Secondary use of EMRs for surgical complication surveillance

二次使用 EMR 进行手术并发症监测

基本信息

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
项目类别：
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

项目摘要

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 亿例大型外科手术，主要术后并发症 (PSC) 的发生率为 3% 至 16%，永久残疾或死亡的发生率为 0.4%至 0.8%。早期发现 PSC 至关重要，因为早期干预可以挽救生命。与此同时，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 进行高级分析来检测 PSC 使得近实时 PSC 监测成为可能。我们开发了一种基于规则的 PSC 监测系统，可从 EMR 近乎实时地检测最常见的结直肠 PSC，其中使用基于模式的自然语言处理 (NLP) 引擎从文本中提取 PSC 相关信息，并使用一组专家规则用于检测 PSC。确定了两个挑战。首先，整合各种相关的信息是非常具有挑战性的。使用专家规则的数据。过去，贝叶斯网络等可以整合多种相关数据的概率方法已在临床决策支持和疾病爆发监测中变得流行。我们可以采用概率方法进行 PSC 监测吗？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，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 模型则无法由人类解释。我们能否结合统计 NLP 来加速 NLP 知识工程进程？我们假设可以开发一种用于 PSC 监测的概率方法来改进病例检测，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 可以加速构建实践中使用的基于模式的 NLP 系统所需的知识工程过程。具体目标包括： i) 开发和评估创新的贝叶斯 PSC 监测系统，该系统融合了结构化和非结构化 EMR 数据的证据； ii) 纳入和评估统计 NLP，以加速用于 PSC 监测的基于模式的 NLP 的 NLP 知识工程过程。鉴于 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，这些系统可以转化为满足医疗保健实践中的 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重大的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及早发现并发症，因此在提高患者安全和医疗质量同时降低成本方面具有巨大潜力。结果可能导致大规模 PSC 监测和质量改进，以实现更安全、更好的医疗保健。