Outcome Metrics Research Articles

Healthcare Value Assessment: A Critical Review of Economic Outcome Metrics and Future Directions

This paper provides a critical review of economic outcome metrics used in healthcare value assessment, emphasizing the evolving landscape of resource allocation, patient-centered approaches, and standardization efforts. With healthcare costs rising globally, the efficient allocation of limited resources is essential. Metrics like Quality-Adjusted Life Years (QALYs), Disability-Adjusted Life Years (DALYs), Incremental Cost-Effectiveness Ratios (ICERs), and Cost-Benefit Analysis (CBA) are central to guiding funding decisions, influencing insurance coverage, and shaping treatment prioritization. Emerging trends, such as the integration of artificial intelligence (AI) and big data, are enhancing the precision of these assessments, while patient-centered metrics underscore the importance of patient satisfaction and quality of life. Additionally, there is a growing push for the standardization of these metrics to create consistent frameworks across diverse health systems. This paper explores case studies, practical applications, and future directions to provide insights into how healthcare systems can adopt more effective, globally aligned economic assessments.

Effectiveness of exercise intervention in improving physical and mental status of patients with alcohol use disorders: A systematic review and meta-analysis.

This meta-analysis and systematic review examined the effects of an exercise intervention on alcohol dependence and physical and mental states in patients with alcohol use disorder (AUD). PubMed, Web of Science, Cochrane Library, EBSCO, and Embase. Randomized controlled trials published in English from the inception of the database until June 30, 2024, were included. All forms of exercise intervention (aerobic, resistance, yoga, mixed exercise, etc.) were included in the study, using an exercise intervention for patients with AUD and a non-exercise control group. Studies that excluded acute exercise or did not describe a specific intervention program; duplicate publications; review articles, conference articles, etc.; and studies that did not report appropriate outcome metrics. This protocol was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol standard. The risk of bias was assessed via the Cochrane risk-of-bias tool as described by the Cochrane Handbook for Systematic Reviews and Interventions. Alcohol dependence (number of drinks per day, number of drinks per week, AUDIT), physical and mental status [maximal oxygen uptake (VO2 max), resting heart rate, anxiety state, depression state, stress level]. Seventeen RCTs with a total of 1,905 patients with alcohol use disorders were included as subjects, and the results revealed that the exercise intervention had a significant effect on alcohol dependence, the number of drinks per day and the AUDIT score, the exercise intervention also had a significant effect on physical and mental status, VO2max, the resting heart rate, the anxiety state, the depression state, and the stress level. High heterogeneity in the number of drinks per day, anxiety, depression and stress outcomes (I2 > 50%), but Egger's test showed no publication bias for all outcome indicators (p > 0.05). Exercise intervention can effectively reduce alcohol dependence and significantly improve the physical and mental states of AUD patients, and exercise intervention as an adjunct to the treatment of AUD patients is significantly effective.

Evaluating Long-Term Outcomes of Children Undergoing Surgical Treatment for Congenital Heart Disease for National Audit in England and Wales.

