JAMIA: EHR data can help determine medication adherence
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Nikki Carroll, MS, of the Kaiser Permanente Colorado Institute for Health Research in Denver, and colleagues developed a medication order algorithm to identify from EHRs the medication order intended for dispensing. They identified medication order data from EHR tables, obtained orders and linked the orders to dispensed medications. These steps were then used to identify patients who had been newly prescribed antihypertensive, antidiabetic or antihyperlipidemic medications and to determine the adherence group of each patient.
The study was conducted at Kaiser Permanente Colorado (KPCO), an integrated delivery network providing care to more than 470,000 people in the Denver/Boulder metropolitan area. The study patient cohort included all KPCO members with a newly initiated order for an oral antihypertensive, antidiabetic or antihyperlipidemic medication between Jan. 1, 2007 and June 30, 2008. Patients were required to have at least 365 days of membership with pharmacy benefits before and at least 180 days after the initial order.
The researchers prepared an inclusive list of oral medications from the First Data Bank data categorization scheme included in the HealthConnect EHR medication tables. This was verified and cross-referenced by drug name to the pharmacy information management system (PIMS) product table and by the National Drug Code system codes (NDC) to the McKesson Medi-Span Generic Product Identifier (GPI) to ensure all drugs in their respective classes were captured.
Medication orders for newly initiated therapies were identified from HealthConnect’s ORDER-MED table. The medication order data for each patient were sorted by order date, order medication number and dispense date, and the medication order occurring on the earliest date was considered the index order, the authors wrote. If the index order was revised within 30 days without being dispensed in the intervening time, the last revision was chosen as the definitive index order.
Dispensed medications associated with the index order were determined from PIMS. The medication orders were linked to PIMS by a patient identifier, the dispense date and the GPI Drug Name level. Any dispensation of drug(s) within the GPI Drug Name level within 180 days after the index order was pulled. If no matching record was found in PIMS, the medication order was considered not dispensed.
After medication orders were linked with dispensings, the proportion of days covered was calculated for each patient and the study cohort was stratified into three drug adherence groups for analysis:
- Early non-persistence: newly initiated chronic medication orders dispensed within 30 days of the initial order with no refills within 180 days;
- Primary non-adherence: newly initiated chronic medication orders not dispensed within 30 days of the initial order; and
- Ongoing: newly initiated chronic medication orders dispensed within 30 days of the initial order with at least one refill within 180 days.
Multiple iterations of programming and chart review were required to accurately identify the definitive medication order, the authors stated.
The drug categorization schemes differed between the EHR (which used First Data Bank and KP-specific) and PIMS (which used Medi-Span). The researchers matched the generic drug name to the categorization scheme used in PIMS, then matched it to the Medi-Span GPI by NDC to ensure complete drug capture. Carroll and colleagues then extracted medication order data from HealthConnect by individual medication identification number.
“Linking medication orders to dispensings is an important preliminary step to capture these two groups of patients, but little research has been done on this linkage,” they wrote. However, “even in a system where medication orders and dispensings can be linked, assumptions about the inherent completeness, appropriateness and accuracy of the information available can be misleading, and can result in classification of patients into incorrect adherence groups.”
A limitation of the study was the fact that the authors weren’t able to differentiate suboptimal medication adherence from medication intolerance or adverse events in the early non-persistence group. For example, they could not identify the subset of patients who stopped taking medication on the advice of the clinician due to an adverse reaction.
“In conclusion, identifying definitive medication orders in the EHR was challenging. In particular, determining the correct order date, identifying external prescriptions, and identifying amended orders was difficult,” wrote Carroll and colleagues. “When scientific investigations include assessment of primary non-adherence and early non-persistence, it is particularly important to accurately identify these factors and to internally validate data extraction methods. Particular attention to iterative evaluations and refinement of the programming code may help develop an accurate medication order algorithm.”
The study was supported by an internal grant from the Kaiser Permanente Institute for Health Research.