Pharmacy Talk with IBM Watson Health

December 21, 2021by PPN Marketing

IBM Watson Health | Pharmacy Talk


Demystifying the use of AI in Pharmacy

Episode 1:

How the use and training of AI-based medication management will impact hospital pharmacy. IBM® Micromedex® with Watson™ combines evidence-based drug content with advanced AI search capabilities to deliver rapid, reliable answers at the point of care. Improve care experiences and efficiency with insights into drug selection, dosing, toxicology and IV compatibility – accessible from your EHR, computer or mobile device.

  • Primer, 101 for AI in Pharmacy – terminology, examples in med use process
  • Augmented intelligence vs. Artificial intelligence, leveraging the strengths of computers and clinicians together to obtain improved outcomes for patients
  • Why should you know this area? Domain expert and contribute as a translator, clinical voice in the room
  • Hierarchy of AI from AI to Deep Learning
  • Critical components of high-fidelity AI – volume of data, algorithms-built w/out confirmational bias, recurring opportunities for evolution, training the models, et.
“Artificial intelligence” is a general term used to describe the theory and development of computer systems to perform tasks that normally would require human cognition, such as perception, language understanding, reasoning, learning, planning, and problem solving. Following the fundamental theorem of informatics, a better term for AI would be “augmented intelligence,” or leveraging the strengths of computers and the strengths of clinicians together to obtain improved outcomes for patients. Understanding the vocabulary of and methods used in AI will help clinicians productively communicate with data scientists to collaborate on developing models that augment patient care. This primer includes discussion of approaches to identifying problems in practice that could benefit from application of AI and those that would not, as well as methods of training, validating, implementing, evaluating, and maintaining AI models. Some key limitations of AI related to the medication-use process are also discussed.
The use of AI gives pharmacists more of an opportunity to take an active role in patient care, which is extremely important as value-based care models continue to take center stage in the health care space. Pharmacists can become overwhelmed with managing drug inventory.  Pharmacists are highly trained in patient care and yet, they too often must act as de facto supply chain experts to keep their hospital stocked with the medications it needs. With AI, pharmacists can direct their energy on patient care, as recognized in an official capacity in some states.
Artificial intelligence (AI) focuses in producing intelligent modelling, which helps in imagining knowledge, cracking problems and decision making. Recently, AI plays an important role in various fields of pharmacy like drug discovery, drug delivery formulation development, polypharmacology, hospital pharmacy, etc. In drug discovery and drug delivery formulation development, various artificial neural networks (ANNs) like deep neural networks (DNNs) or recurrent neural networks (RNNs) are being employed. Several implementations of drug discovery have currently been analysed and supported the power of the technology in quantitative structure-property relationship (QSPR) or quantitative structure-activity relationship (QSAR). In addition, de novo design promotes the invention of significantly newer drug molecules with regard to desired/optimal qualities. In the current review article, the uses of AI in pharmacy, especially in drug discovery, drug delivery formulation development, polypharmacology and hospital pharmacy are discussed.
About IBM® Micromedex® with Watson™

IBM® Micromedex® with Watson™ combines evidence-based drug content with advanced AI search capabilities to deliver rapid, reliable answers at the point of care.


Whitley Yi, PharmD, BCPS: Pharmacy Specialist, Digital Health Clinical Operations, Well, Co-Founder, AI Collective, Lecturer, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences

Scott Nelson, PharmD, MS, CPHIMS, FAMIA, Program Director, MS Applied Clinical Informatics (MSACI), Assistant Professor, Biomedical Informatics, Assistant Clinical Director, HealthIT, Vanderbilt University Medical Center

Arti Bhavsar, PharmD, Clinical Program Director, IBM Watson Health

Drug Shortage Management and Supply Chain Solutions

Episode 2:

Join the Pharmacy Podcast Network and IBM Watson Health for another podcast, episode 2 in the 4 part series, focused on how to improve efficiencies across your organizations’ drug shortage management and supply chain. Listen to three experts discuss:

  • Drug shortage management and prevention – from classification and communication to key stakeholders to how COVID-19 has impacted hospital drug shortages
  • The role of technology & data in assisting pharmacists in solving these inventory and management challenges
  • How automation and emerging technology can help pharmacists provide the best care for their patients

OrbitalRX, now with IBM Micromedex, combines real-time inventory awareness and clinical decision support capabilities to help hospital pharmacies proactively manage drug shortages and efficiently identify effective alternatives. View the latest evidence-based treatment information, check your hospital’s current drug inventory status, and provide purchasers with a consolidated view of procurement options – all within a single solution.

