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

• 批准号: 8209311
• 负责人: Alicia Gruber Kalamas
• 金额: $ 105.3万
• 依托单位: MEDSLEUTH, INC.
• 依托单位国家: 美国
• 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/8209311
• 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

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，用于开发基于网络的、以患者为中心的软件产品，该软件产品可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。 在美国，每年约有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过程在两个方面都福尔斯不足。一个是未能及时识别风险因素，因为大多数术前评估发生在手术前一天或手术当天。第二种是由于术前评估不完整或不准确而未能正确识别风险因素。这些缺点增加了发病率和死亡率，增加了医疗保健成本，并降低了患者满意度。因此，需要及时提供标准化的术前评估。 为了满足这一需求，我们（MedSleuth，Inc.）已经开发了基于网络的软件，该软件利用正在申请专利的算法，根据患者的药物概况和对调查的连续响应，生成定制的患者调查。调查结果以全面的病史为形式，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。保守估计，通过我们的方法，每年可以节省100亿美元（约占总支出的25%），质量和满意度也有相当大的提高。 我们的第一阶段研究将在六个月的时间内评价第一代软件与一个合作医院系统（马萨诸塞州总医院、哈佛医学院）的概念验证。第一阶段将试图证明：（1）患者能够成功完成基于网络的调查;（2）调查生成的结果准确、全面，并且与做出知情的临床决策相关;（3）我们的评估算法在识别围手术期风险方面等同于或上级现状;（4）患者和医疗保健提供者报告满意度高;和（5）术前评估成本可以显著降低。 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。我们将反过来在多个手术部位的更大患者人群中测试这种第二代软件，以验证临床准确性和完整性，节省成本和提高满意度。在第二阶段结束时，我们希望有一个市场准备的产品与记录的结果。 公共卫生相关性：需要一种能够基于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那些患者上，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的以患者为中心的软件产品，该产品简化并简化了患者病史的引出和记录方式。这是通过应用正在申请专利的机器学习技术来根据每位患者的药物情况和调查期间的连续反应定制实时调查来实现的。我们假设：（1）患者可以自行成功完成基于网络的调查;（2）临床医生发现调查结果相关、准确，并且上级当前做出与外科手术相关的知情临床决策的方法;（3）患者和医疗保健提供者对调查的满意度较高;（4）护理质量得到改善;（5）成本降低。