This article is fifth in a Deal Screening Mastery series for emerging investors who want to build LP confidence and for early‑stage companies trying to find the right investor fit. In the fourth article, “Founder-Problem Fit,” I show how an integrative framework like FPF lets you triage your deal flow using high-value signals that combine into a single statement of rationale.
If you sit through enough pitches, you start to notice something about the “Why now?” slide. When it is present, it is almost never doing the job investors need it to do. Founders declare that “AI is transforming healthcare,” “genomics is exploding,” or “telehealth is here to stay.” They gesture at massive trends and assume timing is self‑evident. But as an investor, you are not underwriting vibes. You are underwriting specific structural shifts that create an actual timing advantage for this company, with this product, in this market, right now. “Why now?” is not a slogan. It is a claim about how the world has changed in ways that make this business more possible, faster, cheaper, or less risky today than it was five or ten years ago. In this article, I want to pull “Why now?” out of the land of hand‑waving and put it back where it belongs: as a sharp, screen‑level tool you can use in the first 30 to 60 minutes with a deal. And if you are a founder reading this, I want to give you a framework that forces your timing story to survive contact with reality.
You’ve heard (or repeated!) these lines, or something like them:
“Gen Z expects digital experiences in healthcare.”
“Payers are moving to value‑based care.”
“Sequencing is getting cheaper every year.”
These statements are just not enough. Every generation expects better experiences in healthcare. Every decade has a new payment acronym. Sequencing costs have been falling for twenty years. Forces that have been true and will remain true for a long time are just background noise. And trends are not inflection points. This is not the rationale you should be using as a founder or accepting as an investor.
What does a stronger slide look like? When experienced investors treat “Why now?” as a real investment question, they are asking about very different things:
What exactly changed, and when?
Who made that decision or built that capability?
Where does that change show up—in law, in a billing table, in a cost curve, in a workflow?
How does this company capture the benefit faster or more efficiently than everyone else?
These aspects define timing edges. Don’t accept timing platitudes in their place.
“Why now?” to investors in life sciences and healthcare is:
The specific structural shifts in policy, reimbursement, technology, data, or behavior that make this solution materially more feasible, valuable, or capital‑efficient today than it was 5 to 10 years ago.
This definition does three important things:
1. It insists on specific structural shifts rather than amorphous trends.
2. It anchors the story in time (5 to 10 years), so you can test whether something is really new.
3. It focuses your attention on feasibility, value, and capital efficiency; things that change your underwriting, not just your marketing language.
This definition is different from TAM and unmet need. TAM asks, “Is this problem big?” Unmet need asks, “Is the status quo failing people in a meaningful way?” Instead, our definition asks, “Why is this problem meaningfully more solvable now than it used to be, and why will early movers capture disproportionate value?” If we can’t answer the last question clearly, without a lot of jargon, then the investment proposition is necessarily weak.
In healthcare and life sciences, most genuine “Why now?” edges show up through four levers:
1. Policy and regulation
2. Reimbursement and economic incentives
3. Technology and cost curves
4. Data, workflow, and behavior
Your job in screening is to figure out which of these levers the company is riding, what changed, and how proven that change is. We’ll walk through each one with concrete questions you can use on your next call.
Policy and regulatory shifts are some of the clearest timing drivers in healthcare. They are also the ones founders most consistently under‑specify. A real policy‑driven “Why now?” sounds like:
“In year X, authority Y changed rule Z, which now allows A that was previously prohibited or uneconomic. Our product is designed to exploit exactly that change.”
We see this in action today with evolving guidance on digital tools, real‑world evidence, and new pathways for diagnostics. Regulators have become more open to genomic and advanced diagnostics over time, and agencies have signaled willingness to integrate different kinds of data streams into decision‑making. That’s not a slogan; it’s a pattern you can point to in guidance documents and approvals.
When a founder claims, “regulators are getting more open,” your screen‑level job is to force that into specifics:
Which agency?
Which guidance or rule?
What year?
What did it actually change in terms of requirements, timelines, or permissible evidence?
