The Future is Now: Artificial Intelligence and Machine Learning in Specialty Pharmacy
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.
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 2025.1
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.2-4 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.5 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.5
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.5
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.1
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—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.
- Fagella, Daniel; Machine Learning in Healthcare: Expert Consensus from 50+ Executives; techemergence.com; June 20, 2017; https://www.techemergence.com/machine-learning-in-healthcare-executive-consensus/
- Chilcott, Meghann; How Data Analytics And Artificial Intelligence Are Changing The Pharmaceutical Industry; Forbes.com; May 10, 2018; 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:10.1111/medu.13350
- Jha S, Topol E; Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists; JAMA; Published online November 29, 2016
- Fagella, Daniel; 7 Applications of Machine Learning in Pharma and Medicine; techemergence.com; June 1, 2018; https://www.techemergence.com/machine-learning-in-pharma-medicine/
About the Author
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.