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BACKGROUND/RATIONALE:
There is growing evidence that clinicians workload can adversely affect clinical performance, resulting in adverse events and lower quality of care. However, current methods of measuring clinical workload are often crude (e.g., nurse-patient staffing ratios), retrospective (based on the volume of work units performed), and do not apply to the unique model of the one-patient-at-a-time care performed in the operating room (OR). To address this problem, we developed and piloted an instrument, the Quality and Workload Assessment Tool (QWAT), to measure the perceived clinical workload of individual nurses, surgeons, and anesthesia providers, as well as that of the surgical team as a whole. The QWAT also elicits data about intraoperative non-routine events (or NRE). NRE represent deviations from optimal care and thus may be a measure of quality of care. We have developed a conceptual model that suggests that performance shaping factors (e.g., clinician fatigue or stress) contribute to clinical workload, which is a determinant of processes of care that, in turn, affect patient outcomes. In this project, we will test this conceptualization of surgical clinical workload and its mediating effects on intra-operative quality of care and patient safety.
OBJECTIVE(S):
Building on previous work, the purpose of this study is to: 1) refine a robust conceptual model linking clinical workload and intraoperative care quality; 2) test the validity and reliability of the SQWAT; and 3) examine the relationships between key variables associated with clinical workload and intraoperative care quality. Using data from this study, our team will be well poised to develop a future intervention study to ameliorate mutable factors that adversely affect the quality of care in VHA operating rooms.
METHODS:
In Aim 1, using nominal group interview techniques, a total of 54 surgeons, anesthesia providers, and OR nurses will be asked to provide their insights about the various factors they perceive to be important in understanding the relationships between the concepts of clinical workload (individual and team), performance shaping factors (PSF), and intraoperative quality of care (non-routine events, morbidity and mortality). Based on the results, we will identify PSF that may influence our conceptual model of clinical workload and intraoperative care quality. Then, in Aim 2, we will use written synopses of 18 actual cases, chosen to exemplify different levels of clinical workload and intraoperative care quality, to assess the criterion validity and inter-rater reliability of the QWAT. Finally, in Aim 3, we will conduct a study of at least 750 surgical cases in three VAMC ORs chosen to reflect a cross-section of VA surgical care. For each study case, we will collect data about targeted PSF, ask the OR clinicians to complete the QWAT, and then assess immediate (NRE) and 30-day (patient surgical mortality and serious morbidity) measures of intraoperative care quality. Using a two-stage proof-of-concept analysis, we seek first to show with path analysis that clinical workload is important to understanding intraoperative quality and then, using multivariate regression techniques, to show that mutable PSF can be identified for intervention and clinical improvement.
FINDINGS/RESULTS:
No findings at this time.
IMPACT:
This research will generate new knowledge and methods that will contribute significantly to our understanding of the factors affecting the conduct and quality of surgical care in the VA. This is the first step toward identifying early warning signs of suboptimal and unsafe processes. The results will provide a more rational basis for improving working conditions, clinician training and staffing, care processes, and technology design. The program will enhance the VAs ability to measure and affect surgical workload and quality, augment the value of NSQIP data, and establishing guidelines for institutional workload to improve surgical care.
PUBLICATIONS:
None at this time.
DRA:
Health Services and Systems
DRE:
Quality of Care, Communication and Decision Making, Treatment
Keywords:
Adverse events, Safety, Implementation
MeSH Terms:
none
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