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. 2017 Apr 1;24(e1):e111-e120.
doi: 10.1093/jamia/ocw124.

Identifying Collaborative Care Teams Through Electronic Medical Record Utilization Patterns

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Free PMC article

Identifying Collaborative Care Teams Through Electronic Medical Record Utilization Patterns

You Chen et al. J Am Med Inform Assoc. .
Free PMC article

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.

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).

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.

Discussion: 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.

Keywords: collaborative networks; data mining; electronic medical records; health care organization modeling.

Figures

Figure 1.
Figure 1.
The process by which HCO components are learned through EHR system utilization. ui = EMR user; pi = EMR of a patient; di = diagnosis code assigned to an EMR; oi = operational area affiliated with a user; topici = concept that represents a latent diagnostic pattern.
Figure 2.
Figure 2.
An example of the inference of collaborative networks from EMR utilization data.
Figure 3.
Figure 3.
The organizational components learned from 4 months of inpatient EMR utilization. Note that the smaller the distance between 2 operational areas is, the stronger the collaboration between the affiliated employees. The empty gaps between components are due to cutting the dendrogram above a value of 0.1. They correspond to the inducing of independent operational areas. The composition of each component, in terms of its operational areas in the HCO, can be found online in Supplement S1.
Figure 4.
Figure 4.
The strength of collaboration among the operational areas of a component as a function of its size. C* and C** correspond to the sets of components with 2 and 3 operational areas, respectively.
Figure 5.
Figure 5.
A network view of the organizational components inferred from EMR utilization records. A node corresponds to an operational area, and an edge is the interaction relation between 2 operational areas.
Figure 6.
Figure 6.
The hierarchical structure of the 14 operational areas that comprise C6, Oncology I.

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