Decision Aid Research Articles

Safety and accuracy of dating unwanted pregnancies and detecting ectopic pregnancy with the RCOG decision aid without routine ultrasound - a retrospective analysis form a large abortion service.

Evidence suggests routine pre-abortion ultrasound is unnecessary for women with regular menstrual cycles and without symptoms or risk factors for ectopic pregnancy in early gestation. We evaluated the safety and accuracy of using the decision aid for early medical abortion without ultrasound developed by the RCOG Abortion Care group in a Finnish setting. Using gestational age (GA) expected by last menstrual period (LMP), cycle irregularities, use of hormonal contraceptives, breastfeeding and self-reported symptoms indicative of possible ectopic pregnancy, women were classified into 'no ultrasound needed' and 'ultrasound needed' with a cut-off of 10 + 0 weeks or 70 days. Findings on US were then evaluated to assess classification accuracy. Between September and December 2023, 494 women attending the abortion clinic at the Helsinki University Hospital were assessed. Correct classification of the necessity of an ultrasound was made in 491/494 (99.4%) cases before the women had a scan. The remaining three cases were one woman with an unexpected GA just above 10 + 0, one asymptomatic ectopic pregnancy and one asymptomatic pregnancy of unknown location with high plasma hCG, diagnosed as a partial molar pregnancy on pathology after diagnostic vacuum aspiration. Use of the structured flowchart developed by the RCOG highly accurately identifies women needing an ultrasound examination. The few cases that would have been undetected highlight the importance of informing women and health care providers about symptoms of ectopic or abnormal pregnancy similar to practices in wanted pregnancies not routinely examined and dated before late first trimester.

Patients’ and plastic surgeons’ experiences with an online patient decision aid for breast reconstruction: considerations for nationwide implementation

Patients’ and plastic surgeons’ experiences with an online patient decision aid for breast reconstruction: considerations for nationwide implementation

Explainable correlation-based anomaly detection for Industrial Control Systems

Anomaly detection is vital for enhancing the safety of Industrial Control Systems (ICS). However, the complicated structure of ICS creates complex temporal correlations among devices with many parameters. Current methods often ignore these correlations and poorly select parameters, missing valuable insights. Additionally, they lack interpretability, operating efficiently with limited resources, and root cause identification. This study proposes an explainable correlation-based anomaly detection method for ICS. The optimal window size of the data is determined using Long Short-Term Memory Networks—Autoencoder (LSTM-AE) and the correlation parameter set is extracted using the Pearson correlation. A Latent Correlation Matrix (LCM) is created from the correlation parameter set and a Latent Correlation Vector (LCV) is derived from LCM. Based on the LCV, the method utilizes a Multivariate Gaussian Distribution (MGD) to identify anomalies. This is achieved through an anomaly detection module that incorporates a threshold mechanism, utilizing alpha and epsilon values. The proposed method utilizes a novel set of input features extracted using the Shapley Additive explanation (SHAP) framework to train and evaluate the MGD model. The method is evaluated on the Secure Water Treatment (SWaT), Hardware-in-the-loop-based augmented ICS security (HIL-HAI), and Internet of Things Modbus dataset using precision, recall, and F-1 score metrics. Additionally, SHAP is used to gain insights into the anomalies and identify their root causes. Comparative experiments demonstrate the method's effectiveness, achieving a better 0.96% precision and 0.84% F1-score. This enhanced performance aids ICS engineers and decision-makers in identifying the root causes of anomalies. Our code is publicly available at a GitHub repository: https://github.com/Ermiyas21/Explainable-correlation-AD.

