The effect of insurance-driven medication changes on patient care

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Original Research

The effect of insurance-driven medication changes on patient care

J Fam Pract. 2012 July;61(7):E1-E7

By

Mark N. Rood, MD

Wanda Cruz-Knight, MD

James Cunagin, MD

Stephen J. Zyzanski, PhD

James J. Werner, PhD

Mary Jane Mason, MD

Peter J. Lawson, MA, MPH

Kurt C. Stange, MD

Susan A. Flocke, PhD

Nearly one-quarter of patients in this study encountered problems filling their prescriptions; that led to a mix of adverse outcomes, decreased satisfaction, and increased practice burdens.

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References

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Abstract

Purpose Insurance plans periodically change their formularies to enhance medical efficacy and cost savings. Patients face challenges when formulary changes affect their treatment. This study assessed the impact of insurance-driven medication changes on primary care patients and examined implications for patient care.

Methods We mailed questionnaires to a cross-sectional random sample of 1200 adult patients who had visited one of 3 family medicine practices within the past 6 months, asking them to describe problems they had encountered in filling medication prescriptions. We performed descriptive analyses of the frequency and distribution of demographic variables and conditions being treated. Using logistic regression analysis, we identified demographic and health-related variables independently associated with patient-reported problems caused by formulary changes.

Results Three variables—a greater number of prescription medications taken, younger patient age, and reliance on government insurance—were independently associated with an increased likelihood of encountering a problem filling a medication. Patients who reported an insurance-related issue filling a new or existing prescription over the past year (23%) encountered an average of 3 distinct problems. Patients experienced adverse medical outcomes (41%), decreased satisfaction with the health care system (68%), and problems that burdened the physician practice (83%). Formulary changes involving cardiac/hypertension/lipid and neurologic/psychiatric medications caused the most problems.

Conclusions Insurance-driven medication changes adversely affect patient care and access to treatment, particularly for patients with government insurance. A better understanding of the negative impact of formulary changes on patient care and indirect health care expenditures should inform formulary change practices in order to minimize cost-shifting and maximize continuity of care.

To maintain the cost-effectiveness of health insurance, many organizations, including government agencies, routinely evaluate and choose to adopt alternative treatment modalities. But how do such changes affect patient outcomes? And do near-term cost savings from formulary changes lead to long-term cost benefits?

Chronic disease management and associated complex medication regimens account for most health insurance expenditures.^1,2Changes to prescription formularies are common,³with medications being added or removed to reduce costs or to respond to revised practice guidelines.^4,5

Researchers have examined the clinical risks and merits of changing from one drug to another, as well as the impact of implementing formulary changes on administrative and other costs, overall effectiveness of disease management, and the operational adeptness of health systems.^6-10Routine formulary changes may yield immediate cost savings, but net costs may increase downstream due to disruptions in patient care.^11,12Insurance-driven medication changes have also been shown to negatively affect patient adherence to medical treatment and also disease outcomes.^13,14

Patient-level data related to formulary restrictions are limited,¹⁵and analyses of patients’ experiences of medication changes are rare. A better understanding of patients’ experiences in this context could guide interventions to minimize treatment delays and improve outcomes. Our study assessed the effect of insurance-driven medication changes on primary care patients; specifically, the prevalence of difficulty in filling a prescription, resultant problems, and patient characteristics associated with reporting a problem.

Methods

Data collection

We mailed questionnaires to a random sample of 1200 adult patients (≥40 years) who had been seen within the previous 6 months at one of 3 family practices in northeastern Ohio. We asked respondents to quantify and describe any insurance-driven problems they encountered while attempting to fill or refill a prescription over the past year. We recorded each respondent’s insurance status, the name of the medication at issue and other medications they were taking, and demographic data. Comparative data for age and sex were collected for nonrespondents. The University Hospitals Case Medical Center Institutional Review Board approved all data collection procedures and methods for this cross-sectional study.

Data analysis

We tabulated and analyzed data from the surveys using Statistical Package for the Social Sciences (SPSS). We compared age and sex data (using t-test and chi-square test, respectively) between respondents and nonrespondents. We calculated descriptive statistics for all demographic, control, and outcome variables, and computed measures of association between demographic and health-related variables and insurance-driven problems encountered while filling a prescription. Using logistic regression analysis, we identified demographic and health-related variables independently associated with a problematic prescription.

We calculated the frequencies of problems encountered while trying to fill a prescription, and grouped the problems into 3 mutually exclusive categories: adverse medical outcomes, decreased patient satisfaction, and burden on physician practice. Adverse medical outcomes included missed doses of medication, inability to obtain medication, worsened medical condition, new medication adverse effects, and having to go to the emergency department (ED) because of a medication issue. We sorted medications into categories, and calculated the frequency of problems associated with each category.