Must-Read: Opportunities and Risks in Healthcare Big Data Entrepreneurship
It has been only five years since McKinsey analyzed the value of big data and introduced the concept of the “Big Data Era.” Yet, big data seems to have suddenly permeated people’s lives, with the term on everyone’s lips, even though most individuals do not have a profound personal experience of its impact. As a group at the forefront of the times, entrepreneurs hold their own unique perspectives on this trend. Currently, it is almost embarrassing to pitch for financing without incorporating some big data concepts into one’s project. However, value-oriented investors and rational entrepreneurs should delve deeper into this matter. To this end, Tongdu Capital has released the report “Medical Big Data: Basis and Challenges.” The detailed contents of the report are as follows:
What Exactly Is the Sought-After Big Data?
It is evident that this is indeed a favorable era, as the value of data is increasingly recognized. Whether at the government, capital, or entrepreneurial level, all parties are driving the development of big data. As Jack Ma stated in his “2015 Open Letter to Investors,” “Humanity has moved from the IT era into the DT era.”
However, to truly leverage big data in healthcare, we must first understand what big data is, what healthcare big data entails, and what benefits it can actually bring us.
Big data can be defined in various ways, with IBM’s “4V” definition being widely accepted: Volume (massive data scale), Variety (diverse data types), Velocity (rapid data generation), and Veracity (data authenticity). The first three are relatively straightforward to understand, whereas Veracity emphasizes data quality and reliability, as only authentic data holds analytical value.
Provided that the fundamental characteristics of big data are met, any big data related to the processes and outcomes of medical and health services can be referred to as medical and health big data. We categorize it into directly relevant data and indirectly relevant data. Based on different sources, directly relevant data can be further divided into in-hospital data and out-of-hospital data, while indirectly relevant data can be classified into individual data and institutional data.
The Significance of Big Data
In fact, we believe that data scale is merely one characteristic of big data and serves as the foundation for analysis. However, the significance of big data lies not only in its volume but also in deriving deeper value through data processing. Here, we must reference the DIKW pyramid model, which effectively elucidates the various stages of data utilization. The progression from data to information, from information to knowledge, and from knowledge to wisdom represents a continuous process of refinement and processing. The long-term goal of smart health is also to leverage data-driven approaches to achieve optimal decision-making and solutions.
Where Do the Opportunities Lie in Big Data for Healthcare?
Big data in healthcare is indeed ushering in significant opportunities for development. In addition to advancements in technologies such as data storage, data analytics, and cloud computing, the healthcare sector also benefits from numerous favorable factors:
1) Continuous Improvement in the Construction of Healthcare Information Systems
2) Integration of the Internet and Healthcare
3) High Level of Attention from the Capital Markets
4) The Rapid Development of the Commercial Health Insurance Market
5) No dominant players have yet emerged in niche sectors
Based on the aforementioned opportunities, Tongdu Capital has expressed an overall optimistic outlook on startups in the healthcare big data sector. Drawing on McKinsey’s analysis of application areas for healthcare big data, they have identified several potential entrepreneurial directions and provided subjective feasibility ratings from an investment perspective:
1) Clinical Diagnosis
a) Comparative effectiveness research: Relatively low implementation difficulty with significant impact
b) Clinical Decision Support: The technology has a certain barrier to entry and carries medical risks
c) Medical Data Transparency: Challenges in Monetization
d) Remote Patient Monitoring: Limited measurable data and limited accuracy
e) Patient Profile Analysis: Deep Mining of Patient Value, with Challenges in Conversion
f) Personalized Treatment: Technological Breakthroughs Still Needed
2) Cost Control
a) Identification of Claim Reasonableness: Large market, but lack of information and high difficulty in analysis
b) Pricing Plan Based on Health Economics and Efficacy Studies: Lack of Incentive Mechanisms and Payers
3) Public Health
a) Epidemic Monitoring: Significant in value, but not suitable for startups
4) Pharmaceutical R&D
a) Predictive Modeling: Strong demand with a certain technical barrier to entry
b) Statistical tools and algorithms for improving clinical trial design: small proportion
c) Clinical Trial Data Analysis: Difficulties in Data Acquisition and Small Proportion
d) Personalized Medicine: High Technical Difficulty
e) Analysis of disease patterns: There is demand, but the intervention stage is slightly early, with relatively high risk.
f) Demand Analysis and Promotion: Strong demand, technically feasible
5) Health Management
a) Disease Management: Technically Feasible, but Low Willingness to Pay
b) Health Consumption: Clear monetization models lead to higher consumer acceptance
c) Personal Health Records: High Value, but Difficult to Aggregate
What are the revenue models for big data in healthcare?
During the entrepreneurial process, a viable revenue model is crucial to ensuring the sustainable development of a business. Currently, there are six main payers in the healthcare big data sector: consumers, enterprises, insurance companies, the government, hospitals, and pharmaceutical and medical device companies. In the short term, insurance companies and pharmaceutical firms demonstrate the strongest willingness to pay, with representative companies already beginning to pilot big data applications. Demand from hospitals, the government, and enterprises is also evident, but they remain relatively conservative at this stage. Consumers are currently more inclined to pay for tangible products, showing limited willingness to pay for services such as online light consultations, let alone for big data solutions.
Challenges Facing Big Data in Healthcare
Big Data in Healthcare Indeed Presents Numerous Opportunities, Yet Its Current Development Faces Significant Challenges. We Have Summarized the Following Five Key Points for Your Consideration:
1) Lack of Security and Privacy Safeguards
2) Barriers to Data Sharing and Interoperability
3) High-quality data sources remain limited
4) Barriers Arising from the Inherent Complexity of the Healthcare Sector
5) Difficulty in Implementing the Business Model
Overall, the development of big data in healthcare is still at a relatively early stage, with both China and the United States exploring their paths. However, opportunities and challenges always coexist; significant transformations bring substantial opportunities. At the very least, we will maintain an attitude of cautious optimism and continue to monitor this industry.
（Tongdu Capital is a venture capital firm focused on innovation and entrepreneurship opportunities in the healthcare sector. Since 2014, Tongdu Capital has been dedicated to research and investment practices in this field.