Background: While more attention has been paid of late to utilization plans for big data in the healthcare sector worldwide, few scholars have addressed the value estimation of healthcare data. Accordingly, this study aims to propose an idea of a reasonable price estimation algorithm that can be applied to bidirectional exchange in healthcare data platforms.
Methods: This study incorporates three methodologies for the data valuation, namely: cost-based, market-based, and impact-based approaches. The cost-based approach calculates the value of data based on the costs associated with data creation, management, and utilization. On the other hand, the market-based approach evaluates it by comparing the market price of a service similar to the data. Finally, the impact-based approach estimates the data value with an emphasis on improving future revenue generation and productivity as an effect of using the data.
Results: The trading prices of healthcare data are determined by the sum of two prices—the fundamental price and the dynamic price. Here, the fundamental price can be further subdivided into the beginning value, complexity value, and network value. The beginning value is determined in proportion to the physical file size of the data, and the fundamental price is estimated by adding the complexity value and network value that can reflect the qualitative value (within 20% of the beginning value) of the data to the beginning value. First, the complexity value can increase if more personal information, more relevant information to the national health insurance system, and more recent and long-term information are included in the dimensions of identification, material, and time information inherent in healthcare data. Second, the network value reflects whether the data can be well linked with data from, not only the healthcare sector, but also from other fields and sectors. The higher the match rate between the attribute value keyword of the data and the healthcare search keyword of journals of excellence and portal services, the higher value is given. Finally, dynamic price reflects real-time preferences for the data and changes in data supply and demand as the actual exchange proceeds through healthcare data trading. To this end, dynamic value is determined within the upper and lower 5% band of the previous month's trading price based on the number of monthly views for the data, the number of downloads of summary data, and the number of actual purchases, and this is reflected in the next month's trading price.
Conclusions: If the algorithm for estimating the trading price of healthcare data proposed in this study is applied to actual data trades, it would expand the transactions of healthcare data from both supply and demand sides. Also, in the processes of actual data exchange and the accumulation of actual data trades, continuing studies on the weighting parameters are needed to better reflect reality; such studies would enable the assignment of additional values or penalties.