Exploring the reliability of inpatient EMR algorithms for diabetes identification
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...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2023-06-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/30/1/e100894.full |
| _version_ | 1827153057418313728 |
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| author | Hude Quan Danielle A Southern Jie Pan Sonia Butalia David J T Campbell Cathy A Eastwood Seungwon Lee Elliot A Martin Abdel Aziz Shaheen |
| author_facet | Hude Quan Danielle A Southern Jie Pan Sonia Butalia David J T Campbell Cathy A Eastwood Seungwon Lee Elliot A Martin Abdel Aziz Shaheen |
| author_sort | Hude Quan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T16:47:38Z |
| format | Article |
| id | doaj.art-fecb22f620494599b9f8dc3a91e7707a |
| institution | Directory Open Access Journal |
| issn | 2632-1009 |
| language | English |
| last_indexed | 2025-03-20T22:15:42Z |
| publishDate | 2023-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj.art-fecb22f620494599b9f8dc3a91e7707a2024-08-09T03:55:09ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092023-06-0130110.1136/bmjhci-2023-100894Exploring the reliability of inpatient EMR algorithms for diabetes identificationHude Quan0Danielle A Southern1Jie Pan2Sonia Butalia3David J T Campbell4Cathy A Eastwood5Seungwon Lee6Elliot A Martin7Abdel Aziz Shaheen8Community Health Sciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, CanadaCentre for Health Informatics, Department of Community Health Sciences, University of Calgary, Calgary, Alberta, CanadaCommunity Health Sciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, CanadaDepartment of Medicine, University of Calgary Cumming School of Medicine, Calgary, Alberta, CanadaCommunity Health Sciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, CanadaCommunity Health Sciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, CanadaCommunity Health Sciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, CanadaCommunity Health Sciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, CanadaCommunity Health Sciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, CanadaIntroduction 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.https://informatics.bmj.com/content/30/1/e100894.full |
| spellingShingle | Hude Quan Danielle A Southern Jie Pan Sonia Butalia David J T Campbell Cathy A Eastwood Seungwon Lee Elliot A Martin Abdel Aziz Shaheen Exploring the reliability of inpatient EMR algorithms for diabetes identification BMJ Health & Care Informatics |
| title | Exploring the reliability of inpatient EMR algorithms for diabetes identification |
| title_full | Exploring the reliability of inpatient EMR algorithms for diabetes identification |
| title_fullStr | Exploring the reliability of inpatient EMR algorithms for diabetes identification |
| title_full_unstemmed | Exploring the reliability of inpatient EMR algorithms for diabetes identification |
| title_short | Exploring the reliability of inpatient EMR algorithms for diabetes identification |
| title_sort | exploring the reliability of inpatient emr algorithms for diabetes identification |
| url | https://informatics.bmj.com/content/30/1/e100894.full |
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