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. 2021 Oct 15;23(10):e29301.
doi: 10.2196/29301.

Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study

Luisa Pumplun  1 , Mariska Fecho  1 , Nihal Wahl  1 , Felix Peters  1 , Peter Buxmann  1
Affiliations

Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study

Luisa Pumplun et al. J Med Internet Res. .

Abstract

Background: Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML.

Objective: This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems.

Methods: To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care-specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process.

Results: With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process.

Conclusions: Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.

Keywords: adoption; clinics; diagnostics; machine learning; maturity model.

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Conflict of interest statement

Conflicts of Interest: This paper builds on a conference paper [86]. This paper extends the earlier work, in particular by expanding the underlying sample size and developing a maturity model.

Figures

Figure 1
Figure 1
Overview of research procedure, illustration based on Jöhnk et al [25]. ML: machine learning.
Figure 2
Figure 2
Integrative framework for the adoption process of machine learning systems in clinics. ML: machine learning.
Figure 3
Figure 3
Determine your clinic's readiness for machine learning–supported diagnostics (screenshot 1 of the web application). ML: machine learning.
Figure 4
Figure 4
Thank you for using the maturity model (screenshot 2 of the web application). ML: machine learning.
See this image and copyright information in PMC

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