Lee Feigert, PharmD, MSPBA

Specialty medications treat a wide range of conditions, from Crohn disease to cancer, and new specialty products are introduced every year. The drugs that treat high-touch, costly conditions often have a monthly price tag of a few hundred to tens of thousands of dollars and their spend continues to grow, the result of both an increase in drug cost and specialty therapy utilization. With ongoing growth across the board, plan sponsors are continuously searching for ways to minimize the impact on their bottom line.

Pharmacy benefit managers (PBMs) have begun offering plan sponsors a new prescription drug benefit plan design that revises the historical treatment of manufacturer payments for costly specialty medications. These plans are commonly known as co-pay accumulator programs or co-pay accumulator adjustment programs, depending on the PBM and the plan sponsor, and the industry 

has witnessed their relatively rapid and widespread adoption over the past few years. One of the most discussed and debated topics in 2018, these programs are an issue that various stakeholders grapple with on a daily basis and a challenge that will likely persist in the coming years.

Also called coupon adjustment, out-of-pocket protection, accumulator adjustment, and variable co-pay, the programs function in the same capacity regardless of their name and seek to dull the adverse financial impact on payers of manufacturer co-pay assistance.

How They Work and the Advantage for Plan Sponsors

Traditional plan designs have allowed for manufacturer assistance to count toward a beneficiary’s deductible and overall out-of-pocket maximum. This assistance has normally been in the form of a co-pay coupon or a co-pay card. Once the beneficiary’s out-of-pocket maximum is reached, the plan sponsor absorbs 100% of any further cost of the medication for the rest of the benefit year.

For example (Table 1), take a very simplistic (and likely unrealistic) traditional plan design, with monthly drug spend on a fictional single specialty medication spread over a 12-month benefit year among plan sponsor, manufacturer, and patient. These numbers are fabricated, and for the sake of simplicity, they ignore any other outside factors that could affect a beneficiary’s deductible and/or out-of-pocket maximum.

Assume this plan has an out-of-pocket maximum of $4000, an annual deductible of $2000, and a coinsurance component of 20% for specialty medications. Also, our fictional manufacturer in this example allows for a maximum coupon program amount of $12,000 for this medication, which itself costs $2000 per month.

In this example (Table 1), the manufacturer is paying toward the beneficiary’s deductible and the coinsurance component. After the annual out-of-pocket maximum is reached, the plan sponsor begins to pay the full cost of the prescription. This is the historical design behind plans that did not include a co-pay accumulator program. As shown, the beneficiary ends up paying nothing for their costly specialty medication, the manufacturer pays a respectable amount, and the plan sponsor shoulders the majority of the cost.

Now consider a very simplistic scenario involving a plan design with a co-pay accumulator component (Table 2). Assume all the same figures in our traditional plan assumption, except that any manufacturer payment does not count toward the beneficiary’s deductible, coinsurance, or out-of-pocket maximum.

Here the plan sponsor’s responsibility is significantly minimized, whereas the manufacturer and the patient are paying substantially more toward the overall yearly cost of this medication. This fact is not insignificant. Even with some manufacturers offering $15,000, $20,000, and even upward of $25,000 in coupon program maximums per year, a beneficiary may max out this coupon in 3 to 4 prescription fills because of the nature of the specialty products in question. Therefore, although a beneficiary may already be 3 to 5 months into a benefit year, they will then normally have $0 paid toward their out-of-pocket maximum for the year and toward their deductible, depending on whether the manufacturer includes any patient out-of-pocket obligation with their particular co-pay offset program (eg, $20 per fill). The patient may have some amount, albeit small, already paid toward both.

The patients affected by co-pay accumulator programs do not include Medicare Part D prescription coverage recipients. This is because manufacturer coupons can induce the purchase of an item or a service, such as the medication in question. Therefore, coupons are barred from use under federal health care programs, such as Part D plans, by the federal Anti-Kickback Statute. Still, the number of patients potentially affected by co-pay accumulators is high.

