Predictive COPD RPM Resource Planning
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Solution Overview
Problem
Current remote patient monitoring (RPM) program managers face challenges in estimating long-term capacity management, leading to mismatches between patient needs and available resources, limiting the number of patients that can be enrolled in COPD RPM programs.
Innovation Solution
A system that predicts the number of patients needing enrollment in each RPM program by dynamically re-evaluating disease severity using modular assessment measures, allowing for optimized resource allocation and patient enrollment based on future predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If RPM program managers enroll as many patients as possible to maximize patient support, then the number of patients served increases, but the resource capacity becomes insufficient to manage them effectively
Solution Approach 1:
The system performs preliminary actions by predicting future patient enrollment numbers and resource requirements before they actually occur. The predictive model uses historical data and disease severity assessments to forecast how many patients will need RPM programs in the future, allowing managers to pre-allocate resources such as coaches and devices to match future demand rather than reacting to current enrollment numbers alone
Solution Approach 2:
The system introduces dynamics by continuously updating patient disease severity assessments and dynamically adjusting resource allocation decisions. The model re-evaluates patient needs over time and adjusts predictions based on changing conditions, allowing the RPM program to adapt resource distribution dynamically rather than using static enrollment numbers
2Ease of operation
If RPM program managers base resource allocation on current information only, then the decision-making process is simple, but long-term capacity management cannot be estimated accurately
Solution Approach 1:
The system performs preliminary analysis by building predictive models that incorporate historical enrollment data, disease severity trends, and resource consumption patterns. These models are trained in advance using past information to accurately forecast future capacity requirements, enabling precise long-term planning while maintaining operational simplicity through automated predictions
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual patient enrollment and resource usage against predicted values. The model uses this feedback to refine its predictions over time, improving accuracy of future capacity estimation while the automated feedback loop maintains simple decision-making processes for managers
3Reliability
If the number of patients enrolled in RPM program is limited by available resources, then resource sufficiency is maintained, but the number of patients who could benefit from the program is reduced
Solution Approach 1:
The system uses preliminary predictive modeling to determine optimal resource allocation decisions before enrollment occurs. By forecasting future patient needs and resource requirements in advance, the system identifies the maximum number of patients that can be served sustainably with available resources, allowing managers to enroll the optimal number of patients rather than being constrained by current resource levels alone
Solution Approach 2:
The system changes key parameters such as disease severity thresholds, patient prioritization criteria, and resource allocation ratios to maximize the number of patients served while maintaining resource sufficiency. The model can adjust these parameters dynamically based on predicted future conditions, allowing the program to serve more patients by optimizing how resources are distributed across different patient populations
Data Source
AI summary
A system and method is provided for planning resources for a plurality of COPD RPM programs. Each RPM program is tailored to a different level of disease acuity. Assessment data is obtained relating to the severity of COPD symptoms in respect of a set of patients currently following one of the RPM programs. For each patient, a prediction is made of which RPM program is most likely to be appropriate for the patient at a future time point and from this a required capacity for each RPM program at said future time point can be derived. The required resources for each RPM program can thus be determined.