How to Transform Healthcare by Organizing Data Intelligently ... - HIMSS

Intelligent Data Aggregation Server

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How to Transform Healthcare by Organizing Data Intelligently around People, Places, and Events Value-based reimbursement and care delivery rely on information that has been collected at different points along the care journey. Until true interoperability materializes, exchanging data between disjointed systems requires sophisticated record matching technology to ensure that data relate to the same entity. But even with accurate identity management, the data remain siloed, and therefore do not provide a comprehensive picture of events. This paper discusses a solution approach that enables organizations to capture and correlate data as it flows through their infrastructure. By combining reliable identity management, XDS repository, and sophisticated relationship management, like that found in social media applications, healthcare organizations can match related data according to context and relationships, and then make this information available to existing systems and new applications. Enterprise data that have been aggregated and intelligently organized will supply end users with the accurate and detailed information needed to meet institutional goals and provide safe, quality care. 3579 E. Foothill Blvd., Suite 587 Pasadena, CA 91107

The acceleration of digitization in healthcare is a story familiar to most. It is a story of federal incentives, billions of dollars in technology investment, and a new flood of data that the industry is still struggling to incorporate in meaningful ways within physician workflow. Electronic medical records (EMRs) and health information exchanges (HIEs) have proven insufficient at delivering the level of interoperability required to meet the needs generated by business and regulatory trends in the healthcare industry. New strategies are required to address these trends and ensure better care and a better patient experience at a lower cost. This paper outlines how to overcome the challenges of interoperability that continue to plague the industry-and introduces a solution that uses social media technology to revolutionize how organizations manage and track relationships between data in healthcare.

Organizations need to be able to share and consume data about where patients received care, what care they received, and who provided that care. They need rapid advancement in data management to align patients and providers accurately for optimal care coordination, collaborative disease management, and valuebased reimbursement.

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The State of Interoperability in the Union The rapid digitization of healthcare records inspired a vision of secure, easy movement of patient records across the diverse healthcare ecosystem. In four short years, federal incentives increased the use of EMRs by physicians from less than 50 percent to almost 80 percent.1 Multiple vendors, solutions, and early adopters began to attempt interoperability between these EMR data silos. Numerous HIEs formed across the country to enable data to flow beyond the four walls of any one organization. However, these HIEs, primarily focused on moving limited clinical datasets among a tightly circumscribed network of clinicians, met with varying levels of success. As the need for sharing data and incorporating it into workflow increased, the industry made advances in data exchange. However, organizations today still struggle to harness data relationships to answer basic yet fundamental questions such as:

 Who is on the patient's care team for this specific visit?  Where do I send test results and critical information?  What occurred during a previous encounter?  Who is the patient's PCP so I can share care transition information?  What are the patient's consent requirements? As value-based care and pay for performance steadily replace the long-entrenched fee-for-service reimbursement model, organizations must have the ability to answer

these basic questions and many more if they wish to remain solvent. These reimbursement trends, embraced and championed by both public and private payers, have spawned new care-delivery models- particularly accountable care organizations (ACOs)- that require an unprecedented level of care coordination and patient health management.

of mergers and acquisitions. Hospitals are acquiring competitors and buying up physician groups to ensure downstream care stays within the system. Such acquisitions and mergers require the consolidation of IT assets, but data cleansing and proper alignment rarely receive sufficient attention.

Mergers Happen at Record Pace, Leaving

Gaps in Standardization, Data Governance Reimbursement reform is proving as rapid and dramatic as the move HOSPITAL toward EMR adoption. MERGERS AND 105 In January 2015, the 98 ACQUISITIONS 93 95 Department of Health & Human Services announced 76 its accelerated timeline for the shift to valuebased payments. It plans 50 to move 30 percent of Medicare payments into ACO and bundled-payment models by the end of 2016 and 50 percent by 2009 2010 2011 2012 2013 2014 the end of 2018. 2 At the same time, the numbers Onboarding of practice site and hospital data is often rushed. Opportunity for standardization in data management today and of ACOs nationwide have data governance processes for the future. skyrocketed. According to Leavitt Partners Center for Accountable Care Intelligence, 744 ACOs formed across the In an industry characterized by more country by January 2015, up from just collaborative partners, consolidated 64 at the same time in 2011. 3 Of these systems with larger geographic ACOs, more than half had government footprints, and unprecedented contracts, approximately 30 percent changes in the care-delivery model, had commercial contracts, and 10 greater interoperability is essential. percent had both.4 New reimbursement models will require a more global view of patient In the face of these trends, when and member populations. Organiinteroperable data is more urgent zations need to be able to share than ever, disjointed data still poses a and consume data about where significant challenge for providers and patients received care, what care they staff. Multiple versions of individual received, and who provided that care. patients' records exist throughout the They need rapid advancement in data community, and pertinent medical management to align patients and information doesn't follow patients providers accurately for optimal care throughout their journey across the coordination, collaborative disease healthcare continuum. The problem management, and valued- based is compounded by a record number reimbursement.

