The Case for Primary Care as a Public Utility

What happens if primary care gets treated like a public utility – something that everyone can access as easily as running water?

A new article in JAMA paints a beautiful picture of what that might look like, and even colors it in with a roadmap for how to get there.

Primary care is a critical component of healthcare. It’s also far from universal.

  • More than a third of U.S. adults lack access to primary care, an eye-popping number that unfortunately makes more sense knowing primary care only sees 5 cents of every federal dollar spent on healthcare.

The authors frame up the issue perfectly. 

  • “Primary care has long fit awkwardly as an insurable risk in the marketplace. Insurance is designed to protect against large, unpredictable expenses. Yet primary care is largely predictable, similar to food, housing, and other common necessities.”

The proposed solution? A primary care common fund, which pools primary care spending from public and private purchasers and pays practices directly. Here’s the basic outline:

  • The common fund would comprise current primary care spending from payors, and include the additional spending that states invest into primary care in the future.
  • A state authority – much like a public utility – would administer the funds and pay practices directly.
  • The “pluralistic financing” of primary care would remain intact. Employers and individuals would continue to pay premiums for commercial plans, and Medicaid would continue to be financed by federal and state funds. On the back end, the state would redirect the primary care portion of payor premiums (their contribution) to the common fund.
  • A key point is that the common fund starts with no “new money.” Baseline contributions equal what purchasers are already spending on primary care (ex. Oregon has a primary care spending target of 12%, and would assess 12% of plan premiums). 
  • Payors would no longer need to compete on prices and benefits for primary care, but they’d still compete on their specialty and other lines of business.
  • People remain enrolled in coverage for non-primary care services, but the common fund “assumes responsibility for coverage and payment of primary care and accountability for its spending.”

The Takeaway

If the U.S. wants everyone to have access to the benefits of primary care, a good start might be making sure everyone has access to primary care. This paper charts a path to get there straight down the middle of single-payor and free-market approaches, and a “Medicare Advantage for Primary Care” feels more doable than ripping and replacing the entire system.

Scribes Show Modest Impact at Major Academics

Ambient scribes are back in the spotlight after a new study in JAMA confirmed that they move the needle on productivity metrics, but the jury’s still out on whether that’s the best yardstick for success.

This was a big one. The study examined the impact of AI scribe use on over 1,800 clinicians at five major academic medical centers from 2023 to 2025.

  • The academics: MGB, YNHH, UCSD, UCSF, UC Davis 
  • The scribes: Abridge, Ambience, Microsoft DAX Copilot

Here’s what they found. Clinicians who used AI scribes:

  • Saved 16 minutes of documentation time per eight hours of patient care 
  • Saved 13 minutes of EHR time 
  • Could see one additional patient every two weeks
  • Saw no significant impact on EHR timeoutside of working hours

Usage patterns helped color in the story. While 1,800 AI scribe adopters is one of the largest samples out there, the 6,770 control clinicians were also offered scribes and opted not to use them.

  • The biggest gains went to the biggest users. Clinicians who used the AI scribe for over 50% of visits experienced twice the reduction in EHR time and 3x the reduction in documentation time, yet only 32% of adopters fell into this bucket.

What’s counted? What matters? This isn’t the first study we’ve covered that scores AI scribes based on metrics that researchers can easily measure (EHR time, visits), which isn’t necessarily the same as the metrics that matter most to patients or clinicians.

  • Although this study solidifies that scribes can cut documentation time, the question now is if that time gets reinvested in ways that improve care and outcomes for patients.
  • The results also confirm that the mechanism of action for scribes reducing burnout isn’t through time savings, but it’s still unclear whether it’s from having a couple more moments to take a deep breath throughout the day or from reallocating the extra minutes to things that feel valuable.

The Takeaway

This study offers the most definitive real-world data yet that AI scribes have a modest impact on productivity metrics, but it also confirms that cleaner notes aren’t the only key to improving healthcare experiences.

How to Build Patient Trust in Medical AI

AI might move at the speed of trust, but new research in JAMA Network Open shows that trust only moves at the speed of accuracy.

The study had a solid setup. To determine the factors currently driving patient trust in AI, researchers presented 3,000 U.S. adults with a pair of hypothetical AI-assisted visits for a moderate-risk rash. 

  • Each visit had six randomized attributes, such as whether or not a doctor was present, how well the AI performs relative to human clinicians, and various AI governance mechanisms.

AI performance came out on top by a wide margin. Respondents cared more about how well the AI performs than FDA approval, governance, and even having a doctor in the room.

  • The biggest difference came from AI performing better than a specialist, which increased the likelihood of choosing that visit by 32.5%.
  • AI performing at the same level as a specialist boosted visit preference by 24.8%, slightly more than having AI that performs as well as a general practitioner (19.1%).
  • Having an actual doctor present surprisingly only swayed visit preference by 18.4%.

Governance factors also moved the needle. They just didn’t move it much.

  • FDA approval for the AI increased visit preference by a modest 11.1%.
  • Mayo Clinic AI certifications apparently carry just as much weight – also coming in at 11.1%.
  • Local hospital certifications for the AI only gave visits a 7.8% lift.

AI data quality was important. It just wasn’t as convincing as AI performance. 

  • AI that had nationally representative training data boosted visit preference by 11.9%, but it was interesting to see that disclosing bias in the training data had no effect versus not providing any data details.

The written explanations told the same story. Respondents cited AI performance and clinician involvement as the primary reasons for their choices, with many of them expressing comfort with AI as a tool – but not as a standalone decision-maker.

The Takeaway

Widespread AI adoption requires patient trust, and this study did a great job highlighting the specific areas that should be prioritized for building it.

