A professor of food, pharma, and healthcare business at Saint Joseph’s University’s Erivan K. Haub School of Business.

Thani Jambulingam, PhD

AI enhances traditional HTA by improving accuracy and efficiency.

 Artificial intelligence addresses key limitations of traditional health technology assessments—such as data gaps and time-intensive processes—through automation, real-time data integration, and advanced predictive modeling, leading to faster and more informed decisions.

AI supports personalized medicine and real-world evidence integration.

 By analyzing large datasets from EHRs, patient registries, and genomics, AI improves the evaluation of individualized treatments and helps generate robust RWE to guide value-based pricing and reimbursement decisions.

Challenges remain in AI adoption for HTA.

 Despite its potential, AI faces limitations such as data privacy barriers, algorithm bias, interpretability issues, and the lack of regulatory standards—factors that must be addressed to ensure responsible and equitable implementation in healthcare policy.

Health technology assessment (HTA) for pharmaceuticals is a systematic evaluation of the clinical, economic, and social implications of a drug. It plays a crucial role in informing pricing, reimbursement, formulary decisions, and policy decisions. However, traditional HTA methods have several limitations, including data gaps, time-intensive processes, and challenges in evaluating personalized medicine. Artificial intelligence (AI) presents a transformative opportunity to address these challenges and enhance HTA methodologies while ensuring more informed formulary decisions and efficient allocation of healthcare resources.

In this article, we’ll explore the limitations of traditional HTA; the ways AI can enhance HTA methodologies; AI applications in HTA; its integration with existing frameworks; its role in value-based pricing and real-world evidence (RWE) generation; and the limitations of AI in HTA.

Limitations of traditional HTA for pharmaceuticals

Despite its importance, HTA faces several constraints. Many evaluations rely on clinical trial data, which may not reflect real-world effectiveness. Additionally, long-term safety and efficacy data are often unavailable at the time of assessment. The process itself is highly time-intensive—sometimes taking months or even years—leading to delays in patient access to innovative treatments. Traditional HTA models are also built on fixed assumptions, making it difficult to adapt to new data or individual patient variability. Furthermore, the assessment of personalized medicine—particularly gene therapies—is challenging due to small patient populations and individualized treatment effects. Economic biases often arise because cost-effectiveness thresholds may undervalue treatments for rare diseases or those with long-term benefits.

Many HTA models also do not fully incorporate real-world patient outcomes, limiting predictive accuracy. Additionally, the variability in HTA methodologies and cost-effectiveness thresholds across different countries creates inconsistencies in assessments. Another significant limitation is the lack of real-time adaptability in formulary decision-making, often leading to delays in drug accessibility and inefficient allocation of resources. Furthermore, when a comparator treatment is available, HTA often lacks head-to-head trial data, making comparative effectiveness assessments more uncertain and leading to reliance on indirect or network meta-analyses, which may introduce additional biases and uncertainties.

AI offers solutions to these limitations through advanced predictive modeling, automation, and real-time data integration. It enhances predictive modeling by simulating long-term drug outcomes using real-world data (RWD), thereby improving the accuracy of effectiveness and cost-effectiveness predictions. By automating data extraction, synthesis, and modeling, AI significantly reduces HTA timelines, allowing for faster decision-making. Machine learning models enable dynamic HTA models that continuously update based on new clinical data, RWE, and evolving healthcare costs.

It also helps boost the evaluation of personalized medicine by assessing treatment value on an individual patient basis, making HTA more relevant for precision medicine. Additionally, AI enables better utilization of RWD by analyzing large datasets from EHRs, wearables, and patient registries to improve predictive accuracy. AI-driven methodologies can refine cost-effectiveness models by incorporating indirect benefits, such as productivity gains and quality of life improvements.

With the help of AI-driven standardization, there is the potential to unify HTA methodologies across different countries, making assessments more comparable and scalable. AI also plays a pivotal role in formulary decision-making by dynamically assessing RWE, cost-effectiveness, and treatment outcomes to optimize drug inclusion in formularies, ensuring efficient resource utilization and cost-effective healthcare delivery.

