‘Without data, you’re just another person with an opinion’

ABSTRACT

Introduction: Given the recent impressive digital transformation worldwide, the importance of data has reached a new dimension. It is, therefore, provocative to ask whether data can save healthcare systems from bankruptcy.

Areas covered: We reviewed published examples in the search for the evidence on how the growing amount of data could change the way we used to assess the value of healthcare technologies, ensuring a more holistic approach in the decision-making process while reducing the waste in the healthcare.

Expert opinion: The growing amount of data will continue to provide a multitude of valuable insights that can save healthcare systems from bankruptcy. Electronic medical records, IoT, wearables, and mobile applications generate constant data streams that can be utilized endlessly thanks to methodological advancements such as SNA, unsupervised and supervised machine learning, and natural language programming. However, interoperability across these multiple data sources still pose a challenge for the future development of data-driven healthcare. Already today however, decision makers can utilize Big Data to develop conditional coverage schemes for very expensive and complicated health technologies suitable for personalized healthcare. More advanced payers may utilize even data analytics even further and develop AI-based pricing schemes.

KEYWORDS:

• Big data
• predictive modeling
• descriptive analytics
• prescriptive analytics
• predictive analytics

Article Highlights

• The scarcity of financing resources has clearly led to the transformation of the healthcare sector.

• Health data are growing at a 48% annual rate. The challenge is how to find new ways to convert this flood of structured and unstructured data into meaningful information that supports the optimal allocation of scarce healthcare resources in the era of aging societies.

• Advancements in descriptive, predictive, and prescriptive data analytics may contribute towards further development of personalized medicine and the implementation of holistic mind-sets while facilitating the quest for the optimal allocation of limited healthcare resources.

• Data analytics will grow in importance with algorithms based on traditional predictive modelling or AI along with machine learning.

• Decision makers can utilize Big Data to develop conditional coverage schemes for very expensive and complicated health technologies suitable for personalized healthcare. More advanced payers can utilise data analytics even further and develop AI-based pricing schemes as well.

• New healthcare models based on health data generated through multiple data streams will be patient centric. The patient will become the centre of the financial process.

• Data driven clinical decision support tools enable the discussion of Big Data’s contribution to the search for the optimal allocation of healthcare resources at both the national and local levels.

• Digital innovation is moving so fast that healthcare professionals have difficulty digesting the changes and coping with them. As a consequence, this may become a barrier to faster adoption.

• Interoperability and information exchange standards are evolving to address Big Data’s future. Nevertheless, today more than 80% of medical information remains unstructured and hard to process.

• Security, data privacy issues, and cybersecurity represent increasing setbacks that need to be addressed in the years to come.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.