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）：这是开发基于Web的，以患者为中心的软件产品的快速应用程序，可准确评估患者的围手术期风险，以改善护理质量和降低成本。每年在美国进行大约4000万手术程序[1]。为了确保接受这些程序的患者的安全，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手术前患者的过程在两个方面都缺乏。一种是无法及时识别危险因素，因为大多数术前评估发生在手术前一天或一天。第二个是由于不完整或不准确的术前评估而无法正确识别风险因素。这些缺点会增加发病率和死亡率，增加医疗保健成本并降低患者满意度。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Web的软件，该软件利用专利申请算法来基于患者的药物概况和对调查的连续反应来生成定制的患者调查。调查输出采用了综合病史的形式，根据健康状况将患者进行三叶草的形式，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，估计每年可以通过我们的方法节省100亿美元（占总支出的25％），质量和满意度的提高也有相当大的提高。我们的I阶段研究将在六个月的时间内与一个合作医院系统（哈佛医学院马萨诸塞州综合医院）评估第一代软件的概念验证。第一阶段将寻求证明（1）患者可以成功完成基于网络的调查；（2）调查产生的输出对于做出明智的临床决策是准确，全面和相关的；（3）我们的评估算法在识别围手术期风险方面等效或优于现状；（4）患者和医疗保健提供者报告的满意度很高；（5）术前评估成本可以大大降低。在第二阶段，我们将结合I阶段的患者和医疗保健提供者的反馈，以开发基于Web的软件的更强大的第二代版本。反过来，我们将在多个手术部位的患者人群中测试该第二代软件，以验证临床准确性和完整性，节省成本和增加的满意度。在第二阶段结束时，我们预计将拥有具有记录结果的市场现成产品。公共卫生相关性：对可以根据围手术期风险有效有效分类患者的系统存在需求，从而将资源集中在那些患有复杂医疗问题的患者的同时，同时提高所有人的质量和满意度。我们（Medsleuth，Inc。）开发了一种基于Web的第一代患者软件产品，该产品标准化和简化了患者的病史的引起和记录的方式。这是通过应用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过程中连续的反应来量身定制的实时调查来实现的。我们假设（1）患者可以成功完成基于网络的调查；（2）临床医生发现调查的输出相关，准确且与当前的方法相关，以做出与外科手术相关的知情临床决策；（3）患者和医疗保健提供者报告对调查的满意度很高；（4）提高护理质量；（5）成本降低。