Global Patient Risk Stratification Market Trends

• Cloud-based delivery models: Cloud-based risk stratification solutions are gaining increasing traction owing to benefits such as flexible scalability, rapid deployment, and reduced upfront costs. The cloud enables easy data sharing across diverse systems to create integrated patient views required for holistic risk assessment. Cloud analytics and machine learning resources can quickly run complex risk algorithms without extensive on-site computing infrastructure. Hybrid models blending cloud agility with on-premise data security are emerging. Overall, the cloud delivery model is a prominent trend in the risk stratification market.
• Health information exchange (HIE) integration: Integrating risk stratification solutions with regional health information exchanges and clinical data repositories enables comprehensive patient data access required for accurate models. HIEs serve as hubs for aggregated clinical data sharing between hospitals, physician offices, labs, pharmacies, payers, and other entities within a geographic area. Drawing upon the comprehensive HIE patient record for risk stratification provides more precise risk scoring. Healthcare organizations are thus increasingly looking to deploy risk stratification systems capable of embedded integration with HIEs.
• Ambulatory and home-based risk stratification: Risk models have conventionally relied on hospital claims data. However, there is a shift towards ambulatory and home-based risk assessment using clinical and remote digital data. Analyzing primary care and specialist visits can help prevent avoidable hospital utilization through early interventions. Similarly, remotely monitoring key vitals and health indicators between visits can enable early risk detection. Such community-focused risk assessment beyond hospital walls is an important trend.
• Natural language processing (NLP) for unstructured data: Unstructured clinical notes contain valuable patient information for risk models. NLP and speech recognition techniques are being used to unlock insights from physician narratives, discharge summaries, radiology reports and other narrative documents. NLP can automatically tag clinical concepts, extract risk factors, and encode into structured data. This allows incorporating unstructured clinical data in risk algorithms to make them more precise without extensive manual chart reviews.

Figure . Global Patient Risk Stratification Market Share (%), by Region, 2023