Leveraging the utility of pharmacogenomics in psychiatry through clinical decision support: a focus group study

Abstract

Background Pharmacogenomics is starting to build momentum in clinical utility, perhaps the most in mental and behavioral healthcare. However, efficient delivery of this information to the point of prescribing remains a significant challenge. Clinical decision support has an opportunity to address this void by integrating pharmacogenomics into the clinician workflow. Methods To address the specific needs of mental health clinicians at the point of care, we conducted 3 focus groups with a total of 16 mental health clinicians. Each 1-h focus group was designed to identify the desired clinical decision support features, with a particular interest in pharmacogenomics, and potential negative or unintended consequences of clinical decision support integration at the point of care in a mental healthcare setting. We implemented an iterative design to expand upon knowledge generated in prior focus groups. The results from the guided discussion in the first focus group were used to develop a mental health clinical decision support prototype. This prototype was then presented during the next two focus groups to drive the discussion. Results This study has identified main themes related to the desired clinical decision support features of mental health clinicians, the use of pharmacogenomics in practice, and unintended and negative consequences of clinical decision support integration at the point of care. Clinicians desire a more complete picture of the medication history of patients and guidance to choose medications in relation to cost, insurance coverage, and pharmacogenetics interactions. Mental health clinicians agreed that pharmacogenetics is useful and impacts their prescribing decisions when the data are available. Several negative consequences of clinical decision support integration were identified including alert fatigue and frustration using the tool. Several points of contention were related to the integration of the clinical decision support with the electronic health record, including bidirectional flow of information, speed, location within workflow, and potential incompleteness of information. Conclusions We have identified general and unique considerations of mental health clinicians with regard to clinical decision support. Clinical decision support that incorporates desired features while avoiding negative and unintended consequences will increase clinician usage and will have the potential to improve the care of patients.

Clinical Decision Support and Implications for the Clinician Burnout Crisis

“…The 75 non-review, non-opinion articles used a variety of metrics to evaluate the usability and acceptability of their CDS tools, with many using more than one metric. The most common evaluation was via interviews (n=29) 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 , followed by surveys or questionnaires (n=16) 27 29 50 53 55 56 57 58 59 60 61 62 63 64 65 66 . Other qualitative feedback responses were obtained by focus groups or workshops (n=5) 43 57 61 67 68 .…”

“…There is currently no consensus on how to use CDS without causing alert fatigue [64]. Worries about alert fatigue and the burden of CDS were common in qualitative studies on CDS tools [29,31,34,83,93,99], which is perhaps unsurprising given that one study estimated the average ICU clinician is subjected to over 900 active and passive alerts per day [98]. Alarm fatigue can lead to important alerts being overlooked [29].…”

“…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].…”

“…The most important theme in improving CDS was iteration, meaning evaluating a tool, revising it, and then evaluating the revised tool. Multiple studies used iterative designs to fine-tune their CDS [26,34,42,68,74]. Mann et al provided a description of the development of a CDS tool for antibiotic prescriptions: (i) Pre-deployment, the CDS team collected qualitative feedback during "think aloud" sessions on the draft versions of the tool, (ii) Post-deployment, they solicited feedback in group interviews and measured utilization rates as well as antibiotic prescription rates.…”

Pharmacogenomic Clinical Decision Support: A Review, How‐to Guide, and Future Vision

“…PGx information delivered without consideration of the overall workflow of the clinician is likely to be overridden. 88,89 Several excellent guidelines can be used to design PGx CDS systems to maximize the five rights of CDS. 28,59,90 Khelifi et al created and tested PGx prototypes through heuristic evaluation and provided step-wise recommendations to support PGx-based prescribing.…”