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Click the title above for a link to open the Pharmaceutical Commerce December 2024 issue in an interactive PDF format.

Pharmaceutical Commerce - December 2024 Issue (PDF)

In the blink of an eye, 2024 has come to a close. Or just about. The December issue of 

 represents the final installment of the year, and quite frankly, the editorial team could not have picked topics that were any timelier, beginning with the latest advances in drug traceability.

This niche topic has been headlined routinely by the Drug Supply Chain Security Act (DSCSA), a piece of legislation that was signed into law over a decade ago as a component of the Drug Quality Security Act. It’s intended to provide the industry with a blueprint for safely tracking pharmaceuticals as they move through the supply chain. In his 

, Editor Emeritus Nicholas Basta details the journey that industry has taken in complying with new mandates in the DSCSA on tracking drugs at the package level via an electronic, interoperable system.

Basta's report is a timely one, as the FDA again recently pushed back the enforcement deadline. He notes that the agency has proposed a new schedule for when full compliance becomes mandatory.

In other coverage this month, EVERSANA’s Maria Kirsch 

 the top barriers encountered by patient services programs today, including lack of information; long wait times; rising costs of therapies; socio-economic challenges; fragmented care coordination; and access to technology. Concerning high drug costs, she cites an honest fact that many patients may not want to state aloud: the spikes in price place such a large financial burden on patients—especially those with chronic conditions or those in need of specialty meds—that many of them are forced to seriously consider whether they can afford to continue treatment.

Meanwhile, speaking of the patient journey, Sonam Dubey and Vishal Singal of Beghou Consulting note in a 

 that when traditional data sources, such as claims and diagnostic and procedure codes, are combined with advanced analytics, the end result is a resource that can help lower diagnosis times, understand the “why” of patient behaviors and decisions, and identify patient cohorts for enhanced patient engagement and interventions.

With the popular “Who Moved My Prescription?” now in the rear-view, Bill Roth of IntegriChain welcomes a 

 on “Popping the Gross-to-Net Bubble" in healthcare. The term refers to the widening gap between the gross revenue associated with a product (total sales at list price) and the net revenue (the amount received by the manufacturer after discounts, rebates, fees, and other adjustments).

We welcome your feedback on the coverage highlighted and the rest of our December slate. As always, thanks for reading.

— Mike Hennessy Jr., President and CEO, MJH Life Sciences

Our December analysis of the twists and turns in the Drug Supply Chain Security Act is a timely one, as the FDA recently again pushed back the    deadline for complying with new mandates on tracking and tracing drugs at the package level.

Traceability's Latest Curve 

As I pondered what the angle of this month’s editor's column could possibly be, a thought suddenly crossed my mind: Does RFID technology truly garner the attention it deserves?

Short for radio frequency identification, this type of tech has been on the pharmaceutical scene since the early 2000s, with Pfizer reportedly being one of the early adopters, using tags to track Viagra (sildenafil). For those who may be unfamiliar, the tools in this area have the capability to remotely track and trace products using radio waves. Supporters of RFID note that readers can scan more tags simultaneously from a greater distance, without the need to approach the reader, unlike traditional barcode scanning.

RFID's history, value to the pharma industry, and potential use cases are starting to come to light—as noted by the esteemed panel of subject matter experts that participated in 

’s recent peer exchange on this exact topic (the discussion will be rolled out online in stages; view the first installment 

)—but various obstacles have stood in the way, including the price of adoption. RFID is also not required per the Drug Supply Chain Security Act, which we dig into in our cover feature this month.

However, companies around the industry are beginning to experiment with its capabilities. For example, BD (Becton, Dickinson and Company), a medical technology company, and ten23 health, a Swiss CDMO, are partnering to utilize this technology as a new way to track prefillable syringes, with the goal of augmenting and adding efficiencies to the manufacturing process. And Intelliguard, a provider of medication management and data-intelligence support, is collaborating with the wholesaler Cencora to boost compatibility between RFID-tagged drugs and RFID-based medication management systems. (I spoke to Rob Sobie, chief product officer of Intelliguard, about these topics on a recent 

Overall, it seems pursuits and advances around this technology are on the rise, slowly but surely. We’ll have to see how events transpire heading into the New Year. I wish you all a fantastic 2025.

Nicholas Saraceno is Pharmaceutical Commerce's Editor. He can be reached at nsaraceno@mjhlifesciences.com.

The value of this sometimes-unheralded technology to the pharma industry—as well as potential use cases—are starting to come to light.

An Appreciation for RFID

The commercial, financial, and operational teams that represent pharmaceutical manufacturers gathered at Washington, DC’s Mayflower Hotel in late October for the Access Insights Conference, which is cohosted by IntegriChain, Blue Fin Group, and Federal Compliance Solutions.

 was in attendance, both as a conference sponsor and a provider of editorial content. The event’s mission was to offer the latest strategies, insights, trends, and tactics for data-driven access and commercialization. There was a plethora of educational seminars devoted to those areas. A sample of the featured sessions included:

“Financial Headwinds and Tailwinds to Commercialization” (Keynote)

“Strategies for Using the New Federally Mandated Price Transparency Data in Pharma”

“Healthcare Policy Interpretation: Mapping Out the Third, Fourth, and Fifth Moves on the Chessboard”

“Playing the Chess Endgame–Healthcare Policies Targeting Drug Commercialization”

Although all were impactful, I felt that the keynote—being that it was the very first session—was successful in setting the tone for the rest of the event for a multitude of reasons. Bill Roth, general manager and managing partner of IntegriChain’s consulting business, which includes Blue Fin Group, delivered the keynote. He is also a regular 

. He discussed the frontline trends today impacting healthcare business; described how he believes the gross-to-net bubble will change market access and channels; noted the ways to navigate specific archetypes and portfolios; and provided a summary and recommendation of next steps.

