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Review
. 2024 Jan 22:26:e52085.
doi: 10.2196/52085.

Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study

Affiliations
Review

Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study

Lisa Weik et al. J Med Internet Res. .

Abstract

Background: Various studies propose the significance of digital maturity in ensuring effective patient care and enabling improved health outcomes, a successful digital transformation, and optimized service delivery. Although previous research has centered around inpatient health care settings, research on digital maturity in general practices is still in its infancy.

Objective: As general practitioners (GPs) are the first point of contact for most patients, we aimed to shed light on the pivotal role of GPs' inherent characteristics, especially their personality, in the digital maturity of general practices.

Methods: In the first step, we applied a sequential mixed methods approach involving a literature review and expert interviews with GPs to construct the digital maturity scale used in this study. Next, we designed a web-based survey to assess digital maturity on a 5-point Likert-type scale and analyze the relationship with relevant inherent characteristics using ANOVAs and regression analysis.

Results: Our web-based survey with 219 GPs revealed that digital maturity was overall moderate (mean 3.31, SD 0.64) and substantially associated with several characteristics inherent to the GP. We found differences in overall digital maturity based on GPs' gender, the expected future use of digital health solutions, the perceived digital affinity of medical assistants, GPs' level of digital affinity, and GPs' level of extraversion and neuroticism. In a regression model, a higher expected future use, a higher perceived digital affinity of medical assistants, a higher digital affinity of GPs, and lower neuroticism were substantial predictors of overall digital maturity.

Conclusions: Our study highlights the impact of GPs' inherent characteristics, especially their personality, on the digital maturity of general practices. By identifying these inherent influencing factors, our findings support targeted approaches to drive digital maturity in general practice settings.

Keywords: digital affinity; digital health; digital health adoption; digital maturity; eHealth; family medicine; general practitioners; maturity assessment; personality; primary care; primary care physicians.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Overview of the data cleaning approach. Straightlining was monitored as part of our data quality control procedure, excluding respondents who showed a straight line across >2 survey pages.
Figure 2
Figure 2
Flowchart for the literature review following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Records removed for other reasons shows records removed based on our language and publication date criteria.
Figure 3
Figure 3
Overview of the constituting digital maturity dimensions and indicators based on the literature review and expert interview results. nMM represents the number of maturity models proposing the respective indicator; nEI shows the number of expert interviews in which the indicator was mentioned. All indicators proposed in >3 maturity models or mentioned in >1 interview were included in the survey. Light grey boxes with italic text show indicators not included in the subsequent web-based survey. Individual competence was not included as we assessed general practitioners’ digital affinity separately; Patient empowerment was included owing to expert consensus.
Figure 4
Figure 4
Frequencies, mean, and SD for digital maturity items along the constituting dimensions assessed (N=219). The bar chart shows the number of respondents per answer category. DA: data analytics; GM: governance and management; IO: interoperability; ITC: IT capability; PCC: patient-centered care; PSB: people, skills, and behavior; ST: strategy.
Figure 5
Figure 5
Standardized coefficients for the final regression model predicting overall digital maturity (N=219). Age, practice location size, professional experience, practice type, and current use were dummy coded for the analysis. Age: 36-45, 46-55, 56-65, >65 versus 26-35 years (reference category). Practice location size: 5000-20,000, 20,001-100,000, 100,001-500,000, >500,000 versus 5000 inhabitants (reference category). Professional experience: 6-10, 11-20, 21-30, >30 versus 1-5 years of experience (reference category). Practice type: Practice sharing, group practice, medical care center versus single practice (reference category). Current use of digital health solutions: Less than once per month, monthly, weekly, daily versus never (reference category).

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