A computerized decision support system for depression in primary care

Decision aids for people facing health treatment or screening decisions

BACKGROUND

Patient decision aids are interventions designed to support people making health decisions. At a minimum, patient decision aids make the decision explicit, provide evidence-based information about the options and associated benefits/harms, and help clarify personal values for features of options. This is an update of a Cochrane review that was first published in 2003 and last updated in 2017.

OBJECTIVES

To assess the effects of patient decision aids in adults considering treatment or screening decisions using an integrated knowledge translation approach.

SEARCH METHODS

We conducted the updated search for the period of 2015 (last search date) to March 2022 in CENTRAL, MEDLINE, Embase, PsycINFO, EBSCO, and grey literature. The cumulative search covers database origins to March 2022.

SELECTION CRITERIA

We included published randomized controlled trials comparing patient decision aids to usual care. Usual care was defined as general information, risk assessment, clinical practice guideline summaries for health consumers, placebo intervention (e.g. information on another topic), or no intervention.

DATA COLLECTION AND ANALYSIS

Two authors independently screened citations for inclusion, extracted intervention and outcome data, and assessed risk of bias using the Cochrane risk of bias tool. Primary outcomes, based on the International Patient Decision Aid Standards (IPDAS), were attributes related to the choice made (informed values-based choice congruence) and the decision-making process, such as knowledge, accurate risk perceptions, feeling informed, clear values, participation in decision-making, and adverse events. Secondary outcomes were choice, confidence in decision-making, adherence to the chosen option, preference-linked health outcomes, and impact on the healthcare system (e.g. consultation length). We pooled results using mean differences (MDs) and risk ratios (RRs) with 95% confidence intervals (CIs), applying a random-effects model. We conducted a subgroup analysis of 105 studies that were included in the previous review version compared to those published since that update (n = 104 studies). We used Grading of Recommendations Assessment, Development, and Evaluation (GRADE) to assess the certainty of the evidence.

MAIN RESULTS

This update added 104 new studies for a total of 209 studies involving 107,698 participants. The patient decision aids focused on 71 different decisions. The most common decisions were about cardiovascular treatments (n = 22 studies), cancer screening (n = 17 studies colorectal, 15 prostate, 12 breast), cancer treatments (e.g. 15 breast, 11 prostate), mental health treatments (n = 10 studies), and joint replacement surgery (n = 9 studies). When assessing risk of bias in the included studies, we rated two items as mostly unclear (selective reporting: 100 studies; blinding of participants/personnel: 161 studies), due to inadequate reporting. Of the 209 included studies, 34 had at least one item rated as high risk of bias. There was moderate-certainty evidence that patient decision aids probably increase the congruence between informed values and care choices compared to usual care (RR 1.75, 95% CI 1.44 to 2.13; 21 studies, 9377 participants). Regarding attributes related to the decision-making process and compared to usual care, there was high-certainty evidence that patient decision aids result in improved participants' knowledge (MD 11.90/100, 95% CI 10.60 to 13.19; 107 studies, 25,492 participants), accuracy of risk perceptions (RR 1.94, 95% CI 1.61 to 2.34; 25 studies, 7796 participants), and decreased decisional conflict related to feeling uninformed (MD -10.02, 95% CI -12.31 to -7.74; 58 studies, 12,104 participants), indecision about personal values (MD -7.86, 95% CI -9.69 to -6.02; 55 studies, 11,880 participants), and proportion of people who were passive in decision-making (clinician-controlled) (RR 0.72, 95% CI 0.59 to 0.88; 21 studies, 4348 participants). For adverse outcomes, there was high-certainty evidence that there was no difference in decision regret between the patient decision aid and usual care groups (MD -1.23, 95% CI -3.05 to 0.59; 22 studies, 3707 participants). Of note, there was no difference in the length of consultation when patient decision aids were used in preparation for the consultation (MD -2.97 minutes, 95% CI -7.84 to 1.90; 5 studies, 420 participants). When patient decision aids were used during the consultation with the clinician, the length of consultation was 1.5 minutes longer (MD 1.50 minutes, 95% CI 0.79 to 2.20; 8 studies, 2702 participants). We found the same direction of effect when we compared results for patient decision aid studies reported in the previous update compared to studies conducted since 2015.

AUTHORS' CONCLUSIONS

Compared to usual care, across a wide variety of decisions, patient decision aids probably helped more adults reach informed values-congruent choices. They led to large increases in knowledge, accurate risk perceptions, and an active role in decision-making. Our updated review also found that patient decision aids increased patients' feeling informed and clear about their personal values. There was no difference in decision regret between people using decision aids versus those receiving usual care. Further studies are needed to assess the impact of patient decision aids on adherence and downstream effects on cost and resource use.

Measurement-Based Care for Depression in Youth: Practical Considerations for Selecting Measures to Assess Depression, Associated Features and Functioning

Identification and management of major depressive disorder (MDD) in children and adolescents remains a significant area of public health need. The process for identifying depression (e.g. screening) and management (e.g. measurement based care [MBC]) is substantially enhanced by utilization of clinical measures and rating scales. Measures can be self- or caregiver reported or clinician rated. They can aid recognition of at-risk individuals for future assessment and assist in clinical diagnosis and management of depression. In addition to assessing symptoms of depression, rating scales can be used to assess important associated features (e.g. anxiety, trauma) and functional outcomes (e.g. quality of life, performance/productivity). In this manuscript, we discuss practical considerations for clinicians and researchers when selecting rating instruments for assessing depression, associated factors, functioning, and treatment outcomes (i.e. adherence and side effects) as part of MBC in youth and provide a summary of rating scales commonly used in research and clinical settings.

Stigma towards opioid use disorder in primary care remain a barrier to integrating software-based measurement based care

BACKGROUND

Opioid use disorder (OUD) is a deadly illness that remains undertreated, despite effective pharmacological treatments. Barriers, such as stigma, treatment affordability, and a lack of training and prescribing within medical practices result in low access to treatment. Software-delivered measurement-based care (MBC) is one way to increase treatment access. MBC uses systematic patient symptom assessments to inform an algorithm to support clinicians at critical decision points.

METHOD

Focus groups of faculty clinicians (N = 33) from 3 clinics were conducted to understand perceptions of OUD diagnosis and treatment and whether a computerized MBC model might assist with diagnosis and treatment. Themes from the transcribed focus groups were identified in two phases: (1) content analysis focused on uncovering general themes; and (2) systematic coding and interpretation of the data.

RESULTS

Analysis revealed six major themes utilized to develop the coding terms: "distinguishing between chronic pain and OUD," "current practices with patients using prescribed or illicit opioids or other drugs," "attitudes and mindsets about providing screening or treatment for OUD in your practice," "perceived resources needed for treating OUD," "primary care physician role in patient care not specific to OUD," and "reactions to implementation of proposed clinical decision support tool."

CONCLUSION

Results revealed that systemic and attitudinal barriers to screening, diagnosing, and treating OUD continue to persist. Providers tended to view the software-based MBC program favorably, indicating that it may be a solution to increasing accessibility to OUD treatment; however, further interventions to combat stigma would likely be needed prior to implementation of these programs.

