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. 2024 Feb 27;7(1):47.
doi: 10.1038/s41746-024-01049-0.

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

Lisa Weik  1 , Leonard Fehring  2   3 , Achim Mortsiefer  4 , Sven Meister  5   6
Affiliations

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

Lisa Weik et al. NPJ Digit Med. .

Abstract

Extensive research has shown the potential value of digital health solutions and highlighted the importance of clinicians' adoption. As general practitioners (GPs) are patients' first point of contact, understanding influencing factors to their digital health adoption is especially important to derive personalized practical recommendations. Using a mixed-methods approach, this study broadly identifies adoption barriers and potential improvement strategies in general practices, including the impact of GPs' inherent characteristics - especially their personality - on digital health adoption. Results of our online survey with 216 GPs reveal moderate overall barriers on a 5-point Likert-type scale, with required workflow adjustments (M = 4.13, SD = 0.93), inadequate reimbursement (M = 4.02, SD = 1.02), and high training effort (M = 3.87, SD = 1.01) as substantial barriers. Improvement strategies are considered important overall, with respondents especially wishing for improved interoperability (M = 4.38, SD = 0.81), continued technical support (M = 4.33, SD = 0.91), and improved usability (M = 4.20, SD = 0.88). In our regression model, practice-related characteristics, the expected future digital health usage, GPs' digital affinity, several personality traits, and digital maturity are significant predictors of the perceived strength of barriers. For the perceived importance of improvement strategies, only demographics and usage-related variables are significant predictors. This study provides strong evidence for the impact of GPs' inherent characteristics on barriers and improvement strategies. Our findings highlight the need for comprehensive approaches integrating personal and emotional elements to make digitization in practices more engaging, tangible, and applicable.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Overview of categories and individual barriers (strategies) based on the literature review and expert interview results.
The figure shows categories and corresponding individual barriers (strategies) as well as their appearance in the literature review and expert interviews. nLR represents the number of studies identified in the literature review proposing the barrier (strategy); nEI shows the number of expert interviews in which the barrier (strategy) was mentioned. Light grey boxes with italic text show barriers (strategies) not included in the subsequent online survey. dhs digital health solutions.
Fig. 2
Fig. 2. Characteristics of participating GPs (N = 216).
The figure shows assessed individual and practice-related characteristics of participating GPs.
Fig. 3
Fig. 3. Sample size, mean, standard deviation, agreement rates, and between-group comparison for adoption barriers along the categories assessed.
The figure shows items for adoption barriers per category, the respective sample size, descriptive statistics, and between-group comparison. The dot chart shows the mean value per item. Error bars represent +/− 2 standard errors. Cells with red framing show substantial differences between groups. %A Percentage of respondents agreeing to the statements and thus rating the respective barrier as relevant rating of (4) or (5); TB technological barriers; SB social barriers; OB organizational barriers; dhs digital health solutions.
Fig. 4
Fig. 4. Sample size, mean, standard deviation, agreement rates, and between-group comparisons for improvement strategies along the categories assessed.
The figure shows items for improvement strategies per category, the respective sample size, descriptive statistics, and between-group comparisons. The dot chart shows the mean value per item. Error bars represent +/−2 standard errors. Cells with red framing show substantial differences between groups. %A Percentage of respondents agreeing to the statement and thus rating the respective strategy as important rating of (4) or (5); DS development-related strategies; AS awareness-related strategies; KS knowledge-related strategies; IS implementation-related strategies; PS policy-related strategies; dhs digital health solutions.
Fig. 5
Fig. 5. Univariate ANOVAs and post hoc tests.
Both parts of the figure show the results for Welch ANOVAs (left) and Hochberg GT2 or Games-Howell post hoc tests for significant Welch ANOVAs in the order of appearance (right). The upper part reports results for the strength of barriers, the lower part reports results for the importance of strategies. Blue brackets represent significant comparisons. As gender is a dichotomous variable, we conducted a two-tailed t-test. The results show the t-statistic (in the column ‘Welch’s F’), its’ df, and P-value. MA digital affinity medical assistants’ digital affinity; ATI affinity for technology interaction; N neuroticism; DM digital maturity.
Fig. 6
Fig. 6. Flowchart for the literature review following PRISMA-ScR guidelines.
The flowchart shows the sequential screening process during the literature review. ‘Records removed for other reasons’ shows records removed based on language and publication date criteria.
Fig. 7
Fig. 7. Overview of the data cleaning approach.
The figure shows the sequential data cleaning approach, including the number of questionnaires excluded during each process step. As part of our data quality control procedures, we excluded respondents that showed straight-lining across more than two survey pages and thus in more than one item battery, i.e., that chose the very same answer option for all items in more than one item battery, as this might indicate careless responding as opposed to straight-lining due to respondents‘ actual views.
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