Patient risk stratification involves the use of data analytics and algorithms to identify high-risk patients and classify them according to their risk levels. This allows healthcare providers to prioritize resources for those who need it the most. Key products in this market include cloud-based and on-premise software that aggregates claims, clinical, and other demographic data to calculate risk scores. This provides insights into the type and level of medical services individual patients may need in the future.
The Patient Risk Stratification Market is estimated to be valued at US$ 1.56 Bn in 2023 and is expected to exhibit a CAGR of 22.2% over the forecast period of 2023-2030, as highlighted in a new report published by CoherentMI.
One of the key drivers for the patient risk stratification market is the increasing demand for reduction of healthcare costs. Risk stratification helps providers focus their disease management programs on high-cost, high-risk patients and prevent expensive healthcare expenditures down the line. Additionally, strict government regulations regarding population health management are also driving the market growth. For instance, the Centers for Medicare and Medicaid Services (CMS) in the US evaluate healthcare providers based on quality measures and readmission rates. Patient risk scores assist providers in meeting such CMS objectives. Furthermore, integration of machine learning and artificial intelligence tools for more accurate predictive analytics is gaining traction. This facilitates optimization of health outcomes through personalized care management programs.
The patient risk stratification market is segmented based on component, data type, deployment mode, end-user, and region. By component, the solutions segment dominated the market in 2018. The solutions segment is dominating as it provides advance tools and platform for efficient data collection, analysis and risk assessment.
Global Patient Risk Stratification Market Segmentation:
By Delivery Model
- Predictive Risk Stratification Model
- Retrospective Risk Stratification Model
- Prospective Risk Stratification Model
- Concurrent Risk Stratification Model
- Population Health Management
- Risk Adjustment
- Revenue Cycle Management
- Clinical Workflow
By End User
- Healthcare Providers
- Healthcare Payers
- Other End Users
Political: Government is taking initiatives to promote precision medicine and personalized healthcare which is driving adoption of patient risk stratification solutions.
Economic: Rising healthcare costs is a key factor for implementation of patient risk stratification as it helps in cost reduction by targeted intervention and preventive care.
Social: Growing awareness about value-based care and shift towards patient-centric healthcare is boosting demand for risk assessment and stratification of patients.
Technological: Advancement in big data analytics, AI and machine learning is facilitating development of more effective risk stratification models for improved care management and outcomes.
The global Patient Risk Stratification Market Size is expected to witness high growth, exhibiting CAGR of 22.2% over the forecast period, due to increasing focus on value-based care and population health management. The market was valued at US$ 1.56 Bn in 2023 and is projected to reach around US$ 6 Bn by 2030.
North America is expected to dominate the patient risk stratification market during the forecast period. This is attributed to rising healthcare spending in the US for preventive care and developed healthcare IT infrastructure. The Asia Pacific market is anticipated to exhibit fastest growth owing to growing geriatric population, rising prevalence of chronic diseases and increasing healthcare spending in the region.
Key players: operating in the patient risk stratification market include Cerner Corporation, Epic Systems Corporation, Optum, Inc., Allscripts Healthcare Solutions, Inc., IBM Corporation, Medecision, Inc., Health Catalyst, Inc., Conifer Health Solutions, LLC, Wellcentive, Inc., ZeOmega, Inc., Verscend Technologies, Inc., PreciseDx, CitiusTech Inc., Avero Diagnostics, Lightbeam Health Solutions, LexisNexis Risk Solutions, Milliman, Inc., NVision Health, RLDatix, and Verisk Analytics, Inc.
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1.Source: CoherentMI, Public sources, Desk research
2.We have leveraged AI tools to mine information and compile it