Research Implications for Future Telemedicine Studies and Innovations in Diabetes and Hypertension-A Mixed Methods Study.
Patrick TimpelLorenz HarstPublished in: Nutrients (2020)
(1) Background: The objective of this study was to identify, categorize and prioritize current implications for future research in the use telemedicine for diabetes and hypertension in order to inform policy and practice decisions. (2) Methods: An iterative mixed methods design was followed, including three consecutive steps: An updated umbrella review of telemedicine effectiveness, qualitative content analysis of extracted data on current research needs and a quantitative survey with practitioners and health care researchers in order to prioritize the identified needs. (3) Results: Overall, 32 included records reported on future research implications. Qualitative content analysis yielded five categories as well as subcategories, covering a need for high quality studies, comprehensive technology assessments, in-depth considerations of patients' characteristics, ethics and safety as well as implementation strategies. The online survey revealed that the most pressing future research needs are data security, patient safety, patient satisfaction, implementation strategies and longer follow-ups. Chi² statistics and t-tests revealed significant differences in the priorities of participants with and without experience in telemedicine use, evaluation and development. A factor analysis revealed six over-arching factors. (4) Conclusion: These results may help learning from mistakes previously made and may serve as key topics of a future telemedicine research agenda.
Keyphrases
- healthcare
- current status
- patient safety
- primary care
- quality improvement
- type diabetes
- blood pressure
- patient satisfaction
- cardiovascular disease
- systematic review
- big data
- single cell
- end stage renal disease
- newly diagnosed
- randomized controlled trial
- global health
- electronic health record
- ejection fraction
- chronic kidney disease
- prognostic factors
- cross sectional
- mental health
- social media
- high resolution
- magnetic resonance imaging
- study protocol
- deep learning
- machine learning
- health information
- artificial intelligence
- adipose tissue
- mass spectrometry