Natural language processing (NLP) for literature reviews

HTA requires systematic reviews of clinical trials, RWE, and economic models. In order to automate and enhance these tasks, AI-driven NLP can assist. Utilizing automated systematic reviews allows AI to scan thousands of articles and can extract relevant data much faster than human reviewers; it can also analyze trial reports and summarize how a new drug compares to existing therapies. Sentiment analysis techniques can process social media, patient forums, and electronic health records (EHRs) to assess real-world drug experiences, providing valuable insights into patient-centered outcomes. Additionally, NLP can detect biases in literature by identifying inconsistencies or missing data in clinical studies that may affect HTA evaluations. AI also facilitates multilingual data processing, enabling comprehensive global HTA assessments by translating and analyzing research published in different languages.

Survival analysis is critical in HTA for predicting long-term drug benefits, particularly in oncology, chronic diseases, and rare conditions. AI enhances survival analysis by better handling time-dependent data through advanced techniques such as recurrent neural networks and long short-term memory networks. These models can accurately model changes in patient survival probabilities over time. AI improves patient outcome predictions by combining clinical trial data with RWE, including EHR and genomics. Adaptive models refine predictions as new therapies enter the market, reducing uncertainty in long-term cost-effectiveness. Additionally, deep learning techniques enable personalized risk prediction by assessing individual patient factors such as genetics, biomarkers, and lifestyle to determine who will benefit most from a treatment.

HTA frameworks vary by country, but AI can enhance decision-making for agencies such as NICE (UK), ICER (US), and EUnetHTA (EU). In the UK, AI-driven cost-effectiveness models can refine quality-adjusted life year (QALY) estimates by integrating NHS patient data. NLP can automate literature reviews, reducing assessment delays, while AI-powered evidence synthesis improves predictions of long-term treatment effects. In the US, AI enhances budget impact modeling by integrating RWD from claims databases and EHRs. Deep learning refines long-term drug benefit projections for chronic and rare diseases, and AI can dynamically adjust pricing models based on real-world cost-effectiveness data.

In the EU, AI aids in standardizing cost-effectiveness analyses across different health systems. NLP synthesizes data from multiple EU clinical trials to accelerate drug approvals, and AI facilitates real-time monitoring of drug performance across EU nations. Additionally, AI-driven formulary decisions in these regions help balance cost constraints while ensuring that the most effective treatments remain accessible to patients.

AI in value-based pricing and RWE generation

AI has the potential to transform value-based pricing (VBP) by leveraging machine learning for dynamic pricing models that adjust drug prices based on real-world patient outcomes. It can even support predictive risk-sharing agreements by identifying patient subgroups most likely to benefit, thus refining risk-sharing contracts. NLP can analyze global HTA reports and generate customized VBP recommendations tailored to different healthcare systems. AI enables personalized value assessments by segmenting patient populations and assigning individualized value scores to drugs, ensuring fair pricing models that align with patient needs and treatment efficacy.

AI plays a significant role in RWE generation by automating data extraction from EHRs and insurance claims to provide robust insights into drug performance. Deep learning enhances predictive analytics by detecting early safety signals and unexpected adverse effects from RWD, thereby improving drug safety assessments.

AI can also facilitate real-world treatment comparisons by analyzing patient cohorts to determine which drug or therapy works best under practical conditions. Along with that, AI-powered real-time monitoring methods continuously update drug effectiveness data, allowing for dynamic reimbursement adjustments and ensuring optimal treatment access for patients.

Despite its advantages, AI in HTA has several limitations, including that AI models require large, high-quality datasets, which are often fragmented or inaccessible due to privacy regulations. Additionally, AI-driven predictions can be biased if trained on non-representative or incomplete datasets. The black-box nature of some AI algorithms makes it difficult for HTA stakeholders to interpret results, reducing trust in AI-driven assessments. Regulatory bodies have yet to establish standardized frameworks for AI integration in HTA, leading to variability in adoption. It’s possible that AI may not fully capture qualitative aspects of patient experiences, ethical considerations, and social value, which are crucial in HTA decision-making.