If they cannot answer in that level of detail, you can safely assume they are surfing headlines, not structural change. You don’t need a PhD in regulatory affairs to hear the difference between “we read the press release” and “we changed our development plan around section 3.2 of this guidance.” Regulation is not a background condition. It is a set of discrete chess moves. Strong “Why now?” stories can usually name the moves.
The second lever is where the money moves. Healthcare is full of problems that will never get solved by venture‑backed companies because no one gets paid to solve them. It is also full of markets that turn from desert to rainforest when a new code, coverage decision, or payment model lands. Reimbursement shifts are timing shifts because they change who makes money, who loses money, and how quickly cash flows move. That’s what you’re underwriting, whether you admit it or not.
Let’s consider the telehealth explosion during the COVID‑19 pandemic. Before the pandemic, telehealth in the United States was heavily constrained by Medicare rules around who could perform telehealth, where patients and providers had to be located, and what would be reimbursed. Only certain licensed providers could bill, visits had to originate from specific sites, and video was required. Then March 2020 happened. Congress altered Medicare restrictions, CMS issued waivers, and suddenly telehealth could originate from a patient’s home, cross state lines in many cases, and be delivered by a much wider set of clinicians using a broader set of technologies. States and commercial payers followed Medicare’s lead, improving reimbursement and reducing restrictions. [1] Inside a few months, telehealth went from “nice add‑on” to primary lifeline. Safety‑net systems like New York City’s public hospitals reported that historic regulatory and insurance changes were pivotal to keeping care accessible and solvent. [2]
Founders seldom discuss reimbursement and economic incentives because these are harder to track and quantify. Questions about their payment scenarios will quickly reveal the weak points in their reimbursement strategy, and whether economic factors can play a role. I don’t recommend eliminating a deal based on this lever because reimbursement is difficult and confusing enough for experts. It can be a prime place where your network can add value to the deal.
The third lever is the one founders love to talk about and investors love to mis‑price: technology and the cost of doing science. The genomics story is a canonical example. The first human genome took nearly 15 years to sequence and cost on the order of billions of dollars. Today, we are on the cusp of routinely sequencing whole genomes in less than a day, and the industry is approaching the milestone of a roughly $100 whole genome sequence. Costs have fallen at a rate that outpaced Moore’s Law, driven by successive generations of next‑generation sequencing platforms. Those cost curves are not academic curiosities. They change which diagnostics are economically viable, how many patients you can realistically screen or monitor, and what kind of business models labs and service providers can build. At earlier stages, cost and throughput constraints meant companies had to differentiate on driving down their own cost of goods. As sequencing gets faster and cheaper, competitive dynamics shift toward offering richer multi‑omic content, specialized interpretation, and integration into clinical workflows. [3]
A founder in that space who says “sequencing is getting cheaper” is telling you something true and useless. A founder who says, “At roughly $100 per genome, we can screen this specific population and still maintain gross margins because of X, Y, Z. We could not have done that when sequencing cost $1,000+” is telling you something you can underwrite.
The forward progress of technology isn’t what’s important here. Cost curves become timing edges when they cross thresholds that change at least one of these factors:
The unit economics of reaching a defined population
The feasibility of embedding a test or tool into standard care, given existing reimbursement and workflow constraints
The practicality of running certain R&D programs at startup scale
Tailor your questions to uncover how their technology change creates the economic argument.
Finally, there is the messy, human part: how people behave, how data flows, and how work practically gets done. Telehealth again offers a useful case study. Telemedicine had been technically possible for years. What changed during the pandemic was not just regulation and reimbursement, but also behavior and expectations. Patients were suddenly unwilling or unable to show up in person. Clinicians re‑organized their schedules around virtual visits. Systems invested in new platforms and processes. A review of telehealth before and after COVID‑19 notes that prior to 2020, telemedicine was underused and understudied; after the pandemic hit, reduced regulations and payment parity facilitated a rapid expansion across providers and patients. Medicare’s loosened restrictions and temporary payment parity encouraged adoption, but the actual experience of delivering and receiving care virtually also altered expectations. [1]
That combination matters. Without behavior change, a policy can sit on the books and do nothing. Without policy, a behavior change may be fragile and unmonetized. The same pattern shows up with data interoperability. Standards and APIs may exist on paper, but until enough health systems implement them, enough vendors support them, and enough clinicians use the capabilities, the transformation is more aspiration than reality.