Decision experiences in joint replacement surgery for patients with haemophilic arthritis: A qualitative study

BackgroundPatients with end-stage haemophilic arthritis (HA) are often hesitant about joint replacement surgery, yet little is known about the decision experiences faced by these patients. The aim of this study was to better understand the experiences faced by patients with HA when making decisions about joint replacement surgery and to provide a reference for health care professionals in the development of decision-making aids.MethodsFifteen HA patients who were candidates for joint replacement surgery at a tertiary and first-class hospital in Zhejiang Province were interviewed using a semistructured in-depth interview from January to December 2023. Colaizzi's seven-step method was used to analyse the data and refine the themes.ResultsThe decision experience for patients with HA regarding joint replacement surgery can be summarized into four themes: decision information conflict, decision support conflict, self-perceived conflict, and self-developmental conflict.ConclusionPatients with HA face numerous decision conflicts. Health care professionals should develop joint replacement surgery decision-making aids suitable for patients with HA as soon as possible to reduce the decision conflict.Trial registrationThe study received approval from the Ethics Committee of Zhejiang Provincial Hospital of Traditional Chinese Medicine, China (registration date: September 22, 2023; registration number: 2023-KLS-294–01).

Charting the future of green HRM practices: insights from theories, context, characteristics and methodologies (TCCM) framework and analytical hierarchy process (AHP) analysis

PurposeThe term “green human resource management” (Green HRM) is becoming more prevalent in both academics and business. This study’s main objective is to locate current understanding and trends in Green HRM literature by examining the available Green HRM literature. And prioritize the Green HRM practices in higher education institutions (HEIs). The study also identifies prevailing theories, context, characteristics and methodologies as current research interests to identify potential future research areas in Green HRM.Design/methodology/approachThe study opted for a multi-method approach, combining theories, context, characteristics and methodologies (TCCM) for literature review and the analytical hierarchy process (AHP) for methodological analysis. The article selection process is illustrated via a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart. The study examines Green HRM literature using the TCCM framework as it aids in identifying commonly used theories, constructs and methods, suggesting future research directions (FRDs). After identifying core Green HRM practices, the study prioritizes them for the HEIs using the AHP.FindingsThe comprehensive analysis of the literature is provided by the TCCM framework, which identifies the most influential theories, contexts, characteristics and methodologies used in Green HRM literature. The most used theory was found to be the ability-motivation-opportunity (AMO) theory, followed by social identity theory. In context, the hospitality industry was the dominating sector/industry of the domain. Under the characteristics, various variables in Green HRM literature were identified and classified as antecedents, mediators, moderators and outcomes. The most used research method was quantitative, and the most opted research design was empirical. FRDs are also suggested analyzing the current trends of the Green HRM field. The finding of AHP indicates that green recruitment and selection holds the highest level of importance among Green HRM in HEIs.Research limitations/implicationsThis study is vital for past-future orientation, in which the past is explored in order to build new routes of Green HRM research. It used research publications from the Scopus database, and future studies may use other databases to improve understanding of Green HRM research.Practical implicationsAHP weights and ranks aid decision-makers in assessing Green HRM practice importance, facilitating pairwise comparisons before implementation in the HEIs and other service industries. It guides managers on allocating appropriate importance to each practice for informed decision-making.Social implicationsThis study enriches Green HRM literature using the TCCM framework, categorizing literature into four key areas and offering valuable FRDs for future researchers. Its comprehensive review provides crucial insights into the research landscape of Green HRM, aiding in understanding theories, variables, relevant sectors and methodologies. The study offers comprehensive theoretical implications in the realm of Green HRM.Originality/valueExisting research indicates that no study has employed a multi-method approach combining the TCCM framework and AHP in Green HRM research. The initial TCCM phase conducts a thorough analysis, examining theories, contexts, characteristics and methodology, a unique methodology. It connects TCCM findings to FRDs. In the subsequent phase, AHP prioritizes Green HRM practices for the HEIs based on identified research gaps from TCCM. This innovative fusion of TCCM and AHP, resulting in FRDs and implications, represents the novelty of this paper.

Graded Walking Energetics under Cold Strain.