TRIAL REGISTRATION

ClinicalTrials.gov; NCT04059016; 16 August 2019; retrospectively registered; https://clinicaltrials.gov/ct2/show/NCT04059016 .

Effects of Non-monetary Incentives in Physician Groups-A Systematic Review

Objectives: Healthcare expenditures in western countries have been rising for many years. This leads many countries to develop and test new reimbursement systems. A systematic review about monetary incentives in group settings indicated that a sole focus on monetary aspects does not necessarily result in better care at lower costs. Hence, this systematic review aims to describe the effects of non- monetary incentives in physician groups. Methods: We searched the databases MEDLINE (PubMed), The Cochrane Library, CINAHL, PsycINFO, EconLit, and ISI Web of Science. Grey literature search, reference lists, and authors' personal collection provided additional sources. Results: Overall, we included 36 studies. We identified 4 categories of interventions related to non-monetary incentives. In particular, the category of decision support achieved promising results. However, design features vary among different decision support systems. To enable effective design, we provide an overview of the features applied by the studies included. Conclusions: Not every type of non-monetary incentive has a positive impact on quality of care in physician group settings. Thus, creating awareness among decision-makers regarding this matter and extending research on this topic can contribute to preventing implementation of ineffective incentives, and consequently, allocate resources towards tools that add value.

Systematic review of structured care pathways in major depressive disorder and bipolar disorder

BACKGROUND

Structured care pathways (SCPs) consist of treatment algorithms that patients advance through with the goal of achieving remission or response. These SCPs facilitate the application of current evidence and adequate treatment, which potentially benefit patients with mood disorders. The aim of this systematic review was to provide an updated synthesis of SCPs for the treatment of depressive disorders and bipolar disorder (BD).

METHOD

PubMed, PsycINFO, and Embase were searched through June 2022 for peer-reviewed studies examining outcomes of SCPs. Eligibility criteria included being published in a peer-reviewed journal in the English language, reporting of intervention used in the SCP, and having quantitative outcomes. Studies Cochrane risk of bias tool was used to assess quality of RCTs.

RESULTS

Thirty-six studies including 15,032 patients were identified for qualitative synthesis. Six studies included patients with BD. The studies were highly heterogeneous in design, outcome measures, and algorithms. More than half of the studies reported superiority of SCPs over treatment as usual, suggesting that the standardized structure and consistent monitoring inherent in SCPs may be contributing to their effectiveness. We also found accumulating evidence supporting feasibility of SCPs in different settings, although dropout rates were generally higher in SCPs. The studies included were limited to being published in peer-reviewed journals in English language. The heterogeneity of studies did not allow quantitative evaluation.

CONCLUSIONS

The findings of our study suggest that SCPs are equally or more effective than treatment as usual in depression and BD. Further studies are required to ascertain their effectiveness, particularly for BD, and to identify factors that influence their feasibility and success.

Telehealth-Supported Decision-making Psychiatric Care for Suicidal Ideation: Longitudinal Observational Study

Background

Suicide is a leading cause of death in the United States, and suicidal ideation (SI) is a significant precursor and risk factor for suicide.

Objective

This study aimed to examine the impact of a telepsychiatric care platform on changes in SI over time and remission, as well as to investigate the relationship between various demographic and medical factors on SI and SI remission.

Methods

Participants included 8581 US-based adults (8366 in the treatment group and 215 in the control group) seeking treatment for depression, anxiety, or both. The treatment group included patients who had completed at least 12 weeks of treatment and had received a prescription for at least one psychiatric medication during the study period. Providers prescribed psychiatric medications for each patient during their first session and received regular data on participants. They also received decision support at treatment onset via the digital platform, which leveraged an empirically derived proprietary precision-prescribing algorithm to give providers real-time care guidelines. Participants in the control group consisted of individuals who completed the initial enrollment data and completed surveys at baseline and 12 weeks but did not receive care.

Results

Greater feelings of hopelessness, anhedonia, and feeling bad about oneself were most significantly correlated (r=0.24-0.37) with SI at baseline. Sleep issues and feeling tired or having low energy, although significant, had lower correlations with SI (r=0.13-0.14). In terms of demographic variables, advancing age and education were associated with less SI at baseline (r=−0.16) and 12 weeks (r=−0.10) but less improvement over time (r=−0.12 and −0.11, respectively). Although not different at baseline, the SI expression was evident in 34.4% (74/215) of the participants in the control group and 12.32% (1031/8366) of the participants in the treatment group at 12 weeks. Although the participants in the treatment group improved over time regardless of various demographic variables, participants in the control group with less education worsened over time, after controlling for age and depression severity. A model incorporating the treatment group, age, sex, and 8-item Patient Health Questionnaire scores was 77% accurate in its classification of complete remission. Those in the treatment group were 4.3 times more likely (odds ratio 4.31, 95% CI 2.88-6.44) to have complete SI remission than those in the control group. Female participants and those with advanced education beyond high school were approximately 1.4 times more likely (odds ratio 1.38, 95% CI 1.18-1.62) to remit than their counterparts.

Conclusions

The results highlight the efficacy of an antidepressant intervention in reducing SI, in this case administered via a telehealth platform and with decision support, as well as the importance of considering covariates, or subpopulations, when considering SI. Further research and refinement, ideally via randomized controlled trials, are needed.

Feasibility and acceptability of a novel telepsychiatry-delivered precision prescribing intervention for anxiety and depression

BACKGROUND

Major Depressive Disorder and Generalized Anxiety Disorder are pervasive and debilitating conditions, though treatment is often inaccessible and based on trial-and-error prescribing methods. The present observational study seeks to describe the use of a proprietary precision prescribing algorithm piloted during routine clinical practice as part of Brightside's telepsychiatry services. The primary aim is to determine the feasibility and acceptability of implementing this intervention. Secondary aims include exploring remission and symptom improvement rates.

METHODS

Participants were adult patients enrolled in Brightside who completed at least 12 weeks of treatment for depression and/or anxiety and received a prescription for at least one psychiatric medication. A prescription recommendation was made by Brightside's algorithm at treatment onset and was utilized for clinical decision support. Participants received baseline screening surveys of the PHQ-9 and GAD-7, and at weeks 2,4,6,8,10 and 12. Intent-to-treat (ITT) sensitivity analyses were conducted. Feasibility of the implementation was measured by the platform's ability to enroll and engage participants in timely psychiatric care, as well as offer high touch-point treatment options. Acceptability was measured by patient responses to a 5-star satisfaction rating.

RESULTS

Brightside accessed and treated 6248 patients from October 2018 to April 2021, treating a majority of patients within 4-days of enrollment. The average plan cost was $115/month. 89% of participants utilized Brightside's core medication plan at a cost of $95/month. 13.4% of patients in the study rated Brightside's services as highly satisfactory, averaging a 4.6-star rating. Furthermore, 90% of 6248 patients experienced a MCID in PHQ-9 or GAD-7 score. Remission rates were 75% (final PHQ-9 or GAD-7 score < 10) for the study sample and 59% for the ITT sample. 69.3% of Brightside patients were treated with the medication initially prescribed at intake.