A Zitter Health Insights study noted that as of early 2018, approximately 60% of covered commercial lives were under payers that had already implemented a co-pay accumulator program, whereas an additional approximately 30% of covered commercial lives were encompassed by plans projected to implement such a program in 2019 or beyond.

In 2016, the American Community Survey of the US Census Bureau found the median US household income to be approximately $57,000. A beneficiary on a costly but life-sustaining specialty product can face dire consequences if they cannot shoulder thousands of dollars in out-of-pocket expenses to reach their deductible, especially for an unexpected expense mid benefit year. This can negatively affect adherence and outcomes, as beneficiaries may have no choice but to stop therapy either on a temporary or permanent basis.

Drug manufacturers need to brace for potentially severe financial repercussions as co-pay accumulator programs are adopted. Historically, manufacturers have not needed to account for 100% maximization of their coupon programs because of the previous plan designs. An underestimate of money they will spend toward their co-pay assistance programs could lead to unexpected effects on financial statements. Most manufacturers are now well aware of these perils and take appropriate steps to account for this likely increasing cost. But failing to consider the potential increase in their specialty medication consumers who will max out their coupon program could lead to dire consequences for financial projections and the meeting or exceeding of shareholder expectations.

Transparency of Co-pay Accumulator Programs 

The degree of transparency by plan sponsors regarding co-pay accumulator programs varies. It can often be a challenge for stakeholders to discern whether a plan is actively using such a program across all drug classes, is using a program selectively with only some medications, or has allowed the sponsors to prospectively pivot and use such a program through the wording in their contract, reserving them the right to no longer apply co-pay assistance toward a member’s deductible. As mentioned, the moniker given to such programs is not uniform among plan sponsors and PBMs, which adds to the confusion for any beneficiary who may change plans from one year to the next.

As PBMs continue to champion the adoption of these programs in the coming years, transparency will be paramount in allowing members to make informed choices about their upcoming benefit selections. User-friendly, easy-to-read documents should provide patients with a full grasp of the type of benefits they are choosing, ideally avoiding unexpected and shocking additional monthly drug costs that could balloon into thousands of dollars once their manufacturer coupon assistance is maxed out.

Forced to adapt, manufacturers may continue to find ways to combat decreasing margins directly related to increased costs from more specialty patients maxing out their co-pay assistance cap limits. One obvious, straightforward, yet clearly unpopular option will continue to be increasing drug list prices. Some more novel options manufacturers may consider are prepaid debit cards and/or rebate funds provided directly to patients, an adjustment of rebates to PBMs/plan sponsors in an attempt to offset the additional costs of co-pay assistance program spend, and the use of smaller cap limits on co-pay assistance programs for new products.

Scrutiny of co-pay accumulators will likely increase through 2019 and beyond. Some legal experts consider these programs to reside in a legal gray area, yet a highly transparent and robust disclosure to beneficiaries of the consequences of co-pay accumulator programs could help plan sponsors and PBMs mitigate any potential for legal risk.

As plan sponsors continue to adopt accumulator programs, an interesting topic for future research and ongoing analysis could be any correlation between decreased adherence to high-cost specialty medications and increased non-drug related costs for plan sponsors. As results from prior research related to high-cost specialty medications show, a lack of medication adherence affects not only the patient’s outcome but also a manufacturer’s bottom line, while increasing costs for the payer and the health system via increased trips to the emergency department and inpatient hospitalizations.

By shifting costs back to the beneficiary, does co-pay accumulator adoption ultimately lead to increased costs for payers in other areas because of decreased medication adherence by said beneficiary? The industry—including, potentially, payers&mdash;will likely examine this question in the coming years as the prevalence of co-pay accumulator programs continues to grow.

Although the literature has already extensively covered this topic, the hope here has been to provide more awareness and insight into this ever-pressing issue. Throughout the rest of 2019, expect to see continued growth in the adoption of and the controversary surrounding co-pay accumulator programs.

