Digitalization and Physician Learning

Hult, Helena Vallo; Hansson, Anders; Gellerstedt, Martin

doi:10.1097/ceh.0000000000000303

Cited by 14 publications

(7 citation statements)

References 54 publications

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“…The study's results revealed that the core mission of the managers' professions had been shifted from tasks that were central before, such as being a coaching leader with a "hands-on" approach to leadership, into more digitally manageable tasks with increasingly administrative features. This finding is seen in previous studies, which indicate that some healthcare professions might experience digitalisation-related changes in their original professional core missions (Vallo Hult et al, 2020). This study revealed that increased digitalisation within a workplace might affect managers' health.…”

Section: Discussionsupporting

confidence: 84%

Healthcare digitalisation and its association with quality and employee health, a mixed-methods study

Qvarfordt,

Lagrosen

2023

IJWHM

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PurposePrevious research has identified associations between quality management and employee health. This study's purpose was to (1) examine those associations in a public healthcare organisation and (2) explore and describe the association between digitalisation and employee health.Design/methodology/approachAn online questionnaire including indices to measure quality management values, employee health and digitalisation was answered by 118 managers in Swedish public healthcare. Correlation analysis was used to analyse the data. Based on the survey results, 12 qualitative, in-depth interviews were conducted with healthcare managers.FindingsThe findings show that employee health is associated with quality management and digitalisation. Categories were defined to describe the managers' views of the relationship between digitalisation and health.Research limitations/implicationsCausality was not explicitly tested and cannot be assumed. However, the results strengthen the body of research showing that quality management is related to employee health, and associations between health and digitalisation were identified.Practical implicationsThe findings and model should be helpful for healthcare managers in a digitalising environment who aim to preserve or enhance employee health whilst ensuring high service quality.Originality/valueThe results were used to create an integrated conceptual model depicting the association between quality, digitalisation and health. This association has not previously been studied.

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Section: Discussionsupporting

confidence: 84%

Healthcare digitalisation and its association with quality and employee health, a mixed-methods study

Qvarfordt,

Lagrosen

2023

IJWHM

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“…This evidence adds to a growing body of work understanding the relationships between healthcare technology use, competence and adoption by healthcare professionals. Recent work by Vallo et al [ 30 ] indicates that adoption and use of digital technology are “related to perceived performance, social influence and organisational context” and the role of ICT [Information and Communication Technology] in clinical professional development. This work highlights the need for a holistic service design approach [ 31 ] to reduce external and internal barriers to adoption and optimise the benefits and professional competence of use of these technologies in practice [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

The impact of data from remote measurement technology on the clinical practice of healthcare professionals in depression, epilepsy and multiple sclerosis: survey

Andrews

Craven

Lang

et al. 2021

BMC Med Inform Decis Mak

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Background A variety of smartphone apps and wearables are available both to help patients monitor their health and to support health care professionals (HCPs) in providing clinical care. As part of the RADAR-CNS consortium, we have conducted research into the application of wearables and smartphone apps in the care of people with multiple sclerosis, epilepsy, or depression. Methods We conducted a large online survey study to explore the experiences of HCPs working with patients who have one or more of these conditions. The survey covered smartphone apps and wearables used by clinicians and their patients, and how data from these technologies impacted on the respondents' clinical practice. The survey was conducted between February 2019 and March 2020 via a web-based platform. Detailed statistical analysis was performed on the answers. Results Of 1009 survey responses from HCPs, 1006 were included in the analysis after data cleaning. Smartphone apps are used by more than half of responding HCPs and more than three quarters of their patients use smartphone apps or wearable devices for health-related purposes. HCPs widely believe the data that patients collect using these devices impacts their clinical practice. Subgroup analyses show that views on the impact of this data on different aspects of clinical work varies according to whether respondents use apps themselves, and, to a lesser extent, according to their clinical setting and job role. Conclusions Use of smartphone apps is widespread among HCPs participating in this large European survey and caring for people with epilepsy, multiple sclerosis and depression. The majority of respondents indicate that they treat patients who use wearables and other devices for health-related purposes and that data from these devices has an impact on clinical practice.

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“…Institutions aiming to adopt AI-CDSSs can leverage the important role of social influence by establishing a culture that values technological advancements and by engaging key opinion leaders to advocate and exemplify the use of these systems. Additionally, trainings can be structured not only to educate but also to establish a shared understanding and a community of practice that positively reinforces the application of AI-CDSSs [109,110]. By addressing the social aspects of technology acceptance, healthcare institutions can ensure that their investment in AI is met with a user base that is both competent and willing to integrate these tools into their daily practice.…”

Section: Practical Implicationsmentioning

confidence: 99%

Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology (Preprint)

Dingel,

Kleine,

Cecil

et al. 2024

Preprint

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BACKGROUND Artificial Intelligence-enabled Clinical Decision Support Systems (AI-CDSSs) offer potential for improving healthcare outcomes, but their adoption among healthcare practitioners remains limited. OBJECTIVE The meta-analysis identifies predictors influencing healthcare practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and additional literature. METHODS The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7,731 results, of which 17 studies met the inclusion criteria. Random-effects meta-analysis, relative weights analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships of relevant predictor variables with the intention to use AI-CDSSs. RESULTS The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results show that performance expectancy (rc = .66), effort expectancy (rc = .55), social influence (rc = .66), and facilitating conditions (rc = .66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (rc = .63), trust (rc = .73), anxiety (rc = -.41), perceived risk (rc = -.21), and innovativeness (rc = .54) as relevant additional predictors. Trust emerged as the most influential predictor overall. The results of moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs compared to devices that combined diagnostic and treatment recommendations. Finally, the relationships between facilitating conditions and use intention was mediated through performance and effort expectancy. CONCLUSIONS The meta-analysis contributes to the understanding of the predictors of the intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and to explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for healthcare practitioners to ease the adoption of AI-CDSSs into their practice. CLINICALTRIAL https://osf.io/b4j3t

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Digitalization and Physician Learning

Abstract: Supplemental Digital Content is Available in the Text.

Cited by 14 publications

References 54 publications

Healthcare digitalisation and its association with quality and employee health, a mixed-methods study

Healthcare digitalisation and its association with quality and employee health, a mixed-methods study

The impact of data from remote measurement technology on the clinical practice of healthcare professionals in depression, epilepsy and multiple sclerosis: survey

Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology (Preprint)

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