Dataset Information

Learning bundled care opportunities from electronic medical records.

ABSTRACT: OBJECTIVE:The traditional fee-for-service approach to healthcare can lead to the management of a patient's conditions in a siloed manner, inducing various negative consequences. It has been recognized that a bundled approach to healthcare - one that manages a collection of health conditions together - may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled manner. In this study, we investigate if a data-driven approach can automatically learn potential bundles. METHODS:We designed a framework to infer health condition collections (HCCs) based on the similarity of their clinical workflows, according to electronic medical record (EMR) utilization. We evaluated the framework with data from over 16,500 inpatient stays from Northwestern Memorial Hospital in Chicago, Illinois. The plausibility of the inferred HCCs for bundled care was assessed through an online survey of a panel of five experts, whose responses were analyzed via an analysis of variance (ANOVA) at a 95% confidence level. We further assessed the face validity of the HCCs using evidence in the published literature. RESULTS:The framework inferred four HCCs, indicative of (1) fetal abnormalities, (2) late pregnancies, (3) prostate problems, and (4) chronic diseases, with congestive heart failure featuring prominently. Each HCC was substantiated with evidence in the literature and was deemed plausible for bundled care by the experts at a statistically significant level. CONCLUSIONS:The findings suggest that an automated EMR data-driven framework conducted can provide a basis for discovering bundled care opportunities. Still, translating such findings into actual care management will require further refinement, implementation, and evaluation.

SUBMITTER: Chen Y

PROVIDER: S-EPMC5771885 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Learning bundled care opportunities from electronic medical records.

Chen You Y Kho Abel N AN Liebovitz David D Ivory Catherine C Osmundson Sarah S Bian Jiang J Malin Bradley A BA

Journal of biomedical informatics 20171122

<h4>Objective</h4>The traditional fee-for-service approach to healthcare can lead to the management of a patient's conditions in a siloed manner, inducing various negative consequences. It has been recognized that a bundled approach to healthcare - one that manages a collection of health conditions together - may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled manner. In this study, we investigate if a data-drive ...[more]

PMID: 29174994

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Project description:BACKGROUND: Policies promoting widespread adoption of electronic medical records (EMRs) are premised on the hope that they can improve the coordination of care. Yet little is known about whether and how physician practices use current EMRs to facilitate coordination. OBJECTIVES: We examine whether and how practices use commercial EMRs to support coordination tasks and identify work-around practices have created to address new coordination challenges. DESIGN, SETTING: Semi-structured telephone interviews in 12 randomly selected communities. PARTICIPANTS: Sixty respondents, including 52 physicians or staff from 26 practices with commercial ambulatory care EMRs in place for at least 2 years, chief medical officers at four EMR vendors, and four national thought leaders. RESULTS: Six major themes emerged: (1) EMRs facilitate within-office care coordination, chiefly by providing access to data during patient encounters and through electronic messaging; (2) EMRs are less able to support coordination between clinicians and settings, in part due to their design and a lack of standardization of key data elements required for information exchange; (3) managing information overflow from EMRs is a challenge for clinicians; (4) clinicians believe current EMRs cannot adequately capture the medical decision-making process and future care plans to support coordination; (5) realizing EMRs' potential for facilitating coordination requires evolution of practice operational processes; (6) current fee-for-service reimbursement encourages EMR use for documentation of billable events (office visits, procedures) and not of care coordination (which is not a billable activity). CONCLUSIONS: There is a gap between policy-makers' expectation of, and clinical practitioners' experience with, current electronic medical records' ability to support coordination of care. Policymakers could expand current health information technology policies to support assessment of how well the technology facilitates tasks necessary for coordination. By reforming payment policy to include care coordination, policymakers could encourage the evolution of EMR technology to include capabilities that support coordination, for example, allowing for inter-practice data exchange and multi-provider clinical decision support.

Project description:BackgroundA new learning-based patient similarity measurement was proposed to measure patients' similarity for heterogeneous electronic medical records (EMRs) data.MethodsWe first calculated feature-level similarities according to the features' attributes. A domain expert provided patient similarity scores of 30 randomly selected patients. These similarity scores and feature-level similarities for 30 patients comprised the labeled sample set, which was used for the semi-supervised learning algorithm to learn the patient-level similarities for all patients. Then we used the k-nearest neighbor (kNN) classifier to predict four liver conditions. The predictive performances were compared in four different situations. We also compared the performances between personalized kNN models and other machine learning models. We assessed the predictive performances by the area under the receiver operating characteristic curve (AUC), F1-score, and cross-entropy (CE) loss.ResultsAs the size of the random training samples increased, the kNN models using the learned patient similarity to select near neighbors consistently outperformed those using the Euclidean distance to select near neighbors (all P values < 0.001). The kNN models using the learned patient similarity to identify the top k nearest neighbors from the random training samples also had a higher best-performance (AUC: 0.95 vs. 0.89, F1-score: 0.84 vs. 0.67, and CE loss: 1.22 vs. 1.82) than those using the Euclidean distance. As the size of the similar training samples increased, which composed the most similar samples determined by the learned patient similarity, the performance of kNN models using the simple Euclidean distance to select the near neighbors degraded gradually. When exchanging the role of the Euclidean distance, and the learned patient similarity in selecting the near neighbors and similar training samples, the performance of the kNN models gradually increased. These two kinds of kNN models had the same best-performance of AUC 0.95, F1-score 0.84, and CE loss 1.22. Among the four reference models, the highest AUC and F1-score were 0.94 and 0.80, separately, which were both lower than those for the simple and similarity-based kNN models.ConclusionsThis learning-based method opened an opportunity for similarity measurement based on heterogeneous EMR data and supported the secondary use of EMR data.

Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Dataset Information

Learning bundled care opportunities from electronic medical records.

Publications

Learning bundled care opportunities from electronic medical records.

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