Articles

With ongoing growth across the board, plan sponsors are continuously searching for ways to minimize the impact on their bottom line.

Co-pay Accumulator Programs: Behind the Controversy

The crisis of opioid use, abuse, addiction, and subsequent overdose deaths has reached epidemic proportions in America with no clear end in sight. As of March 2018, the National Institutes of Health reported that more than 115 Americans per day are dying as a direct result of opioid overdoses. In late 2017, it was reported that the US life expectancy had dropped for a second consecutive year, due in part to a surge in fatal opioid overdoses. 

For perspective, US life expectancy had not dropped for a single year since 1993, which at the time was a direct result of the AIDS epidemic, and had not dropped for consecutive years since the 1960s. Further, the CDC has estimated that as a result of only 

opioid abuse, the total yearly economic burden to the United States totaled upwards of $78.5 billion—including the costs of health care, lost productivity, addiction treatment, and involvement of the criminal justice system&mdash;and clearly this shocking figure excludes the abuse of illicit opioids, such as heroin.

Complex problems require multidimensional answers. There are currently firms working to use artificial intelligence (AI), some through the use of machine learning (ML), in hopes that it will contribute to solving this problem or at least minimize its impact. An incredibly elementary explanation of the relationship between AI and ML is as follows:  ML is a subset of AI—and ML itself contains a further subset known as deep learning&mdash;and all ML can be considered AI, but not all AI is considered ML. To delve any deeper into the interconnected relationship between AI, ML, and deep learning would be beyond the scope of this article, but it is important to briefly note the distinction.

At times, there are conflicting views within the chemical dependency treatment field as to what the term 

 means, relating to the substances from which an opioid addict actually abstains. However, most would agree that recovery from opioid addiction, regardless of your view of what recovery means, is a challenging and sometimes seemingly impossible goal to not only achieve but also maintain on a long-term basis. 

Relapse is an unfortunate but common part of the story along the path of an opioid addict seeking recovery. And due to the fact that opioid tolerance can drastically decrease during a period of abstinence, relapse with opioids frequently leads to overdose and death. 

Here lies an area of opportunity in which AI may be useful in minimizing the deaths related to opioid overdose. Since 2014, a Chicago firm called Triggr Health has been using ML through its platform to both predict addiction recovery relapses and help target prevention strategies. 

Clients are referred by their health care provider to use the Triggr platform. As of mid-2018, Triggr was exclusively marketed to health care systems and providers, with future expansion planned that would also target individual consumers. After the client downloads the Triggr app to their smartphone, the ML-based platform monitors the client’s smartphone data and activity, such as texting patterns, phone location, and sleep history. 

The goal of this monitoring is to identify patterns that indicate a stronger likelihood of a relapse. Information gathered related to these patterns is combined with data such as client drug history and keywords such as “craving” from communications between Triggr Health users and staff, in hopes of identifying whether an individual is at risk of relapse. 

Triggr provides access to 24/7 support and in the event of a client’s risk reaching peak levels, a member of the individual’s addiction treatment team is notified of the situation as swiftly as possible so that immediate preventative action can be taken. Through this comprehensive and personalized solution based upon AI technology, Triggr seeks to save lives.

Behaivior (intentionally misspelled) is a Pittsburgh-based startup that also seeks to use an ML platform and algorithms to minimize relapse in recovering opioid addicts. However, Behaivior is attempting to distinguish its solution through the incorporation of wearable technology. 

The wearable device can collect objective data such as heart rate, skin temperature, and heart rate variability in real-time. When assessed in conjunction with the monitoring of behavioral data (such as location via GPS monitoring), the ML platform can assess whether the user is in a pre-relapse state of craving, a dangerous situation for any opioid addict in recovery. Although this company is still in its start-up stage, the idea that AI (through ML) could be used in such a manner to prevent relapse is certainly both intriguing and promising.

