, nurses, and pharmacists—is often unclear, leading to inefficiency and potential for error.16–18 Not surprisingly therefore, a recent national study found that although hospital EHR vendors have been enhancing MedRec functionality over time, more than a third of the hospitals still use partially paper-based processes at admission, discharge, or both.13

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PURPOSE OF THE STUDY

The Augusta University Health System, AU Health, has invested in certified EHR technology throughout its system, which includes an academic medical center and over 80 satellite outpatient clinics. Similar to issues faced by other hospitals, there is consensus among AU Health administrators that a key challenge being encountered at the institution is that physicians who did not originally order the drugs in question are resistant to discontinuing those medications at discharge, leading to frustrated patients with incomplete and inaccurate medication lists. The EHR system requires clinicians to mark MedRec as “complete” with the press of a radio-button before patients can be officially discharged from the facility. However, in 2016, AU Health leadership estimated MedRec to be accurately completed (ie, free of discrepancies between the patient's home and hospital medication lists in regard to drugs, dosages, and frequencies), for less than 25% of discharged cases. During the same period, the average monthly patient satisfaction score for medication instructions (“medications and care at home were explained to me in a way I could understand”) was at the 25th percentile for outpatient visits, 40th percentile for inpatient discharges, and 2nd percentile for the emergency department (ED).

In an effort to reduce medication discrepancies during care transitions, and improve accuracy of the patient's active medication list, AU Health recently conducted a study to identify a comprehensive set of issues related to EHR MedRec from the perspective of various practitioner groups (physicians, nurses, and pharmacists) directly involved in the EHR MedRec process. Such an effort was deemed as an essential first step for understanding the problem, and laying a foundation for identifying effective strategies to address the problem. This article seeks to describe the methods and results of this study.

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DESIGN/APPROACH

The study began after institutional review board approval was obtained from Augusta University. An exploratory mixed-method approach was used for data collection and analysis.19 The study included 2 rounds of data collection in 2017. Round 1 consisted of individual interviews with a small group of physicians, nurses, and pharmacists; while round 2 consisted of a survey of a larger group of physicians, nurses, and pharmacists, based in the inpatient and outpatient medicine service at AU Health.

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Study setting and participants

Based in Augusta, Georgia, AU Health is a health system offering comprehensive primary, specialty, and subspecialty care in the region. Facilities include a 478-bed AU Medical Center, more than 80 outpatient practice sites, a Critical Care Center housing a regional trauma center, and a 154-bed Children's Hospital. AU Health uses certified EHR technology (powered by Cerner Inc), throughout its health system. By definition, the level of EHR implementation at AU Health is “comprehensive” (HIMSS level 6). As part of the EHR, providers can electronically prescribe medications through SureScripts, which enables them to view patients' medication history, including prescriptions filled at participating pharmacies.

The population of interest for this study was all physicians, nurses, and pharmacists in the inpatient medicine and outpatient medicine service of AU Health, including internal medicine, family medicine, hospitalist, and cardiology services, with cardiology being the only medicine subspecialty included in the study. There were a total of 215 practitioners (including physicians, nurses, and pharmacists) in the population of interest, and all were included in the study, with 15 included in round 1 and 200 included in round 2.

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Round 1 approach

Round 1 consisted of 15 individual open-ended interviews with practitioners based in inpatient and outpatient medicine units, including 3 internal medicine physicians, 3 cardiology physicians, 3 hospitalists, 3 staff nurses, and 3 pharmacists. Participants were asked one question: “What are the key issues you have encountered with EHR MedRec at your institution?” Interviews lasted approximately 30 minutes each, and were audio-recorded, with permission of participants.

Audio-recordings from the 15 interviews were transcribed to text, to facilitate thematic analysis using an emergent manual coding process. The thematic analysis team consisted of 4 researchers, including 1 MD in internal medicine, 1 PhD in health management, and 2 PhD students in biomedical sciences, hereafter referred to as coders 1 to 4, respectively. To begin with, 2 coders (coders 1 and 2) completed all key phases of thematic analysis, to develop an initial coding scheme for identifying key themes and concepts. This included (1) data familiarization; (2) manual generation of initial codes; (3) searching for themes; (4) reviewing themes; and (5) defining and naming themes.20–22 The initial coding scheme was used by 2 other coders (coders 3 and 4) to code approximately 33% of the interview data (ie, data from 5 of the 15 interviews). Analysis of initial intercoder agreement among the 4 coders revealed a 75% match. Follow-up discussions among the 4 coders resulted in further refinements to the coding scheme. The revised coding scheme, in turn, was used by all 4 coders to recode 100% the interview data. This iterative process of coding, evaluation, discussion, and refinements to the coding scheme was repeated until final intercoder agreement among the 4 coders was over 95%.

