Development, Implementation, and Evaluation of the Surgical Bed Management System
Profile last edited on: 7/19/2010

Total Award Amount
Award Phase
Principal Investigator
Katherine S Rowell
Activity Indicator

Company Information

QCMetrix Inc

400-1 Totten Pond Road
Waltham, MA 02451
   (781) 290-5900
Multiple Locations:   
Congressional District:   05
County:   Middlesex

Phase I

Phase I year
Phase I Amount
CMetrix, 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. 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

Phase II

Phase II year
2007 (last award dollars: 2009)
Phase II Amount
Long-Term Objective: The duration of hospitalization, commonly known as length of stay (LOS), is a major determinant of hospital costs. The marked variation in LOS between hospitals for groups of patients with similar illnesses suggests there is room for improved efficiency. Our long-term objective is to create a software application linked to the hospital information system to enable hospitals to enhance their efficiency. The major innovation in this proposal is the use of severity of illness, type of surgical procedure, and perioperative complications to predict LOS, which can then be used for shorter-term bed management, longer-term projection of bed capacity and personnel, and compare LOS across providers. Specific Aims: The specific aims of this proposal are to construct, test, and evaluate a software module, the Surgical Bed Management System (SBMS), which will: 1) Use predicted LOS to manage admissions and discharges within a hospital; 2) Use predicted and actual LOS of a historical cohort of admissions (e.g., previous fiscal year) in a simulation model for strategic planning of bed capacity needs (e.g., the next fiscal year); and 3) Adjust LOS for severity of illness, comorbidity, and surgical procedure so that this measure of efficiency may be credibly compared across physicians and hospitals. Research Design and Methods: The SBMS will be a web-based software module linked to the hospital information system that facilitates gathering the necessary patient- and hospital-level data from the hospital's health information system and by direct data entry. The SBMS will estimate each patient's predicted LOS on admission and again shortly following surgery, and will aggregate and present this information as an electronic Predicted Bed-Occupancy Board for use in planning near-term admissions. Additionally, the SBMS will use hospital-specific- historical data in a simulation exercise to forecast long-term bed needs, and will compute risk- adjusted LOS for use in comparing efficiency with other hospitals. This is a two and one-half-year proposal, in which we propose to design and program prototypes, and secure user feedback in first year. We will implement SBMS and train users at the University of Michigan Hospital and conduct alpha testing during months 7 - 15, followed by use for all inpatient general and vascular surgery during months 16 - 30. We will then follow the same steps in a second hospital, Newton-Wellesley. During months 25 - 30, we will collect and analyze data from both hospitals to assess our outcomes. The primary outcome will be the change in risk-adjusted LOS during the last six months of implementation in the two test hospitals compared with the six months immediately preceding implementation. Secondary outcomes will be user satisfaction assessed with a survey instrument and an assessment of marketability based on structured interviews with key hospital leadership personnel