Clearly, there are barriers to adopting some of these technologies, such as patient hesitation to wearable devices, physical and data privacy concerns, HIPAA regulations, and related trepidations. However, the fact remains that we have a tragic problem that has progressively worsened—and although there have been areas of light at various points along the way, overall there is no end in sight regarding the crisis we currently face related to opioid addiction. Less talk and more tangible solutions are needed, such as those offered by Triggr and hopefully by Behaivior in the near future.

The opioid epidemic is a complicated, multifaceted problem and consequently will require a complex solution using numerous entities, stakeholders, and technologies. With the ability to be part of the solution through the application of data analytics and technology coupled with the added ability to scrutinize large amounts of data, AI can contribute to solving this critical and urgent problem.

https://www.npr.org/sections/health-shots/2017/12/21/572080314/life-expectancy-drops-again-as-opioid-deaths-surge-in-u-s

https://www.drugabuse.gov/drugs-abuse/opioids/opioid-overdose-crisis

https://medium.com/ibmindustrious/can-ai-help-stop-the-opioid-crisis-194aabf8d063

https://www.techemergence.com/artificial-intelligence-opioid-epidemic/

Lee Feigert earned his Doctor of Pharmacy from Duquesne University in Pittsburgh, PA.  For over five years, he was employed in a transitional-care pharmacist role at a 300+ bed inpatient psychiatric hospital. Currently he is employed as a consultant pharmacist for a Program of All-Inclusive Care for the Elderly (PACE) program in Pennsylvania. He most recently earned his Master of Science in Pharmacy Business Administration (MSPBA) program at the University of Pittsburgh, a 12-month, executive-style graduate education program designed for working professionals striving to be tomorrow’s leaders in the business of medicines. 

There are currently firms working to use artificial intelligence in hopes that it could contribute to minimizing the impact of the opioid epidemic.

Artificial Intelligence Can be Leveraged to Minimize Casualties of the Opioid Epidemic

The world is rapidly moving towards the adoption and seamless integration of artificial intelligence (AI), including machine learning (ML) as a subset of AI, throughout our daily lives. As a society, we are just now beginning to see how this technology will become an omnipresent part of life in the coming decades. 

Health care, including the pharmaceutical industry, is not immune to this adoption. Conversely, various stakeholders within the health care industry can be found leading the charge with the adoption of AI and ML into strategic planning and business models. In a recent survey of more than 50 executives from health care companies currently leveraging AI technology, greater than 50% of all respondents believe that AI will be ubiquitous within health care by 2025, while more than 25% of all respondents felt that a nearly ubiquitous adoption of AI would occur by 2025.

As a profession, pharmacy can still face challenges in proving the value it contributes to the health care space as a whole. As time progresses, it is likely that the swift adoption and effective use of AI technology will help to solidify the future value of the pharmacy profession.

Specialty pharmacies that desire to be competitive leaders in the field should be early adopters of available AI and ML technologies. These technologies should be viewed as a forward-looking, long-term investment in an asset that will enable differentiation in the market now and for years to come. 

By investing in emerging AI technologies, a specialty pharmacy is sending a signal to the market that it is looking to the future. Through the eyes of manufacturers, prescribers, and payers, such investments will position the pharmacy as a partner that will enable their patients to receive the highest level of precise care, with the greatest potential for positive outcomes at the lowest achievable costs.

How will specialty pharmacy leverage AI and ML to ensure the most beneficial outcomes for all stakeholders? The use of AI will enable an even greater level of personalized, high-touch care for patients. While already a hallmark of specialty pharmacy, this type of care will be augmented by AI technology in the coming years. 

AI will enable specialty pharmacies to provide care in a more precise manner to individual patients, with a focus on prevention and personalization that has not yet been seen. This increase in personalization will be symbiotically related to the ability of AI to process and analyze large amounts of data in a hyper-efficient manner. 

