Dataset Information

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

ABSTRACT: BACKGROUND:Mobile health (mHealth) tools have shown promise in clinical photo and wound documentation for their potential to improve workflows, expand access to care, and improve the quality of patient care. However, some barriers to adoption persist. OBJECTIVE:This study aims to understand the social, organizational, and technical factors affecting clinicians' adoption of a clinical photo documentation mHealth app and its implications for clinical workflows and quality of care. METHODS:A qualitative case study of a clinical photo and wound documentation app called imitoCam was conducted. The data were collected through 20 in-depth interviews with mHealth providers, clinicians, and medical informatics experts from 8 clinics and hospitals in Switzerland and Germany. RESULTS:According to the study participants, the use of mHealth in clinical photo and wound documentation provides numerous benefits such as time-saving and efficacy, better patient safety and quality of care, enhanced data security and validation, and better accessibility. The clinical workflow may also improve when the app is a good fit, resulting in better collaboration and transparency, streamlined daily work, clinician empowerment, and improved quality of care. The findings included important factors that may contribute to or hinder adoption. Factors may be related to the material nature of the tool, such as the perceived usefulness, ease of use, interoperability, cost, or security of the app, or social aspects such as personal experience, attitudes, awareness, or culture. Organizational and policy barriers include the available clinical practice infrastructure, workload and resources, the complexity of decision making, training, and ambiguity or lack of regulations. User engagement in the development and implementation process is a vital contributor to the successful adoption of mHealth apps. CONCLUSIONS:The promising potential of mHealth in clinical photo and wound documentation is clear and may enhance clinical workflow and quality of care; however, the factors affecting adoption go beyond the technical features of the tool itself to embrace significant social and organizational elements. Technology providers, clinicians, and decision makers should work together to carefully address any barriers to improve adoption and harness the potential of these tools.

SUBMITTER: Jacob C

PROVIDER: S-EPMC7542402 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Jacob Christine C Sanchez-Vazquez Antonio A Ivory Chris C

JMIR mHealth and uHealth 20200923 9

<h4>Background</h4>Mobile health (mHealth) tools have shown promise in clinical photo and wound documentation for their potential to improve workflows, expand access to care, and improve the quality of patient care. However, some barriers to adoption persist.<h4>Objective</h4>This study aims to understand the social, organizational, and technical factors affecting clinicians' adoption of a clinical photo documentation mHealth app and its implications for clinical workflows and quality of care.<h ...[more]

PMID: 32965232

Similar Datasets

Project description:BackgroundDespite the existence of adequate technological infrastructure and clearer policies, there are situations where users, mainly physicians, resist mobile health (mHealth) solutions. This is of particular concern, bearing in mind that several studies, both in developed and developing countries, showed that clinicians' adoption is the most influential factor in such solutions' success.ObjectiveThe aim of this study was to focus on understanding clinicians' roles in the adoption of an oncology decision support app, the factors impacting this adoption, and its implications for organizational and social practices.MethodsA qualitative case study of a decision support app in oncology, called ONCOassist, was conducted. The data were collected through 17 in-depth interviews with clinicians and nurses in the United Kingdom, Ireland, France, Italy, Spain, and Portugal.ResultsThis case demonstrates the affordances and constraints of mHealth technology at the workplace, its implications for the organization of work, and clinicians' role in its constant development and adoption. The research findings confirmed that factors such as app operation and stability, ease of use, usefulness, cost, and portability play a major role in the adoption decision; however, other social factors such as endorsement, neutrality of the content, attitude toward technology, existing workload, and internal organizational politics are also reported as key determinants of clinicians' adoption. Interoperability and cultural views of mobile usage at work are the key workflow disadvantages, whereas higher efficiency and performance, sharpened practice, and location flexibility are the main workflow advantages.ConclusionsSeveral organizational implications emerged, suggesting the need for some actions such as fostering a work culture that embraces new technologies and the creation of new digital roles for clinicians both on the hospitals or clinics and on the development sides but also more collaboration between health care organizations and digital health providers to enable electronic medical record integration and solving of any interoperability issues. From a theoretical perspective, we also suggest the addition of a fourth step to Leonardi's methodological guidance that accounts for user engagement; embedding the users in the continuous design and development processes ensures the understanding of user-specific affordances that can then be made more obvious to other users and increase the potential of such tools to go beyond their technological features and have a higher impact on workflow and the organizing process.

