Artificial intelligence (AI) is reshaping industries across the board, and healthcare is no exception. But beyond the rapid technological advancements a critical question remains: How can we move from promising prototypes and basic adoption to using AI to deliver real, measurable benefits in everyday healthcare settings?
After a peak of investment in 2021, many healthcare AI investors are slowing down and becoming more selective, prioritizing solutions that show real-world lasting impact. This shift reflects the high stakes of healthcare, in which the cost of error is steep and proving effectiveness is critical. Often, the challenge isn't the AI tool itself but instead the fragmented, inconsistent, and siloed data that power it. Unless we solve this data dilemma, AI’s full potential will remain out of reach.
Strengthening the data foundation
Inconsistent data quality, privacy concerns, and limited interoperability all hinder AI’s potential. That’s why strong data governance is critical to getting AI right. By aligning data practices with organizational priorities and regulatory standards, governance helps maintain the integrity and responsible use of healthcare data.
AI can also be part of the solution, with emerging technologies helping us improve how we capture, structure, connect, and secure data. We can begin to unlock AI’s full potential across the healthcare ecosystem by using AI to strengthen the foundation it relies on through several efforts:
- Starting with accurate, usable data at the point of care. Electronic health records are a valuable tool, but usability challenges can hinder their efficiency. Emerging AI tools such as ambient listening can help us move away from click lists and manual entry to natural, real-time documentation.
- Enhancing how we structure and standardize data. Existing code sets and standards have helped, but there’s still work to do. Clinical coding, for example, can be overly complex and inconsistent, affecting data quality and slowing workflows. AI, especially large language models, can streamline coding by suggesting context-relevant options and help improve accuracy.
- Generating synthetic data offers a powerful way to fill data gaps. Although the Health Insurance Portability and Accountability Act (known as HIPAA) and related regulations protect patients’ privacy, they limit access to the real-world data that most effectively train AI models. Patients’ limited internet access creates additional gaps, leaving digitally disconnected communities potentially misrepresented or overlooked in AI models. Synthetic data generation can help bridge these gaps by creating artificial data that mimic real patient information without compromising privacy.
- Employing techniques like privacy preserving record linkage helps us create a fuller picture of health in safe ways. Privacy preserving record linkage brings together information from across different systems, such as those used in housing, education, and free-standing clinics, without sharing patients’ identifiers, and it provides a more complete view of a patient without compromising their information security.
With strong governance and high-quality data, we can unlock broader adoption of AI at scale—from routine administrative tasks to more advanced, high-impact applications.
Promising applications
Healthcare organizations are exploring where AI can add real value. Major platforms are integrating AI capabilities, AI-enabled point solutions are flooding the market, and the federal government is signaling support for technology-driven efficiency.
Looking ahead, AI will likely continue to transform healthcare in several promising ways. This includes more AI-powered tools that support clinical decision making by helping diagnose diseases, distill complex patient charts, and assist clinicians in real time. Operational efficiencies will grow as AI streamlines documentation, optimizes scheduling, shapes outreach to and education of patients, and automates routine administrative tasks. On the research front, AI has the potential to accelerate drug discovery and synthetic data generation. It might also enhance risk and quality management by detecting fraud and enabling more responsive public health monitoring.
Many of these applications are already underway and will continue to expand. But AI adoption in healthcare comes with high stakes. When people’s health and well-being are on the line, innovation must be balanced with appropriate caution.
Partnering for safer, smarter AI adoption
The future of healthcare AI lies not just in artificial but in augmented intelligence, where machines and humans work together, each doing what they do best. Although AI may feel like a leap forward to us today, medicine remains a relatively young and evolving field with large gaps in our understanding of the human body and its complexities. That’s why collaborating on solutions and pairing emerging technology with deep domain expertise is key to building something that will actually work.
You don’t have to navigate this path alone.
At Mathematica, our data solutions experts are here to help you every step of the way to ensure your AI strategy is responsible, grounded in strong data practices, and capable of delivering real-world results. If you think your organization is ready to get started, or want to better understand what it will take to get you there, contact us today.
