Automating Clinical Score Calculation within the Electronic Health Record

Christopher Aakre; Mikhail Dziadzko; Mark T. Keegan; Vitaly Herasevich

doi:10.4338/ACI-2016-09-RA-0149

Applied Clinical Informatics, Inhaltsverzeichnis

Appl Clin Inform 2017; 08(02): 369-380
DOI: 10.4338/ACI-2016-09-RA-0149

Research Article

Schattauer GmbH

Automating Clinical Score Calculation within the Electronic Health Record

A Feasibility Assessment

Christopher Aakre

¹Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA

,

Mikhail Dziadzko

²Department of Anesthesiology, Division of Critical Care, Mayo Clinic, Rochester, MN, USA

,

Mark T. Keegan

²Department of Anesthesiology, Division of Critical Care, Mayo Clinic, Rochester, MN, USA

,

Vitaly Herasevich

²Department of Anesthesiology, Division of Critical Care, Mayo Clinic, Rochester, MN, USA

³Multidisciplinary Epidemiology and Translation Research in Intensive Care (METRIC), Mayo Clinic, Rochester, MN, USA

› Institutsangaben

Abstract

Summary

Objectives: Evidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records.

Methods: We manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings.

Results: We identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable.

Conclusion: Complete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments – partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study.

Citation: Aakre C, Dziadzko M, Keegan MT, Herasevich V. Automating clinical score calculation within the electronic health record: A feasibility assessment. Appl Clin Inform 2017; 8: 369–380 https://doi.org/10.4338/ACI-2016-09-RA-0149

Keywords

Automation - decision support algorithm - clinical score - knowledge translation - workflow - clinical practice guideline

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Referenzen

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