There is strong interest in the evaluation of longer-term outcome metrics for congenital heart diseases (CHDs); however, registries focus on postoperative metrics. Informed by user online discussion forums and scoping of national data, we selected sentinel CHDs and long-term outcome metrics suitable for routine monitoring. We then developed sentinel CHD phenotypes and algorithms for identifying treatment pathway procedures using clinical codes. Finally, we calculated the metrics within a retrospective national cohort analysis. The 9 selected sentinel CHDs had a higher-than-average prevalence, typically involved surgery in infancy, and were associated with an increased risk of late mortality. The selected metrics of survival and reinterventions at 1, 5, and 10 years were both important and feasible. The cohort included 29 319 (41.3% of all operated CHD births) English and Welsh children born with sentinel CHDs in 2000 to 2022. Example metrics at age 10 years included: survival-hypoplastic left heart syndrome: 57.6% (95% CI, 54.9%-60.4%), functionally univentricular heart: 86.7% (95% CI, 84.6%-88.9%), transposition of the great arteries: 93.1% (95% CI, 92.2%-93.9%), pulmonary atresia: 81.0% (95% CI, 79.1%-82.9%), atrioventricular septal defect: 88.5% (95% CI, 87.5%-89.5%), tetralogy of Fallot: 95.1% (95% CI, 94.4%-95.8%), aortic stenosis: 94.4% (95% CI, 93.3%-95.6%), coarctation: 96.7% (95% CI, 96.2%-97.3%), and ventricular septal defect: 96.9% 95% CI, (96.4%-97.3%); and (2) cumulative incidence of reintervention-hypoplastic left heart syndrome : 54.5% (95% CI, 51.5%-57.3%), functionally univentricular heart: 57.3% (95% CI, 53.9%-60.5%), transposition of the great arteries: 20.9% (95% CI, 19.5%-22.3%), pulmonary atresia: 66.8% (95% CI, 64.2%-69.1%), atrioventricular septal defect: 21.6% (20.3%-23.0%), tetralogy of Fallot: 26.6% (95% CI, 25.2%-28.0%), aortic stenosis: 31.2% (95% CI, 28.8%-33.6%), coarctation: 19.8% (95% CI, 18.6%-21.1%), and ventricular septal defect: 6.1% (95% CI, 5.5%-6.8%). It is feasible to report important long-term outcomes of survival and reintervention for sentinel CHDs using routinely collected procedure records, adding value to national audit.

Treatment-free remission after discontinuation of tyrosine kinase inhibitors in patients with chronic myeloid leukemia in the chronic phase: a systematic review and meta-analysis

BackgroundTreatment-free remission (TFR) is a new long-term goal for treating selected patients with chronic myeloid leukemia in the chronic phase (CML-CP). Still, the appropriate group in which TFR can be attempted and the factors influencing it have not yet been identified. This meta-analysis aimed to explore TFR in CML-CP patients who achieved a deep molecular response (DMR) before Tyrosine kinase inhibitors (TKIs) discontinuation and to explore possible factors influencing TFR and the safety of discontinuation.MethodsWe performed a systematic review and single-arm meta-analysis with a systematic search of published literature up to September 2023 in PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases. The assessment was performed using the MINORS scale. Random-effects models were used to calculate outcome metrics, including overall mean TFR rates at 12 and 24 months and subgroup differences. Data synthesis and analysis were done by Stata17.0 software.ResultsA total of 19 single-arm trials involving 2336 patients were included in this meta-analysis, with an overall mean TFR rate of 59% [95CI:0.56–0.63] at 12 months and 55% [95CI:0.52–0.59] at 24 months, and no CML-related deteriorations or deaths reported during the TFR period. Our subgroup analysis showed that better TFR was associated with prior interferon therapy (P = 0.003), and molecular response depth MR5.0 (P = 0.020).ConclusionOur study demonstrated that prior interferon therapy and attainment of a molecular response depth of MR5.0 or greater were associated with higher TFR rates, with patients who attained MR5.0 or greater achieving a TFR rate of up to 62% in the second year after TKI discontinuation. Considering the high heterogeneity of the included trials, the above influences still require further validation and more detailed subgroup analysis in future discontinuation trials.Systematic review registration:https://www.crd.york.ac.uk/prospero/ (Registration No. CRD42023471334).

