This systematic literature review investigated the potential effect of population-based RS tools in managing chronic diseases within primary care settings. While other studies have focused on the validation of the prediction capacity of specific RS tools [11], our research provides meaningful evidence on the impact of these tools on population health outcomes, particularly in chronic disease management carried out by primary care services. Specifically, we aimed to examine the effects of the targeted interventions based on RS tools on selected chronic patients and healthcare utilization outcomes.
We focused our search on the most common chronic conditions—heart failure, diabetes mellitus, chronic obstructive pulmonary disease, and dementia. These conditions are highly prevalent in the population and serve as benchmarks for the extensive and effective application of RS tools. These chronic conditions require complex, continuous management and benefit from RS tools, which help healthcare providers prioritize resources and tailor preventive targeted interventions that can be delivered in out-of-hospital care settings [24, 25]. Our research summarized the latest evidence testing the hypothesis that implementing RS tools in primary care settings for these conditions can lead to better health outcomes, reduced hospitalizations, and improved overall quality of life for patients.
Evaluating population health management tools, such as stratification and risk prediction tools, can be challenging. There is a growing body of evidence on their effectiveness and validation, but real-world implementation and impact studies remain limited and heterogeneous. The latter group of studies vary significantly in their designs, definitions of interventions, population sizes, exposure times, and analyzed outcomes. The still limited and fragmented nature of the related literature makes it difficult to draw broad, generalizable conclusions. However, our analysis attempted to untangle the “web” of scientific evidence and identified key study designs, such as CCTs and CBAs, for future research.
The available literature regarding the effectiveness of RS tool adoption, and related interventions, in enhancing outcomes of interest remains limited both in quality and quantity, with the studies reviewed offering mixed evidence. While some of the findings to date are promising, particularly in terms of reductions in hospitalization and mortality rates, the evidence concerning reductions in ED visits is comparatively weaker. Notably, several studies indicate a simultaneous increase in the utilization of outpatient services, whereas the few analyses examining cost implications reports no significant impact.
The comprehensive review conducted in this study highlighted in the first place the very limited number of evaluations that have assessed the impact of RS tools on improving primary care outcomes. This finding outlines the unexploited potential for policymakers to leverage the sophisticated use of data to enhance primary care interventions. Additionally, we emphasized the importance of adequately defining the intervention based on RS models, when applying such tools to primary care settings for chronic conditions. Interventions can be defined either as the simple use of the stratification tool or, more appropriately, as the comprehensive set of measures, actions, and behaviors (e.g., explicitly through protocols/guidelines) implemented when information about a patient’s risk is made available to healthcare professionals (physicians, nurses, or others).
Non-conclusive evidence supports that improving healthcare models relying on RS tools is associated with a reduction in both ED visits and hospital admissions [18, 22]. These outcomes, when considering the management of chronic patients, serve as a proxy for patient mismanagement by primary care services, highlighting their poor capacity to fully address clinical needs, prevent disease recurrence, or clinical decline, leading to overuse of acute care settings [11].
These findings align with other studies indicating that RS can enhance resource allocation and patient management, ultimately improving health outcomes and reducing the strain on healthcare services, in particular reducing hospitalizations [11, 24]. However, as reported by Snooks et al. [17], any interventions using predictive risk stratification tools should have explicit models of how they will work and undergo rigorous evaluation of their clinical and cost-effectiveness before implementation.
In line with the hypothesis investigated, the implementation of RS tools can be associated with an increase in outpatient visits, as reported by Mateo Abad et al., Wan et al., and Gupta et al. [18, 22, 23]. These findings suggest a better capability of primary care services to treat chronic patients, reducing their risk of needing ED or hospital care, and appropriately moving the setting of care from acute hospital attendances to primary care consultations.
Our systematic review also investigated the impact of complete RS tool implementation on mortality in large healthcare organizations. Although the evidence is still relatively limited, we found two studies reporting lower mortality rates in primary care settings where RS had been implemented, leading to better-informed clinicians and healthcare providers [21, 22]. Two studies [17, 23] also examined the potential impact of RS tools and related interventions on costs but found no significant differences between intervention and control groups. One of the key findings emerging from this review is the limited availability of studies assessing the economic impact of interventions based on risk stratification tools. While our PROSPERO protocol initially emphasized cost-effectiveness as a key aspect of the review, the actual body of evidence identified was insufficient to draw meaningful conclusions in this regard. The lack of structured cost-effectiveness analyses suggests that economic considerations may still be an underexplored aspect in the implementation of RS-based interventions in primary care.
RS tools utilize various data inputs, including electronic health records and patient-reported outcomes, to categorize patients based on their risk of adverse health events [17, 19,20,21]. This could enable healthcare providers to tailor interventions more precisely, prioritize resources, and engage in proactive management of high-risk individuals. For example, the Adjusted Clinical Groups (ACG) system and the Hierarchical Condition Categories (HCC) model are widely used to predict healthcare utilization and guide clinical decision-making in primary care [11, 26].
Future directions
To maximize the benefits of RS tools, future research should focus on several key areas. Improving the integration of diverse data sources, including social determinants of health, can enhance the predictive accuracy of RS tools. This requires robust health information exchange systems and standardized data collection practices [27, 28]. The effectiveness of RS tools also depends on their validation and adaptability to different healthcare systems, geographical areas, and disease types. Since RS models perform optimally in populations similar to those in which they were originally developed, their widespread application requires local validation and potential recalibration. Also, a systematic description of the interventions that accompanied the adoption of RS tools should be included in future studies. Such an approach can help disclose the mechanisms behind these interventions and identify the factors that contribute most to their success. It is also essential to ensure adequate training and support for healthcare providers to facilitate the effective use of RS tools, including the development of user-friendly interfaces and clear communication of both the benefits and limitations of these tools. In addition, longitudinal studies are needed to assess the long-term impact of RS tools on patient outcomes and healthcare costs, offering valuable insights into their sustainability and overall effectiveness [28,29,30]. Lastly, developing customizable RS tools that can be tailored to the specific needs of different populations and healthcare settings can improve their relevance and effectiveness, and this should involve collaboration between developers, healthcare providers, and policymakers [31].
Challenges and limitations
Despite their expected benefits, the implementation of RS tools in primary care is not without challenges. One significant issue is the variability in healthcare organization and data quality across different settings, which can affect the accuracy and utility of these tools [9, 24]. Additionally, the readiness of healthcare providers to adopt new technologies and integrate them into existing workflows is crucial for the successful implementation of RS tools [32]. This requires that innovative strategies are endorsed by the entire healthcare system, across all policy and operational levels, to ensure proper transmission of directives and cooperation among the stakeholders involved. Another challenge is ensuring the comprehensiveness of the data used for RS. Many tools primarily rely on biomedical data, potentially overlooking important social determinants of health, such as socioeconomic status and behavioral factors, which can significantly impact patient outcomes [33]. Addressing these gaps requires incorporating broader data sources and enhancing the interoperability of health information systems. Lastly, another limitation emerging from this review is the scarcity of studies assessing the economic impact of interventions based on risk stratification tools. Economic evaluations are crucial for guiding policymakers and healthcare providers in resource allocation and scalability of RS-based interventions. Without a clear understanding of cost-effectiveness, it remains challenging to determine whether the benefits of risk stratification tools outweigh their implementation and operational costs. Future research should incorporate comprehensive economic evaluations, including cost-effectiveness, cost-utility, and budget impact analyses, to provide a more robust evidence base for decision-making.
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