Learn more here: https://www.ibm.com/products/orbitalrx


  • Nate Peaty, PharmD, MS, Chief Strategy Officer and Co-founder | OrbitalRX
  • Brian Spoelhof, PharmD, Assistant Manager of Pharmacy – Medication Utilization Strategy | University of Virginia
  • Chris Virgilio, PharmD, BCPS Clinical Program Director, Micromedex | IBM Watson Health

Enhance Care Decision Making with Evidence & AI

Episode 3: 

In many ways, AI has been applied in medicine, such as in drug development, business intelligence and patient care. It has also been applied to clinical decision support tools – where it can help rapidly surface insights from vast libraries of biomedical information to facilitate fast, informed decisions.

For clinicians to be comfortable with AI in supporting decision making, trust is essential. Trust can be influenced by several human factors, such as user education, past experiences, user biases and perception towards automation, as well as properties and the reliability of the technology itself.1 However, nothing builds trust like results. Over the past two decades of AI use in healthcare, a body of evidence is emerging on how AI can help in areas such as clinical decision support.

Special guests:

Alan Ehrlich, Executive Editor at DynaMed and an Associate Professor in Family Medicine at the University of Massachusetts Medical School in Worcester

Bio: Alan Ehrlich, MD, FAAFP is the Executive Editor at DynaMed and an Associate Professor in Family Medicine at the University of Massachusetts Medical School in Worcester. He is board certified from the American Board of Family Medicine and a Fellow of the American Academy of Family Physicians. Alan graduated from Rutgers New Jersey Medical School in 1984 and completed his residency at the University of Massachusetts Medical Center in 1987. He is involved in the teaching of Evidence-Based Medicine to third year medical students and writes the Evidence-Based Medicine column for Clinical Advisor magazine.

Eileen Yoshida, Deputy Editor of Medication and Clinical Informatics, EBSCO Health

Bio: Eileen Yoshida is the Deputy Editor of Medication and Clinical Informatics at EBSCO Health. In this role, she leads the medication and clinical informatics strategies for the organization. Eileen brings over 25 years of experience in the healthcare industry, most recently leading the Clinical Knowledge Management and Decision Support team at Partners HealthCare. Previously, she served with Capgemini as an EHR implementation strategist and with the University of Chicago Hospitals in a special project’s role. Eileen has extensive experience in clinical informatics, EHR implementation, healthcare consulting and clinical pharmacy and is a published author and speaker on clinical decision support. She holds a degree in Pharmacy from the University of Toronto and a master’s in business administration from the Kellogg School of Management at Northwestern University.


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Courtney Holmes, RN, Executive Clinical Consultant, IBM Watson Health

Bio: Courtney Holmes is a Clinical Program Director supporting IBM’s Micromedex solution suite, as well as an Executive Clinical Consultant with Watson Health. Courtney focuses on providing clinical leadership in the sales and development of cognitive solutions and engagement with key stakeholders to support meaningful use of Watson Health solutions. She provides subject matter expertise for the Healthcare. Provider Segment. This includes providing strategic and clinical expertise to ensure high value development/improvements and intuitive, seamless clinical workflow design.

Courtney collaborates among global product offerings teams to ensure clinically relevant approaches to client engagements, and leads Watson Health solution sales activities through client briefings, demonstration of solutions, workshops and other formats as needed.
Ms. Holmes joined IBM in 2018 and brings 17 years of extensive oncology nursing experience to the team. Her experience ranges from direct clinical care at some of the top hospitals in the country, to developing the nurse educator program for a large pharmaceutical company and leading a team of nurses in her region. Courtney spent time building strong customer relationships with key nursing organizations, targeted accounts, and other identified key customers. She also focused on providing clinical expertise and support to sales to optimize overall customer experience, as well as helping facilities achieve better patient outcomes by improving overall quality of care and providing insight on best practices.

Blog post: https://www.ibm.com/blogs/watson-health/how-ai-infused-clinical-decision-support-may-change-medicine/