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Strategies to Achieve Interoperability for Value-Based Care The industry is rife with opportunity for making data work better for healthcare organizations as they prepare for new partners, new patients, new payment models, and emerging trends in care delivery. However, the majority of today's EMRs, HIEs, and other IT systems do not support the level of interoperability necessary to accommodate these trends. However, technology solutions are available that will enable healthcare organizations to continue to leverage their existing EMRs and systems and to navigate the evolving industry. Organizations need simply to augment their technology with infrastructure that ensures they can do three things:

1

Accurately match patients and providers

Share clinical data securely and efficiently across the continuum 2

Organize data intelligently around people, places, and events 3

These three capabilities — which build on each other — are fundamental to achieving the interoperability that value-based care necessitates.

Accurate Identification and Matching of Patients and Providers No organization can be sure that the right information about the right patient is being sent to the right provider without accurate patient and provider matching. Because effective value-based care requires data aggregation and exchange across heterogeneous platforms and organizations, health systems must have an enterprise strategy in place to uniquely identify records and their contextual relationships. In fact, accurate matching must serve as the foundation of any initiative for achieving interoperability. In such a rapidly evolving industry, patient matching might seem like old news — but the truth is that accurate patient and provider matching is still a real problem. Most hospitals use only the basic patient-identity features embedded in EMR systems, which can produce duplicate record rates of up to 20 percent due to changes in identifying data or data entry errors.6 Poor patient matching can have significant clinical ramifications. Some experts believe that medical error is the third leading cause of death in the United States, and that not having the right patient in context or not having the patient's relevant health information available contributes to medical error.7

significant savings, if a strategy is put in place to resolve existing duplicates and prevent new ones.8 Provider matching is equally important for leveraging data successfully in a value-based care environment. Provider information stored in clinical, financial, and analytics applications is often outdated, inconsistent, and full of duplicate records. Without reliable master data, basic tasks like results reporting, referrals, billing, and care coordination become burdensome and error-prone. A centralized, comprehensive, and up-to-date provider registry — that accounts for individual providers and organizations as a whole — can eliminate these challenges. Such a registry collects data from any source and assembles them into a single best record for each provider entity. As a result, any enterprise system can access a trusted resource to retrieve necessary provider information.

The financial consequences of unmatched patients are also significant. According to AHIMA, duplicates can cost $100 or more per record, and that's primarily labor, not the consequences of any action taken as a result of using information in the record. An organization with 1 million records and a 15% duplicate rate can expect a significant financial burden. Or a

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Clinical Document Exchange Capabilities Once an organization is confident that its data accurately represent patient and provider identities, it needs the ability to share patient health records and other documents between providers and enterprises. Despite advances in data exchange, sharing records across the continuum remains cumbersome for care providers and for patients. Research indicates providers have the information they need only 1 out of 3 times when they are with the patient. An XDS (IHE Cross-Enterprise Document Sharing) registry and repository offers a flexible solution to this problem. XDS is a set of standards around document transport, metadata, and content that supports the exchange of patient-centric clinical documents across the continuum of care. Each care delivery organization publishes clinical information to the repository but retains the actual documents in their

own source system or XDS repository. Such a repository stores documents in a transparent, secure, reliable and persistent manner and responds to document retrieval requests. The repository works in concert with an XDS registry — a centralized store of metadata on published documents. This registry makes it easy for organizations to search for, request, and securely access documents, regardless of where they're actually stored.

Web Applications

Mobile Applications SDK & APIs

PERSON REGISTRY

PROVIDER & RELATIONSHIP ORGANIZATION REGISTRY REGISTRY

EVENT REGISTRY

XDS REGISTRY

XDS MESSAGING REPOSITORY SERVER

NextGate Server (OSGi)

WORKFLOW ENGINE

Intelligent Data Aggregation Server

NEXTGATE

Intelligent Organization of Data around People, Places, and Events Even with accurate matching and clinical document exchange in place, organizations have difficulties linking patients to their provider care network, sharing patient information with the right providers during care transitions, and performing other tasks essential for care coordination. These difficulties remain because organizations lack a way to organize data intelligently around the people, places, and events that it relates to. What is missing is sophisticated relationship engine technology — like that used in social networking applications — that can monitor existing message traffic, correlate relevant data elements to each other,

and assemble a cross-platform view of enterprise information. This kind of technology can automatically link and organize enterprise information into useable and insightful servings that can be injected directly into provider workflow, offering an unprecedented level of interoperability. Mapping out the data relationships between people, places, and events hasn't been possible in the past because of the database infrastructure in use in healthcare organizations and HIEs. Most databases in healthcare today are relational databases. Such databases are composed of multiple tables of

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information, and they work very well for transactional systems like EMRs. Yet, in spite of their name, relational databases are not very good at defining complex relationships between multiple points of data. Relational databases force organizations to represent non-tabular data in a tabular format. Not only is such a database hard to build, it is also difficult to search and to update as the patient's network of relationships expands. For sicker and older patients, that web of relationships and events can be mind-boggling.