Although research supports selecting steeper routes to minimize metabolic rate (Ṁ) in mountain racers, the "steeper is cheaper" strategy has yet to be confirmed for slower, more typical graded walking speeds under cold stress. Confirm whether "steeper is cheaper" is true for typical graded walking speeds in individuals exposed to incremental cold stress. Fourteen healthy, military-age adults (age, 24 ± 6 years; height, 1.72 ± 0.08 m; body mass, 72 ± 16 kg) completed four 20-min treadmill walks in three ambient temperatures (20, 10, and 0 °C) in light clothing (i.e., shorts, t-shirt, light gloves). Each walk involved five stages at incremental vertical speeds (0.00, 1.93, 3.86, 5.79, 7.79 m·min-1) but variable treadmill speeds (0.54, 0.72, 1.07 m·s-1). To verify the "steeper is cheaper" strategy, we compared Ṁ between treadmill speeds at matched vertical speeds in each temperature. We also tested if the 90% confidence interval around the mean percent paired difference between Load Carriage Decision Aid (LCDA) metabolic model predictions and measured Ṁ was within ±10%. Ṁ was significantly higher for the faster treadmill speed at matched vertical speeds in all but two comparisons at 20 °C, all but two comparisons at 10 °C, and all comparisons at 0 °C (p < 0.05). LCDA metabolic model predictions were statistically equivalent to measured Ṁ during graded walking at 20 °C (90% CI, -3.1, 0.6%) and 10 °C (-7.8, -2.6%) but not 0 °C (-16.2, -9.5%). Route planners should recommend steeper but shorter routes to minimize Ṁ in individuals that walk in temperate-to-cold environments. The LCDA metabolic model provides accurate Ṁ predictions in lightly dressed individuals in temperatures down to 10 °C, but users should expect underestimated Ṁ in 0 °C or colder.

A Design Sprint to Develop Human Factors-Based Heuristics to Aid in Product Procurement Decisions

Product substitution is a common dilemma in healthcare settings as products become unavailable due to supply chain shortages or other factors. Substitute products can pose risks to patient safety when not carefully selected. This study developed a human factors-based heuristic evaluation tool to help healthcare professionals make informed decisions when choosing substitute products. The heuristic evaluation tool was developed through a design sprint that involved human factors experts and healthcare practitioners. Stakeholder evaluation led to improvements in heuristic evaluation categories. The final proposed tool evaluated products across five categories: product safety, usability, use, storage and maintenance, organizational support, and cost.

Clinical decision support to improve primary care obesity management in adults with diabetes: Clinic-randomized study design.

To promote use of lifestyle, pharmacologic, and surgical weight management options for adults with body mass index (BMI) ≥35 kg/m2 and type 2 diabetes (T2D), this project implements and evaluates a weight loss clinical decision support (WL-CDS) intervention that provides patient-specific estimates of the risks and benefits of evidence-based obesity management options to primary care clinicians (PCCs) and patients at primary care clinical encounters. We randomize 38 primary care clinics to either (a) usual care (UC), or (b) the WL- CDS intervention that provides patients and PCCs recommendations for lifestyle changes and patient-specific obesity management options and estimates of benefits and risks of weight loss medications, or metabolic bariatric surgery (MBS) when indicated. Outcomes assessed at 18-months after a patient-specific index date are (a) referral of eligible patients for MBS evaluation; (b) initiation or active management of FDA-approved medications for weight loss; and (c) weight trajectory. Outcomes measured within 1 month of index date are patient-reported (d) shared decision making about weight loss options, and (e) intention to engage in weight loss. This study will deepen our understanding of how patients and PCCs use WL-CDS generated information to inform selection of obesity care options for adults with T2D and BMI ≥35 kg/m2.

Scoping review indicates heterogeneous methods for developing and integrating patient decision aids in the context of clinical practice guidelines.

To review the methods used to develop and integrate patient decision aids (PDAs) based on the recommendations of clinical practice guidelines (CPGs). We conducted a scoping review covering bibliographic databases (PubMed, Embase; searched until December 2023), grey literature, references, and expert consultations to identify eligible documents. Documents published from 2000 onwards and describing methods related to guideline-based PDA development or linking CPGs and PDAs were included. Two reviewers independently selected and analyzed the documents. Results were synthesized and presented narratively. Based on 24 included documents, we categorized their methods into four topics. For topic (1), the selection of CPG recommendations for which PDAs are (most) needed, we found a total of 14 selection factors across n=11 documents, with uncertainty/variability in patient preferences and trade-offs between options being the most frequently mentioned. Topic (2) (n=24) covers methods for developing and/or updating guideline-based PDAs, such as forming a multidisciplinary development group, using CPGs and their evidence summaries along with other sources as the evidence base, and using digital solutions for semi-automated development and updating. Topic (3) (n=12) covers methods for PDA quality assessment and/or user testing, such as finalizing and approving the PDAs after a review and feedback process from the CPG group and an iterative user testing process. Topic (4) (n=20) covers methods for linking CPGs and PDAs, often through digital strategies. We identified heterogeneous methods for developing and integrating PDAs based on CPG recommendations. Empirical testing is required to determine the most useful and practically feasible (combination of) methods. CPG organizations should focus on establishing adequate methods for linking CPG and PDA development to foster shared decision making.

