Peer-reviewed study demonstrates Waymark Signal is 90 percent accurate in predicting avoidable ER and hospital visits

同行评审的研究表明，路标信号在预测可避免的急诊室和医院就诊方面准确率为90%

SAN FRANCISCO, CA / ACCESSWIRE / January 23, 2024 / Waymark, the Medicaid provider enablement company, today published a peer-reviewed study in Nature's Scientific Reports comparing the performance of Waymark SignalTM, the company's proprietary machine learning technology, to conventional Medicaid risk models.

加利福尼亚州旧金山/ACCESSWIRE/2024年1月23日/Waymark，医疗补助提供者支持公司，今天在《自然》科学报告中发表了一项同行评审的研究，比较了该公司专有机器学习技术Waymark SignalTM与传统医疗补助风险模型的性能。

The study found that Waymark Signal was 90 percent accurate in predicting avoidable emergency room (ER) and hospital utilization for patients receiving Medicaid - stronger performance than leading Medicaid risk models in the field..

该研究发现，Waymark信号在预测接受医疗补助的患者的可避免急诊室（ER）和医院利用率方面准确率为90%，比该领域领先的医疗补助风险模型表现更好。。

Waymark Signal combines data on social risk factors and patient risk trajectories with healthcare utilization to identify patients at risk for preventable ER and hospital visits. As one of the largest and most representative comparisons of Medicaid risk models to date, the study assessed Waymark Signal's machine learning approach against traditional regression models for Medicaid, which rely primarily on patient demographics, healthcare diagnostic codes, and medications to predict at-risk patients.

Waymark Signal将社会风险因素和患者风险轨迹的数据与医疗保健利用相结合，以识别有风险进行可预防的ER和医院就诊的患者。作为迄今为止医疗补助风险模型的最大和最具代表性的比较之一，该研究评估了Waymark Signal的机器学习方法与传统的医疗补助回归模型，后者主要依赖患者人口统计学，医疗保健诊断代码和药物来预测高危患者。

Researchers found that Waymark Signal was 3x better at identifying at-risk patients and 10x better at predicting costs compared to conventional models..

研究人员发现，与传统模型相比，Waymark信号在识别高危患者方面优于3倍，在预测成本方面优于10倍。。

'This study demonstrates the potential for machine learning models like Waymark Signal to more accurately identify at-risk patients and drive more effective interventions,' said Sadiq Y. Patel, MSW, Ph.D., an author of the study and Data Science Lead for Waymark. 'By enabling care teams to better recognize and act on social and clinical risk factors, Waymark Signal can help them intervene to prevent avoidable disease complications, ER visits, and hospitalizations that negatively impact both patient health and costs.'.

“这项研究证明了像Waymark Signal这样的机器学习模型有潜力更准确地识别高危患者，并推动更有效的干预，”该研究的作者、Waymark数据科学负责人、MSW博士Sadiq Y.Patel说通过使护理团队能够更好地识别和应对社会和临床风险因素，Waymark Signal可以帮助他们进行干预，以预防可避免的疾病并发症，急诊就诊和住院治疗，这些都会对患者的健康和成本产生负面影响。”。

Additionally, the study found that Waymark Signal also reversed the Black-White prediction bias observed in most risk models. Because Black individuals typically have less access to higher-cost tertiary care centers, traditional cost-based models often under-predict their future costs and assume the lower costs reflect lower health needs.

此外，研究发现，路标信号也逆转了大多数风险模型中观察到的黑白预测偏差。由于黑人通常无法进入成本较高的三级保健中心，因此传统的基于成本的模型往往低估了他们未来的成本，并假设较低的成本反映了较低的健康需求。

Waymark Signal reversed this bias, demonstrating higher sensitivity for Black patients' needs and offering one approach for a more equitable application of machine learning in Medicaid risk modeling..

Waymark信号扭转了这种偏见，证明了对黑人患者需求的更高敏感性，并为在医疗补助风险建模中更公平地应用机器学习提供了一种方法。。

'This study makes an important contribution to the underserved area of Medicaid risk prediction,' said Will Shrank, MD, Venture Partner at Andreessen Horowitz and former Director of Evaluation, Innovation Center for the Centers for Medicare & Medicaid Services (CMS). 'While Medicare and commercial insurers have benefitted from numerous risk modeling advances, Medicaid programs have seen far fewer peer-reviewed studies achieving such a significant performance gain.

安德烈森·霍洛维茨（AndreessenHorowitz）风险合伙人、前医疗保险和医疗补助服务中心（CMS）创新中心评估主任威尔·斯莱克（WillShrank）医学博士说，这项研究为医疗补助风险预测服务不足的领域做出了重要贡献虽然医疗保险和商业保险公司受益于众多风险建模的进步，但医疗补助计划取得如此显着绩效收益的同行评审研究却少得多。

By tripling sensitivity without increasing false alerts, the authors' innovative approach using machine learning provides valuable guidance for more effectively addressing care disparities through personalized outreach to those enrolled in Medicaid.'.

