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Rick Whittaker
Author of
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OS23 - Engaging Patients Through Digital Health (ID 28)
- Event: e-Health 2017 Virtual Meeting
- Type: Oral Session
- Track: Clinical
- Presentations: 1
- Coordinates: 6/07/2017, 08:30 AM - 10:00 AM, Room 203CD
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OS23.05 - Who’s the Optimal Candidate for Remote Patient Monitoring? (ID 254)
- Abstract
Purpose/Objectives: Remote patient monitoring (RPM) is a healthcare intervention that aims to improve the management of chronic diseases where information and timely access can improve healthcare as well as reduce system costs by eliminating unnecessary 911 calls and emergency room visits. One of the greatest challenges of a remote patient monitoring (RPM) program is to identify patients who are the best candidates for the technology, patients that will respond well and enable the most benefit. Research focused on value-based approaches to care, such as the Center for Connected Health Policy and the University of Mississippi Medical Center in the United States, have recognized patient identification as an important part of RPM and are leveraging predictive analytics to identify optimal candidates but do not have an answer yet[1]. The Community Paramedic Remote Patient Monitoring program (CPRPM) has used the aggregate data of patients to explore the impact of specific patient characteristics (e.g., age, gender) as well as aspects of RPM training and education that drive better outcomes for patients as well as savings to the health care system. [1] http://m.healthcareitnews.com/news/ummc-pinpoints-ideal-patients-remote-monitoring-predictive-analytics-artificial-intelligence?mkt_tok=eyJpIjoiWW1ZMU16bGxabUptWVRkaiIsInQiOiJKcnk2YmlcL3hHbTVtTWdGQ1BWMjIyM1E0YmIwKzUxZVhMXC9vZTRVbk0wbjR6VFcxelZsSXc4VWIzaGx0RkJFUUhJV0hDRnV6Z3hHR2t5YlNxdW1jWHcyajRidnl0b3hqS3B3dnR4aDVGU0Y4PSJ9
Methodology/Approach: Data for the study was gathered from three administrative datasets. First, Interdev is a private firm that provided information about the number of 911 calls made by patients on the program, and the number of patient transports to the ER. Patient enrollment forms were used to gather demographic data, namely age, gender, and community. Finally, daily readings from patients remote monitoring devices were used to measure number of alerts and compliance rates. Although the study is ongoing, Wave 1 analysis of 79 patients and over 600,000 patient device readings and alerts is complete. Transcripts from interviews with 15 paramedics across seven communities was also used to understand the impact of RPM training and education.
Finding/Results: Our research team specializes in the study of 911:Tranport conversion rates. Our conceptual approach is developed from the perspective that RPM should inform and educate patients to call 911 only when appropriate that is, when they actually need to be transported to the hospital. Our preliminary results show that overall the RPM program increased the conversion rate; however, perhaps more interesting is that patients that were 100% compliant in using the technology had significantly higher conversion rates than patients that were less compliant. As illustrated in the graphs below, compliance rates in using the devices (specifically scale and blood pressure cuff) significantly vary across communities. Our team is currently exploring results further by examining the impact of patient characteristics (e.g., age, gender etc.) as well as provider support and training on conversion rate improvements.
Conclusion/Implication/Recommendations: Our research provides evidence that the ability to select the best candidates for a RPM program will significantly improves both patient and system outcomes. Although RPM has its benefits, it is possible that this approach to care is not for everyone. Knowing what patients are most likely to generate the most value will make RPM programs more efficient.
140 Character Summary: Developing accurate selection tools that identify optimal patients charateristics is critical for a successful and sustainable remote monitoring program.