Doctors Who Use AI Are Viewed Worse by Peers

The research headline of the week belongs to a study out of Johns Hopkins University that found “doctors who use AI are viewed negatively by their peers.”

Clickbait from afar, but far from clickbait. The investigation in npj Digital Medicine surfaced interesting takeaways after randomizing 276 practicing clinicians to evaluate one of three vignettes depicting a physician: using no GenAI (the control), using GenAI as a primary decision-making tool, or using GenAI as a verification tool.

  • Participants rated the clinical skill of the physician using GenAI as a primary decision-making tool as significantly lower than the physician who didn’t use it (3.79 vs. 5.93 control on a 7-point scale). 
  • Framing GenAI as a “second opinion” or verification tool improved the negative perception of clinical skill, but didn’t fully eliminate it (4.99 vs. 5.93 control). 
  • Ironically, while an overreliance on GenAI was viewed as a weakness, the clinicians also recognized AI as beneficial for enhancing medical decision-making. Riddle us that.

Patients seem to agree. A separate study in JAMA Network Open took a look at the patient perspective by randomizing 1.3k adults into four groups that were shown fake ads for family doctors, with one key difference: no mention of AI use (the control), or a reference to the doctors using AI for administrative, diagnostic, or therapeutic purposes (Supplement 1 has all the ads).  

For every AI use case, the doctors were perceived significantly worse on a 5-point scale:

  • less competent – control: 3.85, admin AI: 3.71; diagnostic AI: 3.66; therapeutic AI: 3.58
  • less trustworthy – control: 3.88; admin AI: 3.66; diagnostic AI: 3.62; therapeutic AI: 3.61
  • less empathic – control: 4.00 ; admin AI: 3.80; diagnostic AI: 3.82; therapeutic AI: 3.72

Where’s that leave us? Despite pressure on clinicians to be early AI adopters, using it clearly comes with skepticism from both peers and patients. In other words, AI adoption is getting throttled by not only technological barriers, but also some less-discussed social barriers.

The Takeaway

Medical AI moves at the speed of trust, and these studies highlight the social stigmas that still need to be overcome for patient care to improve as fast as the underlying tech.

OpenEvidence Partners With JAMA Ahead of Next Raise

“The fastest-growing platform for doctors in history” continues to step on the gas, and OpenEvidence is reportedly on the verge of notching a $3B valuation after inking a deal to bring JAMA Network journals to its AI medical search engine.

The multi-year content agreement will make full-text articles from the American Medical Association’s JAMA, JAMA Network Open, and 11 specialty journals available directly within the OpenEvidence platform.

  • OpenEvidence’s medical search engine helps clinicians make decisions at the point of care, turning natural language queries into structured answers with detailed citations.
  • The model was purpose-built for healthcare using training data from strategic partners like the New England Journal of Medicine, which joined the platform through a similar deal earlier this year.

The Disney+ content strategy has arrived in healthcare. OpenEvidence compares its approach to streaming services that drive subscriptions through exclusive movies.

  • If a physician wants information from top journals to support decision making, they’ll either have to get it straight from the source or use OpenEvidence, just like how anyone who wants to stream Moana needs to go to Disney+.
  • The kicker is that OpenEvidence is available at no cost to verified physicians, and advertising generates all of the revenue. 

The blueprint is working like a charm. OpenEvidence has over 350k doctors using its platform plus another 50k joining each month, and it’s apparently close to raising $100M at a $3B valuation just a few months after closing its $75M Series A.

  • It’s rare to find hockey stick growth in digital health, and OpenEvidence is a good reminder that many areas of healthcare change slowly… then all at once.
  • It also isn’t too surprising to hear that VC’s like Google Ventures and Kleiner Perkins are lining up to fund a company with a similar ad-supported business model to Doximity – one of the only successful healthcare IPOs since the start of the pandemic.

The Takeaway

Content is king, and OpenEvidence is locking in partnerships to make sure its platform is wearing the crown. The results have been speaking for themselves, but healthcare’s genAI streaming wars are just getting started.

The Volume and Cost of Quality Metric Reporting

A Johns Hopkins-led study in JAMA reached a conclusion that many health systems are already all-too-familiar with: reporting on quality metrics is a costly endeavor. 

The time- and activity-based costing study estimated that Johns Hopkins Hospital spent over $5M on quality reporting activities in 2018 alone, independent of any quality-improvement efforts.

Researchers identified a total 162 unique metrics:

  • 96 were claims-based (59%) 
  • 107 were outcome metrics (66%) 
  • 101 were related to patient safety (62%) 

Preparing and reporting data for these metrics required over 100,000 staff hours, with an estimated personnel cost of $5,038,218 plus an additional $602,730 in vendor costs.

  • Claims-based metrics ($38k per metric per year) required the most resources despite being generated from “collected anyway” administrative data, which the researchers believe is likely tied to the challenge of validating ICD codes and whether comorbidities were present on admission. 

Although the $5M cost of quality reporting is a small fraction of Johns Hopkins Hospital’s $2.4B in annual expenses, extrapolating those findings to 4,100 acute care hospitals in the US suggests that we’re currently spending billions on quality reporting every year.

That conclusion raises questions that are outside the scope of this study but extremely important for understanding the true value of quality reporting.

  • Do the benefits of quality reporting outweigh the burden it places on clinicians?
  • Would the time and effort required for quality reporting be better spent on patient care?
  • Do quality metrics accurately reflect a hospital’s overall quality of care?

The Takeaway

Non-clinical administrative costs are a giant slice of the healthcare spending pie, and quality measurements unintentionally contribute due to increasing spending on chart review and coding optimization. Quantifying the burden of quality reporting is a key step to understanding its overall cost-effectiveness, and although this study doesn’t tackle that issue directly, it lays the foundation for those who are.

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