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An Electronic Clinical Decision Support System for the Assessment and Management of Suicidality in Primary Care: Protocol for a Mixed-Methods Study.

ABSTRACT: BACKGROUND:Suicide is a global public health concern, but it is preventable. Increased contact with primary care before the suicide or attempted suicide raises opportunities for intervention and prevention. However, suicide assessment and management are areas that many general practitioners (GPs) find particularly challenging. Previous research has indicated significant variability in how GPs understand, operationalize, and assess suicide risk, which subsequently has an impact on clinical decision making. Clinical decision support systems (CDSS) have been widely implemented across different health care settings, including primary care to support practitioners in clinical decision making. A CDSS may reduce inconsistencies in the identification, assessment, and management of suicide risk by GPs by guiding them through the consultation and generating a risk assessment plan that can be shared with a service user or with specialized mental health services. OBJECTIVE:Our aim is to co-develop and test with end users (eg, GPs, primary care attendees, mental health professionals) an electronic clinical decision support system (e-CDSS) to support GPs in the identification, assessment, and management of suicidality in primary care. METHODS:Ours is an ongoing embedded mixed-methods study with four phases: (1) qualitative interviews with GPs to explore their views on the content, format, and use of the e-CDSS, as well as consultation with two service-user advisory groups (people aged ≤25 and people aged ≥25) to inform the content of the e-CDSS including phrasing of items and clarity; (2) participatory co-production workshops with GPs, service users, and clinical experts in suicidality to determine the content and format of the e-CDDS; gain consensus of the relevance of items; establish content validity and identify pathways to implementation, using the Consolidated Framework for Implementation Research; (3) building the e-CDSS so that it guides the GP through a consultation; and (4) usability testing of the e-CDSS with GPs and service users in one primary care practice involving a nonlive and a live stage. RESULTS:The study was funded for four years, to take place between 2015 and 2019, and is currently completing phase 4 data collection. The first results are expected to be submitted for publication in June 2019. The findings will enable us to evaluate the feasibility, acceptability, and usability of a suicide-specific, electronic, guided decision support system in primary care. CONCLUSIONS:This study will be the first to explore the feasibility, acceptability, and usability of an electronic, guided decision support system for use in primary care consultations for the improved assessment and management of suicidality. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR1-10.2196/11135.

SUBMITTER: Horrocks M

PROVIDER: S-EPMC6303673 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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Project description:BACKGROUND:People with low back pain (LBP) in the community often do not receive evidence-based advice and management. Community pharmacists can play an important role in supporting people with LBP as pharmacists are easily accessible to provide first-line care. However, previous research suggests that pharmacists may not consistently deliver advice that is concordant with guideline recommendations and may demonstrate difficulty determining which patients require prompt medical review. A clinical decision support system (CDSS) may enhance first-line care of LBP, but none exists to support the community pharmacist-client consultation. OBJECTIVE:This study aimed to develop a CDSS to guide first-line care of LBP in the community pharmacy setting and to evaluate the pharmacist-reported usability and acceptance of the prototype system. METHODS:A cross-platform Web app for the Apple iPad was developed in conjunction with academic and clinical experts using an iterative user-centered design process during interface design, clinical reasoning, program development, and evaluation. The CDSS was evaluated via one-to-one user-testing with 5 community pharmacists (5 case vignettes each). Data were collected via video recording, screen capture, survey instrument (system usability scale), and direct observation. RESULTS:Pharmacists' agreement with CDSS-generated self-care recommendations was 90% (18/20), with medicines recommendations was 100% (25/25), and with referral advice was 88% (22/25; total 70 recommendations). Pharmacists expressed uncertainty when screening for serious pathology in 40% (10/25) of cases. Pharmacists requested more direction from the CDSS in relation to automated prompts for user input and page navigation. Overall system usability was rated as excellent (mean score 92/100, SD 6.5; 90th percentile compared with similar systems), with acceptance rated as good to excellent. CONCLUSIONS:A novel CDSS (high-fidelity prototype) to enhance pharmacist care of LBP was developed, underpinned by clinical practice guidelines and informed by a multidisciplinary team of experts. User-testing revealed a high level of usability and acceptance of the prototype system, with suggestions to improve interface prompts and information delivery. The small study sample limits the generalizability of the findings but offers important insights to inform the next stage of system development.

