Adoption and Performance of Complementary Clinical Information Technologies: Analysis of a Survey of General Practitioners

Abstract

Background The benefits from the combination of 4 clinical information systems (CISs)—electronic health records (EHRs), health information exchange (HIE), personal health records (PHRs), and telehealth—in primary care depend on the configuration of their functional capabilities available to clinicians. However, our empirical knowledge of these configurations and their associated performance implications is very limited because they have mostly been studied in isolation. Objective This study aims to pursue 3 objectives: (1) characterize general practitioners (GPs) by uncovering the typical profiles of combinations of 4 major CIS capabilities, (2) identify physician and practice characteristics that predict cluster membership, and (3) assess the variation in the levels of performance associated with each configuration. Methods We used data from a survey of GPs conducted throughout the European Union (N=5793). First, 4 factors, that is, EHRs, HIE, PHRs, and Telehealth, were created. Second, a cluster analysis helps uncover clusters of GPs based on the 4 factors. Third, we compared the clusters according to five performance outcomes using an analysis of variance (ANOVA) and a Tamhane T2 post hoc test. Fourth, univariate and multivariate multinomial logistic regressions were used to identify predictors of the clusters. Finally, with a multivariate multinomial logistic regression, among the clusters, we compared performance in terms of the number of patients treated (3 levels) over the last 2 years. Results We unveiled 3 clusters of GPs with different levels of CIS capability profiles: strong (1956/5793, 37.36%), medium (2764/5793, 47.71%), and weak (524/5793, 9.04%). The logistic regression analysis indicates that physicians (younger, female, and less experienced) and practice (solo) characteristics are significantly associated with a weak profile. The ANOVAs revealed a strong cluster associated with significantly high practice performance outcomes in terms of the quality of care, efficiency, productivity, and improvement of working processes, and two noncomprehensive medium and weak profiles associated with medium (equifinal) practice performance outcomes. The logistic regression analysis also revealed that physicians in the weak profile are associated with a decrease in the number of patients treated over the last 2 years. Conclusions Different CIS capability profiles may lead to similar equifinal performance outcomes. This underlines the importance of looking beyond the adoption of 1 CIS capability versus a cluster of capabilities when studying CISs. GPs in the strong cluster exhibit a comprehensive CIS capability profile and outperform the other two clusters with noncomprehensive profiles, leading to significantly high performance in terms of the quality of care provided to patients, efficiency of the practice, productivity of the practice, and improvement of working processes. Our findings indicate that medical practices should develop high capabilities in all 4 CISs if they have to maximize their performance outcomes because efforts to develop high capabilities selectively may only be in vain.

Understanding inherent influencing factors to digital health adoption in general practices through a mixed-methods analysis

“…According to our findings, the strength of barriers and the importance of strategies differed between GPs based on gender, with female participants perceiving barriers as higher and strategies as more important than their male colleagues. This is in line with studies reporting that being male was associated with using digital health technology 27 , 50 while being female was associated with lower digital health adoption 49 . Other studies found no gender-based differences for digital health adoption 24 or even a higher usage for female participants 51 .…”

“…While extensive research has studied influencing factors to digital health adoption 17 , 21 , 24 , 49 , no study has investigated factors influencing adoption barriers or improvement strategies. According to our findings, the strength of barriers and the importance of strategies differed between GPs based on gender, with female participants perceiving barriers as higher and strategies as more important than their male colleagues.…”

A review of cluster analysis techniques and their uses in library and information science research:k-meansandk-medoidsclustering

“…Perhaps if they had sought to identify five clusters, they would have identified more-defined early and late majority groups as well. Beyond Kwon and Johnson (2013), the use of cluster analysis for diffusion research is further evidenced in studies like Schedler et al (2019) and Poba-Nzaou et al (2020). Guillamet et al (2018) utilize clustering methods to analyze electronic health records, identifying clusters of chronic obstructive pulmonary disease (COPD) patients.…”