process to certify the integrity of patient safety data reported externally for purposes of regulation, public reporting, or payment.
Efficient patient safety data systems that can span a continuum of uses will require careful design. Accountability measures usually report high-order, summary data (James, 1994a, b, 2003). Process management and improvement, on the other hand, require detailed decision-level data (Berwick et al., 2003).
Patient safety data systems should be designed to capture, as part of the patient care process, the data needed for learning applications. While data systems designed for learning can supply accountability data, the opposite is not true; summary data collected for accountability usually lack sufficient detail for learning-based uses (James, 2003). In the absence of careful planning, a health care delivery organization with limited resources may find that all of its measurement resources are consumed by special data collection to comply with external reporting requirements, with none remaining for learning and system redesign (Casalino, 1999). By contrast, a carefully designed data system that captures detailed decision-level data for improvement will be able to comply with external reporting requirements through a concept known as “data reuse” (see Chapter 2).
Learning depends on profound knowledge of key work processes. Process and outcome measures provide insight on what fails, how often and how it fails, what works, how it works, and how to make it work better. Data systems designed for learning can integrate data collection directly into work processes. Integrated data collection is usually more timely, accurate, and efficient. More important, properly designed learning data are immediately useful to front-line workers for process management, so that the burden of data collection is less likely to be perceived as an unfunded mandate (Langley et al., 1996).
Patient safety data systems should adhere to national data standards. Many external applications of patient safety data, including accountability, the provision of incentives, and priority setting, make use of comparative data. Standardized data definitions are necessary to make such comparisons. Comparative data also serve learning purposes when used to identify “best in class” providers.