Overall, AI is revolutionizing HTA by enhancing predictive accuracy, accelerating decision-making, and improving the integration of RWE. However, addressing AI's limitations, including data accessibility, interpretability, and ethical concerns, will be essential for its widespread adoption. As AI continues to evolve, it has the potential to transform formulary decisions and healthcare policy, making HTA more responsive, equitable, and efficient.

Thani Jambulingam, PhD, is a professor of food, pharma, and healthcare business at Saint Joseph’s University’s Erivan K. Haub School of Business.

Articles

Ways to address limitations while also enhancing the decision-making process.


Bolstering Health Technology Assessment With the Help of AI

The pharmaceutical manufacturing sector is experiencing a profound transformation driven by technological advancements, regulatory shifts, and market dynamics. Industry leaders are facing both unprecedented challenges and opportunities, making it essential to adopt new strategies to stay competitive. Innovations in manufacturing technologies, coupled with growing demands for personalized medicine and sustainability, are reshaping the landscape.

Moreover, changes in global policies, such as proposed pharmaceutical tariffs, could significantly impact production and pricing strategies. This article explores key trends in pharmaceutical manufacturing, providing industry leaders with insights on how to navigate the evolving landscape effectively.

Advancements in manufacturing technologies

One of the most significant trends in pharmaceutical manufacturing is the shift from traditional batch processing to continuous manufacturing. Continuous manufacturing enables real-time monitoring, enhances efficiency, reduces production times, and ensures better product consistency. Unlike batch processing, which involves discrete production steps that can introduce inefficiencies, continuous manufacturing streamlines production, reduces waste, and maintains stringent quality control. Leading companies like Merck and Johnson & Johnson (J&J) have successfully adopted continuous manufacturing to accelerate production and ensure high-quality standards.

Despite the advantages of continuous manufacturing, its widespread adoption faces regulatory hurdles. Agencies like the FDA and the European Medicines Agency (EMA) have differing compliance requirements, complicating the standardization of production methods across regions. Many companies like Merck and J&J have made significant investments to gain approval for both batch and continuous manufacturing processes, yet they often rely on batch processes due to the lack of harmonization in regulations across countries. This inconsistency not only increases operational costs but also limits the potential benefits of continuous manufacturing, forcing companies to continue using more traditional batch process.

Another emerging innovation is the use of single-use systems in biologics manufacturing. These disposable systems reduce the risks of contamination, simplify production processes, and lower sterilization costs, making them particularly valuable in the production of biologics, vaccines, and personalized medicines. Looking ahead, the rise of smart factories, incorporating internet of things (IoT) devices, artificial intelligence (AI), and robotics, will further enhance predictive maintenance, automation, and quality control. AI-driven analytics will help optimize production, minimize downtime, and improve overall efficiency. Furthermore, 3D printing is poised to revolutionize the production of personalized medicines by enabling the on-demand creation of patient-specific treatments.

Addressing capacity constraints for high-demand therapies

Pharmaceutical manufacturers are also grappling with capacity constraints, particularly in response to the growing demand for high-demand therapies such as gene therapies, glucagon-like peptide-1 (GLP-1) agonists, and personalized medicines like CAR-T cell therapy. These therapies often require specialized production processes and facilities that are both resource-intensive and costly to scale.

A prominent example is the demand for GLP-1 drugs, such as semaglutide, which have shown significant benefits in treating obesity and diabetes. The demand for these therapies has surged as they have demonstrated promising efficacy in managing weight and blood sugar levels. However, the manufacturing of these drugs is complex, requiring specialized facilities and processes to ensure quality and safety. As a result, many manufacturers are struggling to keep up with the growing demand, and in some cases, production constraints are leading to shortages.

