ACOs: Scale Up Data to Achieve APM Success

written by Theresa Hush | July 14, 2021

Last updated July 27, 2022

More data is required for ACOs, now that Alternative Payment Models have moved to high gear. In this article we’ll take the mystery out of how to realistically gauge your data needs and scale up data, so that your ACO can be successful. We’ll show you how to identify the links between what you want to accomplish as an ACO or medical group in value-based payment models and those data requirements, and help you target your data efforts.  Data will drive your analytics, and analytics will fuel your interventions to produce better outcomes and cost performance.

Data Must Scale Up with Need to Manage Risk

When providers were focused on meeting patient volume targets, operations systems like billing and scheduling provided an adequate basis for tracking volume and revenues. But the shift to APMs demands accountability for outcomes, health equity and costs, which means that you need more relevant data. Your participation in payment models should not be opportunistic. Instead, it should be predicated on an understanding of the vulnerabilities and strengths of your patient populations and issues that could derail your budget. The right data gives you the insight you need to avoid cost overruns and plan your Value-Based Care (VBC) strategies.

It’s no secret, however, that ACOs, physician organizations, and small to moderate medical groups are often the most data poor. Less well understood is the fact that calculations defined in VBC demand different data and novel approaches to organizing that data for analysis. The following three approaches, used separately or progressively, will move you from data poor to sufficient and enable you to meet the competition.

Your Approach to Data Sufficiency Must Address Quality, Equity, Costs

The new CMS pathways for ACOs have raised the bar for ACO cost and quality activities. The increased Risk carried by ACOs under the ACO Pathways to Success means that a focus on costs must be laser-like. And the APM Performance Pathway, your gateway to qualifying for shared savings, requires a major change in quality reporting by expanding the denominator to all patients regardless of coverage. Although the program won’t begin until performance year 2024, for some ACOs with low tech practices, it may take two years to implement.

Your simplest path to achieving initial data sufficiency just to remain an ACO is to aggregate the data needed to complete quality reporting tasks and implement interventions and improvements that generate savings. In short, this approach is task-oriented to keep you in compliance. But it will also help you grow, because these data are also valuable for participating in Risk with private health plans.

  • Quality reporting. The new APM Performance Pathway requires ACOs to report a small number of quality measures on all patients, regardless of insurance coverage, beginning in 2024. This puts ACOs on the same footing as medical groups in quality performance measurement. But because some ACOs have many independent medical groups, new APP rules will dramatically increase ACO data needs for this segment, requiring data aggregation from provider systems. Practice data aggregation will be a must for quality reporting, to capture the full denominator of patients in measures.
  • Cost tracking and high cost areas. Increasing levels of provider risk raise a critical need to identify cost drivers. Examination of costs for all patients, specialty services, and practice variations requires all-patient data. The data that must be collected for APP reporting will be essential for filling in the missing details of claims data, but it requires much more manipulation to provide more than basic information on cost categories. However, starting with basics is possible through aggregating and organizing data to identify key cost territories.

What Data is Rich Enough

There is no getting out of covering the basic ACO requirements, but the basics won’t be enough to sustain you against competitive medical groups or payment models that want to lure away your physicians and patients. If your goal is to secure your network of physicians, that goal must include data and infrastructure to support physicians to achieve their financial and clinical goals, and to keep their patients.

Aggregation of provider all-patient data and claims gives you a significant foundation for getting to the next step: identifying your highest potential improvements and Interventions. This is the time to use that data not just to meet external requirements, but also to achieve more for your providers and patients. Start with helping practices transform care for patients, reversing the trajectory of costs for progressive chronic disease, improving patient risks, and rationalizing specialty services.

All-patient data must be rich enough in clinical information to be effective. Practices cannot realistically apply interventions to one payer group; all-patient data permits a broader strategy of improvements and interventions. But this data is a starting point to capturing additional data based on those interventions.

