Amplify Your APP Payoff: Boost Quality and Costs with 3 Essential Data Types

Many ACOs are in the throes of planning to adopt APP Reporting. It’s a massive undertaking that can be costly, depending on your ACO’s configuration of practices. If your ACO is scrambling to sort this out, you’re not alone in your abrupt initiation into the world of EHR data. Welcome to the joy ride.

In our last post, we explained how to maximize the value of data aggregated for quality, as produced by either the HL7 and flat file method, or the QRDA method. Here we break down the data you need to achieve better quality in terms of patient outcomes. Why? Because if you’re aggregating data for quality, you don’t want to miss the important stuff that will make a real difference for your organization and patients in the long run.

Different Goals for Quality-Cost Improvement and Quality Reporting

Achieving better quality and cost performance is an entirely different game than quality reporting. The first is part of the Triple (or Quadruple) Aim at the root of the ACO model. Its goal is to improve outcomes while reducing costs. APP Reporting, on the other hand, provides the aggregate quality status about the ACO’s population and also checks the box to get savings, incentives, or penalties-avoidance. APP Reporting will also standardize how ACO and non-ACO providers measure quality, so that, eventually, there can be some comparisons between the traditional providers and ACOs.

The two “quality” goals, Triple Aim and quality reporting, are not the same, nor do they require the same data.

APP Reporting data is not highly actionable, because the three APP measures give only a limited view of patient status. You can discover the percentage of the population that does not meet standards and which individual patients comprise the population. But that data does not help you to identify any solution to improve the patient’s status. It simply calls out the patient without further enlightenment as to the clinical status of the patient over time.

Three Data Types Essential for Implementing Improvements

By whatever method you aggregate patient data from ACO practices for APP Reporting, you will also need to capture additional quality data that permits an in-depth analysis of your ACO’s patient outcomes and quality of care. This is not for reporting, but for guiding your improvement of outcomes and cost prevention. These three types are key components for your quality analysis and intervention guidance:

1. Time-specified Values for All Quality Numerators

APP Measures, like other Quality Payment Programs (QPP), are satisfied by the latest single instance of the measure’s numerator, e.g. Hemoglobin A1C value at the latest lab visit by any provider. How this value relates to other A1Cs over time is more important, and provides both the clinician and your ACO’s population health initiatives with critical information for interventions:

• Is the patient improving or worsening?
• Has the patient been in poor status over an extended period, or does the value signify a change?
• Across all the patients with poor control, has an improvement occurred over time?

These data, along with other time-related data (visit adherence, for example) can help you determine what actions to take to improve the patient’s status and prevent admissions, emergency care, and progression of disease.

2. Clinical Events

Condition-related events like exacerbations, hypoglycemia or other clinical diagnoses, and disease progressions are important markers for predicting patient crises. Claims data only offers a retroactive view of utilization and lacks the predictive clinical elements for future risk. These data types are essential to view patient risk profiles and establish improvement plans that involve clinicians, as well as outreach and potential social services.

3. Provider and Patient Actions

The previous two data types will reveal patients who are succeeding or at risk. The next task is to examine both provider and patient actions. These include prescribed medications, specialty referrals, patient self-management programs, patient outreach or education, behavioral health or social services, and so on. Populations of patients who have not improved over time, yet for whom no changes are made, can be referred for clinician review. Likewise, patient actions such as poor visit timeliness, missed visits, and no visits are important indicators for ACO action.

There is growing pressure on ACOs to move the needle on costs. Doing so depends on improving patients who are failing in their current therapies. Good EHR data is a gold mine of information that reveals patient histories of diagnoses and therapies, information that can guide your ACO to more targeted strategies for cost and outcomes improvement. Keep your eye on the data horizon, as you plan to expand your data field of view.

Founded in 2002, Roji Health Intelligence guides health care systems, providers and patients on the path to better health through Solutions that help providers improve their value and succeed in Risk.

Image: Simon Berger