Recently, I had the opportunity to judge Accenture’s Healthtech Innovation Challenge. The focus was on AI and its applicability in healthcare. For me, what stood out among the many brilliant ideas was the question of how to price technology products for healthcare. The following is not only a summary of what I could glean from the many conversations I had on this topic but also advice for startup CEOs who aim to succeed in the healthcare technology space through a successful pricing strategy. Reach out to discuss this with us further.
As AI reshapes healthcare, startup CEOs need to think of strategic pricing to drive adoption among payers and providers. Two key observations:
- “Focus on cost savings” may sound like a cliché, but in healthcare, cost is an emotive subject. If cost savings on X is not one of the outcomes of your product X, you are barking up the wrong tree
- Prepare to flex models as you evolve in the environment. Moving from a flat fee to pay per use to tiered pricing may be considered hara-kiri in big tech pricing strategies, but in the startup world it is considered “having your ears to the ground”
Now, on to how healthcare buys. Traditionally, healthcare was not a big buyer of large monolithic platforms such as CRM and ERP. The industry was (and continues to be) fragmented, and hence, technology purchases were also erratic – a mix of bespoke and non-standard COTS. This world underwent a change post meaningful use. Core administration (payers) and EMRs (providers) became that monolith. However, instead of solving for that erratic product buying behavior, what we got was just a better name for it – point-solution centricity. Two ways to explain this phenomenon:
- Healthcare loves bolt-on solutions that are not only cheaper to adopt but also plug into their legacy technology
- Healthcare also desires an evolved “platform” that herds together the benefits of all the bolt-ons and legacy technologies
This is the reason why technology adoption strategies vary by organization. Across functions and stakeholders, buyers’ willingness to pay depends not only on direct business outcomes but also on tailored solutions that correlate spend with outcomes.
Hence, a nuanced approach to pricing is crucial in driving adoption and ensuring that AI solutions are accessible and valuable across the diverse landscape of healthcare organizations.
These strategies not only enhance market penetration but also build long-term relationships with healthcare providers and payers by aligning pricing with organizational needs and capabilities. What we have noted below is drawn from a slew of interactions with buyers of technology at different healthcare organizations and CEOs of startups who are working with them.
- Large payers and providers
Large healthcare organizations typically have significant budgets and complex needs, necessitating flexible and comprehensive pricing models. The key strategies observed in this segment include:
- Enterprise pricing model (most preferred model currently):
- This model offers a flat fee for unlimited access across the organization. It simplifies budgeting and supports widespread adoption across multiple departments
- Common among large hospital networks and national insurers like UnitedHealthcare and Kaiser Permanente
- Value-based pricing:
- Pricing is aligned with the outcomes achieved, such as improvements in patient outcomes or operational efficiencies. This model resonates well with large entities focused on ROI
- Utilized by companies like Epic Systems in their AI-driven EHR enhancements
- Bundled services:
- Comprehensive packages that include not only the AI tools but also integration, training, and ongoing support. This model adds significant value and ensures that the AI solution is fully embedded in the organization’s operations
- Seen in offerings by Teladoc Health for their AI-driven telehealth services
- Mid-sized payers and providers
Mid-sized organizations, such as regional hospital systems or mid-tier insurance companies, often require scalable solutions that can grow with their needs. The pricing strategies here are more varied to accommodate differing capabilities and budgets:
- Tiered pricing (most preferred model currently):
- Offers different levels of service and functionality, allowing organizations to choose a package that best fits their current needs and budget
- Common in platforms like Olive AI, where mid-sized hospitals can start with basic automation tools and scale up to more advanced AI-driven operations
- Pilot programs with clear ROI metrics:
- Healthcare AI startups often implement pilot projects that allow these organizations to test solutions with clear, short-term ROI metrics before committing to larger investments
- Examples include pilot programs from startups like Aidoc, which provides AI-driven radiology solutions
- Volume discounts:
- Discounts are offered for bulk commitments or long-term contracts, helping mid-sized entities manage costs while planning for future growth
- Seen in pricing strategies from Change Healthcare for their AI-driven revenue cycle management tools
- Small payers and providers
Small healthcare providers, such as independent clinics or small regional insurers, have limited budgets and require affordable, flexible solutions. The pricing models in this segment are designed to lower barriers to entry:
- Subscription-based pricing (most preferred model currently):
- Monthly or annual subscriptions make AI solutions more accessible by spreading costs over time, which aligns with the cash flow constraints of smaller organizations
- Healthcare AI startups like Zebra Medical Vision utilize this model to provide AI-driven imaging solutions to small clinics
- Freemium models:
- Basic features are offered for free, with the option to upgrade to premium versions. This model allows small providers to try the technology before committing financially
- Used by some digital health startups like Buoy Health for their AI-driven symptom checkers
- Pay-as-you-go:
- This usage-based pricing model allows small providers to pay only for what they use, making it ideal for those with fluctuating patient volumes
- Common in AI-driven telemedicine platforms like Amwell, which serves small practices
As healthcare AI startups navigate the complexities of pricing strategies, it is crucial to continuously monitor the evolving demands of payers and providers. Market dynamics, technological advancements, and regulatory changes can all influence what organizations value in AI solutions.
Startups must remain agile, regularly reassessing their pricing models to ensure they align with the shifting priorities and financial capacities of their target segments. By staying attuned to these changes, startups can not only maintain relevance but also capture new opportunities for growth, ensuring that their solutions remain accessible and attractive across stakeholders in the healthcare landscape.
To explore AI in the healthcare industry and how startup CEOs can succeed in the healthcare technology space, reach out to Abhishek Singh, Rahul Gehani, or Abhishek Sharma.
Watch our LinkedIn live event, The Role of Technology in Advancing Member and Patient Engagement, to learn about potential investments in this space and strategies for their implementation.
