Development of an Automated Web-based C. difficile infection (CDI) Surveillance System for a State Wide Performance Improvement Collaborative

Background:

In coordination with the Ohio Hospital Association (OHA), and The Ohio State University (OSU) CDC Prevention Epicenter, a large collaboration spanning 18 months was formed to initiate a performance improvement project (PIP) to reduce C. difficile infection (CDI) rates by standardizing surveillance methods, definitions, and data reporting tools.

Objective:

To develop and implement an automated web based CDI surveillance system, for a state wide collaborative. To provide monthly feedback to the participating hospitals of their rates compared to other similar bed-sized facilities and the overall collaboration to reduce CDI rates, share successes, and serve as the basis for public reporting.

Methods:

Using CDC/NHSN CDI definitions, an algorithm was created to standardize and automatically classify CDI cases into hospital or community onset or community associated cases. Classification was based on dates of positive CDI stool, previous infections, and admissions. Participating facilities also collected patient (pt) census data and entered all information, monthly, into a data repository housed and maintained on the OHA website. Case data were collected for the entire facility and pre-specified facility specific PIP unit(s). Pt days data excluded rehabilitation, psychiatry and patients < 1 yr. The surveillance system calculated CDI rates /10,000 pt days for each facility. Hospitals could view their individual cases, classifications, and CDI rates over time. De-identified data was downloaded quarterly by OHA and sent to OSU Epicenter for analysis. Detailed reports with CDI rates among all participating hospitals, by bed size, and by adult vs. pediatric days over time were generated by OSU and shared with the hospitals to assess CDI trends and potential outbreaks.

Results:

Development and implementation of the automated web based CDI surveillance system allowed 63 participating hospitals across Ohio to collect and enter data in a standardized manner. Automated determination of classification of CDI cases reduced human bias and validated facility case counting methods. Generation of individual facility reports detailing CDI rates with comparisons among similar bed sized hospitals allowed hospitals to evaluate CDI trends over time.

Conclusions:

This automated web-based CDI surveillance system for our state wide collaborative not only standardized surveillance methods, definitions, and data reporting tools, but also led to a potential increase in timeliness, efficiency, and feedback to exchange CDI surveillance data for analysis and process improvement.  These efforts have provided the best evidenced based CDI prevention strategies and surveillance methods to serve as a basis for public reporting.