A web-based clinical decision tool to support treatment decision-making in psychiatry: a pilot focus group study with clinicians, patients and carers

Abstract

BackgroundTreatment decision tools have been developed in many fields of medicine, including psychiatry, however benefits for patients have not been sustained once the support is withdrawn. We have developed a web-based computerised clinical decision support tool (CDST), which can provide patients and clinicians with continuous, up-to-date, personalised information about the efficacy and tolerability of competing interventions. To test the feasibility and acceptability of the CDST we conducted a focus group study, aimed to explore the views of clinicians, patients and carers.MethodsThe CDST was developed in Oxford. To tailor treatments at an individual level, the CDST combines the best available evidence from the scientific literature with patient preferences and values, and with patient medical profile to generate personalised clinical recommendations. We conducted three focus groups comprising of three different participant types: consultant psychiatrists, participants with a mental health diagnosis and/or experience of caring for someone with a mental health diagnosis, and primary care practitioners and nurses. Each 1-h focus group started with a short visual demonstration of the CDST. To standardise the discussion during the focus groups, we used the same topic guide that covered themes relating to the acceptability and usability of the CDST. Focus groups were recorded and any identifying participant details were anonymised. Data were analysed thematically and managed using the Framework method and the constant comparative method.ResultsThe focus groups took place in Oxford between October 2016 and January 2017. Overall 31 participants attended (12 consultants, 11 primary care practitioners and 8 patients or carers). The main themes that emerged related to CDST applications in clinical practice, communication, conflicting priorities, record keeping and data management. CDST was considered a useful clinical decision support, with recognised value in promoting clinician-patient collaboration and contributing to the development of personalised medicine. One major benefit of the CDST was perceived to be the open discussion about the possible side-effects of medications. Participants from all the three groups, however, universally commented that the terminology and language presented on the CDST were too medicalised, potentially leading to ethical issues around consent to treatment.ConclusionsThe CDST can improve communication pathways between patients, carers and clinicians, identifying care priorities and providing an up-to-date platform for implementing evidence-based practice, with regard to prescribing practices.

Patients' and clinicians' perspectives on shared decision‐making regarding treatment decisions for depression, anxiety disorders, and obsessive‐compulsive disorder in specialized psychiatric care

“…Given the important role of patients' treatment preferences, other personal aspects in treatment selection, and the suggested benefits of SDM in psychiatric care, efforts are underway to implement and to promote SDM in clinical practice. SDM interventions and tools, varying in design and delivery, have been developed or customized to support patients in treatment decision‐making, to promote their understanding of the issues, and/or to guide them in asking relevant questions . These interventions/tools are mostly based on the theoretical SDM construct and related steps as described above or inspired by existing initiatives in other clinical fields.…”

“…[34][35][36][37][38][39][40] These interventions/tools are mostly based on the theoretical SDM construct and related steps as described above or inspired by existing initiatives in other clinical fields. Interventions include traditional decision aids (DAs) for use before the consultation that contain information about treatment options and ask patients about experiences and preferences (and can also be taken to the consultation) 37 ; shorter versions for use by patients and clinicians together (eg, Option Grids 34 ); web-based decision support systems (eg, Common Ground web application, 38 computerized clinical decision support tool [CDST], 39 and e-health programs using routine outcome monitoring [SDM-DI] 35,36,41 ). Most SDM interventions resulted in better informed patients, showed an increase of consumer involvement facilitated by the clinician, an increase of consumer satisfaction and treatment adherence, and often resulted in less decisional conflict (DC).…”

Identifying Ethical Considerations for Machine Learning Healthcare Applications

“…Machine learning requires large amounts of training data. The aggregation and curation of these large datasets raises not only issues regarding specifying the standards that high-quality reference standard data must achieve, but also issues regarding data privacy and data ownership (Aboueid et al 2019;Amarasingham et al 2016;Cohen et al 2014;Gruson et al 2018;Henshall et al 2017;Jaremko et al 2019; Nicholson Price and Glenn Cohen 2019; Racine et al 2019; SFR-IA Group 2018; Vayena and Blasimme 2018). For diagnostic ML-HCAs, training data will likely be based on data collected from individual patients obtained during routine clinical care (such as laboratory test values, biopsy findings, or diagnostic images) or from individual enrollees in health insurance plans (such as medical diagnoses from medical encounters or health care utilization patterns), along with personal demographic information.…”

Ethical implications of poor comparative effectiveness evidence: obligations in industry-research partnerships

“…Treatment algorithms have enabled advances in many fields of medicine, 3 and computerised decision systems can provide ongoing assistance to clinicians. 4 Developing trustworthy and valid treatment algorithms, however, is complex. Much has been written about the technical problems of algorithms (rubbish in, rubbish out).…”