Searchable abstracts of presentations at key conferences in endocrinology
Endocrine Abstracts (2016) 43 OC19 | DOI: 10.1530/endoabs.43.OC19

WCTD2016 Abstract Topics Design a Clinical Program for Success (17 abstracts)

Development of an electronic clinical decision support system: “mWellcare – an Integrated mHealth System for Prevention and Care of Chronic Diseases”

Devraj Jindal 1 , Dilip Jha 1 , Priti Gupta 1 , Ajay S. Vamadevan 1 , Ambuj Roy 4 , Vidya Venugopal 1 , David Prieto-Merino 3 , Pablo Perel 3 , Nikhil Tandon 2 , Vikram Patel 3 & Dorairaj Prabhakaran 1


1The Centre for Chronic Conditions and Injuries, Public Health Foundation of India, New Delhi, India; 2Department of Endocrinology & Metabolism, All India Institute of Medical Sciences, New Delhi, India; 3Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK; 4Department of Cardiology, All India Institute of Medical Sciences, New Delhi, India.


Background: Diabetes and hypertension are among leading causes of premature adult deaths in India. Innovative approaches such as electronic clinical decision support systems (DSS) could play major role in improving quality and access to diabetes and hypertension care at primary care settings.

Objective: The main objective of this study is to develop an innovative tablet-computer based DSS namely “m-WELLCARE”, and other essential support healthcare processes for facilitating evidence-based diabetes and hypertension care at primary care.

Methods: A multidisciplinary team of researchers, clinicians, administrators and software experts used mixed methods to design and develop the mWellcare in six iterative steps: 1) Literature review and expert consultation; 2) Needs assessment; 3) Adapting the clinical management guideline to local context; 4) Validation of clinical algorithms 5) Identifying support healthcare processes and 6) Field testing of the mWellcare at five Community Health Centers in India.

Results: The above steps provided inputs for designing core-features of the DSS which include: Computation of personalized evidence-based management plan for diabetes, hypertension and co-morbid conditions (depression and alcohol use disorder); Assessment of cardiovascular risk using a re-calibrated Framingham-Risk function; Graphical display of patient clinical parameters; Minimum or nil chance of duplicate records; Access to patient’s previous visit records; Case data sharing between doctors and nurses electronically or through printout; and Short Message Service reminder for the patients.

Conclusion: Development of electronic DSS for diabetes and hypertension care for the use at resource poor settings is a complex process. Learning from this study can serve as resource for developing similar applications for decision support enabled interventions.

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