Health information technology (HIT) can lower costs, strengthen productivity, and promote safety. To realize such benefits on a large scale, healthcare organizations (HCOs) are adopting electronic health records (EHRs) to provide various capabilities. Yet, as EHRs and the healthcare workforce grow in diversity, so does their complexity. This is a concern because evidence suggests complex HIT can interrupt care delivery, contribute to medical errors, and expose patient data to privacy breaches. Moreover, such events tend to be discovered only after they transpire en masse, leading to negative media coverage, loss of patients'trust, and sanctions. Federal regulations now enable patients to receive accountings of who accessed their medical records during treatment, payment, and operations related activities. Yet, for patients to make sense of such accountings, they need to be provided with explanations regarding the extent to which accesses are normal in the context of routine HCO activities. We believe that relating specific accesses to patterns of healthcare operations can help explain how medical records are utilized. Unfortunately, many of the aforementioned problems manifest because EHR utilization patterns rarely guide the design and refinement of healthcare management practices. Thus, the overarching objective of our research is to develop novel strategies to automatically learn HCO behavior based on EHR usage. The past several years has witnessed a flurry of activity in this field, but it remains in its infancy and has only scratched the surface of care patterns and the types of anomalies that can be detected. Through this project, we propose to develop anomaly detection methods that integrate the semantics of healthcare operations and allow for the detection of workflows over time. This will enable HCOs and patients to audit in a meaningful way. Moreover, we believe the innovation and dissemination of such data mining strategies will enable HCOs to detect anomalous events that indicate system misuse and patients who require special attention, but also effectively audit business practices and discover inefficient workflows.
The specific aims of this project are (1) to develop machine learning approaches, based on intrasession utilization patterns, to streamline EHR interface configuration and detect anomalous sessions, (2) to design a data mining framework, based on intersession EHR access patterns, to characterize HCO departmental interactions in patient treatment and detect anomalous events, and (3) to infer patient management pathways to consolidate redundant processes and detect deviations from anticipated workflows. In support of these goals, we will evaluate, compare, and contrast the workflows and anomalies in the EHR systems of two large medical centers. Additionally, we will ensure that our methods are integrated into an open source software system that can assist HCOs to extract, transform, and load (ETL) access data from EHRs, analyze such data for anomalies, and visualize the results in interfaces that enable review by healthcare administrators and patients. In doing so, we will be able to compare and contrast behavior of the workflows and multiple institutions and develop methods that appropriately generalize across EHR systems.
As electronic health record systems, and healthcare organizations, grow in diversity, so do their complexity, which can lead to inefficient documentation, management of patients, and expose patient data to privacy breaches. In this research, we will develop technologies, to be disseminated through an open source software suite, to learn patterns associated with healthcare operations, upon which anomaly detection techniques can be based. The specific goals of this project are to 1) model HCO users'intrasession behaviors when interacting with an EHR, 2) learn interdepartmental relations based on the accesses of common patients, and 3) infer patient management pathways based on the sequence of accesses to a patient's record.
| Li, Thomas; Gao, Cheng; Yan, Chao et al. (2018) Predicting Neonatal Encephalopathy From Maternal Data in Electronic Medical Records. AMIA Jt Summits Transl Sci Proc 2017:359-368 |
| Chen, You; Lorenzi, Nancy M; Sandberg, Warren S et al. (2017) Identifying collaborative care teams through electronic medical record utilization patterns. J Am Med Inform Assoc 24:e111-e120 |
| Hedda, Monica; Malin, Bradley A; Yan, Chao et al. (2017) Evaluating the Effectiveness of Auditing Rules for Electronic Health Record Systems. AMIA Annu Symp Proc 2017:866-875 |
| Gao, Cheng; Kho, Abel N; Ivory, Catherine et al. (2017) Predicting Length of Stay for Obstetric Patients via Electronic Medical Records. Stud Health Technol Inform 245:1019-1023 |
| Chen, Robert; Sun, Jimeng; Dittus, Robert S et al. (2016) Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program. IEEE J Biomed Health Inform : |
| Yan, Chao; Chen, You; Li, Bo et al. (2016) Learning Clinical Workflows to Identify Subgroups of Heart Failure Patients. AMIA Annu Symp Proc 2016:1248-1257 |
| Chen, You; Xie, Wei; Gunter, Carl A et al. (2015) Inferring Clinical Workflow Efficiency via Electronic Medical Record Utilization. AMIA Annu Symp Proc 2015:416-25 |
| Chen, You; Ghosh, Joydeep; Bejan, Cosmin Adrian et al. (2015) Building bridges across electronic health record systems through inferred phenotypic topics. J Biomed Inform 55:82-93 |
| Chen, You; Lorenzi, Nancy; Nyemba, Steve et al. (2014) We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records. Int J Med Inform 83:495-506 |
| Chen, You; Nyemba, Steve; Zhang, Wen et al. (2012) Specializing network analysis to detect anomalous insider actions. Secur Inform 1: |
Showing the most recent 10 out of 19 publications
