B5 - Embracing the technology revolution in pharmacy – How can you help lead the movement?

Paris 1

Organised by the FIP Academic Pharmacy Section in collaboration with FIP’s Community Pharmacy Section & Social and Administrative Pharmacy Section & IPSF & YPG


Safeera Hussainy, Monash University, Australia and Whitley Yi, University of North Carolina Medical Center, USA


Technologies such as the internet, smartphones, cloud-based apps, artificial intelligence (AI) and machine learning (ML) methods have changed the way we live. These technologies offer unprecedented access to information, dramatically enhanced ability to communicate remotely, and increasingly sophisticated decision support tools. Healthcare is not immune to these ‘disruptive’ technologies, and long-held paradigms of care are being challenged.

It is impossible for the pharmacy profession to ignore the potential these technologies offer for patient care and already the use of certain digital technologies has become standard practice in some jurisdictions. There are however potential barriers to uptake of   these technologies by pharmacists, such as concerns for patient privacy, lack of motivation to change, absence of appropriate reimbursement models or legislation, potential costs and knowledge and familiarity with the technologies. Being able to effectively research, evaluate and apply these emerging technologies within healthcare and pharmacy also requires skills and competency in interpreting AI/ML literature and therefore practicing evidence-based AI/ML.

One of the key steps to closing the implementation gap of AI/ML in healthcare is making them more accessible to pharmacists. This session has three aims: 1.) demystify AI/ML and increase familiarity with key AI/ML concepts; 2.) provide the tools needed to interpret and understand the evidence behind AI/ML applications; and 3.) present case studies of such technologies in use by pharmacists, including identifying lessons learnt to support effective further dissemination. Subsequent small group discussions will critique these case studies and draw on their own experience to identify (1) barriers and enablers to introducing such new technologies internationally, (2) priorities for research and development, and (3) adaptations required to existing models of care for successful implementation in other health systems.

It is desired to generate a report to FIP of session outcomes to illustrate best practice and identify future opportunities for the uptake of digital technologies and AI/ML by pharmacists. We will also seek to illustrate the use of the case study technologies either via live demonstrations or video presentations.


  1. Introduction to artificial intelligence (AI)/machine learning (ML) methods, process of building an AI algorithm and understanding available best practices
    Whitley Yi, University of North Carolina Medical Center, USA
  2. Using AI to assist health workers in Tanzania
    Ally Jr Salim, Inspired Ideas, Tanzania
  3. Using cloud-based technology to enable assessment in primary care
    Ross Tsuyuki, University of Alberta, Canada
  4. The use of telepharmacy to provide disease management services for rural communities
    Christine Bond, University of Aberdeen, Scotland, UK
  5. Small group discussions critiquing the case studies
    Moderator: Kevin McNamara, Deakin University, Australia

Learning Objectives

At the end of this session, participants will be able to:

  1. Describe the process for building simple machine learning algorithms, utilizing appropriate open-access resources and available tools (workshop)
  2. Identify clinical areas where digital technologies and AI can impact patient care, applying principles of evidence-based medicine to AI/ML applications
  3. Define the risks and benefits of using new technologies and AI/ML to support community pharmacist prevention and management of chronic disease
  4. Evaluate and document approaches to adapting innovative models of care using new technologies and AI/ML across health systems (small group work)

Type of session: Application-based