Healthcare systems face long-term workforce challenges

Economic variables also play a central role. Rising GDP per capita increases healthcare utilization rates as households are better able to access medical services. Additionally, out-of-pocket health expenditures, which remain relatively high in Turkey, generate more demand for primary and specialist care.

Healthcare systems around the world are facing mounting pressure as populations age, medical needs rise and the demand for skilled physicians continues to accelerate. Many countries are confronting widening gaps between the number of doctors available and the number of patients seeking care, raising concerns about long-term workforce sustainability. New forecasting research shows that this challenge is becoming increasingly urgent, especially in regions experiencing rapid demographic and economic shifts.

A recent machine learning analysis focusing on Turkey provides one of the clearest warnings yet. The study, “Workforce Forecasting with Machine Learning for Healthcare Management,” applies advanced predictive models to forecast both the supply and demand for physicians through 2030. Using decades of OECD health, demographic and economic indicators, the paper demonstrates how artificial intelligence can expose long-term vulnerabilities that traditional forecasting methods often overlook.

Machine learning models reveal clear shortfall in physician supply

Physician training requires long, high-cost investment, meaning that the labor supply is highly inelastic. Even when demand spikes, medical schools cannot rapidly increase output, nor can newly trained physicians enter the workforce quickly enough to close the gap. This reality makes forward-looking, data-driven forecasting essential.

The study covers Turkey’s NACE Q sector, which includes human health and social services and accounts for about 5.5 percent of the national workforce. Despite its importance, the sector faces increasing pressure from aging demographics, rising healthcare consumption, expanding health tourism and changing economic conditions. Combined, these trends are expected to strain the physician workforce in ways that past planning frameworks have not fully accounted for.

To evaluate future labor dynamics, the authors implemented machine learning models such as XGBoost, Random Forest, LSTM, LightGBM and Prophet. These algorithms processed historical data on physician numbers, medical school graduates, population age distribution, GDP per capita, out-of-pocket healthcare spending and physician migration patterns. Because annual health data are limited in size, the researchers also used the Synthetic Data Vault (SDV) to generate statistically validated synthetic datasets and applied distribution-similarity tests to ensure reliability.

Forecasts from the real-data models indicate that Turkey will reach 3.04 physicians per 1,000 inhabitants by 2030, while medical demand is projected to require 3.12 physicians per 1,000, a measurable shortfall. Models trained on synthetic data show an even wider gap, predicting 2.03 physicians per 1,000 in supply versus 2.86 per 1,000 in demand. These parallel results from both real and synthetic datasets reinforce the study’s central conclusion: shortages are unavoidable under current conditions.

Machine learning’s ability to incorporate nonlinear patterns allows the models to capture dynamics that traditional linear forecasting often misses. Rising life expectancy, increased chronic disease prevalence, health-tourism growth and shifts in socioeconomic conditions all contribute to rising demand. On the supply side, long training durations, limited foreign physician participation and significant physician emigration create downward pressure.

The study positions machine learning not only as a forecasting tool but as a diagnostic instrument that reveals structural vulnerabilities in Turkey’s healthcare labor pipeline.

Migration, aging and health tourism are accelerating physician demand

An aging population is one of the largest contributors. As Turkey’s elderly population grows, so does the incidence of chronic conditions requiring long-term medical care. This shift alone increases demand for physicians, particularly in internal medicine, geriatrics, cardiology and chronic-disease specialties.

At the same time, Turkey’s role as a destination for health tourism has expanded significantly in recent years. Competitive pricing, well-equipped facilities and strong international interest in elective procedures have increased patient inflows. Although beneficial for economic growth, this surge adds further strain to the domestic medical workforce.

Economic variables also play a central role. Rising GDP per capita increases healthcare utilization rates as households are better able to access medical services. Additionally, out-of-pocket health expenditures, which remain relatively high in Turkey, generate more demand for primary and specialist care.

In view of this, physician supply has not grown at a pace sufficient to match these pressures. The study highlights that physician emigration from Turkey rose by 87 percent between 2014 and 2019, driven by wage disparities, workplace challenges and broader socioeconomic factors. While many OECD countries attract foreign doctors to mitigate shortages, Turkey’s share of foreign physicians remains low, creating a structural deficit.

The authors point up that health workforce planning becomes particularly challenging under these conditions. Policymakers must anticipate long-term demographic and economic changes, not just respond to existing gaps. Machine learning’s high sensitivity to emerging patterns provides a clearer understanding of how multiple forces collectively shape future physician requirements.

Synthetic data strengthens forecast accuracy and reveals deeper risks

The study leverages synthetic data to overcome small-sample constraints, a common problem in healthcare labor forecasting. Annual physician statistics create datasets with limited observations, restricting model accuracy. By generating synthetic datasets using SDV, the researchers expanded the available data while preserving statistical properties.

The synthetic data were validated using Kolmogorov–Smirnov scores, distribution overlays and correlation comparisons to ensure that they reflected real-world trends. The similarity between real-data and synthetic-data forecasts strengthens confidence in the projections.

Importantly, the synthetic datasets revealed an even more pronounced shortage of physicians by 2030. This finding suggests that Turkey’s real shortage may be deeper than what historical data alone indicate. Synthetic data capture rare but plausible scenarios, such as accelerated emigration, slower medical-school expansion or higher-than-expected aging rates.

The models also expose sector-specific vulnerabilities. For example, GDP-driven demand for healthcare services tends to rise faster than population-based estimates, meaning economic growth alone can create disproportionate stress on physicians. Meanwhile, supply variables like medical school capacity and net migration operate at slower, less flexible rates.

The authors note that Turkey remains behind many OECD countries in the physician-to-population ratio. Even if current policies succeed in increasing medical graduates, demographic and economic forces may still produce sustained shortages unless broader reforms are enacted.

Implications for health Ppolicy, medical education and workforce strategy

Immediate and strategic intervention is necessary to prevent physician shortages from weakening the Turkish healthcare system by the end of the decade. One recommendation is to expand medical school capacity, which would increase long-term physician supply. However, this solution requires balancing educational quality, training resources and faculty availability.

Improving physician salaries and working conditions is another key priority, especially as outward migration continues to erode the domestic workforce. Retention efforts must address both economic incentives and non-economic factors, including workplace environment, workload pressures and career development opportunities.

Policymakers may also need to revise regulations governing foreign medical professionals. Compared with many OECD nations, Turkey’s utilization of foreign physicians is low. Streamlined recognition processes, targeted recruitment and integration support could help mitigate short-term shortages.

From a strategic management perspective, the paper argues that machine learning should be integrated into national workforce planning frameworks. Traditional forecasting tools may underestimate future gaps, whereas AI-driven models can identify nonlinear interactions between demographic change, economic expansion and workforce mobility.

The study also highlights opportunities for sub-specialty forecasting. Certain medical fields, such as oncology, neurology, geriatrics and emergency medicine, may experience shortages at different rates. Machine learning models can be adapted to specialty-specific datasets once sufficient data are available.

Furthermore, the research underscores the growing importance of health-sector resilience. The COVID-19 pandemic exposed vulnerabilities in global health workforces, and Turkey is no exception. Future crises will require a robust physician pipeline, flexible staffing strategies and advanced predictive monitoring to ensure rapid response capacity.