Development and evaluation of a decision aid for HPV vaccination among adults aged 27-45 years old in the United States.

To design, develop, and field test the HPV Decide decision tool to facilitate shared clinical decision-making recommendation for mid-adult HPV vaccination. The 'HPV Decide' online tool was developed through a 6-step process, involving community and provider advisory boards, usability testing with 10 end users (unvaccinated adults aged 27-45), field testing interviews with another 10, and interviews with 18 healthcare providers. The process incorporated both inductive and deductive qualitative data analyses. The logic model, based on the Ottawa Decision Support Framework, highlighted the importance of objective knowledge and managing decisional conflict. Iterative modifications were informed by the advisory boards, alpha testing, and usability testing. Field testing showed utility to end users, highlighting the straightforward design, and lack of medical jargon. Many expressed a desire to access the tool before doctor appointments, emphasizing its value in a healthcare context and in partnership with providers. Lastly, healthcare provider feedback highlighted information quality, structure, acceptability, and future implementation. While users recognized the tool's information quality and user-centric design, full evaluation focused on changes in informed decision making and clinic-based implementation are needed. The HPV Decide decision tool demonstrates promise in supporting informed decisions about HPV vaccination among mid-adults.

Machine learning model for predicting DIBH non-eligibility in left-sided breast cancer radiotherapy: Development, validation and clinical impact analysis.

Multi-day assessments accurately identify patients with left-sided breast cancer who are ineligible for irradiation in Deep Inspiration Breath Hold (DIBH) and minimise on-couch treatment time in those who are eligible. The challenge of implementing multi-day assessments in resource-constrained settings motivated the development of a machine learning (ML) model using data only from the 1st day of assessment to predict DIBH ineligibility. This prospective cohort study used data from 202 patients collected between January and December 2023 for model development. Patient-related and DIBH assessment-related variables (upper, lower, and average breath-hold amplitude; average breath-hold duration; breath-hold consistency) were included. Nine ML algorithms (and three modelling strategies) were evaluated, and a decision curve analysis was used to select the best model. The best model was temporally validated on a prospective dataset of 47 patients (January to March 2024). Further, a clinical impact study on another prospective cohort of 64 patients (April to August 2024) was performed, to assess its practical utility by comparing its predictions with the clinical team's decision to treat a patient in DIBH or not. The uncalibrated gradient-boosting ensemble model demonstrated the highest performance [AUC (95 % CI) = 0.803 (0.686-0.941); Recall = 0.526] and net benefit in decision curve analysis. Key predictors included average breath-hold duration and lower breath-hold amplitude levels. The clinical impact study suggests that the model reduces the need for additional DIBH assessments by up to 20 % without misclassifying eligible patients. The developed ML model accurately predicts DIBH ineligibility using only first-day DIBH assessment data and could be a decision aid for patient selection in resource-constrained or busy departments. External validation is necessary to confirm its generalizability.

