A Novel Approach for the Investigation of Multidisciplinary Collaboration using Social Network Analysis on Electronic Health Record Data

Social network analysis (SNA) is widely used to study multidisciplinary collaboration among healthcare professionals. Most of the earlier works have however relied on survey and observational data, which do not scale, and have been limited to only descriptive studies without providing insight on how to improve patient outcomes. However, since the widespread adoption of electronic health records (EHR) for care delivery, there has been progressively increasing interest in exploiting the rich collection of activity data that are captured in EHR systems. Ability to exploit EHR data has the potential to offer unprecedented capacity to study and improve multidisciplinary teams. Unfortunately, the methodologic approaches used so far have had significant limitations, which have hampered the realization of this promise. In this dissertation, I describe a novel, process-mining based methodologic approach for applying SNA to study multidisciplinary collaboration using metadata of clinical activities captured in EHR. First, I described the process of linking the EHR activity metadata to trauma registry data, which is rich in quality clinical and encounter data to produce a linked dataset that was used for the dissertation. Second, I described and applied the methodology to identify collaborative EHR usage patterns and correlated them to patient outcomes. I demonstrated that a more collaborative EHR usage pattern were associated with shorter emergency department length of stay, in the process, identifying meaningful insight that can be the focus of further research or intervention. And finally, I described and applied a modification of the methodology to identify and compare diurnal variations in collaborative care teams at various locations in the hospital. I demonstrated the presence of multi-team systems and described how the composition and collaborative patterns of the multi-team systems varied with the time of day. This dissertation provides a promising new direction for harnessing EHR data, and in doing so, sets the stage for future studies

A Novel Approach for the Investigation of Multidisciplinary Collaboration using Social Network Analysis on Electronic Health Record Data

Social network analysis (SNA) is widely used to study multidisciplinary collaboration among healthcare professionals. Most of the earlier works have however relied on survey and observational data, which do not scale, and have been limited to only descriptive studies without providing insight on how to improve patient outcomes. However, since the widespread adoption of electronic health records (EHR) for care delivery, there has been progressively increasing interest in exploiting the rich collection of activity data that are captured in EHR systems. Ability to exploit EHR data has the potential to offer unprecedented capacity to study and improve multidisciplinary teams. Unfortunately, the methodologic approaches used so far have had significant limitations, which have hampered the realization of this promise. In this dissertation, I describe a novel, process-mining based methodologic approach for applying SNA to study multidisciplinary collaboration using metadata of clinical activities captured in EHR. First, I described the process of linking the EHR activity metadata to trauma registry data, which is rich in quality clinical and encounter data to produce a linked dataset that was used for the dissertation. Second, I described and applied the methodology to identify collaborative EHR usage patterns and correlated them to patient outcomes. I demonstrated that a more collaborative EHR usage pattern were associated with shorter emergency department length of stay, in the process, identifying meaningful insight that can be the focus of further research or intervention. And finally, I described and applied a modification of the methodology to identify and compare diurnal variations in collaborative care teams at various locations in the hospital. I demonstrated the presence of multi-team systems and described how the composition and collaborative patterns of the multi-team systems varied with the time of day. This dissertation provides a promising new direction for harnessing EHR data, and in doing so, sets the stage for future studies

Complexity of the pediatric trauma care process: Implications for multi-level awareness.

Trauma is the leading cause of disability and death in children and young adults in the US. While much is known about the medical aspects of inpatient pediatric trauma care, not much is known about the processes and roles involved in in-hospital care. Using human factors engineering (HFE) methods, we combine interview, archival document and trauma registry data to describe how intra-hospital care transitions affect process and team complexity. Specifically, we identify the 53 roles directly involved in patient care in each hospital unit and describe the 3324 total transitions between hospital units and the 69 unique pathways, from arrival to discharge, experienced by pediatric trauma patients. We continue the argument to shift from eliminating complexity to coping with it and propose supporting three levels of awareness to enhance the resilience and adaptation necessary for patient safety in health care, i.e. safety in complex systems. We discuss three levels of awareness (individual, team and organizational) and describe challenges and potential sociotechnical solutions for each. For example, one challenge to individual awareness is high time pressure. A potential solution is clinical decision support of information perception, integration and decision making. A challenge to team awareness is inadequate non-technical skills, e.g., leadership, communication, role clarity; simulation or another form of training could improve these. The complex, distributed nature of this process is a challenge to organizational awareness; a potential solution is to develop awareness of the process and the roles and interdependencies within it, by using process modeling or simulation