The thematic analysis process resulted in identifying a total of 55 individual issue items related to EHR MedRec, grouped into 9 issue categories (themes), including (1) care coordination issues (CCI); (2) patient education issues (PEI); (3) ownership and accountability issues (OAI); (4) process-of-care issues (PCI); (5) IT-related issues (ITRI); (6) workforce training issues (WTI); (7) workflow issues (WI); (8) resources issues (RI); and (9) documentation issues (DI). A full listing of the 55 issue items grouped into 9 issue categories is provided in Table 1.

Table 1

Table 1

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Round 2 approach

Round 2 consisted of a survey administered to the larger population of interest (ie, 200 practitioners) including 50 nurses, 50 pharmacists, and 100 physicians (including residents, hospitalists, and physicians from internal medicine, family medicine, and cardiology services), based in inpatient and outpatient medicine units at AU Health. To ensure a fair assessment and validation of the issues in round 2, the 15 individuals who completed the interviews in round 1 were excluded from the survey (round 2). The survey was conducted electronically, and contained 3 sections:

  1. Section 1 captured participants' demographic characteristics, including age, gender, race, unit affiliation, professional affiliation, and length of tenure at AU Health.
  2. Section 2 captured participants' importance rating (on a 7-point Likert scale) of the 55 issue items related to EHR MedRec (grouped into 9 “issue categories”), identified in round 1 interviews.
  3. Section 3 was open-ended, and captured comments and suggestions from participants.

The survey received a total of 127 responses, translating to a 63% response rate. The STATA 14 software package was used for analysis. A variety of descriptive analyses were conducted to evaluate respondents' importance ratings and demographic characteristics, with the latter including unit affiliation and professional affiliation.

  • Unit affiliation consisted of the following 5 categories for analysis: (1) inpatient; (2) outpatient subspecialty; (3) outpatient primary care; (4) emergency department; and (5) outpatient pharmacy.
  • Professional affiliation consisted of the following 6 categories for analysis: (1) internal medicine physician; (2) family medicine physician; (3) cardiology physician; (4) hospitalist; (5) pharmacist; and (6) nurse. The first 3 physician categories included residents and attending faculty members.

Next, factor analysis was used to validate the issue categories and generate an index for the validated issue categories, to facilitate assessment of differences in importance rating by unit and professional affiliation. Factor analysis served the purpose of assessing whether issue items grouped under each issue category were correlated or loading on to a single common factor. This involved generating a principal factors output, including the eigenvalue for each factor.23 An eigenvalue of more than 1 (for a factor) means that the variables (issue items) are meaningfully correlated with that particular factor. If the eigenvalue indicates that the variables are loading on to a single factor, the next step is to calculate the Cronbach α to assess whether all issue items included in a single issue category are sufficiently related to one another to be considered part of a single index. The common rule is that the Cronbach α should be at least 0.70, with many analysts seeking 0.75 or higher before the set of items is accepted as being related to a single latent factor.24,25 Once the factor loadings were verified through eigenvalue and Cronbach α, the next decision to make was whether or not to generate an index. An index was created if the eigenvalue was more than 1 and the Cronbach α was more than 0.75. The creation of indexes, in turn, enabled significance testing of differences in importance rating by unit and professional affiliation for each index, using the analysis of variance (ANOVA) technique. Finally, the open-ended comments section was reviewed to ensure that no additional issue items related to EHR MedRec (not included in the survey) were reported by participants.

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RESULTS

Round 1 results

The thematic analysis of interviews helped identify 55 issue items related to EHR MedRec, which were, in turn, grouped into 9 issue categories or themes. To begin with, participants discussed issues related to care coordination across multiple settings (eg, across the hospital, the outpatient cardiology clinic, and the primary care clinic). For example, one cardiologist commented that “no arrangements are made for post-discharge follow-up with the patient's clinic to monitor interactions of new medicines with the patient's existing medications. It is not sufficient if medications are managed well during the hospital stay alone. It is more important for patients to be in control after they have transitioned to the community and their usual customary care.” A pharmacist discussed how the “current process of ‘discharge-then-scripts,’ poses a serious time constraint, which results in pharmacy not being able to meet many discharged patients before they leave the facility, at the most crucial point of transition, before they return to the community. Several opportunities for reconciliation (related to insurance, drug brand etc.) and patient education are missed by pharmacy, as a result.”

