The Shared Decision-Making Process in the Pharmacological Management of Depression

Background Depression is highly prevalent, but knowledge is scarce as to whether increased public awareness and strengthened government focus on mental health have changed how general practitioners (GPs) help their depressed patients. This study aimed to examine national time trends in GP depression care and whether trends varied regarding patient gender, age, and comorbidity. Methods Nationwide registry-based cohort study, Norway. The study population comprised all residents aged 20 years or older with new depression diagnoses recorded in general practice, 2009–2015. We linked reimbursement claims data from all consultations in general practice for depression with information on demographics and antidepressant medication. The outcome was type(s) of GP depression care during 12 months from the date of diagnosis: (long) consultation, talking therapy, antidepressant drug treatment, sickness absence certification, and referral to secondary mental health care. Covariates were patient gender, age, and comorbidity. The data are presented as frequencies and tested with generalized linear models. Results We included 365,947 new depression diagnoses. Mean patient age was 44 years (SD = 16), 61.9 % were women, 41.2 % had comorbidity. From 2009 to 2015, proportions of patients receiving talking therapy (42.3–63.4 %), long consultations (56.4–71.8 %), and referral to secondary care (16.6–21.6 %) increased, while those receiving drug treatment (31.3–25.9 %) and sick-listing (58.1–50 %) decreased. The trends were different for gender (women had a greater increase in talking therapy and a smaller decrease in sick-listing, compared to men), age (working-aged patients had a smaller increase in talking therapy, a greater increase in long consultations, and a smaller decrease in antidepressant drug use, compared to older patients) and comorbidity (patients with mental comorbidity had a smaller increase in talking therapy and a greater increase in long consultations, compared to those with no comorbidity and somatic comorbidity). Conclusions The observed time trends in GP depression care towards increased provision of psychological treatment and less drug treatment and sick-listing were in the desired direction according to Norwegian health care policy. However, the large and persistent differences in treatment rates between working-aged and older patients needs further investigation.

Background Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction. Aims Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction. Method Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback. Results All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction. Conclusions The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

Background A high quality treatment decision means patients are informed and receive treatment that matches their goals. This research examined the reliability and validity of the Depression Decision Quality Instrument (DQI), a survey to measure the extent to which patients are informed and received preferred treatment for depression. Methods Participants were aged 18 and older from 17 US cities who discussed medication or counseling with a physician in the past year, and physicians who treated patients with depression who practiced in the same cities. Participants were mailed a survey that included the Depression-DQI, a tool with 10 knowledge and 7 goal and concern items. Patients were randomly assigned to either receive a patient decision aid (DA) on treatment of depression or no DA. A matching score was created by comparing the patient’s preferred treatment to their self-reported treatment received. Concordant scores were considered matched, discordant were not. We examined the reliability and known group validity of the Depression-DQI. Results Most patients 405/504 (80%) responded, 79% (320/405) returned the retest survey, and 60% (114/187) of physicians returned the survey. Patients’ knowledge scores on the 10-item scale ranged from 14.6 to 100% with no evidence of floor or ceiling effects. Retest reliability for knowledge was moderate and for goals and concerns ranged from moderate to good. Mean knowledge scores differentiated between patients and physicians (M = 63 [SD = 15] vs. M = 81 [SD = 11], p < 0.001), and between patients who did and didn’t receive a DA (M = 64 [SD = 16] vs. M = 61 [SD = 14], p = 0.041). 60.5% of participants received treatment that matched their preference. Based on the multivariate logistic regression, ‘avoiding taking anti-depressants’ was the only goal that was predictive of taking mediation (OR = 0.73 [0.66, 0.80], p < 0.01). Shared Decision Making Process scores were similar for those who matched their preference and those who didn’t (M = 2.18 [SD = 0.97] vs. M = 2.06 [SD = 1.07]; t(320) =  − 1.06, p = 0.29). Those who matched had lower regret scores (matched M = 1.72 [SD = 0.74] vs. unmatched M = 2.32 [SD = 0.8]; t(301) =  − 6.6, p < .001). Conclusions The Depression DQI demonstrated modest reliability and validity. More work is needed to establish validity of the method to determine concordance. Trial registration: NCT01152307.