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OS29 - e-Health Solutions for Patient Self Management (ID 47)
- Event: e-Health 2018 Virtual Meeting
- Type: Oral Session
- Track: Clinical Delivery
- Presentations: 1
- Coordinates: 5/30/2018, 10:30 AM - 12:00 PM, Granville II Room, Conference Level
OS29.05 - A Business Case for Artificial Intelligence in Remote Patient Monitoring (ID 470)
Purpose/Objectives: The use of Remote Patient Monitoring (RPM) technology to manage chronic disease patients is gaining popularity. Dealing with chronic conditions only at the acute state in an episodic manner has led to high demand and inefficiencies in most health care delivery systems (Thorpe et al. 2004). It has been suggested that early diagnosis, continuous care, and coordinated health care would reduce system burden and make chronic disease management more effective (Rajan et al. 2013). However inefficient use of technology can in fact lead to less efficiency itself. In our recent CPRPM study paramedics using RPM technology observed that close to 50% of the patient interactions were non-coachable moments. The introduction of Artificial Intelligence (AI) in the RPM system not only increases the efficiency of the system but also increases the engagement of patients for coaching purposes and to keep patients engaged. Preliminary research has differentiated between three roles: 1) patient monitoring provides patients with access to their daily readings, 2) provider feedback helps patients understand and learn how to self-manage their condition, and 3) digital leverages artificial intelligence to predict health risks and exacerbations. The objective of this study is to examines RPM technologies used by patients not in isolation, but rather within an ecosystem that includes learning technology, providers, patients, and feedback that supports and gently pushes or nudges positive health behaviors and outcomes.
Methodology/Approach: Observational study that utilizes a prospective longitudinal cohort design to compare the role of RPM across two use cases. The first use case is human-centric as its design is highly dependent on the active role of the provider to respond to systematic alerts and provide feedback. The second use case is technology-centric as its design incorporates data analytics and artificial intelligence to generate more customized alerts and predict risks and exacerbations. The outcomes evaluated are the impact of both use cases on healthcare system utilization (i.e., reduction in 911 calls, reduction in ED visits). EMS Service Rurality Target # Patients Use Case 1 Target # Patients Use Case 2 Total Sample EMS 1 Rural 10 10 20 EMS 2 Rural 15 15 30 EMS 3 Urban 10 10 20 EMS 4 Urban 15 15 30 Total 50 50 100
Finding/Results: A previous study of 212 participants involved in the first use case resulted in a 42% reduction in 911 calls and 40% reduction in ED visits. Although results were extremely promising, it is difficult to scale results to a larger population due to high demand on provider influence. By comparing the original use case with the technology-centric alternative, benefits and challenges for both use cases will be identified.
Conclusion/Implications/Recommendations: The implications of our study are relevant to organizations and government bodies involved in connecting the business of health and technology to transform health care into a more sustainable delivery model.
140 Character Summary: The introduction of smart home AI in the remote monitoring system not only increases the efficiency of the system but also increases the engagement of patients.
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