Bibliographic Details
| Title: |
Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research |
| Authors: |
Devin Mann, Rachel Hess, Thomas McGinn, Rebecca Mishuris, Sara Chokshi, Lauren McCullagh, Paul D. Smith, Joseph Palmisano, Safiya Richardson, David A. Feldstein |
| Source: |
Digital Health, Vol 5 (2019) |
| Publisher Information: |
SAGE Publishing, 2019. |
| Publication Year: |
2019 |
| Collection: |
LCC:Computer applications to medicine. Medical informatics |
| Subject Terms: |
Computer applications to medicine. Medical informatics, R858-859.7 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2055-2076
20552076 |
| Relation: |
https://doaj.org/toc/2055-2076 |
| DOI: |
10.1177/2055207619827716 |
| Access URL: |
https://doaj.org/article/649b4665d7b64147a6567569b0f38337 |
| Accession Number: |
edsdoj.649b4665d7b64147a6567569b0f38337 |
| Database: |
Directory of Open Access Journals |