Reducing research participant disparities through use of a co-developed video intervention: A cohort nested pre-post study

Background Evidence on effective approaches for the recruitment of diverse populations remains limited. This study aimed to assess whether a co-developed study information video would improve participant ethnic diversity within a paediatric study cohort. Methods Pre-post study nested within the Uveitis in childhood prospective national inception cohort study, UNICORNS. Intervention of interest: study information and recruitment video with format and content co-developed with a patient expert group. Outcome of interest: the proportion of families from ethnic minority groups, and of families with primary caregivers without a higher educational degree who were recruited to the study, and the proportion of families who engaged in study activities (returned patient reported outcome metrics following recruitment) pre and post intervention implementation. Results During the study period, 191 children were recruited to UNICORNS (110 pre-intervention era, 81 post-intervention era). A larger proportion of families recruited in the post-intervention era were of an ethnic minority background (36% versus 20%, Fisher’s exact test p=0.02). A larger proportion of families returned self-descriptions of their sociodemographic background (100% versus 87%, Fisher’s exact test p=0.0003) and a larger proportion returned study specific patient reported outcome measures (70% versus 55%, Fisher’s exact test p=0.003). Conclusion Co-created digital research information resources can be an effective tool for ensuring better representation in research studies and may also support participant engagement overall. Our reported methods and findings should support other researchers in developing patient-centred and patient co-developed strategies and tools for the purposes of equitable access to medical research.

Reimagining higher education: The impact of learner outcome metrics in Ireland and beyond

AbstractA college is an institution that exists to provide instruction. Subtly but profoundly, we are shifting to a new paradigm: a college is an institution that exists to produce learning (Barr & Tagg, 1995, p. 13). This paper traces the evolution of learner outcomes, from their progeny as a diagnostic tool in accurately measuring learning gains and facilitating institutional self‐improvement, to their present distorted incarnation, in propagating “governance by numbers”. The paper proceeds in a number of steps. Firstly, it begins with a brief contextualization of the topography of higher education. It then moves to explore the OECD influences regarding quantifying outcomes and aligning them with performance metrics. From here, it looks to EU deliberations in the shape of the Bologna Process with its enduring iterations, the purpose of which benchmarks HEIs' (higher education institutions) success against the successful achievement of learner outcomes. To conclude, taking the example of the Irish funding and strategy, Hunt Report (National Strategy for Higher Education to 2030), I provide an explanatory account detailing how this “learning outcomes race” and its association with “success metrics” within the higher education landscape is mistaken and harmful.

Treatment and Outcome Metrics for Speech-Language Pathology Treatment of Upper Airway Disorders: A Systematic Review.

Clinical treatment strategies and progress metrics for patients with inducible laryngeal obstruction (ILO) and exercise-induced laryngeal obstruction (EILO) have not been systematically applied across clinics. The goals of this review were to identify the intervention strategies used to treat upper airway disorders and determine the clinical metrics by which improvement or resolution of ILO/EILO is determined in clinical studies of speech-language pathology intervention. A systematic review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Systematic Reviews Checklist. English language, full-text publications were identified through MEDLINE (Ovid), APA PsycINFO, and Web of Science for publications that described clinical outcomes following behavioral treatment for ILO provided by a speech-language pathologist (SLP). A total of 238 sources were retrieved, of which 75 were duplicates. Titles and abstracts of 163 were screened, with 22 sources included for full-text review. Citation review yielded nine additional resources. Thirty-one sources were included for data extraction. Most sources described use of respiratory/laryngeal retraining. There was a lack of consistency for clinical and standardized metrics used to evaluate improvement or resolution of upper airway symptoms. Behavioral and standardized outcome metrics for evaluating therapy outcomes from SLP behavioral intervention of ILO/EILO are not consistently applied. Clinical metrics standards for clinicians are needed to provide a means to evaluate graded improvement in treatment and changes in health care utilization as well as for training less-experienced clinicians. https://doi.org/10.23641/asha.27172683.

How to optimize government R&D budget allocation? A review of recent studies and research gaps

Purpose Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation. Design/methodology/approach This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process. Findings The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics. Practical implications Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms. Originality/value The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.

Respiratory pathogen analysis in pediatric inpatients unraveled the infection pattern of Mycoplasma pneumoniae post the COVID-19 pandemic.