CONCLUSIONS

Results suggest that the present intervention may be feasible and acceptable within the assessed population. Exploratory analyses suggest that Brightside's course of treatment, guided by precision recommendations, improved patients' symptoms of anxiety and depression.

The impact of electronic health record functions on patterns of depression treatment in primary care

BACKGROUND

Many individuals with depression are not being linked to treatment by their primary care providers. Electronic health records (EHRs) are common in medicine, but their impact on depression treatment is mixed. Because EHRs are diverse, differences may be attributable to differences in functionality. This study examines the relationship between EHR functions, and patterns of depression treatment in primary care.

METHODS

secondary analyses from the 2013-2016 National Ambulatory Medical Care Survey examined adult primary care patients with new or acute depression (n = 5,368). Bivariate comparisons examined patterns of depression treatment by general EHR use, and logistic regression examined the impact of individual EHR functions on treatment receipt.

RESULTS

Half the sample (57%; N = 3,034) was linked to depression treatment. Of this, 98.5% (n = 2,985) were prescribed antidepressants, while 4.3% (n = 130) were linked to mental health. EHR use did not impact mental health linkages, but EHR functions did affect antidepressant prescribing. Medication reconciliation decreased the odds of receiving an antidepressant (OR = .60, p < .05), while contraindication warnings increased the likelihood of an antidepressant prescription (OR = 1.91, p < .001).

CONCLUSIONS

EHR systems did not impact mental health linkages but improved rates of antidepressant prescribing. Optimizing the use of contraindication warnings may be a key mechanism to encourage antidepressant treatment.

Creation of an algorithm for clinical decision support for treatment of opioid use disorder with buprenorphine in primary care

BACKGROUND

The treatment capacity for opioid use disorder (OUD) lags far behind the number of patients in need of treatment. Capacity is limited, in part, by the limited number of physicians who offer office based OUD treatment with buprenorphine. Measurement based care (MBC) has been proposed as a means to support primary care physicians in treating OUD. Here, we propose a set of measures and a clinical decision support algorithm to provide MBC for the treatment of OUD.

METHODS

We utilized literature search and expert consensus to identify measures for universal screening and symptom tracking. We used expert consensus to create the clinical decision support algorithm.

RESULTS

The Tobacco, Alcohol, Prescription medication, and other Substance use (TAPS) tool was selected as the best published measure for universal screening in primary care. No published measure was identified as appropriate for symptom tracking or medication adherence; therefore, we created the OUD Symptom Checklist from the DSM-5 criteria for OUD and the Patient Adherence Questionnaire for Opioid Use Disorder Treatment (PAQ-OUD) to assess medication adherence. We developed and present a clinical decision support algorithm to provide direct guidance regarding treatment interventions during the first 12 weeks of buprenorphine treatment.

CONCLUSION

Creation of these tools is the necessary first step for implementation of MBC for the treatment of OUD with buprenorphine in primary care. Further work is needed to test the feasibility and acceptability of these tools. Trial Registration ClinicalTrials.gov; NCT04059016; 16 August 2019; retrospectively registered; https://clinicaltrials.gov/ct2/show/NCT04059016.

Implementing Measurement-Based Care for Depression: Practical Solutions for Psychiatrists and Primary Care Physicians

Measurement-based care (MBC) can be defined as the clinical practice in which care providers collect patient data through validated outcome scales and use the results to guide their decision-making processes. Despite growing evidence supporting the effectiveness of MBC for depression and other mental health conditions, many physicians and mental health clinicians have yet to adopt MBC practice. In part, this is due to individual and organizational barriers to implementing MBC in busy clinical settings. In this paper, we briefly review the evidence for the efficacy of MBC focusing on pharmacological management of depression and provide example clinical scenarios to illustrate its potential clinical utility in psychiatric settings. We discuss the barriers and challenges for MBC adoption and then address these by suggesting simple solutions to implement MBC for depression care, including recommended outcome scales, monitoring tools, and technology solutions such as cloud-based MBC services and mobile health apps for mood tracking. The availability of MBC tools, ranging from paper-pencil questionnaires to mobile health technology, can allow psychiatrists and clinicians in all types of practice settings to easily incorporate MBC into their practices and improve outcomes for their patients with depression.

Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction

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.

Setting Measurement-Based Care in Motion: Practical Lessons in the Implementation and Integration of Measurement-Based Care in Psychiatry Clinical Practice

Measurement-based care (MBC) involves the systematic use of standardized measurements to inform treatment decisions. MBC can enhance clinical decision-making and quality of care by prompting personalized changes in treatment based on measured patient outcomes. MBC can also promote more precise communications between patients and clinicians around individual patient care. While commonly employed in psychiatric clinical research, the use of MBC in everyday practice can be complicated by clinic operations and variability across patients. We implemented MBC in the UT Southwestern Psychiatry Multispecialty Outpatient Clinic during the expansion of our general psychiatry clinic and subspecialty targeted programs. This article describes the top 10 lessons we learned as we confronted practical obstacles around implementing the ideals of MBC into a pre-existing, busy psychiatric clinical practice and how doing so impacts care, provider engagement, patient engagement, and research opportunity.

Effectiveness of clinical decision support systems and telemedicine on outcomes of depression: a cluster randomized trial in general practice

BACKGROUND

Computerized Clinical Decision Support Systems (CCDSS) are information technology tools, designed to improve clinical decision-making. Telemedicine is a health care service delivery using videoconferencing, telephone or messaging technologies.

OBJECTIVES

Our project aimed at testing the effectiveness of a composite CCDSS and telemedicine approach designed to treat depression in primary care.

METHODS

This cluster randomized trial involved four GP clinics located in Northern Italy. Two clinics were assigned to the experimental protocol, and two served as controls. The study compared the telemedicine group (TG), in which GPs had access to a CCDSS platform, with the control group (CG) in which GPs provided treatment as usual (TAU). Patients scoring ≥11 on Patient Heath Questionnaire and ≥26 on the Inventory of Depressive Symptomatology-Self-Report were eligible for participation. Patients were also administered the World Health Organization Quality of Life-BREF to assess quality of life and Medical Interview Satisfaction Scale 21 to assess satisfaction with the medical interview.

RESULTS

Overall, 2810 patients were screened and 66 in the experimental group and 32 in the CG passed the screening stages and met inclusion criteria. The percentage of remitters at 6 months was significantly higher in the TG than in the CG group (24.1% versus 3.1%, χ 2 = 6.6, P = 0.01). This difference remained significant after adjusting for baseline confounders. Physical and psychological quality of life improved significantly from baseline in both groups. Patients reported, on average, good satisfaction with the medical interview.

CONCLUSIONS

Our study showed that a combined CCDSS and telemedicine approach may be more effective than the TAU offered by GPs to patients with depression.

TRIAL REGISTRATION

The trial was registered on https://clinicaltrials.gov/ on 5 October 2012 with identifier: NCT01701791. The first participant was enrolled on 5 May 2014 and the study was completed on May 2016.

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

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.