To address these capacity challenges, pharmaceutical companies are investing in modular production units. These flexible units can be rapidly deployed and scaled to meet fluctuating demand without the need for lengthy and expensive facility construction. Modular production systems offer manufacturers the agility needed to adapt to market changes while ensuring efficiency and compliance. Additionally, many companies are expanding partnerships with contract manufacturing organizations (CMOs) to increase production capacity. However, reliance on CMOs introduces risks such as supply chain bottlenecks and potential quality control issues. Careful evaluation of these partnerships is necessary to ensure stability, regulatory compliance, and product quality.

Navigating the impact of pharmaceutical tariffs

The imposition of tariffs on pharmaceutical imports, particularly from India—the largest exporter of generic drugs to the United States—presents a significant challenge for pharmaceutical manufacturers. India supplies $8.7 billion worth of pharmaceuticals to the US annually, accounting for nearly 31% of its pharmaceutical exports, according to the Pharmaceuticals Export Promotion Council of India (Pharmexcil).

Proposed tariffs, such as a 25% tariff on Indian pharmaceutical imports, could significantly increase the cost of generic drugs in the U.S. and undermine their affordability.

The Association for Accessible Medicines (AAM) notes that generic medicines saved the US healthcare system approximately $408 billion in 2022 alone, and these savings would be jeopardized by tariffs. As the cost of medications rises, patients may experience reduced access to vital treatments, and healthcare costs could increase across the board. In light of these potential disruptions, pharmaceutical companies must consider diversifying their supply chains, exploring alternatives like 

 or expanding domestic production capabilities. Although reshoring offers supply chain resilience, it requires considerable capital investment and regulatory coordination.

Pharmaceutical leaders should also explore ways to balance cost-effective sourcing with maintaining affordable drug pricing. The imposition of tariffs could also divert financial resources away from research and development, potentially stalling innovation in critical therapeutic areas. As industry leaders navigate these complexities, they must strike a balance between cost control and long-term sustainability.

Sustainability as a competitive imperative

Sustainability has become a major focus in the pharmaceutical industry, driven by regulatory pressures, investor demands, and growing public awareness of environmental issues. Pharmaceutical manufacturers are increasingly adopting green chemistry principles, optimizing energy consumption, reducing waste, and shifting toward more sustainable practices. Leading companies, such as Novartis, have committed to carbon neutrality and set ambitious sustainability goals, accelerating their transition to renewable energy sources like solar and wind power.

The pharmaceutical sector is also innovating in solvent recovery and waste minimization processes to reduce environmental impact. Additionally, manufacturers are prioritizing eco-friendly raw material sourcing and biodegradable packaging to align with global sustainability standards. These efforts not only help companies reduce their environmental footprint but also create new business opportunities and enhance their reputation among consumers, regulators, and investors.

In this context, sustainability is not just about reducing environmental impact—it has also become a key differentiator in a competitive market. As consumers and investors increasingly prioritize sustainability, pharmaceutical manufacturers that embrace eco-friendly practices will be better positioned to lead the industry in the coming years.

Personalized medicine and small-batch production strategies

The rise of personalized medicine is fundamentally reshaping pharmaceutical manufacturing. Therapies like CAR-T cell treatment for cancer are tailored to individual patients, requiring flexible, small-batch production systems. These therapies are typically produced on a patient-by-patient basis, which calls for highly specialized production processes that can accommodate these unique needs.

Image Credit: Adobe Stock Images/Zaleman.com

To improve efficiency and scalability, companies are leveraging digital twin technology, which uses virtual simulations of manufacturing processes to optimize workflows, reduce inefficiencies, and predict potential bottlenecks. Digital twins allow manufacturers to test and refine processes before implementation, saving time and resources while improving production outcomes. Additionally, many companies are exploring ways to integrate biologics and small-molecule production capabilities into a single facility to streamline production and better serve diverse therapeutic pipelines.