Roth's candor when kicking things off appeared to be appreciated by attendees.

“The information I’m going present to you guys this afternoon is not rosy. We’re not in a good spot as an industry,” he said. “There are lots of businesses that are in pain right now. ... We’re not here to sell anything. We’re not here to pitch anything. This is not about money for us. This is about our industry needing help, and I think if we recognize where we have pain and we come together as a community, we’ll be able to solve the problems that are in front of us.”

The editorial team also conducted on-site video interviews with multiple subject matter experts, including the aforementioned Roth; William Sarraille, a professor at the University of Maryland Francis King Carey School of Law; and Albert Thigpen, co-founder of Talentwise Consulting.

Access Insights will once again be held at the Mayflower Hotel next year, slated for Oct. 27-29, 2025.

For more on this year's event, and our conference coverage in general, including session write-ups and exclusive video interviews, please visit 

pharmaceuticalcommerce.com/latest-conference

Access Insights Conference delves into the latest obstacles,   while mapping out potential solutions to address them. 

Conference Connect: Tackling the Issues that Matter in the Access Space

Like a football game where the ball is fumbled at the goal line, the US pharmaceutical supply chain must wait yet again for a complete traceability solution to take effect. When the Drug Supply Chain Security Act (DSCSA) was passed in 2013, it set a 10-year process of industry preparation for tracking pharmaceuticals at the package level, from point of manufacture to point of dispensing. In 2023, the FDA allowed for a one-year “stabilization period” before enforcing the law. Now, a year later, it has again deferred enforcement, depending on the type of trading partner, for six months to two years.

Going forward, the FDA has proposed a schedule for exemption expiration (i.e., when compliance becomes mandatory):

Manufacturers and repackagers: May 27, 2025

Dispensers with 26 or more full-time employees: Nov. 27, 2025

Previously, dispensers with 25 or fewer employees were granted an exemption until Nov. 27, 2026.

Industry representatives generally supported the action, recognizing that a significant portion of the supply chain was still not ready. The position of the Healthcare Distribution Alliance (HDA), the trade group of wholesalers, was to “applaud” the decision, saying that “Given the interdependency of the pharmaceutical supply chain, FDA’s phased-in approach will allow supply chain partners to better align their data exchange processes to ultimately achieve full implementation.”

At the National Association of Chain Drug Stores (NACDS), the trade group “expresses appreciation to the FDA for making a sound decision,” while noting that its members were reporting receiving complete DSCSA data “for only about 25% to 50% of prescription drug products that dispensers receive.” Per DSCSA rules, “without compliant data and transactions, dispensers cannot accept ownership of the product, and the product either must be quarantined or rejected and sent back to the trading partner.”

But unofficially, there is some grumbling, including a belief that the string of deferrals will continue.

“The industry has taken massive, monumental steps to get to where we are today,” says Greg Cathcart, senior vice president at NNIT and president of Excelis Health Solutions, a longtime DSCSA specialist. “I’m disappointed that FDA couldn’t push this through and start slapping the bad guys.”

He is also suspicious that after the current postponements are expired, other delays could appear.

Another industry consultant, Scott Pugh, principal of Jennason LLC, is even more pessimistic.

“The [pharma supply chain participants] are not being forthright in why we continue to have delays,” he notes. “It won’t surprise me at all that there will be more delays in the future.”

The basic problem is that no one is asking why the interoperability of diverse systems, from a group of solution providers, is difficult to achieve.

“The EPCIS (Electronic Product Code Information Services) standard is robust, but different vendors are interpreting it different ways,” says Pugh, leading to incompatibilities that have to be resolved on a point-to-point basis.

EPCIS version 2.0, a standard developed by the GS1 organization for supply chain visibility, is used in a variety of industries besides pharma. Pugh’s comments hark back to the early days of pharmaceutical package “pedigrees,” and the belief that eventually, pharma supply chains would exchange information as smoothly as, say, international credit-card transactions by banks.

A more positive view comes from Elizabeth Gallenagh, general counsel and senior vice president, supply chain integrity, at HDA. She points out that each successive postponement by the FDA has led to tighter, stricter requirements to continue; the latest delay gives carte blanch to pharma trading partners but only if they are already testing electronic data exchange. The latest exemption from DSCSA compliance “is not meant to be a free-for-all if you haven’t done anything, or that you should stop doing anything," says Gallenagh. "Organizations need to continue to work through lagging trading partners, bringing the system to fruition.”

Dan Walles, serialization and traceability business leader at TraceLink, a leading solution provider, adds that many pharma trading partners are already up and running with their systems; within the TraceLink client network, a “terabyte a month” is being generated, representing billions of DSCSA transactions cumulatively.

