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Study protocol for the antidepressant advisor (ADeSS): a decision support system for antidepressant treatment for depression in UK primary care: a feasibility study.

ABSTRACT: INTRODUCTION:The Antidepressant Advisor Study is a feasibility trial of a computerised decision-support tool which uses an algorithm to provide antidepressant treatment guidance for general practitioners (GPs) in the UK primary care service. The tool is the first in the UK to implement national guidelines on antidepressant treatment guidance into a computerised decision-support tool. METHODS AND ANALYSIS:The study is a parallel group, cluster-randomised controlled feasibility trial where participants are blind to treatment allocation. GPs were assigned to two treatment arms: (1) treatment-as-usual (TAU) and (2) computerised decision-support tool to assist with antidepressant choices. The study will assess recruitment and lost to follow-up rates, GP satisfaction with the tool and impact on health service use. A meaningful long-term roll-out unit cost will be calculated for the tool, and service use data will be collected at baseline and follow-up to inform a full economic evaluation of a future trial. ETHICS AND DISSEMINATION:The study has received National Health Service ethical approval from the London-Camberwell St Giles Research Ethics Committee (ref: 17/LO/2074). The trial was pre-registered in the Clinical Trials.gov registry. The results of the study will be published in a pre-publication archive within 1?year of completion of the last follow-up assessment. TRIAL REGISTRATION NUMBER:NCT03628027.

SUBMITTER: Harrison P

PROVIDER: S-EPMC7252992 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Study protocol for the antidepressant advisor (ADeSS): a decision support system for antidepressant treatment for depression in UK primary care: a feasibility study.

Harrison Phillippa P Carr Ewan E Goldsmith Kimberley K Young Allan H AH Ashworth Mark M Fennema Diede D Barrett Barbara B Zahn Roland R

BMJ open 20200524 5

<h4>Introduction</h4>The Antidepressant Advisor Study is a feasibility trial of a computerised decision-support tool which uses an algorithm to provide antidepressant treatment guidance for general practitioners (GPs) in the UK primary care service. The tool is the first in the UK to implement national guidelines on antidepressant treatment guidance into a computerised decision-support tool.<h4>Methods and analysis</h4>The study is a parallel group, cluster-randomised controlled feasibility tria ...[more]

PMID: 32448796

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Project description:BackgroundThe number of people receiving antidepressants has increased in the past 3 decades, mainly because of people staying on them longer. However, in many cases long-term treatment is not evidence based and risks increasing side effects. Additionally, prompting general practitioners (GPs) to review medication does not improve the rate of appropriate discontinuation. Therefore, GPs and other health professionals may need help to support patients discontinuing antidepressants in primary care.ObjectiveThis study aims to develop a digital intervention to support practitioners in helping patients discontinue inappropriate long-term antidepressants (as part of a wider intervention package including a patient digital intervention and patient telephone support).MethodsA prototype digital intervention called Advisor for Health Professionals (ADvisor HP) was planned and developed using theory, evidence, and a person-based approach. The following elements informed development: a literature review and qualitative synthesis, an in-depth qualitative study, the development of guiding principles for design elements, and theoretical behavioral analyses. The intervention was then optimized through think-aloud qualitative interviews with health professionals while they were using the prototype intervention.ResultsThink-aloud qualitative interviews with 19 health professionals suggested that the digital intervention contained useful information and was readily accessible to practitioners. The development work highlighted a need for further guidance on drug tapering schedules for practitioners and clarity about who is responsible for broaching the subject of discontinuation. Practitioners highlighted the need to have information in easily and quickly accessible formats because of time constraints in day-to-day practice. Some GPs felt that some information was already known to them but understood why this was included. Practitioners differed in their ideas about how they would use ADvisor HP in practice, with some preferring to read the resource in its entirety and others wanting to dip in and out as needed. Changes were made to the wording and structure of the intervention in response to the feedback provided.ConclusionsADvisor HP is a digital intervention that has been developed using theory, evidence, and a person-based approach. The optimization work suggests that practitioners may find this tool to be useful in supporting the reduction of long-term antidepressant use. Further quantitative and qualitative evaluation through a randomized controlled trial is needed to examine the feasibility, effectiveness, and cost-effectiveness of the intervention.

