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Mixed-methods approach to understanding clinician macrocognition in the design of a clinical decision support tool: a study protocol.

ABSTRACT: INTRODUCTION:The anatomic variants of congenital heart disease (CHD) are multiple. The increased survival of these patients and disposition into communities has led to an increase in their acute presentation to non-CHD experts in primary care clinics and emergency departments. Given the vulnerability and fragility of these patients in the face of acute illness, new clinical decision support systems (CDSS) are urgently needed to better translate the best practice recommendations for the care of these patients. This study aims to understand the perceived confidence and macrocognitive processes of non-CHD experts (emergency medicine physicians) and CHD experts (paediatric cardiac intensivists) when treating children with CHD during acute illness and apply this to optimise the design of a CDSS (MyHeartPass™) for these patients. METHODS AND ANALYSIS:The first phase of the study involves a survey of non-CHD experts and CHD experts to understand their perceived confidence as it relates to treating acutely ill patients with CHD. The second phase is a qualitative cognitive task analysis using critical decision method to characterise and compare the macrocognitive processes used by non-CHD experts and CHD experts during the critical decision making. In phases 3 and 4, heuristic evaluation and usability testing of the CDSS will be completed. These results will be used to inform design changes to the chosen CDSS (MyHeartPass™). In the final phase, a within-participant simulation design will be used to study the effect of the CDSS on clinical decision making compared with baseline (without use of CDSS). ETHICS AND DISSEMINATION:Ethics approval from The Hospital for Sick Children in Toronto, Ontario, Canada has been obtained for all phases. Results will be published in peer-reviewed journals and presented at relevant conferences. On successful completion of these studies, it is anticipated that there will be a controlled implementation of the redesigned CDSS.

SUBMITTER: Assadi A

PROVIDER: S-EPMC7170622 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Mixed-methods approach to understanding clinician macrocognition in the design of a clinical decision support tool: a study protocol.

Assadi Azadeh A Laussen Peter P Trbovich Patricia P

BMJ open 20200325 3

<h4>Introduction</h4>The anatomic variants of congenital heart disease (CHD) are multiple. The increased survival of these patients and disposition into communities has led to an increase in their acute presentation to non-CHD experts in primary care clinics and emergency departments. Given the vulnerability and fragility of these patients in the face of acute illness, new clinical decision support systems (CDSS) are urgently needed to better translate the best practice recommendations for the c ...[more]

PMID: 32213525

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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:BackgroundHomecare settings across the United States provide care to more than 5 million patients every year. About one in five homecare patients are rehospitalized during the homecare episode, with up to two-thirds of these rehospitalizations occurring within the first 2 weeks of services. Timely allocation of homecare services might prevent a significant portion of these rehospitalizations. The first homecare nursing visit is one of the most critical steps of the homecare episode. This visit includes an assessment of the patient's capacity for self-care, medication reconciliation, an examination of the home environment, and a discussion regarding whether a caregiver is present. Hence, appropriate timing of the first visit is crucial, especially for patients with urgent health care needs. However, nurses often have limited and inaccurate information about incoming patients, and patient priority decisions vary significantly between nurses. We developed an innovative decision support tool called Priority for the First Nursing Visit Tool (PREVENT) to assist nurses in prioritizing patients in need of immediate first homecare nursing visits.ObjectiveThis study aims to evaluate the effectiveness of the PREVENT tool on process and patient outcomes and to examine the reach, adoption, and implementation of PREVENT.MethodsEmploying a pre-post design, survival analysis, and logistic regression with propensity score matching analysis, we will test the following hypotheses: compared with not using the tool in the preintervention phase, when homecare clinicians use the PREVENT tool, high-risk patients in the intervention phase will (1) receive more timely first homecare visits and (2) have decreased incidence of rehospitalization and have decreased emergency department use within 60 days. Reach, adoption, and implementation will be assessed using mixed methods including homecare admission staff interviews, think-aloud observations, and analysis of staffing and other relevant data.ResultsThe study research protocol was approved by the institutional review board in October 2019. PREVENT is currently being integrated into the electronic health records at the participating study sites. Data collection is planned to start in early 2021.ConclusionsMixed methods will enable us to gain an in-depth understanding of the complex socio-technological aspects of the hospital to homecare transition. The results have the potential to (1) influence the standardization and individualization of nurse decision making through the use of cutting-edge technology and (2) improve patient outcomes in the understudied homecare setting.Trial registrationClinicalTrials.gov NCT04136951; https://clinicaltrials.gov/ct2/show/NCT04136951.International registered report identifier (irrid)PRR1-10.2196/20184.

