TY - JOUR
T1 - Identifying collaborative care teams through electronic medical record utilization patterns
AU - Chen, You
AU - Lorenzi, Nancy M.
AU - Sandberg, Warren S.
AU - Wolgast, Kelly
AU - Malin, Bradley A.
N1 - Funding Information:
This research was supported, in part, by the National Institutes of Health under grants R00LM011933 and R01LM010685.
Funding Information:
We would like to thank Dr Mark Frisse for providing constructive directions. We further thank Dr Matthew Rioth and Dr Travis Osterman for detailed interpretation of the inferred oncology organizational component. The datasets used for the analyses described were obtained from Vanderbilt University Medical Center’s BioVU, which is supported by institutional funding and by Vanderbilt Clinical and Translational Science Awards grant ULTR000445 from the National Center for Advancing Translational Sciences and the National Institutes of Health.
Publisher Copyright:
© 2016 The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
PY - 2017/4/1
Y1 - 2017/4/1
N2 - 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.
AB - 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.
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U2 - 10.1093/jamia/ocw124
DO - 10.1093/jamia/ocw124
M3 - Article
C2 - 27570217
AN - SCOPUS:85035796987
SN - 1067-5027
VL - 24
SP - e111-e120
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - e1
ER -