The intent of DSCSA all along has been to tighten up US supply chains and prevent diversions or counterfeiting of drugs—at the unit-of-use level—from the point of manufacture to the point of dispensing. According to HDA data, 99% of drug shipments received have these barcodes now. Earlier DSCSA compliance milestones called for sharing bulk data with trading partners. The “enhanced drug distribution system” nominally in effect now requires the individual barcodes to be tracked, and to be accompanied by trading-partner data to validate the transaction. The sticky part has been the ability to align the physical shipment with the electronic transaction (for example, shipments received without transaction data, or the latter without the former.)

Under full DSCSA compliance, a shipment must be quarantined at the respective trading partner until the data are verified. HDA data (as of the end of 2023) show that the wholesalers themselves were able to scan only 80% of incoming shipments for serial data; the task is also complicated in that 5% of the data were unreadable. These vagaries are why pharmacies, at the tail end of the supply chain, report 20% to 50% missing data for incoming shipments.

When there are such data gaps, what is to be done? The planned-for response is called “exceptions handling”—a manual, semi-automatic or fully automated system to correct the data gaps. LSPedia, a DSCSA software provider, has had considerable success recently with a product called Investigator, a tool that can automate some of the EPCIS syntax or data errors, and automatically generate an email to the originating trading partner for verification. According to Riya Cao, CEO, Investigator reduces the time spent resolving exceptions from days to hours (or less).

“We’ve run surveys of our customers on the exception frequency they are experiencing, and it’s gone down from 9.8% at the beginning of this year to 6.5% currently,” she says.

The currently indefinite regulatory situation has another component: the status of exempted manufacturers or other trading partners. In late 2023, FDA issued instructions

 for "waivers, exceptions, and exemptions (WEEs)" to the then-looming DSCSA deadline. The program enables a party, typically a manufacturer, to apply for an exemption from DSCSA compliance when barcodes and related documentation are not finalized. The WEE is intended to have a set time limit, after which the party can re-apply.

The latest enforcement delay, announced in early October,

 granted exemption to trading partners who are already exchanging DSCSA data. Trading partners who are not, and hadn’t previously obtained a WEE, were able to request a WEE until the end of November. (Left unsaid was the status of companies not exempted and not possessing a WEE.) This state of confusion could be a knotty problem for a wholesaler who is receiving exempted data, WEE data, and non-WEE data. LSPedia’s Cao says that her company is readying a new compliance module that will provide current data on trading-partner WEE status—in effect, a new data-tracking service to make up for the lack of compliant data.

Yet another subsidiary component of DSCSA traceability is the process of handling saleable returns—a longstanding practice among trading partners whereby unsold drug stocks are returned for a refund. Some of these returns are discarded, but some of them (amounting to tens of millions of dollars annually in the US) can be resold. But to do that legally, the products’ tracking data must be received, validated, and reissued when a transaction is made.

The mechanics of processing these data was not formalized under DSCSA, but industry, led by HDA, created a verification router service (VRS) to enable the current possessor of product to obtain tracking data from its origin. (Since the product might have changed hands multiple times in the supply chain, it is not automatically known who the originator of the product is.) According to industry sources, the VRS system, which requires collaboration among the solution providers that are generating and storing DSCSA data for their clients, is up and running, although its use is not universal. (Trading partners who are bothering to collect the data can also find its source manually through their own efforts.)

According to HDA, the current members of the VRS Provider Network are: Antares Vision Group (which includes the solution provider rfXcel); LSPedia; Movilitas.Cloud; the National Association of Boards of Pharmacy (NABP); Optel; Tracelink; and Trust.med.

Within the VRS Provider Network, NABP, which represents state-level pharmacy regulation, has set up a service for its members overseeing retail pharmacy. The service, Pulse, is designed to make it easy to verify the identity of a trading partner. This becomes more necessary as transactions move from a face-to-face negotiation between buyer and seller to an online marketplace abetted by DSCSA-validated transactions. In other words, both the product and its seller can be authenticated with the appropriate digital signatures.

Pulse, in turn, has a group of vendors that offer interoperability with their data: Antares Vision Group (rfXcel); Axway; Engineering Industries eXcellence (owners of Movilitas.Cloud); InfiniTrak; LedgerDomain; LSPedia; Optel; and Systech.

LSPedia’s Cao calls NABP’s effort a “noble vision,” but notes that take-up among retail pharmacies has been limited (recently, Walgreens announced its participation). The trading-partner-identification process also affects the rest of the supply chain; trading partners worry about an anonymous request within a network seeking either to extract or to inject information that could affect downstream transactions. Yet another effort, managed by the Open Credentialing Initiative (OCI), seeks to provide technological solutions to validated identification. The sponsors of this organization (all of which specialize in digital credentialing) are: LedgerDomain; Legisym; Spherity; and Trust.med.

A vendor that has established a preferential position in the DSCSA compliance space is TraceLink, which is aggressively moving to take advantage of the relatively large client base it has. According to Walles, TraceLink has more than 1,600 clients globally, to which some 290,000 trading partners have linked. A long-standing part of TraceLink’s business model is that a client can onboard its preferred trading partners; and once those trading partners are in the network, they are identifiable to other TraceLink clients.

