VP of Biometrics and Data Operations, Michael Goedde, was interviewed for the Alldus podcast to speak about how Artificial Intelligence (AI) and Machine Learning (ML) are transforming the healthcare industry. He discusses the move towards the advancement of medical services, decentralised trials and how ICON is leading these trends in the industry.
The healthcare industry generates large datasets consisting of useful information on patients’ medical procedures, demographics, insurance, treatment plans and diagnostic details. Data science helps to manage, analyse and assimilate this massive pool of fragmented, structured and unstructured data created by the healthcare systems. The insights gained from this data aids in strategic decision-making in healthcare and production of drugs. In this podcast, Michael shares his journey in technology and his role in building the digital platform for healthcare systems at ICON. As the head of a team of around 650 people, he brings a global team together that builds databases to support this digital transformation. They typically engage with electronic data capture systems (EDC), ensuring data integrity meets the required standard for global submissions. Their work is to affirm that the various systems that collect this clinical data, such as sensors, electronic patient reported outcomes, lab data and third-party data are efficient and maintain the highest integrity standards. Their aim is to use the most sophisticated technology such as Machine Learning (ML) and Artificial intelligence (AI) to ingest and review this data. Michael explains that clinical data has augmented in volume and complexity which requires more advanced methods of analysing them.
He particularly talks about ICON and its industry leading role in using data science in healthcare. In the present times, there has been a decisive shift towards more and more decentralised trials where non-traditional methods of data collection have become rampant. A central lab is now not the only source of data for clinical trials but there are several different sources such as EDC, Electronic Patient Reported Outcome (EPRO) and specialty labs from where clinical data can be collected. This has led to the increasing complexity of managing this data. ICON uses sophisticated methods in the management, analysis and review of this data at item, patient, region, size and site level to gather useful insights into current data trends that will facilitate the improvisation of tools to identify life-saving solutions. Decentralised trials also promote health equity by bringing medical data of people from diverse backgrounds and economies. Therefore, digital transformation in healthcare has also extended its benefits to people who are in need of care but lack access.
At the management level, structural changes have been made to support AI and ML led digital transformation in healthcare. The role of the lead data manager has been split to expand the scope of the role and enhance process efficiency. Instead of a single data manager overseeing a delivery, there is a Clinical Data Scientist Lead and a Clinical System Lead to share the responsibilities between them. The Data Scientist is primarily responsible for the analytics side of the work, verifying validity of clinical data, thereby improving quality of care. The Clinical Systems Lead, on the other hand, ensures protocol requirements are met. They primarily engage in clinical system development activities and delivery of the clinical systems. The data scientist and system lead need to work closely together for a successful delivery.
Michael reinforces the need within healthcare industry to constantly re-invent itself. He talks about the generational shift in people’s mindsets, and how people are more eager to adapt and adjust to new ideas and trends, compared to before when people would suffer from change fatigue. He is particularly happy with how ICON has adapted to the changes and shifts in the healthcare industry by adopting sophisticated technologies to analyse clinical data and developing personalised healthcare solutions that are cost-efficient and easily accessible, besides creating a dynamic work environment within the organisation which has contributed to ICON’s success globally.
