Solving for Simplified Care Coordination and Predicting the Future of Value Based Initiatives

Let’s face it, coordinating care for patients can be an incredibly complex task, and it isn’t about to get easier. Recently payers like United HealthHumana and CMS have ramped up their efforts for implementing value based payment models. As we know, these reimbursement models place greater demand on strong patient care coordination.

Therefore, simplifying the care coordination process is imperative, and some providers are currently seeking solutions to help their care efforts.

This week the HEI Knowledge Center examined how organizations may use predictive analytics tools, specifically the Johns Hopkins ACG System®, to potentially simplify their care coordination efforts.

Here are 3 ways the ACG predictive analytics tool shows potential for simplifying the future of care coordination:


1) Establishing a Baseline

We believe that an important starting point for improving any process is to establish a clear picture of baseline performance. With a clear picture of today’s performance, leaders can look for the right opportunities to make continuous improvement in their care coordination efforts.

The ACG system comes equipped with metrics aimed to assist providers in the creation of a clear picture of their baseline performance. Such as the Predicted ACG Score, which includes highly accurate estimations for the anticipated care needs of an individual patient and for their cost of care.


2) Targeted Chronic Care Management

According to the CDC 48% of the U.S. population has one or more chronic conditions, accounting for 86% of healthcare spending. As value-based penalties don’t appear to be going away any time soon, patients with chronic conditions will be a greater risk for organizations.

To create more accurate predictions in care utilization, the ACG system does not solely rely on identifying the top diagnosis. Instead, it identifies comorbidities, both related and unrelated, to determine an individual’s need for health services. The ACGs system categorizes people while most other systems categorize events or episodes.

Using Predicted ACG Scores with hospitalization and readmission data, care managers can efficiently target the highest risk patients and proactively implement care management plans.


3) Meeting Future Healthcare Needs

Finally, an important component of efficient care coordination is having resources available to manage the given needs of a patient population. But how do we know which population is most in need of additional care resources?

There are many approaches to resolving this challenge with varying degrees of success, but the ACG system presents a data driven solution to assist providers in making care allocation decisions. By using geographic markers like ZIP codes combined with Predicted ACG Scores to locate at-risk populations, providers may tactically allocate resources according to community demand.

What do you think? Will future patient care coordination be affected by systems like the John Hopkins ACG?

 Let us know what you think by emailing or via LinkedIn.

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