Harnessing the power of CTSA-CDRN data networks: Using social determinants of health, frailty and functional status to identify at-risk patients and improve risk adjustment

少数族裔和低社会经济地位者术后并发症和再住院率较高。 病人。低SES与虚弱有关，这是术后30天并发症的最佳预测因子之一 以及提前再入院。尽管脆弱和社会风险因素对健康结果有影响，但它们不会 在报销和质量措施的风险调整中考虑。CMS制定了财务激励措施- 以改善护理质量为基础的计划。然而，这一战略不成比例地惩罚了为少数群体服务的主要 教育和安全网医院(SNH)，进一步限制了照顾弱势群体的资源。 我们的长期目标是利用虚弱和社会风险因素来识别高危患者，以设计更多 有效的临床护理路径。脆弱可以追溯到使用美国外科医生学会 国家外科质量改进计划(ACS NSQIP)数据集。 数据网络是强大的研究工具，可以用来回答重要的问题。但是，提取 来自EHR的数据具有挑战性。以患者为中心的结果研究所(PCORI)制定了13项 与临床有相当多重叠成员的临床数据研究网络(CDRN) 翻译科学奖(CTSA)机构。虽然取得了稳步进展，但仍存在多重障碍 以高效地访问和使用数据。我们将与3个CTSA中心合作，每个中心属于不同的CDRN成员，以在当地 合并确定的数据集，制定不同机构的数据访问和链接策略 在CDRN内的站点之间传播，并最终在CDRN上进行类似的研究。我们将使用 智能IRB依赖平台，以尽可能地协调监管审批流程 本项目的步骤是确定在数据网络中使用的障碍。我们提出以下目标： 1)确定种族、种族、社会经济地位和脆弱程度对术后并发症、死亡率的预测力 和重新入院，以改善3个CTSA/CDRN的风险调整 2)使用自然语言处理(NLP)和机器学习来评估术后功能状态 急性冠脉综合征患者住院理疗(PT)、作业治疗(OT)的算法及护理要点 患者可以预测长期的功能状态 3)开发预测大手术后长期丧失独立性的方法 4)确定按SES、脆弱性和少数群体地位分层的医院资源利用情况 本研究的意义在于将社会风险因素、脆弱性和功能状态纳入风险中 调整是未来干预的基础，针对术后风险最高的患者 减少并发症和减少医疗保健差距。我们的创新方法利用了各种不同的数据源 这些机构的目标是在3个CDRN和CTSA网络中传播这些方法。

Postoperative complications and readmissions rates are higher in minority and low socioeconomic status (SES) patients. Low SES is associated with frailty, one of the best predictors of 30-day postoperative complications and early hospital readmission. Despite their influence on health outcomes, frailty and social risk factors are not considered in risk adjustment for reimbursement and quality measures. CMS developed financial incentive- based programs to improve quality of care. Yet this strategy disproportionately penalizes minority-serving, major teaching and safety net hospitals (SNH), further constraining resources for the care of vulnerable populations. Our long-term goal is to use frailty and social risk factors to identify at-risk patients to design more effective clinical care pathways. Frailty can be derived retrospectively using the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) dataset. Data networks are powerful research tools that can be used to answer important questions. However, extracting data from EHR is challenging. The Patient-Centered Outcomes Research Institute (PCORI) developed 13 Clinical Data Research Networks (CDRN) that have considerable overlapping membership with Clinical Translational Science Award (CTSA) institutions. While steady progress has been made, multiple barriers exist to efficiently access and use data. We will engage 3 CTSA hubs, each members of a different CDRN, to locally merge identified datasets developing data accessing and linking strategies at diverse institutions for dissemination across sites within CDRNs and to ultimately perform similar studies across CDRNs. We will use the SMART IRB reliance platform to harmonize the regulatory approval process as much as possible for each step of this project to identify barriers to use in data networks. We propose the following Aims: 1) Determine the predictive power of ethnicity, race, SES, and frailty for postoperative complications, mortality and readmissions to improve risk adjustment at 3 CTSA/CDRNs 2) Estimate postoperative functional status using natural language processing (NLP) and machine learning algorithms on inpatient physical therapy (PT), occupational therapy (OT) and nursing notes for ACS NSQIP patients to predict long-term functional status 3) Develop methods to predict long-term loss of independence after major surgery 4) Determine hospital resource utilization stratified by SES, frailty and minority status The significance of our study is the incorporation of social risk factors, frailty and functional status in risk adjustment forming the basis for future interventions by targeting patients at the highest risk for postoperative complications and reducing health care disparities. Our innovative approach harnesses data sources at diverse institutions with the goal of disseminating these methods across 3 CDRNs and the CTSA network.