JMIR Publications

ABSTRACT

Background:

The combination of the four clinical information systems (CISs)—Electronic health records (EHR), Electronic health information exchange (HIE), Personal health records (PHR), and Telehealth—at primary care practice encompasses a high potential, including improving the coordination of health care services, and saving lives. As well, the effects of the four CISs on practice performance depend on the configuration of their functional capabilities available to clinicians. However, our empirical knowledge of these configurations and associated performance implications is very limited because they have mostly been studied in isolation.

Objective:

Three main objectives are pursued (1) characterize General Practitioners (GPs) by uncovering typical profiles of combination of four major CIS capabilities; (2) identify physicians and practice characteristics that predict cluster membership; (3) assess the variation in levels of performance outcomes associated with each configuration.

Methods:

We used data from a European Union-wide survey of GPs (N=9596). First, four factors of EHR, HIE, PHR and Telehealth were created after checking Cronbach's alphas. Second, we conducted cluster analysis to derive GPs empirical clusters based on the four factors. Third, we compared the clusters according to three performance outcomes using ANOVA and Tamhane T2 post hoc test. Forth, univariate and multivariate multinomial logistic regressions were used to identify predictors on the clusters. Fifth, with multivariate multinomial logistic regression, we compared among the clusters the performance of the number of patients (3-levels) over the last two years.

Results:

We unveiled three GPs clusters with different levels of CIS capability profiles: “strong” (31.8%, n=2508), “medium” (59.1%, n=4661), “weak” (9.2%, n=724). Logistic regression indicates that physicians (younger, female, less experienced) and practice (solo) characteristics are significantly associated with the weak profile. The ANOVAs revealed the strong cluster associated with significantly high practice performance in quality of care, productivity, and improvement of working processes, and the two non-comprehensive medium and weak profiles associated with low (equifinal) practice performance outcomes. Logistic regression also revealed that physicians in the weak profile are associated with a decrease in the number of patients over the last two years.

Conclusions:

Different CIS capability profiles may lead to the same equifinal performance outcomes. This underlines the importance to look beyond the adoption of one CIS capability versus a cluster of capabilities when studying CISs. GPs in the strong cluster exhibit a comprehensive CIS capabilities profile and outperforms the other two clusters with non-comprehensive profiles and leads to significantly high performance in terms of quality of care provided to patients, productivity of the practice, and improvement of working processes. In particular, our results indicate that medical practices should develop high capabilities in all four CISs if they have to maximize their performance outcomes because efforts of developing high capabilities selectively may only remain in vain.