Walles says that the current version of its DSCSA platform, Magnum Opus, is being built out with 50 types of business transactions, all of which depend on “serialized data as a tool to provide visibility across the supply chain.” One example of this functionality is a digital recalls tool, which automates the workflow for managing a product recall from pharmacy shelves, typically a highly manual process. With the appropriate serialization data, the recall can be targeted to the specific lot or item numbers, and where they are stored. He says that this capability is an example of the “value beyond compliance” that practitioners in the field have been hoping for; and it’s “making a meaningful positive impact” on pharmacy operations.

Last year, the company announced the Multienterprise Information Network Tower (MINT), a resource to enable the trading partners along a supply chain to share information and coordinate activities. Between that and the Magnum Opus platform, the solution provider expects to foster an environment where other supply chain specialists can link their systems and distribute trading-partner data faster and more effectively. Most recently, it announced a collaboration with Tecsys, a software provider of warehouse management systems, to this end.

Even as some participants in the pharma supply chain struggle to get compliant DSCSA data systems up and running, others, already well along in implementation, are looking at business improvements. At Excelis Health Solutions, CEO Greg Cathcart says his company is involved in an effort with a major retail organization to rationalize inventories across a large number of retail outlets.

“The company wants to be able to decide, when inventory is low at one outlet, should it reorder from the supplier, or move inventory from another outlet,” he says.

The pharma industry is at the point where high-performing organizations will out-compete the poorer ones, through better use of their DSCSA investments.

Waivers, Exceptions, and Exemptions from the Requirements of Section 582 of the Federal Food, Drug, and Cosmetic Act: Guidance for Industr

https://www.fda.gov/media/113342/download

Waivers and Exemptions Beyond the Stabilization Period 

https://www.fda.gov/drugs/drug-supply-chain-security-act-dscsa/waivers-and-exemptions-beyond-stabilization-period

— Nicholas Basta is Founder and Editor Emertus of Pharmaceutical Commerce

Because of the potential for product shortages at pharmacies, the FDA has not only given industry more time for compliance—but time to catch up on 
boosting the supply chain’s interoperable efforts.

Did Industry Fumble the DSCSA Deadline?

What if we could flip the patient data iceberg to expose what is typically hidden from sight—the full narrative of a patient’s healthcare journey? In the 10% of the iceberg we see above the surface are the traditional data sources such as claims and diagnostic and procedure codes, which give only us a snapshot of the patient. It is the vast base underwater that provides the rich detail we need. When paired with advanced analytics, this data can help us reduce time to diagnosis, understand the “why” of patient behaviors and decisions, and identify patient cohorts for enhanced patient engagement and interventions.

The industry’s current approaches to patient data analytics focus on structured data (e.g., height, weight, standardized test results, diagnosis codes, medications, stages of disease diagnosis). Although this data helps capture the patient’s journey, it does not represent 100% of the patient’s interactions with the healthcare system. In fact, only 20% of patient data is captured in structured form, with the remaining 80% comprising unstructured data (e.g., physician notes, progress notes, infusion summaries, lab reports). Furthermore, according to RBC Capital Markets, the compound annual growth rate of data for healthcare will reach 36%, and there will be nearly 5,000 digital device interactions daily per person by 2025.

 All this will significantly increase the amount of unstructured data.

The combination of increasing variance, volume, and velocity of patient data overall and complex unstructured data, which is not easily codified, has limited the industry’s ability to understand the full patient journey. For life science companies developing niche and targeted therapies, the resulting lack of insight creates challenges for identifying the right patients and intervening at the right moments to support treatment journeys.

Moving the patient health paradigm from 'what' to 'why'

Structured data primarily answers the “what” of patient health—clinical diagnoses, current disease status, treatment initiation, and discontinuation. Unstructured data provides the context around their health—the “why” —a comprehensive longitudinal view of the patient, a road map to their physicians’ thought processes, and the ability to understand the clinical and socioeconomic aspects of patients’ reactions to therapies and diagnoses.

For example, clinical notes capture nuanced observations about patient behavior, drug responses, and lifestyle factors; laboratory narratives and genomic data reveal deeper biological patterns and potential therapeutic targets; and detailed pathology reports provide context around disease progression. Advanced natural language processing (NLP) algorithms can extract meaningful insights from these unstructured sources, converting them into analyzable data points. Combining these with structured data sources in a way that is compliant with privacy requirements can generate a more comprehensive patient data set.

Accelerating diagnoses using unstructured data

A delayed rare disease diagnosis can be not only mentally and emotionally draining for patients, but also financially draining. According to a study by The EveryLife Foundation for Rare Diseases, the “diagnostic odyssey” for a patient with a rare disease can result in an average of more than $220,000 in medical costs and lost income.

 Each year saved in diagnosis directly impacts patients. We have seen in our work with life sciences companies that it can take up to eight years and 15 (or more) clinical encounters for patients to secure a diagnosis.

Therefore, finding new ways to speed diagnoses could significantly improve patient outcomes and finances. Utilizing artificial intelligence (AI) on unstructured data can fill gaps in a patient’s diagnostic journey.

For example, relatively simple documentation of the progression of fatigue in primary care notes, when combined with other longitudinal elements of the patient journey, can be an early indicator of rare pediatric diseases with a genetic predisposition that might be asymptomatic at birth. NLP-based analysis of physician notes could flag the need for appropriate specialist referrals for further diagnostic tests, ultimately reducing the time to diagnosis.

