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Comprehensive evaluation of two elec...

Gaikwad, Rekha K.

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Comprehensive evaluation of two electronic medical record (EMR) systems in primary health care for accuracy of drug interaction alerts.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Comprehensive evaluation of two electronic medical record (EMR) systems in primary health care for accuracy of drug interaction alerts./
Author:	Gaikwad, Rekha K.
Description:	154 p.
Notes:	Source: Masters Abstracts International, Volume: 45-01, page: 0277.
Contained By:	Masters Abstracts International45-01.
Subject:	Engineering, Biomedical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR16574
ISBN:	9780494165744

Comprehensive evaluation of two electronic medical record (EMR) systems in primary health care for accuracy of drug interaction alerts.
Gaikwad, Rekha K.

Comprehensive evaluation of two electronic medical record (EMR) systems in primary health care for accuracy of drug interaction alerts. - 154 p.

Source: Masters Abstracts International, Volume: 45-01, page: 0277.

Thesis (M.H.I.)--Dalhousie University (Canada), 2006.

This research evaluated the accuracy of two electronic medical record systems (EMR) used by physicians and pharmacists to accurately identify drug-drug interactions (DDI) seen in elderly patients in primary health care by triggering accurate genuine drug interaction (DI) alerts. Adverse drug reactions due to unrecognized drug interactions and inappropriate prescribing commonly seen in elderly patients are a growing health care concern and are often preventable risk events. Elderly people often have comorbidities, consume multiple medications and find it onerous to recall their medications. Primary care physicians and pharmacists find it difficult to keep up to date with the expanding medical knowledge base. Automated DI alerts triggered by EMR systems are designed to support the clinical decision-making process of physicians and the dispensing process of pharmacists through the availability of evidence-based knowledge. Interacting drug-drug pairs representing "hypothetical" patient scenarios were used to obtain DI alerts triggered by the two EMR systems. Regressions and correlation analyses were used to evaluate the performance of the two EMR systems. Sensitivity for both the EMR systems was low (26% and 33%) and specificity was high (96% and 100%). Positive predictive (0.286 and 1) and negative predictive (0.956 and 0.961) values were obtained on evaluating the accuracy of DI alerts triggered by them. This suggests that EMR systems should be regularly evaluated for their effectiveness and updates of their software and databases. EMR systems and drug interaction resources were found to differ in their rating scales used to determine severity levels of drug-drug interactions.

ISBN: 9780494165744Subjects--Topical Terms:

1017684
Engineering, Biomedical.

Comprehensive evaluation of two electronic medical record (EMR) systems in primary health care for accuracy of drug interaction alerts.
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520 $a This research evaluated the accuracy of two electronic medical record systems (EMR) used by physicians and pharmacists to accurately identify drug-drug interactions (DDI) seen in elderly patients in primary health care by triggering accurate genuine drug interaction (DI) alerts. Adverse drug reactions due to unrecognized drug interactions and inappropriate prescribing commonly seen in elderly patients are a growing health care concern and are often preventable risk events. Elderly people often have comorbidities, consume multiple medications and find it onerous to recall their medications. Primary care physicians and pharmacists find it difficult to keep up to date with the expanding medical knowledge base. Automated DI alerts triggered by EMR systems are designed to support the clinical decision-making process of physicians and the dispensing process of pharmacists through the availability of evidence-based knowledge. Interacting drug-drug pairs representing "hypothetical" patient scenarios were used to obtain DI alerts triggered by the two EMR systems. Regressions and correlation analyses were used to evaluate the performance of the two EMR systems. Sensitivity for both the EMR systems was low (26% and 33%) and specificity was high (96% and 100%). Positive predictive (0.286 and 1) and negative predictive (0.956 and 0.961) values were obtained on evaluating the accuracy of DI alerts triggered by them. This suggests that EMR systems should be regularly evaluated for their effectiveness and updates of their software and databases. EMR systems and drug interaction resources were found to differ in their rating scales used to determine severity levels of drug-drug interactions.
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