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Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record
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| Zeitschriftentitel: | Journal of Primary Care & Community Health |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Journal of Primary Care & Community Health, 2, 2011, 1, S. 49-53 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
SAGE Publications
|
| Schlagwörter: |
| author_facet |
Ivers, Noah Pylypenko, Bogdan Tu, Karen Ivers, Noah Pylypenko, Bogdan Tu, Karen |
|---|---|
| author |
Ivers, Noah Pylypenko, Bogdan Tu, Karen |
| spellingShingle |
Ivers, Noah Pylypenko, Bogdan Tu, Karen Journal of Primary Care & Community Health Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record Public Health, Environmental and Occupational Health Community and Home Care |
| author_sort |
ivers, noah |
| spelling |
Ivers, Noah Pylypenko, Bogdan Tu, Karen 2150-1327 2150-1327 SAGE Publications Public Health, Environmental and Occupational Health Community and Home Care http://dx.doi.org/10.1177/2150131910382251 <jats:p> Purpose: Increasing utilization of electronic medical records (EMRs) presents an opportunity to efficiently measure quality indicators in primary care. Achieving this goal requires the development of accurate patient-disease registries. This study aimed to develop and validate an algorithm for identifying patients with ischemic heart disease (IHD) within the EMR. Methods: An algorithm was developed to search the unstructured text within the medical history fields in the EMR for IHD-related terminology. This algorithm was applied to a 5% random sample of adult patient charts (n = 969) drawn from a convenience sample of 17 Ontario family physicians. The accuracy of the algorithm for identifying patients with IHD was compared to the results of 3 trained chart abstractors. Results: The manual chart abstraction identified 87 patients with IHD in the random sample (prevalence = 8.98%). The accuracy of the algorithm for identifying patients with IHD was as follows: sensitivity = 72.4% (95% confidence interval [CI]: 61.8-81.5); specificity = 99.3% (95% CI: 98.5-99.8); positive predictive value = 91.3% (95% CI: 82.0-96.7); negative predictive value = 97.3 (95% CI: 96.1-98.3); and kappa = 0.79 (95% CI: 0.72-0.86). Conclusions: Patients with IHD can be accurately identified by applying a search algorithm for the medical history fields in the EMR of primary care providers who were not using standardized approaches to code diagnoses. The accuracy compares favorably to other methods for identifying patients with IHD. The results of this study may aid policy makers, researchers, and clinicians to develop registries and to examine quality indicators for IHD in primary care. </jats:p> Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record Journal of Primary Care & Community Health |
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Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
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Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
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Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
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Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
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Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
| title_short |
Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
| title_sort |
identifying patients with ischemic heart disease in an electronic medical record |
| topic |
Public Health, Environmental and Occupational Health Community and Home Care |
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http://dx.doi.org/10.1177/2150131910382251 |
| publishDate |
2011 |
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49-53 |
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<jats:p> Purpose: Increasing utilization of electronic medical records (EMRs) presents an opportunity to efficiently measure quality indicators in primary care. Achieving this goal requires the development of accurate patient-disease registries. This study aimed to develop and validate an algorithm for identifying patients with ischemic heart disease (IHD) within the EMR. Methods: An algorithm was developed to search the unstructured text within the medical history fields in the EMR for IHD-related terminology. This algorithm was applied to a 5% random sample of adult patient charts (n = 969) drawn from a convenience sample of 17 Ontario family physicians. The accuracy of the algorithm for identifying patients with IHD was compared to the results of 3 trained chart abstractors. Results: The manual chart abstraction identified 87 patients with IHD in the random sample (prevalence = 8.98%). The accuracy of the algorithm for identifying patients with IHD was as follows: sensitivity = 72.4% (95% confidence interval [CI]: 61.8-81.5); specificity = 99.3% (95% CI: 98.5-99.8); positive predictive value = 91.3% (95% CI: 82.0-96.7); negative predictive value = 97.3 (95% CI: 96.1-98.3); and kappa = 0.79 (95% CI: 0.72-0.86). Conclusions: Patients with IHD can be accurately identified by applying a search algorithm for the medical history fields in the EMR of primary care providers who were not using standardized approaches to code diagnoses. The accuracy compares favorably to other methods for identifying patients with IHD. The results of this study may aid policy makers, researchers, and clinicians to develop registries and to examine quality indicators for IHD in primary care. </jats:p> |