Project description:IntroductionAdvanced oral cancer and its ensuing treatment engenders significant morbidity and mortality. Patients are often elderly with significant comorbidities. Toxicities associated with surgical resection can be devastating and they are often highlighted by patients as impactful. Given the potential for suboptimal oncological and functional outcomes in this vulnerable patient population, promotion and performance of shared decision making (SDM) is crucial.Decision aids (DAs) are useful instruments for facilitating the SDM process by presenting patients with up-to-date evidence regarding risks, benefits and the possible postoperative course. Importantly, DAs also help elicit and clarify patient values and preferences. The use of DAs in cancer treatment has been shown to reduce decisional conflict and increase SDM. No DAs for oral cavity cancer have yet been developed.This study endeavours to answer the question: Is there a patient or surgeon driven need for development and implementation of a DA for adult patients considering major surgery for oral cancer?Methods and analysisThis study is the first step in a multiphase investigation of SDM during major head and neck surgery. It is a multi-institutional convergent parallel mixed-methods needs assessment study. Patients and surgeon dyads will be recruited to complete questionnaires related to their perception of the SDM process (nine-item Shared Decision-Making Questionnaire, SDM-Q-9 and SDM-Q-Doc) and to take part in semistructured interviews. Patients will also complete questionnaires examining decisional self-efficacy (Ottawa Decision Self-Efficacy Scale) and decisional conflict (Decisional Conflict Scale). Questionnaires will be completed at time of recruitment and will be used to assess the current level of SDM, self-efficacy and conflict in this setting. Thematic analysis will be used to analyse transcripts of interviews. Quantitative and qualitative components of the study will be integrated through triangulation, with matrix developed to promote visualisation of the data.Ethics and disseminationThis study has been approved by the research ethics boards of the Nova Scotia Health Authority (Halifax, Nova Scotia) and the University Health Network (Toronto, Ontario). Dissemination to clinicians will be through traditional approaches and creation of a head and neck cancer SDM website. Dissemination to patients will include a section within the website, patient advocacy groups and postings within clinical environments.

Project description:BackgroundDiagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability.ObjectiveThis study aims to develop an information retrieval (IR)-based framework that accommodates data sparsity to facilitate broader diagnostic decision support.MethodsWe introduced an IR-based diagnostic decision support framework called CliniqIR. It uses clinical text records, the Unified Medical Language System Metathesaurus, and 33 million PubMed abstracts to classify a broad spectrum of diagnoses independent of training data availability. CliniqIR is designed to be compatible with any IR framework. Therefore, we implemented it using both dense and sparse retrieval approaches. We compared CliniqIR's performance to that of pretrained clinical transformer models such as Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) in supervised and zero-shot settings. Subsequently, we combined the strength of supervised fine-tuned ClinicalBERT and CliniqIR to build an ensemble framework that delivers state-of-the-art diagnostic predictions.ResultsOn a complex diagnosis data set (DC3) without any training data, CliniqIR models returned the correct diagnosis within their top 3 predictions. On the Medical Information Mart for Intensive Care III data set, CliniqIR models surpassed ClinicalBERT in predicting diagnoses with <5 training samples by an average difference in mean reciprocal rank of 0.10. In a zero-shot setting where models received no disease-specific training, CliniqIR still outperformed the pretrained transformer models with a greater mean reciprocal rank of at least 0.10. Furthermore, in most conditions, our ensemble framework surpassed the performance of its individual components, demonstrating its enhanced ability to make precise diagnostic predictions.ConclusionsOur experiments highlight the importance of IR in leveraging unstructured knowledge resources to identify infrequently encountered diagnoses. In addition, our ensemble framework benefits from combining the complementary strengths of the supervised and retrieval-based models to diagnose a broad spectrum of diseases.

