Objective The goal of this investigation was to determine whether automated approaches can learn patient-oriented care teams via utilization of an electronic medical record (EMR) system.#N##N#Materials and Methods To perform this investigation, we designed a data-mining framework that relies on a combination of latent topic modeling and network analysis to infer patterns of collaborative teams. We applied the framework to the EMR utilization records of over 10 000 employees and 17 000 inpatients at a large academic medical center during a 4-month window in 2010. Next, we conducted an extrinsic evaluation of the patterns to determine the plausibility of the inferred care teams via surveys with knowledgeable experts. Finally, we conducted an intrinsic evaluation to contextualize each team in terms of collaboration strength (via a cluster coefficient) and clinical credibility (via associations between teams and patient comorbidities).#N##N#Results The framework discovered 34 collaborative care teams, 27 (79.4%) of which were confirmed as administratively plausible. Of those, 26 teams depicted strong collaborations, with a cluster coefficient > 0.5. There were 119 diagnostic conditions associated with 34 care teams. Additionally, to provide clarity on how the survey respondents arrived at their determinations, we worked with several oncologists to develop an illustrative example of how a certain team functions in cancer care.#N##N#Discussion Inferred collaborative teams are plausible; translating such patterns into optimized collaborative care will require administrative review and integration with management practices.#N##N#Conclusions EMR utilization records can be mined for collaborative care patterns in large complex medical centers.
['You Chen', 'Nancy M. Lorenzi', 'Warren S. Sandberg', 'Kelly A. Wolgast', 'Bradley Malin']
Identifying collaborative care teams through electronic medical record utilization patterns