Secondary use of EMRs for surgical complication surveillance

二次使用 EMR 进行手术并发症监测

• 批准号: 9251814
• 负责人: HONGFANG LIU
• 金额: $ 30万
• 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 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9251814
• 关键词: Abscess; Address; Adoption; Age; Anesthetics; Area; Bayesian Method; Cessation of life; Clinic; Clinical; Clinical Research; Colorectal; Complex; Complication; Computerized Medical Record; Data; Decision Making; Detection; Development; Disease Outbreaks; Early Diagnosis; Early Intervention; Educational workshop; Engineering; Goals; Health Care Costs; Healthcare; Hemorrhage; Human; Ileus; Knowledge; Lead; Manuals; Methods; Minor; Motivation; Natural Language Processing; Nature; Nutritional; Operative Surgical Procedures; Output; Patients; Pattern; Perioperative; Pharmaceutical Preparations; Physicians; Process; Registries; Reporting; Research; Risk Factors; Science; Severities; Specialist; Statistical Models; Structure; Surgeon; Surgical complication; System; Testing; Text; Time; Translating; Uncertainty; Work; Wound Infection; base; clinical practice; computer based statistical methods; cost; disability; health care quality; health information technology; improved; innovation; patient safety; public health relevance; rapid growth; statistics

DESCRIPTION (provided by applicant): Recent statistics indicate that worldwide almost 234 million major surgical procedures are performed each year with the rates of major postsurgical complications (PSCs) range from 3% to 16% and rates of permanent disability or death range from 0.4% to 0.8%. Early detection of PSCs is crucial since early intervention could be lifesaving. Meanwhile, with the rapid adoption of electronic medical records (EMRs) and the accelerated advance of health information technology (HIT), detection of PSCs by applying advanced analytics on EMRs makes it possible for near real-time PSC surveillance. We have developed a rule-based PSC surveillance system to detect most frequent colorectal PSCs near real-time from EMRs where a pattern-based natural language processing (NLP) engine is used to extract PSC related information from text and a set of expert rules is used to detect PSCs. Two challenges are identified. First, it is very challenging to integrate a diverse set of relevant data using expert rules. In the past, probabilistic approaches such as Bayesian Network which can integrate a diverse set of relevant data have become popular in clinical decision support and disease outbreak surveillance. Can we implement probabilistic approaches for PSC surveillance? Secondly, a large portion of the clinical information is embedded in text and it has been quite expensive to manually obtain the patterns used in the NLP system since it requires team effort of subject matter experts and NLP specialists. In the research field, statistical NLP has been quite popular. However, decision making in clinical practice demands tractable evidences while models for statistical NLP are not human interpretable. Can we incorporate statistical NLP to accelerate the NLP knowledge engineering process? We hypothesize that a probabilistic approach for PSC surveillance can be developed for improved case detection which can integrate multiple evidences from structured as well as unstructured EMR data. We also hypothesize that empirical NLP can accelerate the knowledge engineering process needed for building pattern- based NLP systems used in practice. Specific aims include: i) developing and evaluating an innovative Bayesian PSC surveillance system that incorporates evidences from both structured and unstructured EMR data; and ii) incorporating and evaluating statistical NLP in accelerating the NLP knowledge engineering process of pattern-based NLP for PSC surveillance. Given the significance of HIT, our study results will advance the science in developing practical NLP systems that can be translated to meet NLP needs in health care practice. Additionally, given the significance of PSCs, our study results will address significant patient safety and quality issues in surgical practice. Utilizing automated methods to detect postsurgical complications will enable early detection of complications compared to other methods and therefore have great potential of improving patient safety and health care quality while reducing cost. The results could lead to large scale PSC surveillance and quality improvement towards safer and better health care.

描述（由申请人提供）：最近的统计数据表明，全球每年进行近2.34亿例大手术，严重术后并发症（PSC）的发生率范围为3%至16%，永久性残疾或死亡的发生率范围为0.4%至0.8%。PSC的早期检测至关重要，因为早期干预可能挽救生命。同时，随着电子病历（EMR）的快速采用和健康信息技术（HIT）的加速发展，通过对EMR应用高级分析来检测PSC使得近实时PSC监测成为可能。我们已经开发了一个基于规则的PSC监测系统，以检测最常见的结直肠PSC近实时的EMR，其中基于模式的自然语言处理（NLP）引擎被用来提取PSC相关的信息，从文本和一组专家规则被用来检测PSC。确定了两个挑战。首先，整合一套多样化的相关 数据使用专家规则。在过去，概率方法，如贝叶斯网络，可以集成不同的相关数据集已经成为流行的临床决策支持和疾病暴发监测。我们能否实施概率方法进行PSC监测？其次，很大一部分临床信息嵌入在文本中，手动获取NLP系统中使用的模式非常昂贵，因为它需要主题专家和NLP专家的团队努力。在研究领域中，统计NLP已经相当流行。然而，临床实践中的决策需要易于处理的证据，而统计NLP模型不是人类可解释的。我们能否结合统计NLP来加速NLP知识工程过程？我们假设可以开发一种用于PSC监测的概率方法，以改进病例检测，该方法可以整合来自结构化和非结构化EMR数据的多个证据。我们还假设，经验NLP可以加速知识工程的过程需要建立模式为基础的NLP系统在实践中使用。具体目标包括：i）开发和评估一个创新的贝叶斯PSC监测系统，该系统结合了来自结构化和非结构化EMR数据的证据;以及ii）在加速PSC监测的基于模式的NLP的NLP知识工程过程中结合和评估统计NLP。考虑到HIT的重要性，我们的研究结果将推动开发实用NLP系统的科学发展，这些系统可以被翻译以满足医疗实践中的NLP需求。此外，鉴于PSC的重要性，我们的研究结果将解决手术实践中的重大患者安全和质量问题。与其他方法相比，利用自动化方法检测术后并发症将能够早期检测并发症，因此在降低成本的同时具有提高患者安全性和医疗保健质量的巨大潜力。研究结果可能导致大规模PSC监测和质量改进，以实现更安全和更好的医疗保健。