Project description:Rationale & objectiveMost adults with chronic kidney disease (CKD) in the United States are cared for by primary care providers (PCPs). We evaluated the feasibility and preliminary effectiveness of an electronic clinical decision support system (eCDSS) within the electronic health record with or without pharmacist follow-up to improve the management of CKD in primary care.Study designPragmatic cluster-randomized trial.Setting & participants524 adults with confirmed creatinine-based estimated glomerular filtration rates of 30 to 59mL/min/1.73m2 cared for by 80 PCPs at the University of California San Francisco. Electronic health record data were used for patient identification, intervention deployment, and outcomes ascertainment.InterventionsEach PCP's eligible patients were randomly assigned as a group into 1 of 3 treatment arms: (1) usual care; (2) eCDSS: testing of creatinine, cystatin C, and urinary albumin-creatinine ratio with individually tailored guidance for PCPs on blood pressure, potassium, and proteinuria management, cardiovascular risk reduction, and patient education; or (3) eCDSS plus pharmacist counseling (eCDSS-PLUS).OutcomesThe primary clinical outcome was change in blood pressure over 12 months. Secondary outcomes were PCP awareness of CKD and use of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker and statin therapy.ResultsAll 80 eligible PCPs participated. Mean patient age was 70 years, 47% were nonwhite, and mean estimated glomerular filtration rate was 56±0.6mL/min/1.73m2. Among patients receiving eCDSS with or without pharmacist counseling (n=336), 178 (53%) completed laboratory measurements and 138 (41%) had laboratory measurements followed by a PCP visit with eCDSS deployment. eCDSS was opened by the PCP for 102 (74%) patients, with at least 1 suggested order signed for 83 of these 102 (81%). Changes in systolic blood pressure were-2.1±1.5mm Hg with usual care, -2.8±1.8mm Hg with eCDSS, and -1.1±1.1 with eCDSS-PLUS (P=0.7). PCP awareness of CKD was 16% with usual care, 26% with eCDSS, and 32% for eCDSS-PLUS (P=0.09). In as-treated analyses, PCP awareness of CKD was significantly greater with eCDSS and eCDSS-PLUS (73% and 69%) versus usual care (47%; P=0.002).LimitationsRecruitment of smaller than intended sample size and limited uptake of the testing component of the intervention.ConclusionsAlthough we were unable to demonstrate the effectiveness of eCDSS to lower blood pressure and uptake of the eCDSS was limited by low testing rates, eCDSS use was high when laboratory measurements were available and was associated with higher PCP awareness of CKD.FundingGrants from government (National Institutes of Health) and not-for-profit (American Heart Association) entities.Trial registrationRegistered at ClinicalTrials.gov with study number NCT02925962.

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:BackgroundElectronic health record (EHR)-based clinical decision support tools can improve the use of evidence-based clinical guidelines for preeclampsia management that can reduce maternal mortality and morbidity. No study has investigated the organizational capabilities that enable hospitals to use EHR-based decision support tools to manage preeclampsia.ObjectiveTo examine the association of organizational capabilities and hospital adoption of EHR-based decision support tools for preeclampsia management.MethodsCross-sectional analyses of hospitals providing obstetric care in 2017. In total, 739 hospitals responded to the 2017-2018 National Survey of Healthcare Organizations and Systems (NSHOS) and were linked to the 2017 American Hospital Association (AHA) Annual Survey Database and the Area Health Resources File (AHRF). A total of 425 hospitals providing obstetric care across 49 states were included in the analysis. The main outcome was whether a hospital adopted EHR-based clinical decision support tools for preeclampsia management. Hospital organizational capabilities assessed as predictors include EHR functions, adoption of evidence-based clinical treatments, use of quality improvement methods, and dissemination processes to share best patient care practices. Logistic regression estimated the association of hospital organizational capabilities and hospital adoption of EHR-based decision support tools to manage preeclampsia, controlling for hospital structural and patient sociodemographic characteristics.ResultsTwo-thirds of the hospitals (68%) adopted EHR-based decision support tools for preeclampsia, and slightly more than half (56%) of hospitals had a single EHR system. Multivariable regression results indicate that hospitals with a single EHR system were more likely to adopt EHR-based decision support tools for preeclampsia (17.4 percentage points; 95% CI, 1.9 to 33.0; P < .05) than hospitals with a mixture of EHR and paper-based systems. Compared with hospitals having multiple EHRs, on average, hospitals having a single EHR were also more likely to adopt the tools by 9.3 percentage points, but the difference was not statistically significant (95% CI, -1.3 to 19.9). Hospitals with more processes to aid dissemination of best patient care practices were also more likely to adopt EHR-based decision-support tools for preeclampsia (0.4 percentage points; 95% CI, 0.1 to 0.6, for every 1-unit increase in dissemination processes; P < .01).ConclusionStandardized EHRs and policies to disseminate evidence are foundational hospital capabilities that can help advance the use of EHR-based decision support tools for preeclampsia management in the approximately one-third of US hospitals that still do not use them.

