Global Patient Risk Stratification Market Analysis

The patient risk stratification market size is expected to reach US$ 11.44 Bn by 2030, from US$ 1.56 Bn in 2023, at a CAGR of 22.2% during the forecast period.

Patient risk stratification is the process of identifying and grouping patients according to their risk of developing certain outcomes such as hospital readmission or complications from chronic diseases. It allows providers to target interventions toward patients who will benefit the most by patient risk satisfaction software and services. The key advantages of patient risk stratification include improved patient outcomes, reduced healthcare costs, and better population health management.

The market growth is driven by the rising geriatric population, increasing prevalence of chronic diseases, the need to curb healthcare costs, and technological advancements. The patient risk stratification market is segmented based on component, delivery model, type, application, end user, and region. By component, the market is segmented into software and services. The software segment accounts for the largest share owing to the growing adoption of predictive analytics and data integration solutions for risk stratification.

Patient Risk Stratification Market Drivers

• Growing prevalence of chronic diseases: The rising prevalence of chronic diseases such as diabetes, cardiovascular diseases, chronic respiratory diseases, and cancer is a major factor driving the growth of the patient risk stratification market. Chronic diseases are long-lasting health conditions that require continuous health monitoring and care management. Risk stratification enables providers to identify patients at high risk of developing complications from chronic diseases and target preventive interventions accordingly. For instance, predictive analytics can identify diabetic patients at high risk of hospital readmission so they can be enrolled in care management programs to avoid adverse outcomes. The growing burden of chronic diseases is creating a need for advanced risk stratification to optimize disease management.
• Need to reduce healthcare costs: With rising healthcare costs, payers and providers are looking for innovative ways to improve outcomes while reducing costs. Patient risk stratification provides actionable insights to identify high-cost patients and align care delivery to their needs. For example, concurrent risk models can identify patients likely to incur high costs so care teams can prevent adverse events leading to unnecessary utilization and spend. Risk stratification is enabling value-based population health models aimed at reducing avoidable healthcare costs. According to estimates, the successful implementation of risk stratification can result in cost savings ranging from US$ 100 to US$ 1,000 per member per month for at-risk populations.
• Favorable government initiatives: Government policies and initiatives to promote preventive care, value-based reimbursement, and population health management are contributing to the adoption of patient risk stratification solutions. For instance, the CMS Medicare Shared Savings Program encourages accountable care organizations (ACOs) to use risk stratification to provide efficient and coordinated care. Additionally, risk adjustment programs require payers to stratify plan members accurately for capitated payments. Such programs are creating a conducive environment for the growth of the risk stratification market.
• Advancements in analytics, Artificial Intelligence (AI), and machine learning: Ongoing advancements in predictive analytics, artificial intelligence, machine learning, and big data techniques are enhancing the capabilities of modern risk stratification solutions. Natural language processing and unsupervised learning can extract insights from unstructured clinical notes and identify unknown patterns predictive of risk conditions. AI-based models can continuously self-learn and improve their predictive accuracy. Access to comprehensive real-world data is enabling more granular and precise risk stratification algorithms. Such technological enhancements are expected to significantly expand the adoption of risk stratification going forward.

Patient Risk Stratification Market Opportunities

• Leveraging real-world evidence (RWE) and big data: Real-world data from clinical settings and insurance claims provide extensive insights into patient health outcomes, treatment patterns, resource utilization, and costs. Leveraging RWE and big data in risk models can lead to more accurate predictions of clinical risks and trajectories. For instance, integrating socioeconomic and genomic data could account for social determinants of health inequities. Big data analytics can also power personalized risk assessment at an individual patient level. Overall, RWE and big data present significant opportunities to develop enhanced risk stratification frameworks.
• Adoption of patient risk satisfaction softwares in emerging economies: Emerging economies present untapped opportunities for growth owing to improving healthcare infrastructure, rising middle-class and health insurance, and increasing incidence of chronic diseases. Governments in developing countries are focused on enhancing access to affordable healthcare services. Risk stratification can assist providers in prioritizing resource allocation among high-risk low-income groups. Localizing risk models as per population health challenges will be key to adoption in emerging markets.
• Incorporation of social determinants of health: Social determinants of health (SDOH), such as food access, housing stability, transportation barriers, and financial toxicities, are being recognized as pivotal predictors of health risks. Incorporating SDOH data from community service providers could lead to better risk stratification, care coordination, and outcome improvements among disadvantaged populations. Addressing SDOH through risk-adjusted interventions is an impactful opportunity for payers and accountable care organizations.
• Patient-centered risk prediction models: Existing risk models predominantly rely on clinical and claims data inputs. Developing patient-centered models using patient-reported outcomes, wearable data, and other real-world data can enable more personalized risk assessment. For instance, remote patient monitoring and patient-reported data can provide real-time visibility into health changes between office visits. Such granular data can allow timely interventions based on dynamic risk predictions. Patient engagement in shared decision-making around risk information also promotes satisfaction and outcomes.

Patient Risk Stratification Market Restraints

• Data privacy and security concerns: Concerns regarding data privacy and security associated with aggregating large volumes of patient data are hindering the market growth. There are apprehensions about the privacy of sensitive health data used for risk modeling by third parties. Stringent regulations governing cross-border patient data transfer also restrain market expansion. Healthcare organizations are often reluctant to embrace risk stratification due to cybersecurity risks and compliance obligations related to protected health information.
• High upfront costs: The high upfront costs of implementing risk stratification software, infrastructure, and resources is challenging, especially for smaller healthcare organizations with budget constraints. Substantial investments are required for EHR (Electronic Health Record) integrations, data warehouse setup, analytics platforms, and specialist hiring. The complex data infrastructure and long ROI (Return on investment) deters many providers. However, SaaS(Software as a service)-based models are emerging to provide more flexible and affordable options.
• Interoperability issues: Effective risk stratification relies on complete patient data aggregation across care settings. However, disparate vendor systems and fragmented data silos make assembling comprehensive records with full context challenging. Underlying interoperability issues between diverse EHRs, claims systems, registries, and other data sources hinder holistic risk analysis. Lack of standardized terminologies and specifications also hampers data integration.