For other diseases, the use of AI to analyze patient data could help reduce misdiagnosis or underdiagnosis. The biggest challenge for one of Beghou’s clients with a rare disease drug was finding patients and initiating treatment. Misdiagnosis often occurred because of the multiple levels of symptoms, most of which were mild in the initial disease phase, and considerable delays in the confirmatory genetic test for most patients. Therefore, instead of using only claims data, which identified very few patients, we identified patterns of symptoms from patient notes. These symptoms ranged from simple skin spotting, vertigo-like symptoms, decreased ability to sweat, gastrointestinal issues, kidney-related issues, and irregular heartbeat. Using AI models, a substantial number of patients, each with a bundle of these symptoms, were identified within regional hotspots, which enabled focused outreach and facilitated diagnosis.

Enhancing engagement and personalizing interventions

Detailed patient profiles can also be developed using advanced analytic techniques such as machine learning and predictive modeling on clinical data (e.g., electronic health records, lab results, treatment responses), behavioral patterns (e.g., medication adherence, appointment attendance, portal usage), and socioeconomic factors (e.g., education, income, access to care) from both structured and unstructured data.

These profiles can be used to identify distinct patient microsegments with unique needs and preferences based on subtle patterns in disease progression; objective and subjective treatment effectiveness; predictions of potential complications; engagement preferences and compliance tendencies; and barriers to care and access to resources.

Patient microsegments facilitate the development of more effective, personalized intervention strategies and patient-centric, outcome-driven engagement plans, such as simplified medication schedules for patients with complex situations, digital health tools for tech-savvy individuals, and high-touch support programs for those requiring additional assistance.

For example, for patient cohorts predicted to be less adherent to their treatment, providers can understand underlying factors such as adverse effects, social determinants of health, or misunderstandings about the importance of treatment. Appropriate intervention strategies could include adverse effect management, reminder notifications, digital app-based prompts, and education about the treatment itself.

Improving insight quality and reducing cycle times with GenAI: A real-world example

We used data science and AI to improve the quality of insights by 25% and reduce intervention implementation cycle times by 20% for a specialty client. The GenAI program enabled and scaled AI infrastructure across the enterprise and functions to enable timely interactions with the healthcare providers and patients. Raw data from various unstructured data sources were enriched with the client’s specific ontologies and medical vocabulary to ensure the model could address ill-framed or abstract questions and integrate insights using retrieval-augmented generation. The latter allows the model to handle large amounts of data with relative ease and provide contextually relevant responses for the brand from all data sources, with a focus on usability via conversational AI, executive summaries, insights dashboards, and semantic search.

In some ways, claims data sets represent only the tip of the iceberg. With rapid technological advancements, especially with GenAI, we are on the brink of a large-scale transformation where combining structured and unstructured data sets will truly revolutionize how we understand patients and their journeys, engage and provide meaningful support, and impact patient outcomes.

To turn this potential into reality, we must radically change how healthcare stakeholders use unstructured data. This relies not only on tech stack, data storage, AI, and machine learning advancements, but also on data security and structural and change management. Cross-functional collaboration with clinicians and data scientists is fundamental to:

Understanding the nuances of medical ontologies.

Normalizing advanced AI algorithm use across healthcare systems.

Optimizing storage across disparate systems using common data models.

Refining definitions in metadata to evolve with advancements in medicine.


Instituting change management across functions for data democratization.

With a holistic, collaborative approach to designing richer data ecosystems and implementing advanced technologies, we can gain an in-depth understanding of health-related behaviors and decisions. Interventions centered around the patient then become possible, providing benefits for patients, physicians, and life science companies alike. Are we ready?

Sonam Dubey and Vishal Singal are partners at Beghou Consulting.

1. RBC Capital Markets. The Healthcare Data Explosion. 2020. 

https://www.rbccm.com/en/gib/healthcare/episode/the_healthcare_data_explosion

2. EveryLife Foundation for Rare Diseases. The Cost of Delayed Diagnosis in Rare Disease: A Health Economic Study. 

https://everylifefoundation.org/wp-content/uploads/2023/09/CombinedInfo_PDF.pdf

Filling the gaps in the patient journey within an increasingly complex data environment. 

Caught Beneath the Surface: The Hidden Potential of Patient Data 

Patients today too often find themselves navigating a myriad of challenges that can make accessing quality healthcare a daunting task. Various common obstacles range from financial barriers to long wait times, to geographical and technological limitations and much more.

As someone deeply invested in improving patient health outcomes, I’ve had numerous conversations with patients, healthcare providers (HCPs), and pharmaceutical manufacturers who echo the sentiment that these barriers can significantly impede patient access, affordability, and adherence.

In light of all of these complexities, it’s crucial to understand the specific hurdles patient services programs must overcome. Here are some of the top ones.

Over 60% of patients report wanting more information when deciding where to get care. They often feel lost navigating their treatment options and need clearer guidance on therapies available to them. This gap in reliable healthcare information can lead to poor decision-making, and, often, non-adherence to prescribed drugs, which only worsens health outcomes.

The average wait time for a new patient appointment in the US is a staggering 26 days. Imagine being in pain or facing a health issue, and having to wait nearly a month to see a provider. This is not just inconvenient; it can lead to delayed treatments and increase reliance on already overburdened emergency services.

Between 2019 and 2022, hospital drug expenses per patient increased by a reported 19.7%. This rise in the cost of therapies can place an incredible financial burden on patients, particularly those with chronic conditions or requiring specialty medications. As a result, many patients are forced to make tough decisions about whether they can afford to continue treatment.

