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J Manag Care Spec Pharm. 2017 May;23(5):566-572. doi: 10.18553/jmcp.2017.23.5.566.

Exploring Electronic Medical Record and Self-Administered Medication Risk Screening Tools in a Primary Care Clinic.

Author information

1
1 Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada.
2
2 Grey Nuns Community Hospital, Edmonton, Alberta, Canada.

Abstract

BACKGROUND:

Electronic medical record (EMR) screening for indicators of medication risk could improve efficiency in identifying primary care clinic patients in need of clinical pharmacist care compared with patient self-reporting.

OBJECTIVES:

To (a) compare the performance of an EMR medication risk assessment questionnaire (MRAQ) with a self-administered (SA) MRAQ and (b) explore each tool's ability to predict indicators of health behavior, health status, and health care utilization.

METHODS:

A prospective cohort study was conducted with 143 adults who attended an academic family medicine center and were taking ≥ 2 medications. All participants completed the 10-item SA-MRAQ, Morisky Medication Adherence Scale, Chew's health literacy screener, Stanford Health Distress Scale, and SF-36 overall rating of health. A blinded investigator completed the EMR-MRAQ and a chart review to ascertain 6 months of health care utilization. Outcome measures included the following: (a) scores from the 5- and 10-item SA-MRAQs and 5-item EMR-MRAQ; (b) sensitivity and specificity to determine the accuracy of the 5-item EMR versus the 5-item SA risk scores; (c) correlations between risk assessments and health behavior/status scales; and (d) area under the receiver operator curve to determine how well a high-risk score predicted health care utilization.

RESULTS:

The 5-item SA-MRAQ, the 5-item EMR-MRAQ, and the 10-item SA-MRAQ categorized 52.9% (55/104), 69.2% (99/143), and 17.6% (18/102) of participants as high risk, respectively. For the 104 participants who completed both 5-item MRAQ tools, the EMR-MRAQ had a sensitivity of 81.8% and specificity of 49.0% in detecting a high-risk SA-MRAQ score. Both 5-item risk assessments showed weak correlations with health distress and overall health, while the 10-item SA-MRAQ additionally showed weak correlations with medication adherence. The EMR-MRAQ was most effective in predicting all-cause emergency room visits/hospitalization (c-statistic = 0.69; 95% CI=0.57-0.81) and high clinic utilization (≥ 4 visits per 6 months; c-statistic = 0.77; 95% CI = 0.69-0.85). The EMR-MRAQ had high sensitivities but low specificities for these health care utilization outcomes, respectively (82.6% and 33.3%; 88.9% and 42.7%).

CONCLUSIONS:

This pilot study suggests that EMR-MRAQ screening has high sensitivity but low specificity in comparison with self-reporting and was able to discriminate between those who would and would not experience health care utilization outcomes. These results justify further development and validation of an automated EMR-based tool to predict patient-important consequences of medication-related problems.

DISCLOSURES:

This work was funded by the Canadian Society of Hospital Pharmacists Research and Education Foundation, which had no role in the analysis or interpretation of data or the decision to submit the manuscript for publication. The authors have no conflict of interests, potential or otherwise, to report. Makowsky had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design were contributed by Makowsky and Cor. Makowsky and Wong collected the data, and data interpretation was performed by Makowsky, Cor, and Wong. The manuscript was written by Makowsky and was critically reviewed for intellectual content by Makowsky, Cor, and Wong.

PMID:
28448775
DOI:
10.18553/jmcp.2017.23.5.566
[Indexed for MEDLINE]
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