How are new medication orders recorded while the electronic system is unavailable, and how are they entered into the system when it is restored?
How much data from the patient’s medication history should be provided when paper backup records are needed?
Without some parameters on the amount of information needed from paper backup records, several facilities realized they could end up with complete paper records of 100 or more pages for some patients.
Although the HFMEA™ teams addressed the same topic, each designed its own solutions to the questions raised by the analysis. VA facilities are now on their own to select topics for a proactive risk assessment in 2003. Topics selected include reporting of laboratory or radiology results, patient identification procedures, and patient backlogs for procedures. The VA’s first experience with HFMEA™ also provided the agency with additional lessons to improve the process for proactive risk analysis. Some of the lessons learned from the VA’s first application of HFMEA™ include the following:
Assign an HFMEA™ team member the task of mapping the flow diagram before the team’s first meeting. This ensures that the team moves in the right direction from the start.
Ensure that the steps to a process are numbered and the subprocesses are lettered. These simple measures help to keep the HFMEA™ team organized and prevent the team from overlooking potential failure modes.
Limit the flow diagram of the process to no more than 10 to 12 steps; otherwise the diagram gets too large.
Make testing of proposed changes a formal part of the HFMEA™ process. Testing can evaluate whether any of the proposed changes introduce unintended consequences.
Additional information and tools for HFMEA™ are available from the VA National Center for Patient Safety Web site (http://www.patientsafety.gov).
In the only published study of Probabilistic Risk Assessment and patient safety that we could identify, Dr. Elisabeth Paté-Cornell extended PRA—called “engineering risk analysis”—to the study of anesthesia patient risk to show how this tool can incorporate human and organizational factors to support patient safety decisions before complete datasets can be gathered and in cases where key factors are not directly observable.29
In assessing the risk of severe anesthesia accidents, technical failures