Tackling population health’s “big three”

July 28, 2015
Larry Schor, Senior Vice President, Medecision

As healthcare organizations embrace risk-bearing payment models, they must proactively manage the health of patient populations, improve outcomes, and avoid unnecessary, reactive, and high-cost interventions.

But what is “population health”?

The “big three”

Population health management, as the latest buzz word in our industry, suffers from a lack of consistent definition. Three key elements must exist to achieve a sustainable and successful population health management strategy:

  1. Small data management and actionable analytics. The big data hype is high, but the work is in managing the bits. We are facing big data management challenges of new and multiplying data sources, growing data volumes, and real-time processing capabilities, but the first priority is building out an infrastructure that can manage and govern all of those data sources. This infrastructure must deliver data that is trusted, properly curated to identify and remediate anomalies, and be meaningful and actionable.
  2. Data-driven workflow processes and provider performance management. Risk identification and care coordination are the cornerstones of an effective population health management initiative. The intelligence and insights generated from the analytic method can’t stop with a report or a “chase list.” To perform at scale and do so with consistent quality and results, the analytics payload must be embedded into the workflow so that clinical program logic can be tailored for that patient, and to remediate the risks identified. Finally, we need to measure our impact and outcomes to learn from empirical findings and drive performance.
  3. Patient engagement. We won’t achieve the Triple Aim without the patient. Genuine, sustained patient engagement and adherence are critical objectives for every health-delivery organization; frankly, these issues were around long before reform legislation was passed and remain a steep challenge. Achieving patient engagement will require expertise in big data architecture, as well as applications and technology that have matured in consumer-focused industries, like retail and entertainment. We have much to learn from Apple, Amazon, Google, and Facebook (to name a few) who have developed an extraordinary understanding of consumer engagement, preferences, and purchasing behavior. An effective population health management program will entail the use of consumer analytics to support a deep understanding of population risk and highly tailored consumer engagement models that drive adherence and positive behavior change.
Expanding the scope of analytics

Transactional documentation systems like electronic health records (EHRs) and practice management are important repositories of data, but these tools are not enough to manage population risk. Care management teams also need to be supported by an agile, on-demand infrastructure that can aggregate data from multiple sources, including EHRs, claims information, pharmacy records, and consumer-generated content, to create a holistic view of each patient, with insights on the psycho-social and behavioral context for that patient, to inform the plan of care and optimize the patient’s own participation and engagement.

Consider a healthcare organization proactively trying to reach high-risk patients within an identified population. Engagement must be highly personal, contextual, and timely. When data regarding a patient’s support system, lifestyle situation, and external risk factors can be used in the risk and predictive modeling, healthcare organizations will be far better equipped to achieve meaningful patient engagement that actually drives a positive impact on health outcomes and total cost.

A look into the future

The first generation of accountable care and alternative payment systems is already underway. While payment is tied to parameters like documentation, accurate coding, and some quality measures, many providers still operate under a fee-for-service (FFS) model. This is a holding pattern that cannot last much longer.

The landscape is rapidly changing for providers, as CMS accelerates payment policies that replace FFS with value-based alternative payment reimbursement. Success in the next generation of accountable care will not be measured by compliance alone, but by how well providers actually perform.

A redesign of the care model will build in risk assessment and mitigation strategies early and implement remote and virtual technologies that enable patients to be monitored between scheduled visits, and to drive performance as a system of care, where all participants on the care chain operate at the top of their license, in the right setting (increasingly the patient’s home). Care plans can then be designed to specifically address the proactive interventions, workflow strategies, and patient engagement tactics needed to circumvent high-cost surgeries.

Committing to population health management through data

While data is crucial to every turn of population health management, healthcare organizations must make a commitment to a model of care that considers the big picture of success across the continuum, which is driven by analytics. Allowing data to drive performance improvement and engage patients and clinical stakeholders will help create a unified effort to advance the Triple Aim.

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