You should be able to pick out three care areas where your data highlights clinical and/or administrative initiatives that are core to your mission, have a substantial effect on your patient care spending, and where there is a payoff to you and your patients. Focusing on clearly defined clinical areas creates a foundation of interventions to fortify savings that are long-term, rather than one-time. It also builds an internal energy source that fires growth for your organization. Based on your own patient populations and clinician involvement, you could choose from among these strategies to facilitate programmed approaches to care that can be adopted broadly:

  • Diabetes prevention in patients with a variety of progressive risk factors;
  • Improving control in patients with poor control in diabetes and hypertension;
  • Behavioral health collaboration with community providers to avoid emergency and admissions through better screening and referrals;
  • Falls programs to improve balance, gait, and strength of vulnerable patients;
  • Reduction of obesity with medication, nutrition, and coaching.

Many physicians and patients alike are frustrated by ineffective or repeated efforts to reduce patient risk. But those efforts have largely depended solely on patient visit interactions to effect changes in patient behavior, which is unrealistic. Or, they have called in resources for care coordination, which, without real data, produce moderate results, at best.

Old approaches do little to ensure that physicians themselves have the sustained support necessary for targeting new pharmaceutical therapies, nutritional or behavioral health services, or motivating patients between appointments. Data analysis and sharing among clinicians, coupled with population health, can create more effective and consistent programs of outreach and patient participation in risk efforts. But to help physicians really deliver state-of-the-art care, it must make sense both clinically and organizationally.

Engage Providers and Patients in Growth with Data-Based Tools

Equipped with foundational data and initial interventions, growth is typically the next goal for ACOs and medical groups engaging in Risk. Getting there requires a bit more data and infrastructure for transforming care and for identifying and preventing specific areas of cost escalation. The latter include the use of low value services, poor patient selection for therapies, and patients with poor control or disease progression on continued ineffective therapies.

Illuminating patterns of patient behavior and clinical practices that are driving costs and outcomes requires two additional data developments. First, there must be a reorganization of data you have already collected from practices and claims into comparable units for analysis. Patient episodes of care, which can be condition- or procedure/treatment-based, eliminate unnecessary noise from the data—visits for incidental care, for example. Episodes enable comparison of patients as well as practices, therapies and their outcomes, and costs.

Mature ACOs and medical groups will need episodes as a lens to clarify outcomes and costs. Let’s say you want to see how your patient population with diabetes is faring. The diabetes episode reveals patients with persistent poor control, their risk factors, other clinical and service events, and what pharmaceutical agents are currently being prescribed. You will see patients with circulatory problems and chronic kidney disease, and those with dramatic events like hypoglycemia admissions. By viewing patients in populations through this approach, you’ll find that the available clinical opportunities are remarkably clear, as are the inconsistencies in patient results.

Second, ACOs should seek additional sources of data and integrate them into existing databases, such as values from patient devices, patient-reported outcomes, and self-care surveys. Claims for filled prescriptions and patient activity levels through health apps could also be of value for certain interventions.

Transformative data approaches deploy technology and data to engage patients and physicians in an ongoing dialogue with results. Programs involving continuous glucose monitoring, for example, are having positive results because they provide continual feedback.

Episode data helps physicians see how the critical decision points of treatment turn into costs and outcomes across their patients and help create future approaches for improvement. The data clearly reveal both extremes—unpreventable high-cost, complicated episodes where no actions would have changed the results, and episodes where intervention is possible and could have significantly reduced costs.

Data Sufficiency Is Iterative and Achievable

Some ACOs start out with the assumption—based on experience—that they can economize data and technology. In our competitive, increasingly venture-backed practice environment, this is a path to extinction.

The initial costs of data aggregation are high for low-financed ACOs, but they are more feasible if those costs are either spread across practices or supported by a growth strategy that helps physicians adopt common technology with shared cost. ACOs will need to build data aggregation strategies like any business dependent on data for fuel—because they can no longer afford not to have it.

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: Toa Heftiba