Developer Experience (Dev-X) has emerged as a critical driver of software quality, developer productivity, and project success. However, the empirical literature remains fragmented, with limited understanding of how specific Dev-X interventions affect downstream software and business outcomes. This study aims to synthesize and map the current empirical evidence on Dev-X interventions, identifying how they influence software outcomes (e.g., code readability, collaboration) and propagate to organizational key performance indicators (KPIs) such as product quality and process efficiency. We conducted a systematic mapping study of 160 empirical articles published between 2006 and 2024. We identified 146 unique interventions that impacts software outcomes such as role match may impact on developers’ workload, likewise managing burnout in developer feedback may impact organizational culture. Using grounded coding, we also classified such mappings to 8 KPIs and 5 Dev-X dimensions based on the strength of empirical relationships (correlational, causal, classification-based) reported in the literature. Our analysis reveal that quality-related KPIs are more frequently impacted than productivity metrics, challenging traditional emphasis. Emotional state and values-oriented interventions dominate current practices, while cognitive load and motivation-focused strategies remain underexplored. Only a minority of studies (39 out of 160) explicitly trace full chains of intervention–outcome–KPI, highlighting persistent fragmentation; in other cases, we reconstructed these chains using cross-sectional synthesis of study data. Notably, recent years show an emergence of AI-assisted and value-centered intervention designs. This study consolidates the fragmented Dev-X landscape by offering an evidence-based, open-access Ready-Reckoner tool for navigating interventions and their effects. While not prescriptive, it enables structured, context-aware exploration of empirical findings and lays the foundation for future causal, longitudinal, and multi-channel Dev-X research. • We systematically map 160 empirical studies to trace how Developer Experience (Dev-X) interventions influence software outcomes and business-critical KPIs. • The study identifies 146 unique interventions and categorizes them by their targeted Dev-X facets, intermediate outcomes, and relationship strength (causal, correlational, qualitative). • Our results highlight a gap in empirical traceability between Dev-X practices and long-term organizational success, emphasizing the need for integrated evaluation frameworks. • The study identifies underexplored connections across Dev-X dimensions and advocates for research into their combined influence on developer performance and project outcomes. • We deliver an open-access, practitioner-oriented Dev-X Ready-Reckoner Tool that supports evidence-based selection of interventions aligned with organizational goals.

A new soft sensing method based on serial-parallel GRU with self-attention mechanism for complex multi-unit industrial processes.

HIV-Gag VLP-based vaccine purification at manufacturing scale: Combination of counterflow centrifugation and nuclease treatment as a key to DNA depletion.

Carbon emissions from the construction industry have attracted significant public attention, and the sector's low-carbon transition is crucial for achieving carbon neutrality. Although extensive research has been conducted in this field, limited studies have investigated the combined effects of structural performance design and seismic safety technologies on carbon emission reduction. This study aims to simultaneously enhance structural safety and reduce carbon emissions using the seismic energy dissipation technology. Eight reinforced concrete frame structures with varying numbers of floors are selected as case studies in accordance with the Chinese design code. By incorporating additional energy dissipation devices, the required dimensions of structural components in structures with damper (SWD) are reduced compared with structures without damper (SWOD), thereby lowering carbon emissions and improving seismic performance. Both SWOD and SWD systems are designed for each of the eight reinforced concrete frame structures for comparative analysis. Structural component dimensions are calculated using SAUSG software, and key performance indicators, including the natural period and inter-story drift ratio, are analyzed to verify compliance with code-specified safety requirements. Engineering quantities and life-cycle energy consumption are quantified, including the production and transportation of materials, as well as construction and dismantling stages. The results indicate that SWDs reduce material and energy consumption, with average carbon emissions 17.4% lower than those of SWODs. This study provides a novel perspective on carbon emission reduction during the design phase and offers an effective technical pathway for the coordinated development of low-carbon buildings and seismic resilience.