Project description:BackgroundAttempts to utilize eHealth in diabetes mellitus (DM) management have shown promising outcomes, mostly targeted at patients; however, few solutions have been designed for health care providers.ObjectiveThe purpose of this study was to conduct a feasibility project developing and evaluating a mobile clinical decision support system (CDSS) tool exclusively for health care providers to manage chronic kidney disease (CKD) in patients with DM.MethodsThe design process was based on the 3 key stages of the user-centered design framework. First, an exploratory qualitative study collected the experiences and views of DM specialist nurses regarding the use of mobile apps in clinical practice. Second, a CDSS tool was developed for the management of patients with DM and CKD. Finally, a randomized controlled trial examined the acceptability and impact of the tool.ResultsWe interviewed 15 DM specialist nurses. DM specialist nurses were not currently using eHealth solutions in their clinical practice, while most nurses were not even aware of existing medical apps. However, they appreciated the potential benefits that apps may bring to their clinical practice. Taking into consideration the needs and preferences of end users, a new mobile CDSS app, "Diabetes & CKD," was developed based on guidelines. We recruited 39 junior foundation year 1 doctors (44% male) to evaluate the app. Of them, 44% (17/39) were allocated to the intervention group, and 56% (22/39) were allocated to the control group. There was no significant difference in scores (maximum score=13) assessing the management decisions between the app and paper-based version of the app's algorithm (intervention group: mean 7.24 points, SD 2.46 points; control group: mean 7.39, SD 2.56; t37=-0.19, P=.85). However, 82% (14/17) of the participants were satisfied with using the app.ConclusionsThe findings will guide the design of future CDSS apps for the management of DM, aiming to help health care providers with a personalized approach depending on patients' comorbidities, specifically CKD, in accordance with guidelines.

Project description:ObjectiveTo determine the feasibility of a definitive trial in primary care of electronic clinical decision support (eCDS) for possible oesophago-gastric (O-G) cancer.Design and settingFeasibility study in 42 general practices in two regions of England, cluster randomised controlled trial design without blinding, nested qualitative and health economic evaluation.ParticipantsPatients aged 55 years or older, presenting to their general practitioner (GP) with symptoms associated with O-G cancer. 530 patients (mean age 68 years, 58% female) participated.InterventionPractices randomised 1:1 to usual care (control) or to receive a previously piloted eCDS tool for suspected cancer (intervention), for use at the discretion of the GPs, supported by a theory-based implementation package and ongoing support. We conducted semistructured interviews with GPs in intervention practices. Recruitment lasted 22 months.OutcomesPatient participation rate, use of eCDS, referrals and route to diagnosis, O-G cancer diagnoses; acceptability to GPs; cost-effectiveness. Participants followed up 6 months after index encounter.ResultsFrom control and intervention practices, we screened 3841 and 1303 patients, respectively; 1189 and 434 were eligible, 392 and 138 consented to participate. Ten patients (1.9%) had O-G cancer. eCDS was used eight times in total by five unique users. GPs experienced interoperability problems between the eCDS tool and their clinical system and also found it did not fit with their workflow. Unexpected restrictions on software installation caused major problems with implementation.ConclusionsThe conduct of this study was hampered by technical limitations not evident during an earlier pilot of the eCDS tool, and by regulatory controls on software installation introduced by primary care trusts early in the study. This eCDS tool needed to integrate better with clinical workflow; even then, its use for suspected cancer may be infrequent. Any definitive trial of eCDS for cancer diagnosis should only proceed after addressing these constraints.Trial registration numberISRCTN125595588.

