Pharmaceutical export resilience is an important health economics and healthcare management issue because instability in international medicine trade can affect pharmaceutical markets, medicine supply security, and the capacity of health systems to prepare for external shocks. Previous pharmaceutical-trade research has concentrated mainly on retrospective competitiveness and trade-structure indicators, leaving limited comparative evidence on how classical time-series and machine-learning approaches differ in forward-looking assessments of pharmaceutical export dynamics. This study compares four forecasting approaches for pharmaceutical exports from India and China, evaluates their out-of-sample point-forecast accuracy, and examines whether their longer-horizon trajectories remain consistent with the historical trend and seasonal structure of each series. The dataset contains 218 monthly observations for each country from July 2007 to August 2025 obtained from the International Trade Centre Trade Map. The period from July 2007 to December 2021 is used for model training, the period from January to June 2022 is excluded from the reported accuracy evaluation, and the period from July 2022 to August 2025 is used as the out-of-sample evaluation window. The empirical strategy applies logarithmic transformation, stationarity testing, differencing, seasonal autoregressive integrated moving average modeling, Holt-Winters exponential smoothing, Prophet modeling, random forest forecasting, residual diagnostics, root mean squared error, mean absolute percentage error, and Diebold-Mariano tests using Python 3.13.7. For India, random forest achieved the lowest forecast errors (RMSE = 340,611; MAPE = 0.1639), and Diebold-Mariano comparisons with the SARIMA benchmark were significant for Holt-Winters (p = 0.032), Prophet (p = 0.006), and random forest (p = 0.001). For China, random forest also produced the lowest point-forecast errors (RMSE = 138,080; MAPE = 0.1172), while Holt-Winters was the strongest classical model (RMSE = 229,843; MAPE = 0.2164); all three competing models differed significantly from SARIMA at p < 0.01. The relatively flat long-horizon random forest trajectories nevertheless show that lower test-period error does not by itself validate structural reliability through 2032. Comparative forecasting can therefore complement retrospective analysis of international pharmaceutical markets as a decision-support input for monitoring export stability and medicine-supply preparedness, while long-horizon projections should be interpreted cautiously and validated in future research.
Unequal pharmaceutical service availability is an important health economics and healthcare management concern because the spatial and institutional distribution of pharmacies influences the capacity of health systems to provide equitable access to medicines and support universal health coverage. Previous South African research has documented medicine-access barriers, public-sector constraints, and geographic inequalities, but a nationally integrated registry-based profile that simultaneously differentiates pharmacy premises by regulatory sector, province, and public–private institutional pharmacy distribution remains insufficiently developed. This study assesses the national distribution of registered pharmaceutical service infrastructure and examines sectoral, geographic, and institutional patterns relevant to health-system planning. The analysis uses the South African Pharmacy Council registry dated 19 January 2026, comprising 5,645 registered pharmacy premises across nine provinces. The study used a quantitative cross-sectional design and applied descriptive statistics, concentration measures, a chi-square goodness-of-fit test used as an equal-share reference benchmark, and a Pearson chi-square test of homogeneity and Cramér’s V, with calculations performed in R statistical software version 4.3.1. Community pharmacies account for 74.1 percent of registered premises, while academic and consultant pharmacies represent 0.2 percent and 0.1 percent, respectively. Gauteng, KwaZulu-Natal, and the Western Cape contain 65.5 percent of all registered pharmacies; Gauteng alone accounts for 34.7 percent of the national total, and 72.7 percent of registered manufacturing pharmacies are located in Gauteng, demonstrating pronounced concentration of registered pharmaceutical infrastructure. Public institutional pharmacies constitute 65.8 percent of institutional premises compared with 34.2 percent in the private sector, and the provincial public–private composition differs significantly (chi-square statistic of 80.75; Cramér’s V of 0.29), with Gauteng being the only province in which registered private institutional pharmacies outnumber public ones (110 compared with 76), whereas the public-to-private ratio reaches 3.8:1 in the Eastern Cape, 3.9:1 in Limpopo, and 4.1:1 in the Northern Cape. The findings provide a national registry baseline for health economics, healthcare management, and public health policy and support future district-level, longitudinal, and geospatial analyses linking registered premises to population need, travel time, service capacity, utilization, and quality of pharmaceutical care.