• Adaptive cascaded control applied to grid-forming inverter. • Voltage direct-tracking using a model reference adaptive controller. • Controller validated through extensive tests with wide parameter variations. • Comparison between adaptive and fixed-gain control techniques. • Model-free adaptive control applied to microgrid synchronization. This paper presents a cascaded adaptive control strategy for the inner loop of a grid-forming inverter. The control structure combines an inner proportional regulator for current limiting with an outer Model Reference Adaptive Controller (MRAC) for voltage regulation, tracking references from the primary control layer. The primary control utilizes a droop mechanism with virtual impedance to enhance active and reactive power decoupling. For grid-reconnection scenarios, the secondary control implements an adaptive proportional-integral-derivative (PID) controller in the synchronization loop, effectively compensating for non-deterministic communication delays while maintaining a simple design framework. The control system is validated through hardware-in-the-loop (HIL) simulations, evaluating five critical scenarios: steady-state operation (islanded and grid-connected), reconnection transients, unplanned islanding, and a microgrid fault. A comparative analysis with a fixed-gain controller demonstrates the superior performance of the proposed adaptive approach in managing uncertainties and dynamic grid conditions, with consistent improvements across all scenarios and error reductions of up to 90% in key performance indices.

• Incorporating asset interdependencies markedly improves the accuracy of infrastructure performance predictions • Fusion of long short-term temporal dependencies with spatial neighbourhood correlations in the proposed hybrid model, BaGTA, which is temporally aware and spatially informed, captures the spatiotemporal relationships between drainage performance and track degradation • Incorporation of a Bayesian inference module enables dynamic and probabilistically grounded spatiotemporal predictions under complex interdependent infrastructure systems • The multi-level modelling framework addresses decision-making needs across both asset and system levels Effective prediction of infrastructure performance is essential for informed asset management. However, traditional approaches often treat different types of assets in isolation, overlooking critical interdependencies (such as those between track and drainage systems) that significantly influence asset degradation and risk. This paper proposes a hybrid model, BaGTA, that is temporally aware, spatially informed and probabilistically grounded to predict railway track performance while accounting for both uncertainty and inter-asset dependencies. The model was trained and validated on a dataset comprising 6,072 track segments and 31,628 drainage assets across four UK railway routes. We demonstrate that incorporating track-drainage interdependencies improves prediction accuracy in both classification and regression tasks. Specifically, the inclusion of interdependencies reduced the prediction error for the Vertical Settlement Standard Deviation (VSD), which is a key indicator of track performance, by 24.65%. The proposed method not only captures complex spatiotemporal relationships but also quantifies uncertainty in predictions, offering a robust decision-support tool for infrastructure operators. This approach has the potential to transform maintenance strategies by enabling proactive, risk-informed, and cost-effective asset management.

ObjectivesThis study aimed to develop a cost-benefit analysis (CBA) process to guide the evidence-based selection of healthcare flooring materials that balance safety, environmental sustainability, and economic viability.BackgroundFlooring selection in healthcare environments significantly influences patient safety and well-being, operational costs, maintainability, and environmental performance. However, tools that support material choices aligned with both sustainability goals and long-term cost efficiency remain limited, especially in the predesign and specification stages.MethodsA CBA approach was applied, integrating discounted cost analysis for economic assessment, sustainability key performance indicators for environmental and maintenance evaluation, and a benefit-cost ratio model for final decision-making. This quantitative approach compared flooring materials' "first costs" to long-term benefits concerning safety, environmental sustainability, and maintainability, supporting trade-off analysis in healthcare design.ResultsBased on the data used for this research, common flooring materials such as rubber and linoleum demonstrated a high balance of durability, safety, and sustainability performance. Conversely, vinyl-based options scored lower on composite criteria for long-term performance and sustainable value proposition. This examination culminated in a generalizable cost-benefit model for healthcare material decisions.ConclusionsThis study introduces a design decision-support approach that complements choice management during the environment of care predesign stage for practitioners and healthcare facility operations specialists. The parameters used in this study offer the potential to better inform environmental planning lifecycle choices by considering healthcare flooring material safety and sustainability as equally weighted criteria, without compromising operational fiscal prudence.

Crude awakening: Crude oil exposure under warming scenarios alters escape performance and metabolic enzyme activity in Pomacentrus amboinensis.

Emergency general surgery (EGS) services are under increasing pressure in New Zealand and internationally. This study examined trends in EGS volume, incidence, patient complexity, and system performance over 15 years at North Shore Hospital, Waitematā, New Zealand. We conducted a retrospective cohort study of adult patients (≥ 16 years) undergoing emergency general surgical procedures at North Shore Hospital between 2010 and 2024. Annual procedure volumes, incidence rates using census-derived catchment populations, patient demographics, procedure type and key system performance indicators were analysed over time. A total of 39 996 emergency general surgical procedures were performed. Case volume increased by 32.3% over the study period, with a corresponding rise in total operative minutes. The overall population of Waitemata increased from 524 610 to 670 174 people (a 27.8% increase, 1.65% per year). The overall procedure incidence rate increased modestly (453.1 vs. 469.1 per 100 000 person-years) but did not reach statistical significance. The median age of surgical patients increased more rapidly than the underlying population. The proportion of patients with ASA ≥ 3 rose from 23.4% to 34.5%. Median time from booking to surgery increased from 7 to 16 h, while length of stay remained stable. This study demonstrated a significant increase in the emergency general surgical workload at a large metropolitan hospital in New Zealand, with population growth as the primary driver. It also demonstrates that the patients are ageing and becoming increasingly medically complex. These findings highlight the need for proactive workforce planning and service design.

Bone fragility is a major cause of morbidity and reduced quality of life among adult solid organ transplant (SOT) recipients. Despite guideline recommendations, bone health screening and posttransplant treatment remain inconsistent, and optimal therapeutic strategies are not well standardized. We reviewed the relevant literature, along with professional society guidance, and integrated institutional clinical experience to develop a streamlined multidisciplinary care pathway and define key performance indicators for transplant and bone health specialist teams. All non-kidney SOT candidates should undergo spine and hip dual-energy X-ray absorptiometry (DXA) with vertebral fracture assessment at listing, whereas kidney transplant candidates should undergo DXA only when the results are expected to influence management. High-risk patients, defined by a fragility fracture or a T-score ≤-2.5, should be referred for specialist care. After transplantation, non-kidney SOT recipients should receive osteoporosis therapy within 2 to 4 months if they have not been treated in the prior year, with treatment guided by renal function and bone turnover status. Kidney transplant recipients should undergo DXA, with referral and treatment tailored to fracture risk, bone turnover, and renal function. Bone biopsy should be considered when the underlying bone disease is unclear. Early risk stratification, standardized assessment, and coordinated multidisciplinary management are essential for fracture prevention and improved outcomes in SOT recipients.

Nursing workload in intensive care units affects outcomes and staff well-being. Objective measures such as Nine Equivalents of Nursing Manpower Score (NEMS) capture task demands, whereas subjective ratings reflect nurses' workload; how these relate is unclear (1-4). All shifts over 6 months in an adult ICU of a tertiary hospital were analyzed. For each shift, staffing (number of nurses, patients, patient-to-nurse ratio PNR), number of interventions, admissions and discharges, cumulative Richmond agitation sedation scale RASS score and intensivist identity were recorded. Shift workload was measured as cumulative NEMS and subjective rating on a 1-7 scale (LEP). Analyses used Pearson correlations, linear regression, one-way ANOVA, and multiple regression. 531 shifts showed a mean NEMS of 347.7 and subjective rating of 4.6 with a mean PNR of 1.42. Early shifts had the highest NEMS; the highest subjective ratings occurred in late shifts (4.8 late, 4.6 early, 4.4 night; p < 0.0001). Subjective workload correlated weakly with NEMS overall (r = 0.254, p < 0.0001), but moderately in early (r = 0.31) and late (r = 0.43) shifts with no association at night. PNR correlated with subjective workload in early (r = 0.37) and late (r = 0.24) but not night shifts. Multiple regression including NEMS, PNR, RASS, admissions and discharges and consultant identity explained 12.4% of variance in subjective workload (p < 0.001). NEMS (B = 0.0030, p < 0.0001), admissions (B = 0.0687, p = 0.048) and interventions (B = 0.0603, p < 0.008) were independent predictors. PNR, cumulative RASS, discharges and consultant identity were not independently associated. NEMS, admissions and interventions were related to perceived workload but explained little of what makes a shift demanding. The NEMS-subjective workload association was confined to early and late shifts, suggesting night-time workload is driven by other factors. ICU workload is multidimensional and cannot be adequately captured by objective task counts alone. Routinely recorded key performance indicators from intensive care units can only partially reflect the fluctuations in the subjective workload perceived by nurses. Not applicable.

To meet Yantai City's 2030 hydrogen demand (1000 tons for 12 refueling stations), this study develops a quantitative model to assess high-pressure gaseous (CGH2, 700bar) and liquid hydrogen (LH2) across truck, railway, maritime, and pipeline modes. The model systematically analyzes key performance indicators to optimize supply configurations based on Yantai's geography. For short-distance transport (e.g., Qingdao → Yantai, 200km), CGH2 by truck is optimal, with a unit cost of 5.22 CNY/kg, low loss (0.1%), and superior energy efficiency. For long-distance inland routes (e.g., Ordos → Yantai, 1500km), CGH2 by rail offers a viable pathway at 3.22 CNY/kg, ideal for inland green hydrogen bases. For international imports (e.g., Australia → Yantai, 8000km), LH2 by ship shows a high cost of 14.35 CNY/kg, rendering it economically unattractive due to shipping costs and evaporative losses. Pipeline transport (e.g., Tianjin → Yantai, 300km) yields prohibitively high costs (CGH2: 57.23; LH2: 81.53 CNY/kg) at this scale, due to high upfront infrastructure investment. Thus, pipelines should be a long-term strategic option for future large-scale networks. This study confirms that hydrogen form and transport distance are pivotal factors influencing total cost. It advocates for prioritizing a multimodal CGH2-based network in the near term. The findings provide quantitative guidance for Yantai's hydrogen planning and establish a transferable methodological framework for designing hydrogen strategies in comparable coastal cities.

