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Why today’s models will always be interim ones-andhow the consumerization of healthcare will drive change.
Value-based care continues to gain momentum, and population health-focused alternative care models such as accountable care organizations (ACOs) are becoming more prevalent. 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. With newer models of value-based care come new payment methods: In the mix are episodes of care (EOC), bundled payments and more.
Increasingly, these newer payment and delivery systems require robust, adaptable and scalable technology solutions to enable automation, real-time metrics tracking, and quality reporting.
BennettBased on a survey by Leavitt Partners, in the coming years, it’s likely that the newer value-based care models will move even farther away from the original ACO approach, which will require healthcare organizations to be even more proactive, with better integration. Most likely, we will see a mix of value-based delivery and payment models on the illness-to-wellness continuum, with a mix of newer ACO models, bundled payments, full risk sharing, upside and downside risk sharing.
Essentially, today’s ACO model (or any other alternative care model) is destined 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. This evolution will be powered by innovative data analytics and other technologies tied to population health and precision medicine that enables more proactive management of potentially high-risk patients leading to improved care coordination.
Here’s a sneak peek at innovative approaches to the value-based care models of the future and the technologies that will enable them:
McKayDifferent 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, patient-centric tools, according to IMIA Yearbook of Medical Informatics. 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 or change in medication efficacy and a specific 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-centric 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.
Increasingly, healthcare organizations that participate in data-driven emerging care models need access to open source-distributed database systems that are large, scalable and support real-time data sets. These technologies, which are widely used by large consumer companies, allow for the storage and management of large amounts of data across commodity servers.
A rich, scalable environment that supports a large number of data sets in real time to enable precision medicine can improve patient outcomes in a value-based environment. Not only can social determinants of health become part of patients’ care plans, but data from wearables, injectables, remote patient monitoring devices (e.g., glucose devices) and such can be readily available to providers and care coordinators. It’s important to have ready access to this data so that those who are part of the patient’s ecosystem can make ad hoc queries about it 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’ longitudinal medical records and other key data (i.e., social determinants of health) for more proactive care and better quality.
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 with results available prior to the patient’s appointment. Proactive care models also include access to data that help avoid unnecessary duplicate medical testing and provider appointments.
With proactive care models, costs are decreased and patients have the opportunity to engage in their own care and 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 can be placed on medication adherence and addressing social factors or barriers to care that 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 re-hospitalizations.
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 with quality measures that have crossover among different care areas, 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 criteria.
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.
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.
With the evolution in care delivery and payment models, many transitions 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 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.
David Bennett is executive vice president of product and strategy at Orion Health, which delivers open-data technology solutions to drive interoperability, population health management and precision medicine.
Cheryl McKay, PhD, RN, is chief nursing officer at Orion.