Scottsdale Health Partners (SHP), a physician-led clinical integration network (CIN), was founded with a mission of transforming healthcare delivery in the greater Scottsdale, AZ, community.

SHP is a joint venture between the HonorHealth health system and the Scottsdale Physician Organization, representing a broad spectrum of medical specialties with a pluralistic model, allowing providers to remain independent and entrepreneurial. Its focus is on achieving the Triple Aim of healthcare: improving the health of the patient population, improving the patient experience, and reducing costs.

Today SHP has contracts with seven major insurance companies and covers more than 40,000 patients. In January 2014, SHP was awarded accountable care organization (ACO) status in the Medicare Shared Savings Program. During that first year, SHP successfully achieved cost savings of nearly $3.7 million by engaging physicians, evolving and integrating care delivery, and launching an open, flexible, clinician-friendly technology solution to manage the health of its population groups.

Challenge

Moving closer to the goal of achieving the Triple Aim hinged on the following:

• Building a system of engagement to support the physicians that joined and bought into the SHP model. SHP needed to identify the most effective way to share a maximum of actionable information with providers and bring transparency to its CIN participants. Achieving SHP’s goals meant partnering closely with both primary care and specialty practices. SHP needed to facilitate data sharing, identify patients most at risk, help clinicians close gaps in care, and simplify reporting on quality measures.
• Identifying an open, easy-to-use IT platform with flexibility to run multiple contracts and adapt to emerging models of care. During the two-year planning that preceded SHP’s 2012 launch, the executive team determined that it needed an open health information exchange (HIE) platform that would make complete, accurate patient data from 40+ different EMR systems available to all 700+ participating physicians.

This meant the IT infrastructure would need to integrate with various health information systems with a robust underlying HIE technology. The IT platform also would need to be flexible enough to meet evolving regulatory requirements.

Finally, preparing for the future was a paramount concern. SHP was seeking a standards-based platform with an open application programming interface (API) to support a wide range of population health management applications, with a very modern, scalable database and analytics at the heart of the system.

Solution

SHP knew that solving this challenge would require:

• A strong physician engagement strategy;
• A care management program with two distinct yet highly integrated services: transitional care management and comprehensive care coordination programs; and
• An innovative technology platform to support the engagement and collaboration among 700+ physicians at more than 230 practices with more than 40 EMRs.

SHP required an open-standard, open-access approach to its technology architecture. The platform needed to be clinician friendly and easy to use, to encourage adoption, and improve communication and coordination.

The CIN/ACO also needed to ensure that its physicians, who are spread out in disparate practices, would be able to use the EMRs they were already comfortable with – while also having the ability to share health information and coordinate care. Achieving this goal would be no easy feat.

After a thorough review of available options, SHP partnered with Orion Health, a precision medicine-focused company with advanced population health technologies and deep knowledge of what it takes to make HIEs work. Orion Health’s flexible open platform technology provides a common interface to facilitate the seamless exchange of crucial health information for SHP’s clinicians.

Within the platform provided by Orion Health, there are multiple ways to view a patient record, including a timeline view. A notification hub feeds SHP’s secure messaging solution, TigerText.

Traditional hospital-based technologies fail to support the critical needs of ambulatory care management. They are focused on inpatient processes, not ambulatory processes. SHP found innovative ways to leverage the power of an open-architecture platform to build applications that effectively support care management in the community.

Connected care results inbetter outcomes, improved ACO performance

This transition has resulted in significant improvements in care quality and cost savings.

More than anything else, the resulting improvements in transition of care and care coordination have correlated to positive outcomes for patients and efficient operations for the ACO.

SHP custom built census reporting and real-time alerting for care transitions and care coordinators into its technology offering, demonstrating the flexibility of development with Orion Health’s open platform. SHP is now working with its partner on designing and testing an expanded care management solution. SHP care coordinators are able to build custom assessments, communicate with the patient’s care team, record and track patient goals, create care team tasks, and document efficiently, all within a single platform that integrates all the patient data.

SHP’s challenge – to find a robust population health management platform that allows clinicians using dozens of different EMR systems to exchange health information in real time – was a lofty one. Partnering with Orion Health to leverage its open-platform technology was the ideal solution, as evidenced by the dramatic improvements in SHP’s metrics that matter in today’s value-based care environment.

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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.

“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.”

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.

Reference:

1. https://www.urac.org/news/leavitt-partners-aco-survey-reveals-need-attention-culture/

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With the onslaught of data created by the internet of things (IoT) and wearables, data sets are now appearing everywhere. In the next few years, device manufacturers will continue to create all kinds of highly specialized gadgetry – from sensors that note changes in glucose levels and dispense insulin like an actual pancreas, to facial masks that help users voluntarily move weakened cheek muscles – that will significantly contribute to the generation of up to 2 terabytes of health data per person.