Project description:BACKGROUND:There is a growing body of evidence highlighting the potential of mobile health (mHealth) in reducing health care costs, enhancing access, and improving the quality of patient care. However, user acceptance and adoption are key prerequisites to harness this potential; hence, a deeper understanding of the factors impacting this adoption is crucial for its success. OBJECTIVE:The aim of this review was to systematically explore relevant published literature to synthesize the current understanding of the factors impacting clinicians' adoption of mHealth tools, not only from a technological perspective but also from social and organizational perspectives. METHODS:A structured search was carried out of MEDLINE, PubMed, the Cochrane Library, and the SAGE database for studies published between January 2008 and July 2018 in the English language, yielding 4993 results, of which 171 met the inclusion criteria. The Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines and the Cochrane handbook were followed to ensure a systematic process. RESULTS:The technological factors impacting clinicians' adoption of mHealth tools were categorized into eight key themes: usefulness, ease of use, design, compatibility, technical issues, content, personalization, and convenience, which were in turn divided into 14 subthemes altogether. Social and organizational factors were much more prevalent and were categorized into eight key themes: workflow related, patient related, policy and regulations, culture or attitude or social influence, monetary factors, evidence base, awareness, and user engagement. These were divided into 41 subthemes, highlighting the importance of considering these factors when addressing potential barriers to mHealth adoption and how to overcome them. CONCLUSIONS:The study results can help inform mHealth providers and policymakers regarding the key factors impacting mHealth adoption, guiding them into making educated decisions to foster this adoption and harness the potential benefits.

Project description:ObjectiveTo understand the frequency of social determinants of health (SDOH) diagnosis codes (Z-codes) within the electronic health record (EHR) for patients with prediabetes and diabetes and examine factors influencing the adoption of SDOH documentation in clinical care.Data sourcesEHR data and qualitative interviews with health care providers and stakeholders.Study designAn explanatory sequential mixed methods design first examined the use of Z-codes within the EHR and qualitatively examined barriers to documenting SDOH. Data were integrated and interpreted using a joint display. This research was informed by the Framework for Dissemination and Utilization of Research for Health Care Policy and Practice.Data collection/extraction methodsWe queried EHR data for patients with a hemoglobin A1c > 5.7 between October 1, 2015 and September 1, 2020 (n = 118,215) to examine the use of Z-codes and demographics and outcomes for patients with and without social needs. Semi-structured interviews were conducted with 23 participants (n = 15 health care providers; n = 7 billing and compliance stakeholders). The interview questions sought to understand how factors at the innovation-, individual-, organizational-, and environmental-level influence SDOH documentation. We used thematic analysis to analyze interview data.Principal findingsPatients with social needs were disproportionately older, female, Black, uninsured, living in low-income and high unemployment neighborhoods, and had a higher number of hospitalizations, obesity, prediabetes, and type 2 diabetes than those without a Z-code. Z-codes were not frequently used in the EHR (<1% of patients), and there was an overall lack of congruence between quantitative and qualitative results related to the prevalence of social needs. Providers faced barriers at multiple levels (e.g., individual-level: discomfort discussing social needs; organizational-level: limited time, competing priorities) for documenting SDOH and identified strategies to improve documentation.ConclusionsProviders recognized the impact of SDOH on patient health and had positive perceptions of screening for and documenting social needs. Implementation strategies are needed to improve systematic documentation.

Project description:ObjectivesThis study uses the diffusion of innovations (DOI) theory to comprehensively understand the adoption of shared decision-making (SDM) in clinical practice, specifically focusing on the 'knowledge' and 'persuasion' stages within DOI. We aim to understand the challenges and dynamics associated with SDM adoption, offering insights for more patient-centred decision-making in healthcare.DesignThis qualitative study employs a modified framework analysis approach, integrating ethnographic and interview data from prior research, along with additional interviews. The framework used is based on the DOI theory.Study setting and participantsThis study was conducted in the obstetrics and gynaecology department of a tertiary teaching hospital in the Eastern region of the Netherlands. It included interviews with 20 participants, including gynaecologists, obstetrics registrars and junior doctors currently practising in the department. Additionally, data from prior research conducted within the same department were incorporated, ensuring the maintenance of contextual consistency.ResultsFindings reveal a complex interplay between SDM's benefits and challenges. Clinicians value SDM for upholding patient autonomy and enhancing medical practice, viewing it as valuable for medical decision-making. Decision aids are seen as advantageous in supporting treatment decisions. Challenges include compatibility issues between patient and clinician preferences, perceptions of SDM as time-consuming and difficult and limitations imposed by the rapid pace of healthcare and its swift decisions. Additionally, perceived complexity varies by situation, influenced by colleagues' attitudes, with limited trialability and sparsely observed instances of SDM.ConclusionsClinicians' decision to adopt or reject SDM is multifaceted, shaped by beliefs, cognitive processes and contextual challenges. Cognitive dissonance is critical as clinicians reconcile their existing practices with the adoption of SDM. Practical strategies such as practice assessments, open discussions about SDM's utility and reflective practice through professional development initiatives empower clinicians to make the best informed decision to adopt or reject SDM.

Project description:There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets