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If there is one essential focal point that I gathered by attending LogiPharma USA in Boston, MA, back in late September—a conference that revolves about supply chain and logistics—it’s this: Sustainability is not only alive and well, it is top of mind for the pharmaceutical industry.

Many of you may be familiar with Energize, an initiative created in 2021 that “aims to accelerate renewable energy adoption and enable bold climate action within the pharmaceutical value chain.” The initiative is essentially designed to provide pharma suppliers with the information necessary to further learn about adopting renewable energy. AstraZeneca, for example, which happens to be a participating member of Energize, reminded conference attendees on day one of its pledge to invest up to $1 billion toward a program striving for zero carbon emissions from AZ's global operations by 2025, along with a goal of having its entire value chain be carbon negative by 2030.

There are various companies in the cold chain container sector that are working to change the game when it comes to efficiently transporting pharmaceutical products. For instance, AeroSafe Global, which prides itself on providing sustainable, temperature-sensitive supply chain solutions, is offering AeroLoop, a program featuring a reusable container that can be utilized more than 50 times. Between 2020 and 2022, the company calculated that AeroLoop saved and will save 33 million kilograms/33 billion grams of CO2. And when compared to single-use alternatives, the company says that the program has resulted in the following:

Sonoco ThermoSafe, another player in this space, has committed to reducing its absolute Scope 1 and Scope 2 emissions by at least 25% by 2030; it also aims to lower its normalized energy use in its manufacturing plants by 8% in that same time frame.

From Dec. 12-14, I will be attending Informa’s Trade & Channels Strategies event in Philadelphia, PA, which will feature a plethora of topics, ranging from current and future trends impacting trade and distribution to navigating regulatory reform in the space. Let us not forget one more—yes, you guessed it—sustainability. I am honored to be presenting on the topic, specifically as an overview of ways to apply sustainable practices in the pharma industry.

I would also be remiss not to mention that I have assumed the role of 

’s new editor, where I will be coordinating the day-to-day editorial content of the brand. Special thanks to my predecessor, Julian Upton, for his guidance in helping me settle into this position—I look forward to helping to provide you, the reader, with valuable insight from around the industry.

Here’s to a happy and healthy New Year!

Nicholas Saraceno is Pharmaceutical Commerce's Editor. He can be reached at

Companies along the pharma value chain strive for carbon neutrality

Sustainability: Its Importance Can't Be Ignored                    

Efforts to leverage the valuable insights surfaced from real-world evidence (RWE) throughout the biopharmaceutical and healthcare sectors have skyrocketed in recent years—and for good reason. Two parallel factors are enabling this trend.

Growth in the number of real-world data (RWD) sources that are developed during the course of routine patient care.

Ongoing advances in both data-extraction tools and data-analytics capabilities based on artificial intelligence (AI), machine learning (ML) and natural language processing (NLP), and other modeling and simulation methodologies.

All of this has kicked the doors wide open, ushering in a new era for high-quality, regulatory-grade RWE, growing trust in the process and increasing support from many regulatory bodies.

Historically, three primary sources of RWD have been the focal point for analyses to create RWE—electronic health record (EHR) data, medical and pharmacy claims data, and published literature. But more recently, additional data sources have demonstrated their value in helping to create richer RWE insights, by more fully capturing the patient experience as they manage their medical conditions in the face of available or non-existent treatment options. They include:

Aggregated clinical laboratory-testing and genomics data.

Digital patient-generated health data (PGHD) that is collected from in-home, mobile, and wearable devices.

Data captured in patient and product registries.

“In recent years, RWE has exploded in terms of what it can tell us, and how it can support and supplement randomized controlled trials (RCTs). Importantly, trust in the use of RWE has also expanded, which opens up many new possibilities,” says Christina Mack, chief scientific officer, RWE, for IQVIA Real World Solutions. “Where it was once a novelty, RWE is now baked into most conversations with our clients these days, and it’s no longer seen as a special case.”

RWE is now helping stakeholders to advance critical objectives in all phases of the drug discovery, development, and commercialization lifecycle. These include:

Aiding internal decision-making on portfolio development.

Improving clinical trial design (by, for instance, improving selection of appropriate endpoints, surrogate endpoints, and trial duration), as well as goal-setting for trial diversity and patient enrollment.

Elucidating the disease incidence, prevalence, natural history, and disease burden of the target indications.

Informing clinical practice by closing evidence gaps that remain after the clinical trial.

Supporting initial regulatory approval submissions and approval of post-marketing label/indication expansions by adding critical context to trial data.

Enabling data-informed negotiations with regard to drug pricing, formulary placement, and performance-based contracts with payers (thereby removing barriers to access for today’s advanced and high-cost therapies).

“RWE that could be based on thousands or millions of patient records provides a goldmine to support your regulatory submission and build out your therapy’s overall value story,” says Nandini Hadker, partner and head of evidence strategy for Trinity Life Sciences. “Pre-launch, companies can demonstrate unmet need by developing RWE to identify gaps in the current care paradigm. Post-launch, companies can develop RWE to help quantify their product’s performance compared to competitor products across a number of dimensions. Both types of findings are of significant interest to regulatory bodies, payers and physicians.”

Combined together, these insights can be integral to constructing the value proposition of a particular treatment. “RWE studies that can help stakeholders to better understand the burden of disease can be used to demonstrate the full economic impact of the disease and the specific clinical interventions on both the patient and the health plan," says Mack. She believes the potential of novel high-cost therapeutics to offset healthcare costs should be brought to light during negotiations between drug developers and payers.

“Similarly, as a drug sponsor, you should be engaging with regulators as early as possible to demonstrate to FDA that your strategically and scientifically sound approach is the best way to meet unmet clinical need,” adds Won Chan Lee, principal and head of Axtria’s health economics and outcomes research (HEOR)/RWE practice.

Recently, there has been increasingly vocal, top-down support from FDA and other global regulators, and the number of regulatory approvals that were based entirely or in part on RWE presented by sponsors has continued to grow. In October, FDA announced that it would begin accepting applications into its new Advancing Real-World Evidence Program.

 FDA's new program “provides sponsors ... the opportunity to meet with agency staff—before protocol development or study initiation—to discuss the use of RWE in medical product development,” says the agency.

“FDA’s new program is huge,” adds Anissa Cyhaniuk, senior vice president, RWE, for EVERSANA. “It reinforces the agency’s commitment to finding situations where RWE can provide efficacy data, and we’ve seen early work here across many extensions in different disease states and age groups, including pediatric and rare diseases.”

