Improving Health Outcomes: The Impact of Care and Disease Management on Population Health 

Discover how Dr. Pariksith Singh's innovative approach to population health management is transforming care delivery, improving outcomes, and reducing costs through strategic risk stratification and comprehensive data analysis.

As Dr. Singh reflects on their ongoing journey towards becoming a Learning Organization, he underscores the importance of a holistic approach to Population Health. With a commitment to leveraging data-driven insights and fostering collaborative care models, Dr. Singh and his team are poised to continue making meaningful strides in Population Medicine and Analytics.

Understanding Population Medicine and Health Analytics is similar to the classic parable of the elephant and the blind men. In the story, one man feels the elephant’s leg and thinks it’s a pillar. Another touches its ear and believes it’s a fan. A third man, feeling the trunk, imagines it to be a long pipe. And the fourth, holding the tail, concludes it’s a rope with frayed ends. Each man’s description is accurate from his limited perspective, yet incomplete. They all miss the broader reality of the elephant. Similarly, viewing Population Medicine and Health Analytics from a single standpoint offers only a partial glimpse of its comprehensive scope.

Population Health Analysis can be interpreted in several ways: it might examine the entire community’s population, focus on the group under a specific set of healthcare providers, or analyze a subset of the population distinguished by certain criteria such as age, gender, or medical condition. In our approach, we initially concentrate on all patients under the care of our medical group, which includes both employed and affiliated providers. Subsequently, we expand our focus to embrace the broader community, reflecting our deep-rooted involvement over two decades.

For our patient panel managed by our Independent Providers’ Association and our Accountable Care Organization (ACO), we’ve adopted a strategy of risk stratification. This involves categorizing patients into Very High, High, Moderate, and Low-Risk groups based on their medical history and previous health experiences. We apply this stratification uniformly and payer-blind, using a methodology developed by our medical systems and processes.

Our initial analysis relies on straightforward data systems aiming for real-time and accurate information. We utilize various data streams, encompassing clinical, financial, and operational inputs from both internal and external sources. Clinical data may come from provider referrals, case managers, our care coordination center, electronic medical records, patient requests, census data, or reports from Health Maintenance Organizations or the Centers for Medicare and Medicaid Services. Financial data is sourced from claims reports, pharmacy usage, or our claims platform, while operational insights are drawn from interactions with non-clinical staff and advanced data reviews.

Clinical interactions occur across multiple settings, including offices, hospitals, skilled nursing facilities (SNFs), assisted living facilities (ALFs), acute rehabs, and home health services. By integrating data from these various sources and employing a team of case managers to ensure continuity of care from hospitals to SNFs to homes or ALFs, we’ve developed responsive and proactive systems. Our utilization management system oversees referrals and their medical necessity based on Organization Determination Appeals and Grievances (ODAG) standards and coverage determinations at both local and national levels. Compiling this data in an enterprise data warehouse and monitoring it across different service points ensures our patients receive comprehensive healthcare.

In our hospital settings, we meticulously monitored the Geometric Length of Stay (GLOS) and observation hours, ensuring the medical necessity of each admission. Additionally, we conducted thorough analyses on readmissions, successfully reducing them from SNFs from 30% to nearly 10%, achieving a GLOS reduction to about 100-105% from 120%, and decreasing observation hours to an average of less than 24. Our refined data analysis methods aim to predict patients at higher risk for readmissions and severe illnesses.

Broadening our focus to encompass community health, we launched initiatives like health education lectures on topics such as personal hygiene, nutrition, and exercise, making these resources globally accessible online. This population-based approach reflects our commitment to enhancing community health.

Our patient population is stratified into four categories: low, moderate, high, and very high risk. Low-risk patients typically manage well with preventive care and minimal medication. Moderate-risk patients benefit from integrated care in a Patient-Centered Medical Home (PCMH) setting, requiring coordinated care and comprehensive education. As patients’ complexity increases, so does the need for intensive case management and team-based care, especially for those with multiple serious conditions.

High-risk patients receive multifaceted interventions, including enhanced case and care management, ensuring seamless communication and care continuity. Very high-risk patients, at the apex of care complexity, necessitate unprecedented levels of support, including personalized communication and intensive case management to coordinate care plans effectively.

Our comprehensive approach spans the entire care continuum, significantly enhancing patient safety, engagement, compliance, and quality. This strategy has led to improved Medicare Risk-Adjustment scores, STAR ratings, HEDIS scores, CAHPS, and HOS scores, while also reducing healthcare costs through evidence-based practices.

Our commitment to being a Learning Organization and addressing Population Health in its truest form has just begun. We view the entire community as our patient population, a perspective we believe is essential for advancing Population Medicine and Analytics.

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