European Psychiatric Association (EPA) guidance on quality assurance in mental healthcare

Purpose:

To advance the quality of mental healthcare in Europe by developing guidance on implementing quality assurance.

Methods:

We performed a systematic literature search on quality assurance in mental healthcare and the 522 retrieved documents were evaluated by two independent reviewers (B.J. and J.Z.). Based on these evaluations, evidence tables were generated. As it was found that these did not cover all areas of mental healthcare, supplementary hand searches were performed for selected additional areas. Based on these findings, fifteen graded recommendations were developed and consented by the authors. Review by the EPA Guidance Committee and EPA Board led to two additional recommendations (on immigrant mental healthcare and parity of mental and physical healthcare funding).

Results:

Although quality assurance (measures to keep a certain degree of quality), quality control and monitoring (applying quality indicators to the current degree of quality), and quality management (coordinated measures and activities with regard to quality) are conceptually distinct, in practice they are frequently used as if identical and hardly separable. There is a dearth of controlled trials addressing ways to optimize quality assurance in mental healthcare. Altogether, seventeen recommendations were developed addressing a range of aspects of quality assurance in mental healthcare, which appear usable across Europe. These were divided into recommendations about structures, processes and outcomes. Each recommendation was assigned to a hierarchical level of analysis (macro-, meso- and micro-level).

Discussion:

There was a lack of evidence retrievable by a systematic literature search about quality assurance of mental healthcare. Therefore, only after further topics and search had been added it was possible to develop recommendations with mostly medium evidence levels.

Conclusion:

Evidence-based graded recommendations for quality assurance in mental healthcare were developed which should next be implemented and evaluated for feasibility and validity in some European countries. Due to the small evidence base identified corresponding to the practical obscurity of the concept and methods, a European research initiative is called for by the stakeholders represented in this Guidance to improve the educational, methodological and empirical basis for a future broad implementation of measures for quality assurance in European mental healthcare.

Improving the identification and treatment of depression in low-income primary care clinics: a qualitative study of providers in the VitalSign6 program

QUALITY PROBLEM

Despite its global burden and prevalence, Major Depressive Disorder often goes undetected and untreated, and is particularly pervasive in the primary care setting.

INITIAL ASSESSMENT

One in four Texans lack health insurance, and people with behavioral health disorders are disproportionately affected. It is possible to provide high-quality depression treatment in primary care settings with outcomes equal to those provided by specialty care. The Center for Depression Research and Clinical Care offered an opportunity to transform service delivery practices in underserved primary care practices to improve quality, health status, patient experience and coordination.

CHOICE OF SOLUTION

A point-of-care, web-based, self-report based software program, VitalSign6, was developed to provide universal depression screening in primary care practices and assist providers in monitoring and treating patients' symptoms using principles of Measurement-Based Care.

IMPLEMENTATION

Implementation included a multi-faceted training program designed to build confidence and competence in participating clinics' medical providers and staff as well as ongoing performance improvement delivered by the VitalSign6 team.

EVALUATION

Primary care providers (N = 11) were interviewed, using a semi-structured interview guide, with a focus on barriers and challenges to full integration, perceptions of the most/least valuable aspects of the program, and the program's impact on knowledge, attitudes and behaviors about depression screening and treatment.

LESSONS LEARNED

More efficient technology is needed to reduce time wasted, as is training to reduce stigma and correct misconceptions about antidepressant medications. Provider buy-in is essential.

CONCLUSIONS

Despite barriers, VitalSign6 increased knowledge, changed attitudes and enhanced providers' depression screening and treatment skills over time.

Collaborative Care for Depression of Adults and Adolescents: Measuring the Effectiveness of Screening and Treatment Uptake

OBJECTIVE

This study analyzed effectiveness of screening, referrals, and treatment uptake of a collaborative care for depression intervention across 10 primary care clinics in Chicago.

METHODS

Between November 2016 and December 2017, patients (N=25,369) were screened with the Patient Health Questionnaire-2 and the Patient Health Questionnaire-9 on the basis of an eligibility algorithm. Electronic health record data were analyzed for sample characteristics, screening rates, referrals, and treatment pathways. To identify disparities, a test of proportions was conducted between eligible and screened patients as well as referred and treated patients.

RESULTS

Screenings, referrals, and uptake occurred proportionately across subgroups except for patients ages 12-17. Adolescent age was associated with disproportionate Patient Health Questionnaire-9 screenings and with treatment disengagement.

CONCLUSIONS

The intervention shows promise in expanding access to care and reducing disparities. Greater access to psychotherapies and innovative treatment modalities, particularly for adolescents, may improve overall treatment uptake.

A model for digital mental healthcare: Its usefulness and potential for service delivery in low- and middle-income countries

BACKGROUND

Using digital technology to deliver mental health care can possibly serve as a viable adjunct or alternative to mainstream services in lessening the mental health gap in a large number of resource deficient and LAMI countries. Conventional models of telepsychiatric services available so far, however, have been inadequate and ineffective, as these address only a small component of care, and rely on engagement of specialists who are grossly insufficient in numbers.

AIM

To describe an innovative digital model of mental health care, enabling and empowering the non-specialists to deliver high quality mental health care in remote areas.

METHODS

The model is powered by an online, fully automated clinical decision support system (CDSS), with interlinked modules for diagnosis, management and follow-up, usable by non-specialists after brief training and minimal supervision by psychiatrist, to deliver mental health care at remote sites.

RESULTS

The CDSS has been found to be highly reliable, feasible, with sufficient sensitivity and specificity. This paper describes the model and initial experience with the digital mental health care system deployed in three geographically difficult and remote areas in northern hill states in India. The online system was found to be reasonably comprehensive, brief, feasible, user-friendly, with high levels of patient satisfaction. 2594 patients assessed at the three remote sites and the nodal center represented varied diagnoses.

CONCLUSIONS

The digital model described here has the potential to serve as an effective alternative or adjunct for delivering comprehensive and high quality mental health care in LAMI countries like India in the primary and secondary care settings.

Telepsychiatry clinical decision support system used by non-psychiatrists in remote areas: Validity & reliabilityof diagnostic module

Background & objectives:

A knowledge-based, logically-linked online telepsychiatric decision support system for diagnosis and treatment of mental disorders was developed and validated. We evaluated diagnostic accuracy and reliability of the application at remote sites when used by non-psychiatrists who underwent a brief training in its use through video-conferencing.

Methods:

The study was conducted at a nodal telepsychiatry centre, and three geographically remote peripheral centres. The diagnostic tool of application had a screening followed by detailed criteria-wise diagnostic modules for 18 psychiatric disorders. A total of 100 consecutive consenting adult outpatients attending remote telepsychiatry centres were included. To assess inter-rater reliability, patients were interviewed face to face by non-specialists at remote sites using the application (active interviewer) and simultaneously on online application via video-conferencing by a passive assessor at nodal centre. Another interviewer at the nodal centre rated the patient using Mini-International Neuropsychiatric Interview (MINI) for diagnostic validation.

