Joshua W Joseph1, David T Chiu2, Larry A Nathanson3, Steven Horng4. 1. Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, One Deaconess Road, 2nd Floor, Boston, MA 02215, United States. Electronic address: jwjoseph@bidmc.harvard.edu. 2. Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, One Deaconess Road, 2nd Floor, Boston, MA 02215, United States. Electronic address: dtchiu@bidmc.harvard.edu. 3. Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, One Deaconess Road, 2nd Floor, Boston, MA 02215, United States. Electronic address: lnathans@bidmc.harvard.edu. 4. Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, One Deaconess Road, 2nd Floor, Boston, MA 02215, United States. Electronic address: shorng@bidmc.harvard.edu.
Abstract
OBJECTIVES: To evaluate the sensitivity and specificity of a problem list automatically generated from the emergency department (ED) medication reconciliation. METHODS: We performed a retrospective cohort study of patients admitted via the ED who also had a prior inpatient admission within the past year of an academic tertiary hospital. Our algorithm used the First Databank ontology to group medications into therapeutic classes, and applied a set of clinically derived rules to them to predict obstructive lung disease, hypertension, diabetes, congestive heart failure (CHF), and thromboembolism (TE) risk. This prediction was compared to problem lists in the last discharge summary in the electronic health record (EHR) as well as the emergency attending note. RESULTS: A total of 603 patients were enrolled from 03/29/2013-04/30/2013. The algorithm had superior sensitivity for all five conditions versus the attending problem list at the 99% confidence level (Obstructive Lung Disease 0.93 vs 0.47, Hypertension 0.93 vs 0.56, Diabetes 0.97 vs 0.73, TE Risk 0.82 vs 0.36, CHF 0.85 vs 0.38), while the attending problem list had superior specificity for both hypertension (0.76 vs 0.94) and CHF (0.87 vs 0.98). The algorithm had superior sensitivity for all conditions versus the EHR problem list (Obstructive Lung Disease 0.93 vs 0.34, Hypertension 0.93 vs 0.30, Diabetes 0.97 vs 0.67, TE Risk 0.82 vs 0.23, CHF 0.85 vs 0.32), while the EHR problem list also had superior specificity for detecting hypertension (0.76 vs 0.95) and CHF (0.87 vs 0.99). CONCLUSION: The algorithm was more sensitive than clinicians for all conditions, but less specific for conditions that are not treated with a specific class of medications. This suggests similar algorithms may help identify critical conditions, and facilitate thorough documentation, but further investigation, potentially adding alternate sources of information, may be needed to reliably detect more complex conditions.
OBJECTIVES: To evaluate the sensitivity and specificity of a problem list automatically generated from the emergency department (ED) medication reconciliation. METHODS: We performed a retrospective cohort study of patients admitted via the ED who also had a prior inpatient admission within the past year of an academic tertiary hospital. Our algorithm used the First Databank ontology to group medications into therapeutic classes, and applied a set of clinically derived rules to them to predict obstructive lung disease, hypertension, diabetes, congestive heart failure (CHF), and thromboembolism (TE) risk. This prediction was compared to problem lists in the last discharge summary in the electronic health record (EHR) as well as the emergency attending note. RESULTS: A total of 603 patients were enrolled from 03/29/2013-04/30/2013. The algorithm had superior sensitivity for all five conditions versus the attending problem list at the 99% confidence level (Obstructive Lung Disease 0.93 vs 0.47, Hypertension 0.93 vs 0.56, Diabetes 0.97 vs 0.73, TE Risk 0.82 vs 0.36, CHF 0.85 vs 0.38), while the attending problem list had superior specificity for both hypertension (0.76 vs 0.94) and CHF (0.87 vs 0.98). The algorithm had superior sensitivity for all conditions versus the EHR problem list (Obstructive Lung Disease 0.93 vs 0.34, Hypertension 0.93 vs 0.30, Diabetes 0.97 vs 0.67, TE Risk 0.82 vs 0.23, CHF 0.85 vs 0.32), while the EHR problem list also had superior specificity for detecting hypertension (0.76 vs 0.95) and CHF (0.87 vs 0.99). CONCLUSION: The algorithm was more sensitive than clinicians for all conditions, but less specific for conditions that are not treated with a specific class of medications. This suggests similar algorithms may help identify critical conditions, and facilitate thorough documentation, but further investigation, potentially adding alternate sources of information, may be needed to reliably detect more complex conditions.