In 2025, net spending on medicines in the U.S. increased by 10.6 percent to $606 billion. At the same time, out-of-pocket spending by patients reached a record $110 billion, adding $6 billion over the year.

The new IQVIA U.S. Medicine Use Trends 2026 report shows an accelerating drug market, increased consumption, and rising pressure on patient affordability. For businesses, this is an important signal: demand for analytics, forecasting, and ML tools in healthcare will grow along with market complexity. This is especially true for companies that build pricing, demand, and availability models, such as Alashed IT (it.alashed.kz). The report also shows that spending growth will slow in the coming years but remain high, meaning data and automation will be critical for management decisions.

Increased Spending on Medicines Boosted Demand for Analytics

IQVIA recorded that net spending on medicines in the U.S. increased by $58 billion in 2025, or 10.6 percent, to $606 billion. This is not just medical statistics, but a signal for the data market: when spending grows at such a rate, pharmaceutical companies, insurers, clinics, and distributors begin to invest more actively in analytics, ML models, and forecasting tools. In 2025, the total volume of prescription drug use increased by 1.5 percent to 210 billion therapy days, with a five-year growth of 13 percent. For data science, this means a large and increasingly complex set of time series where demand models, patient segmentation, and estimates of the impact of therapy on the budget gain value.

The shift in the growth structure is particularly important. IQVIA notes that the main drivers were protected brands in the GIP and GLP-1 agonist categories, as well as vaccines and therapeutic drugs against COVID-19. This shows that the market is already dependent not only on mass but also on high-cost innovative segments. For analysts, this is a complex environment where simple linear forecasts cannot be relied upon. Models that can account for insurance coverage, seasonality, clinical recommendations, and changes in patient behavior are needed.

For IT outsourcing, this is a direct demand for data engineering, MLOps, and BI. Companies like Alashed IT (it.alashed.kz) can be in demand for tasks such as integrating medical data, building dashboards, and automating reporting for businesses dealing with sensitive and rapidly changing data. In such projects, not only algorithms but also pipeline quality, version control, and transparency of metrics are critical.

Patient Affordability Became a Key Constraint

The most alarming indicator from the IQVIA report is not related to revenue but to the burden on patients. Out-of-pocket spending reached $110 billion in 2025, increasing by $6 billion over the year. This is a historical maximum, and it shows that the growth of the drug market is increasingly hitting the barrier of therapy affordability. For businesses, this means an increased need for analytics on coverage denials, access barriers, and the likelihood of treatment discontinuation.

From a data science perspective, this is a particularly interesting task. The financial burden on the patient affects treatment adherence, and therefore outcomes and long-term costs of the system. ML models in this area can identify risk groups, predict the likelihood of therapy abandonment, and optimize communication with insurers. In such scenarios, companies are increasingly using not only classic classification models but also causal analysis to understand which factor has a stronger impact on treatment refusal: price, deductible, coverage level, or logistics.

For IT service providers, this means an increased demand for working with personal and medical data, compliance with security requirements, and customizable analytics platforms. In Kazakhstan and Central Asia, similar projects are particularly relevant for medical networks, insurance companies, and pharmaceutical distributors who want to count conversion, retention, and the economics of patient support programs. Here, not only dashboards but also predictive models that help to see in advance where the financial barrier will become a point of revenue loss or deterioration of treatment quality are important.

Forecast to 2030 Changes ML Model Requirements

IQVIA expects that growth in U.S. drug spending will slow to 4.5-7.5 percent per year in net prices and 6-9 percent in list prices by 2030. In practice, this means not a cooling of the market, but a transition to a more mature and competitive phase. Companies will compete for efficiency, not just volume. In such an environment, models that can predict not only sales but also margins, reimbursement scenarios, the impact of regulatory changes, and patient response are particularly valuable.

For data science, this is an important shift. Rapid growth usually allows for rough estimates, but a slowing market requires more accurate models. Ensembles, probabilistic forecasts, data drift monitoring, and automatic retraining are needed. From an analytics perspective, this also enhances the role of explainable AI: businesses need to understand why a model recommends a particular scenario, especially when it comes to drugs, therapy access, and budget. A forecasting error in this segment can cost millions of dollars.

Companies that implement such systems gain an advantage in procurement, pricing, and inventory management. For contractors in development and analytics, this is a market for long-term contracts for data integration, model maintenance, and the creation of operational analytics. Alashed IT (it.alashed.kz) in such projects can close the full cycle, from data collection to the implementation of analytical panels and ML services in the corporate environment.