Participants from all groups also discussed several issues related to patient education. One cardiologist mentioned that “Patients do not understand when a medication is being replaced by another one, i.e., that they need to stop taking a previous prescription and start taking the more updated one; this is particularly an issue with beta-blockers and ACE [angiotensin-converting enzyme] inhibitors.” Similarly, within the hospital setting, hospitalists, nurse, and pharmacists commented on how patients' lack of awareness of the medications, they are on, can result in providers being unable to compile a complete and accurate medication list.

Participants also discussed concerns related to ownership and accountability. For example, a cardiologist mentioned that “a patient may have a beta-blocker drug that is on hospital formulary ordered to replace the home beta-blocker drug, during the hospital stay, but the former may not be discontinued at discharge, allowing both formulary and home (i.e., duplicate) prescriptions of the beta-blocker to be continued on the patient medication list, creating room for confusion for the patient/family and the next provider of care.” A nurse mentioned that “since you cannot indicate on the medication list that a patient is ‘not taking’ a medication, without removing it from the list; these notes are usually made by nurses within the medical record for the attending doctor to review and discontinue medications as appropriate. However, these notes are often missed by doctors.” Correspondingly, several drugs that that patient is “not taking” and should be discontinued remain on the list even after discharge, causing potential for error at the next level of care. Similarly, a pharmacist discussed how the “lack of role clarity prompts multiple practitioners (nurses, pharmacists, residents) to go through the throes of reconciliation, and yet, nobody takes full responsibility for inputting accurate information related to drug names, frequency, and dosages. This problem in turn, results in multiple medication lists for one patient, which affects not only patient safety, but also patient satisfaction during hospital stay.”

There were also a number of issues related to process of care expressed by participants. For example, an internal medicine physician mentioned that “MedRec is often performed only at admission or discharge; and not during the hospital stay, to supplement data gathered at admission, based on new information from family; which in turn, affects the accuracy of reconciliation at discharge.” Participants from all practitioner groups also described how the lack of a comprehensive medication history at the time of admission (front end) could result in duplication of work, confusion related to the regimen, and an inaccurate or incomplete medication list at the time of discharge (back end).

Several interviews also revealed concerns with IT-related issues. For example, an internal medicine physician commented that “patients are highly portable; however, there is considerable lack of EHR interoperability among hospitals within the local community.” Importantly, participants from all groups commented on providers' unanimous recognition of the need for workarounds with the EHR MedRec system: “Simply having a check box against discharge reconciliation does not mean it's done. We understand that the button needs to be checked for us to bill for it, but we also know that the medication list is likely to be inaccurate or incomplete.”

Participants also spoke of workforce training issues: An internal medicine physician mentioned that “often times, residents do not understand the importance of continuously updating the medication list even while the patient is in the facility, to facilitate smooth discharge and prevent readmissions.” A pharmacist mentioned that “pharmacy could play a key role in educating residents on selecting the right drug and dose; improving drug documentation; and identifying substitutes when patients cannot afford the drugs prescribed.”

Participants from all groups also spoke of workflow issues. For example, a nurse mentioned that “in a busy clinic or ED, data on medication history may not be recorded comprehensively, which in turn leads to incomplete and sometimes false medication information inputted into a patient's EHR.” Finally, yet importantly, practitioners commented on resource issues and documentation issues. In regard to the former, a hospitalist physician and a pharmacist commented on the “need for a supplemental staffing in the ED (e.g., pharmacy techs) to assist with MedRec during triage,” and in regard to the latter, a pharmacist mentioned the need for better documentation on “details related to medications names, types, and dosages on the discharge medication list.”

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Round 2 results

Results from the interviews provided a foundation for conducting a survey of a larger group of 200 practitioners (physicians, nurses, and pharmacists) based in inpatient and outpatient medicine units at AU Health. The survey received a total of 127 responses, translating to a 63% response rate. Table 2 summarizes the demographic characteristics of respondents.

