Unmet (and unlimited) demand for the scarcest healthcare resource, clinical expertise, results in delayed and inconsistent care that contributes to 20% greater odds of death. While any licensed clinician can theoretically prescribe any of the tens of thousands of medications and diagnostic tests a patient may need, we are well past the point where the escalating “complexity of modern medicine exceeds the capacity of the unaided expert mind.” The result is sub-specialization fragmentation with limited care coordination. Over 25 million in the US alone have deficient access to specialty care, with a projected shortage of over 100,000 physicians by 2030. (Let alone the masses in low and middle income countries who cannot even access basic medical care.)
Advances in clinical practice that gain efficiencies are among the few ways to simultaneously improve all ends in the “iron triangle” of healthcare quality, cost, and access. Most advances will otherwise cause trade offs or increase disparities that impact underserved populations, for there is no quality without access.
Electronic consultation systems allow clinicians to request specialty advice through messages, teleconferencing, or online crowdsourcing. These illustrate the potential to treat patients with up to 71% fewer in-person specialist visits. This eliminates patient time and travel to initiate a treatment plan (days vs. months), which could be lost completely when up to 40% of specialty referral requests are not completed. A recent literature review of such systems found improvements in process measures such as timely access to care across a range of studies, but also noted the need for further research on how best to design such systems to ensure improvements in clinical outcomes. Despite benefiting patients and providers, existing (electronic) consult systems remain fundamentally constrained by the availability of human experts.
In the face of ever escalating complexity in medicine, integrating informatics solutions is the only credible approach to systematically address challenges in healthcare. Tapping into real-world clinical data streams like electronic medical records with machine learning and data analytics will reveal the community’s latent knowledge in a reproducible form. Delivering this back to clinicians, patients, and healthcare systems as clinical decision support will uniquely close the loop on a continuously learning health system. Our research group seeks to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches to medicine that will deliver better care than what either can do alone.