Mar 20, 2025 · 32m · a16z
Scaling Medicaid Innovation with Rajaie Batniji, Sanjay Basu, and Afia Asamoah
Rajaie Batniji · 10m spoken Vineeta Agarwala · 8m spoken Sanjay Basu · 6m spoken Afia Asamoah · 4m spoken
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this panel discussion hosted by a16z's Vineeta Agarwala, Waymark co-founders Rajaie Batniji, Afia Asamoah, and Sanjay Basu detail how their public benefit corporation combines community-based care teams, predictive machine learning, and innovative value-based contracting to improve health outcomes and reduce costs for Medicaid populations.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. How this is scored →
The host as informed peer 5.0 Guest teaching 4.1 Guest disagreement 0.9 The host pushing back 1.1
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 26:37 Medical malpractice claim
Rajaie humorously reframes Vineeta's biopharma analogy with a provocative claim that withholding their non-drug intervention would constitute medical malpractice.
Hardest push from the host ▶ 20:15 Challenging market execution barriersVineeta directly challenges the guests on market reality by asking why other healthcare organizations fail to target rising-risk populations if the strategy is standard.
Biggest teaching moment ▶ 18:07 Flaws in traditional high-cost claimant algorithmsRajaie educates the host on how standard Medicaid predictive algorithms identify high-cost patients only after their spend trajectory has already peaked and started declining naturally.
The host holds their own ▶ 25:23 Biopharma precision medicine parallelVineeta demonstrates high expertise by reframing the conversation using advanced pharma concepts like precision biomarkers and number needed to treat to evaluate AI model rigor.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The host as informed peer | Guest teaching | Guest disagreement | The host pushing back | Why |
|---|---|---|---|---|---|---|
| Afia Asamoah's Background and Journey to Waymark | 3 | 2 | 0 | 0 | Vineeta opens the interview by asking Afia about her background transitioning from law into health tech product development at Google. Afia provides a collaborative narrative about her career and motivation for joining Waymark. | |
| Sanjay Basu on Clinical Practice and Social Interventions | 4 | 3 | 0 | 0 | Vineeta frames Sanjay's dual role as clinical lead and practicing primary care physician before turning to Rajaie's background as a repeat founder. The exchange is warm and informative, establishing company origin stories. | |
| The Care Delivery Model: Moving Care into Communities | 4 | 5 | 1 | 1 | Vineeta notes that roughly 20-25 percent of Americans rely on Medicaid and gently questions how a tiny startup of three people could address such a daunting challenge. Rajaie explains that 39 percent of Medicaid acute care is avoidable and details how productizing community health worker interventions makes scaling possible. | |
| Waymark's Structure as a Public Benefit Corporation | 3 | 3 | 0 | 0 | Vineeta validates Waymark's product strategy before asking Afia to explain the choice to incorporate as a Public Benefit Corporation. Afia describes how a PBC structure binds leadership to a dual fiduciary and social charter. | |
| NEJM Catalyst Study Results and Machine Learning Signal | 5 | 6 | 1 | 1 | Vineeta introduces the New England Journal Catalyst study results and asks probing follow-up questions regarding clinical mechanics. Sanjay and Rajaie present trial results showing a 23 percent reduction in ER visits driven by machine learning risk prediction. | |
| Defining and Targeting the Rising Risk Patient Population | 6 | 5 | 1 | 3 | Vineeta pushes the guests on market friction, asking why other health groups fail to target rising-risk patients if the strategy is so obvious. Rajaie and Afia explain why legacy algorithms miss rising-risk cohorts and how risk-based contracting enables community care. | |
| Participatory Data Science and AI Task Automation | 8 | 4 | 2 | 2 | Vineeta demonstrates impressive domain mastery by connecting Waymark's machine learning model to biopharma clinical trial concepts like precision biomarkers and numbers needed to treat. Rajaie responds with a witty comment that if their model were a drug, withholding it would be medical malpractice. | |
| Scaling Strategy, Value-Based Care, and Community Networks | 7 | 5 | 2 | 2 | Vineeta actively builds on Rajaie's criticism of 'value veneers' in Medicaid, elaborating on how superficial ACO contracts lack actual care delivery resources. The guests discuss local community trust and wrap up the discussion. |
Statements from this episode (13)
Agarwala: Every healthcare company could eventually resemble a consumer company
“As we've said on this podcast before, every healthcare company somewhere deep down could start to look like a consumer company.”
Batniji: Medicaid is US healthcare's largest and most addressable challenge
“All of the creativity, all the capital is going into largely Medicare and employer. And Medicaid is actually the biggest, and frankly, I think the most readily addressed challenge in, in US healthcare. And there was very little happening to take that on.”
Batniji: 39% of Medicaid acute care is avoidable, triple commercial rates
“40% of all acute care or 39% of all acute care in Medicaid is avoidable. That's like three times what it is in Medicare, three times what it is in commercial.”
Batniji: Missing software and funding models prevented community healthcare from scaling
“The challenge is nobody had ever built the product to reliably implement those proven interventions at scale, and nobody had ever created the funding mechanism to make those sustainable.”
Basu: Waymark improved Medicaid care quality metrics by 11 percentiles
“In addition to reducing emergency room visits by about a quarter versus that control group, we achieved a lot of improvement in access and quality outcomes. So nationally, they're measured by this Health Effectiveness Data Info System, or HEDIS, which a lot of…”
Batniji: Waymark reduced Medicaid acute care by 23% and hospitalizations by 48%
“Reducing overall acute care in the intervene cohort by 23%, reducing what are called ambulatory care sensitive hospitalizations by 48%.”
Batniji: Standard Medicaid algorithms identify high-cost patients too late to help
“If you look at those high cost claimants, and you look at the algorithms that are used to identify them, there's a couple of very common ones, By the time they identify a patient, that patient's cost, and this is according to the National Medicaid Database, wh…”
Batniji: Waymark trains risk models on clinical and social data
“Our algorithm is, is trained not on cost or prior utilization, but on condition and social risk factors, and the various other data feeds that tell us about behavior, the kind of admission discharge transfer feeds, we can see whether they're picking up their m…”
Asamoah: Fee-for-service models fail to reimburse community-based healthcare
“Community based care is not reimbursed in a fee for service system, and so we are doing risk based contracting to make it work”
Batniji: Waymark's ML model boosts acute care prediction accuracy to 90%
“If you didn't have signal, you'd have about 30% accuracy in identifying Future acute care use. I mean, seven out of the 10 patients that you reach out to, you're not going to actually benefit in a way that changes the outcome. With Signal, you have 90% accurac…”
Batniji: Withholding Waymark's intervention would be medical malpractice if a drug
“I think the biggest difference is that if this were a drug, it'd be medical malpractice not to give it.”
Batniji: Superficial value-based care agreements impede Medicaid delivery reform
“And I think that's actually been one of the biggest, like, challenges and impediments to adopting non-fee-for-service models in Medicaid is You know, the default mentality is, you know, if there is a value-based flag planted somewhere, then that territory is c…”