Putting RWE to work earlier in drug lifecycle

In the early days, RWE was primarily used to satisfy post-marketing requirements and address evidence gaps that remain after RCT data is submitted to regulators, points out John Doyle, group vice president of health sciences at Exponent, and adjunct assistant professor in the department of epidemiology, Mailman School of Public Health at Columbia University. But in recent years, he adds, the RWE framework is being applied to the drug-development process much earlier, shaping how the trial is designed and executed and how the regulatory submission package is developed. “When it comes to drug-discovery efforts for orphan drugs, biomarker-directed therapies for specific subsets of patients in oncology and other diseases, and highly personalized cell and gene therapies (CGTs), sponsors and regulatory agencies are aligned in their willingness to consider conditional approval based on smaller trial populations—but then working to validate the long-term safety and effectiveness profile of the approved therapy and evaluate its benefit-to-risk ratio in a larger, more diverse cohort of real-world patients over longer-time horizons,” says Doyle. 

In a recently published analysis of FDA’s new oncology drug approvals in 2020,

 investigators found that most initial cancer drug approvals are based on a single, non-randomized trial design using surrogate endpoints. “Specifically, 17 of the oncology drugs accounted for 18 pivotal trials; 13 of them (72.2%) used historical control, only six of them (35.3%) had a randomized component, and the majority (17) used a surrogate endpoint as a primary endpoint (94.4%),” explains Javier Jimenez, chief medical officer, Flatiron Health, a cancer-focused technology and services company. “The ongoing evolution in the complexity of today’s new medicines—now including biologicals, [CGTs], and targeted therapies aimed at rare populations—will increase the need for post-marketing evidence generation derived from RWD.”

Follow-up studies are particularly important in the realm of CGTs, many of which are considered to be potentially curative for a wide range of chronic or deadly diseases. “The sponsor may not be able to validate a CGT’s ‘cure’ in a pre-market trial of limited scope and duration, so they must carry out longer-term studies to develop valid, reliable, and durable RWE that verifies the full clinical value of these breakthrough therapies over time,” says Doyle.

Much earlier in the development lifecycle, the use of RWE to support pre-launch efforts is being used in three ways, 

explains Nagasubramanya Nabha, assistant vice president of data science for Trinity Life Sciences. They include developing look-alike models that identify favorable patient populations; identifying early adopters among healthcare professionals; and developing healthcare segmentation to enable more focused messaging and value communication.

“RWE strategies can help sponsors explore different possible endpoints, different patient subgroups, and different expressions of the disease based on biomarker stratification,” adds Anila Dede, executive director, RWE solutions for Syneos Health. “You can find these critical signals if the data set is big enough and diverse enough.”

Today, there is growing effort to integrate multiple linked data sources to enable deeper evidentiary insights. “EHR data will always be valuable to help answer a lot of questions, but it still needs to be supplemented with other data sources,” says Mack of IQVIA. “The ability to appropriately curate the RWD from multiple sources to augment the analyses—that’s where the magic happens.”

“Many feasibility studies attempt to leverage EHR and claims data but are then unable to fully define the target patient condition and/or clinical context within the available data,” adds Adrienne Lovink, partner and co-leader of Trinity’s RWE function. “Claims data often lacks specificity, and much of the relevant information that resides in EHR systems is in the form of harder-to-access unstructured data.”

The growing ability today to use NLP to mine the unstructured data that is found in physician notes within the EHR system is particularly important “because so often, the codes used in the structured EHR data are not nuanced enough—nor do they exist for a new category of disease for which the manufacturer is pursuing a therapy—to correctly identify particular patients, so text mining using NLP can help to identify hidden patients,” says Lee of Axtria.

In a recent study completed with one of its pharma clients, IQVIA used NLP and ML to analyze clinical information found in both structured and unstructured EHR data to identify a particular subset of asthma patients. “The findings led to a 30% increase in the ability to identify uncontrolled asthma, and a 17% reduction in uncontrolled respiratory diseases, by helping doctors to accurately diagnose patients, initiate the right therapy more quickly, and track patients to ensure improved outcomes,” explains Mack.

Similarly, imaging data created during routine healthcare also remains an important, albeit untapped resource that can be mined to support drug discovery and development. “The ability to train computer models to understand and analyze clinical pathology images, match insights from malignant tumors, and marry those insights with data related to patient outcomes and genetic profiling can provide critical insights to improve trial recruitment and treatment,” says Dede.

Lovink believes that in several disease areas, particularly oncology, it is hard to reasonably characterize a patient’s clinical condition without imaging data. “However, imaging data has traditionally been difficult to locate, retrieve, view, de-identify, and normalize at scale," she notes. "Recognizing that imaging is one of the most important and critical clinical endpoints for many therapies, I expect the emerging companies will continue to advance these capabilities and eventually change the data landscape.”

Meanwhile, according to Lee, two particularly rich sources of RWD—PRO and digital health data from wearable devices—are just starting to be appreciated and utilized, and can enable deeper insights into drug adherence, side effects, and adverse events (which are important for managing chronic conditions), offering a path forward to improved patient care. For example, RWE related to treatment patterns, persistence, adherence, and change in treatment can help run trials more efficiently by reducing their duration, cost, and burden on patients.

Biomarkers offer a treasure trove to be mined

Today, the number of approved biomarker-directed therapies continues to grow. One recent paper

 analyzed the US-based de-identified Flatiron Health-Foundation Medicine clinicogenomic database and was able to systematically identify 458 mutations “that predict the survival of patients on specific immunotherapies, chemotherapy agents, or targeted therapies,” says Jimenez. “The research spanned across eight common cancer types, allowing for the generation of insights and hypotheses in mutation–treatment interactions and mutation–mutation interactions that can be used to guide new targeted cancer therapy development.”

In the coming years, the practice of routinely sequencing patients for a panel of targeted mutations is expected to grow. However, it is notable that the remaining mutations from any broad panel screening, which are captured in the patient’s EHR profile may not necessarily be relevant today to inform therapy selection, experts contend. "But such data can be studied on an aggregate level to help drug sponsors better understand how patients with particular mutations are faring on their existing prescribed therapies,” notes Viraj Narayanan, chief commercial officer for Ontada, an oncology real-world evidence, technology, and insights business of McKesson. “Efforts to carry out RWE studies that pair genomic data with outcomes data can then help to inform ongoing development efforts in the pursuit of biomarker-related therapies.”

However, one barrier is the fact that many clinicians are not yet routinely running comprehensive biomarker profiling upfront, adds Narayanan. Ontada, he says, is working with another McKesson unit, The US Oncology Network, to address this problem by developing educational outreach and digital tools to incorporate biomarker testing more smoothly into the physician’s workflow.