The adoption of adaptive manufacturing strategies will be crucial to ensuring that personalized therapies can be scaled without sacrificing accessibility or patient-specific care. By building production flexibility into their operations, pharmaceutical companies can continue to meet the rising demand for personalized treatments.

Regulatory harmonization and policy adaptation

Pharmaceutical manufacturers operate in a complex, fragmented global regulatory environment. Different countries and regions impose varying compliance requirements, approval timelines, and post-market surveillance regulations, creating challenges for companies that operate internationally. To navigate this complexity, pharmaceutical leaders must engage proactively with regulatory bodies and work toward the harmonization of global regulatory standards.

Regulatory harmonization is crucial to reducing operational complexity and ensuring smoother market access across regions. As the global regulatory landscape continues to evolve, pharmaceutical companies must remain agile and responsive to changing policies to maintain compliance and stay competitive.

Digital transformation and data-driven decision making

The integration of digital technologies is transforming pharmaceutical manufacturing, making operations more efficient, cost-effective, and innovative. Advanced analytics, AI-powered quality control, and blockchain for supply chain transparency are enabling pharmaceutical companies to optimize their processes. Predictive analytics, for example, help manufacturers anticipate fluctuations in demand, optimize inventory management, and prevent production disruptions.

Real-time data sharing across global supply chains enhances collaboration, streamlines regulatory compliance, and allows for better decision-making. By embracing digital transformation, pharmaceutical companies can improve productivity, reduce operational costs, and make more informed, data-driven decisions that ultimately lead to better patient outcomes.

Pharmaceutical manufacturing is undergoing a significant transformation, shaped by technological advancements, evolving regulatory policies, and increasing demand for personalized treatments. To stay competitive, industry leaders must embrace emerging technologies, optimize production systems, and adapt to shifting market conditions. By investing in innovation, sustainability, and digital transformation, pharmaceutical manufacturers can enhance operational efficiency, improve patient access to life-saving therapies, and position themselves for long-term success in an increasingly complex global market.

Thani Jambulingam, PhD, is a professor of food, pharma & healthcare business at Saint Joseph’s University’s Erivan K. Haub School of Business.

This link in the pharma supply chain is undergoing a major transformation propelled by technological advancements, regulatory changes, and evolving market dynamics, requiring industry leaders to adopt innovative strategies in order to remain competitive.

Strategic Trends in Pharmaceutical Manufacturing for Industry Leaders

The emergence of glucagon-like peptide-1 (GLP-1) drugs such as liraglutide (Saxenda), semaglutide (Ozempic, Wegovy), and tirzepatide (Mounjaro, Zepbound) has transformed the management of type 2 diabetes and obesity. These drugs are primarily administered via pre-filled injectable pens, offering patients convenience. However, the growing use of these pens introduces significant challenges concerning their disposal. Classified as medical waste, these pens raise environmental, health, and regulatory concerns.

Addressing these issues is essential to ensuring that the advantages of GLP-1 medications are not overshadowed by the unintended consequences of improper waste disposal. This article examines the challenges associated with the disposal of these drug pens and presents potential solutions to mitigate the associated risks.

Challenges in disposing of GLP-1 drug pens

One of the primary issues with GLP-1 drug pens is the presence of built-in needles, which are classified as "sharps." Sharps are considered hazardous waste because they pose a risk of injury or infection. Improper disposal of used pens can result in accidental needle sticks, exposing waste handlers, healthcare workers, and even the public to potential biohazards, including bloodborne pathogens. This risk is exacerbated when these pens are discarded in regular household trash instead of in designated sharps disposal containers. Given the increasing use of GLP-1 drugs for chronic conditions like diabetes and obesity, the volume of sharps waste generated is substantial, raising the potential for injuries if proper disposal protocols are not followed.

GLP-1 pens are single-use devices made from a combination of plastic, metal, and residual medication. These materials can negatively impact the environment if not disposed of properly. Non-biodegradable plastics contribute to the growing problem of plastic waste in landfills, while residual medication can seep into soil and water sources, contaminating ecosystems and harming wildlife. As the number of patients using GLP-1 drugs grows, millions of these pens are discarded each year, amplifying their environmental impact.