When a founder leans on “Patients are more open to remote monitoring” or “Clinicians want data at the point of care,” your job is to push:
What has changed in your user’s day‑to‑day life that makes your solution more acceptable now than 5 to 10 years ago?
Where do you see that change: in adoption stats, in workflows, in staffing, in budget lines?
How dependent are you on that behavioral change persisting once emergency conditions, promotional budgets, or temporary incentives fade?
Here is one simple, brutal question for founders, and for anyone getting carried away by a story:
“If you had launched this exact product in 2015, what would have been impossible that is possible now?”
By now I hope you won’t accept answers like “The tech wasn’t as good” or “People weren’t ready.” Instead, look for answers like:
“We could not have billed for this, because there was no code and no coverage policy.”
“Sequencing throughput and cost were too low to screen this cohort at a price payers would tolerate.”
“Clinicians were not allowed to deliver this from home, so the care model could not work economically.”
“The data we need were locked in systems that had no viable integration path.”
These are answers that reveal the founders have put some thought into regulations, reimbursement, unit economics, and product integration. All good signs that they are building an operating business.
Let’s go back to telehealth and look at it in more detail. Before 2020, telehealth was technically feasible but heavily constrained. Medicare limited who could perform and receive telehealth services, where visits could originate, and what technologies could be used. For example, patients had to be in designated rural areas or specific facilities to qualify, clinicians had to provide telehealth from their practice location, and only audiovisual technologies on approved platforms were reimbursable. Following the global spread of the COVID-19 pandemic in early March 2020, Congress and CMS made major policy changes. State and private payers followed Medicare’s lead, introducing payment parity and broadening coverage. [1] One public safety‑net system in New York City documented how these regulatory and reimbursement changes were critical to their rapid shift to virtual care. Medicaid expanded coverage for a greater range of telehealth services, including telephone‑based care, and this combination of policy and payment moves “propelled telehealth adoption” within that public system. [2]
If you map that to our four levers:
Policy/regulatory: Medicare and state waivers radically expanded who could do telehealth, from where, and with what technology.
Reimbursement: Payment parity and broadened coverage meant telehealth visits became financially viable at scale.
Technology/cost: Videoconferencing tools were already widely available and inexpensive, and consumer hardware penetration was high.
Behavior/workflow: Patients and clinicians were suddenly forced to adopt virtual care, which normalized the modality and created a new baseline expectation. Now, telehealth is one of many options users consider when seeking care.
That is a perfect storm “Why now?” story. In mid-2020 and later, the telehealth companies worth your time were the ones that could say, “Here are the specific waivers, coverage decisions, and behavior changes that de‑risk our model, and here is how we’re building to a world where some of those might sunset, but the new baseline sticks. This is timing that is more investable.
Now consider advanced diagnostics and genomic testing. For years, sequencing was the shiny object in every deck. Now we are at the point where whole genomes can now be sequenced in less than 24 hours and the industry is approaching the $100 whole genome milestone. That cost collapse has shifted competitive dynamics for next‑generation sequencing labs. A recent analysis from BCG highlights that as sequencing becomes very fast and cheap, labs can no longer differentiate mainly on lowering sequencing cost; instead, they must offer richer content (e.g., multi‑omic panels, whole genome services) and build capabilities in data interpretation, management, and workflow integration. The economics of what services you can offer, to whom, and at what price point change when the underlying sequencing cost per genome falls to that level. [3]
Here is what matters about this case study:
There is a quantifiable cost curve with specific price points over time.
Certain clinical applications only become economically sensible once the per‑sample cost drops below a threshold.
Competitive moats move from “we can sequence cheaply” to “we can ask and answer more complex health research and drug development questions, deliver faster, and plug into real workflows.”
A more compelling “Why now?” in 2026 is something like, “At roughly $100 per genome and sub‑day turnaround, we can design a high-content multi-omics tumor profiling service for this specific oncology indication that can be used as an effective patient stratification tool in clinical trials, whereas at $1,000 per screen with lower content data we would have been dead on arrival.”