But what sort of systems can actually handle this quantity of data? Certainly not the same systems that handle the relatively modest amount of IoT and wearables data being generated today. Some of this new data will be serialized, streamed, or unstructured, while some new data sets, like those generated by IoT devices and wearables, will be truly enormous.

A provider can try to navigate this new world as much as he or she wants, but once 2 terabytes of variable data are plopped in front of the clinician, how is he or she ever supposed to base a meaningful decision on that data in the 15 minutes allotted to see a patient? It’s just not possible.

What is possible is the use of a real-time healthcare analytics platform that will crunch that data and enable the clinician to have the cognitive support to make an informed decision right then and there.

Yet, despite the feasibility of such a system, most healthcare analytics platforms aren’t built that way today. Instead, they are built to accommodate an unremarkable sequence:

1. A transactional system dumps data into an operational store.
2. That operational store dumps the data into a data warehouse.
3. Some analytics are performed within that data warehouse.
4. The results are dumped into a workflow or engagement engine.

That sequence won’t fit into the data-rich future of healthcare. Tomorrow’s platforms will need to:

• Answer all the integration challenges that arise in the healthcare environment;
• Scale to handle all that data;
• Be flexible enough to keep pace with society’s ever-evolving quality-measurement needs;
• Integrate directly into care-delivery processes and workflows; and
• Leverage machine-learning techniques to more accurately predict outcomes in a healthcare environment.

So, with all of that in mind, here are the two essential steps to take to guarantee a platform will meet the unprecedented challenges tomorrow will surely bring.

1. Eliminate the problems created by a lack of interoperability.

Despite the best intentions of regulatory incentives and standards consortia, inconsistencies and quality issues continue to plague many health information exchange (HIE) interfaces, which can inhibit the effectiveness of most analytics platforms. If a platform is going to handle all those terabytes of data generated by the IoT and wearables, it will have to support high-quality integrations, too. This is a job for tools that:

• Understand the message formats of source systems;
• Address the specific challenges presented by event-based integration;
• Support and analyze real-time message feeds to identify variable data and data-quality issues;
• Accommodate message loading and output analysis while offering both robust monitoring and an error-handling infrastructure;
• Offer a way to store virtually unlimited data in a single repository;
• Allow acquired data to be processed and mapped to ever-evolving models;
• Provide an infrastructure that handles message re-ordering and variable data update modes; and
• Offer a set of prebuilt, standards-based models that cover core clinical-, claims-, and device-data domains.

It’s worth noting that HL7’s Fast Healthcare Interoperability Resources (FHIR) standard has rapidly evolved to provide a quality data model set to satisfy the demands of healthcare integration, so it’s the obvious model for the platform described here.

2. Consider the importance of accommodating IoT-device and wearables data at scale.

As organizations look to integrate all of this new data, healthcare analytics platforms will naturally encounter ever-increasing scalability challenges.

Other sectors have already begun addressing these challenges, and technologies such as distributed databases and computing engines are striving to meet the demand. Databases built for speed, such as Cassandra and Elasticsearch – and engines for big-data processing, such as Apache Spark – allow storage and processing capacity to be distributed over a number of servers, which makes it possible to incrementally ramp up capacity by increasing the size of the server clusters supporting the deployment. There are already many examples of these technologies being deployed across countless clusters, managing petabytes of data, and processing millions of transactions in an instant.

For software developers, creating these technologies requires a large research-and-development investment and dedicated integration/deployment teams. Waiting until the current technology reaches a breaking point will leave many developers without sufficient time to accommodate the transition.

When selecting an analytics platform, look for one that has the ability to deploy and scale solutions on demand by its explicit use of technologies such as Cassandra, Elasticsearch, and Apache Spark, and make sure it’s tied to one of the big cloud providers, like Amazon Web Services.

The anxieties that the onslaught of IoT devices and wearable data is causing are understandable, so it seems absolutely reasonable that users of today’s healthcare analytics platforms are wondering how they are expected to make sense of the tsunami of self-reported data headed toward their organizations’ doorsteps.

Those worries, however, are actually unfounded.

The technology already exists to make short work of literally any amount of data the IoT devices and wearables can and will throw at the industry. By following the steps outlined here, the onslaught of IoT devices and wearables data that continues to roll in will be both manageable and useful.

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The adoption of precision health—the process, according to Stanford Medicine,¹ of “tapping health data to provide targeted, predictive and personalized care”—will call for the entire industry to be much more proactive in its care models and adopt real-time technologies that don’t require truck-loading data sets to different workflow engines, patient tools, or provider tools.

