Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research
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 effect...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2019-02-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/2055207619827716 |
| _version_ | 1818282099144130560 |
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| author | Devin Mann Rachel Hess Thomas McGinn Rebecca Mishuris Sara Chokshi Lauren McCullagh Paul D. Smith Joseph Palmisano Safiya Richardson David A. Feldstein |
| author_facet | Devin Mann Rachel Hess Thomas McGinn Rebecca Mishuris Sara Chokshi Lauren McCullagh Paul D. Smith Joseph Palmisano Safiya Richardson David A. Feldstein |
| author_sort | Devin Mann |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T00:15:37Z |
| format | Article |
| id | doaj.art-649b4665d7b64147a6567569b0f38337 |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-12-13T00:15:37Z |
| publishDate | 2019-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-649b4665d7b64147a6567569b0f383372022-12-22T00:05:48ZengSAGE PublishingDigital Health2055-20762019-02-01510.1177/2055207619827716Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS researchDevin MannRachel HessThomas McGinnRebecca MishurisSara ChokshiLauren McCullaghPaul D. SmithJoseph PalmisanoSafiya RichardsonDavid A. FeldsteinOBJECTIVE 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.https://doi.org/10.1177/2055207619827716 |
| spellingShingle | Devin Mann Rachel Hess Thomas McGinn Rebecca Mishuris Sara Chokshi Lauren McCullagh Paul D. Smith Joseph Palmisano Safiya Richardson David A. Feldstein Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research Digital Health |
| title | Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research |
| title_full | Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research |
| title_fullStr | Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research |
| title_full_unstemmed | Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research |
| title_short | Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research |
| title_sort | adaptive design of a clinical decision support tool what the impact on utilization rates means for future cds research |
| url | https://doi.org/10.1177/2055207619827716 |
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