18542971. INTELLIGENT DROP-OUT PREDICTION IN REMOTE PATIENT MONITORING simplified abstract (KONINKLIJKE PHILIPS N.V.)
Contents
INTELLIGENT DROP-OUT PREDICTION IN REMOTE PATIENT MONITORING
Organization Name
Inventor(s)
Dieter Maria Alfons Van De Craen of Eindhoven (NL)
INTELLIGENT DROP-OUT PREDICTION IN REMOTE PATIENT MONITORING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18542971 titled 'INTELLIGENT DROP-OUT PREDICTION IN REMOTE PATIENT MONITORING
The present disclosure focuses on predicting patient dropout from remote patient monitoring programs using a data-driven approach to identify root causes and prevent dropout through targeted interventions.
- The methods and systems described aim to address the clinical challenge of early detection of dropout risk in virtual care programs.
- The dropout prediction engine accurately identifies likely root causes of dropout, enabling timely interventions to prevent it.
- By promoting continued engagement with virtual care, the technology leads to lower costs of care, better health outcomes, and improved patient and staff experience.
Potential Applications: This technology can be applied in various healthcare settings utilizing remote patient monitoring programs to improve patient engagement and prevent dropout.
Problems Solved: The technology addresses the challenge of early detection of patient dropout from virtual care programs and enables targeted interventions to prevent it, leading to better outcomes and experiences for patients and staff.
Benefits: The technology offers a data-driven approach to predict and prevent patient dropout, ultimately improving the effectiveness of remote patient monitoring programs and enhancing patient care.
Commercial Applications: Title: Predictive Patient Dropout Technology in Remote Patient Monitoring This technology has commercial applications in healthcare organizations, telemedicine providers, and remote patient monitoring companies looking to improve patient engagement and outcomes.
Prior Art: For prior art related to predictive patient dropout technology in remote patient monitoring, researchers can explore existing literature on patient engagement in virtual care programs and predictive analytics in healthcare.
Frequently Updated Research: Researchers and healthcare professionals can stay informed on the latest developments in predictive analytics for patient engagement and dropout prevention in remote patient monitoring programs through industry conferences, academic journals, and healthcare technology forums.
Questions about Predictive Patient Dropout Technology in Remote Patient Monitoring: 1. How does this technology impact patient engagement in virtual care programs? 2. What are the key features of the dropout prediction engine in preventing patient dropout?
Original Abstract Submitted
The present disclosure is directed to methods and systems for predicting patient dropout from a remote patient monitoring (RPM) program, as well as root dropout causes, based on clinical features using a dropout prediction engine. As described herein, the methods and systems address the clinical challenge of early detection of dropout risk of patients from these virtual care programs through a data-driven approach that accurately identifies the likely root cause(s) of the dropout and enables the prevention of the dropout by applying timely interventions targeting the root causes of the dropout. As a result, dropout prevention effectuated through targeted interventions will promote continued engagement with virtual care, thereby leading to lower costs of care, better health outcomes, and better patient and staff experience.