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:BackgroundAlmost every patient admitted to hospital will receive medication during their stay. Medication errors are an important cause of patient morbidity and mortality, as well as an economic burden for healthcare institutions. Research suggests that current methods of storing medication on hospital wards are not fit for purpose, contributing to inefficiency and error.AimTo improve medication storage in inpatient areas, by exploring variation and challenges related to medication storage and designing a prototype solution.MethodsSet in four hospitals in an English teaching hospital trust, the study used a mixed methods approach comprising a quantitative descriptive survey of storage facilities and practices followed by mixed methods observations of medication rounds and interviews with patients, nurses and pharmacy staff. Quantitative data were presented descriptively and qualitative data analysed thematically and using a human-centered design approach.ResultsWe identified wide variation in medication storage facilities and practices across 77 wards. Observations and staff interviews in six wards revealed five problem areas: poor management of multiple storage facilities; lack of visibility and organisation of medication within trolleys; inadequate size of storage; lack of ownership and knowledge of standard practice; and use of key locks. Patients were largely satisfied with receiving their medication. Systematic and consistent physical organisation of medication in medication trolleys, and integrating and implementing principles of best practice, were identified as areas for intervention.Discussion and conclusionVariation in medication storage facilities and practices existed both across the organization and on individual wards. Multiple challenges were identified in how medication was stored, which if addressed may improve the efficiency and safety of medication administration and in turn, staff and patient experience. The use of design principles alongside a research approach resulted in a rapid, iterative process for developing and refining potential solutions to improve inpatient medication storage.

Project description:BackgroundIndividualized selection of antiretroviral (ARV) therapy is complex, considering drug resistance, comorbidities, drug-drug interactions, and other factors. HIV-ASSIST (www.hivassist.com) is a free, online tool that provides ARV decision support. HIV-ASSIST synthesizes patient and virus-specific attributes to rank ARV combinations based upon a composite objective of achieving viral suppression and maximizing tolerability.ObjectiveTo evaluate concordance of HIV-ASSIST recommendations with ARV selections of experienced HIV clinicians.DesignRetrospective cohort study.PatientsNew and established patients at the Johns Hopkins Bartlett HIV Clinic and San Francisco Veterans Affairs HIV Clinic completing clinic visits were included. Chart reviews were conducted of the most recent clinic visit to generate HIV-ASSIST recommendations, which were compared to prescribed regimens.Main measuresFor each provider-prescribed regimen, we assessed its corresponding HIV-ASSIST "weighted score" (scale of 0 to 10 +, scores of < 2.0 are preferred), rank within HIV-ASSIST's ordered listing of ARV regimens, and concordance with the top five HIV-ASSIST ranked outputs.Key resultsAmong 106 patients (16% female), 23 (22%) were ARV-naïve. HIV-ASSIST outputs for ARV-naïve patients were 100% concordant with prescribed regimens (median rank 1 [IQR 1-3], median weighted score 1.1 [IQR 1-1.2]). For 18 (17%) ARV-experienced patients with ongoing viremia, HIV-ASSIST outputs were 89% concordant with prescribed regimens (median rank 2 [IQR 1-3], median weighted score 1 [IQR 1-1.2]). For 65 (61.3%) patients that were suppressed on a current ARV regimen, HIV-ASSIST recommendations were concordant 88% of the time (median rank 1 [IQR 1-1], median weighted score 1.1 [IQR 1-1.6]). In 18% of cases, HIV-ASSIST weighted score suggested that the prescribed regimen would be considered "less preferred" (score > 2.0) than other available alternatives.ConclusionHIV-ASSIST is an educational decision support tool that provides ARV recommendations concordant with experienced HIV providers from two major academic centers for a diverse set of patient scenarios.

Project description: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.

Dataset Information

Mixed-methods approach to understanding clinician macrocognition in the design of a clinical decision support tool: a study protocol.

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Mixed-methods approach to understanding clinician macrocognition in the design of a clinical decision support tool: a study protocol.

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