Exploring the reliability of inpatient EMR algorithms for diabetes identification
Autor: | Hude Quan, Danielle A Southern, Jie Pan, Sonia Butalia, David J T Campbell, Cathy A Eastwood, Seungwon Lee, Elliot A Martin, Abdel Aziz Shaheen |
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Jazyk: | angličtina |
Rok vydání: | 2023 |
Předmět: | |
Zdroj: | BMJ Health & Care Informatics, Vol 30, Iss 1 (2023) |
Druh dokumentu: | article |
ISSN: | 2023-1008 2632-1009 |
DOI: | 10.1136/bmjhci-2023-100894 |
Popis: | Introduction Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes phenotyping algorithms.Materials and methods A chart review on 3040 individuals was completed, and 583 had diabetes. We linked EMR data on these individuals to the International Classification of Disease (ICD) administrative databases. The following EMR-data-based diabetes algorithms were developed: (1) laboratory data, (2) medication data, (3) laboratory and medications data, (4) diabetes concept keywords and (5) diabetes free-text algorithm. Combined algorithms used or statements between the above algorithms. Algorithm performances were measured using chart review as a gold standard. We determined the best-performing algorithm as the one that showed the high performance of sensitivity (SN), and positive predictive value (PPV).Results The algorithms tested generally performed well: ICD-coded data, SN 0.84, specificity (SP) 0.98, PPV 0.93 and negative predictive value (NPV) 0.96; medication and laboratory algorithm, SN 0.90, SP 0.95, PPV 0.80 and NPV 0.97; all document types algorithm, SN 0.95, SP 0.98, PPV 0.94 and NPV 0.99.Discussion Free-text data-based diabetes algorithm can yield comparable or superior performance to a commonly used ICD-coded algorithm and could supplement existing methods. These types of inpatient EMR-based algorithms for case identification may become a key method for timely resource planning and care delivery. |
Databáze: | Directory of Open Access Journals |
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