How data science is changing the face of healthcare: Michael Goedde podcast

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.