Background

Red blood cell (RBC) transfusion is a common medical procedure. While it offers clinical benefits for many, hemodynamically stable patients are often subjected to unwarranted transfusions, with the potential to lead to adverse consequences. We created a real-time clinical decision support (CDS) tool in the electronic health record system to address this problem and optimize transfusion practice as part of an institutional multidisciplinary, team-based patient blood management program.

Methods

The real-time CDS tool incorporated the transfusion guidelines published by the AABB. The tool was deployed as a dynamic order set within the computerized provider order entry interface. Prior to implementation, extensive education and outreach to increase provider engagement were provided. The CDS tool was launched in September 2015.

Results

The percentage of guideline-indicated RBC transfusions increased from a baseline of 43.6 to 54.2% while the percentage of multiunit (≥ 2 units) RBC transfusions decreased from 31.3 to 22.7% between September 2014 and July 2019. The estimated minimum cost saving over the entire study period was $36,519.36.

Conclusion

Our intervention increased guideline-indicated transfusions by 10.6% and reduced multiunit transfusions by 8.6%. The adoption of a dynamic order set for the CDS tool, as opposed to an interruptive alert that displays static alert messages, allowed for more customized and tighter control of RBC orders, leading to a sustained improvement in our transfusion practice.

Objectives

Reduction in unnecessary services is one strategy for increasing the value of health care. Reference laboratory, or send-out, tests are associated with considerable costs. We investigated whether displaying cost and turnaround time (TAT), or time-to-result, for reference laboratory tests at the time of order entry in the electronic health record (EHR) system would impact provider ordering practices.

Methods

Reference laboratory test cost and TAT data were randomized prior to the study and only displayed for the intervention group. A 24-month dataset composed of 12 months each for baseline and study periods was extracted from the clinical data mart. A difference-in-differences (DID) analysis was conducted using a linear mixed-effects model to estimate the association between the intervention and changes in test-ordering patterns.

Results

In the inpatient setting, the DIDs of aggregate test-order costs and volume were not different among the control and intervention groups (p = 0.31 and p = 0.26, respectively). In the ambulatory setting, the DIDs of aggregate test-order costs and volume were not different among the control and intervention groups (p = 0.82 and p = 0.51, respectively). For both inpatient and ambulatory settings, no significant difference was observed in the DID of aggregate test-order costs and volumes calculated in respect to stratified relative cost and TAT groups (p > 0.05).

Conclusion

Lack of alternative tests, test orders placed at a late step in patient management, and orders facilitated by trainees or mid-level providers may have limited the efficacy of the intervention. Our randomized study demonstrated no significant association between the display of cost or TAT display and ordering frequency.