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use cases

Remote Patient Monitoring

AI-based models can accurately predict which patients are most likely to experience adverse events.

Problem

Remote patient monitoring (RPM) presents a key opportunity to improve care management for chronic diseases—the leading cause of death and disability (1). The potential impact on health and financial outcomes is immense. The United States spends roughly $3.4 trillion annually on people with chronic conditions, and a staggering 60% of adults have at least one chronic disease (1,2). RPM’s ability to collect and transmit health data outside of a conventional care setting is ideal to help alleviate the burden of chronic conditions on healthcare organizations (HCOs). It provides the data necessary to consistently measure and respond to changes in health, enable better healthcare resource allocation, and foster improved patient engagement.

Size of the Problem

  • $3.4 trillion is the total U.S. spending annually on chronic conditions (1).
  • Up to 40% reduction in hospitalizations for some chronic diseases is achievable through RPM programs (4).
  • $6,500 is the estimated annual savings per chronic disease patient through RPM (4).
  • 60% of adults in the U.S. have at least one chronic disease (2).

Why it matters

RPM facilitates regular assessment without relying on frequent appointments and can help to potentially prevent acute clinical events. Rather than having to delay examinations and treatment until a scheduled appointment, RPM provides sustained collection of vital data and behaviors, such as changes in blood pressure, heart rate, or activity levels. Armed with this information, care teams and patients can proactively take action before chronic conditions worsen to the point of requiring hospitalization or a visit to the emergency department.

Solution

As a source of data, RPM has incredible potential to improve outcomes, but to fully realize its value, HCOs must integrate it with the right analytics capabilities. AI-based models can ingest this ongoing data stream and accurately predict which patients are most likely to experience adverse events, surface the specific risk factors assessed in making predictions, and optimize proactive engagement and interventions. For care teams, AI can display these insights in existing clinical workflows to streamline outreach. It can also promote patient engagement by providing them with far greater insight into their own health and recommendations that foster healthy behaviors.Leveraged proactively, RPM programs can significantly improve disease management. In a study from the national association of America’s Health Insurance Plans, an HCO reported their Medicare members enrolled in the program were 76% less likely to experience readmissions (3). Another HCO reported that they saved $3.30 for every $1 spent on implementation.

Datasources

  • Remote Monitoring Data: Remote monitoring data capture key vital signs and health behaviors (e.g. blood pressure, heart rate, blood glucose, activity levels, etc.).
  • Electronic Health Records: EHR data with comprehensive patient histories of vital signs and symptoms, problem lists and chief complaints, tests results, diagnoses and procedures, and prescriptions.
  • ADT Records: Data from Admit, Discharge, and Transfer feeds and patient data notification services that synchronize patient demographic, diagnostic, and visit information across healthcare systems.

Citations

  1. “About Chronic Diseases.” Centers for Disease Control and Prevention, 2021. Accessed 14 May 2021.
  2. Buttorff, Christine, et al. “Multiple Chronic Conditions in the United States.” Rand Corporation, 2017, doi. Accessed 14 May 2021.
  3. Coalition to Transform Advanced Care. “Leveraging Telehealth To Support Aging Americans.” America's Health Insurance Plans, Oct. 2018. Accessed 14 May 2021.
  4. Hodin, Michael. “The Medical Technology That Could Save the US Billions Each Year.” The Fiscal Times, 3 Mar. 2017. Accessed 14 May 2021.

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