Computer-Based Driving in Dementia Decision Tool With Mail Support: Cluster Randomized Controlled Trial

Abstract

BackgroundPhysicians often find significant challenges in assessing automobile driving in persons with mild cognitive impairment and mild dementia and deciding when to report to transportation administrators. Care must be taken to balance the safety of patients and other road users with potential negative effects of issuing such reports.ObjectiveThe aim of this study was to assess whether a computer-based Driving in Dementia Decision Tool (DD-DT) increased appropriate reporting of patients with mild dementia or mild cognitive impairment to transportation administrators.MethodsThe study used a parallel-group cluster nonblinded randomized controlled trial design to test a multifaceted knowledge translation intervention. The intervention included a computer-based decision support system activated by the physician-user, which provides a recommendation about whether to report patients with mild dementia or mild cognitive impairment to transportation administrators, based on an algorithm derived from earlier work. The intervention also included a mailed educational package and Web-based specialized reporting forms. Specialists and family physicians with expertise in dementia or care of the elderly were stratified by sex and randomized to either use the DD-DT or a control version of the tool that required identical data input as the intervention group, but instead generated a generic reminder about the reporting legislation in Ontario, Canada. The trial ran from September 9, 2014 to January 29, 2016, and the primary outcome was the number of reports made to the transportation administrators concordant with the algorithm.ResultsA total of 69 participating physicians were randomized, and 36 of these used the DD-DT; 20 of the 35 randomized to the intervention group used DD-DT with 114 patients, and 16 of the 34 randomized to the control group used it with 103 patients. The proportion of all assessed patients reported to the transportation administrators concordant with recommendation did not differ between the intervention and the control groups (50% vs 49%; Z=−0.19, P=.85). Two variables predicted algorithm-based reporting—caregiver concern (odds ratio [OR]=5.8, 95% CI 2.5-13.6, P<.001) and abnormal clock drawing (OR 6.1, 95% CI 3.1-11.8, P<.001).ConclusionsOn the basis of this quantitative analysis, in-office abnormal clock drawing and expressions of concern about driving from caregivers substantially influenced physicians to report patients with mild dementia or mild cognitive impairment to transportation administrators, but the DD-DT tool itself did not increase such reports among these expert physicians.Trial RegistrationClinicalTrials.gov NCT02036099; https://clinicaltrials.gov/ct2/show/NCT02036099 (Archived by WebCite at http://www.webcitation.org/6zGMF1ky8)

Clinical Decision Support and Implications for the Clinician Burnout Crisis

“…Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated. [20,24,97] -Role tailoring of alerts [21,82] -Tiering of alerts [20,80] -Iterative design [25] Harmful -Interruptive or hard stop alerts [21,25,80] -Lack of "right information" [18] -Lack of relevance [80,97,108] -Lack of integration [78]…”

“…Although metrics of relevance such as sensitivity, specificity, and positive predictive value (PPV) were referenced several times 77 84 85 86 87 88 , only one study evaluated these metrics as pre-specified outcomes 49 . Tools for capturing clinician burnout, stress, or satisfaction after using a CDS tool were used in nine studies 26 44 78 80 82 89 90 91 92 . Psychometric measures of the user interface, such as mouse clicks, eye movement tracking, time spent on the page, errors using the tool, or direct observation were captured in seven studies 29 49 54 59 82 89 93 .…”

“…Other qualitative feedback responses were obtained by focus groups or workshops (n=5) [43,57,61,67,68]. The alert firing rate (n=12) [43,55,62,[69][70][71][72][73][74][75][76][77] and alert acceptance rate (n=15) [27,29,43,58,61,62,69,74,[78][79][80][81][82][83][84] were also common metrics used to understand alarm fatigue and acceptability. Although metrics of relevance such as sensitivity, specificity, and positive predictive value (PPV) were referenced several times [77,[84][85][86][87][88], only one study evaluated these metrics as pre-specified outcomes [49].…”

“…The lack of integration of CDS into EHRs leads to needing to "do work twice" -once to satisfy the CDS alert and then again, for example, to actually place the order in the EHR [99]. Better integration of CDS into the EHR was identified as a key change that would improve acceptance of decision support systems [34,78,83,85]. For example, automatic documentation of patient vital signs that feed into CDS systems decreased both clinician workload and frustration [91].…”

A virtual second opinion: Acceptability of a computer-based decision tool to assess older drivers with dementia

“…Similarly, in our quantitative evaluation of the RCT of the DD-DT, we found no difference in physicians' perception of their relationship with the patient or caregiver based on whether they were in the intervention or in the control group. 34 An unanticipated use of the tool was as a teaching resource, although a cautionary note was sounded with regard to over-reliance on tools in lieu of clinical judgement, especially by less experienced clinicians. Another finding was that uptake of the tool will depend not only on the sophistication of the algorithm generating the tool recommendations but also on logistics and workflow issues specific to various healthcare providers.…”