Table 2

Table 2

Descriptive analysis of importance ratings by respondents revealed that all 55 issue items related to EHR MedRec were rated as “important” or “extremely important” (ie, “6” or “7” respectively, on a 7-point Likert scale), by at least 70% of all respondents. The Figure shows the average importance rating of issue items within each of the 9 issue categories.

Figure

Figure

Table 3 shows results of factor analysis for the 9 sets of issue items (or issue categories). The second column shows the eigenvalue generated by factor analysis for the first factor for each set of issue items. For example, for CCI, the eigenvalue for the first factor generated from the issue items was 2.41. No other factor from the output had an eigenvalue greater than 1.00, so the rest were disregarded. This suggested that the issue items were all loading on to a single common underlying concept. As indicated in Table 3, all sets of issue items, except DI and RI, had an eigenvalue more than 1. The next step was to calculate the Cronbach α test for all sets of issue items, to assess interitem covariance. As indicated in Table 3, 3 of the 9 issue categories did not meet the minimum cutoff for the Cronbach α of 0.75, including DI, RI, and WI. For the remaining 6 out of the original 9 issue categories, the Cronbach α of more than 0.75 suggested that these sets of issue items were strongest in capturing a single latent concept. The next step was to generate indexes for these 6 validated issue categories, which, in turn, enabled assessment of differences in importance rating by unit and professional affiliation, for each index, through the ANOVA technique.

Table 3

Table 3

Table 4 shows results of the index ANOVA analysis by unit and professional affiliation. The results showed no statistically significant differences (at the 5% significance level), in importance rating by unit affiliation for any of the 6 indexes. However, the results were mixed by professional affiliation. While there were no statistically significant differences, in importance rating by professional affiliation, for 2 indexes, CCI and PEI, there were some statistically significant differences for the 4 remaining indexes, OAI, PCI, ITRI, and WTI.

Table 4

Table 4

Table 5 shows the results of post-ANOVA regression analysis for the 4 significant indexes by professional affiliation. These results help understand which practitioner groups differed significantly from the base category of internal medicine physicians. For example, results for OAI index indicate that nurses and pharmacists rated issues with higher importance compared with internal medicine physicians, whereas results for ITRI index indicate that family medicine and hospitalist physicians rated issues with lower importance compared with internal medicine physicians; and for the WTI index, family medicine physicians rated issues with lower importance compared with internal medicine physicians.

Table 5

Table 5

To identify the specific issue items within the indexes where these differences occurred, we performed an ANOVA analysis by issue item for the 4 indexes. This analysis revealed a total of 7 issue items showing significant differences by professional affiliation, including 2 in the OAI issue category (OAI-1 and OAI-4); 1 in PCI (PCI-6); 2 in ITRI (ITRI-4 and ITRI-6); and 1 in WTI (WTI-2 and WTI-3). Further analysis of the actual importance ratings for these issue items revealed that nurses and pharmacists had a higher proportion of respondents rating the 2 OAI issues as “extremely important” compared with a higher proportion of internal medicine physicians rating them as “important.” Similarly, for the ITRI and WTI issue items, the differences boiled down to a higher proportion of hospitalists and/or family medicine physicians rating these issues as “important” compared with a higher proportion of internal medicine physicians rating them as “extremely important.” In summary, although the rating differences by professional affiliation were statistically significant for a small number of issue items, they were not practically meaningful.

The analysis of survey results concluded with a review of open-ended comments received from respondents. We received several comments that served to corroborate the various issue items related to EHR MedRec included on the survey. Importantly, the comments helped verify that no additional issue items related to EHR MedRec (not included on the survey) were identified by respondents. This, in turn, served to reinforce the gleaning that the mixed-method approach had indeed served the purpose of identifying a comprehensive set of issues related to EHR MedRec encountered by multiple practitioner groups at AU Health.