Quality Assurance (QA) in education as a continuous process focused on monitoring, evaluating, and enhancing the quality of teaching, learning, and overall educational outcomes has evolved and became a prominent feature of higher education reforms worldwide. In the Philippines, the Commission on Higher Education (CHED) Memorandum Order Number 46 series of 2012 defined quality as the continuity and integrity of the learning environment with the vision, purpose and goals of the organization, demonstrated by excellent results in learning and service, as well as the creation of a successful culture. To provide quality education and to address the global concern for excellence in education, the CHED memorandum stated that quality assurance mechanism should be in two components such as the internal and external mechanism. Despite the implementation of quality assurance mechanisms, autonomous higher education institutions were experiencing a myriad of challenges which were affecting the effectiveness of these quality assurance practices. Any Quality Assurance mechanisms, either locally or internationally recognized, that are being utilized and adopted by the autonomous university, may be greatly influenced by the quality assurance practices implemented by the faculty. The roles and functions of the Autonomous University's quality assurance mechanisms may not be well understood by the average faculty member. Teachers' effectiveness was largely dependent on the full usage of quality assurance practices such as capacity building, supervision, physical facilities, and accreditation. Teacher’s involvement and practices in the quality assurance (QA) endeavors of their institutions need to be examined since they were the direct implementer of their QA mechanisms. This study assessed the quality assurance practices of the teachers and the challenges they encountered in the College of Education in the different autonomous universities in the National Capital Region in the Philippines with the goal of crafting a proposed action plan for improvement. Utilizing descriptive-evaluative, comparative, and correlational designs, the study surveyed 505 College of Education faculty members from 26 private autonomous Higher Education Institutions (HEIs) in the National Capital Region, Data were collected through validated questionnaires and analyzed using statistical tools, including Pearson correlation, at a 0.05 level of significance. Ethical considerations were observed throughout the conduct of the study emphasizing the use of informed consent and confidentiality of responses. The findings revealed that there was a significant difference in the assessment of the respondents on the external quality assurance practices implemented by the College of Education for voluntary accreditation as to "IQuAME" when age (P=0.035), faculty rank (P=0.047), status of employment (P=0.043), and subject taught (P=0.023) were considered, in the assessment of the respondents on the External Quality Assurance practices implemented by the College of Education for voluntary accreditation as to "ISO Certification" when status of employment (P=0.049) and subject taught (P=0.042) were considered. There was a significant relationship between the internal quality assurance practices and challenges encountered by the College of Education (Pearson r of -0.629), with a qualitative description of "moderate negative relationship", which was significant since the computed P-value of 0.000 was less than 0.05 level of significance. Also, the external quality assurance practices and challenges encountered by the College of Education were also significantly correlated with the calculated Pearson r of -0.729, p &lt; 0.05. The Quality Assurance practices of faculty members positively influence the quality of teaching, learning, and overall educational outcomes. Hence, school administrators are encouraged to provide relevant trainings and other workshops pertaining to Quality Assurance Management alongside training in Digital Technology and Information Systems Management, including the proper allocation of sufficient financial support for their faculty members who would like to enroll in the doctoral program. The study proposes an action plan delving on the following Key Performance Indicators (KPIs) that could be utilized to monitor any intervening discrepancies or non -compliance to Quality Assurance Protocols via: a) determination of the actual percentage reduction in non-conformances or defects, b) identification of staff quality assurance training completion rate, c) performance of regular audit compliance score improvement schemes, d) regular monitoring of the customer/stakeholder satisfaction ratings, and e) strict adherence and compliance to turnaround time for corrective actions.

This study introduces a comprehensive methodology for improving production system workstations, leveraging Virtual Reality (VR), motion capture technology, and data-driven performance assessment. The methodology defines and justifies a set of Key Performance Indicators (KPIs), including ergonomics (RULA scores), operation frequency, safety of operator, complexity of task, productivity, and user feedback, to holistically evaluate workstation performance. A key innovation of the methodology is the visualisation of these KPIs, which gives designers intuitive feedbacks and enables effective identification of design deficiencies. By bridging the gap between real and immersive workstation environments, the methodology ensures reliable assessments of operator behaviours and interactions. A case study demonstrates the practical value of this approach, showcasing its capacity to address ergonomic challenges, improve operation space, and enhance operator satisfaction through iterative design refinement.

ABSTRACT This study examined key game performance indicators and their thresholds associated with win probability in men’s elite handball, analysing data from 388 matches across the four most recent Men’s World Championships. Using an XGBoost machine learning model with SHAP (Shapley additive explanations) for interpretability, we identified the relative contributions of 18 performance indicators to match outcomes. The model demonstrated strong predictive accuracy (test area under the curve = 0.896; accuracy = 0.791). The 9 m shot success rate, block rate, technical fault rate, assist rate, and steal rate were the most influential indicators. Threshold analysis suggested that a 9 m shot success rate above approximately 40% and a block rate above approximately 3% may be associated with a higher probability of winning, while a technical fault rate above approximately 20% may be associated with a lower probability of winning. Marginal gains from any single indicator plateaued, suggesting an upper limit to their impact. These findings provide insights into the importance of specific performance indicators and their reference thresholds for coaching and tactical decision-making in elite handball. The integration of machine learning with explainable outputs provides practical insights for performance analysis in invasion sports.