In this section
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Digital Disruption
- AI and clinical trials
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Clinical trial data anonymisation and data sharing
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Clinical Trial Tokenisation
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Closing the evidence gap: The value of digital health technologies in supporting drug reimbursement decisions
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Digital disruption in biopharma
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Disruptive Innovation
- Remote Patient Monitoring
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Personalising Digital Health
- Real World Data
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The triad of trust: Navigating real-world healthcare data integration
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Patient Centricity
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Agile Clinical Monitoring
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Capturing the voice of the patient in clinical trials
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Charting the Managed Access Program Landscape
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Developing Nurse-Centric Medical Communications
- Diversity and inclusion in clinical trials
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Exploring the patient perspective from different angles
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Patient safety and pharmacovigilance
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A guide to safety data migrations
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Taking safety reporting to the next level with automation
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Outsourced Pharmacovigilance Affiliate Solution
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The evolution of the Pharmacovigilance System Master File: Benefits, challenges, and opportunities
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Sponsor and CRO pharmacovigilance and safety alliances
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Understanding the Periodic Benefit-Risk Evaluation Report
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A guide to safety data migrations
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Patient voice survey
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Patient Voice Survey - Decentralised and Hybrid Trials
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Reimagining Patient-Centricity with the Internet of Medical Things (IoMT)
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Using longitudinal qualitative research to capture the patient voice
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Agile Clinical Monitoring
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Regulatory Intelligence
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An innovative approach to rare disease clinical development
- EU Clinical Trials Regulation
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Using innovative tools and lean writing processes to accelerate regulatory document writing
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Current overview of data sharing within clinical trial transparency
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Global Agency Meetings: A collaborative approach to drug development
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Keeping the end in mind: key considerations for creating plain language summaries
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Navigating orphan drug development from early phase to marketing authorisation
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Procedural and regulatory know-how for China biotechs in the EU
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RACE for Children Act
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Early engagement and regulatory considerations for biotech
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Regulatory Intelligence Newsletter
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Requirements & strategy considerations within clinical trial transparency
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Spotlight on regulatory reforms in China
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Transfer of marketing authorisation
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An innovative approach to rare disease clinical development
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Therapeutics insights
- Endocrine and Metabolic Disorders
- Cardiovascular
- Cell and Gene Therapies
- Central Nervous System
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Glycomics
- Infectious Diseases
- NASH
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Oncology
- Paediatrics
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Respiratory
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Rare and orphan diseases
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Advanced therapies for rare diseases
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Cross-border enrollment of rare disease patients
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Crossing the finish line: Why effective participation support strategy is critical to trial efficiency and success in rare diseases
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Diversity, equity and inclusion in rare disease clinical trials
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Identify and mitigate risks to rare disease clinical programmes
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Leveraging historical data for use in rare disease trials
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Natural history studies to improve drug development in rare diseases
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Patient Centricity in Orphan Drug Development
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The key to remarkable rare disease registries
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Advanced therapies for rare diseases
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Transforming Trials
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Accelerating biotech innovation from discovery to commercialisation
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Ensuring the validity of clinical outcomes assessment (COA) data: The value of rater training
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Linguistic validation of Clinical Outcomes Assessments
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Optimising biotech funding
- Adaptive clinical trials
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Best practices to increase engagement with medical and scientific poster content
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Decentralised clinical trials
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Biopharma perspective: the promise of decentralised models and diversity in clinical trials
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Decentralised and Hybrid clinical trials
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Practical considerations in transitioning to hybrid or decentralised clinical trials
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Navigating the regulatory labyrinth of technology in decentralised clinical trials
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Biopharma perspective: the promise of decentralised models and diversity in clinical trials
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eCOA implementation
- Flexible delivery models
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Implications of COVID-19 on statistical design and analyses of clinical studies
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Improving pharma R&D efficiency
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Increasing Complexity and Declining ROI in Drug Development
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Innovation in Clinical Trial Methodologies
- Partnership insights
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Risk Based Quality Management
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Transforming the R&D Model to Sustain Growth
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Accelerating biotech innovation from discovery to commercialisation
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Value Based Healthcare
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Strategies for commercialising oncology treatments for young adults
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US payers and PROs
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Accelerated early clinical manufacturing
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Cardiovascular Medical Devices
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CMS Part D Price Negotiations: Is your drug on the list?
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COVID-19 navigating global market access
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Ensuring scientific rigor in external control arms
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Evidence Synthesis: A solution to sparse evidence, heterogeneous studies, and disconnected networks
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Global Outcomes Benchmarking
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Health technology assessment
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Perspectives from US payers
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ICER’s impact on payer decision making
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Making Sense of the Biosimilars Market
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Medical communications in early phase product development
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Navigating the Challenges and Opportunities of Value Based Healthcare
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Payer Reliance on ICER and Perceptions on Value Based Pricing
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Payers Perspectives on Digital Therapeutics
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Precision Medicine
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RWE Generation Cross Sectional Studies and Medical Chart Review
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Survey results: How to engage healthcare decision-makers
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The affordability hurdle for gene therapies
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The Role of ICER as an HTA Organisation
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Strategies for commercialising oncology treatments for young adults
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