To counteract the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) were implemented globally, exerting a profound influence on a wide spectrum of infectious diseases, encompassing respiratory tract infections (RTIs). Subsequent to the easing of NPIs, China experienced a significant outbreak of Mycoplasma pneumoniae (MP). Over a decade from 2015 to 2024, our study scrutinized 12 common infectious diseases among pediatric children. Etiologically diagnostic data and clinical outcome metrics of children with RTIs, tested for 13 pathogens, were analyzed to evaluate changes during and after the pandemic compared to pre-pandemic periods, with a notable emphasis on age profile and coinfection patterns of MP. Among 57,471 hospitalized children, 23,178 were diagnosed with infectious diseases. Under NPIs, most respiratory infections declined compared to pre-pandemic levels, rebounding by 69.64% in 2023. While the infection rate of common respiratory pathogens decreased, cases of respiratory syncytial virus increased during the period of extensive NPI implementation. In 2023, pediatric intensive care unit durations for these pathogens increased, suggesting greater severity of illness compared to 2019. MP exhibited the highest infection rate (31.38% average), with a notable outbreak post-pandemic due to severity increase in <3 year olds and rise among older children. NPIs reduced MP coinfections and mitigated their severity, while exerting a significant influence on bacterial coinfections with MP over the span of 5 years, in contrast to their impact on viral pathogens. NPIs effectively curb transmission of respiratory infections by most pathogens, resulting in increased average age of MP infections and altered patterns of coinfection post-pandemic.

Optimizing influenza vaccination among oncology patients through quality improvement cycles.

285 Background: Influenza poses significant risks to cancer patients, who are especially vulnerable due to immunosuppression caused by both their disease and treatment. (Cooksley et al., 2005) Sidney Kimmel Cancer Center – Jefferson Health (SKCC)’s Quality Improvement (QI) initiative aimed to increase influenza vaccination rates among adult cancer patients in our Center City clinic from October 2023 to March 2024. At baseline in the 2022-2023 flu season, 49% of our patients had a documented vaccination against influenza, leaving most of our patient population at increased risk for influenza infection. Methods: A series of four Plan-Do-Study-Act (PDSA) cycles were implemented over the course of the vaccination season. Cycle 1 collected baseline data on vaccination status via electronic health records (EHRs) and identified gaps in patient knowledge and vaccine hesitancy. Cycle 2 trained medical assistants (MAs) to engage in effective vaccination conversations, emphasizing the use of presumptive communication techniques and the reconciliation of immunization records in EHRs. Cycle 3 integrated patient education materials on vaccination in the clinical encounter. Cycle 4 scaled assessment protocols across teams. Results: Cumulatively, 68% (2,592/3,824) of unique oncology patients had a documented influenza vaccination by the completion of the season in March 2024. Integration of vaccine reconciliation into the MA workflow was the iteration that led to the biggest increase in vaccination rate among the PDSA cycles tested. We surpassed our initial aim for the season (59%) in this metric. Conclusions: The QI project at SKCC demonstrates that layered strategies involving both patients and care teams can improve vaccination rates among adult cancer patients. Integration of vaccine assessment into clinic workflows was an effective tactic. Future efforts will focus on replicating these successes with other vaccines. Rates of influenza vaccination by PDSA cycle. Cycle # Date Cycle Description Outcome Metric: Cumulative Vaccination Rate (%) 1 Oct 2023 Provider perception survey of vaccination knowledge 65 2 Nov 2023 Integration of vaccine reconciliation in MA workflow 69 3 Dec 2023 Integration of patient education materials to clinical encounter 70 4 Jan 2024 – Mar 2024 Scaling of updated workflows to entire clinic 68

Exploring Trade-Offs for Online Mental Health Matching: Agent-Based Modeling Study.

Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped or highly naive. In this study, we collaborated with one of the world's largest OMHCs; our contribution is to show the application of agent-based modeling for the design of online community matching algorithms. We developed an agent-based simulation framework and showcased how it can uncover trade-offs in different matching algorithms between people seeking support and volunteer counselors. We used a comprehensive data set spanning January 2020 to April 2022 to create a simulation framework based on agent-based modeling that replicates the current matching mechanisms of our research site. After validating the accuracy of this simulated replication, we used this simulation framework as a "sandbox" to test different matching algorithms based on the deferred acceptance algorithm. We compared trade-offs among these different matching algorithms based on various metrics of interest, such as chat ratings and matching success rates. Our study suggests that various tensions emerge through different algorithmic choices for these communities. For example, our simulation uncovered that increased waiting time for support seekers was an inherent consequence on these sites when intelligent matching was used to find more suitable matches. Our simulation also verified some intuitive effects, such as that the greatest number of support seeker-counselor matches occurred using a "first come, first served" protocol, whereas relatively fewer matches occurred using a "last come, first served" protocol. We also discuss practical findings regarding matching for vulnerable versus overall populations. Results by demographic group revealed disparities-underaged and gender minority groups had lower average chat ratings and higher blocking rates on the site when compared to their majority counterparts, indicating the potential benefits of algorithmically matching them. We found that some protocols, such as a "filter"-based approach that matched vulnerable support seekers only with a counselor of their same demographic, led to improvements for these groups but resulted in lower satisfaction (-12%) among the overall population. However, this trade-off between minority and majority groups was not observed when using "topic" as a matching criterion. Topic-based matching actually outperformed the filter-based protocol among underaged people and led to significant improvements over the status quo among all minority and majority groups-specifically, a 6% average chat rating improvement and a decrease in blocking incidents from 5.86% to 4.26%. Agent-based modeling can reveal significant design considerations in the OMHC context, including trade-offs in various outcome metrics and the potential benefits of algorithmic matching for marginalized communities.

Efficacy of ultrasonography and color-Doppler for early prediction of hemodialysis arteriovenous fistula unassisted maturation.

Arteriovenous fistula (AVF) failure is a prevalent concern for patients with end-stage kidney disease on hemodialysis. Recognizing the efficacy of ultrasound Doppler in post-operative AVF evaluation, this study sought to discern the predictive capabilities of various ultrasonographic and color-Doppler metrics for early AVF outcomes. This single-center, prospective cohort study spanned 1 year and, post ethical clearance, included all patients who underwent native AVF creation surgery and were subsequently referred for standard post-operative ultrasound Doppler assessment. Parameters such as fistula size, cephalic vein area and diameter, and AVF flow velocity and rates were assessed on post-operative day 2, week 2, and week 6. These initial findings were juxtaposed with later outcomes to determine unassisted AVF results. Of the initial cohort of 40 patients, 75% encountered AVF failure, whereas 25% realized successful unassisted AVF maturation. A notable observation was the significant variance in AVF flow rates as early as post-operative day 2. A threshold of >246 ml/min was indicative of successful unassisted AVF maturation, leading to a sensitivity of 80% and a specificity of 70%. Although the cephalic vein diameter on post-operative day 2 lacked a robust association with AVF outcomes, a cut-off of >3.4 mm, when combined with flow rate testing, augmented the cumulative sensitivity to 92%. Ultrasound Doppler stands out as a valuable quantitative imaging modality, adept at prognosticating AVF outcomes from as early as post-operative day 2. In particular, a flow rate exceeding 246 ml/min and a cephalic vein diameter surpassing 3.4 mm are salient indicators for the early prediction of successful AVF outcomes.

Acceptable outcomes of liver transplantation in uninsured patients under the coverage of a state assistance program.

