The following HTML text is provided to enhance online
readability. Many aspects of typography translate only awkwardly to HTML.
Please use the page image
as the authoritative form to ensure accuracy.
Patient Safety: Achieving a New Standard for Care
Whether the adverse event potentially represents a previously unknown problem—a judgment call drawing on the collective expertise of the patient safety team. Access to a database of adverse events also helps here.
The resources available to carry out such analyses—another judgment call for the patient safety team.
The potential for correction—depends on the expertise of the patient safety team.
A number of risk assessment indices have been developed to help in making the decision as to whether a root-cause analysis should be carried out. Chapter 9 provides further discussion on risk assessment as well as on methods for classifying root cause data.
Once a root-cause analysis has been completed, its results, including the following, should be fully documented and acted upon:
Failed (and successful) defenses and recoveries for the patient
Outcome for the patient
Lessons learned and ways to improve patient safety
Here there is an important difference between adverse events and near misses. Adverse events require the formal instigation of defenses (for example, a medication is discontinued, a prescription for diphenhydramine is written), whereas near misses involve built-in defenses (for example, automatic compensation through stand-by equipment; see Chapter 7).
An examination of public health surveillance systems reveals the importance of refining these datasets, while health services research reminds us that collecting less structured data early in the process will reduce respondent burden and potentially remove inherent biases in the types of data collected. Therefore, it may be important to define an outcome of interest precisely and then allow knowledge gained from the reporting process (both accountability and learning) to inform system developers about data whose collection in the aggregate will be useful. As knowledge about these outcomes and known or suspected causes accrues, the inclusion of elements in a minimum dataset will evolve.