Objective: To develop a sustainable, automated method to perform comprehensive internal data validation [IDV], reporting and dissemination of quality reports [QRs] to participating facilities.
Methods: Since March 2009, we conducted internal validation of CLABSI and SSI data reported to NHSN. We automated procedures that check for attributes of data quality such as missing information, accuracy, (e.g., # of patient days ≥ # of beds per ICU * # days in month), internal consistency (pathogen listed/CLABSI criteria) and timeliness (reported denominator data) and generated data quality reports [QR]. QRs were disseminated via blast email on a monthly basis to NHSN facility administrators and NHSN contacts [NFANC] extracted from an existing state database. For each facility, a single QR with up to a dozen worksheets that showed errors was generated in Excel, archived, and sent by e-mail as an attached file to the appropriate NFANC. We used SAS version 9.2, MS Access and Excel for data processing and reporting.
Results: The time taken to complete the whole process, particularly dissemination of QRs, was dramatically reduced to a few seconds, compared to sending manual emails (minimum of 1 hour). Regular QRs from the TDH has motivated facilities to increase communication with the TDH. The number of phone call and email inquiries by facilities to TDH increased dramatically since QR were initiated. The proportion of facilities with an error-free quality report rose from 6% initially to >50% over a six month period. Many QR errors were related to issues surrounding appropriate conferring of rights. Facilities that used electronic data sources to capture denominator data (central line- and patient days) frequently had inflated denominators (up to 2.5 times maximum possible denominator); these problems were identified systematically through IDV. IDV helped lessen the time and work load on staff conducting on-site visits for more resource and time intensive external data validation.
Conclusions: Electronic data sources data may inflate denominator data resulting in underestimates of CLABSI rates; IDV can identify this. Our model for IDV is sustainable, effective and can be replicated by other State Health Departments [