♻️ Facts: The global Artificial Intelligence in healthcare market size is projected to reach $164.10 billion by 2029
Facts and principles tips #2023-26
Data overload plagues many healthcare organizations. As a result, their boards, executives, and managers lack the data required to identify areas for development and measure progress toward addressing them.
→ This difficulty, however, can be solved through collaborations with healthcare organizations. But it requires time and public/private budgets.
→ The global Artificial Intelligence in healthcare market size was valued at USD 10.54 billion in 2021 and is projected to reach USD 164.10 billion by 2029, exhibiting a CAGR of 42.4% during the forecast period.
→ During the pandemic, the increasing digitalization of healthcare has only increased the amount of health-related data available—and reinforced its importance in care delivery, disease prediction and diagnosis, biopharma and medtech innovation, and patient outcomes.
What we know/ what we need:
Quest for discovery: accelerating medical research to advance disease prevention, diagnosis, and treatment.
Global health insights: increasing our shared understanding of health and longevity to protect against global health crises.
Health equity: reducing health inequity and improving access to care for underserved populations.
Research capabilities: supporting fundamental research capabilities, including data collaboratives and differential privacy.
Now more than ever, and despite the exponential growth in data generated across the healthcare ecosystem, notable gaps remain. Data is central to discussions about public health and is core to enabling continued scientific advancement for healthcare and well-being.
Insufficient availability and analysis of specific health data undermine advancements in disease-state understanding and limit asset discovery opportunities across medical conditions with significant unmet needs.
These data disparities ultimately influence health outcomes for gender and populations globally by creating blind spots in the insights that drive research design, investment decisions, and pipeline priorities.
One such area is women’s health, in which gaps span the entire data value chain—from defining women’s health (pre–data generation) to diagnosing (data generation) to tracking at the national level (data collection) to translating data into insights at the global level through epidemiological studies (data analysis).
→ The list is very long and deserves more attention. So, to spare your time I will dedicate a full series on gender gap and sustainability during the next few weeks.
Takeaways
How will organizations know if their data strategy is working? More particularly institutions like hospitals and care services?
Data is foundational to our understanding of disease states and is a crucial catalyst for continued life-sciences innovation. But most leaders and decision-makers believe that they are getting the data they need to set priorities, make decisions, and track progress. They aren't investing time in looking for patterns in the sea of information.
Their data collection investment yields dividends in the form of new insights that are revealed on a regular basis.
They also spend more time addressing performance issues and less time discussing data issues. This is probably a huge opportunity for founders, creators and new leaders.
♻️ Facts and principles tips
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Mc Kinsey | Closing the data gaps in women’s health: https://www.mckinsey.com/industries/life-sciences/our-insights/closing-the-data-gaps-in-womens-health