title: Identifying collaborative care teams through electronic medical record utilization patterns.
abstract: 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.
  
  
  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).
  
  
  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.
  
  
  CONCLUSIONS
  Inferred collaborative teams are plausible; translating such patterns into optimized collaborative care will require administrative review and integration with management practices.
  
  
  CONCLUSIONS
  EMR utilization records can be mined for collaborative care patterns in large complex medical centers.
authors: Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.
authors: You
authors: Y
authors: Chen
authors: Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.|School of Nursing, Vanderbilt University.
authors: Nancy M
authors: NM
authors: Lorenzi
authors: Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.|Department of Anesthesiology, Vanderbilt University.
authors: Warren S
authors: WS
authors: Sandberg
authors: School of Nursing, Vanderbilt University.|Healthcare Leadership Program, School of Nursing, Vanderbilt University.
authors: Kelly
authors: K
authors: Wolgast
authors: Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.|Department of Electrical Engineering and Computer Science, Vanderbilt University.
authors: Bradley A
authors: BA
authors: Malin
mesh_terms: D003299:Cooperative Behavior; D057225:Data Mining; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D007400:Interprofessional Relations; D010348:Patient Care Team; D018802:Patient-Centered Care
keywords: collaborative networks; data mining; electronic medical records; health care organization modeling