Results:

Screening sub-module had high sensitivity (80-100%), low positive predictive values (PPV) (0.10-0.71) but high negative predictive value (NPV) (0.97-1) for most disorders. For the diagnostic sub-modules, Cohen's kappa was >0.4 for all disorders, with kappa of 0.7-1.0 for most disorders. PPV and NPV were high for most disorders. Inter-rater agreement analysis revealed kappa >0.6 for all disorders.

Interpretation & conclusions:

Diagnostic tool showed acceptable to good validity and reliability when used by non-specialists at remote sites. Our findings show that diagnostic tool of the telepsychiatry application has potential to empower non-psychiatrist doctors and paramedics to diagnose psychiatric disorders accurately and reliably in remote sites.

Elusive search for effective provider interventions: a systematic review of provider interventions to increase adherence to evidence-based treatment for depression

Background

Depression is a common mental health disorder for which clinical practice guidelines have been developed. Prior systematic reviews have identified complex organizational interventions, such as collaborative care, as effective for guideline implementation; yet, many healthcare delivery organizations are interested in less resource-intensive methods to increase provider adherence to guidelines and guideline-concordant practices. The objective of this systematic review was to assess the effectiveness of healthcare provider interventions that aim to increase adherence to evidence-based treatment of depression in routine clinical practice.

Methods

We searched five databases through August 2017 using a comprehensive search strategy to identify English-language randomized controlled trials (RCTs) in the quality improvement, implementation science, and behavior change literature that evaluated outpatient provider interventions, in the absence of practice redesign efforts, to increase adherence to treatment guidelines or guideline-concordant practices for depression. We used meta-analysis to summarize odds ratios, standardized mean differences, and incidence rate ratios, and assessed quality of evidence (QoE) using the GRADE approach.

Results

Twenty-two RCTs promoting adherence to clinical practice guidelines or guideline-concordant practices met inclusion criteria. Studies evaluated diverse provider interventions, including distributing guidelines to providers, education/training such as academic detailing, and combinations of education with other components such as targeting implementation barriers. Results were heterogeneous and analyses comparing provider interventions with usual clinical practice did not indicate a statistically significant difference in guideline adherence across studies. There was some evidence that provider interventions improved individual outcomes such as medication prescribing and indirect comparisons indicated more complex provider interventions may be associated with more favorable outcomes. We did not identify types of provider interventions that were consistently associated with improvements across indicators of adherence and across studies. Effects on patients’ health in these RCTs were inconsistent across studies and outcomes.

Conclusions

Existing RCTs describe a range of provider interventions to increase adherence to depression guidelines. Low QoE and lack of replication of specific intervention strategies across studies limited conclusions that can be drawn from the existing research. Continued efforts are needed to identify successful strategies to maximize the impact of provider interventions on increasing adherence to evidence-based treatment for depression.

Trial registration

PROSPERO record CRD42017060460 on 3/29/17

Electronic supplementary material

The online version of this article (10.1186/s13012-018-0788-8) contains supplementary material, which is available to authorized users.

A mobile and web-based clinical decision support and monitoring system for diabetes mellitus patients in primary care: a study protocol for a randomized controlled trial

BACKGROUND

Physicians' guideline use rates for diagnosis, treatment and monitoring of diabetes mellitus (DM) is very low. Time constraints, patient overpopulation, and complex guidelines require alternative solutions for real time patient monitoring. Rapidly evolving e-health technology combined with clinical decision support and monitoring systems (CDSMS) provides an effective solution to these problems. The purpose of the study is to develop a user-friendly, comprehensive, fully integrated web and mobile-based Clinical Decision Support and Monitoring System (CDSMS) for the screening, diagnosis, treatment, and monitoring of DM diseases which is used by physicians and patients in primary care and to determine the effectiveness of the system.

METHODS

The CDSMS will be based on evidence-based guidelines for DM disease. A web and mobile-based application will be developed in which the physician will remotely monitor patient data through mobile applications in real time. The developed CDSMS will be tested in two stages. In the first stage, the usability, understandability, and adequacy of the application will be determined. Five primary care physicians will use the developed application for at least 16 DM patients. Necessary improvements will be made according to physician feedback. In the second phase, a parallel, single-blind, randomized controlled trial will be implemented. DM diagnosed patients will be recruited for the CDSMS trial by their primary care physicians. Ten physicians and their 439 patients will be involved in the study. Eligible participants will be assigned to intervention and control groups with simple randomization. The significance level will be accepted as p < 0.05. In the intervention group, the system will make recommendations on patient monitoring, diagnosis, and treatment. These recommendations will be implemented at the physician's discretion. Patients in the control group will be treated by physicians according to current DM treatment standards. Patients in both groups will be monitored for 6 months. Patient data will be compared between 0th and 6th month of the study. . Clinical and laboratory outcomes will be assessed in person while others will be self-assessed online.

DISCUSSION

The developed system will be the first of its kind to utilize evidence based guidelines to provide health services to DM patients.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02917226 . 28 September 2016.

Digital technology and clinical decision making in depression treatment: Current findings and future opportunities

Clinical decision making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision making. In this paper, we provide "snapshots" of research and current directions on ways that digital technologies can support clinical decision making in depression treatment. Practical facets of clinical decision making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision making for depression by (1) improving tools to incorporate depression symptom data into existing electronic health record systems, (2) enhancing measurement of treatment fidelity and treatment processes, (3) harnessing smartphone and biosensor data to inform clinical decision making, (4) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (5) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains.

Decision aids for people facing health treatment or screening decisions

BACKGROUND

Decision aids are interventions that support patients by making their decisions explicit, providing information about options and associated benefits/harms, and helping clarify congruence between decisions and personal values.

OBJECTIVES

To assess the effects of decision aids in people facing treatment or screening decisions.

SEARCH METHODS

Updated search (2012 to April 2015) in CENTRAL; MEDLINE; Embase; PsycINFO; and grey literature; includes CINAHL to September 2008.

SELECTION CRITERIA

We included published randomized controlled trials comparing decision aids to usual care and/or alternative interventions. For this update, we excluded studies comparing detailed versus simple decision aids.

DATA COLLECTION AND ANALYSIS

Two reviewers independently screened citations for inclusion, extracted data, and assessed risk of bias. Primary outcomes, based on the International Patient Decision Aid Standards (IPDAS), were attributes related to the choice made and the decision-making process.Secondary outcomes were behavioural, health, and health system effects.We pooled results using mean differences (MDs) and risk ratios (RRs), applying a random-effects model. We conducted a subgroup analysis of studies that used the patient decision aid to prepare for the consultation and of those that used it in the consultation. We used GRADE to assess the strength of the evidence.

