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A novel use of web-based software to efficiently triage pre-surgical patients bas

一种基于网络的软件的新颖用途，可有效对术前患者进行分类

基本信息

批准号：
8233257
负责人：
Alicia Gruber Kalamas
金额：
$ 54.61万
依托单位：
MEDSLEUTH, INC.
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-08-01 至 2012-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8233257
关键词：
Address Adoption Agreement Algorithms Ambulatory Care Facilities American Anesthesia procedures Classification Client satisfaction Clinic Clinical Complex Computer software Control Groups Cost Savings Day Surgery Diagnostic tests Ensure Evaluation Failure Feedback General Hospitals Generations Goals Gold Growth Health Care Costs Health Personnel Health Status Hospitals Interview Laboratories Legal patent Machine Learning Marketing Massachusetts Measures Medical Medical History Methods Morbidity - disease rate Online Systems Operative Surgical Procedures Outcome Outcome Study Output Patient Schedules Patients Perioperative Persons Pharmaceutical Preparations Phase Procedures Process Protocols documentation Quality of Care Relative Risks Reporting Research Design Research Methodology Resources Risk Risk Factors Site Small Business Innovation Research Grant Societies Surveys System Technology Testing Time Triage United States base cost falls improved medical schools mortality novel patient oriented patient population patient safety phase 1 study physical state prototype public health relevance response satisfaction

项目摘要

DESCRIPTION (provided by applicant): This is a Fast-Track application to develop a web-based, patient-centered software product that accurately assesses a patient's perioperative risk as a means of improving quality of care and reducing costs. Approximately 40 million surgical procedures are performed annually in the United States [1]. To ensure the safety of patients undergoing these procedures, it is imperative to identify and mitigate perioperative risk. Unfortunately, the process used by most hospitals and surgical centers to evaluate pre-surgical patients falls short on two fronts. One is a failure to identify risk factors in a timely fashion, as most preoperative evaluations occur the day before or day of surgery. The second is a failure to properly identify risk factors due to incomplete or inaccurate preoperative evaluations. These shortcomings increase morbidity and mortality, increase healthcare cost, and lower patient satisfaction. Therefore, a standardized preoperative assessment delivered in a timely fashion is needed. To address this need, we (MedSleuth, Inc.) have developed web-based software that utilizes a patent- pending algorithm to generate a customized patient survey, based on the patient's medication profile and successive responses to the survey. The survey output takes the form of a comprehensive medical history, triages patients based on health status, and provides the patient-specific information required by healthcare providers to identify and mitigate perioperative risk. Conservatively, it is estimated $10 billion could be saved annually (~25% of total spend) through our approach, with similarly sizable improvements in quality and satisfaction. Our Phase I study will evaluate proof of concept for the first-generation software with one collaborating hospital system (Massachusetts General Hospital, Harvard Medical School) over the course of a six- month period. Phase I will seek to prove (1) patients can successfully complete the web-based survey; (2) the output generated by the survey is accurate, comprehensive and relevant for making informed clinical decisions; (3) our assessment algorithm is equivalent or superior to the status quo in identifying perioperative risk; (4) patients and healthcare providers report high levels of satisfaction; and (5) preoperative evaluation costs can be substantially reduced. In Phase II we will incorporate patient and healthcare provider feedback from Phase I to develop the more robust second-generation version of the web-based software. We will in turn test this second- generation software on a much larger patient population across multiple surgical sites to verify clinical accuracy and completeness, cost savings, and increased satisfaction. At the conclusion of Phase II, we expect to have a market ready product with documented outcomes. PUBLIC HEALTH RELEVANCE: A need exists for a system that can efficiently and effectively triage patients based on perioperative risk, thereby focusing resources on those patients with complex medical problems while improving quality and satisfaction for all. We (MedSleuth, Inc.) have developed a first-generation web-based patient- centric software product that standardizes and streamlines the way a patient's medical history is elicited and recorded. This is accomplished by applying patent-pending machine learning technology to tailor a real-time survey based on each patient's medication profile and successive responses during the survey. We hypothesize that (1) patients can successfully complete the web-based survey on their own; (2) clinicians find the output of the survey relevant, accurate, and superior to current methods for making informed clinical decisions related to the surgical procedure; (3) patients and healthcare providers report high levels of satisfaction with the survey; (4) quality of care is improved; and (5) costs are reduced.

描述（由申请人提供）：这是一个快速通道应用程序，用于开发基于网络、以患者为中心的软件产品，该产品可以准确评估患者的围手术期风险，作为提高护理质量和降低成本的一种手段。美国每年进行大约 4000 万例外科手术[1]。为了确保接受这些手术的患者的安全，必须识别和减轻围手术期风险。不幸的是，大多数医院和外科中心用于评估术前患者的流程在两个方面存在缺陷。一是未能及时识别危险因素，因为大多数术前评估发生在手术前一天或手术当天。第二是由于术前评估不完整或不准确而未能正确识别危险因素。这些缺点增加了发病率和死亡率，增加了医疗费用，并降低了患者满意度。因此，需要及时进行标准化的术前评估。为了满足这一需求，我们（MedSleuth, Inc.）开发了基于网络的软件，该软件利用正在申请专利的算法，根据患者的用药概况和对调查的连续响应来生成定制的患者调查。调查输出采用全面病史的形式，根据健康状况对患者进行分类，并提供医疗保健提供者所需的患者特定信息，以识别和减轻围手术期风险。保守地说，通过我们的方法，估计每年可以节省 100 亿美元（约占总支出的 25%），同时质量和满意度也能得到类似的大幅提高。我们的第一阶段研究将在六个月的时间内与一个合作医院系统（麻省总医院、哈佛医学院）评估第一代软件的概念验证。第一阶段将力求证明 (1) 患者能够成功完成网络调查； (2) 调查产生的结果准确、全面且与做出明智的临床决策相关；（3）我们的评估算法在识别围手术期风险方面相当于或优于现状； (4) 患者和医疗保健提供者的满意度很高； (5)术前评估费用可大幅降低。在第二阶段，我们将结合第一阶段的患者和医疗保健提供者的反馈，开发更强大的第二代网络软件版本。反过来，我们将在多个手术部位的更多患者群体中测试该第二代软件，以验证临床准确性和完整性、节省成本并提高满意度。在第二阶段结束时，我们预计将有一个市场就绪的产品，并记录结果。公共卫生相关性：需要一种能够根据围手术期风险高效且有效地对患者进行分类的系统，从而将资源集中在具有复杂医疗问题的患者身上，同时提高所有人的质量和满意度。我们（MedSleuth, Inc.）开发了第一代基于网络、以患者为中心的软件产品，该产品标准化并简化了患者病史的获取和记录方式。这是通过应用正在申请专利的机器学习技术来根据每个患者的药物概况和调查期间的连续响应定制实时调查来实现的。我们假设（1）患者能够自行成功完成网络调查； (2) 临床医生发现调查结果相关、准确，并且优于当前做出与手术程序相关的明智临床决策的方法； (3) 患者和医疗保健提供者对调查表示高度满意； (4) 护理质量提高； (5)降低成本。