A new model for diabetes-focused capacity building - lessons from Sri Lanka.
Anjan K SahaNaresh GunaratnamRashmi PatilMonica ChooDevika P BagchiEkta JhaveriJennifer WyckoffGaneika BahuUlysses BalisPaul ClydeWilliam H HermanPublished in: Clinical diabetes and endocrinology (2018)
Sri Lanka is experiencing a rapid increase in the number of people with diabetes mellitus (DM) due to population growth and aging. Physician shortages, outdated technology, and insufficient health education have contributed to the difficulties associated with managing the burden of disease. New models of chronic disease management are needed to address the increasing prevalence of DM. Medical students, business students, and faculty members from the University of Michigan partnered with the Grace Girls' Home, Trincomalee General Hospital, and Selvanayakapuram Central Hospital to identify and train diabetes-focused medical assistants (MAs) to collect and enter patient data and educate patients about their disease. Return visits to these MAs were encouraged so that patient progress and disease progression could be tracked longitudinally. Data entry was conducted through a cloud-based mechanism, facilitating patient management and descriptive characterization of the population. We implemented this pilot program in June 2016 in coordination with Trincomalee General Hospital and Selvanayakapuram Central Hospital. Over a 12-month period, 93 patients were systematically assessed by the medical assistants. All patients received education and were provided materials after the visit to better inform them about the importance of controlling their disease. Fifteen percent (14/93) of patients returned for follow-up consultation. Trained MAs have the potential to provide support to physicians working in congested health systems in low-resource settings. Public investment in training programs for MAs and greater acceptance by physicians and patients will be essential for handling the growing burden associated with chronic illnesses like DM. Trained MAs may also play a role in improved patient education and awareness regarding diabetes self-management.
Keyphrases
- healthcare
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- type diabetes
- cardiovascular disease
- prognostic factors
- case report
- primary care
- clinical trial
- peritoneal dialysis
- public health
- risk factors
- machine learning
- risk assessment
- medical students
- patient reported
- adverse drug
- mental health
- quality improvement
- metabolic syndrome
- acute care
- patient reported outcomes
- social media
- high resolution
- skeletal muscle
- sensitive detection
- data analysis