The Future is Now: Artificial Intelligence and Machine Learning in Specialty Pharmacy

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.
 
References
  1. 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/
  2. 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
  3. Hodges, B. D. (2018), Learning from Dorothy Vaughan: Artificial Intelligence and the Health Professions. Med Educ, 52: 11-13. doi:10.1111/medu.13350
  4. Jha S, Topol E; Adapting to Artificial Intelligence:  Radiologists and Pathologists as Information Specialists; JAMA; Published online November 29, 2016
  5. 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. 
 



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