Urban pipeline infrastructure plays a vital role in ensuring the operational efficiency and service reliability of modern utility systems, especially in industrial regions. While previous studies have focused primarily on pipe failure prediction, limited research has addressed the forecasting of key pipeline performance indicators such as velocity, pressure, and head loss within the context of infrastructure asset management. This study investigates the performance of advanced machine learning (ML) models, PSO-ANN, Genetic CNN, Quantum SVR, Fuzzy Logic Tree, and Bayesian GPR, in predicting three critical output variables: velocity, head loss, and pressure. A dataset comprising 91 instances with geometric and hydraulic descriptors was employed, and descriptive statistics revealed significant variability in flow-dependent parameters. SHAP-based sensitivity analysis highlighted elevation (0.9287) as the dominant factor for pressure prediction, while flow rate (0.4574) and diameter (0.2273) strongly influenced head loss. For velocity, flow rate (0.1139) emerged as the most influential, though other parameters also contributed, justifying their inclusion in the modeling framework. The models were trained using data from the Gadhra Water Distribution Network (District Metered Area-03) in East Singhbhum, Jamshedpur, India. Model evaluation was conducted using R², RMSE, MAE, and MAPE. Results demonstrated a clear performance hierarchy, with Bayesian GPR and Fuzzy Logic Tree exhibiting superior accuracy and stability (R² ≥ 0.98, RMSE ≤ 0.06, MAPE ≤ 0.13), whereas PSO-ANN and Genetic CNN showed relatively weaker performance. The near-perfect R² observed for Fuzzy Logic Tree reflects the small dataset size and its high capacity, highlighting that generalization may be limited in larger or unseen datasets. The analysis of regressor plots, residual distributions, and normalized accuracy matrices further validated these findings. Overall, the study establishes Bayesian GPR and Fuzzy Logic Tree as robust predictive tools for hydraulic modeling while acknowledging dataset constraints that may affect generalization.

Under-five mortality (U5M) remains a key indicator of child health outcomes and health system performance. In Ghana, while national trends in U5M are well documented, limited evidence exists on how socioeconomic and sex-disaggregated inequalities have evolved and how future trajectories may unfold. This study, therefore, assessed three-decade trends, identified inequalities in U5M, and projected future patterns in Ghana. A descriptive analytical design was employed using the WHO Health Equity Assessment Toolkit. Absolute and relative measures of inequality, including Difference (D), Ratio (R), Population Attributable Risk (PAR), and Population Attributable Fraction (PAF), were computed using the WHO HEAT online built-in software. Moreover, temporal trends were examined using the Joinpoint Regression programme to estimate the Annual Percentage Change (APC) and Average Annual Percentage Change (AAPC) at p < 0.05. In addition, time-series forecasting was conducted using an Auto-Regressive Integrated Moving Average (ARIMA) model to project U5M trends in Ghana from 2023 to 2035. Between 1992 and 2022, Ghana's U5M declined substantially from 120.20 to 42.34 deaths per 1,000 live births. Six distinct periods of statistically significant decline were identified, with the steepest annual decrease occurring between 2008 and 2016 (APC = -4.50%, 95% CI: -4.60, -4.04). The overall AAPC was - 3.42% (95% CI: -3.44, -3.40). U5M remained consistently higher among males than females, although the absolute difference narrowed from - 16.15 deaths per 1,000 in 1992 to -8.90 in 2022. Socioeconomic inequalities also persisted, with children from the poorest quintile consistently experiencing higher mortality than those from the richest, despite reductions in both absolute (D: 62.36 to 20.81 deaths per 1,000) and relative (R: 1.73 to 1.65) measures. Forecasts from 2023 to 2035 indicate a continued decline in U5M, although the rate of reduction is projected to be gradual. U5M, along with persistent sex and socioeconomic inequalities, has declined over the past 3 decades. The rate of U5M is also projected to decline further from 2023 to 2035. This echoes the need to expand access to quality maternal care, improve nutrition, and address social determinants of health. These interventions are essential for accelerating progress and ensuring equitable survival outcomes.