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| description | <jats:p> Purpose: Increasing utilization of electronic medical records (EMRs) presents an opportunity to efficiently measure quality indicators in primary care. Achieving this goal requires the development of accurate patient-disease registries. This study aimed to develop and validate an algorithm for identifying patients with ischemic heart disease (IHD) within the EMR. Methods: An algorithm was developed to search the unstructured text within the medical history fields in the EMR for IHD-related terminology. This algorithm was applied to a 5% random sample of adult patient charts (n = 969) drawn from a convenience sample of 17 Ontario family physicians. The accuracy of the algorithm for identifying patients with IHD was compared to the results of 3 trained chart abstractors. Results: The manual chart abstraction identified 87 patients with IHD in the random sample (prevalence = 8.98%). The accuracy of the algorithm for identifying patients with IHD was as follows: sensitivity = 72.4% (95% confidence interval [CI]: 61.8-81.5); specificity = 99.3% (95% CI: 98.5-99.8); positive predictive value = 91.3% (95% CI: 82.0-96.7); negative predictive value = 97.3 (95% CI: 96.1-98.3); and kappa = 0.79 (95% CI: 0.72-0.86). Conclusions: Patients with IHD can be accurately identified by applying a search algorithm for the medical history fields in the EMR of primary care providers who were not using standardized approaches to code diagnoses. The accuracy compares favorably to other methods for identifying patients with IHD. The results of this study may aid policy makers, researchers, and clinicians to develop registries and to examine quality indicators for IHD in primary care. </jats:p> |
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| spelling | Ivers, Noah Pylypenko, Bogdan Tu, Karen 2150-1327 2150-1327 SAGE Publications Public Health, Environmental and Occupational Health Community and Home Care http://dx.doi.org/10.1177/2150131910382251 <jats:p> Purpose: Increasing utilization of electronic medical records (EMRs) presents an opportunity to efficiently measure quality indicators in primary care. Achieving this goal requires the development of accurate patient-disease registries. This study aimed to develop and validate an algorithm for identifying patients with ischemic heart disease (IHD) within the EMR. Methods: An algorithm was developed to search the unstructured text within the medical history fields in the EMR for IHD-related terminology. This algorithm was applied to a 5% random sample of adult patient charts (n = 969) drawn from a convenience sample of 17 Ontario family physicians. The accuracy of the algorithm for identifying patients with IHD was compared to the results of 3 trained chart abstractors. Results: The manual chart abstraction identified 87 patients with IHD in the random sample (prevalence = 8.98%). The accuracy of the algorithm for identifying patients with IHD was as follows: sensitivity = 72.4% (95% confidence interval [CI]: 61.8-81.5); specificity = 99.3% (95% CI: 98.5-99.8); positive predictive value = 91.3% (95% CI: 82.0-96.7); negative predictive value = 97.3 (95% CI: 96.1-98.3); and kappa = 0.79 (95% CI: 0.72-0.86). Conclusions: Patients with IHD can be accurately identified by applying a search algorithm for the medical history fields in the EMR of primary care providers who were not using standardized approaches to code diagnoses. The accuracy compares favorably to other methods for identifying patients with IHD. The results of this study may aid policy makers, researchers, and clinicians to develop registries and to examine quality indicators for IHD in primary care. </jats:p> Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record Journal of Primary Care & Community Health |
| spellingShingle | Ivers, Noah, Pylypenko, Bogdan, Tu, Karen, Journal of Primary Care & Community Health, Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record, Public Health, Environmental and Occupational Health, Community and Home Care |
| title | Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
| title_full | Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
| title_fullStr | Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
| title_full_unstemmed | Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
| title_short | Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
| title_sort | identifying patients with ischemic heart disease in an electronic medical record |
| title_unstemmed | Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record |
| topic | Public Health, Environmental and Occupational Health, Community and Home Care |
| url | http://dx.doi.org/10.1177/2150131910382251 |