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

描述(由申请人提供)：这是一个快速通道应用程序，旨在开发基于网络的、以患者为中心的软件产品，作为提高护理质量和降低成本的一种手段，准确评估患者的围手术期风险。 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，确定和减少围手术期的风险是当务之急。不幸的是，大多数医院和外科中心用来评估手术前患者的程序在两个方面都不足。一个是未能及时识别风险因素，因为大多数术前评估都是在手术前一天或一天进行的。第二个原因是由于术前评估不完整或不准确，未能正确识别危险因素。这些缺陷增加了发病率和死亡率，增加了医疗成本，降低了患者满意度。因此，及时提供标准化的术前评估是必要的。 为了满足这一需求，我们(MedSleuth，Inc.)开发了基于网络的软件，该软件利用一种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反应，生成定制的患者调查。调查结果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识别和降低围手术期风险所需的特定于患者的信息。保守地估计，通过我们的方法，每年可以节省100亿美元(约占总支出的25%)，同时在质量和满意度方面也有类似的大幅改善。 我们的第一阶段研究将在六个月的时间内评估第一代软件与一个协作医院系统(马萨诸塞州综合医院、哈佛医学院)的概念验证。第一阶段将努力证明：(1)患者可以成功地完成基于网络的调查；(2)调查产生的结果是准确、全面和相关的，可以做出明智的临床决策；(3)我们的评估算法在识别围手术期风险方面与现状相当或更好；(4)患者和医疗保健提供者报告的满意度很高；(5)可以大幅降低术前评估成本。 在第二阶段，我们将结合患者和医疗保健提供者从第一阶段的反馈，以开发基于网络的软件的更强大的第二代版本。我们将反过来在多个手术地点的更大患者群体上测试这一第二代软件，以验证临床准确性和完整性、成本节约和更高的满意度。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：需要一种系统，能够根据围手术期风险对患者进行高效和有效的分类，从而将资源集中在那些有复杂医疗问题的患者身上，同时提高质量和所有人的满意度。我们(MedSleuth，Inc.)已经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实现的，该技术根据每个患者的用药概况和调查期间的连续反应定制实时调查。我们假设：(1)患者可以自己成功地完成基于网络的调查；(2)临床医生发现调查的结果是相关的、准确的，并且优于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很高；(4)护理质量得到提高；以及(5)成本降低。

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.