This is the second post in our series on AI in healthcare.
Read the first post, “Medicine 3.0: The Next Era of Healthcare Has Already Begun.”
There was a time when much of the information needed to evaluate a patient could fit inside a paper medical chart – medical history, physical examination notes, several laboratory results, perhaps an X-ray, and the relevant medical literature within the physician’s specialty. That world no longer exists. A single person can now generate years of electronic health records, laboratory results, and more, while the world’s medical knowledge continues expanding at a pace no physician could possibly read, remember, and apply independently. This creates one of the central challenges of modern healthcare: We no longer have a shortage of medical information, but we have a shortage of the ability to intelligently organize, interpret, and act upon it.
That is where artificial intelligence has the potential to become transformative.
Analyzing and Interpreting Continuous Medical Information
Traditional healthcare was largely episodic – you visited a physician, your blood pressure was measured, blood was drawn, perhaps an image was taken, then the physician evaluated that snapshot and made a decision.
Medicine 3.0 is increasingly longitudinal. A continuous glucose monitor can generate a measurement every few minutes. A wearable can track physiology throughout the day. Laboratory values can now be analyzed as trajectories developing over months or years rather than as isolated numbers, and imaging studies can be compared computationally over time.
When genomic and molecular information are integrated with medications, medical history, environment, and treatment outcomes, the opportunities are extraordinary, but simply generating more data does not produce better healthcare. More information without better intelligence only creates more noise.
The information challenge extends beyond the individual patient. Medical research itself is expanding at an extraordinary rate. Thousands of scientific papers are published across medicine, biology, genomics, pharmacology, imaging, artificial intelligence, and related fields. Clinical expertise, continuing education, and critical appraisal remain essential. But the information environment has become too large for unaided human cognition.
This is where AI can extend the cognitive reach of the physician. Imagine an intelligent system capable of helping identify questions such as: Are laboratory values gradually moving in a concerning direction? Has an imaging abnormality subtly changed over several years? Could multiple findings scattered throughout the medical record actually be connected? Does this person’s genomic or molecular profile influence the likelihood that a particular treatment will work? Are wearable or remote-monitoring signals showing deterioration before symptoms become obvious? Has new research emerged that could materially alter the diagnostic or treatment strategy?
Supporting Patients and Providers Through Care Navigation
AI alone will not solve the problem. The healthcare system must also create a pathway that gets the right information to the right physician, helps members navigate to appropriate care, supports providers with the tools they need, and turns intelligence into action.
An algorithm can produce extraordinary insight, but that insight has little value if it remains isolated inside a computer. This is one of the most important distinctions in Medicine 3.0. Suppose technology identifies that a member may benefit from a more specialized evaluation. Who helps that person determine what to do next and connects them to the appropriate provider and treatment?
This is why care navigation is an essential component of Medicine 3.0. Members should not be expected to understand every emerging diagnostic technology or search the country for the physician most qualified to use it. A better healthcare ecosystem should help members move from information to appropriate evaluation and, when necessary, from one level of care to another.
The physician cannot be separated from the healthcare infrastructure surrounding the physician. Providers practice within a system of reimbursement, technology, staffing, regulation, and limited time, and healthcare naturally evolves around what that system makes practical and sustainable. If we want physicians to incorporate advanced diagnostics, longitudinal information, remote monitoring, precision technologies, and AI-assisted decision support, we cannot simply tell them to practice differently; we have to build an ecosystem that supports them doing it. That can include making relevant medical information easier to access, reducing fragmented records, connecting providers with specialty expertise, giving physicians intelligent tools that help synthesize years of medical information before the patient ever enters the examination room, and/or helping create access to advanced imaging, genomic or molecular diagnostics, remote monitoring, or other technologies when clinically appropriate.
Building the Medicine 3.0 Ecosystem
The future of healthcare is not one artificial intelligence platform, one laboratory test, or one extraordinary physician; it is an ecosystem with the member at the center. Around that member are physicians, specialists, care navigators, diagnostic laboratories, imaging centers, virtual-care providers, remote-monitoring technologies, precision medicine tools, artificial intelligence, and increasingly sophisticated clinical data. The challenge is connecting them.
At America’s HealthShare, this is an important part of how we think about Medicine 3.0. Our responsibility cannot end with access to healthcare after someone becomes sick. We need to create pathways that allow members to find appropriate care, help providers access meaningful information and technology, and support the implementation of innovations that can genuinely improve health.
Artificial intelligence gives healthcare an unprecedented ability to generate intelligence from enormous amounts of data. Care navigation connects people to that intelligence, provider support makes it clinically usable, and implementation turns it into better healthcare. That is how data ultimately become outcomes – and that is the healthcare ecosystem America’s HealthShare is committed to building.
In Health,
Dr. John Oertle
Chief Medical Officer