Barriers to Improving the Digital Performance of the Pharmaceutical Industry
Author(s)
Vutova Y1, Dacheva A2, Djambazov S3
1HTA Ltd., Sofia, 22, Bulgaria, 2HTA Ltd., Sofia, 23, Bulgaria, 3Medical University Pleven, Sofia, 23, Bulgaria
Presentation Documents
OBJECTIVES: Even though we live in the digital era and digitalization has a huge impact on our lives and businesses already, digital initiatives are still fragmented and scattered in the healthcare sector. The aim of this study is to present some of the practices that represent pharma’s biggest barriers to improving its digital performance.
METHODS:: A literature search in the PubMed and Cochrane databases undertaken in May 2022 resulted in the identification of relevant publications. The findings on the pharma’s biggest barriers to improving its digital performance are included in the subsequent analysis.
RESULTS: The main internal barriers were identified as: pharma companies do not pay enough attention to the customer decision journeys; pharma companies align on their strategy first and treat digital engagement as an aspect of execution, rather than as a central consideration in strategic planning; some operational gaps such as lack of digital roles and lack of tracking the digital budget. External barriers include the current healthcare regulatory structure, current reimbursement methodology, and fragmented sources of consumer data
CONCLUSIONS: Building on automation, digital technologies have the potential to revolutionize healthcare and help address some of the launch challenges of the current changing healthcare ecosystem. Digital technologies can even get life-saving treatments to market faster and strengthen innovations. Despite the recognized potential of the digital era, pharma companies still do not put the digital strategy as a main priority.
Conference/Value in Health Info
Value in Health, Volume 25, Issue 12S (December 2022)
Code
MSR63
Topic
Methodological & Statistical Research, Organizational Practices, Study Approaches
Topic Subcategory
Best Research Practices, Confounding, Selection Bias Correction, Causal Inference, Prospective Observational Studies
Disease
No Additional Disease & Conditions/Specialized Treatment Areas