“The preliminary cost of a drug and its budget impact on any health plan can be refined and articulated over time by carrying out comparative effectiveness studies based on RWE once the drug has launched," explains Lee. "This is particularly important for any drug that is entering a crowded therapeutic space. Then you can go back to the payers and renegotiate pricing and formulary tier to maximize patient access.”

RWE also plays a role in clarifying budget impact and cost-effectiveness models for payers. “For example, studies involving RWD can help to identify which subgroups of patients within a given indication account for the highest cost, which one shows the greatest benefit from the treatment, and so on,” says Cyhaniuk. “We can then provide payers with a better understanding of how large these respective groups may be, thereby driving more-informed decision-making and linking clinical effect with cost.”

Axtria recently carried out a study using an approach called prevalence-based budget impact modeling (BIM) to demonstrate the value and affordability of the insulin biosimilar glargine (Gla-300) over basal insulins for treating type 2 diabetes. The study

 showed that redistributing the market share (by switching patients) from first-generation to second-generation basal insulins, along with increasing market share of glargine biosimilars over the years, demonstrated incremental cost reductions over time thanks to increased utilization of second-generation basal insulins. “We look forward to the findings to help guide decision-makers in terms of formulary placement and utilization controls,” says Lee.

In the US, “RWE provides an avenue to continue discussions with payers and to inform the most appropriate management of a product, but to be successful it must be coupled with other strategies, such as innovative and traditional contracting arrangements,” adds Monica Martin de Bustamante, senior partner and head of evidence, value, access, and pricing for Trinity Life Sciences. “Thinking through the best outcomes to capture, the methodology to deploy, and how else to supplement these data upon submission is key for success.”

Use cases: Supporting regulatory submissions

The growing number of scenarios in which RWE has played a pivotal role in regulatory approval or label expansion are making headlines. A few examples are discussed here:

In May 2022, Novartis received approval from the European Medicines Agency (EMA) for the approval of Kymriah (tisagenleceleucel), a CAR-T cell therapy for treatment of adult patients with relapsed or refractory follicular lymphoma.

 This approval was noteworthy, says Jimenez, because it was based on a single-arm trial, plus two studies based on RWD—a retrospective chart review of US and EU patients, and a non-interventional study using Flatiron Health data. Jimenez notes, “While EMA flagged some limitations with the RWD, the agency ultimately said the studies provided valuable context and were generally deemed supportive of the pivotal study, due to the clear differences in outcomes they show.”

Trinity Life Sciences conducted a real-world study using a chart audit to study the off-label use of a product for one pharma client. “We were able to demonstrate that the product was producing improved clinical outcomes for a particular subpopulation,” says Hadker. “This finding was pure gold because it showed the drug was able to address a population with high unmet need. The effort helped our client to de-risk its expensive and risky RCT, and drive multiple scientific publications. Today, the company has an expanded label.”

A similar effort bore fruit in 2019, when Pfizer was able to use RWE as a primary source of evidence to support the FDA label expansion of its breast cancer therapy Ibrance (palbociclib) for the treatment of males with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) advanced or metastatic breast cancer. The expanded US approval relied solely on an RWE study of off-label use of Ibrance in male patients.

The first-in-class tropomyosin receptor kinase (TRK) inhibitor Vitrakvi (larotrectinib) from Bayer and Loxo Oncology received the second-ever “pan-tumor” approval from FDA based on the high overall response rate 75% in a small clinical study

 of real-world patient cohort data from off-label use involving 55 patients with 17 different tumor types. “Neurotrophic tropomyosin receptor kinase (NTRK) gene fusions drive oncogenesis in a small subset of solid tumors, with estimated US annual cases number roughly 3,000,” explains Dede. “The use of EHR data enabled real-world patients with NTRK-mutant tumors (an exceedingly rare population) to be identified from a geographically diverse clinic-genomic database.”

RWD from Flatiron Health recently supported FDA approval of a supplemental biologics license application for Erbitux (cetuximab) from Eli Lilly.

 This approval provides a new, biweekly dosing regimen for the drug's indicated use in patients with certain forms of metastatic colorectal cancer or squamous cell carcinoma of the head and neck—significantly reducing the frequency of patient visits to an infusion center, a particular benefit for those with advanced cancer, says Jimenez.

In July 2021, FDA approved a new use for Prograf (tacrolimus) from Astellas, based on an observational study providing RWE of effectiveness.

 Originally approved to prevent organ rejection in patients receiving liver transplants, the drug was later approved to prevent organ rejection for kidney, heart, and lung transplants.

In April 2022, FDA granted accelerated approval to Novartis’ Vijoice (alpelisib) as the first and only drug indicated for select patients with PIK3CA-related overgrowth spectrum (PROS), a spectrum of rare conditions characterized by atypical overgrowths and anomalies in blood vessels, the lymphatic system, and other tissues. The approval was based solely on RWE developed from a retrospective chart review of just 57 patients, according to the company.

 To validate the clinical findings across a broader cohort of patients, Novartis is now carrying out additional clinical trials, and a follow-up trial involving patients from the initial study.

“Today, drug sponsors can and should proactively engage with all payers and with regulators in a parallel process informed by patients’ and providers’ input, as early as possible to investigate what types of RWE would be most meaningful to support approval and enhance coverage of the medication,” says Doyle.

Ontada's Narayanan notes that these efforts, on a broad scale, are providing real opportunities to change the course of healthcare by accelerating drug development and eliminating the evidence gaps that exist between drug approval and real-world care. “We’re on a maturity curve here, and we’re still in early days," he says. "While we’re making important strides, there is still a lot of untapped opportunity.”

1. US Food and Drug Administration press release, Oct. 19, 2022, Advancing Real-World Evidence Program; 

https://www.fda.gov/drugs/development-resources/advancing-real-world-evidence-program

2. Mitra-Majumdar, Mayookha, et al.,Analysis of Supportive Evidence for U.S. Food and Drug Administration Approvals of Novel Drugs in 2020, 

. 2022;5(5):e2212454. doi:10.1001/jamanetworkopen.2022.12454; 

https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2792372#:~:text=A%20total%20of%2075%20pivotal,46)%20were%20double%2Dmasked

3. Liu, Ruishan, et al., Systematic Pan-Cancer Analysis of Mutation-Treatment Interactions Using Large Real-World Clincogenomics Data, 

https://www.nature.com/articles/s41591-022-01873-5

4. Ponomareva, E., et al., The Economic Value Of Insulin Glargine 300 U/Ml (GLA-300) In People ≥18 Years of Age With Type 2 Diabetes Mellitus: A Value-based Economic Model From A U.S. Payer Perspective; ISPOR, 2022; 

https://www.ispor.org/heor-resources/presentations-database/presentation/euro2022-3567/122288

