Department of Management Science and Engineering, Stanford University, Stanford, CA, United States.
Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States.
Front Endocrinol (Lausanne). 2022 Nov 11;13:1021982. doi: 10.3389/fendo.2022.1021982. eCollection 2022.
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.
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.
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.
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.
基于连续血糖监测（CGM）数据审查的人群水平算法支持的远程患者监测（RPM）已被证明可以改善糖尿病患者的临床结果，尤其是儿童。然而，现有的报销模式是针对直接提供诊所护理，而不是人群健康管理。我们开发了一种财务模型，以帮助儿科 1 型糖尿病（T1D）诊所根据 CGM 数据的算法审查设计财务可持续的 RPM 计划。
从 Lucile Packard 儿童医院 302 名患有 T1D 的儿科患者的每周 RPM 计划中收集数据。我们创建了一个可定制的财务模型，以计算提供糖尿病教育的每年边际成本和收入。我们考虑了基线或现状情况，并将其与两种不同的护理提供情况进行了比较，在这两种情况下，根据患者的需要，常规预约会补充以算法为基础、灵活的基于消息的联系。我们使用该模型来估计远程医疗联系所需的最低报销率，以保持收支平衡，不会对底线产生不利影响。
财务模型估计，在这两种情况下，平均每一次远程医疗互动约 10.00 美元的报销率就足以保持收支平衡。算法支持的 RPM 可以通过使用现有的 RPM CPT 代码进行计费，并可能导致利润率扩大。
我们设计了一个模型，该模型评估了在一家为 T1D 患者服务的儿科内分泌诊所采用基于算法的 RPM 的财务影响。该模型为维持收支平衡确定了一个明确的报销值阈值，以及一个可计费的潜在 RPM 报销收入估计。它可以作为儿科 T1D 诊所的一种有用的财务规划工具，旨在利用基于算法的 RPM 为其患者提供灵活、更及时的干预措施。