Project description:ObjectivesTo analyze current practices in shared decision-making (SDM) in primary care and perform a needs assessment for the role of information technology (IT) interventions.Materials and methodsA mixed-methods study was conducted in three phases: (1) ethnographic observation of clinical encounters, (2) patient interviews, and (3) physician interviews. SDM was measured using the validated OPTION scale. Semistructured interviews followed an interview guide (developed by our multidisciplinary team) informed by the Traditional Decision Conflict Scale and Shared Decision Making Questionnaire. Field notes were independently coded and analyzed by two reviewers in Dedoose.ResultsTwenty-four patient encounters were observed in 3 diverse practices with an average OPTION score of 57.2 (0-100 scale; 95% confidence interval [CI], 51.8-62.6). Twenty-two patient and 8 physician interviews were conducted until thematic saturation was achieved. Cohen's kappa, measuring coder agreement, was 0.42. Patient domains were: establishing trust, influence of others, flexibility, frustrations, values, and preferences. Physician domains included frustrations, technology (concerns, existing use, and desires), and decision making (current methods used, challenges, and patients' understanding).DiscussionGiven low SDM observed, multiple opportunities for technology to enhance SDM exist based on specific OPTION items that received lower scores, including: (1) checking the patient's preferred information format, (2) asking the patient's preferred level of involvement in decision making, and (3) providing an opportunity for deferring a decision. Based on data from interviews, patients and physicians value information exchange and are open to technologies that enhance communication of care options.ConclusionFuture primary care IT platforms should prioritize the 3 quantitative gaps identified to improve physician-patient communication and relationships. Additionally, SDM tools should seek to standardize common workflow steps across decisions and focus on barriers to increasing adoption of effective SDM tools into routine primary care.

Project description:IntroductionCOVID-19 catalysed a rapid move to provide care away from the hospital using online communication platforms. Technology enabled care (TEC) continues to be an important driver in progressing future healthcare services. Due to the complex and chronic nature of conditions seen within paediatric rheumatology, TEC may lead to better outcomes. Despite some growth in published literature into the adoption of TEC in paediatric rheumatology, there is limited synthesis. The aim of this review is to provide a comprehensive understanding and evaluation of the adoption of TEC by patients in paediatric rheumatology services, to establish best practices.Methods and analysisThis proposed mixed-methods systematic review will be conducted by searching a wide variety of healthcare databases, grey literature resources and associated charities and societies, for articles reported in English language. Data extraction will include population demographics, technology intervention, factors affecting adoption of intervention and consequent study outcomes. A parallel-results convergent synthesis design is planned, with independent syntheses of quantitative and qualitative data, followed by comparison of the findings of each synthesis using a narrative approach. Normalisation process theory will be used to identify, characterise and explain implementation factors. The quality of included articles will be assessed using the Mixed Methods Appraisal Tool for research papers and the Authority, Accuracy, Coverage, Objectivity, Date, Significance checklist for grey literature. Overall confidence in quality and strength of evidence will be assessed using the Confidence in the Evidence from Reviews of Qualitative Research tool.Ethics and disseminationEthical approval is not required due to the nature of this mixed-methods systematic review. The findings will be disseminated via a peer-reviewed journal, relevant conferences and any other methods (eg, via NHS Trust or NIHR YouTube channels) as advised by paediatric rheumatology patients.Prospero registration numberCRD42023443058.

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An Electronic Clinical Decision Support System for the Assessment and Management of Suicidality in Primary Care: Protocol for a Mixed-Methods Study.

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