Factors such as income, education, and location have a significant impact on healthcare access. Patients living in rural areas or low-income households often face additional barriers to receiving care, contributing to growing health disparities.

When care is not well-coordinated, patients often find themselves bouncing between HCPs without clear communication or cohesive treatment plans. This fragmented care leads to miscommunication, duplicated tests, and inconsistent treatments.

In an increasingly digital world, access to and basic knowledge of technology is becoming more essential to access care, yet only 58.5% of adults reportedly used the internet to look for health or medical information in 2022. This digital divide means many patients are missing out on the benefits of telehealth, online appointment booking, and digital health management tools, limiting their engagement and access to services.

Turning obstacles into opportunities for success

It’s clear these significant obstacles can prevent patients from receiving timely, affordable care and contribute to a healthcare environment where many patients struggle to navigate their treatment journeys. These barriers not only hinder patient outcomes but also increase the burden on HCPs and patient services programs.

Addressing these issues requires a multifaceted approach. We must focus on advancements such as enhancing patient education and access to reliable information, along with the utilization of digital platforms that empower patients to make informed decisions. Reducing wait times can be achieved through process improvements and leveraging telehealth to expand access to care. By prioritizing these strategies and others, we can begin to create a healthcare system that better serves patients in need.

In recent years, there has been a notable shift in how manufacturers launch new therapies, acquire patients, and provide ongoing support. The concept of "patients as consumers" is finally becoming a reality as manufacturers seek to meet the evolving expectations of today’s healthcare marketplace. With patients expecting more convenience, information, and engagement, direct-to-patient (DTP) models have emerged as a game-changing approach to meet these demands.

DTP models offer a transformative approach to addressing these challenges, providing numerous benefits for both patients and manufacturers. These models bypass traditional healthcare intermediaries, providing patients with direct access to therapies and services. Not only do DTP models streamline processes, they also enhance the overall patient experience.

By leveraging DTP models, we can address many of the systemic issues that hinder patient care. DTP models help simplify the healthcare journey by ensuring patients receive timely and accurate information, personalized support, and more affordable treatment options. The following are some of the key benefits that DTP models bring to the table:

By utilizing DTP models, patients are empowered with accurate, timely information about their therapies.

I’ve spoken with physicians who have witnessed this firsthand: When patients are given clear, direct communication about their treatments or interventions, they feel more in control of their healthcare decisions and are more likely to adhere to their prescribed regimens.

 By streamlining processes, DTP models can reduce the time it takes for patients to receive their medications or therapies, ensuring quicker access to care when it matters most.

DTP models allow for more personalized care. Patients appreciate having direct access to tailored support services, such as educational resources and adherence programs, which can improve their overall health outcomes and satisfaction with their care.

DTP models foster direct communication with patients, encouraging them to stay engaged with their healthcare. Because patients are more informed and supported, they’re more likely to adhere to their prescribed treatments.

 In many parts of the country, physician shortages and geographic isolation prevent patients from accessing the care they need. DTP models help eliminate these “medical deserts” by making therapies accessible, regardless of location. This is especially critical for patients with rare or complex conditions who may not have easy access to specialists.

DTP models leverage data and analytics to provide a more personalized and seamless healthcare experience. By collecting real-time patient data, manufacturers can tailor support services, adjust treatment plans, and deliver proactive care that meets individual patient needs.

Charting a path toward a healthier tomorrow

While the challenges facing patients and patient services programs today are numerous and complex, there are successful pathways to improving health outcomes and creating a more equitable healthcare system. Through collaborative efforts and implementing innovative solutions such as direct-to-patient models, we can tackle obstacles and help ensure patients receive the quality care they deserve. As we continue to address these challenges head-on, I am optimistic we can pave the way for a healthier future.

Maria Kirsch is President of Patient Services at EVERSANA.

A guide to overcoming the top obstacles facing patient services programs today. 

From Challenges to Solutions:  Breaking Barriers to Revolutionize Patient Services 

Since my involvement with an expert group on artificial intelligence (AI) began in 2018, helping to craft the guidelines that laid the groundwork for the EU’s AI Act proposal, I’ve been engaged in fiery debates about AI’s future. I’ve consistently supported one specific stance: competition is the lifeblood of progress, and the dance of innovation always outstrips the sluggish steps of regulation.

Allowing AI innovators to build algorithms with the freedom to learn and adapt without excessive constraint is essential for fostering innovation. However, this autonomy must be guided by universal values that prioritize humanity. Just as we teach our children fundamental values—including not to steal, not to lie, and to be kind—we should instill these same values into AI.

It’s important to acknowledge that AI is a tool, and like any such tool, its impact depends on how it’s used. Currently, much of AI’s potential is being channeled into business models that prioritize profit over societal benefit. Too often, we see AI being used for trivial purposes, such as personalizing advertisements, rather than addressing critical issues like healthcare innovation.

In 2015, I founded OKRA.ai with the mission of leveraging AI to accelerate the development and delivery of innovative treatments to patients. This illustrates how AI—with the right intention—can be implemented as part a business model. Now, as chairwoman of the AI Innovation Board at Envision Pharma Group, I lead the integration of AI technology and data with human expertise to accelerate decision-making and drive faster, smarter patient outcomes.