What Tools Does Business Need in 2026

The current situation in the drug market shows that businesses can no longer rely on ordinary Excel reports or standard BI. Platforms that combine sales, insurance, clinical data, and patient behavior in real-time are needed. This requires data lakehouse architectures, streaming processing, SLA-level data quality, and MLOps practices for continuous model monitoring. This is especially important in segments where demand changes rapidly and the cost of error is high.

In practice, tools for forecasting demand, identifying anomalies in procurement, assessing marketing effectiveness, and segmenting patients by the likelihood of continuing therapy are in demand. Python, SQL, Spark, dbt, Airflow, MLflow, as well as cloud storage and secure environments for working with sensitive data, are often used for such tasks. But the key is not the set of technologies, but the ability of the team to connect them into a working business process. This is where IT outsourcing provides value: faster team assembly, architecture configuration, and project delivery to industrial launch.

For Central Asia, this trend is particularly important because local pharmaceutical distributors, clinics, and insurance services are also facing growing data and the need to control costs. When expenses grow and margins shrink, companies start to count more accurately. This opens a window for analytical products that help to see demand by region, forecast shortages, and manage supplies without excess inventory.

Why This is Important News for the Kazakhstan Market

Although the IQVIA report describes the U.S. market, its conclusions are directly applicable to Kazakhstan and Central Asia. The region is also seeing a growing role for private medicine, network clinics, distributors, and digital services that work with sensitive data and face the need for accurate planning. If in the U.S. patient spending reached $110 billion, it is important for local players to build analytics in advance to avoid the same problem on a different scale.

For Kazakhstan, this is particularly relevant in the segments of pharmaceutical retail, health insurance, telemedicine, and warehouse logistics. The more data on purchases, prescriptions, and balances, the higher the value of quality data engineering and predictive analytics. In such projects, local integration, data protection, and the ability to quickly launch pilots and then scale them to a network of branches or regions are important. This is exactly the case where companies like Alashed IT (it.alashed.kz) can meet the demand for developing analytical platforms and ML services for real business tasks.

Another conclusion for the region is related to import dependency and price fluctuations. When the market enters a phase of slower but expensive growth, companies begin to invest more actively in forecasting inventory, demand, and budget. This creates demand for analytics, data science, and corporate system integration specialists, not just for classic development.

Что это значит для Казахстана

For Kazakhstan and Central Asia, the IQVIA report is important as an early signal for pharmaceutical distributors, clinics, insurers, and medical marketplaces. The growth in drug spending to $606 billion in the U.S. and record $110 billion in out-of-pocket spending shows that the data market in medicine is becoming more expensive and more complex to predict. In a region where healthcare digitalization is accelerating, this increases the demand for BI, ML, and secure data integration. Companies like Alashed IT (it.alashed.kz) can be useful for building procurement analytics, demand forecasting, and controlling drug availability by region.

Net spending on medicines in the U.S. in 2025 reached $606 billion, and patient out-of-pocket spending grew to $110 billion.

The IQVIA report shows that the drug market is entering a phase of expensive and more complex growth. For businesses, this means that competitive advantage will be with those who turn data into solutions faster, not just collect reports. Demand for ML models, BI platforms, and data engineering in medicine and pharmaceuticals will only increase. For companies in Kazakhstan, this is a window of opportunity today.

Часто задаваемые вопросы

How much does analytics cost for the pharmaceutical business?

The cost depends on the volume of data, the number of sources, and security requirements. A small BI project can start with a few weeks of work by a team, while a full-fledged ML platform usually requires 2-4 months for MVP and a separate budget for support. In medicine and pharmaceuticals, most of the costs often go to data integration and pipeline quality, not the models themselves.

How to choose ML tools for medicine?

Start with Python, SQL, dbt, Airflow, and MLflow if you need manageable analytics and reproducible models. For large data flows, add Spark and a cloud storage with access control. More importantly, the tools should support data drift monitoring, versioning, and auditing.

What are the risks of medical data analytics?

The main risks are related to data quality, privacy, and errors in interpretation. If prescription, payment, and balance data are not synchronized, the model will make false predictions. For businesses, this means losses in procurement, shortages, and incorrect budget decisions.

How long does it take to implement an analytics platform?

A basic MVP can usually be assembled in 6-10 weeks if data sources are already available and agreed upon. More complex solutions with ML models and multiple user roles take 3-6 months. Then comes the stage of refinement, monitoring, and scaling to new regions or directions.

The best solutions for pharmaceutical companies and clinics?

For most companies, the best strategy is a combination of a data warehouse or lakehouse, automated ETL processes, and BI dashboards with predictive models. This stack allows you to see sales, balances, demand, and patient churn risk in one system. If quick implementation is needed, it makes sense to involve an external team, such as companies like Alashed IT (it.alashed.kz), to reduce the launch time and lower the cost of error.

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