Development of algorithmic detection report for surgical site infections

Background:

Increasing requirements for public reporting of healthcare associated infections (HAI) is resulting in a diversion of infection preventionists.  Automatically capturing data within electronic medical records (EMR), could potentially reduce the time required for these activities.

Objective:

Develop algorithmic infection detection reports using an electronic medical record to reduce time required by IPs (infection preventionists) to review medical records, perform case finding, collate data, report data.         

Methods:

During 2009 one large integrated healthcare system developed an algorithmic detection report to identify surgical site infection (SSI) after hernia repair procedures.   The triggers used to detect infection are:  (1) Antibiotic Order >48 hours and <30 days after procedure; (2) Wound Culture Order; (2) Diagnosis of SSI by ICD-9 code; (4) ER visit or hospital admission within 30 days of procedure.  A second, denominator report was also designed to automatically collate approximately 70% of data elements required by NHSN for surgical procedures. 

Results:

The accuracy of the SSI report was compared with traditional infection surveillance using manual record review for hernia, total hip, total knee, colon, laminectomy, spine fusion, breast and abdominal hysterectomy procedures.  Accuracy was found to be equal.  Surveillance time was reduced by approximately 70% , and collation of over two-thirds of data elements required for NHSN reporting was automated.

Conclusions:

This approach will now be applied in development of additional algorithmic detection reports including CLABSI, VAP, C diff, MRSA and VRE.  These reports are critical to reduce diversion of IP resources and have the potential also to improve the quality of surveillance data by standardizing and reducing inter rater reliability issues.