5. Novartis press release, Novartis Kymriah receives EC approval as first CAR-T cell therapy for adults with relapsed or refractory follicular lymphoma, May 4, 2022.; 

https://www.novartis.com/news/media-releases/novartis-kymriah-receives-ec-approval-first-car-t-cell-therapy-adults-relapsed-or-refractory-follicular-lymphoma

6. Miksad, R.A., et al., Small But Mighty: The Use of Real-World Evidence to Inform Precision Medicine, 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617709/

7. Flatiron press release, Flatiron Health Real-World Data Support FDA Approval of New Dosing Regimen for Erbitux (cetuximab), July 20, 2021; 

https://flatiron.com/press/press-release/flatiron-health-real-world-data-support-fda-approval-of-new-dosing-regimen-for-erbitux-cetuximab/

8. FDA press release, FDA approves new use of transplant drug based on real-world evidence (RWE), July 16, 2021; 

https://www.fda.gov/drugs/news-events-human-drugs/fda-approves-new-use-transplant-drug-based-real-world-evidence

9. Novartis press release, April 6, 2022, FDA approves Novartis Vijoice (albelisib) as first and only treatment for select patients with PIK3CA-Related Overgrowth Spectrum (PROS); 

https://www.novartis.com/news/media-releases/fda-approves-novartis-vijoice-alpelisib-first-and-only-treatment-select-patients-pik3ca-related-overgrowth-spectrum-pros

Stakeholders are investing heavily in their ability to curate appropriate data sets and devise advanced data-analytics capabilities to harness real-world evidence (RWE) across the entire pharma lifecycle—from drug discovery and development through product launch and commercialization

Pharma and RWE: Hitting Its Stride

The recently approved Inflation Reduction Act (IRA) of 2022

 is highlighting an issue that the biopharmaceutical industry has failed to address and that can no longer be ignored. Now that the US is effectively introducing price controls for at least part of the prescription drug market, it is time to consider payer and provider needs more seriously in drug development planning. The IRA comes at a time when outside the US, the biopharma industry is increasingly faced with payer rejections of new drug value propositions and subsequent market withdrawals or poor commercial results. Bluebird Bio’s withdrawal of Zynteglo from Europe after failed price negotiations was just one example of misalignment between biopharma expectations and commercial reality.

Inadequately addressing payer evidence needs is already leading to suboptimal pricing, and sometimes, an inability to launch in international markets. It is time that the industry realizes that placebo-controlled clinical trials, single arm trials, and surrogate endpoints may pass the FDA hurdle but may not lead to commercial viability. Biopharma companies now need to address a lingering value issue that they have so far largely ignored.

The IRA includes a redesign of Medicare Part D, allows the Centers for Medicare and Medicaid Services (CMS) to negotiate drug prices for Medicare Parts B and D, and penalizes inflationary price increases with Medicare rebates. The most concerning part about the new law is the “negotiation” aspect. The level of penalties for not complying with CMS rules gives a strong scent of direct price control rather than negotiations; this is also likely a basis for legal challenge of the IRA.

We must wait for further details on the implementation of the CMS negotiation framework. The IRA seems to have a relatively favorable impact on biologics vs. small molecules and on pharmacy benefits vs. medical benefits, although it remains to be seen how managed care will adjust its formulary management after patient cost for Medicare Part D is capped at $2,000 per year.

Assuming that (after legal challenge or not) negotiations will be based on value and competition, there is a high probability that the steepest discounts will fall on drugs that are medically deemed undifferentiated or insufficiently substantiated with evidence of patient or healthcare system value.

In many countries outside the US, drug price controls have dominated the commercial landscape for decades. Increasingly strict clinical and economic evidence requirements create steep commercial hurdles beyond the efficacy and safety-oriented regulatory approvals, such as by the European Medicines Agency (EMA) that officially signify market authorization, but in reality, just start payer discussions.

Amplified by COVID-19-related general economic and healthcare budget pressures, global governments are engaging in cost-cutting programs that include strict containment of prescription drug spending. ZS analysis

 shows that ongoing healthcare budget deficits will likely lead to more severe restrictions and lower prices or price cuts for undifferentiated drugs, increased scrutiny on value of high-budget impact therapies, increased use of biosimilars, and price pressures on combination oncology treatments and orphan drugs. As an illustration, the share of reimbursement submissions in France that were successful in securing a rating of ASMR I, II, or III, thus having a potential for a meaningful price premium over the standard of care, has declined from only 7.1% from 2006-2010 to an even lower 3.6% from 2016-2020. In Germany in 2021 and the first half of 2022, nine new drugs were withdrawn from the German market over health technology assessment (HTA) results and resulting pricing problems. Prior to 2020 this was a very rare event.

Payers increasingly scrutinize a lack of longer-term outcomes as proof of drug benefit. Where the medical community and FDA or EMA tend to accept surrogate endpoints as evidence of efficacy, payers demand evidence of sustained effectiveness. For example, payers look for a meaningful improvement in overall survival rather than a tumor response rate, or they demand a reduction in cardiovascular event rates instead of lowered cholesterol. Biopharma companies often do not take these requirements seriously, sometimes because they mistakenly believe these hurdles can be overcome and other times because they are simply not willing to take the clinical risk or make the additional time and monetary investment to show compelling data.

In an early 2022 poll, we asked payers in the US, Germany, France, Italy, Spain, and the UK to give their perspectives on biopharma pricing and reimbursement submissions and the main gaps that they experience. Four evidence gaps were highlighted as having the largest impact on payer assessments:

Figure 1 above illustrates that the ratings across countries vary somewhat. Overall, France and Germany tend to be most critical in their evidence requirements; ironically, these are also the most-referenced countries among the set, making pricing and access success in these two countries critical for ex-US global commercialization. In France, the choice of trial comparator is the highest-ranked concern, while in Germany, the single-arm trial is highlighted most. Spain and Italy also express substantial concerns. The US and UK are on the lower end of the concern spectrum and tend to be more willing to find ways of overcoming the evidence gaps through other analyses. For the US, the question is how this may change post-IRA.

The selection of the primary endpoint in pivotal clinical trials is the most frequently stated payer concern expressed across countries. US payers also express strong concern about this aspect. It reflects the consistent feedback from payers that, while clinicians make decisions based on surrogate endpoints as immediate signals on patient management, payers are highly skeptical whether, for example, in oncology, tumor response rates, or even progression-free survival (PFS) are leading to meaningful survival improvements. As a result, drugs with lack of compelling evidence of long-term patient health improvements are heavily penalized on price in countries such as France and Germany. An example is the CDK4/6 class, where Ibrance, Verzenio, and Kisqali received relatively poor pricing outside the US despite compelling PFS data, as these therapies lacked evidence of overall survival improvement at launch.

