KONINKLIJKE PHILIPS N.V. (20240212849). INTELLIGENT DROP-OUT PREDICTION IN REMOTE PATIENT MONITORING simplified abstract

INTELLIGENT DROP-OUT PREDICTION IN REMOTE PATIENT MONITORING

Organization Name

KONINKLIJKE PHILIPS N.V.

Inventor(s)

Dieter Maria Alfons Van De Craen of Eindhoven (NL)

Marten Piji 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 20240212849 titled 'INTELLIGENT DROP-OUT PREDICTION IN REMOTE PATIENT MONITORING

The present disclosure focuses on methods and systems for predicting patient dropout from a remote patient monitoring program and identifying root causes of dropout using a dropout prediction engine. This approach aims to detect dropout risk early and prevent it through targeted interventions.

• Data-driven approach to identify likely root causes of patient dropout
• Timely interventions to prevent dropout by addressing root causes
• Promotion of continued engagement with virtual care programs
• Lower costs of care, better health outcomes, and improved patient and staff experience
• Targeted interventions for dropout prevention

Potential Applications: - Healthcare industry for remote patient monitoring programs - Telemedicine services - Patient engagement platforms

Problems Solved: - Early detection of patient dropout risk - Prevention of dropout through targeted interventions - Improved patient engagement with virtual care programs

Benefits: - Lower healthcare costs - Better health outcomes - Enhanced patient and staff experience - Increased efficiency in remote patient monitoring programs

Commercial Applications: Title: "Innovative Dropout Prediction Engine for Remote Patient Monitoring Programs" This technology can be utilized by healthcare providers, telemedicine companies, and patient engagement platforms to enhance patient retention and improve overall outcomes. The market implications include increased efficiency, cost savings, and improved patient satisfaction.

Prior Art: Readers can explore prior research on patient dropout prediction in remote monitoring programs, data-driven approaches to healthcare interventions, and strategies for improving patient engagement in virtual care.

Frequently Updated Research: Stay informed about the latest developments in patient dropout prediction, remote monitoring technologies, and data-driven healthcare interventions to enhance patient outcomes and engagement.

Questions about Remote Patient Monitoring Dropout Prediction: 1. How does the dropout prediction engine analyze clinical features to identify root causes of patient dropout? 2. What are the key benefits of preventing patient dropout in remote monitoring programs?

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.