Technology

AI has huge potential, but the key to ROI is in the application, writes Alan White.

With a growing abundance of clinical and health information now available and achievable, the need for industry to apply common structures and rules to interpret and act on this data is critical.

Realizing the full potential of enabling technologies such as AI and machine learning in measuring treatment value and outcomes.

In keeping pace with the digital transformation, pharma companies are doing pretty well with data and infrastructure, but a fundamental reshaping of operating models remains the biggest hurdle to moving forward.

While commercial websites can employ analytics to gain better snapshots of their visitors, pharma faces far greater challenges in using technology as a way to track and understand its consumers.

Pharm Exec speaks to Katya Svoboda, Taneesha Chawla, and Tanvi Ahuja about the growing practice of applying AI and machine learning in value-based and outcomes-based contracts.

The importance of KOIs in tapping into today’s market mindset.

The key steps to evading automation-fueled data crises.

William Golden, founder, chairman and CEO of Noveome Biotherapeutics, talks about the potential of the company's lead product, ST266.

Sathyanarayanan Krishnamurthy looks at the potential impact of Pharma 4.0 on product development and regulatory operations.

Biopharma companies hungry for digital talent must evaluate their real estate portfolio and workplace strategies to access new talent pools, says Joanne Henderson.

It is worth keeping patients, clinicians, pharmacists and the wider public front of mind when evaluating priorities and best next steps towards Identification of Medicinal Products(IMDP) and other emerging standards, writes Frits Stulp.

Every big shift will stir up dissenters, but these negative forces can actually offer a useful contribution if their concerns are fed into a continuous change program. This could make all the difference in regulatory information management transformation, writes Steve Gens.

Patient support program providers should incorporate new technologies for more effective processes, but this should be matched with empathy, human connectivity and personalized clinical interventions, writes Tommy Bramley.

Many real-world data sources contain unstructured text, making it difficult and time-consuming to glean actionable insights from the data. Natural language processing technology can alleviate this problem, writes Jane Z. Reed.

Aiden Flynn discusses mining real-world data pools to transform drug development.

Clive Glover and Mark Szczypka discuss the complexities of scaling up the manufacturing of gene therapies and gene-modified cell therapies to industrial levels.

The cell and gene therapy space remains fertile territory for growth, exploration, and discovery. How applying a data-driven model may be the best way to approach this complex ecosystem and assess the innovations of tomorrow.

How to navigate the production and reimbursement intricacies of bringing regenerative medicines from bench to bedside.

Exploring the EU’s struggles and new efforts in promoting cell and gene drugs.

Why Key Online Influencers are so important and how to leverage them.

By the end of 2020, it is predicted that AI will create more jobs than it is taking. Should reps be concerned, asks David Logue.

There’s no need for companies to turn to fancy technologies in order to meet Drug Supply Chain Security Act compliances by the deadline, writes Gina Parry.

More signs of alignment between startups, pharma, and payers.

Automation is an essential aspect of data management and processing today in the pharma industry. At the same time, it’s important for companies to strike a balance between humans and machines, write Emily Eller and Kevin Frymire.