2839 Decision aids for the Care of Patients with multimorbidity: a systematic review

Abstract Introduction Clinical decision making for older adults with multimorbidity can be complex and demanding. When facing complex decision-making, patients may experience decisional conflicts, leading to low treatment adherence, adverse health outcomes, and increased utilisation of health services, etc. To address these issues, patient decision aids (DAs) have been developed and utilised in the decision-making process to facilitate informed decisions. The aim of this study is to identify DAs developed for patients with multimorbidity and assess their quality. Method We searched full-text papers on nine databases. Any article utilising a DA for patients with multimorbidity was eligible and DAs for making medical decisions at any point were eligible. We used the International Patient Decision Aid Standards (IPDAS) checklist to assess the quality of DAs. Results In total, ten articles including six DAs were included. Two DAs targeted for the older patients with multimorbidity. Most DAs didn’t focus on specific treatment choices but rather aimed at improving the overall quality of life for patients with multimorbidity. The targets of these DAs are including setting goals about health care, preparing for conversation with doctors and taking ownership for the decisions. IPDAS checklist revealed that only one DA met all qualifying criteria and provided comprehensive choice. Three DAs were deemed to have poor quality due to their failure to provide the pros and cons of decisions. The quality of the remaining DAs was difficult to judge due to incomplete versions. Conclusions DAs for patients with multimorbidity were few and had poor quality. Designing DAs for this patient population presents challenges given the complex nature of multimorbidity and its lack of specific treatment options. Future development should focus on adhering to the IPDAS checklist, provide more information and possibility, and aim at improving the quality of life for patients with multimorbidity.

Impact of Online Interactive Decision Tools on Women's Decision-Making Regarding Breast Cancer Screening: Systematic Review and Meta-Analysis.

The online nature of decision aids (DAs) and related e-tools supporting women's decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs. This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy. We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023. We included studies reporting on populations at average risk of breast cancer, which utilized DAs or related e-tools, and assessed women's participation in BCS by mammography or other key cognitive determinants of decision-making as primary or secondary outcomes. We conducted meta-analyses on the identified randomized controlled trials, which were assessed using the revised Cochrane Risk of Bias 2 (RoB 2) tool. We further explored intermediate and high heterogeneity between studies to enhance the validity of our results. In total, 22 different e-tools were identified across 31 papers. The degree of tailoring in the e-tools, specifically whether the tool was fully tailored or featured with tailoring, was the most influential factor in women's decision-making regarding BCS. Compared with control groups, tailored e-tools significantly increased women's long-term participation in BCS (risk ratio 1.14, 95% CI 1.07-1.23, P<.001, I2=0%). Tailored-to-breast-cancer-risk e-tools increased women's level of worry (mean difference 0.31, 95% CI 0.13-0.48, P<.001, I2=0%). E-tools also improved women's adequate knowledge of BCS, with features-with-tailoring e-tools designed and tested with the general population being more effective than tailored e-tools designed for or tested with non-BCS participants (χ21=5.1, P=.02). Features-with-tailoring e-tools increased both the rate of women who intended not to undergo BCS (risk ratio 1.88, 95% CI 1.43-2.48, P<.001, I2=0%) and the rate of women who had made an informed choice regarding their intention to undergo BCS (risk ratio 1.60, 95% CI 1.09-2.33, P=.02, I2=91%). Additionally, these tools decreased the proportion of women with decision conflict (risk ratio 0.77, 95% CI 0.65-0.91, P=.002, I2=0%). Shared decision-making was not formally evaluated. This review is limited by small sample sizes, including only a few studies in the meta-analysis, some with a high risk of bias, and high heterogeneity between the studies and e-tools. Features-with-tailoring e-tools could potentially negatively impact BCS programs by fostering negative intentions and attitudes toward BCS participation. Conversely, tailored e-tools may increase women's participation in BCS but, when tailored to risk, they may elevate their levels of worry. To maximize the effectiveness of e-tools while minimizing potential negative impacts, we advocate for an "on-demand" layered approach to their design.

P-202. 7- Year experience with Clostridioides difficile Infection (CDI): pre- and post-COVID-19 pandemic, effect of diagnostic stewardship and the gender difference

