Dian A. Chase



Document Type


Degree Name



Dept. of Medical Informatics and Clinical Epidemiology.


Oregon Health & Science University


Background: Electronic health records (EHRs) and collaborative healthcare delivery are two innovations implemented to meet the triple aim: improved patient satisfaction, better population outcomes, and more efficient service delivery. Both represent significant changes in how healthcare is provided. With any significant innovation there will be unanticipated results, both positive and negative. With two significant innovations, we should expect unintended consequences from the interaction of the EHR with Patient Centered Medical Homes. The EHR, then, has the potential to be a facilitator or a barrier for increased collaboration. Thus far, the literature on this topic is scant. Objectives: In this dissertation we had three aims: 1) provide qualitative data to increase our understanding of the interaction between these two changes, 2) develop a tool to measure collaborative behaviors, and 3) analyze the association between the EHR as a communication channel and changes in collaboration behaviors between clinicians. Methods: Aim 1 used qualitative methods, the development of the scale (aim 2) used a mixed methods approach, and aim 3 relied on principal component analysis and multi-level regression techniques. Settings: Fourteen health care systems and clinics that had already implemented EHRs and were in the process of implementing patient centered medical homes. These settings were purposively selected to include a diversity of organizational models, geographic locations, sizes, and EHR vendors. Results: Aim 1: The EHR played several different roles in collaboration – repository, messenger, orchestrator, and monitor, with mixed success. As a repository, the EHR can help build common ground, but can also destroy trust when documentation quality is poor. As a messenger it is easy to use, at the risk of creating an illusion of communication. In the orchestrator role, the EHR has not yet fulfilled its promise in supporting coordination of care. Finally, in the monitor role it can help bring people together, but adaptation requires that people meet and talk rather than just send notes through the EHR. Aim 2: We were able to develop and validate a brief tool to measure frequency of collaboration behaviors (trust/respect, communication, coordination, and adaptation). We found that while these behaviors are linked, they are not prerequisites for one another. Aim 3: Principal components analysis divided the communication channels into EHR-based channels, primarily voice-based channels, and other written channels. We found that use of the EHR for communication did not increase trust, but did increase reported levels of communication frequency and coordination for collaborations outside the clinic only. Use of the EHR for communication also did not increase the frequency of reported adaptive behaviors. Increased use of voice-based channels of communication did increase reported frequency of adaptive behaviors. Discussion: Results from our qualitative and quantitative studies (Aim 1 and Aim 3) are very similar. Both studies found the EHR can both build and damage trust; it can facilitate communication (but not necessarily quality communication); it can help with coordination across distance, and it is not rich enough to facilitate adaptation. Because these studies were done in different settings and provider samples, this triangulation suggests the potential generalizability of study results. This research is only the start; are many confounders that need to be further explored. Future work includes examining the relationship between increased frequency of collaboration behaviors and triple aim results and finding ways changes in EHR design and implementation can increase collaboration. Conclusion: We have demonstrated, using different methodologies and in different clinics, that the EHR has the potential to affect how providers collaborate. We also provide a brief, unbiased, flexible tool to measure collaboration behaviors. Organizational leaders, technology designers, and implementation teams can use this tool to measure how their innovations affect collaborations. By doing so, they can better anticipate and monitor adverse effects and maximize the positive benefits of their work.




School of Medicine



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