ACO's move from infancy to adolescence

July 20, 2016 Cheryl McKay, PhD, R.N., Chief Nursing Officer and David Bennett, Executive Vice President of Product and Strategy

As the country’s healthcare system continues to move away from fee-for-service and toward value-based care, population health-focused alternative care models such as accountable care organizations (ACOs) have become more prevalent.

These new payment and delivery systems – which incentivize high-quality, lower cost care – require robust, adaptable technology solutions to enable automation, real-time metrics tracking, and quality reporting.

The U.S. Department of Health and Human Services (HHS) announced its goal in early 2015 of tying at least 50 percent of Medicare payments by 2018 to quality or value through alternative payment models such as ACOs or bundled payment arrangements. To date, Medicare ACO programs have been the principal contributor to achieving this goal, according to a Leavitt Partners survey conducted in cooperation with the Accountable Care Learning Collaborative.1 The analysis identified 838 ACOs across the country, with coverage in all 50 states and the District of Columbia.

Both public and private payers are driving change. While ACOs are often primarily associated with CMS, the majority of ACO patient lives (17.2 million of 28.3 million) are covered by private payers.

Evolving care models

As with any innovation in healthcare, ACOs have evolved over time, from the original Pioneer ACO Model to the Medicare Shared Savings Program (MSSP) to the Next Generation ACO Model, which includes more downside risk for providers but potentially greater rewards. Also in the mix are episodes of care (EOC), bundled payments, and more.

Essentially, today’s ACO model (or any other alternative care model) is always going to be an interim one. Therefore, the technology that enables them must be adaptable, flexible, scalable, and future-proofed so that it’s still relevant five, 10, or 20 years from now as care models continue to evolve.

In the coming years, newer value-based care models will move even farther away from the original ACO approach. Most likely, we will see a mix of value-based delivery and payment systems on the illness-to-wellness continuum, with a mix of newer ACO models, bundled payments, full risk sharing, upside risk sharing, and downside risk sharing. These changes will require health systems and other providers to be more proactive, with better integration.

This evolution will be powered by innovative data analytics and other technologies tied to population health and precision medicine that enable better, more proactive management of high-risk patients and improved care coordination.

Here’s a sneak peek at innovative approaches to care models of the future and the technologies that will enable them:

Different ways to share risk: Payers and providers will share risk, both upside and downside, in a variety of ways. In addition, now that the concept of risk sharing has become part of the landscape, even broader ways to do so may emerge. For example, there’s already been some talk about medical device manufacturers (think heart implants or stents) taking on their share of risk for cardiac patients with these devices. Clearly, automation, open data platforms, and data integration are critical to tracking patient outcomes and supporting these types of risk-sharing models.

Care delivery will become more patient centric and consumerized: Care models of the future will be driven by high-tech, consumer-like tools. Mobile is everywhere, and care models of the future will push information proactively to patients via devices such as smartphones and tablets, dependent on preference and access.

Providers will leverage these tools, too. Imagine an environment where providers receive a streaming notification before a patient comes to see them with a timely issue that needs to be addressed, based on real-time data analytics technology. Or perhaps that notification comes in before the patient’s virtual visit, since patient-centered care means more care delivery options. In addition, technology will allow providers to proactively identify opportunities that support their patients’ health prior to knowing there is a problem, such as a genome variation causing risk for a certain disease and an intervention to manage the risk.

Technology that supports many diverse data sets and real-time interactions: Population health management is, of course, at the center of value-based care models. Traditionally, population health management has focused on chronic care and care management solutions, with a focus on compliance and the delivery of evidence-based medicine. While those certainly are important considerations, the reality is that patient care is not linear, and technology will need to support the ebb and flow of the human health experience.

Providers and payers also need ready access to genomic, social, environmental, and behavioral data sets to drive effective patient care plans. The right systems need to be in place to support real data sets with all of those components, and providers need ready access to that information to achieve IHI’s Triple Aim of healthier populations, lower costs, and improved patient experience.

The technologies that are necessary to support these interactions are different than traditional healthcare technologies. Supporting real-time data sets is something that consumer-driven companies do well. Twitter, Reddit, and Netflix are all driven by Apache Cassandra, an open-source distributed database system designed for storing and managing large amounts of data across commodity servers.