The choice of a comparative treatment arm in a randomized controlled head-to-head clinical trial is critical in many payer systems. Having chosen an inappropriate or no-longer-relevant comparator can have a drastic impact on the accepted benefit level. An example of an unfortunate outcome has been the withdrawal of Piqray

 from Germany and France after payers deemed the comparator treatment arm in the trial no longer the relevant choice since the adoption of CDK4/6 inhibitors. Particularly in France, Germany, Spain, and Italy, this is one of the most common reasons for an unsuccessful price negotiation.

When an appropriate comparator is not selected, payer acceptance comes down to the justification. It may not be a surprise that, what is often the real reason (“I am not sure I can win against product X”) is exactly the reason why payers insist on this point. Payers may say, “If you don’t have the stomach to show value, why do you expect us to pay for it?”

Single-arm studies are only deemed appropriate in situations where a properly powered head-to-head trial showing statistically significant improvements on important endpoints is not feasible, such as in the situation of many orphan drugs. Yet even in this situation, payers have become very critical. Germany is increasingly rejecting benefit claims for orphan drugs over lack of evidence through the single-arm trial, which we saw clearly illustrated by the German responses in our survey. This should be seen in the context of the German government’s actions to clamp down on what it considers an explosion of orphan-drug applications following Arzneimittelmarkt-Neuordnungsgesetz's (AMNOG) automatic exclusion of “no-benefit” designations for orphan drugs with limited revenue.

Payers are also concerned with a sustained effect of treatment. Shorter trials can satisfy claims regarding some surrogate endpoints. But initially, observed improvements can be reversed over time, resulting in a lack of longer-term improvements in outcomes. There is some overlap between this concern and the insistence on endpoints that reflect longer-term outcomes. For example, in kidney disease, a sustained change in estimated glomerular filtration rate (eGFR) may support claims of disease modification, although payers usually want to see a verification in terms of fewer patients progressing to end-stage renal disease.

The medical community has great expectations for real-world data (RWD). RWD offers opportunities to learn from statistical analyses of real-life outcomes across patient populations and treatments. But RWD can only be obtained in support of benefit claims after widespread use in medical practice and thus has limited use for initial approvals for new drugs. Since the 21st Century Cures Act of 2016, FDA has become more engaged in addressing the role of RWD in US marketing authorization approvals. Recent examples

 show some FDA willingness to use RWD as an external control arm and in addressing concerns regarding single-arm studies. However, payers are only slowly and highly selectively accepting RWD.

In our survey, payers strongly indicated that while RWD can be useful for treatment protocols and to orient physician prescribing, they do not accept uncontrolled observational data as a substitute for randomized controlled trial data. More specifically, payers indicated that RWD will generally not be accepted to overcome an inappropriate endpoint selection or trial comparator, or for choosing to conduct a single-arm trial (see Figure 2). Therefore, we should be very careful in assuming that evidence gaps can be fixed with RWD.

Many biopharma companies are still coming to grips with the need to demonstrate evidence of value beyond the very limited requirements that FDA is imposing. This is often a deep organizational issue, where development decision-making is heavily focused on speed and clinical risk reduction rather than a holistic evaluation of multiple options with respect to impact on commercialization. Many companies make market share projections based on theoretical potential, but ignore evidence needs to ensure that the potential comes to fruition. They tend to choose the fastest road to FDA approval, largely ignoring the evidence needs of the medical community, payers, providers, and prescribers. As a result, they lack acceptance in guidelines and have poor formulary placement and slow adoption.

There are several things that biopharma companies can do today to be better prepared for the evolving needs of payers and the emerging needs under the IRA.

Enforce better-structured access involvement in the drug development and corporate business development decision-making processes.

 The creation of a chief access officer is long overdue to ensure that access has a strong voice next to R&D, to add a sense of urgency, to build a stronger organizational understanding of payers and providers, and to pursue meaningful customer partnerships.

Incorporate access and pricing in forecasting and business decision-making. 

Medical community adoption in guidelines, payer/provider formulary uptake, and physician prescribing are highly correlated with the evidence provided beyond the FDA-mandated placebo-controlled trial. Forecasting needs to support trade-off analyses and decision-making that considers multiple development options. Scenario planning is important to address uncertainties with respect to further US market evolution within and beyond the IRA implementation.

Evolve global strategies to address commercial evidence needs in the US, Japan, Europe, and

 Consider regional evidence needs, and not just as a US afterthought. In some cases, it may make sense to launch in the US and Japan first, and strengthen US market uptake and European/Canadian pricing and access requirements with more robust evidence. However, price control timelines under the IRA could change that.

Ed Schoonveld is a managing partner and founder of the value and access practice at ZS, a management consulting and technology firm.

https://www.zs.com/insights/inflation-reduction-act-what-biopharma-needs-to-know

2. Stefan Kloss and Emmanuel Colliot, “Once bent out of shape: Implications from the latest cost containment measures to address post-COVID healthcare budget deficits in Western Europe,” ZS white paper, Oct. 2022.

3. Neena Brizmohun, “Piqray: German Withdrawal, European Reimbursement Deals & Broader Indication Plans,” Pink Sheet, May 6, 2022. 

https://pink.pharmaintelligence.informa.com/PS144276/Piqray-German-Withdrawal-European-Reimbursement-Deals--Broader-Indication-Plans#:~:text=Novartis%20has%20withdrawn%20Piqray%20(alpelisib,use%20across%20Europe%20last%20year

4. Bridget Silverman. “RWE On The Runway: Trio Of Upcoming Approval Decisions Will Test US FDA’s Thinking On RWE For Efficacy,” Pink Sheet, Sept. 5, 2022. 

https://pink.pharmaintelligence.informa.com/PS146958/RWE-On-The-Runway-Trio-Of-Upcoming-Approval-Decisions-Will-Test-US-FDAs-Thinking-On-RWE-For-Efficacy

Exploring the legislation's diverging requirements between regulatory agencies and global payers, and its impact on the commercial results of biopharma companies

The IRA is Industry's Wake-up Call

Artificial intelligence/machine learning (AI/ML) technologies have become increasingly mainstream in commercial life sciences, particularly in the form of next-best-action (NBA) systems for sales and marketing teams, as well as medical science liaisons (MSLs). The technology has evolved as many innovations evolve, progressing from basic algorithms to well-fueled intelligence engines. Now, NBA solutions represent the new era of data science in drug commercialization.