通过在不增加错误警报的情况下将灵敏度提高三倍，作者使用机器学习的创新方法为通过个性化推广到医疗补助计划的参与者中更有效地解决护理差异提供了有价值的指导。

Waymark works directly with Medicaid health plans and primary care providers (PCPs) to deliver technology-enabled, community-based care for people enrolled in Medicaid. The company's local care teams use Waymark Signal to identify and outreach patients at-risk of avoidable ER and hospital utilization.

Waymark直接与医疗补助健康计划和初级保健提供者（PCP）合作，为参加医疗补助的人提供技术支持的社区护理。该公司的当地护理团队使用路标信号来识别和推广有可能避免急诊室和医院使用风险的患者。

As a public benefit company, Waymark has published the key methodological advancements from this study to enable Medicaid programs to apply them to their own data and populations..

作为一家公益公司，Waymark发布了这项研究的关键方法学进展，使医疗补助计划能够将其应用于自己的数据和人群。。

'This study shows that advanced data science and tools like Waymark Signal offer the potential to help us deliver more equitable and effective care that improves outcomes for the chronically underserved,' said Sanjay Basu, MD, Ph.D., Co-Founder and Head of Clinical at Waymark. 'Ultimately, that's why we founded Waymark.'.

Waymark联合创始人兼临床负责人桑杰·巴苏（SanjayBasu）博士说：“这项研究表明，Waymark Signal等先进的数据科学和工具有可能帮助我们提供更公平有效的护理，从而改善长期服务不足者的预后。”最终，这就是我们创建Waymark的原因。”。

Researchers used CMS data from 2017-2019 across 26 states and Washington, D.C., to compare the predictive power of Waymark Signal to conventional Medicaid risk models. The states used in the study were identified based on CMS' Data Quality Atlas, which assesses each state's enrollment benchmarks, claim volume, and data completeness..

研究人员使用了26个州和华盛顿特区2017-2019年的CMS数据，比较了Waymark信号与传统医疗补助风险模型的预测能力。研究中使用的州是根据CMS的数据质量地图集确定的，该地图集评估了每个州的入学基准，索赔数量和数据完整性。。

'Waymark's community-based care teams have been using Signal to identify and outreach at-risk Medicaid members in partnership with our health plan and provider partners,' said Aaron Baum, Ph.D., Analytics and Economics Lead at Waymark. 'These findings reaffirm what we've also seen in our data: that leveraging our data science capabilities can play a critical role in creating more accessible and equitable pathways to better health for underserved communities.'.

Waymark的分析和经济学负责人AaronBaum博士说，Waymark的社区护理团队一直在利用Signal与我们的健康计划和提供者合作伙伴合作，识别和推广处于风险中的医疗补助成员这些发现重申了我们在数据中所看到的：利用我们的数据科学能力可以在为服务不足的社区创造更容易获得和公平的健康途径方面发挥关键作用。”。

The full article titled 'Prediction of Non-Emergent Acute Care Utilization and Cost Among Patients Receiving Medicaid' was published in Scientific Reports, a peer-reviewed journal published by Nature. The authors for this article were Sadiq Y. Patel, MS, Ph.D., of Waymark; Aaron Baum, Ph.D., of Waymark; and Sanjay Basu, MD, Ph.D., of Waymark..

这篇题为“接受医疗补助的患者的非紧急急性护理利用率和成本预测”的全文发表在《科学报告》上，这是一本由《自然》杂志出版的同行评审期刊。本文的作者是Waymark的Sadiq Y.Patel，医学博士；Waymark的Aaron Baum博士；以及Waymark医学博士Sanjay Basu。。

About Waymark

关于Waymark

Waymark is a public benefit company dedicated to improving access and quality of care for people receiving Medicaid. We partner with health plans and primary care providers to reduce disparities and improve outcomes through technology-enabled, community-based care. Our local teams of community health workers, pharmacists, therapists and care coordinators use proprietary data science and machine learning technologies to deliver evidence-based interventions to hard-to-reach patient populations.

Waymark是一家公益公司，致力于改善接受医疗补助的人获得医疗服务的机会和质量。我们与卫生计划和初级保健提供者合作，通过技术支持的社区护理来减少差距并改善结果。我们当地的社区卫生工作者、药剂师、治疗师和护理协调员团队使用专有的数据科学和机器学习技术，为难以接触到的患者人群提供循证干预。

For more information, visit www.waymarkcare.com.

有关更多信息，请访问www.waymarkcare.com。

Contact Information

联系方式

Iman Rahim

Rahim游戏

Communications

通信

iman.rahim@waymarkcare.com

iman.rahim@waymarkcare.com

SOURCE: Waymark

来源：Waymark

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