The lack of health insurance is a major barrier in the access to healthcare even in case of life-saving procedures such as liver transplantation (LT). Concerns about worse outcomes in uninsured patients have also discouraged the evaluation and transplantation of patients without adequate health insurance coverage. The aim of this study is to evaluate outcomes from the largest cohort of uninsured patients who underwent LT with the support of a state payment assistance program (also called charity care). This study included all consecutive patients who underwent LT at a single center from 2002 to 2020. Demographic, clinical, social variables and outcome metrics were collected and compared between insured and uninsured patients. Among a total of 978 LT recipients, 594 had private insurance, 324 government insurance (Medicare/Medicaid), 60 were uninsured and covered under a state charity care program. In the charity care group, there was a higher proportion of Hispanic subjects, single marital status, younger age, and high-MELD score patients. The 1- and 3-year patient survival rates were 89.0% and 81.8% in private insurance patients, 88.8% and 80.1% in government insurance recipients, and 93.3% and 79.6% in those with charity care (p=0.49). There was no difference in graft survival between insured and uninsured patients (p=0.62). The three insurance groups presented similar hospital length-of-stay and 30-day readmission rates. In both univariate and multivariate analysis, uninsured status (charity care) was not associated with worse patient survival (HR:1.23, 95%CI: 0.84-1.80, p=0.29) or graft survival (HR:1.22, 95%CI:0.84-1.78, p=0.29). In conclusion, there was no difference in outcomes after LT between insured and uninsured patients. A charity care program may be an effective tool to mitigate socio-economic disparities in both outcomes and access to LT.

A Bone Health Optimization Framework for Malaysia: a position paper by the Malaysian Bone Health Optimization Network (MyBONe).

Osteoporosis and osteopenia are highly prevalent among older patients scheduled for elective arthroplasties and spinal surgeries. This position paper aims to establish, promote, and standardise effective Bone Health Optimization (BHO) strategies for such patients within orthopaedic practices in Malaysia. It emphasises the need for bone health assessments to be undertaken at the pre-operative, intra-operative, and post-operative stages, with tailored management strategies to meet individual patient needs. A comprehensive literature review was conducted, focusing on articles published from 2019 to 2024. Twelve broad themes were defined including definitions and importance of BHO, epidemiological data, assessment techniques, risk stratification, management strategies, and outcome metrics. Elective surgeries on patients with poor bone health are associated with adverse outcomes, such as periprosthetic fractures, aseptic loosening of implants, and complications after spinal surgeries. This position paper advocates for routine bone health assessments and monitoring during the pre-operative, intra-operative, and post-operative phases. It provides summaries of imaging modalities, risk assessment tools, and techniques for each phase. By adapting the successful "5IQ" approach from secondary fracture prevention, we propose 5IQ-based Clinical Standards for BHO, including 18 Key Performance Indicators. A Malaysian BHO Registry is proposed to benchmark care in real-time and support a national quality improvement programme. Practical resources, such as a BHO algorithm and key practice points, are included. This position paper proposes a paradigm shift in the management of bone health for patients undergoing elective orthopaedic surgery in Malaysia, aiming to improve surgical outcomes and patient care through standardised BHO strategies.

Do clinical decision support tools improve quality of care outcomes in the primary prevention of cardiovascular disease: A systematic review and meta-analysis

