Yiye Zhang1, Rema Padman. 1. Weill Cornell Medicine of Cornell University, 425 E 61st St, Ste 301, New York, NY 10065. E-mail: yiz2014@med.cornell.edu.
Abstract
OBJECTIVES: This study illustrates a systematic methodology to embed medical costs into the exact flow of clinical events associated with chronic care delivery. We summarized and visualized the results using clinical and cost data, with the goal of empowering patients and care providers with actionable information as they navigate through a multitude of clinical events and medical expenses. STUDY DESIGN: We analyzed the electronic health records (EHRs) and medication cost data of 288 patients from 2009 to 2011, whose initial diagnoses included chronic kidney disease stage 3, hypertension, and diabetes. METHODS: We developed chronological pathways of care and costs for each patient from EHR and medication cost data. Using a data-driven method called clinical pathway (CP) learning, which leverages statistical machine-learning algorithms, we categorized patients into clinically similar subgroups based on progressing clinical complexity and associated care needs. The CP-based subgroups were compared against cost-based subgroups stratified by quartiles of total medication costs, and visualized via pathways that are color-coded by costs. RESULTS: Our methods identified 3 CP-based, and 4 cost-based, patient subgroups. Two sets of subgroups from each approach indicated some clinical similarity in terms of average statistics, such as number of diagnoses and medication needs. However, the CP-based subgroups displayed significant variation in costs; conversely, large differences in clinical needs were observed among cost-based subgroups. CONCLUSIONS: This study demonstrates that CPs extracted from EHRs can be enhanced with appropriate cost information to potentially provide detailed visibility into the variability and inconsistencies in current best practices for chronic care delivery.
OBJECTIVES: This study illustrates a systematic methodology to embed medical costs into the exact flow of clinical events associated with chronic care delivery. We summarized and visualized the results using clinical and cost data, with the goal of empowering patients and care providers with actionable information as they navigate through a multitude of clinical events and medical expenses. STUDY DESIGN: We analyzed the electronic health records (EHRs) and medication cost data of 288 patients from 2009 to 2011, whose initial diagnoses included chronic kidney disease stage 3, hypertension, and diabetes. METHODS: We developed chronological pathways of care and costs for each patient from EHR and medication cost data. Using a data-driven method called clinical pathway (CP) learning, which leverages statistical machine-learning algorithms, we categorized patients into clinically similar subgroups based on progressing clinical complexity and associated care needs. The CP-based subgroups were compared against cost-based subgroups stratified by quartiles of total medication costs, and visualized via pathways that are color-coded by costs. RESULTS: Our methods identified 3 CP-based, and 4 cost-based, patient subgroups. Two sets of subgroups from each approach indicated some clinical similarity in terms of average statistics, such as number of diagnoses and medication needs. However, the CP-based subgroups displayed significant variation in costs; conversely, large differences in clinical needs were observed among cost-based subgroups. CONCLUSIONS: This study demonstrates that CPs extracted from EHRs can be enhanced with appropriate cost information to potentially provide detailed visibility into the variability and inconsistencies in current best practices for chronic care delivery.
Authors: Haochang Shou; Jesse Y Hsu; Dawei Xie; Wei Yang; Jason Roy; Amanda H Anderson; J Richard Landis; Harold I Feldman; Afshin Parsa; Christopher Jepson Journal: Clin J Am Soc Nephrol Date: 2017-07-27 Impact factor: 8.237
Authors: Luiza Siqueira do Prado; Samuel Allemann; Marie Viprey; Anne-Marie Schott; Dan Dediu; Alexandra L Dima Journal: BMJ Open Date: 2020-03-18 Impact factor: 2.692