Abstract Background Diagnostic stewardship interventions, such as order sets, reflex orders, hard and soft stop alerts and decision algorithms have shown to improve facility C. difficile rates. We aimed to examine the impact of COVID-19 infection and diagnostic stewardship on incidence and prevalence of CDI at our institution, based on gender. Methods Using National Healthcare Safety Network (NHSN) surveillance Laboratory Identification (Lab ID) events, 926 CDI cases, ≥ 18 years of age were identified among patients hospitalized at Hurley Medical Center from 01/2015 to 07/2022. NHSN surveillance definitions were used to describe the incident, recurrent cases and determine the epidemiologic categories. Hospital onset (HO) incident rate is described as per 10,000 patient days and inpatient community-onset (CO) CDI prevalence rate described as per 10,000 admissions (Figure 1).Table 1:Diagnostic stewardship and infection control interventions Results During the 7.6-year study period, overall HO CDI incident rate and inpatient CO CDI prevalence rate decreased, a percentage decrease of 34% and 53% respectively. The rates were lowest in 2020, onset of COVID-19 pandemic (percentage decrease of 22% for HO CDI incident and 7% for CO CDI prevalence rates as compared to 2019 pre-COVID-19 pandemic). There was a transient increase in 2021 (percentage increase of 36% for HO CDI incident and 14% for CO CDI prevalence rates as compared to 2019) (Figure 2, 3). HO CDI incident rates were slightly higher in men (average 11.4% difference) as compared to women except in 2017. CO CDI prevalence rates were much higher in women as compared to the men (average 13.4% difference) with the exception of 2017 and 2022.Figure 2:HO CDI Incidence rate 2015-2022, with 95% confidence intervals Conclusion HO CDI incident rate and inpatient CO CDI prevalence rates were lowest in 2020 (the onset of COVID-19 pandemic), likely secondary to computerized clinical decision support (CCDS) interventions introduced in 2019 (Table 1). The transient increase in rates seen in 2021, was likely due to increase in antibiotic utilization during the pandemic. Diagnostic stewardship played a key role in optimizing the diagnosis of CDI and improving CDI rates at our institution. There was a discrepancy of the CO CDI and HO CDI rates between the sexes, likely owing to the higher outpatient antibiotic use in women as compared to men, highlighting the importance of ambulatory stewardship.Figure 3:Inpatient CO CDI prevalence rate, with 95% confidence intervals Disclosures All Authors: No reported disclosures

Barriers and drivers of near-term climate change mitigation: a Canadian case study

Abstract This work investigates, through semi-structured interviews, the prospects for rapid transitions towards low-carbon civil infrastructure systems. Rapid greenhouse gas (GHG) mitigation is critical to facilitate the near-term (i.e. within five years) reductions needed to limit global temperature rise to 2 °C. In addition, ongoing delays in climate action in many countries and sectors mean that rapid interventions will be needed in the 2030s and 2040s as climate change evolves and the need to mitigate becomes more urgent. This work examines, among twenty decisionmakers involved in developing, operating, or using Canadian infrastructure: (1) ongoing and expected near-term GHG mitigation actions (2) barriers constraining faster change, and (3) mitigation goals and expectations of the near future. Interviews were coded to identify common perspectives. Results indicate that organizations prioritize enabling deep change in a more distant future over executing rapid change, that detailed roadmaps for meeting near-term (e.g. 2030) goals were rare, and that participants view government policy certainty as crucial to near-term action. This work identifies deficits in action on the near-term scale and aids policymakers and decisionmakers by describing planned near-term mitigation actions and assessing barriers to going faster.

A generalized approach for multi-criteria decision aid methods

Abstract Outranking methods were developed for solving Multi-Criteria Decision-Making (MCDM) problems with a finite set of alternatives. In this paper, we propose a comprehensive framework that extends the application of outranking methods for solving MCDM problems with continuous sets of alternatives. This is achieved through the integration of multi-objective optimization methods and outranking methods. The derived Three-phases integration procedure is illustrated by the integration of the Weighted Sum method for continuous MCDM problems and the outranking Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) method. Moreover, the proposed framework incorporates the use of the Principal Component Analysis (CPA) to address the problem of selecting a Pareto optimal solution when the phase of application of the outranking method generates a large set of ‘best’ Pareto optimal solutions. The application potential of the procedure is illustrated by a multi-dimensional financial portfolio selection problem.

Barriers and facilitators to sustaining a lupus patient decision aid in regular rheumatology outpatient care in the US.

