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Secondary use of EMRs for surgical complication surveillance

EMR 二次用于手术并发症监测

基本信息

批准号：
10471838
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
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-05-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10471838
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 Medicare Modeling Monitor Nature Operative Surgical Procedures Organ Outcome Pancreas Patient Care Patient Monitoring Patients Performance Predictive Value 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 machine learning model medical specialties microbiome next generation novel payment predictive modeling rapid growth risk prediction model tertiary care transfer learning unstructured data

项目摘要

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.

项目概要术后并发症 (PSC) 已成为医院日益关注的问题，特别是考虑到支付改革侧重于较长的发作以及 30 天再入院和不良医疗保险的处罚结果包括深部或器官间隙手术部位感染（DOS-SSI）。因事后重新入院由于许多此类事件，出院并发症尤其已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）的广泛采用导致了许多 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区域（例如结直肠手术）以及应用复杂的统计建模/机器学习，以允许丢失数据、交互和非线性。然而，无论是在准确性还是普适性方面，仍有很大的改进空间。在我们的在当前资助期间，我们已经证明了临床记录对 PSC 的预测价值。然而，一当前模型的明显局限性是它们接受过大型三级医院的大量外科专业培训拥有高质量临床数据并使用先进信息学方法的护理机构。这件事的推动力该提案本质上有两个方面：(i) 可以为规模较小的机构创建准确的模型，通过转移学习和通过未经证实的结果（即那些模仿的结果）利用更多数据来获得专业知识黄金标准结果，但不太可靠），并适当考虑可靠性。 (ii) 决策可以是通过利用时变的实时数据（例如实验室、生命体征和临床记录）来显着改善使用所有可用信息为患者提供当前 PSC 风险。我们的目标是 i) 发展并应用 PSC 的纵向风险模型，以明确解释某些风险的随时间变化的性质信息（例如实验室、生命体征、临床记录）实时可用，以便可以集成到临床医生的决策； ii) 开发迁移学习并将其应用于 PSC 风险模型； iii) 开发建模允许使用更广泛可用的未经证实的结果，同时明确核算的方法与较少的结果相比，由于使用此类未经证实的结果而导致额外的不确定性和偏差可用的黄金标准； iv) 开发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通常不会像在研究中那样。该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，以便研究能够产生直接的临床影响。成功开发将打开为所有外科专业和所有领域提供下一代患者监测、警报和干预的大门机构。我们将公开相关的建模结果，以便交易量较小的机构能够利用此处开发的迁移学习方法，无需我们的实际数据。这将通过及早发现并发症并限制并发症，最终改善患者护理并降低总体成本由于梅奥诊所和全国其他机构的 PSC 再次入院。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery.

DOI：
10.4236/iim.2016.83006
发表时间：
2016-05
期刊：
Intelligent information management
影响因子：
0
作者：
Shen F;Liu H;Sohn S;Larson DW;Lee Y
通讯作者：
Lee Y

Leveraging Terminology Services for Extract-Transform-Load Processes: A User-Centered Approach.

利用术语服务进行提取-转换-加载过程：以用户为中心的方法。

DOI：
发表时间：
2016
期刊：
AMIA ... Annual Symposium proceedings. AMIA Symposium
影响因子：
0
作者：
Peterson,KevinJ;Jiang,Guoqian;Brue,ScottM;Liu,Hongfang
通讯作者：
Liu,Hongfang

Real-time risk prediction of colorectal surgery-related post-surgical complications using GRU-D model.

使用 GRU-D 模型实时预测结直肠手术相关术后并发症的风险。

DOI：
10.1016/j.jbi.2022.104202
发表时间：
2022
期刊：
Journal of biomedical informatics
影响因子：
4.5
作者：
Ruan,Xiaoyang;Fu,Sunyang;Storlie,CurtisB;Mathis,KellieL;Larson,DavidW;Liu,Hongfang
通讯作者：
Liu,Hongfang

Distinction between medical and non-medical usages of short forms in clinical narratives.

临床叙述中简短形式的医学和非医学用法的区别。

DOI：
发表时间：
2017
期刊：
AMIA ... Annual Symposium proceedings. AMIA Symposium
影响因子：
0
作者：
Moon,Sungrim;Ihrke,Donna;Zeng,Yuqun;Liu,Hongfang
通讯作者：
Liu,Hongfang

Ease of adoption of clinical natural language processing software: An evaluation of five systems.

DOI：
10.1016/j.jbi.2015.07.008
发表时间：
2015-12
期刊：
Journal of biomedical informatics
影响因子：
4.5
作者：
Zheng K;Vydiswaran VGV;Liu Y;Wang Y;Stubbs A;Uzuner Ö;Gururaj AE;Bayer S;Aberdeen J;Rumshisky A;Pakhomov S;Liu H;Xu H
通讯作者：
Xu H