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A model to design financially sustainable algorithm-enabled remote patient monitoring for pediatric type 1 diabetes
Paul Dupenloup1, Ryan Leonard Pei1, Annie Chang1
1Department of Management Science and Engineering, Stanford University, Stanford, CA, United States.
Insights
Developing financially sustainable remote patient monitoring (RPM) programs for pediatric type 1 diabetes (T1D) is possible with algorithm-enabled continuous glucose monitor (CGM) data review. A financial model indicates a ~$10 reimbursement per telehealth interaction is needed for revenue neutrality.
Area of Science:
- Endocrinology
- Health Informatics
- Health Economics
Background:
- Population-level remote patient monitoring (RPM) using continuous glucose monitor (CGM) data improves outcomes in pediatric diabetes patients.
- Current reimbursement models do not adequately support population health management for RPM.
- Pediatric type 1 diabetes (T1D) clinics need financial models for sustainable RPM programs.
Purpose of the Study:
- To develop a financial model for pediatric T1D clinics to design sustainable algorithm-enabled RPM programs.
- To determine the minimum reimbursement rate for revenue neutrality in RPM telehealth interactions.
- To estimate potential revenue from RPM billing using existing CPT codes.
Main Methods:
- Data collected from a weekly RPM program for 302 pediatric T1D patients.
- A customizable financial model was created to calculate costs and revenues.
- Compared a baseline scenario with two RPM care delivery scenarios using algorithm-enabled, need-based messaging.
Main Results:
- An average reimbursement rate of approximately $10.00 USD per telehealth interaction is estimated to maintain revenue neutrality.
- Algorithm-enabled RPM can potentially be billed using existing RPM CPT codes.
- The model suggests potential for margin expansion through RPM implementation.
Conclusions:
- A financial model was designed to evaluate the impact of algorithm-enabled RPM in pediatric T1D clinics.
- The model identifies a reimbursement threshold for revenue neutrality and estimates potential RPM revenue.
- This tool can aid pediatric T1D clinics in planning for financially sustainable RPM programs offering flexible interventions.
Introduction:
Population-level algorithm-enabled remote patient monitoring (RPM) based on continuous glucose monitor (CGM) data review has been shown to improve clinical outcomes in diabetes patients, especially children. However, existing reimbursement models are geared towards the direct provision of clinic care, not population health management. We developed a financial model to assist pediatric type 1 diabetes (T1D) clinics design financially sustainable RPM programs based on algorithm-enabled review of CGM data.
Methods:
Data were gathered from a weekly RPM program for 302 pediatric patients with T1D at Lucile Packard Children's Hospital. We created a customizable financial model to calculate the yearly marginal costs and revenues of providing diabetes education. We consider a baseline or status quo scenario and compare it to two different care delivery scenarios, in which routine appointments are supplemented with algorithm-enabled, flexible, message-based contacts delivered according to patient need. We use the model to estimate the minimum reimbursement rate needed for telemedicine contacts to maintain revenue-neutrality and not suffer an adverse impact to the bottom line.
Results:
The financial model estimates that in both scenarios, an average reimbursement rate of roughly $10.00 USD per telehealth interaction would be sufficient to maintain revenue-neutrality. Algorithm-enabled RPM could potentially be billed for using existing RPM CPT codes and lead to margin expansion.
Conclusion:
We designed a model which evaluates the financial impact of adopting algorithm-enabled RPM in a pediatric endocrinology clinic serving T1D patients. This model establishes a clear threshold reimbursement value for maintaining revenue-neutrality, as well as an estimate of potential RPM reimbursement revenue which could be billed for. It may serve as a useful financial-planning tool for a pediatric T1D clinic seeking to leverage algorithm-enabled RPM to provide flexible, more timely interventions to its patients.
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