MAIN RESULTS

We included 105 studies involving 31,043 participants. This update added 18 studies and removed 28 previously included studies comparing detailed versus simple decision aids. During the 'Risk of bias' assessment, we rated two items (selective reporting and blinding of participants/personnel) as mostly unclear due to inadequate reporting. Twelve of 105 studies were at high risk of bias.With regard to the attributes of the choice made, decision aids increased participants' knowledge (MD 13.27/100; 95% confidence interval (CI) 11.32 to 15.23; 52 studies; N = 13,316; high-quality evidence), accuracy of risk perceptions (RR 2.10; 95% CI 1.66 to 2.66; 17 studies; N = 5096; moderate-quality evidence), and congruency between informed values and care choices (RR 2.06; 95% CI 1.46 to 2.91; 10 studies; N = 4626; low-quality evidence) compared to usual care.Regarding attributes related to the decision-making process and compared to usual care, decision aids decreased decisional conflict related to feeling uninformed (MD -9.28/100; 95% CI -12.20 to -6.36; 27 studies; N = 5707; high-quality evidence), indecision about personal values (MD -8.81/100; 95% CI -11.99 to -5.63; 23 studies; N = 5068; high-quality evidence), and the proportion of people who were passive in decision making (RR 0.68; 95% CI 0.55 to 0.83; 16 studies; N = 3180; moderate-quality evidence).Decision aids reduced the proportion of undecided participants and appeared to have a positive effect on patient-clinician communication. Moreover, those exposed to a decision aid were either equally or more satisfied with their decision, the decision-making process, and/or the preparation for decision making compared to usual care.Decision aids also reduced the number of people choosing major elective invasive surgery in favour of more conservative options (RR 0.86; 95% CI 0.75 to 1.00; 18 studies; N = 3844), but this reduction reached statistical significance only after removing the study on prophylactic mastectomy for breast cancer gene carriers (RR 0.84; 95% CI 0.73 to 0.97; 17 studies; N = 3108). Compared to usual care, decision aids reduced the number of people choosing prostate-specific antigen screening (RR 0.88; 95% CI 0.80 to 0.98; 10 studies; N = 3996) and increased those choosing to start new medications for diabetes (RR 1.65; 95% CI 1.06 to 2.56; 4 studies; N = 447). For other testing and screening choices, mostly there were no differences between decision aids and usual care.The median effect of decision aids on length of consultation was 2.6 minutes longer (24 versus 21; 7.5% increase). The costs of the decision aid group were lower in two studies and similar to usual care in four studies. People receiving decision aids do not appear to differ from those receiving usual care in terms of anxiety, general health outcomes, and condition-specific health outcomes. Studies did not report adverse events associated with the use of decision aids.In subgroup analysis, we compared results for decision aids used in preparation for the consultation versus during the consultation, finding similar improvements in pooled analysis for knowledge and accurate risk perception. For other outcomes, we could not conduct formal subgroup analyses because there were too few studies in each subgroup.

AUTHORS' CONCLUSIONS

Compared to usual care across a wide variety of decision contexts, people exposed to decision aids feel more knowledgeable, better informed, and clearer about their values, and they probably have a more active role in decision making and more accurate risk perceptions. There is growing evidence that decision aids may improve values-congruent choices. There are no adverse effects on health outcomes or satisfaction. New for this updated is evidence indicating improved knowledge and accurate risk perceptions when decision aids are used either within or in preparation for the consultation. Further research is needed on the effects on adherence with the chosen option, cost-effectiveness, and use with lower literacy populations.

Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention

BACKGROUND

Management of uncontrolled symptoms is an important component of quality cancer care. Clinical guidelines are available for optimal symptom management, but are not often integrated into the front lines of care. The use of clinical decision support (CDS) at the point-of-care is an innovative way to incorporate guideline-based symptom management into routine cancer care.

OBJECTIVE

The objective of this study was to develop and evaluate a rule-based CDS system to enable management of multiple symptoms in lung cancer patients at the point-of-care.

METHODS

This study was conducted in three phases involving a formative evaluation, a system evaluation, and a contextual evaluation of clinical use. In Phase 1, we conducted iterative usability testing of user interface prototypes with patients and health care providers (HCPs) in two thoracic oncology clinics. In Phase 2, we programmed complex algorithms derived from clinical practice guidelines into a rules engine that used Web services to communicate with the end-user application. Unit testing of algorithms was conducted using a stack-traversal tree-spanning methodology to identify all possible permutations of pathways through each algorithm, to validate accuracy. In Phase 3, we evaluated clinical use of the system among patients and HCPs in the two clinics via observations, structured interviews, and questionnaires.

RESULTS

In Phase 1, 13 patients and 5 HCPs engaged in two rounds of formative testing, and suggested improvements leading to revisions until overall usability scores met a priori benchmarks. In Phase 2, symptom management algorithms contained between 29 and 1425 decision nodes, resulting in 19 to 3194 unique pathways per algorithm. Unit testing required 240 person-hours, and integration testing required 40 person-hours. In Phase 3, both patients and HCPs found the system usable and acceptable, and offered suggestions for improvements.

CONCLUSIONS

A rule-based CDS system for complex symptom management was systematically developed and tested. The complexity of the algorithms required extensive development and innovative testing. The Web service-based approach allowed remote access to CDS knowledge, and could enable scaling and sharing of this knowledge to accelerate availability, and reduce duplication of effort. Patients and HCPs found the system to be usable and useful.

Development and impact of computerised decision support systems for clinical management of depression: A systematic review

One of the proposals for improving clinical practice is to introduce computerised decision support systems (CDSS) and integrate these with electronic medical records. Accordingly, this study sought to systematically review evidence on the effectiveness of CDSS in the management of depression. A search was performed in Medline, EMBASE and PsycInfo, in order to do this. The quality of quantitative studies was assessed using the SIGN method, and qualitative studies using the CASPe checklist. Seven studies were identified (3 randomised clinical trials, 3 non-randomised trials, and one qualitative study). The CDSS assessed incorporated content drawn from guidelines and other evidence-based products. In general, the CDSS had a positive impact on different aspects, such as the screening and diagnosis, treatment, improvement in depressive symptoms and quality of life, and referral of patients. The use of CDSS could thus serve to optimise care of depression in various scenarios by providing recommendations based on the best evidence available and facilitating decision-making in clinical practice.

Food for thought: understanding the value, variety and usage of management algorithms for major depressive disorder

By 2020, depression is projected to be among the most important contributors to the global burden of disease. A plethora of data confirms that despite the availability of effective therapies, major depressive disorder continues to exact an enormous toll; this, in part, is due to difficulties reaching complete remission, as well as the specific associated costs of both the disorder's morbidity and mortality. The negative effects of depression include those on patients' occupational functioning, including absenteeism, presenteeism, and reduced opportunities for educational and work success. The use of management algorithms has been shown to improve treatment outcomes in major depressive disorder and may be less costly than "usual care" practices. Nevertheless, many patients with depression remain untreated. As well, even those who are treated often continue to experience suboptimal quality of life. As such, the treatment algorithms in this article may improve outcomes for patients suffering with depression. This paper introduces some of the principal reasons underlying these treatment gaps and examines measures or recommendations that might be changed or strengthened in future practice guidelines to bridge them.

Decision aids for people facing health treatment or screening decisions

BACKGROUND

Decision aids are intended to help people participate in decisions that involve weighing the benefits and harms of treatment options often with scientific uncertainty.

OBJECTIVES

To assess the effects of decision aids for people facing treatment or screening decisions.

SEARCH METHODS

For this update, we searched from 2009 to June 2012 in MEDLINE; CENTRAL; EMBASE; PsycINFO; and grey literature. Cumulatively, we have searched each database since its start date including CINAHL (to September 2008).

