Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research

  • Authors
  • David A. Feldstein
  • Dillon Chrimesb
  • Joseph Palmisano
  • Lauren McCullagh
  • Paul Smith
  • Rebecca Mishuris
  • Safiya Richardson
  • Thomas McGinn
  • Rachel Hess
  • Sara Chokshi
  • Published
  • Digital health

Abstract

Objective

We employed an agile, user-centered approach to the design of a clinical decision support tool in our prior integrated clinical prediction rule study, which achieved high adoption rates. To understand if applying this user-centered process to adapt clinical decision support tools is effective in improving the use of clinical prediction rules, we examined utilization rates of a clinical decision support tool adapted from the original integrated clinical prediction rule study tool to determine if applying this user-centered process to design yields enhanced utilization rates similar to the integrated clinical prediction rule study.MATERIALS & METHODS: We conducted pre-deployment usability testing and semi-structured group interviews at 6 months post-deployment with 75 providers at 14 intervention clinics across the two sites to collect user feedback. Qualitative data analysis is bifurcated into immediate and delayed stages; we reported on immediate-stage findings from real-time field notes used to generate a set of rapid, pragmatic recommendations for iterative refinement. Monthly utilization rates were calculated and examined over 12 months.

Results

We hypothesized a well-validated, user-centered clinical decision support tool would lead to relatively high adoption rates. Then 6 months post-deployment, integrated clinical prediction rule study tool utilization rates were substantially lower than anticipated based on the original integrated clinical prediction rule study trial (68%) at 17% (Health System A) and 5% (Health System B). User feedback at 6 months resulted in recommendations for tool refinement, which were incorporated when possible into tool design; however, utilization rates at 12 months post-deployment remained low at 14% and 4% respectively.

Discussion

Although valuable, findings demonstrate the limitations of a user-centered approach given the complexity of clinical decision support.

Conclusion

Strategies for addressing persistent external factors impacting clinical decision support adoption should be considered in addition to the user-centered design and implementation of clinical decision support.

  • Focus
  • User Experience​
  • Clinical Decision Support​