Using Pre-Operative Variables to Predict Length of Stay

Qcmetrix, Inc., Waltham MA

Investigators

Abstract

[unreadable] DESCRIPTION (provided by applicant): [unreadable] Abstract: QCMetrix, Inc. is proposing to research and develop innovative software that will use patient-specific peri-operative risk variables to project in real-time that patient's post-operative length of stay. The proposed software will combine proven statistical modeling techniques with a database of over 1 million surgical cases performed at fourteen major academic health centers nationwide and in the Veterans Administration hospital system. These data continue to be accumulated in the database of the National Surgical Quality Improvement Program (NSQIP) through continued data capture in the VA and through the AHRQ-funded Patient Safety in Surgery Study (PSS). Beyond the term of this project, the objective is to extend the modeling of patient's peri-operative risk factors using NSQIP data to predict post-operative morbidity and mortality for the purpose of real-time clinical management of the surgical patient and advanced, prospective clinical quality improvement. [unreadable] [unreadable] Proposed Commercial Applications: The potential users of this software include surgeons, hospital managers, nursing and affiliated clinical support staffs involved in the care and management of the surgical patient. This software will be the first to use real-time, patient-specific information to project that patient's likely length of stay. This is a critical tool for clinicians and managers who are faced with the daily burden of balancing the demands for surgical bed capacity between the demand from elective surgeries and the demands of emergency care. Additionally, this tool may prove useful to project and manage staffing demands for the surgical floors. Beyond the scope of this project, this underlying statistical projection techniques will be utilized to inform clinicians and patients about the risks of post-operative morbidity and mortality related to their specific surgery and to define, using evidence-based medicine, optimal treatment of the surgical patient in real time. [unreadable] [unreadable]