Priya Ramaswamy1,2, Jen J Gong3, Sameh N Saleh4,5,6, Samuel A McDonald4,7, Seth Blumberg8,9,10, Richard J Medford4,11, Xinran Liu1. 1. Division of Hospital Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California, USA. 2. Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, California, USA. 3. Center of Clinical Informatics and Improvement Research, Department of Medicine, University of California, San Francisco, San Francisco, California, USA. 4. Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA. 5. Section of Hospital Medicine, Division of General Internal Medicine, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA. 6. Department of Biomedical & Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. 7. Department of Emergency Medicine, University of Texas Southwestern Medical Center, Clinical Informatics Center, Dallas, Texas, USA. 8. Francis I. Proctor Foundation, University of California San Francisco, San Francisco, California, USA. 9. Centers of Disease Control's Modeling infectious Diseases (MInD) Healthcare Program, USA. 10. Department of Medicine, University of California San Francisco, San Francisco, California, USA. 11. Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern Medical Center, Clinical Informatics Center, Dallas, Texas, USA.
Abstract
OBJECTIVE: There is a need for a systematic method to implement the World Health Organization's Clinical Progression Scale (WHO-CPS), an ordinal clinical severity score for coronavirus disease 2019 patients, to electronic health record (EHR) data. We discuss our process of developing guiding principles mapping EHR data to WHO-CPS scores across multiple institutions. MATERIALS AND METHODS: Using WHO-CPS as a guideline, we developed the technical blueprint to map EHR data to ordinal clinical severity scores. We applied our approach to data from 2 medical centers. RESULTS: Our method was able to classify clinical severity for 100% of patient days for 2756 patient encounters across 2 institutions. DISCUSSION: Implementing new clinical scales can be challenging; strong understanding of health system data architecture was integral to meet the clinical intentions of the WHO-CPS. CONCLUSION: We describe a detailed blueprint for how to apply the WHO-CPS scale to patient data from the EHR.
OBJECTIVE: There is a need for a systematic method to implement the World Health Organization's Clinical Progression Scale (WHO-CPS), an ordinal clinical severity score for coronavirus disease 2019 patients, to electronic health record (EHR) data. We discuss our process of developing guiding principles mapping EHR data to WHO-CPS scores across multiple institutions. MATERIALS AND METHODS: Using WHO-CPS as a guideline, we developed the technical blueprint to map EHR data to ordinal clinical severity scores. We applied our approach to data from 2 medical centers. RESULTS: Our method was able to classify clinical severity for 100% of patient days for 2756 patient encounters across 2 institutions. DISCUSSION: Implementing new clinical scales can be challenging; strong understanding of health system data architecture was integral to meet the clinical intentions of the WHO-CPS. CONCLUSION: We describe a detailed blueprint for how to apply the WHO-CPS scale to patient data from the EHR.
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