Technology such as Cassandra does not need to be limited to consumer applications. Healthcare organizations that participate in data-driven emerging care models can position themselves for success by working with healthcare technology companies that offer modern database solutions such as Cassandra.

The rich, scalable environment enabled by Cassandra supports a large number of data sets in real time for precision medicine that can improve patient outcomes in a value-based environment. Not only can social determinants of health become part of the patient’s care plan, but data from wearables, injectables, remote patient monitoring devices (e.g., glucose devices), and such can be readily available to providers and care coordinators. The speed at which Cassandra enables real-time data availability and intelligence is critical when focusing on proactive care and early interventions.

This rich data can be sliced and diced in many ways, and elastic search capabilities – with a Google-like search engine – mean that anyone who is part of the patient’s ecosystem can make ad-hoc queries in real time for better informed patient care decisions.

Proactive care models: Open-data platforms allow all members of the care team to access patients’ full medical records and other key data (i.e., social determinants of health) for better quality, more proactive care.

Preventive care (e.g., screening mammograms) can occur proactively instead of retrospectively when the right systems are in place. Instead of patients making a yearly appointment with a provider and scheduling a mammogram, the testing is done prior to the appointment with the provider. Proactive care models also include access to data that helps avoid unnecessary duplicate medical testing and provider appointments.

With proactive care models, costs are decreased and patients stay healthier. Ultimately, patient satisfaction improves due to better continuity of care.

Care coordination and transitions of care will be more important than ever: More sophisticated data analytics allow providers and care coordinators to better – and more quickly – identify members of their populations who are high risk. Instead of waiting for payers to identify patients at risk weeks or months after a qualifying event, providers can leverage analytics to build their own cohorts and flag them proactively for real-time interventions that lead to better outcomes.

Patients who are ready to be discharged and have a high defined risk score based on data analytics can be placed on a pathway before they leave, and follow-up appointments and calls can be scheduled. Emphasis is placed on medication adherence and addressing social factors that may affect patient outcomes (i.e, a lack of transportation to a follow-up appointment). Data-driven interventions can improve care and prevent issues such as unnecessary rehospitalizations.

This approach of using analytics to drive interventions is scalable and can also support care coordination in areas such as chronic disease, especially when leveraged in conjunction with technologies such as remote patient monitoring, mobile device health information collection, and more.

Documentation requirements may increase, but technology will make it easier: With so many care models emerging, documentation will continue to be important from an accountability perspective. Optimal integration solutions will align quality measures that have crossover across care models, so essentially providers will simply document what they are accountable for and move on to the next patient, without worrying about what documentation ties to which care model.

ACOs and other healthcare delivery systems can gain flexibility by using dynamic registry capabilities and workflows that go with them to manage shared savings contracts or any risk-bearing relationship. Innovative specialty applications running on top of open-data platforms allow quick action on quality measure reporting that requires pulling data from many disparate provider groups in a short time frame. This capability is especially important when dealing with payers, such as Medicare, that often don’t provide details on their updated reporting requirements until shortly before they are due.

The importance of the payer component

As these care models of the future continue to evolve, payers also need to make changes to enable them – especially as related to clinical data integration. Increasingly, payer data will need to be integrated with clinical data from across communities, along with device and genomics data, to give payers one comprehensive source of information about their members that can be easily accessed.

This can be done via an open application programming interface (API) layer using existing analytics, care management, and consumer engagement tools. Real-time access to this amount and type of data supports value-based providers, a critical factor in the success of the care models of the future.

Conclusion

With the evolution in care delivery and payment models, many transitions will need to occur within healthcare organizations related to workflows, governance, and change management. Ultimately, technology is the enabler to support it all.

The technology that enables all of these changes must be flexible and adaptable so that it, too, can evolve along with the care models. Healthcare organizations that are equipped with the right technology to change – whether that change is driven by consumer, regulatory, or market demands – will drive the care of the future. They will succeed in moving the healthcare community toward the Triple Aim.

Link to the original article.

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