Chronic diseases such as diabetes are among the most widespread, expensive, and preventable of all health problems, accounting for approximately 86 percent of the United States’ $2.7 trillion annual health care expenditures. In the face of such staggering numbers, it is surprising that our current approach to chronic disease care management has remained largely unchanged for decades, where the care team evaluates the patient and related data infrequently and episodically. However, mobile care management (mCare) information system use is growing, whereby individuals with chronic medical conditions such as diabetes are taught to monitor and manage their disease through the use of a mobile application for tracking, education and feedback, along with monitoring of vital signs with “connected” medical devices, and the support of a remote health coach. These mCare systems offer promise, but many unanswered questions exist surrounding their effects on the health and healthcare of the users, and how user individual differences may influence these effects.
Informed primarily by the mobile health systems and health behavior literatures, this study provided a deeper understanding of the effects of an mCare platform on health outcomes and health services utilization of chronic disease patients, principally those with diabetes mellitus, and the effects of a user’s social support on these outcomes. This study analyzed administrative claims, device readings, app usage, demographic and social determinant data of 163 diabetic mCare users from a 21-week observation period from mCare initiation, along with a well-matched control group of diabetic non-users, and a supplemental cohort of 127 non-diabetic mCare users with other chronic medical conditions.
mCare had a significant positive effect on users’ adherence to physician’s office visits, suggesting greater continuity of care, chronic care management, and a possible reduction in inpatient use (1.2 fewer encounters over 5 months, on average).
The findings show that mCare had a significant beneficial effect, on average, towards the cardiovascular health of the users as measured by the change in their diastolic blood pressure (−2.8 mmHg, −3.3%) and systolic blood pressure (−6.7 mmHg, −4.9%) in the five-month observational period, which is a primary therapeutic target for diabetes care and clinically important. Furthermore, linear mixed models of cardiovascular outcomes uncovered how those mCare users with a moderate degree of social support are likely to achieve greater benefit in from mCare on average relative to those with very high or very low social support in their lives. This additional impact equated to on average a 2.4 mmHg drop (2.9%) in diastolic blood pressure and a 3.9 mmHg (3.1%) drop in systolic blood pressure over the five-month observational period, which is clinically significant. These results provide evidence to support a more precisely tailored future healthcare paradigm beyond the current one-size-fits-all archetype.
A primary goal of mCare is triaging emergency department use where appropriate; however, this study found that this did not happen in a significant manner in the treatment group compared to the control group. Furthermore, the study identified specific medical problems where improved mCare design is needed, including processes to prevent hyperglycemia, hypoglycemia and exacerbations of hypertension and pulmonary issues (such as asthma and chronic obstructive pulmonary disease), and a need to assess pain more effectively to foster more appropriate healthcare utilization.
Additional training for health coaches, as well as training and development of machine intelligence algorithms to better triage patient problems to appropriate sites of care, are productive directions for future research. mCare designers should seek to better gauge the severity of pain, and develop new sensor technologies to assess emergent issues, especially abdominal pain. mCare vendors should also seek to refine their processes to better manage glucose and respiratory issues to avoid exacerbations, and predict exacerbations earlier to intervene.
|Advisor:||St. Jean, Beth, Butler, Brian|
|Commitee:||Bjarnadottir, Margret, Choe, Eun Kyoung, Gao, Guodong (Gordon)|
|School:||University of Maryland, College Park|
|Department:||Library & Information Services|
|School Location:||United States -- Maryland|
|Source:||DAI-A 81/8(E), Dissertation Abstracts International|
|Subjects:||Information science, Health sciences, Information Technology|
|Keywords:||Diabetes, Digital health, Healthcare management, Information systems, mCare, Mobile health|
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