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Data integrity considerations in Pharma and Life Sciences

European Pharmaceutical Review

Life science companies are increasingly using advanced technologies like artificial intelligence (AI), machine learning (ML) and blockchain to improve data quality and integrity by detecting anomalies in data and establishing tamper-proof audit trails for all data-related activities Data governance. Good documentation practices.

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Transforming pharmaceutical manufacturing: The AI revolution

European Pharmaceutical Review

Accelerated drug discovery: Simulations and predictive models powered by AI will speed up the drug development process, effectively slashing time to market for drugs. A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. Available from: [link] Rajkomar A, Dean J, Kohane I. N Engl J Med.

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Data-Driven Connections: Linking Rare Disease Patients to Clinical Trials, Experts, and Resources

PM360

For more than a decade, access to deidentified sources of real-world data—especially administrative claims, patient registries, and more recently electronic health records (EHRs)—has been informing unmet needs, population sizing and forecasting, and commercialization and marketing activities.

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Uri Goren

Cadensee

21:40 At what scale is breaking away from traditional channel to market is happening on a wide scale in the industry? 52:21 Uri's opinion about the main problem that needs to be solved for the healthcare market with technology. It might be silly question but bringing a drug to market takes many, many years.

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Understanding the current and future state of complex health data protection laws

Pharmaceutical Technology

Rigorous standards established by governing bodies include consent forms and protocols related to storing and sharing of PHI. Violations specifically included lack of analysis to determine vulnerabilities to electronic PHI and failure to implement an authentication process to safeguard information. million consumers.