That is a timing claim you can interrogate by asking the founder to:
Show you the cost components: sequencing, interpretation, other lab steps.
Show you how the costs map to DRGs, APCs, or bundled payments in your target setting.
Show you how much headroom you have if your technology stalls or R&D moves to a different indication.
If the economics make sense because of real changes that have occurred in the last 5 to 10 years, you have a genuine timing wedge.
In your first pass screening, you do not need a 20‑item rubric. You need a small set of sharp questions you can ask out loud and answer in your notes. Here is the version that fits on a Post‑it:
What exact policy or regulatory move are you depending on? Year, document, and agency.
What exact reimbursement or incentive change makes someone willing to pay for this now? Code, contract type, or risk arrangement.
What cost or performance curve has crossed a meaningful threshold for this use case? Roughly when?
What concrete change has occurred in user or buyer behavior in the last 3 to 5 years that makes adoption more likely?
You’re not trying to score all of this in the first 30 minutes. You’re trying to see whether there is a timing story that can be made concrete, or whether it collapses into buzzwords the moment you apply pressure.
If you’ve been following this series, you know we’ve already talked about commercialization plans, great teams, great ideas, and founder-problem fit. “Why now?” sits right in the middle of those. It validates whether the commercialization plan is built on a stable, real-world foundation or on temporary anomalies and hopeful policy. It stress‑tests the team’s depth of understanding about the ecosystem they’re stepping into—have they mapped incentives, regulatory paths, and cost curves, or are they skating on generalities? It often forces you to narrow the “great idea” down to the slice of the market where timing is on your side.
One of the most practical ways to use “Why now?” is as a forcing function. If you cannot identify a specific timing edge, you may decide that this is a perfectly nice business idea, but not one where your fund can earn the returns it needs. You don’t need to be dogmatic about it. There will be exceptional teams and exceptional assets where the timing edge is “we will create the inflection.” But that should be the exception, not the norm.
If you’re noticing that many of the strongest “Why now?” edges are regulatory or reimbursement edges, you’re right. In early‑stage healthcare, a lot of your timing alpha lives in places founders either don’t see or don’t want to talk about, because they feel unsexy or constraining. Examples include:
The fact that new coverage policies created unexpected pressure on hospitals to reduce readmissions in specific conditions.
The reality that new therapeutic categories aren’t eligible for existing reimbursement paths, necessitating engagement for new codes and benefit categories.
The way Medicare Advantage risk adjustment, denials, and documentation requirements are reshaping what providers care about when they evaluate tools. [4]
In the next article, we’re going go deeper on Regulatory and Reimbursement Readiness as its own screening lens. “Why now?” tells you whether the wind is at your back. Regulatory and reimbursement readiness will tell you whether the sail is even pointed in roughly the right direction. Put together, they can shift timing rationale into a disciplined part of your deal screening practice.
Thanks for reading Thinking Kat! If you found this issue valuable, please pass it to someone else who would benefit.
[1] Shaver, J. (2022). The state of telehealth before and after the COVID-19 pandemic. Primary Care: Clinics in Office Practice, 49(4), 517–530. https://doi.org/10.1016/j.pop.2022.04.002
[2] Chokshi, D. A., Felknor, S. A., & Greiner, A. L. (2020). Staying connected in the COVID-19 pandemic: Telehealth at the largest safety-net system in the United States. Health Affairs, 39(8), 1437–1442. https://doi.org/10.1377/hlthaff.2020.00903
[3] Haller, M., Magiera, J., Meagher, K., & Steinhubl, S. R. (2024, May 15). How genomic sequencing may change advanced diagnostics. Boston Consulting Group. https://www.bcg.com/publications/2024/how-genomic-sequencing-may-change-advanced-diagnostics
[4] CPa Medical Billing. (2026, March 17). How Medicare Advantage is reshaping revenue cycle management. CPa Medical Billing. https://cpamedicalbilling.com/how-medicare-advantage-is-reshaping-revenue-cycle-management/
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