SELECTION CRITERIA

We included published randomized controlled trials of decision aids, which are interventions designed to support patients' decision making by making explicit the decision, providing information about treatment or screening options and their associated outcomes, compared to usual care and/or alternative interventions. We excluded studies of participants making hypothetical decisions.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. The primary outcomes, based on the International Patient Decision Aid Standards (IPDAS), were:A) 'choice made' attributes;B) 'decision-making process' attributes.Secondary outcomes were behavioral, health, and health-system effects. We pooled results using mean differences (MD) and relative risks (RR), applying a random-effects model.

MAIN RESULTS

This update includes 33 new studies for a total of 115 studies involving 34,444 participants. For risk of bias, selective outcome reporting and blinding of participants and personnel were mostly rated as unclear due to inadequate reporting. Based on 7 items, 8 of 115 studies had high risk of bias for 1 or 2 items each.Of 115 included studies, 88 (76.5%) used at least one of the IPDAS effectiveness criteria: A) 'choice made' attributes criteria: knowledge scores (76 studies); accurate risk perceptions (25 studies); and informed value-based choice (20 studies); and B) 'decision-making process' attributes criteria: feeling informed (34 studies) and feeling clear about values (29 studies).A) Criteria involving 'choice made' attributes:Compared to usual care, decision aids increased knowledge (MD 13.34 out of 100; 95% confidence interval (CI) 11.17 to 15.51; n = 42). When more detailed decision aids were compared to simple decision aids, the relative improvement in knowledge was significant (MD 5.52 out of 100; 95% CI 3.90 to 7.15; n = 19). Exposure to a decision aid with expressed probabilities resulted in a higher proportion of people with accurate risk perceptions (RR 1.82; 95% CI 1.52 to 2.16; n = 19). Exposure to a decision aid with explicit values clarification resulted in a higher proportion of patients choosing an option congruent with their values (RR 1.51; 95% CI 1.17 to 1.96; n = 13).B) Criteria involving 'decision-making process' attributes:Decision aids compared to usual care interventions resulted in:a) lower decisional conflict related to feeling uninformed (MD -7.26 of 100; 95% CI -9.73 to -4.78; n = 22) and feeling unclear about personal values (MD -6.09; 95% CI -8.50 to -3.67; n = 18);b) reduced proportions of people who were passive in decision making (RR 0.66; 95% CI 0.53 to 0.81; n = 14); andc) reduced proportions of people who remained undecided post-intervention (RR 0.59; 95% CI 0.47 to 0.72; n = 18).Decision aids appeared to have a positive effect on patient-practitioner communication in all nine studies that measured this outcome. For satisfaction with the decision (n = 20), decision-making process (n = 17), and/or preparation for decision making (n = 3), those exposed to a decision aid were either more satisfied, or there was no difference between the decision aid versus comparison interventions. No studies evaluated decision-making process attributes for helping patients to recognize that a decision needs to be made, or understanding that values affect the choice.C) Secondary outcomes Exposure to decision aids compared to usual care reduced the number of people of choosing major elective invasive surgery in favour of more conservative options (RR 0.79; 95% CI 0.68 to 0.93; n = 15). Exposure to decision aids compared to usual care reduced the number of people choosing to have prostate-specific antigen screening (RR 0.87; 95% CI 0.77 to 0.98; n = 9). When detailed compared to simple decision aids were used, fewer people chose menopausal hormone therapy (RR 0.73; 95% CI 0.55 to 0.98; n = 3). For other decisions, the effect on choices was variable.The effect of decision aids on length of consultation varied from 8 minutes shorter to 23 minutes longer (median 2.55 minutes longer) with 2 studies indicating statistically-significantly longer, 1 study shorter, and 6 studies reporting no difference in consultation length. Groups of patients receiving decision aids do not appear to differ from comparison groups in terms of anxiety (n = 30), general health outcomes (n = 11), and condition-specific health outcomes (n = 11). The effects of decision aids on other outcomes (adherence to the decision, costs/resource use) were inconclusive.

AUTHORS' CONCLUSIONS

There is high-quality evidence that decision aids compared to usual care improve people's knowledge regarding options, and reduce their decisional conflict related to feeling uninformed and unclear about their personal values. There is moderate-quality evidence that decision aids compared to usual care stimulate people to take a more active role in decision making, and improve accurate risk perceptions when probabilities are included in decision aids, compared to not being included. There is low-quality evidence that decision aids improve congruence between the chosen option and the patient's values.New for this updated review is further evidence indicating more informed, values-based choices, and improved patient-practitioner communication. There is a variable effect of decision aids on length of consultation. Consistent with findings from the previous review, decision aids have a variable effect on choices. They reduce the number of people choosing discretionary surgery and have no apparent adverse effects on health outcomes or satisfaction. The effects on adherence with the chosen option, cost-effectiveness, use with lower literacy populations, and level of detail needed in decision aids need further evaluation. Little is known about the degree of detail that decision aids need in order to have a positive effect on attributes of the choice made, or the decision-making process.

Feasible evidence-based strategies to manage depression in primary care

According to the World Health Organization, major depressive disorder (MDD) is a leading cause of disability-adjusted life years worldwide. However, recent evidence suggests depression remains undertreated in primary care settings. Measurement-based care (MBC) is an evidence-based strategy that can feasibly assist primary care physicians in managing patients with MDD. Utilizing health information technology tools, such as computer decision support software, can improve adherence to evidence-based treatment guidelines and MBC at the point of care.

Effect of communicating depression severity on physician prescribing patterns: findings from the Clinical Outcomes in MEasurement-based Treatment (COMET) trial

OBJECTIVE

In this secondary analysis from the Clinical Outcomes in MEasurement-based Treatment trial (COMET), we evaluated whether providing primary care physicians with patient-reported feedback regarding depression severity affected pharmacological treatment patterns.

METHOD

Intervention-arm physicians received their patients' 9-item Patient Health Questionnaire scores monthly. Odds of having no change in antidepressant treatment during the 6-month study period were calculated. Relationships between depression symptom status (partial or nonresponse) at month 3 and treatment changes in months 3 through 6 were assessed.

RESULTS

Among 503 intervention and 412 usual care (UC) patients with major depressive disorder, most received antidepressant monotherapy at baseline (79.4% UC vs. 88.4% intervention; P=.047). Few switched their baseline antidepressant (17.4%), increased their dose (12.4%) or augmented with a second medication (2%). Odds of having no change in antidepressant therapy did not differ significantly between study arms (odds ratio 1.21; 95% confidence interval 0.78-1.88; P=.392). Few month 3 partial or nonresponders had a regimen change over the following 3 months; the study arms did not differ significantly (partial responders: 4.1% UC vs. 7.7% intervention; P=.429; nonresponders: 14.6% UC vs. 15.9% intervention; P=.888).

CONCLUSIONS

Among depressed patients treated in primary care, little active management was observed. The lack of treatment modification for the majority of partial and nonresponders was notable.

