Proprietary Datasets in HealthTech: Value Proposition and Strategic Advantages

Proprietary datasets have become a critical asset and a major source of competitive advantage in the healthtech industry. Unlike publicly available data, proprietary data is unique to a company, often collected through its own products, services, or partnerships. This exclusivity creates a powerful moat that is difficult for competitors to replicate.

I. Value Proposition

The value proposition of proprietary data sets in healthtech is centered on their ability to create unique, high-quality, and actionable insights that improve health outcomes and reduce costs. This value is delivered to various stakeholders in the healthcare ecosystem:

For Patients: Improved patient outcomes through personalized medicine, earlier disease diagnosis, and more effective treatment plans. Data from wearables, for example, can be used to provide real-time, continuous monitoring, and personalized health recommendations.

For Healthcare Providers: Enhanced clinical decision-making through predictive analytics and AI-powered tools that identify at-risk patients, streamline workflows, and optimize resource allocation. Proprietary datasets can provide a more holistic view of a patient's health, including social determinants of health and lifestyle factors.

For Payers and Insurers: The ability to reduce wasteful spending, predict and manage high-cost populations, and develop more effective value-based care models.

For Life Sciences Companies (Pharma, Biotech): Accelerated drug discovery and development, faster clinical trial validation, and a deeper understanding of real-world evidence. This can lead to more targeted therapies and a higher success rate for new drugs.

A strong value proposition is built on the unique benefits, cost savings, and differentiation a company's data offers compared to current practices. This is often backed by robust evidence and a clear demonstration of how the data addresses an unmet need.

II. Value

The value of proprietary healthtech data is multifaceted and can be quantified in several ways:

Clinical Value: This is the most direct measure, showing how the data improves health outcomes. Examples include a diagnostic tool with a lower false positive rate, an algorithm that predicts disease progression, or a platform that improves medication adherence.

Economic Value: This relates to cost savings and new revenue streams. The data can lead to reduced hospital readmissions, more efficient clinical trials, or the ability to offer premium pricing for a unique solution. Some estimates suggest that a single patient dataset can be worth hundreds or even thousands of dollars depending on its therapeutic focus and type.

Operational Value: This includes improvements in efficiency, such as automating tasks, reducing administrative burdens, and optimizing hospital operations. Predictive analytics, for instance, can help healthcare organizations anticipate demand for services.

Research Value: The data can be a valuable asset for research, driving new discoveries and fueling innovation in drug development and medical device creation. Companies can monetize this data through partnerships or by offering it to researchers.

The value of a dataset is determined by several factors:

Volume and Variety: A large and diverse dataset (e.g., combining claims, clinical, and social data) is more valuable for training powerful AI models.

Uniqueness and Novelty: Data that is difficult for competitors to obtain, such as real-time streams from connected devices or rare genomic data, holds higher differentiation potential.

Quality and Completeness: The data must be accurate, clean, and free from significant gaps. 

Context and Integration: Data that is well-contextualized and harmonized across different sources is more valuable for generating actionable insights.

III. Strategic Advantages

A proprietary data set is a powerful strategic asset that can create a sustainable competitive advantage, also known as a "data moat." This advantage is built on several key pillars:

Defensible Competitive Moat: Unlike a patent, which eventually expires, a proprietary dataset can grow and become more valuable over time. As more data is collected, the company's algorithms and insights become more accurate, creating a self-reinforcing loop that is incredibly difficult for new entrants to replicate.

Enhanced AI and Machine Learning Capabilities: Proprietary data is the essential fuel for developing and training superior AI models. Companies with unique datasets can build models that are more accurate, less biased, and better at addressing specific, high-value problems in healthcare. For example, a company that has trained a large language model (LLM) on thousands of healthcare entities and concepts can offer an AI assistant that is more accurate and less prone to "hallucinations" than a general-purpose model.

Personalization and Customization: Unique data allows healthtech companies to build products and services that are highly personalized to individual patients, providers, or organizations. This leads to better engagement, improved outcomes, and stronger customer loyalty.

New Product and Service Development: The insights derived from proprietary data can be the basis for entirely new product lines or business models. For instance, a company analyzing real-world evidence from its data could identify new therapeutic applications for an existing drug or pinpoint a previously unknown disease pattern.

Increased Valuation and Attractiveness for M&A: Companies with valuable, proprietary datasets are often more attractive to investors and potential acquirers. The data itself is a key asset that can be leveraged for future growth and innovation.

In summary, proprietary datasets are not just a tool for healthtech companies; they are the foundation for building a defensible market position, creating outsized value for all stakeholders, and driving the future of healthcare innovation.

To discuss how Nelson Advisors can help your HealthTech, MedTech or Digital Health company, please email [email protected]