Along the same trajectory, life sciences companies have made significant investments in their data science teams to develop in-house capabilities. Spending by pharmaceutical manufacturers on data analytics is 

 to grow by a 27% compound annual growth rate and be worth $1.2 billion by 2030. Between maturing technology from external providers and growing data science capabilities within pharma companies, the industry is betting big on the power of intelligence technologies to improve commercialization.

However, is this substantial AI investment translating into a substantially better customer experience?

Not yet. We have the data and many of the right analytics models, but the resulting insights are not yet achieving the hoped-for improvement in healthcare professional (HCP) experiences with companies. Commercial AI is still experiencing growing pains—and the biggest pain is translating insights into action.

Many of today’s NBA recommendations are not delivered in a format that makes them easy for users to leverage in their daily workflows. Instead, recommendations are either delivered in mass batches at once, poorly timed, or sent without context. Consider this all-too-common scenario: a recommendation to email a physician comes two days 

 the sales rep has already met with that doctor in person. Such ill-timed recommendations are moot, since it contradicts the sales rep’s instinct not to overwhelm the physician and users can’t act. Even worse, reps start to grow distrustful of a technology that is already unfamiliar to them.

Today, internal and external AI resources are converging as early adopters like Genentech, Novartis, and Sanofi are working more closely with technology providers to fill in last-mile gaps and drive meaningful engagements with HCPs that are coordinated across channels and teams. A more refined approach that addresses the last mile first will help organizations turn intelligence into action and get the full return from their considerable AI investments.

One part of today’s data science revolution is the technology—the AI/ML systems, the algorithms being applied, and the analytics being created. The other part is the data itself. Traditional data sets have included prescription and sales data, pharmacy and medical claims data, and activity reports generated by field sales teams. Now, additional data sources, drawn from social media, mobile phone usage, and online activity add new dimensions to what can be fed into the analytical systems. The NBA suggestions that emerge from these systems are the “intelligence” guide commercial teams.

Consider, as examples, how pooling these data sources can generate answers to questions like these:

Which physician is most in need of a personal visit soon, based on queries each has been making on brand websites?

How can I arrange my day’s calls to make the best use of my time across my territory?

Of a variety of benefits that can be promoted for a brand, which ones are most significant at this time, given the relative trends of that brand and its competitors?

What changes in messaging should accompany a price change of a brand (a company’s or competitor's)?

How vital is having reimbursement and copay information, along with resources, for this physician at this time?

With all the various data sources available, it is not difficult today for NBA systems to generate many recommendations—too many, even. In fact, the number of NBA suggestions can paralyze users if those insights aren’t prioritized based on the individual user needs, territories, past engagement, and other real-world considerations. Advanced intelligence technology can serve as a curator or traffic cop for NBA recommendation, ranking them based on various criteria and parceling them out to users in palatable chunks while also doing a one-for-one accounting of results. NBA recommendations must be contextually logical to individual users so that they will act.

In this way, advanced NBA solutions address the last mile—first. Proven algorithms are incorporated into the systems during the initial solution implementation process to ensure that recommendations are provided to users in a format and timeline that makes it easy for them to act on. For example, delivering each recommendation in a workflow that makes action just one or two clicks away. In addition, the solution should take into consideration actions being taken with each HCP by other people in the organization so that customer engagement is well orchestrated. If, for instance, an HCP has just been sent a promotional email from the brand marketing team, a well-orchestrated recommendation would trigger a rep to follow up a few days later through the HCP's preferred channel.

The final stretch of this last-mile solution must also include a mechanism for user feedback. If users do not have any way to provide a reason for ignoring a recommendation, it can frustrate them. On the other hand, if users can easily provide fast feedback, then the engine will adapt, perform better, and instill greater confidence in its recommendations for a continuous improvement loop. This is a benefit particularly important in the life sciences industry, where things change quickly: new people, performance goals, territories, doctors. Nothing is static, and AI is particularly adept at managing change in real time.

Case study: Getting AI right in real life

One top-20 US-based biotech company is an innovator in leveraging data analytics for commercialization. The company came to Aktana shortly after it switched to a new CRM system. The switchover gave it the opportunity to better organize its salesforce data, which was combined with other sources to kickstart its NBA program. After the initial buildout for four therapeutic areas, the biotech began to add new therapeutic areas monthly; the company now has a total of eight.

The biotech, known for creating the first medicine for primary progressive multiple sclerosis, has reported positive experience with the NBA program and its sales representatives anecdotally reported improved customer engagement as a result of the suggestions. Further, by solving the last-mile challenge, more than 70% of the company’s sales representatives are actively using the system, exceeding initial benchmarks. Generally, Aktana customers see a 22% increase in priority HCP coverage and frequency, a 20% reduction in time spent pre-call planning, and 60% to 80% adoption of NBA tools.

By far, the most critical step of implementing a successful NBA program is driving internal behavioral change. Part of that responsibility falls on technology providers who need to take the time to understand corporate culture, commercial operations, and the people who will use the system. It is eminently possible to “soft launch” the solution, first by integrating existing sales call planning, then incorporating HCP-specific data such as channel preferences, and finally optimizing for omnichannel journeys and higher-level analytics.

Even as life sciences companies invest in internal data science capabilities, they struggle to solve for the last-mile challenge: the key to building user trust and improving customer experiences. It’s not an either/or decision, but rather, a mix of both an internally developed AI model and an open platform that can incorporate all data and AI models while making insights more palatable to users so they can translate easily into action.

Graham Rapier is the Vice President of Enterprise Accounts at Aktana.

How to make commercial AI actionable in the last mile, helping to better realize      data science investment and improve customer engagement

'Next-Best-Action' for Data Analytics

Companies across a wide range industries use machine learning (ML) to peel back layers of data and uncover valuable insights. These insights can help these organizations improve how they engage with their customers. Though biopharma has historically lacked the rich data of other industries, the rise in anonymized patient-level data has unlocked new opportunities for biopharma companies to use ML techniques to improve customer targeting efforts.

Using ML techniques, a drug manufacturer can mine large data sets to identify potential new patients (and prescribing physicians) and more effectively target those patients and healthcare professionals (HCPs). In the end, these efforts can help a biopharma company engage HCPs it may have ignored completely if it relied solely on traditional targeting methods.

Traditional targeting and the limits of human analysis

Traditional targeting techniques involve indexing and ranking a universe of physicians based on the company’s assessment of their propensity to prescribe. Using a mix of data (a company’s own, as well as data on analog drugs), marketing research, and market knowledge, a company can rank physicians based on their potential commercial value.