Laboratory medicine remains the cornerstone of disease detection, clinical management, monitoring, and public health surveillance. Increasing population needs and rising disease burdens, particularly in low- and middle-income countries (LMICs), continue to expand the volume of diagnostic testing, resulting in substantial generation of biomedical waste. Effective laboratory waste management is therefore essential for protecting personnel, maintaining high-quality service delivery, and minimizing environmental harm. However, many LMICs face persistent challenges due to inadequate infrastructure, insufficient training, limited financial resources, and weak regulatory enforcement-that compromise safe and efficient waste handling. This review explores affordable, scalable and sustainable measures to strengthen laboratory waste management in resource-constrained settings. It categorizes the main types of laboratory waste-infectious/biological, chemical, sharps, and general waste-and examines common barriers to appropriate disposal. Guided by various international standards including International Organization for Standardization (ISO) 15189 and 14001, Eco-Management and Audit Scheme (EMAS) and World Health Organization (WHO) recommendations, and drawing on experiences from other LMICs, the review discusses practical, low-cost interventions such as improved test utilization, workflow optimisation, Artificial Intelligence (AI)-supported waste classification and digital tracking systems. The importance of workforce training, government engagement, public-private partnerships, and involvement of non-governmental organisations is also emphasised. To foster a culture of responsible waste management, the review proposes behavioural and accountability mechanisms including linking efficiency to key performance indicators, providing institutional recognition, documenting non-conformances, and applying consequence management where necessary. Sustainable and cost-effective waste management is achievable in resource-limited settings when supported by committed leadership, coordinated stakeholder action, and continuous quality improvement. Corporate bodies can play a pivotal role by providing strategic leadership, mobilising resources and strengthening workforce capacity.

The degree of convergency is routinely cited as a hallmark of efficient syntheses, yet a simple quantitative score still remains to be applied. After deriving a new and easy-to-use metric based on a common understanding of convergency, this key performance indicator is applied to two dozen syntheses of various lengths and complexities. To reach a holistic picture, said metric is then combined with Baran's ideality approach to allow for the estimation of an overall efficiency, which can be equally applied to linear and convergent approaches. Finally, angles for optimization of synthetic planning can be derived by analyzing the influencing factors driving convergency and synthetic efficiency.

BackgroundType 2 diabetes mellitus (T2DM) affects approximately 17% of adults in Qatar, making improved primary care management a national health priority. Despite established guidelines, significant gaps persist between recommended care and real-world practice, particularly in preventive and patient-centered services. To address this, a structured, data-driven quality improvement (QI) framework was implemented.MethodsWe conducted a mixed-methods, pre-implementation QI study across 31 primary health care facilities in Qatar. The three-phase approach included: (1) a Delphi consensus with 65 primary care physicians to validate 17 key performance indicators (KPIs) for T2DM care; (2) a retrospective audit of 450 adult T2DM patient records to assess adherence to each KPI; and (3) prioritization of improvement interventions using the Analytic Hierarchy Process (AHP), based on clinical impact (50%), feasibility (30%), and urgency (20%).ResultsThe Delphi panel reached unanimous consensus on all 17 KPIs (median importance rating 5/5), covering preventive care, metabolic monitoring, therapy management, and patient education. The audit revealed high adherence to laboratory-based indicators (∼82%) but low performance in preventive and patient-centered care: ASCVD risk assessment (19.6%), foot examinations (24.0%), and individualized HbA1c goal documentation (1.3%). KPI adherence varied widely across centers (e.g., foot exam rates ranged from 0% to 95.6%). AHP analysis identified five top-priority gaps accounting for 63% of total priority weight: ASCVD risk assessment (16%), foot examinations (16%), lifestyle education (12%), follow-up HbA1c testing (10%), and individualized HbA1c goal-setting(9%).ConclusionThis study highlights critical deficits in preventive and patient-centered diabetes care in primary care facilities. By integrating expert consensus, real-world performance data, and structured prioritization through the Delphi–AHP framework, we developed a robust, evidence-based model for QI planning. Importantly, involving frontline clinicians in both KPI selection and intervention prioritization fostered ownership, contextual relevance, and alignment with clinical realities. This participatory approach enhanced the credibility and feasibility of proposed interventions and laid a strong foundation for sustainable practice change.