219949 Leveraging electronic medical records in the surveillance of surgical site infections in a total joint replacement population

Sunday, November 7, 2010

Maria Inacio, MS , Graduate School of Public Health, San Diego State University, San Diego, CA
Elizabeth Paxton, MA , Clinical Analysis, Surgical Outcome & Analysis Department, Kaiser Permanente, Southern California Permanente Medical Group, San Diego, CA
Yuexin Chen, BS , Clinical Analysis, Surgical Outcome & Analysis Department, Kaiser Permanente, Southern California Permanente Medical Group, San Diego, CA
Jessica Harris, MS , Clinical Analysis, Surgical Outcome & Analysis Department, Kaiser Permanente, Southern California Permanente Medical Group, San Diego, CA
Enid Eck, RN, MPH , Quality and Risk Management, Infection Prevention and Control Department, Kaiser Permanente, Pasadena, CA
Sue Barnes, RN, BSN, CIC , Program Offices, Infection Prevention and Control Department, Kaiser Permanente, Oakland, CA
Robert Namba, MD , Orthopedics Department, Kaiser Permanente, Southern California Permanente Medical Group, Irvine, CA
  
Current Center for Disease Control and Prevention (CDC) guidelines for the prevention of surgical site infection (SSI) do not offer recommendations on post operative SSI surveillance methods. At a large health maintenance organization, the surveillance of Total Joint Replacement (TJR) surgeries' SSIs were historically performed by reviewing all medical charts. A hybrid electronic SSI screening algorithm that leverages electronic medical records and a TJR registry post-operative follow up system sensitivity was tested in a large population of TJRs, chart review burden was also evaluated. Using ICD9 diagnostic and procedural codes for infection, wound complications, cellullitis, procedures related to infections, and surgeon reported complications captured at the point of care, we screened each TJR procedure between 01/2006 and 12/2008 for one year post-operative. Experts in TJR complications then reviewed the flagged charts to confirm SSI. SSIs identified using the electronic screening algorithm were compared to SSIs identified using traditional methodology. Positive predictive, negative predictive, specificity, and sensitivity values were calculated for the overall algorithm and absolute reduction of number of chart reviews was calculated. The algorithm identified 4001 (9.5%) possible infections in our TJR population of 42173. Of the possible cases only 440 (11.0%) were true SSIs. The overall algorithm sensitivity was 97.8% with 91.5% specificity. While this algorithm may still not be specific enough to hone in on the cases with new SSIs related to TJR using only electronic sources we created a 97.8% sensitive algorithm and reduced the chart review work burden by 90.5%.

Learning Areas:
Epidemiology

Learning Objectives:
1. Describe the development of a surgical site infection electronic screening algorithm 2. Describe the sensitiviy, specificity, positive predictive, negative predictive value of a surgical site infection electronic screening algorithm

Keywords: Surveillance, Infectious Diseases

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I developed the algorithm being described by the abstract and helped implement its use in the organization being described.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

Back to: 2023.0: Vaccination and infectious disease epidemiology poster session