Moreover, the disposal of GLP-1 pens is subject to various regulations, which differ across regions. In many areas, these pens are classified as medical waste, requiring disposal in compliance with specific guidelines. Unfortunately, not all patients are aware of these regulations or have access to appropriate disposal facilities. Consequently, many pens are discarded in regular household waste or recycling bins, which is not only unsafe but also illegal in some regions. The lack of consistent regulations across countries and states makes it difficult for pharmaceutical companies to establish uniform disposal protocols.

For patients, the proper disposal of GLP-1 pens can be both inconvenient and costly. Sharps disposal containers, essential for safe disposal, are often not provided for free. Patients may need to purchase these containers or pay for specialized disposal services. In areas where needle drop-off sites or take-back programs are unavailable, patients may have to travel considerable distances to safely dispose of their pens. This inconvenience often leads to noncompliance with disposal guidelines, prompting some patients to discard pens in regular trash due to a lack of accessible alternatives.

One of the most effective ways to address the issue of sharps waste is by expanding the availability of sharps disposal programs. Pharmacies and healthcare providers can distribute sharps containers to patients using GLP-1 pens and educate them about the importance of proper disposal. Local governments and healthcare institutions can also increase the number of needle drop-off sites and mobile collection units, making it more convenient for patients to safely dispose of their pens. Collaborating with pharmaceutical companies to offer free or subsidized sharps containers could also boost compliance rates.

A more sustainable solution involves the implementation of pen take-back programs. Similar to existing programs for expired medications, these initiatives would allow patients to return their used pens to pharmacies or designated collection centers for safe disposal. While some pharmaceutical companies already offer such programs, expanding them globally would provide patients with a convenient, cost-free way to dispose of their pens. Governments could further encourage the establishment of take-back programs by offering tax incentives or subsidies to pharmacies and healthcare providers who participate.

In the long term, transitioning toward reusable injection pens offers a promising solution. In this model, the pen would be reusable, with only the medication cartridge needing replacement. This shift would significantly reduce the amount of plastic and sharps waste generated by each patient. While reusable injection pens already exist for other medications, pharmaceutical companies could develop GLP-1 formulations compatible with this technology. Though the initial cost of reusable pens may be higher, the long-term savings in waste management and environmental impact would be substantial.

The pharmaceutical industry should also explore the possibility of using biodegradable or recyclable materials in the production of GLP-1 pens. Currently, the combination of plastic and metal components makes recycling difficult. However, advancements in biodegradable plastics and eco-friendly materials could provide a solution. Some companies are already researching plant-based or compostable plastics for medical devices, and applying this technology to GLP-1 pens could significantly reduce their environmental footprint.

Educating patients about the importance of proper disposal practices is also crucial to mitigating the risks associated with GLP-1 pens. Healthcare providers, pharmacies, and pharmaceutical companies should include clear instructions on safe disposal with every prescription. Public awareness campaigns, both online and in healthcare settings, can emphasize the risks of improper disposal and inform patients about available resources, such as sharps containers and take-back programs.

The disposal of GLP-1 drug pens presents significant challenges due to the risks posed by sharps, environmental pollution, regulatory inconsistencies, and patient inconvenience. However, these challenges can be addressed by implementing comprehensive sharps disposal programs, promoting take-back initiatives, encouraging the use of reusable pens, developing eco-friendly materials, and raising public awareness. Pharmaceutical companies, healthcare providers, and governments must collaborate to ensure that the growing use of GLP-1 drugs does not result in unintended environmental and public health consequences. By adopting these measures, the medical community can continue to reap the benefits of GLP-1 medications while ensuring responsible waste management.

With great power comes great responsibility. In this case, tackling these obstacles is vital to making sure that the positives of GLP-1s are not outweighed by the downfalls of improper waste disposal.