When it comes to AI regulation, we need to embrace the messy, unpredictable nature of the technology and adapt our approach accordingly. Traditional rule-based systems do not suffice in the AI era. Rules should serve as guardrails to ensure that AI operates within ethical boundaries, promoting accountability.

We must also address the prevalent issue of data exploitation by certain business models, often referred to as “Trojan business models.” These models offer seemingly free services while surreptitiously harvesting user data for profit.

For instance, many social media companies have been under scrutiny for their business models using personal data to target users with adverstisements. The result is a pervasive culture of exploitation and manipulation, where our data is often used against us for profit, rather than for the purposes for which it was intended. This exploitation not only undermines user privacy but also erodes trust in AI technologies. We need the alignment of intention with the business model with the outcome.

In order to combat this, we need regulatory frameworks that prioritize data privacy and security while promoting transparency and accountability.

Coming together for a common goal: WEF meeting analysis

Having participated at the beginning of the year in sessions at the 54

 Annual Meeting of the World Economic Forum (WEF), I was struck by the common ground shared by leaders from diverse backgrounds when it comes to AI regulation. Amid the complexities of global governance, a consensus emerged around the importance of upholding universal values in shaping the future of AI.

One poignant example came from Ursula von der Leyen, president of the European Commission, and Li Qiang, premier of the People’s Republic of China,as highlighted in their special address at the meeting. Despite their differing geopolitical contexts, both leaders embraced the value of not lying, emphasizing their commitment to combating disinformation and misinformation in the digital sphere. This shared dedication underscores the universal value of truthfulness, a cornerstone of ethical AI regulation.

In a session titled “360° on AI Regulations,” the international panel echoed this sentiment, highlighting the borderless nature of AI’s impact. As AI technologies transcend national boundaries, there is the need for collaborative, cross-border solutions and global cooperation among stakeholders—governments, businesses, civil society, and academia—in shaping responsible AI governance frameworks. AI regulation necessitates a holistic approach that transcends geopolitical divides and embraces the principles of transparency, accountability, and inclusivity.

By encouraging businesses to embrace open data practices, we can ensure greater accountability, trust, and ethical standards in AI development and deployment. Open data initiatives empower researchers, entrepreneurs, developers, and policymakers with access to diverse datasets, enabling them to create more robust and unbiased AI systems.

At the same time, it’s important to acknowledge that not all values are universally aligned. While values such as “do not lie” find resonance across cultures, others, such as “do not steal,” may present divergent perspectives. China, for instance, champions collaboration and global exchange, advocating for a level playing field where all nations can participate in the AI revolution.

This calls for a shift toward a more open-minded approach and the implementation of measures that foster international cooperation in science and technology, ensuring a level playing field for all.

However, achieving genuine openness and equity in technology development requires addressing hurdles related to international trade and investment. For instance, while Chinese companies may establish headquarters in Europe, the reverse is often impeded by restrictions within China. This asymmetry not only impedes fair competition, but also raises concerns about intellectual property rights and technology transfer.

Furthermore, the issue of intellectual property theft and unauthorized replication of products remains a significant obstacle to innovation and investment. When international companies manufacture products in China, they face the risk of having their designs and technologies replicated without consent, eroding their competitive edge and profitability. This dilemma may deter investors within the EU and US from engaging in business ventures with China.

To overcome these challenges, there’s a pressing need for the harmonization of regulatory frameworks and standards across borders. If each country enforces its own rules without considering the global ramifications, it creates a fragmented and uncertain landscape for businesses and investors alike. In the realm of AI regulation, adherence to European standards should be expected of international companies seeking to operate in the EU market, ensuring a
little playing field and fostering trust among stakeholders.

In conclusion, as we navigate the transformative landscape of the AI revolution, one value remains constant: the importance of respecting our universal human values.

Every revolution throughout history has ultimately circled back to this fundamental principle: the recognition of our shared humanity and the imperative to uphold values such as honesty, integrity, and respect.

But how do we ensure that these values guide our actions in the realm of AI? How do we find common ground amid diverse perspectives and interests? The answer lies in dialogue, collaboration, and a willingness to listen and learn from one another.

Loubna Bouarfa, PhD, is the chairwoman of the AI Innovation Board at Envision Pharma Group. She also founded OKRA.ai.

The importance of embracing universal values and business models that prioritize humanity. 

Navigating the Landscape of Global AI Governance 

The future of medicine is being shaped by cell and gene therapies (CGTs), which involve complex manufacturing steps. Because they are mostly personalized medicines today, production is limited to one patient dose at a time, and slowed by cumbersome and largely manual processes.

In addition to these inefficiencies, the field is still just learning which manufacturing parameters are most critical to patient outcomes, and how to optimize them.

As with so many areas, new tools such as artificial intelligence (AI) and machine learning are explored to optimize the steps to cut costs and increase efficiency while improving outcomes. However, doing so relies on access to reliable data, which has been a challenge.

Pharma’s penchant for data siloing is throttling the lifeblood of algorithms, leaving those working across the industry—particularly makers of enabling technologies—as key partners for CGT developers to ensure that crucial data can be leveraged in ways that allow AI to learn from it.

Since the first CAR T-cell therapy approval in 2017, the field of gene-modified cell therapies has progressed rapidly. Over the last seven years, FDA has approved 12 of these therapies, including the first T-cell receptor gene therapy for the treatment of metastatic synovial sarcoma in adults.

