Association Between Personal Health Record Enrollment and Patient Loyalty
Published Online: July 12, 2012
Marianne Turley, PhD; Terhilda Garrido, BSE, MPH; Alex Lowenthal, MPA; and Yi Yvonne Zhou, PhD
Among American healthcare consumers, considerable interest exists in integrated personal health records (PHRs). Across several consumer-focused surveys, 76% to 86% of respondents expressed interest in having Internet access to their health information, yet far fewer—7%—had experience doing so.1-3 In a national survey, the majority of respondents reported the belief that online access to health information would have personal benefits that would improve the quality of healthcare.4 People pay more attention to and become more engaged in their health and medical care when they have easy
online access to their health information.5
Most investigations report on adoption and the characteristics and preferences of users of online access websites or patient portals integrated with electronic health records (EHRs).6 Far fewer reports document the impact on outcomes of patient portals.7 Documented impacts include increased efficiency, increased patient contacts of all types, and improved Healthcare Effectiveness Data and Information Set (HEDIS) scores.8-11 Operational cost savings related to reduced mailed and telephone communications have also been documented.12
An important potential outcome of patient access to an integrated PHR, in addition to clinical benefits, is increased loyalty to the delivery system as reflected in membership retention. Beyond the obvious negative impact to a healthcare organization’s bottom line of member terminations, terminations are associated with significant costs for acquiring new members, transferring health records, and to some extent, liability related to transferring patients to other providers.13 Indirect costs may also occur related to the organization’s reputation and physician dissatisfaction with the voluntary departure of patients from their care.
Our objective was to examine the association between the use of My Health Manager (MHM), Kaiser Permanente’s integrated PHR, and health plan member retention.
Population and Data Source
Kaiser Permanente is one of the nation’s leading healthcare providers and not-for-profit health plans, with 9 million members in 9 states and the District of
Columbia. We conducted a retrospective cohort analysis of 394,215 eligible Kaiser Permanente Northwest members between the fourth quarter of 2005 and the third quarter of 2008. Eligible members included those in a mixture of zero- to high-deductible health maintenance organization (HMO) and point-of-service plans who were continuously enrolled or whose membership was terminated. Nearly 94% of the eligible members subscribed to HMO plans. We excluded members under the age of 13 years as they are not eligible to register for MHM access. Data on termination and member characteristics were obtained from KP HealthConnect, Kaiser Permanente’s integrated electronic health record, and other administrative sources.
MHM as Treatment
By registering to use MHM on website kp.org, members gain access to a robust set of online functionalities. Registered users can view portions of their medical record—eg, a health summary including allergies, immunizations, health conditions, and clinical laboratory results—schedule or change office visit appointments, securely e-mail physicians and other healthcare providers, order prescription refills, view summaries from recent appointments and reminders for needed services, request an update to their medical record, and perform other healthcare-related activities. We defined MHM use as registration, activation, and at least 1 login event during the observation period; in our exploratory analyses, this definition was highly correlated with the number of login events and online functionalities used. During the study period, 162,022 members became MHM users. By March of 2012, 53.3% (218,456) of eligible Kaiser Permanente members in the Northwest region were registered users.
The outcome measure was retention of Kaiser Permanente membership. Health plan members participate in an annual “open period” during which they elect to retain an existing health plan or replace it; every member retained is a membership termination avoided. We identified retention as occurring in the same month in which the member’s employer group renewed its contract with Kaiser Permanente; if the group renewed the contract and the individual did not terminate membership, we categorized the membership as retained. We considered involuntary termination as equivalent to retention; ie, members did not choose to discontinue membership. In contrast, we also identified voluntary termination as occurring in the same month as group contract renewal; if the individual did not renew membership, we categorized the termination as voluntary.
We performed exploratory data analyses on the raw data and calculated retention rates for MHM users and nonusers. To control for self-selection bias in our observational design, we used matching methods to minimize any systematic differences of the known factors between the MHM users and nonusers.14 This method required a design phase to select a subset of data matched on significant covariates of MHM use; we used SAS 9.1 software to estimate propensity scoreson each of the observations using logistic regression between the covariates and MHM use or nonuse, without including the outcome measure of membership retention.15 The covariates included age, gender, tenure (length of KP membership in years), membership type (subscriber, spouse/partner, or dependent), illness burden (measured by average per year, concurrent Diagnostic Cost Group scores), and the presence of diabetes and hypertension. Explanatory variables were treated as categorical; modeling them as continuous variables did not improve model fit.
After propensity scores were estimated to each observation, those with equivalent propensity scores, ie, similar to one another with respect to the multiple covariates, were matched 1 to 1 without replacement for MHM users and nonusers.14 We used the concordance index to assess the quality of the matching.16
In the second phase of the matching method, member retention rates were estimated for the matched data by MHM use and nonuse, and differences were tested between them. The matched data were also modeled with logistic regression with member retention as the outcome measure to estimate the effect of the covariates and MHM use on the outcome. We assessed overall model fit with the likelihood ratio test and the adjusted R2 and used the Wald test to assess the model coefficients.
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