Project description:The predictD is an intervention implemented by general practitioners (GPs) to prevent depression, which reduced the incidence of depression-anxiety and was cost-effective. The e-predictD study aims to design, develop, and evaluate an evolved predictD intervention to prevent the onset of major depression in primary care based on Information and Communication Technologies, predictive risk algorithms, decision support systems (DSSs), and personalized prevention plans (PPPs). A multicenter cluster randomized trial with GPs randomly assigned to the e-predictD intervention + care-as-usual (CAU) group or the active-control + CAU group and 1-year follow-up is being conducted. The required sample size is 720 non-depressed patients (aged 18-55 years), with moderate-to-high depression risk, under the care of 72 GPs in six Spanish cities. The GPs assigned to the e-predictD-intervention group receive brief training, and those assigned to the control group do not. Recruited patients of the GPs allocated to the e-predictD group download the e-predictD app, which incorporates validated risk algorithms to predict depression, monitoring systems, and DSSs. Integrating all inputs, the DSS automatically proposes to the patients a PPP for depression based on eight intervention modules: physical exercise, social relationships, improving sleep, problem-solving, communication skills, decision-making, assertiveness, and working with thoughts. This PPP is discussed in a 15-min semi-structured GP-patient interview. Patients then choose one or more of the intervention modules proposed by the DSS to be self-implemented over the next 3 months. This process will be reformulated at 3, 6, and 9 months but without the GP-patient interview. Recruited patients of the GPs allocated to the control-group+CAU download another version of the e-predictD app, but the only intervention that they receive via the app is weekly brief psychoeducational messages (active-control group). The primary outcome is the cumulative incidence of major depression measured by the Composite International Diagnostic Interview at 6 and 12 months. Other outcomes include depressive symptoms (PHQ-9) and anxiety symptoms (GAD-7), depression risk (predictD risk algorithm), mental and physical quality of life (SF-12), and acceptability and satisfaction ('e-Health Impact' questionnaire) with the intervention. Patients are evaluated at baseline and 3, 6, 9, and 12 months. An economic evaluation will also be performed (cost-effectiveness and cost-utility analysis) from two perspectives, societal and health systems.Trial registrationClinicalTrials.gov, identifier: NCT03990792.

Project description:BackgroundApproximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.ObjectiveThis study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction.MethodsOwing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews.ResultsData were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change.ConclusionsOur findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies.Trial registrationClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.

Project description:Informed consent is regarded as a cornerstone of ethical healthcare research and is a requirement for most clinical research studies. Guidelines suggest that prospective randomised controlled trial (RCT) participants should understand a basic amount of key information about the RCTs they are being asked to enrol in in order to provide valid informed consent. This information is usually provided to potential participants in a patient information leaflet (PIL). There is evidence that some trial participants fail to understand key components of trial processes or rationale. As such, the existing approach to information provision for potential RCT participants may not be optimal. Decision aids have been used for a variety of treatment and screening decisions to improve knowledge, but focus more on overall decision quality, and may be helpful to those making decisions about participating in an RCT. We investigated the feasibility of using a tool to identify which items recommended for good quality decision making are present in UK PILs.PILs were sampled from UK registered Clinical Trials Unit websites across a range of clinical areas. The evaluation tool, which is based on standards for supporting decision making, was applied to 20 PILs. Two researchers independently rated each PIL using the tool. In addition, word count and readability were assessed.PILs scored poorly on the evaluation tool with the majority of leaflets scoring less than 50%. Specifically, presenting probabilities, clarifying and expressing values and structured guidance in deliberation and communication sub-sections scored consistently poorly. Tool score was associated with word count (r=0.802, P <0.01); there was no association between score and readability (r=-0.372, P=0.106).The tool was feasible to use to evaluate PILs for UK RCTs. PILs did not meet current standards of information to support good quality decision making. Writers of information leaflets could use the evaluation tool as a framework during PIL development to help ensure that items are included which promote and support more informed decisions about trial participation. Further research is required to evaluate the inclusion of such information.

Dataset Information

Study protocol for the antidepressant advisor (ADeSS): a decision support system for antidepressant treatment for depression in UK primary care: a feasibility study.

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Study protocol for the antidepressant advisor (ADeSS): a decision support system for antidepressant treatment for depression in UK primary care: a feasibility study.

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