Why Population Health Analytics Are Critical to Outcomes Improvement

Editor’s Note: This article draws on insights gathered from in-house experts and a range of articles linked throughout.

By harnessing the power of population health analytics and data, health systems can discover opportunities for system-level improvements in several areas, including identifying health trends, improving resource allocation, and facilitating data-driven decision-making.

Yet, organizational leaders and providers must proactively evolve their data tools for population health management. Devoid of new approaches or solutions, health systems risk lagging innovators who embrace modern solutions and may struggle to effectively meet the needs of their communities.

Understanding Population Health Management

Population health strategies guide transformation across the continuum of care. Because population health data comprises the complete picture of patient populations, including socio-economic factors influencing their health status, organizations leveraging population health analytics can better identify needs and initiate and measure quality improvement initiatives.

Community stakeholders, comprising health information exchanges (HIEs), providers, payers, and public health agencies, also actively engage in collaborative efforts using population health data.

Significance of High-Value Analytics in Population Health Management

Population health analytics can inform targeted interventions, ensuring equitable care delivery for at-risk patient groups or those less inclined to seek preventative care.

Allina Health, for example, identified opportunities to reduce disparities, including improving colorectal cancer screening (CRC) rates among minority groups with low screening participation. Allina Health’s leaders leveraged population health analytics and research, which informed a program to develop home-based testing and screening kits using culturally sensitive education materials written in the patient’s primary language.

They monitored the program’s effectiveness, and the resulting data showed that the intervention led to a three percent relative improvement in CRC screening rates for those patient populations.

Leveraging Population Health Analytics for Risk Stratification

Patient risk stratification is fundamental to helping the most vulnerable patients get needed care and enabling providers to deliver more personalized and efficient care to all patients. In addition to focusing on high-risk patients, organizations that leverage population health data and analytics can consider cost- and time-effective interventions for low-risk and rising-risk patients. However, identifying and evaluating outcomes improvement requires an analytics infrastructure that scales to address evolving cost, organizational, and clinical challenges. 

Overcoming Population Health Management Challenges

Despite the promise of population health management, health system leaders and providers face widespread challenges in using population health data and analytics to inform outcomes improvement efforts, including:

1. Interoperability.
2. Data quality and completeness.
3. Data volume and complexity.
4. Patient privacy and confidentiality.

One critical goal of leveraging population health data is aligning data exchange standards across various healthcare entities and providers through standardized formats and protocols for sharing seamlessly between stakeholders. Indeed, there’s been an increased push to improve interoperability in the population health data exchange realm to ensure data consistency, accuracy, and transparency.

Overcoming these barriers requires a concerted effort to improve data infrastructure, enhance stakeholder partnerships, and implement robust security protocols. Only then can health systems prioritize and integrate diverse internal and external data sources to proactively identify opportunities for improved outcomes. This integrated approach also fosters transparency throughout the population health management journey.

What’s Next for Population Health Management?

In conclusion, population health management’s future in healthcare will likely involve several analytics trends and industry advancements. Experts predict that the landscape will be driven by:

1. Artificial or augmented intelligence (AI) integration.
2. Broader interoperability.
3. Value-based care models.
4. Social Determinants of Health (SDoH) data integration.
5. Federal policy and regulation concerning quality, reimbursement, and healthcare access.

Organizations that commit to fully adopting population health analytics and data and prioritize investments in new tools and strategies pave the way for more effective and efficient patient care. As leaders in the evolving landscape of health information exchange, they can drive positive outcomes and improve the health of their communities.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

Three Ways HIEs Empower Population Health Management

Four Population Health Management Strategies that Help Organizations Improve Outcomes

Three Key Strategies Put Population Health Goals Within Reach