A Model for Assessing Necessary Conditions for Rural Health Care’s Mobile Health Readiness: Qualitative Assessment of Clinician-Perceived Barriers
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Abstract
Background: Mobile health (mHealth) technology dissemination has penetrated rural and urban areas alike. Yet, health care organization oversight and clinician adoption have not kept pace with patient use. mHealth could have a unique impact on health and quality of life for rural populations. If organizations are prepared to manage mHealth, clinicians may improve the quality of care for their patients, both rural and urban. However, many organizations are not yet prepared to prescribe or prohibit third-party mHealth technologies. Objective: This study explored organizational readiness for rural mHealth adoption, the use of patient-reported data by clinical care teams, and potential impact on improving rural health care delivery. Methods: Semistructured, open-ended interviews were used to investigate clinicians’ current practices, motivators, and perceived barriers to their use of mHealth technologies in rural settings. Results: A total of 13 clinicians were interviewed, and 53.8% (7/13) reported encouraging use of mHealth apps or wearable devices with rural patients. Perceived barriers to adoption were categorized into three primary themes: (1) personal (clinician), (2) patient, and (3) organizational. Organizational was most prominent, with subcodes of time, uniformity, and policy or direction. Thematic analysis revealed code-category linkages that identify the complex nature of a rural health care organization’s current climate from a clinician’s perspective. A thematic map was developed to visualize the flow from category to code. Identified linkages guided the development of a refined rural mHealth readiness model. Conclusions: Clinicians (including physicians) have limited time for continuing education, research, or exploration of emerging technologies. Clinicians are motivated to learn more, but they need guidance through organization-led directives. Rural health care institutions should consider investing in mHealth analysis, tool development, and formal recommendations of sanctioned tools for clinicians to use with patients.
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Teledermatology improves expert access for CU patients in rural regions.
“Studies highlight teledermatology’s benefits in such regions, improving expert access for CU patients [42, 43].”
From Acceptance and perceived usefulness of digital health services in the management of chronic urticaria: a survey of patients and physicians (2025) · cites this paper as [43]
The Results > Interviews passage (page unavailable) describes clinicians seeing patients remotely through telehealth, and Discussion > Conclusions says rural populations may benefit from mobile technologies. Neither addresses teledermatology, improved access to experts, or CU patients; searches for teledermatology and urticaria found no relevant passage. (LLM, conf=0.76)
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Cites
A General Inductive Approach for Analyzing Qualitative Evaluation Data
Thomas, D. R. (2006) · American Journal of Evaluation · doi:10.1177/1098214005283748 · Cited by 10,858 · see citing works
The Values and Value of Patient-Centered Care
Epstein, R. M., & Street, R. L. (2011) · The Annals of Family Medicine · doi:10.1370/afm.1239 · Cited by 1,956 · see citing works
m-Health adoption by healthcare professionals: a systematic review
Gagnon, M.-P., Ngangue, P., Payne-Gagnon, J., & Desmartis, M. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv052 · Cited by 622 · see citing works
Can Mobile Health Technologies Transform Health Care?
Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2013) · JAMA · doi:10.1001/jama.2013.281078 · Cited by 496 · see citing works
The Changing Nature of Rural Health Care
Ricketts, T. C. (2000) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.21.1.639 · Cited by 246 · see citing works
The science of real-time data capture: self-reports in health research
Domingo‐Salvany, A. (2008) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2007.068551 · Cited by 241 · see citing works
Leon, N., Schneider, H., & Daviaud, E. (2012) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-12-123 · Cited by 200 · see citing works
Silva, E. H., Lawler, S., & Langbecker, D. (2019) · Journal of Cancer Survivorship · doi:10.1007/s11764-018-0730-8 · Cited by 198 · see citing works
e-Health Readiness Assessment Tools for Healthcare Institutions in Developing Countries
Khoja, S., Scott, R. E., Casebeer, A. L., Mohsin, M., Ishaq, A. F. M., & Gilani, S. (2007) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2006.0064 · Cited by 174 · see citing works
Determinants of readiness to adopt mHealth in a rural community of Bangladesh
Khatun, F., Heywood, A. E., RAY, P. K., Hanifi, S., Bhuiya, A., & Liaw, S. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.06.008 · Cited by 130 · see citing works
James, K. M., Ziegenfuss, J. Y., Tilburt, J. C., Harris, A. M., & Beebe, T. J. (2010) · Health Services Research · doi:10.1111/j.1475-6773.2010.01181.x · Cited by 107 · see citing works
Cho, Y.-M., Lee, S., Islam, S. M. S., & Kim, S. (2018) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2017.0249 · Cited by 106 · see citing works
Improving the Quality of Surveys of Physicians and Medical Groups
Klabunde, C. N., Willis, G., McLeod, C. C., Dillman, D. A., Johnson, T. P., Greene, S. M., & Brown, M. L. (2012) · Evaluation & the Health Professions · doi:10.1177/0163278712458283 · Cited by 93 · see citing works
Acceptability of mHealth augmentation of Collaborative Care: A mixed methods pilot study
Bauer, A. M., Iles-Shih, M., Ghomi, R. H., Rue, T. C., Grover, T., Kincler, N., Miller, M., & Katon, W. J. (2017) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2017.11.010 · Cited by 84 · see citing works
Leung, G. M., Johnston, J. M., Saing, H., et al. (2004) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2003.12.021 · Cited by 50 · see citing works
Recruitment issues in healthcare research: the situation in home care
Miller, K., McKeever, P. D., & Coyte, P. C. (2003) · Health & Social Care in the Community · doi:10.1046/j.1365-2524.2003.00411.x · Cited by 41 · see citing works
Mengesha, W., Steege, R., Kea, A. Z., Theobald, S., & Datiko, D. G. (2018) · Journal of Public Health · doi:10.1093/pubmed/fdy200 · Cited by 37 · see citing works
Mobile Health for All: Public-Private Partnerships Can Create a New Mental Health Landscape
Ben‐Zeev, D. (2016) · JMIR Mental Health · doi:10.2196/mental.5843 · Cited by 20 · see citing works
Rönnby, S., Lundberg, O., Fagher, K., Jacobsson, J., Tillander, B. M., Gauffin, H., Hansson, P.-O., Dahlström, Ö., & Timpka, T. (2018) · JMIR mhealth and uhealth · doi:10.2196/10270 · Cited by 17 · see citing works
Sakakibara, B. M., Chakrabarti, S., Krahn, A. D., Mackay, M., Sedlak, T., Singer, J., Whitehurst, D. G. T., & Lear, S. A. (2018) · JMIR Research Protocols · doi:10.2196/12322 · Cited by 12 · see citing works
Thompson, D., Mackay, T., Matthews, M., et al. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.8317 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [17]Bernard HR Research Methods in Anthropology: Qualitative and Quantitative Methods. Third Edition 2002 Walnut Creek, CA AltaMira Press
- [21]Roger G Diffusion Of Innovations. Third Edition 2003 New York Free Press
Cited by
Sorted by citations
Hindelang, M., Zink, A., Knitza, J., Darkow, R., Welcker, M., Biedermann, T., May, S., & Muehlensiepen, F. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13043-7 · Cited by 4 · see citing works
“Studies highlight teledermatology’s benefits in such regions, improving expert access for CU patients [42, 43].”
- Authors
- Weichelt, B., Bendixsen, C., & Patrick, T.
- Affiliations
- Marshfield Clinic Research Foundation (US)
- University of Wisconsin–Milwaukee (US)
- Corresponding author
- Weichelt, Bryan
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2019-11-08
- Volume
- 7
- Issue
- 11
- Pages
- e11915
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6874803
- OpenAlex
- W2971997848
- Cited by
- 16
- References
- 21
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 3
- FWCI
- 2.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- mobile health, rural health care, organizational readiness, wearable devices, patient-reported data
- MeSH terms
- Adult, Attitude to Computers, Computer Simulation, Female, Health Personnel, Humans, Interviews as Topic, Male, Middle Aged, Patient Acceptance of Health Care, Patients, Rural Population, Wisconsin, Telemedicine, Rural Health Services, Qualitative Research
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