Understanding Clinicians’ Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks

Abstract

<jats:sec> <jats:title>Background</jats:title> <jats:p>Although there is a push toward encouraging mobile health (mHealth) adoption to harness its potential, there are many challenges that sometimes go beyond the technology to involve other elements such as social, cultural, and organizational factors.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>This review aimed to explore which frameworks are used the most, to understand clinicians’ adoption of mHealth as well as to identify potential shortcomings in these frameworks. Highlighting these gaps and the main factors that were not specifically covered in the most frequently used frameworks will assist future researchers to include all relevant key factors.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>This review was an in-depth subanalysis of a larger systematic review that included research papers published between 2008 and 2018 and focused on the social, organizational, and technical factors impacting clinicians’ adoption of mHealth. The initial systematic review included 171 studies, of which 50 studies used a theoretical framework. These 50 studies are the subject of this qualitative review, reflecting further on the frameworks used and how these can help future researchers design studies that investigate the topic of mHealth adoption more robustly.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The most commonly used frameworks were different forms of extensions of the Technology Acceptance Model (TAM; 17/50, 34%), the diffusion of innovation theory (DOI; 8/50, 16%), and different forms of extensions of the unified theory of acceptance and use of technology (6/50, 12%). Some studies used a combination of the TAM and DOI frameworks (3/50, 6%), whereas others used the consolidated framework for implementation research (3/50, 6%) and sociotechnical systems (STS) theory (2/50, 4%). The factors cited by more than 20% of the studies were usefulness, output quality, ease of use, technical support, data privacy, self-efficacy, attitude, organizational inner setting, training, leadership engagement, workload, and workflow fit. Most factors could be linked to one framework or another, but there was no single framework that could adequately cover all relevant and specific factors without some expansion.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>Health care technologies are generally more complex than tools that address individual user needs as they usually support patients with comorbidities who are typically treated by multidisciplinary teams who might even work in different health care organizations. This special nature of how the health care sector operates and its highly regulated nature, the usual budget deficits, and the interdependence between health care organizations necessitate some crucial expansions to existing theoretical frameworks usually used when studying adoption. We propose a shift toward theoretical frameworks that take into account implementation challenges that factor in the complexity of the sociotechnical structure of health care organizations and the interplay between the technical, social, and organizational aspects. Our consolidated framework offers recommendations on which factors to include when investigating clinicians’ adoption of mHealth, taking into account all three aspects.</jats:p> </jats:sec>

Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports

“…As a result of a systematic review on mHealth-related apps, Llorens-Vernet and Miró [79] offer a list of criteria, grouped into categories, aimed at guiding the development of mHealth apps. The systematic review ensured the categories (usability, privacy, security, appropriateness and suitability, transparency and content, safety, technical support and updates, and technology) are consistent with those identified by other authors in related contexts [7][8][9][10].…”

“…Many factors impact the adoption of mobile health (mHealth) tools by professionals and their target population, as observed in Australia [1], Canada [2], USA [3,4], and in Europe [5,6]. As an example, clinicians' concerns when considering an mHealth tool include usefulness, ease of use, compatibility, technical issues, content, personalization, convenience, strict data privacy, workload, workflow, communication, management support, and policies [7][8][9]. Such themes align with those highlighted by Chinese public hospitals' managers [10], including perceived ease of use, system security and reliability, top management support, and government policy.…”

Digital Monitoring and Management of Patients With Advanced or Metastatic Non-Small Cell Lung Cancer Treated With Cancer Immunotherapy and Its Impact on Quality of Clinical Care: Interview and Survey Study Among Health Care Professionals and Patients

“…The questions in each were informed by factors included in the original technology acceptance model (TAM) [ 31 ], notably perceived usefulness and perceived ease of use [ 32 ]. The TAM was selected for its simplicity and for being one of the most commonly used frameworks for assessing user acceptance of new technologies in general health care [ 33 - 36 ] and, specifically, mobile health [ 37 ]. The questions were also informed by other elements frequently reported in the literature as influencing adoption of mobile health solutions, for example, patient-clinician communications [ 38 - 41 ], quality of care [ 42 - 46 ], empowerment of patients and care teams [ 47 - 53 ], and efficiency [ 54 - 57 ].…”

Factors Impacting Clinicians’ Adoption of a Clinical Photo Documentation App and its Implications for Clinical Workflows and Quality of Care: Qualitative Case Study

“…Our themes were mostly influenced by sociotechnical theory and Leonardi’s methodological guidance [ 17 ] looking into the technical, social, and organizational factors interacting with shape technology adoption. We also took into account the emerging themes in the most used frameworks for studying mHealth adoption based on a systematic review that we had published earlier [ 23 ]. Accordingly, our themes were also influenced by other prominent frameworks such as the Technology Acceptance Model [ 24 ], the Diffusion of Innovation theory [ 25 ], different forms of extensions of the Unified Theory of Acceptance and Use of Technology [ 26 - 29 ], and the Consolidated Framework for Implementation Research [ 30 , 31 ].…”