Despite the widespread adoption of electronic health records (EHR) over the last couple of decades, essential patient information often gets trapped within the walls of individual hospital systems. Here's a potential solution.
If AI is going to be used in MSK care — or any area of healthcare — it needs clear, non-negotiable rules around it.
Small practices play a critical role in healthcare delivery, but they cannot continue to absorb ever-increasing administrative demands without consequences.
The path to scalable AI in healthcare will not be defined by a single breakthrough or technology. It will be shaped by the industry’s ability to address the foundational challenges that have existed for decades.
EHRs not only document care but can also act as a financial engine to bridge the gap between patient care and prompt reimbursement. Here's how to bring the two sides together to alleviate tedious administrative burdens.
Sleep threads through cardiovascular function, metabolism, immune activity, and mental health. It happens nightly, which means it can produce a steady flow of information that, measured well, could tell us a lot about near-term changes and long-range risk.
Building a medical practice that’s truly resilient can feel like pushing a rock up a hill. However, physicians who are ready, willing and able to stay adaptable and seize opportunities will give their medical practices staying power.
By reducing administrative burden and redesigning workflows around human needs, it creates space for what matters most: connection between clinicians and patients.
While AI makes VC faster, it’s not necessarily wiser. In healthcare, where breakthroughs rarely resemble anything the market has seen before, that distinction is everything.
Accuracy, completeness, and efficiency all matter when benchmarking a successful AI platform. But there’s another metric that’s often overlooked: the minutes of eye contact returned to the exam room.
Technology alone is not the answer to the capacity crisis facing healthcare. But when thoughtfully integrated into care delivery operations, digital tools can help healthcare organizations rebalance workloads and stabilize patient access.
Layoffs tied to AI adoption will not be uniform; they will vary by sector, job function, and regulatory exposure. Nowhere is this more complex than in healthcare, where legal constraints, patient safety obligations, and labor dynamics intersect with rapid technological change.
How to turn analytics into actual policy outcomes.
In today’s cash- and staffing-strapped, high-stakes healthcare environment, COEs are an operational necessity. The most successful organizations treat them as long-term strategic investments, or engines for scalable, sustainable transformation.
As biologics modalities diversify and processes grow complex, the success of tech transfer depends on integrated execution rather than each procedural completion.
Many health plans are discovering that even after digitizing medical policies and implementing FHIR APIs, prior authorization turnaround times remain slow and provider frustration remains high. The reason is straightforward: digitization alone does not make medical‑necessity determinations executable by automated systems at scale.
In dental practices, where reimbursement cycles are tighter and administrative inefficiencies are immediately visible, AI is already being evaluated not for novelty, but for operational impact.
Transportation is a vital part of the care journey, and if we are serious about improving outcomes, reducing missed appointments, and supporting healthier members, we have to be honest about where friction still exists and focus on removing it.