However, while traditional targeting techniques can help identify top potential prescribers, these techniques fall short when a team has to determine how to allocate finite sales and marketing resources among the lower deciles of physicians. Though these ostensibly lower-tier physicians aren’t anywhere near a company’s top prescribers, they can still offer value and a meaningful return on commercial investment. In a competitive marketplace, it’s well worth the effort to optimize targeting across the entire physician universe.

The problem is that in these lower deciles, the difference between physicians is difficult for even the most skilled analysts to define. And targeting all doctors in this group would require a large amount of resources. Further, differences between these physicians may be hidden deep within a big pool of data, where only a powerful algorithm can uncover them. Traditional targeting methods are inherently limited by the capacity of humans to identify and consider relevant metrics and variables. Digging through this data and uncovering hidden value is the role of ML, which can help a company grab the marginal value it may miss with traditional targeting methods. In biopharma, marginal value can equal significant revenue growth.

Therefore, the most important thing ML can do from a commercial perspective is expand the vantage point of a biopharma company as it surveys its universe of potential prescribers. By uncovering physician characteristics and insights about patient treatment journeys that are hidden in the data, ML techniques can help a company properly allocate resources to areas of commercial opportunity across the full universe of prescribers—from the top ones to the lowest decile.

While physicians are the focus of traditional targeting approaches, ML can enable commercial teams to look beyond the physician to the true customer, the patient. The increased availability of anonymized patient-level data enables companies to identify patients who could benefit from their therapies and then find physicians via the patient. Using ML, the team can paint a detailed picture of the patient treatment journey. With this knowledge, they can not only determine which physicians to target but also identify the proper time during the treatment cycle to intervene, raise awareness of the drug, and, ideally, help patients gain access to effective treatment.

For example, patients only become eligible for late-stage oncology treatments after they go through several other rounds of therapy. However, these treatment decisions are made on a case-by-case basis, and there’s no clear marker for when a patient is ready for the next line of therapy. However, ML can help drugmakers understand the pattern of treatment and begin to predict when patients will be ready for a late-stage course.

With all this in mind, the value of ML in physician targeting is clear. However, it’s important for commercial leaders to understand that ML is not the right solution for every company. Nor is it magic. Instead, it’s a computational algorithm that uses data and statistical modeling to make better predictions. ML leverages the power of a computer to do deep mathematical analysis based on the inputs provided by skilled data scientists. When constructed properly, a ML model will enable insight-gathering that goes beyond the surface level. But as with anything related to data analytics, it’s garbage in, garbage out. If a company doesn’t supply the model with reliable data that is relevant to the business questions the team seeks to answer, the model will fail to deliver valuable insights.

ML gains more power as it gains more data. With sufficient data, a drug manufacturer can configure, train, and validate the model. Then, it can put that model into action and use it to analyze, score, and predict. Without enough data, however, the model’s output will be limited.

Data also helps a company continually improve the model. Organizations should never view ML as a one-and-done activity. Instead, they should deploy ML models that are inherently iterative. In particular, with a supervised approach, data scientists train the model with known facts (e.g., key features of the company’s current patient pool) and ask the model to uncover datapoints that match those known facts.

A company, therefore, needs to take a hard look at where it stands in its data journey before committing to ML. A cash-strapped startup preparing for its first launch would be better off using a traditional targeting approach. But a company with a therapy on the market for several years that faces well-established and new competitors may need to expand the breadth and depth of its market penetration. A company in this position would benefit from taking a deeper look at its lower decile prescribers.

Once a biopharma company decides to embed ML into its commercial efforts, it should keep the following in mind:

 It’s important to go through a regular process of evaluating and recalibrating the model. Every six to 12 months, a company should retrain its ML models. The marketplace is constantly changing, a drugmaker’s patients may be changing, and, as a result, data is always in flux. The team must ensure the ML model captures these changes and continues to produce accurate and valuable insights.

An organization shouldn’t toss aside traditional targeting methods while it embraces ML. The optimal approach is often a blend of methods. For instance, marketing research can provide valuable qualitative insight into the patient journey and physician behavior. Additionally, companies should listen to their sales forces and build their local knowledge into any targeting plan. They can complement these methods with ML, which, as discussed, is designed to squeeze untapped value out of a company’s targeting efforts.

There are many software platforms on the market that can help biopharma commercial teams execute ML projects. However, to make the most of these platforms, a company needs access to a team with biopharma-specific data science expertise. Without access to this expertise, an organization will struggle to unlock the full potential of these ML platforms.

ML techniques, when designed well at the outset and optimized properly over time, can help a brand team gain valuable insight into its potential patient universe, those patients’ treatment journeys, and physician prescribing behavior.

Huan Tan, PhD, is an Associate Manager at Beghou Consulting.

How biopharma commercial teams can leverage machine learning techniques to improve targeting of HCPs and patients

The Value of ML in Customer Targeting

In the 1800s, people flocked to the mountains of the Pacific Coast to seek their fortune during the Gold Rush. A few decades later, the hot commodity was oil. Today, the landscape is digital, and, in the biopharmaceutical industry and healthcare settings, patient data is the new gold. The pick, shovel, and pan have been replaced with new tools and technologies like the internet of things (IoT), sensors, devices, and blockchain, all of which enable a new gold rush. These new tools and technologies can address ownership, trust, and privacy; manage the velocity of patient data growth; and create a path forward for new treatments and improved health outcomes.

Data generated by companies versus patients

Patient data comprises two broad categories. First is the patient data generated by healthcare institutions in a clinical trial or disease treatment setting, such as electronic medical records (EMRs), labs, and pharmacies. Second is the data generated by patients, whose use of genetic tools, smartphones, IoT ecosystems, websites, sensors, and surveys that monitor their lifestyles creates a new digital and non-digital environment. The resulting data are available for patients to download, but without medical training, some patients may not comprehend it.

Additionally, patients do not have total ownership of their data. After gaining customer consent, companies can sell patient data to third parties. While consent is great, the forms signed by patients are so long and complex that they may not fully understand what they’ve agreed to. These forms primarily free companies from liability when privacy breaches occur. True ownership of data means a patient should be technologically literate and understand how, when, and where their health data is used when it is sold to a third party. Privacy is important, but control of the data is equally important as, without proper controls, even the strongest patient data vaults can be breached.