AimTo assess the effectiveness of Clinical Decision Support Tools (CDSTs) in enhancing the quality of care outcomes in primary cardiovascular disease (CVD) prevention. MethodsA systematic review was undertaken in accordance with PRISMA guidelines, and included searches in Ovid Medline, Ovid Embase, CINAHL, and Scopus. Eligible studies were randomized controlled trials of CDSTs comprising digital notifications in electronic health systems (EHS/EHR) in various primary healthcare settings, published post-2013, in patients with CVD risks and without established CVD. Two reviewers independently assessed risk of bias using the Cochrane RoB-2 tool. Attainment of clinical targets was analysed using a Restricted Maximum Likelihood random effects meta-analysis. Other relevant outcomes were narratively synthesised due to heterogeneity of studies and outcome metrics. ResultsMeta-analysis revealed CDSTs showed improvement in systolic (Mean Standardised Difference (MSD)=0.39, 95 %CI=-0.31, -1.10) and diastolic blood pressure target achievement (MSD=0.34, 95 %CI=-0.24, -0.92), but had no significant impact on lipid (MSD=0.01; 95 %CI=-0.10, 0.11) or glucose target attainment (MSD=-0.19, 95 %CI=-0.66, 0.28). The CDSTs with active prompts increased statin initiation and improved patients’ adherence to clinical appointments but had minimal effect on other medications and on enhancing adherence to medication. ConclusionCDSTs were found to be effective in improving blood pressure clinical target attainments. However, the presence of multi-layered barriers affecting the uptake, longer-term use and active engagement from both clinicians and patients may hinder the full potential for achieving other quality of care outcomes. Lay SummaryThe study aimed to evaluate how Clinical Decision Support Tools (CDSTs) impact the quality of care for primary cardiovascular disease (CVD) management. CDSTs are tools designed to support healthcare professionals in delivering the best possible care to patients by providing timely and relevant information at the point of care (ie. digital notifications in electronic health systems). Although CDST are designed to improve the quality of healthcare outcomes, the current evidence of their effectiveness is inconsistent. Therefore, we conducted a systematic review with meta-analysis, to quantify the effectiveness of CDSTs. The eligibility criteria targeted patients with CVD risk factors, but without diagnosed CVD. The meta-analysis found that CDSTs showed improvement in systolic and diastolic blood pressure target achievement but did not significantly impact lipid or glucose target attainment. Specifically, CDSTs showed effectiveness in increasing statin prescribing but not antihypertensives or antidiabetics prescribing. Interventions with CDSTs aimed at increasing screening programmes were effective for patients with kidney diseases and high-risk patients, but not for patients with diabetes or teenage patients with hypertension. Alerts were effective in improving patients' adherence to clinical appointments but not in medication adherence. This study suggests CDSTs are effective in enhancing a limited number of quality of care outcomes in primary CVD prevention, but there is need for future research to explore the mechanisms and context of multiple barriers that may hinder the full potential for cardiovascular health outcomes to be achieved.

Evaluating the impact of rapid antimicrobial susceptibility testing for bloodstream infections: a review of actionability, antibiotic use and patient outcome metrics.

Antimicrobial susceptibility testing (AST) is a core function of the clinical microbiology laboratory and is critical to the management of patients with bloodstream infections (BSIs) to facilitate optimal antibiotic therapy selection. Recent technological advances have resulted in several rapid methods for determining susceptibility direct from positive blood culture that can provide turnaround times in under 8 h, which is considerably shorter than conventional culture-based methods. As diagnostic results do not directly produce a medical intervention, actionability is a primary determinant of the effect these technologies have on antibiotic use and ultimately patient outcomes. Randomized controlled trials and observational studies consistently show that rapid AST significantly reduces time to results and improves antimicrobial therapy for patients with BSI across various methods, patient populations and organisms. To date, the clinical impact of rapid AST has been demonstrated in some observational studies, but randomized controlled trials have not been sufficiently powered to validate many of these findings. This article reviews various metrics that have been described in the literature to measure the impact of rapid AST on actionability, antibiotic exposure and patient outcomes, as well as highlighting how implementation and workflow processes can affect these metrics.

Flexible Endoscopic Evaluation of Swallowing: A Proposed Checklist.

Flexible endoscopic evaluation of swallowing (FEES) is not only a well-recognized and ubiquitous tool in dysphagia research but also possesses features that make the assessment vulnerable to shortcomings in transparency and rigor in published literature. Therefore, FEES was considered an important addition to the Framework for RigOr aNd Transparency In REseaRch on Swallowing (FRONTIERS), a multisite collective effort to establish a tool for the critical appraisal of reporting in all forms of dysphagia and swallowing-related research on human subjects. From the FRONTIERS collective, a team of three clinician researchers completed a review of FEES-related literature to determine all components crucial for generalizable and reproducible reporting of FEES research. These components were developed and refined through an iterative process. This review culminated in a 26-item series of "yes/no" questions, forming the FEES section of FRONTIERS. These questions are grouped into the following five components: (a) Equipment, (b) Rater(s), (c) Rating Process, (d) Outcome Metrics, and (e) Miscellaneous Factors. The results of this review support that FEES possesses unique characteristics to other aspects of dysphagia research and is consequently a crucial addition to FRONTIERS to ensure that clinical researchers have access to critical appraisal of FEES-related research inquiry.