Improvements in budgeting, lower operational costs, and improved overall organizational efficiency will be seen as a positive result of AI data analysis. For instance, although not directly related to pharmacy, a University of Toronto professor has recently used AI technology to plan radiation treatment for patients. 

This advancement allowed for the completion of treatment plans in 4 minutes, itself a fractional amount compared with the previous average of greater than 2 hours, prior to the implementation of the AI technology.

 This same concept of exponentially increased organizational efficiency can be applied to pharmacy platforms for patient therapy, including within the specialty pharmacy arena. 

Most importantly, the increase in efficiency gained elsewhere throughout the organization will be the catalyst that enables pharmacists (and other clinicians) to devote even more time providing personalized, detailed, high-touch care to their patients. Developing approaches to personalized drug combinations that treat various disease states, working towards increased understanding of disease processes, enabling a better and more efficient design of effective treatment options, and research and development of both diagnostic and treatment options in multiple areas are all ways in which manufacturers will continue to leverage AI and ML in their business models. 

Clearly, companies such as these will want to partner with like-minded pharmacies that also use sophisticated technology as part of their strategic and business planning. As an example dating to 2016, IBM’s Watson Genomics (offered through IBM Watson Health) formed an alliance with Quest Diagnostics to leverage AI in the understanding of disease states. 

In particular, this partnership seeks to further develop the field of precision medicine with an integration of cognitive computing and genomic tumor sequencing.

 With a focus on developing approaches to personalizing drug combination therapy for acute myeloid leukemia (AML) through an increased understanding of precision medication, Microsoft’s Hanover Project has partnered with the Knight Cancer Institute in the advancement of AI and ML technologies to positively impact outcomes for AML patients.

ML also has high potential for reducing the overall drug development process timeframe, ultimately leading to lower costs and an increased volume of specialized agents coming to market. A common grievance in American society today is that even with recent attempts at improvement, the current FDA drug approval process still moves at a much slower pace than almost all stakeholders would ideally desire. 

One principal reason for this sluggish pace is the large amount of money and time spent in the various stages of clinical trials. Despite being in minimal practice currently, McKinsey & Company predicts that in the very near future, the use of AI and ML technologies will increase the breadth, depth, efficiency, and monitoring of clinical trials across multiple areas. 

Some ways in which the clinical trial space will benefit from AI and ML technology include applying predictive analytics in the identification of candidates for clinical trials through previously untapped channels, such as social media and doctor visits, real-time remote monitoring and analysis of clinical data, increased ability for targeting precise sample sizes, and using electronic medical records for reducing data errors. All of these examples will increase clinical trial safety and efficiency while decreasing overall costs and time, which in turn will ultimately increase the speed with which new medications can be brought to market.

The reciprocal relationship of expedited drug development and increased market volume of specialized agents will have a direct impact on specialty pharmacy in an obvious sense—with more drugs coming to market, there will be greater opportunity for specialty pharmacies to win contracts and business. Consequently, there will be even greater competition among pharmacies to show their individual value and relevance in the space with the hope of gaining the long-term partnership and repeat business of manufacturers. 

What better way to complete this cyclical relationship, showing value and relevance, by being a specialty pharmacy that can offer the use of AI technologies as an integrated component of their business model. Prescribers will also view specialty pharmacies that use AI more favorably than specialty pharmacies that do not adopt this technology. 

Showing how a specialty pharmacy can save time for a prescriber and/or their office staff is paramount when communicating value to prescribers. Minimizing the time prescribers and their staff need to spend dealing with paperwork, administrative problems, and other housekeeping issues will immediately set a specialty pharmacy apart from their peers. 

With the use of AI, operational processes can become exponentially streamlined. Therefore, more support staff are available to assist prescribers with a variety of functions, such as prior authorizations and copay/financial assistance. Overall, time and money are saved for the prescriber, drastically increasing that pharmacy’s value proposition to this important stakeholder. 