Manufacturing processes account for a major portion of the cost of life-changing therapies, which has meant commercial access is so far limited to a few thousand patients per year, which could be a threat to continued progress.

The process starts with cell collection, followed by cell manipulation steps such as cell isolation, activation, modification, expansion, and final formulation. The final drug product is infused under the supervision of trained medical professionals. At each of these steps, there are opportunities to optimize.

Doing so starts from automating unit operations and enhancing digital data capture capabilities. Over the last decade, the CGT field has moved in this direction, and advanced automated unit operations are available. However, adopting these technologies in manufacturing could be faster.

The challenge begins with early-stage developers, who generally rely on manual processes with limited digital data capture. As the target drug candidate moves through development stages, manual data needs to be digitized, which may require more than easy transfer and integration among the manufacturing steps.

Currently, the industry is focused on data capture in the cell collection, transport logistics, and therapy manufacturing processes. Often overlooked is the cell collection process, where apheresis is a commonly used technique and can be automated. Optimizing apheresis protocols has become a crucial part of clinical development for cell-based therapies, and the opportunities for data-driven AI are becoming more evident.

Researchers led by John Manis from Boston Children’s Hospital and Harvard Medical School demonstrated what is possible.­

 The team performed a retrospective analysis of collection procedures for patients with sickle cell disease (SCD), performed on automated apheresis systems. The process is difficult for these patients, because SCD changes blood viscosity and causes components to clump. But by implementing new methods driven by the analysis of this data, the group observed that CD34+ collection efficiency increased from 4.9% to 36.8%—more cells in less time. The real-world applications became evident shortly after the results were presented, when regulators approved two autologous cell-based therapies for SCD.

The study is just one example of on-going data-based optimization efforts in cell collection. In the coming years, the industry would expect to see greater implementation of prediction algorithms and machine learning models that allow us to understand and adapt processes to improve efficiency, especially in cell collection methods.

Cell culture is another critical step in cell therapy production that may benefit from enhanced data collection, and could potentially lead to AI-based optimization. It is a tedious and complex process, and each subtype within the diverse set of cell therapy applications utilizes cell culture under a unique set of conditions.

The data collection and analysis for this step is centered on the utilization of biosensing tools in order to monitor the cell culture environment. In a recent study, researchers used such data to predict the optimal cell culture parameters using a machine learning model,

achieving high cell densities in a stirred tank bioreactor while decreasing the use of process reagents, shrinking the cost of goods by 35% to 50%.

Similar opportunities are available with other automated platforms, which can be connected with biosensors to enable on-line monitoring of critical process parameters.

 Metabolic data such as lactate generation and glucose consumption during the cell culture could be used to predict the number of cells during the culture, and an appropriate time to harvest the cells. But researchers need to collect more data on these parameters than is currently available in order to train the prediction models and optimize cell culturing systems.

The fast-evolving landscape of CGTs must lean into the potential of AI and machine learning to revolutionize manufacturing processes. The importance of data collection throughout the value chain is evident, and it is a clear area where enabling technologies can improve understanding of the processes. As more processes are automated throughout the manufacturing chain, it is becoming critical to integrate different platforms and the data they can produce.

The focus on data-driven approaches to improve cell collection and culture processes underscores the ongoing efforts to enhance the production of impactful therapies. These advancements pave the way for a future where innovative treatments are more accessible and efficient, ultimately benefiting patients and the healthcare industry as a whole.


1. FDA Approves First Gene Therapy to Treat Adults with Metastatic Synovial Sarcoma. FDA. August 2, 2024.

 https://www.fda.gov/news-events/press-announcements/fda-approves-first-gene-therapy-treat-adults-metastatic-synovial-sarcoma

2. Sethi, D.; Duemke, J. Automated Apheresis Platforms Function as a Therapeutic and an Enabling Technology for Cell-Based Therapies. 

https://pharmaceuticalmanufacturer.media/pharmaceutical-industry-insights/latest-pharmaceutical-manufacturing-industry-insights/automated-apheresis-platforms-function-as-a-therapeutic-and-/

3. Safford, M.; Jerbi, T.; Koudji, M.; et al. Machine Learning Enables High-Density T-cell Expansion with Lower Costs While Maintaining Product Quality. 

https://www.isct-cytotherapy.org/article/S1465-3249(24)00130-0/abstract

Addressing the bottlenecks in cell and gene therapy manufacturing through a well-designed artificial intelligence approach is essential to avoiding data siloes. 

Pharmaceutical Commerce - December 2024

Pharmaceutical Commerce - December 2024 Issue (PDF)

Traceability's Latest Curve

An Appreciation for RFID

Conference Connect: Tackling the Issues that Matter in the Access Space

Did Industry Fumble the DSCSA Deadline?

Caught Beneath the Surface: The Hidden Potential of Patient Data

From Challenges to Solutions: Breaking Barriers to Revolutionize Patient Services

Navigating the Landscape of Global AI Governance

Why AI Advances in CGT Require Getting Data Right

Policy and Population Health

The Advantages of New Medicare Rx Payment Plan

Going Inside the Gross-to-Net Bubble and Its Nuances

Beyond the Finish Line: Why Post-Approval Regulatory Strategy Matters