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DISCUSSION

The issues identified in this study related to EHR MedRec shed light on not only the complexities inherent in clinical transitions of care in the MedRec process, but also the complexities arising from practitioners' interaction with the EHR system, or sociotechnical challenges in implementing EHR MedRec at the study institution. In regard to the latter, the results suggest that sociotechnical challenges may be a “cross-cutting” theme across a majority of issue categories. To provide an example of an issue item under the core issue category of “ownership and accountability” with an added sociotechnical dimension, it would be useful to consider the issue of “not taking” meds not being discontinued at discharge (OAI-2). Currently, the sociotechnical dimension to the issue is that the “not taking” information is not visible on the active medication list within the EHR system. However, even if this issue were to be addressed through EHR system redesign (to facilitate the provider access to this information on the active medication list), the core issue of “ownership and accountability” could persist on the part of the treatment provider, in deciding whether or not to use the information to modify the medication list. There may be resistance on the part of providers to discontinue a “not taking” medication, simply because they did not originally order it. This helps illustrate how sociotechnical challenges could create an added layer of complexity to issues related to EHR MedRec unearthed by this study, making it a cross-cutting theme across several issue categories.

In addition, regardless of whether or not there were statistically significant differences in importance rating by professional affiliation, the full stock of issues related to EHR MedRec, unearthed by this study, is reflective of a central theme (ie, the absence of shared understanding across multiple practitioner groups), including:

  1. Absence of shared understanding of what the responsibilities are of each practitioner group in the MedRec process. For example, admitting providers are not clear on who does what in the medication history and admission process.
  2. Absence of shared understanding of how the EHR MedRec system is being used by other clinicians. For example, outpatient subspecialists are not convinced that MedRec marked as complete in the system at the time of hospital discharge translates to a complete and accurate medication list.
  3. Absence of shared understanding of the why (ie, the value of EHR MedRec in preventing discrepancies and promoting patient safety). For example, outpatient providers expressed the concern that inpatient clinicians may not realize the importance of ensuring a medication list that is free of discrepancies at discharge, to enable patients to effectively transition into the community.
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Practice implications

The results of this study provide substantive implications for practice by providing insight into:

  1. Best practices for managing clinical transitions of care in the EHR MedRec process; and
  2. Best practices for managing socio-technical challenges in EHR MedRec implementation.

In regard to the former, the study suggests that clinical transitions of care in the EHR MedRec process could be effectively managed by creating shared understanding of the process for MedRec and responsibilities for each step of the process among all practitioner (stakeholder) groups involved in the EHR MedRec process. In addition, it would be important to create shared understanding of the value and importance of EHR MedRec in generating an accurate medication list to promote patient safety.

In regard to best practices for managing sociotechnical challenges encountered in EHR MedRec implementation, the study helps understand the importance of concurrent attention to workflow redesign and EHR system redesign for addressing issues related to EHR MedRec. For example, one issue that surfaced under “ownership and accountability” was that physicians need to review nurses' notes on compliance status to know whether a patient is “not taking” a medication, to decide whether or not to discontinue the medication at the time of discharge. This issue could be resolved by physicians and nurses taking ownership in redesigning the patient care workflow to include a compliance status check prior to completing MedRec. Likewise, the issue could also be addressed by redesigning the system to enable compliance status to be accessed within the medication list. However, even after system redesign, the “ownership and accountability” issue might persist; for example, physicians may resist discontinuing a “not taking” medication if they did not previously order it. Nevertheless, this example highlights how improvements could be made through redesign of the workflow and/or the EHR system. It also suggests that improvements to both patient care workflow and the EHR system may need to go hand in hand to facilitate meaningful use and successful implementation of EHR MedRec.

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CONCLUSION

This article describes the results of a study that sought to identify a comprehensive set of issues related to EHR MedRec from multiple practitioner groups at a health system. There were 2 key lessons learned from the study: (1) there was a sociotechnical dimension to all issues related to EHR MedRec unearthed by the study, creating an added layer of complexity; and (2) there was an absence of shared understanding among practitioners in regard to the value of EHR MedRec in promoting patient safety, which, in turn, led to workarounds and suboptimal use of the EHR MedRec system. Results provide insights into best practices for managing both (1) clinical transitions of care in the EHR MedRec process and (2) sociotechnical challenges encountered in EHR MedRec implementation. Taken together, the results suggest that strategies and interventions seeking to concurrently (1) enable shared understanding of the value of EHR MedRec across practitioner groups and (2) address sociotechnical challenges through workflow and system redesign, by way of practitioner/stakeholder engagement, may be most effective for promoting meaningful use and successful implementation of EHR MedRec in health care organizations.

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Keywords:

change implementation; electronic health records; medication reconciliation; patient safety; quality improvement; transitions of care

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