Today, there is a trust deficit between patients and companies, as the latter continue to be lax on ethical standards in favor of monetizing data. Governance of patient data is also an issue. Who ensures that patient data is stored and leveraged according to current laws and the consent given? As more patient data is generated and used, the need for appropriate governance and stewardship of patient data will increase. Privacy laws are in their infancy and will continue to evolve. New roles in patient data governance will require familiarity with the sensitivity of patient data, rapid technology advancements, and awareness around emerging ethical and legal compliance issues. Policymakers must stay up-to-date with the technological advancements and loopholes of digital ecosystems. Currently, legal and ethical frameworks are too slow to keep pace with the rate this data is being generated. In addition, some estimate that, in the US, up to $30 billion per year is spent on unnecessary expenses. And based on "Big Data Healthcare Global Market Report 2022," released by The Business Research Company, the global big data healthcare market is expected to grow to $20.27 billion in 2022 at a compound annual growth rate (CAGR) of 11.02%, and reach $44.53 billion in 2026 at a CAGR of 21.75%.

But suppose medical devices were better integrated with IT systems and made to follow standards for interoperability, so that stakeholders could quickly and seamlessly access a patient’s medical information across various touchpoints and sources during a patient’s journey. This dream system would also be HIPAA compliant and able to reduce healthcare spending significantly.

The IoT, sensors, and healthcare devices are some of the main generators of patient data. These technologies are driven by increased demand, the broader use of cloud platforms, and a rise in the number of mobile apps. Therefore, we cannot escape the hyper-digitization in our lives. Aggregating, storing, and analyzing different patient data types is changing how we approach patient care. For instance, we are now able to detect diseases early and provide better health outcomes for patients because of the precision medicine and hyper-personalized care made possible by the massive amounts of patient data generated from fitness trackers, implantable devices, and such.

We already realize benefits from this increase in available data, but systems, policies, and ethical standards are not keeping pace with the incredible speed at which digital ecosystems are growing. These changes have created instability in the value chain, as stakeholders find themselves at varying places in their digital ecosystem learning journey and cannot adequately adapt to the velocity of data growth. Therefore, all stakeholders must collaborate to unlock the potential of patient data across the value chain.

The tremendous growth of patient data presents both opportunities and challenges. We now have access to the data necessary to develop new treatments, enhance patient outcomes, and predict personalized interventions more precisely than in the past. But the generalized mistrust between patients and healthcare stakeholders, as well as varying data standards, prevents us from unlocking the benefits of digital transformation. To be truly successful, patients must take center stage. They must have an equitable relationship in the use of their data. Only then can we define ownership so patients have control over their data and are involved in decisions about how their data is used to benefit the entire patient community.

Moving forward, we must use a combination of new technologies, policies, and trust to shape the future. Policies must use common language devoid of hidden agendas, so everyone understands. In today’s world, patients are open to giving up their data if they have explicit knowledge of its use and benefits. A cross-functional approach to forming governing bodies, where government, domain experts, patients, and technologists are at the core of policymaking, will allow patients to be involved early in the process. This kind of collaboration will develop the trust necessary to unlock the value of digital ecosystems.

The biggest problem is consent management. Today, companies can shrug off responsibility for data breaches and inappropriate personal data use by getting patients to sign consent forms without giving them options to negotiate the agreement. Moreover, the complexity of most consent policies reminds many of their high school reading of Shakespeare’s 

: To consent, or not to consent? That is the question. Consent and privacy documents must be simple and easy to read if they are to enable an equitable partnership among stakeholders.

While technology has caused some of these problems, it can also help resolve them by providing efficiencies across the value chain. Rapid strides in data science make it possible to store data in the cloud, lowering costs and increasing ease of access. Artificial intelligence and machine learning provide increased sophistication and access to processing and computation, making it easier to leverage patient data

Technologically, there are two broad approaches to managing patient data safely and securely. The first approach relies on centralized patient data lakes that move patient data into highly secure but completely centralized repositories where a centralized authority controls all safeguards and governance policies. This traditional approach is easier for companies and researchers because it enables tighter control and rapid analytics. However, in this approach, patients lose control over the ownership and usage of their data. Once consent is provided, the line between patient and patient data is lost forever, and patients must rely on others to ensure privacy and appropriate usage. Additionally, this approach is time-consuming and expensive. It cannot keep pace with the rate of growth patient data is experiencing.

A blockchain-based approach is more patient-centric and allows patients to own their personal data forever. It gives them a clear line of sight on data usage, purpose, and monetization. Currently, companies monetize data with no patient involvement other than their consent. Patients must have a more significant say in the monetization of their data. Pricing should be dynamic, based on use cases, and patients must be able to control privileges and set pricing. This method could create a marketplace for companies and researchers that results in pricing dictated by supply and demand. The resulting, encrypted health information can create a more comprehensive database, giving scientists access to valuable data faster than ever before. Blockchain smart contracts can automate processes, including payment and actions conditional on payment.

Blockchain can increase interoperability, protecting patients’ data confidentiality without compromising security or needing any central authority. The three pillars of blockchain, namely decentralization, transparency, and immutability, make it the perfect backbone for building patient data applications that incorporate appropriate safeguards and controls. In a decentralized system like a blockchain, information is stored in chunks and copied to the blockchain, spreading it across a network of computers. With blockchain smart contracts, patients own and control their digital, identity-containing data, reputation, and digital assets. Any counterparty using the data can only access data that the patient explicitly permits. Patients who want to send their health information to their new provider can do so directly without going through a third party. If a researcher or company wants patient data for their clinical trials, they can access data that the patient approves for use.

By allowing digital information to be distributed but not copied, blockchain is creating the foundation of a new type of internet. Moreover, in blockchain networks, counterparties will not need to hold sensitive data to verify transactions, thus reducing liability and increasing compliance and interoperability.

Blockchain is also one of the most secure ways to store patient data because a person’s identity is hidden with complex cryptography that helps maintain an incorruptible, decentralized, and transparent log of all patient data applications. Until now, this level of security has never existed within a healthcare system. Blockchain is also immutable, meaning it is nearly impossible to tamper with once something has been entered into the blockchain. The fact that no one can tamper with their health record can inspire patients’ trust. Lastly, a blockchain carries minimal transaction costs, but an infrastructure must be set up to achieve maximum benefits. It is extremely patient-friendly, a significant consideration as patient data applications become mainstream. The blockchain is a simple yet ingenious way of passing patient data in a fully-automated, safe, and cost-effective way from the patient to anyone needing it.

How privacy and technology literacy can coexist behind a blockchain-based approach  to patient data analytics and control   

Pharmaceutical Commerce - December 2022

Pharmaceutical Commerce - December 2022 Issue (PDF)

Sustainability: Its Importance Can't Be Ignored

Pharma and RWE: Hitting Its Stride

The IRA is Industry's Wake-up Call

'Next-Best-Action' for Data Analytics

The Value of ML in Customer Targeting

Patient Data: Solving Ownership Question

Succeeding with Fit-for-Purpose AI

Creating the Internet of Supply Chains