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.

National Institute of Health (NIH)
National Center for Advancing Translational Sciences (NCATS)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
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Special Emphasis Panel (ZRG1-HDM-F (12))
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Sachs, Jody
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Medsleuth, Inc.
San Francisco
United States
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