Measurement-based care for unipolar depression

This article outlines the role of measurement-based care in the management of antidepressant treatment for patients with unipolar depression. Using measurement-based care, clinicians and researchers have the opportunity to optimize individual treatment and obtain maximum antidepressant treatment response. Measurement-based care breaks down to several simple components: antidepressant dosage, depressive symptom severity, medication tolerability, adherence to treatment, and safety. Quick and easy-to-use, empirically validated assessments are available to monitor these areas of treatment. Utilizing measurement-based care has several steps-screening and antidepressant selection based upon treatment history, followed by assessment-based medication management and ongoing care. Electronic measurement-based care systems have been developed and implemented, further reducing the burden on patients and clinicians. As more treatment providers adopt electronic health care management systems, compatible measurement-based care antidepressant treatment delivery and monitoring systems may become increasingly utilized.

General and comparative efficacy and effectiveness of antidepressants in the acute treatment of depressive disorders: a report by the WPA section of pharmacopsychiatry

Current gold standard approaches to the treatment of depression include pharmacotherapeutic and psychotherapeutic interventions with social support. Due to current controversies concerning the efficacy of antidepressants in randomized controlled trials, the generalizability of study findings to wider clinical practice and the increasing importance of socioeconomic considerations, it seems timely to address the uncertainty of concerned patients and relatives, and their treating psychiatrists and general practitioners. We therefore discuss both the efficacy and clinical effectiveness of antidepressants in the treatment of depressive disorders. We explain and clarify useful measures for assessing clinically meaningful antidepressant treatment effects and the types of studies that are useful for addressing uncertainties. This includes considerations of methodological issues in randomized controlled studies, meta-analyses, and effectiveness studies. Furthermore, we summarize the differential efficacy and effectiveness of antidepressants with distinct pharmacodynamic properties, and differences between studies using antidepressants and/or psychotherapy. We also address the differential effectiveness of antidepressant drugs with differing modes of action and in varying subtypes of depressive disorder. After highlighting the clinical usefulness of treatment algorithms and the divergent biological, psychological, and clinical efforts to predict the effectiveness of antidepressant treatments, we conclude that the spectrum of different antidepressant treatments has broadened over the last few decades. The efficacy and clinical effectiveness of antidepressants is statistically significant, clinically relevant, and proven repeatedly. Further optimization of treatment can be helped by clearly structured treatment algorithms and the implementation of psychotherapeutic interventions. Modern individualized antidepressant treatment is in most cases a well-tolerated and efficacious approach to minimize the negative impact of otherwise potentially devastating and life-threatening outcomes in depressive disorders.

Predictors of depression screening rates of nurses receiving a personal digital assistant-based reminder to screen

The purpose of this study was to determine if race/ethnicity, payer type, or nursing specialty affected depression screening rates in primary care settings in which nurses received a reminder to screen. The sample comprised 4,160 encounters in which nurses enrolled in advanced practice training were prompted to screen for depression using the Patient Health Questionnaire (PHQ)-2/PHQ-9 integrated into a personal digital assistant-based clinical decision support system for depression screening and management. Nurses chose to screen in response to 52.5% of reminders. Adjusted odds ratios showed that payer type and nurse specialty, but not race/ethnicity, significantly predicted proportion of patients screened.

[Severe depression : pharmacological treatments]

Depression is a common illness with a large clinical phenotype, and clinicians have numerous guidelines to treat this disorder : many antidepressant drugs are available with different pharmacological profiles and stepped strategies are proposed to obtain a remission. It exists a relationship between baseline depression symptom severity and treatment response and patient with higher levels of severity received significantly more intervention visits, more months of antidepressant treatment and more antidepressant trials, but there is not accepted and consensual definition for severe depression. By using cut-off scores on rating scales severe depression is at one extreme of a continuum of severity (but scales which serve for quantifying the intensity of the depression with thresholds present an interest and also limits, in the current practice, they are rarely used), in the other hand some symptoms contributes to severity (psychotics features, suicidal ideation), evolution and prognosis is a part of severity too (recurrences, chronicity), severe depression can influence a somatic pathology contributing to severity (could be considered itself as a major risk factor) and have an impact on treatment outcome, finally by its role on morbi-mortality and handicap, depression is often a severe disorder. So, concerning the therapeutic choices, there are few data to choose specific options because the concept of severity in the depression is not still clearly defined in studies and few randomized contolled studies have been done in this indication and adapted to different modality of the severity expression. Symptom-free remission is a goal for treatment in severe depression, but complications have to be considered in medication algorithms. In this paper, we review the modalities of prescription of antidepressants according to these differences of the severity in depression.

The effects of on-screen, point of care computer reminders on processes and outcomes of care

BACKGROUND

The opportunity to improve care by delivering decision support to clinicians at the point of care represents one of the main incentives for implementing sophisticated clinical information systems. Previous reviews of computer reminder and decision support systems have reported mixed effects, possibly because they did not distinguish point of care computer reminders from e-mail alerts, computer-generated paper reminders, and other modes of delivering 'computer reminders'.

OBJECTIVES

To evaluate the effects on processes and outcomes of care attributable to on-screen computer reminders delivered to clinicians at the point of care.

SEARCH STRATEGY

We searched the Cochrane EPOC Group Trials register, MEDLINE, EMBASE and CINAHL and CENTRAL to July 2008, and scanned bibliographies from key articles.

SELECTION CRITERIA

Studies of a reminder delivered via a computer system routinely used by clinicians, with a randomised or quasi-randomised design and reporting at least one outcome involving a clinical endpoint or adherence to a recommended process of care.

DATA COLLECTION AND ANALYSIS

Two authors independently screened studies for eligibility and abstracted data. For each study, we calculated the median improvement in adherence to target processes of care and also identified the outcome with the largest such improvement. We then calculated the median absolute improvement in process adherence across all studies using both the median outcome from each study and the best outcome.

MAIN RESULTS

Twenty-eight studies (reporting a total of thirty-two comparisons) were included. Computer reminders achieved a median improvement in process adherence of 4.2% (interquartile range (IQR): 0.8% to 18.8%) across all reported process outcomes, 3.3% (IQR: 0.5% to 10.6%) for medication ordering, 3.8% (IQR: 0.5% to 6.6%) for vaccinations, and 3.8% (IQR: 0.4% to 16.3%) for test ordering. In a sensitivity analysis using the best outcome from each study, the median improvement was 5.6% (IQR: 2.0% to 19.2%) across all process measures and 6.2% (IQR: 3.0% to 28.0%) across measures of medication ordering. In the eight comparisons that reported dichotomous clinical endpoints, intervention patients experienced a median absolute improvement of 2.5% (IQR: 1.3% to 4.2%). Blood pressure was the most commonly reported clinical endpoint, with intervention patients experiencing a median reduction in their systolic blood pressure of 1.0 mmHg (IQR: 2.3 mmHg reduction to 2.0 mmHg increase).

AUTHORS' CONCLUSIONS

Point of care computer reminders generally achieve small to modest improvements in provider behaviour. A minority of interventions showed larger effects, but no specific reminder or contextual features were significantly associated with effect magnitude. Further research must identify design features and contextual factors consistently associated with larger improvements in provider behaviour if computer reminders are to succeed on more than a trial and error basis.