Insights

Korea's current healthcare expenditure has doubled in a decade. This structural pressure is shifting predictive healthcare from a technological curiosity to an economic necessity.

Samjong KPMG Economic Research Institute's report deep-dives “predictive healthcare” — the new paradigm of digital healthcare. Korea's current healthcare expenditure expanded roughly 2x from KRW 112.5 trillion in 2015 (6.5% of GDP) to KRW 213.1 trillion in 2024 (8.4% of GDP), and the burden on the government's mandatory insurance system grew from KRW 62.7 trillion (55.8%) to KRW 127.7 trillion (59.9%) — intensifying fiscal pressure on the state. The medical paradigm shift from reactive illness treatment (Illness) to proactive prevention and management (Wellness) is the era's response to this cost structure.

What predictive healthcare is: a new medical logic built on data. Predictive healthcare uses AI to analyze genomic data, wearable biosensors, electronic medical records (EMR), and lifestyle data to intervene before disease occurs. Digital healthcare is categorized into seven types — medical devices, software, and platforms; wellness devices, solutions, and platforms; and digital infrastructure. Wearables in particular are progressing from portable → attachable → implantable/ingestible. Commercialization of patch-based vital-sign sensing and implantable devices is expected to accelerate in the near future.

Three reasons predictive healthcare is in focus now: demand, supply, and technology. On the demand side, ageing and rising healthcare costs push the paradigm toward prevention. On the supply side, expanding fiscal burden and the need to contain medical costs are growing. On the technology side, the combination of large-scale medical data and AI has reached a level of diagnostic accuracy and predictive reliability applicable in clinical practice.

Three barriers to growth. The report identifies the barriers blocking predictive-healthcare diffusion as: ① data quality and lack of standardization; ② regulatory barriers (data-mobility limits from personal information protection law, medical law, insurance reimbursement, etc.); and ③ difficulty expanding into direct-to-consumer (B2C) markets. In particular, the absence of trust/accountability frameworks and reimbursement bases prevents predictive healthcare from becoming a mainstream form of medical innovation.

What this means for M&A. The growth of predictive healthcare creates three M&A opportunities. First, companies combining clinical data with AI algorithms for diagnostic-assist solutions are emerging as strategic bolt-on targets for traditional medical-device and pharmaceutical companies. Second, cross-border investment and partnerships between global big pharma and Korean digital-health startups are likely to accelerate in wearable sensors, EMR integration, and digital biomarkers. Third, in the current early stage with high regulatory uncertainty, companies that demonstrate health-economics evidence (clinical effectiveness and cost-effectiveness) will have a competitive edge in hospital and insurer channel entry — becoming a differentiating deal-sourcing criterion for investment institutions.