Population health strategies revolve around cohorts of people, and those strategies require the right kinds of data and effective analytics to drive health improvement and lower cost.
Herman Jenich, senior vice president of product development and information management for xG Health Solutions, and Janet Tomcavage, RN, senior vice president and chief of value-based care strategic initiatives for Geisinger Health System in Danville, Pa., and chief of population health for xG Health Solutions, shared how to incorporate data and analytics into population health strategies in a recent webinar hosted by Becker's Hospital Review.
"Both Janet and I are deeply committed to the belief that data-driven decision-making is key to managing any at-risk population," said Mr. Jenich.
The first step to using data to produce valuable analytic insights is to collect the "right" types of data. Key types of data include monthly payer administrative data — which includes medical and pharmacy claims, eligibility data, and provider demographic data — as well as lab results, quality metrics, health risk assessment data, structured data from care management systems, biometric results, EHR data and scheduling data. Genomic and consumer data is also becoming increasingly useful, according to Mr. Jenich.
Gathering data isn't always easy between data inconsistencies, reluctance from payers and other challenges.
"I don't want to give the impression that aggregating, cleaning and normalizing data is a breeze," said Mr. Jenich. "When you've seen data from one payer, you've seen data from one payer."
Still, gathering data is a necessary step.
Once organizations gather the necessary data, there are three stages to transforming data into valuable analytic insights, according to Mr. Jenich.
1. Data foundation. The first stage includes gathering raw data, cleaning the data and creating a strong data governance infrastructure to ensure the data's reliability.
2. Enhance and enrich data. Raw data is limited in its capacity to produce valuable insights, so it is important to enhance the data by:
- Assigning a standard, unique patient identifier to the data
- Attributing patients to providers
- Applying a wide range of groupers to the information
- Deploying a variety of predictive models
- Identifying gaps in care
- Producing useful benchmarks and
- Calculating risk scores to adjust benchmark comparisons
3. Analytic assets and capabilities. There are five key types of analytics that drive value and are most the most critical for population health, including:
- Targeting and stratifying patients for interventions
- Ongoing cost, utilization and quality metrics
- Gaps in care dashboards and other patient-level insights for providers
- Individual provider and provider group efficiency and quality
- Care management operational performance
Following these three steps can help hospitals and health systems better understand their population, their population's needs and how to proceed to best care for their population.
"Understanding the population that you're going to manage is really, really critical before you design strategies," said Ms. Tomcavage. "We've worked with folks who have gone out and designed a strategy only to realize once they've assessed the population that the strategy is probably not going to work."
On the clinical side, Ms. Tomcavage also underlined the importance of developing the right team — including case managers, health coaches, social workers and community-case workers, depending on the population's needs — to leverage the data and analytics gathered to produce optimal outcomes.
"Understanding the population and the data will help direct how to build your team, they are critical first steps to take before you build your care management model," said Ms. Tomcavage.
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