This study investigates the barriers and facilitators to sustaining a decision aid (DA) tool for patients with systemic lupus erythematosus (SLE) in routine rheumatology outpatient care across the U.S. The DA was initially developed for assisting Patients with SLE in making informed medication choices by providing personalized information on their treatment process. We conducted semi-structured interviews with clinicians, administrators, and information technology (IT) professionals from 15 geographically diverse rheumatology clinics that participated in a large-scale DA implementation trial. Interviews were transcribed and analyzed using NVivo, with thematic coding to identify key factors influencing DA sustainability. Analysis identified several facilitators, including the DA's user-friendly design and its effectiveness in simplifying complex medical information, aiding informed decision-making for Patients with SLE. Challenges included inconsistent staff knowledge about the DA, difficulties integrating the DA into clinical workflows, a lack of executive and physician champions, and a lack of structured training and communication strategies. Additionally, concerns were raised about the availability of staff support and the technological capabilities required to maintain the DA's use. To sustain the DA in clinical practice, there is a need for enhanced training programs, better workflow integration, in-clinic physician champions, and effective use of electronic medical records for DA dissemination. Addressing these areas can improve the DA's impact on patient education and decision-making, ensuring its continued use in routine care.

Integrating Shared Decision-Making Into Treatment Planning for Clinicians Who Treat Individuals With Serious Mental Illness.

Background: Shared decision-making (SDM) has the potential to significantly improve patient outcomes by enabling clinicians and patients to make health care decisions as partners. However, its implementation in mental health care has been a slow process. Objective: The objective of the study was to implement SDM during outpatient mental health treatment planning. Methods: In an integrated health care clinic, mental health clinicians (n = 4) participated in SDM training to increase their use of SDM. The 9-item Shared Decision-Making Questionnaire Physician Version (SDM-Q9-DOC) tool was used in a pre-post design to evaluate the training's effectiveness in increasing the implementation of SDM during treatment planning. Results: SDM-Q9-DOC scores improved from a median of 49-54, suggesting a clinically significant improvement in SDM implementation. Project limitations included short duration and lack of feedback from clinicians and patients about their SDM experience. Conclusions: While guided SDM training can increase the use of SDM in outpatient mental health care, we acknowledge the existing barriers to its implementation. These barriers, including clinician pessimism, time constraints, lack of appropriate decision aids, and workplace expectations, should be further studied and addressed to enhance the implementation of SDM in the mental health setting. Implications for Nursing: Psychiatric nurse practitioners and registered nurses can assist with implementing SDM by encouraging effective communication between clinicians and behavioral health clients.

Marrying math and mind: towards production planning systems for industry 5.0

How can a sophisticated Production Planning (PP) system help planners? To answer this question, we study the PP literature and focus on human-system interaction. We find that the PP literature mainly focuses on designing sophisticated algorithms. By synthesising forecasting and information system (IS) literature, we provide findings for designing a human-centred PP system. From the forecasting literature, we learn that certain biases and heuristics become relevant when humans work with systems. We categorise this literature into three system elements: information received, delivery system, and decision aid provided, as well as two human factors: cognitive limitations and capabilities. From the IS literature, we learn that different factors impact behaviour over the system acceptance and continued use phases. Behaviour is mainly impacted by the Perceived Ease of Use in the acceptance phase. The Perceived Usefulness mainly determines interventions to the solutions in the continued use phase. We combine the two phases distinguished in the IS literature with the three system elements distinguished in the forecasting literature and thus propose a two-phase × three-element matrix for designing human-centred systems, obtain findings that serve as guidelines for designing human-centred PP systems, and propose an agenda for future research in PP.

The Choice to Use Automation: Improvements from Evidence Accumulation

This study examined discretionary automation – the choice to engage optional, automated support. In a dynamic decision-making task, after manual and aided trials, participants chose whether to engage a machine learning decision aid. The aid improved performance but participants frequently disused automation and lagged in compliance. Experiment 2 forced greater evidence accumulation, improving compliance, automation use, and performance. Experiment 3 required an initial judgment coupled with subsequent evidence accumulation. This reduced automation use but retained higher levels of compliance and performance. Across experiments, trust in automation and self-confidence influenced use decisions, but task difficulty had little impact. Implications for human-automation systems are discussed.