From clinical data to genomic data, to pharmacy data, to claims data to device data created by wearables,² we’re already seeing data sets appearing everywhere. In the coming years, we’re going to see device manufacturers create all kinds of highly specialized gadgetry that generates heaps of data, from sensors that note changes in glucose levels and dispense insulin like an actual pancreas, to facial masks that help users voluntarily move weakened cheek muscles.³

This gadgetry will generate a lot of data—up to two terabytes per person, per day—that will, if utilized properly, drive a much more precise care plan for its users.⁴ In the value-based era, where outcomes matter for reimbursement, care that is developed precisely for the unique needs of a patient seems like a trend that would evolve organically. After all, what better best practice for care than one that caters to the precise biology of an individual?

New data formats needed

Yet, one obstacle remains: The sheer amount of data. What sort of systems can handle all this new precision health data? Certainly not the same systems that handle the traditional clinical, claims, and pharmacy data today. Some of this new data is serialized, streamed, or unstructured. Some, like variation data, is exceedingly large. A clinician can try to navigate through this data as much as she wants, but with up to two terabytes of variables plopped in front of her, how is she ever supposed to base a meaningful decision on that data in the 15 minutes she’s allotted for a patient? It’s just not possible.

What is possible is the use of a real-time system that will crunch that data and enable that clinician to have the cognitive support to make an informed decision right then and there.
This will require a different kind of patient record altogether—one that’s more actionable, more focused on engagement, utilizes machine learning, and features a far more comprehensive set of dimensions (e.g., clinical claims, care plans, and pharmacy data), and then treats those dimensions with the proper alignment, analysis, integrated models, calculations, and aggregations they deserve.

This might sound ambitious, but is supporting different clinical, social, behavioral, and genome data sets through open APIs (application programming interfaces) any more ambitious than what Apple does with open-source, real-time-enabling, scalable software like Cassandra to support data sets for music and video files, notifications, messaging, backups, and more? Is it any more ambitious than what Netflix, Twitter, and Instagram—services that trade directly on their real-time reputations—do with their data sets?⁵

It’s not. Biometrics yielded from wearables that track glucose, blood pressure, weight, activity, and more adhere to the exact same read/write process that tweets yielded from iPhones adhere to, the only difference being that the “followers” in the biometrics case would be a trusted network authorized by the patient to view that data (e.g., providers, payers, care coordinators, caregivers, and specialists).

APIs to the rescue

In fact, with the exposure of so many APIs—including standard APIs using FHIR, non-standard APIs, and aggregated APIs—in a precision health platform like the one I’m describing comes a real opportunity for innovation that’s limited only by our imaginations.6

When viewed this way, precision health represents much more than the shift described in the beginning of the post—a shift to a model that population health currently represents, where we’re merely more proactive about care.

If you’ll indulge the analogy, it represents a sort of positive “hydra”—that is, the serpent in Greek mythology that grew back two more heads whenever one was cut off. In this case, a new service emerges with every piece of data collected and API exposed, and when our imaginations are applied to that service, more services then emerge.

With conditions like that, who can say what ingenious innovations, methods, and techniques are on their way?

It’s impossible to know.

But I can tell you what I do know: With the right participation from the industry, precision health will work, and our journey as a society is about to get healthier and happier because of it.

References

1. Precision Health: Predicting and Preventing Disease — Not Just Treating it. June 2015. http://med.stanford.edu/news/all-news/2015/06/precision-health-predicting-and-preventing-disease.html
2. Precision Medicine and You. February 2016. https://orionhealth.com/us/knowledge-hub/blogs/precision-medicine-and-you/
3. 10 Medical-Device Wearables To Improve Patients’ Lives. January 2016. https://www.informationweek.com/healthcare/mobile-and-wireless/10-medical-device-wearables-to-improve-patients-lives/d/d-id/1323544
4. Patients Key to Making Sense of Medical Data. March 2016. http://mitsloan.mit.edu/newsroom/articles/patients-key-to-making-sense-of-medical-data/
5. Netflix Foretells ‘House of Cards’ Success with Cassandra Big Data Engine. March 2013. https://www.techworld.com/news/apps-wearables/netflix-foretells-house-of-cards-success-with-cassandra-big-data-engine-3437514/
6. Investing in the Future: New Market-Ready, User-Friendly Health Technology App and Infrastructure Support. March 2016. https://www.healthit.gov/buzz-blog/interoperability/investing-in-the-future-new-market-ready-user-friendly-health-technology-app-and-infrastructure-support/

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