Assessing the long-term economic value of cholesterol-lowering therapies is essential for allocating constrained public healthcare budgets to cardiovascular prevention without compromising equitable access to effective treatment. Existing evaluations of inclisiran offer limited insight into how local acquisition prices, cardiovascular event costs, and uncertainty in projected treatment effects jointly determine its value for an Egyptian public payer. This study evaluates the lifetime costs, health gains, and price conditions associated with adding inclisiran to intensive lipid-lowering standard care for secondary prevention of atherosclerotic cardiovascular disease. The analysis uses a hypothetical cohort of 1,000 patients entering at age 65, a 35-year horizon, 2024 Egyptian medicine prices and public-hospital tariffs, trial-derived cholesterol reductions and published clinical risk and health-utility assumptions, with costs and outcomes discounted annually at 3.5%. A five-state Markov transition model was implemented in Python, incorporating endpoint-specific logarithmic relative-risk scaling, sixteen one-way sensitivity tests, fourteen structural scenarios, threshold-price optimization, and 10,000 paired probabilistic simulations across 21 uncertain parameters. In the original specification, inclisiran increased discounted quality-adjusted life-years from 9.008731 to 9.149510 per patient, while raising costs from 124,481 to 1,603,815 Egyptian pounds, yielding an incremental gain of 0.141 quality-adjusted life-years, an additional 1,479,334 Egyptian pounds, and an incremental cost-effectiveness ratio of 10,508,150 Egyptian pounds per quality-adjusted life-year; modeled myocardial infarctions and strokes decreased by 50.1 and 38.0 events per 1,000 patients. The endpoint-specific specification yielded 0.225670 additional quality-adjusted life-years at an incremental cost of 1,484,298 Egyptian pounds and a ratio of 6,577,307 Egyptian pounds per quality-adjusted life-year; probabilistic simulations estimated a mean gain of 0.225696 quality-adjusted life-years, with a central 95% simulated interval of 0.101774–0.364243, and no cost-effective draws at either illustrative benchmark of 150,000 or 450,000 Egyptian pounds. The original model’s one-way ratios spanned 6.33–30.82 million Egyptian pounds per quality-adjusted life-year, whereas fourteen structural scenarios yielded 7.79–26.55 million; threshold calculations required reducing the acquisition price from 58,500 to 706.47 or 2,380.33 Egyptian pounds per dose, equivalent to reductions of 98.79% and 95.93%, respectively. These findings establish a transparent, scenario-dependent basis for health economic appraisal and medicine-price negotiations, while highlighting the need for locally calibrated event risks, population-specific health utilities, and direct cardiovascular outcome evidence before reimbursement decisions can be justified.
Information-system capability and provider market structure are central to hospital performance in the United States, yet their joint association with service delivery remains unclear. Previous studies have examined artificial intelligence adoption, digital maturity, and hospital consolidation separately, mostly in relation to prices, financial results, or quality indicators, and rarely within one specification based on transparently linked public data. This study examines whether the configuration of certified electronic health record systems and local inpatient-provider concentration are associated with inpatient length of stay and Medicare payment per discharge. The analysis links 2023 public administrative records for 2,221 United States hospitals in 1,100 counties and 50 state-level jurisdictions, combining certified health information technology listings of the Office of the National Coordinator for Health Information Technology with Original Medicare inpatient utilization summaries and hospital cost reports of the Centers for Medicare & Medicaid Services. Three nested ordinary least-squares models with county-clustered standard errors were estimated in Python, with county concentration measured by the Herfindahl–Hirschman index of provider discharge shares, and seven sensitivity analyses tested alternative samples, outcomes and uncertainty estimators. The coefficient on the log-transformed certified developer count changed from −0.0516 in the baseline model to 0.0305 in the fully adjusted model, so that an increase from one to two listed developers corresponded to approximately 1.25% longer predicted stays, conditional on the included controls. The adjusted concentration coefficient of −0.0345 implied that a 0.1-point higher concentration index was associated with approximately 0.34 percent shorter stays, but the sign reversed (0.0202) and the confidence interval included zero when the 630 hospitals in single-provider counties were excluded. Restricting cost reports to calendar-year 2023 (1,099 hospitals) removed statistical evidence for both associations, whereas in the payment model concentration was negatively associated with Medicare payment per discharge (−0.1194) and developer configuration was not. These findings do not establish improvements in service delivery attributable to information-system configuration or hospital concentration and highlight the need for observed artificial intelligence adoption measures, validated market definitions, and longitudinal or quasi-experimental designs before causal conclusions can be drawn.
Sustained occupational stress among healthcare workers affects well-being, workforce functioning, and the delivery of care, making the management of modifiable workplace stressors and coping resources an important healthcare-management concern. This review examined how artificial-intelligence-enabled technologies may function as workplace coping resources for healthcare workers while distinguishing direct psychosocial support, upstream organizational mechanisms, resilience-related capacities, predictive monitoring, implementation risks, and the limits of the available evidence. A structured narrative review covered English-language publications from January 2016 to July 2026 that addressed artificial-intelligence-enabled interventions or technologies, digital mental-health support, workplace stress, coping, burnout, or resilience among healthcare workers. Searches were conducted in Google Scholar, PubMed/MEDLINE and Scopus. Approximately 5,200 records were identified, 220 duplicates were removed, 4,980 titles and abstracts were screened, 69 full texts were assessed, and 62 publications were retained for narrative synthesis. Evidence was classified by source type, although no formal methodological-quality or risk-of-bias appraisal was conducted. First, automated documentation, workflow support, scheduling, and workforce analytics may reduce repetitive administrative demands and improve resource allocation, but these technologies primarily modify upstream organizational stressors rather than acting as psychological coping resources. Second, conversational agents, teletherapy, digital coaching, and related tools may support problem-focused or emotion-focused coping, although their value depends on usability, trust, infrastructure, privacy protection, and continued access to human support. Third, adaptive learning and coaching systems may contribute to resilience, while predictive models may identify burnout-related risk patterns earlier; however, prediction alone does not establish prevention, and evidence remains heterogeneous across study designs and populations. The principal contribution of the review is a management-oriented framework that separates organizational stressor modification, direct coping support, and predictive monitoring, thereby clarifying mechanisms that are often grouped together under the broad label of artificial-intelligence support. For healthcare organizations, the findings support problem-first, human-supervised implementation and careful evaluation of workload, equity, trust, governance, and technostress, while future research should prioritize longitudinal and experimental studies that test whether technological support produces sustained improvements in worker well-being.