Designing an integrated care pathway for spondyloarthritis: A Lean Thinking approach.

Integrated care pathways (ICPs) are crucial for delivering individualised care. However, the development of ICPs is challenging and must be well designed to provide the expected benefits. Regarding this, healthcare organisations are increasingly adopting management systems based on Lean Thinking to improve their organisational processes by eliminating non-value-added steps. This study elucidates the process and evaluates the impact of applying Lean Thinking to redesign an ICP for patients with spondyloarthritis, a chronic inflammatory disease affecting young adults. A multidisciplinary team was assembled and trained in Lean Thinking. Patient's perspective was gathered through a focus group. Guided by an expert methodologist, the team constructed a value stream map of the entire care pathway and analysed each step. Five work streams were defined to increase value at each step, leading to targeted process improvements. Key process and outcome metrics were collected and compared in 2-month baseline and post-implementation audits. A total of 118 patients were included in the baseline audit (September-October 2022), and 116 in the post-implementation audit (January-February 2023). Process redesign resulted in statistically significant improvements (p < 0.05), including a reduction in the mean number of hospital visits per patient over a 2-month period from 2.54 (SD = 0.93) to 1.84 (SD = 0.79), an increase in complementary exams scheduled on the same day (81.4% to 94.8%) and an increase in baseline disease and treatment education (from 22.2% to 84.2% and from 18.2% to 84.6%, respectively). Regarding standardisation of clinical practice, there were significant increases in collecting data for medical records on composite activity indices (76.3% to 95.7%), reporting of pharmacological treatment adherence (68.6% to 94%) and providing nonpharmacological recommendations (31.3% to 95.7%). The application of Lean Thinking to redesign the spondyloarthritis ICP led to significant improvements in outpatient appointment scheduling, reduced patient hospital visits, improved interdepartmental coordination and standardised clinical practice.

Matching Occupational Injury and Illness Data Using Augmented Twin Neural Networks

When record linkage efforts involve complex characteristics there is ample potential for general purpose machine learning (ML) techniques to succeed where traditional probabilistic approaches might fall short. However, there can still be pre-processing (e.g. geocoding) and hand-picked comparators that can further improve linkage outcomes using standard ML models. In this project we present a fusion of these sides we are calling an Augmented Twin Neural Network. This approach leverages the inherent flexibility of Twin Neural Networks in a record linkage context while adding additional layers to allow for hand curated comparators that may be difficult for ML optimizers to implicitly identify without sufficiently large, labeled data sets. The framework is used to match establishments from the BLS Survey of Occupational Injuries and Illnesses to establishments in the OSHA Injury Tracking Application data. The difficulties inherent in matching company names and addresses and the existence of multi-establishment firms make this a prime application for testing. Linkage outcome metrics of this augmented algorithm are compared both with results from probabilistic methods (e.g. Fellegi-Sunter) and standard machine learning methods to illustrate the added benefits.

Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.

Artificial intelligence (AI) has numerous applications in radiology. Clinical research studies to evaluate the AI models are also diverse. Consequently, diverse outcome metrics and measures are employed in the clinical evaluation of AI, presenting a challenge for clinical radiologists. This review aims to provide conceptually intuitive explanations of the outcome metrics and measures that are most frequently used in clinical research, specifically tailored for clinicians. While we briefly discuss performance metrics for AI models in binary classification, detection, or segmentation tasks, our primary focus is on less frequently addressed topics in published literature. These include metrics and measures for evaluating multiclass classification; those for evaluating generative AI models, such as models used in image generation or modification and large language models; and outcome measures beyond performance metrics, including patient-centered outcome measures. Our explanations aim to guide clinicians in the appropriate use of these metrics and measures.