Secondary use of EMRs for surgical complication surveillance

EMR 二次用于手术并发症监测

• 批准号: 10001498
• 负责人: HONGFANG LIU
• 金额: $ 64.37万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001498
• 关键词: Accounting; Adoption; Adverse event; Area; Blood Vessels; Breast; Clinic; Clinical; Clinical Data; Clinical Research; Colorectal Surgery; Complex; Complication; Computerized Medical Record; Data; Data Science; Decision Making; Development; Electronic Health Record; Evaluation; Event; Feedback; Funding; Glare; Gold; Health Care Costs; Hospitals; Image; Informatics; Institution; Intervention; Lead; Light; Machine Learning; Medicare; Modeling; Monitor; Nature; Operative Surgical Procedures; Organ; Outcome; Pancreas; Patient Care; Patient Monitoring; Patients; Performance; Predictive Value; Psychological Transfer; Reporting; Research; Research Proposals; Risk; Statistical Models; Surgical Specialties; Surgical Wound Infection; Surgical complication; Text; Time; Training; Translating; Uncertainty; adverse outcome; clinical implementation; clinical practice; clinical risk; cost; data quality; experience; genetic information; health care quality; high dimensionality; hospital readmission; improved; indexing; medical specialties; microbiome; next generation; novel; payment; predictive modeling; rapid growth; risk prediction model; tertiary care; unstructured data

Project Summary Post-surgical complications (PSCs) have been an increasing concern for hospitals, particularly in light of payment reform focusing on longer episodes and Medicare penalties for 30-day readmissions and adverse outcomes including deep or organ space surgical site infections (DOS-SSIs). Readmission due to post- discharge complications in particular has become a target for quality improvement since many of these events are considered preventable. The wide adoption of electronic health records (EHRs) has led to a number of clinical risk models for PSCs. These modeling efforts have primarily been targeted at the surgical specialty areas within which a large number of events occur (such as colorectal surgery) as well as applying sophisticated statistical modeling / machine learning to allow for missing data, interactions, and nonlinearities. However, there is still considerable room for improvement both in terms of accuracy and generalizability. In our current funding period, we have demonstrated the predictive value of clinical notes for PSCs. However, one glaring limitation of current models is that they are trained on high volume surgical specialties at large tertiary care institutions with high quality clinical data and use of advanced informatics approaches. The impetus of this proposal is essentially two-fold: (i) Accurate models can be created for lower volume institutions and specialties via transfer learning and leveraging more data via unconfirmed outcomes (i.e., those that mimic gold standard outcomes, but are less reliable) with proper accounting of reliability. (ii) Decision making can be significantly improved by leveraging time varying, real-time data such as labs, vitals, and clinical notes to provide the current risk of PSCs for patients using all information as it becomes available. We aim to i) develop and apply longitudinal risk models for PSCs to explicitly account for the time varying nature of some of the information (e.g., labs, vitals, clinical notes) as it becomes available in real-time so that it can be integrated into the clinician’s decision making; ii) develop and apply transfer learning to PSC risk models; iii) develop modeling approaches that allow for the use of more widely available unconfirmed outcomes, while explicitly accounting for the additional uncertainty and bias due to the use of such unconfirmed outcomes when compared to a less available gold standard; and iv) develop a widely applicable framework for model evaluation and monitoring. Models will often not perform in practice as they do in research for a variety of reasons. This framework will allow us to identify these issues and more efficiently translate and apply these complex predictive models into practice so that the research can have an immediate clinical impact. Successful development would open the door for next generation patient monitoring, alerts, and interventions for all surgical specialties and all institutions. We will make the relevant modeling results publicly available so that lower volume institutions can leverage the transfer learning approach developed here without the need for our actual data. This will ultimately lead to improved patient care and lower overall cost by identifying complications early and limiting readmission due to PSCs at Mayo Clinic and other institutions across the nation.

项目摘要 术后并发症（PSC）已成为医院日益关注的问题，特别是考虑到 支付改革，重点是更长的发作和医疗保险的30天再入院和不利的处罚 结果包括深部或器官间隙手术部位感染（DOS-SSI）。因术后再入院- 特别是出院并发症已经成为质量改进的目标， 被认为是可以预防的。电子健康记录（EHR）的广泛采用导致了一些 PSC的临床风险模型。这些建模工作主要针对外科专业 发生大量事件的领域（如结直肠手术）以及应用 复杂的统计建模/机器学习，以允许缺失数据、交互和非线性。 然而，在准确性和普遍性方面仍有相当大的改进余地。在我们 在当前的资助期内，我们已经证明了临床记录对PSC的预测价值。不过有一 当前模式的明显局限性是，他们在大部分高等教育中接受了大量外科专业培训， 医疗机构提供高质量的临床数据和使用先进的信息学方法。这件事的推动力 建议基本上是双重的：（i）可以为低容量机构创建准确的模型， 专业通过迁移学习和利用更多的数据通过未经证实的结果（即，那些模仿 金标准结果，但不太可靠），并适当考虑可靠性。(ii)决策可以是 通过利用实验室、生命体征和临床记录等时变实时数据， 使用所有可用信息为患者提供PSC的当前风险。我们的目标是i）发展 并应用PSC的纵向风险模型，明确说明一些 信息（例如，实验室、生命体征、临床记录），以便能够将其集成到 临床医生的决策; ii）开发并将迁移学习应用于PSC风险模型; iii）开发建模 允许使用更广泛获得的未经证实的结果的方法，同时明确说明 由于使用此类未经证实的结果而产生的额外不确定性和偏倚， 现有的黄金标准;以及四）制定一个广泛适用的模型评估和监测框架。 由于各种原因，模型在实践中的表现往往不如在研究中的表现。这一框架将 使我们能够识别这些问题，并更有效地将这些复杂的预测模型转化和应用到 实践，以便研究能够立即产生临床影响。成功的开发将开启 为所有外科专业和所有医疗器械提供下一代患者监测、警报和干预的大门 机构职能体系我们将公开相关的建模结果，以便低容量机构可以 利用这里开发的迁移学习方法，而不需要我们的实际数据。这将 通过早期发现并发症并限制患者的死亡率， 由于PSC在马约诊所和全国其他机构再次入院。