As AI and ML technologies become more complex and refined, prescribers will more readily adopt the use of these technologies to assist in the diagnosis of conditions and choice of therapies to maximize patient outcomes. In the study referenced at the beginning of this article, the executives who responded to the survey ranked “Decision Support Systems” (for improving patient outcomes) as the most likely application that AI would improve in the next 5 to 10 years.

If a prescriber uses AI in their decision-making processes, they will certainly be more inclined to partner with a compatible specialty pharmacy that utilizes AI in their strategic planning and business model. For a payer, partnering with an AI-enabled specialty pharmacy will be a mutually beneficial proposition. 

The specialty pharmacy will gain access to the plethora of lives covered by the payer, while the payer will know its specialty pharmacy partner will be providing the highest level of precise care possible at the lowest costs, due to the many aforementioned advantages provided by AI and ML. Although it is always an ever-present issue, payers have increased their scrutiny of costs over recent years, particularly in the specialty drug spend category. 

Any ability to decrease costs that a specialty pharmacy can show to a payer is clearly an advantage. As mentioned previously, a specialty pharmacy known to have adopted and integrated AI technology into its business model and operations sends the signal that has the ability to decrease costs compared with its competition.

Health care continues to move towards value-based contracting and care. Consequently, payers will continue to favor partnerships with other entities, including specialty pharmacies, that can show value in the care they provide and who are comfortable with—and even champion the use of&mdash;a value-based contract.

Since AI and ML allow for an increase in the sophistication and depth of data collection and analysis, payers will prefer to choose AI-enabled specialty pharmacies as partners for the potential to more robustly show value in the care provided. Currently, there are multiple companies striving to propel AI adoption within the health care space, including within the specialty pharmacy arena. 

Large, international companies such as Google and Philips seek to be leaders in health care AI, but there are also multiple smaller companies seeking to leverage their flexibility and versatility as they build to make a big impact both now and in years to come.

This use of ML results in streamlined processes, decreased waste, and decreased costs, all of which ultimately unburden health care providers and organizations so they are able to more effectively focus on their patients in a high-touch manner.

We are only now beginning to scratch the surface of the potential held by AI and ML within health care.  Increases in organizational efficiency and time available for pharmacists to provide high-touch patient care, coupled with lower costs and waste are all positive results that a specialty pharmacy will see with the adoption of AI and ML technology. 

In the pharma health care sector, particularly within specialty pharmacies, the opportunity to be a ground-floor adopter of these technologies will provide a unique and differentiating competitive advantage now and for years to come.

Machine Learning in Healthcare:  Expert Consensus from 50+ Executives

https://www.techemergence.com/machine-learning-in-healthcare-executive-consensus/

How Data Analytics And Artificial Intelligence Are Changing The Pharmaceutical Industry

https://www.forbes.com/sites/forbestechcouncil/2018/05/10/how-data-analytics-and-artificial-intelligence-are-changing-the-pharmaceutical-industry/#47189a553644

		Hodges, B. D. (2018), Learning from Dorothy Vaughan: Artificial Intelligence and the Health Professions. Med Educ, 52: 11-13. doi:

		Jha S, Topol E; Adapting to Artificial Intelligence:  Radiologists and Pathologists as Information Specialists; 

7 Applications of Machine Learning in Pharma and Medicine

https://www.techemergence.com/machine-learning-in-pharma-medicine/

Lee Feigert earned his Doctor of Pharmacy from Duquesne University in Pittsburgh, PA.  For over five years, he was employed in a transitional-care pharmacist role at a 300+ bed inpatient psychiatric hospital. Currently he is employed as a consultant pharmacist for a Program of All-Inclusive Care for the Elderly (PACE) program in Pennsylvania. He most recently earned his Master of Science in Pharmacy Business Administration program at the University of Pittsburgh, a 12-month, executive-style graduate education program designed for working professionals striving to be tomorrow’s leaders in the business of medicines. 

The world is rapidly moving towards the adoption and seamless integration of artificial intelligence (AI), including machine learning as a subset of AI, throughout our daily lives. 