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An innovative graph database, on the other hand, is designed to handle these complex relationships. Designed specifically to interpret relationships between different sets of data, the graph database is the foundational technology that lets social media applications — such as Facebook and LinkedIn — build sophisticated social networks around each account owner. For instance, it is what enables users on Facebook to easily browse the profiles of friends of friends and to navigate through their interests and connections. A graph database doesn't force a restructuring of the web of relationships between entities and events into tables. Rather, it accepts the web of relationships exactly as they are, because a graph database is inherently structured as objects connected by relationships. A solution external to a healthcare organization's EMR and other systems can leverage graph database technology to elucidate the complex web of relationships surrounding each patient, provider, and event in healthcare. In fact, such a solution can capture associations, correlations, and relationships in a graph database as a natural artifact of simply "listening in" on the flow of data between existing systems. While existing System A sends data to Systems B and C (and each system consumes the data according to its data model), this external engine monitors the data traffic and makes associations and relationships between data elements. For example, an HL7 message flowing from System A to System B might

PEOPLE

RELATIONSHIPS

PLACES

EVENTS

INTELLIGENT DATA AGGREGATION SERVER

reveal that John is being treated by Dr. Yu. Another message flowing from C to B contains the information that John is also being treated by Dr. Brown and Dr. Hernandez. In a third message, John has received treatment from Dr. Clark. Looking in any of these individual systems, we would not see

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all of these providers related to John, because these systems do not keep track of the totality of data relationships. But a graph database can and does make these associations, making it possible for a query of the database to show that John's care team consists of all four of those doctors.

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INTELLIGENT DATA AGGREGATION SERVER Use Cases for ACOs Innovative uses of graph technology make it possible to liberate data in practical and unprecedented ways to support a variety of workflows, many of which are necessary in an ACO environment. Graph database technology enables organizations to aggregate and organize data according to relationships, revealing how the various data points fit together, to give a more complete, accurate picture of patient care and, in turn, enable better and more informed care. The core premise of such technology remains the accurate identification of entities (patients, providers, events, and places) to ensure that the correct information is related to each another, establishing confidence in the data among decision-makers. The following are just a few of the use cases where graph databases empower ACOs to overcome significant challenges in care coordination and business operations.

Intelligent Data Aggregation Server

Disconnected Data Sources

Master Identity & Create Relationships

Create a Patient's Provider Map Many healthcare organizations have a real challenge finding out which doctors or team of doctors across the continuum are participating in the care of a particular patient. As alluded to above, graph database technology enables organizations to dynamically create and access a care team map. As data about a particular patient flows into the system, new members of the patient's care team can be added to the database and related appropriately to that patient. Any nurse or doctor who needs to know who is providing care to the patient can simply pull up the patient's provider map. They can see not only the names of the providers and the specific care events that link them to the patient but also their roles, where they practice, their schedules and how to get in touch with them. This transparency improves patient care and also has important implications for value-based reimbursement and bundled payments. A graph database enables organizations to clearly see all of the providers associated with a clinical event and to make sure bills and payments are appropriately allocated and submitted.

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Combined data for your applications

Create a Full, Contextualized View of a Patient's Care For many years, organizations and technology vendors have worked to create complete longitudinal care records for patients. Unfortunately, most of these records remain incomplete or, at the very least, lack contextual information surrounding a patient's care. A typical longitudinal health record, for example, might show that a patient had a lab test and display the result. It might even trend a series of results for a repeated lab test. A graph database linked to an XDS repositorycan deliver that information as well as the context surrounding that information. For example, it could show not only the lab order and result but also the provider responsible for that lab order, his practice schedule, his provider ID and how many times in the last year the patient has seen that provider. It can raise awareness of other events that have taken place in the patient's care, such as a medication prescribed based on that lab result, how long the patient has been on the medication and when she last filled her prescription. If the organization has the permissions to do so, it could then look at how many other patients that provider has treated with the same condition and how often he prescribes that medication. (626) 376-4100

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INTELLIGENT DATA AGGREGATION SERVER Send Alerts and Notifications to the Care Team Graph technology simplifies the process of notifying the right members of any care team about a patient's meaningful events, especially transitions of care. Simply put, when an organization has a complete picture of the providers who compose a patient's care team, it is much easier to alert the right people about the patient's clinical events and care needs. Imagine that a hospital is discharging a patient to a cardiac rehab center. Because the hospital has access to the patient's care team map, it can send out an alert about this discharge to all of the relevant providers: the primary care physician, cardiologist, etc. It can send a discharge summary and other critical information. The rehab center in turn can use this same care map to ensure better care coordination as the patient leaves the center.

Understand Patient Utilization9 Getting a full picture of patient utilization without advanced analytics in place is very difficult for ACOs and other healthcare organizations. Because graph database technology captures the complex web of data and events surrounding a patient, it can generate canned utilization reports without requiring an advanced analytics infrastructure. Such reports can identify such things as:

 Patients who have been seen in the emergency department more than three times in one month  Patients who have been seen in the emergency department more than six times in as many months  Patients who have been re-admitted with the same discharge diagnosis in the past 30 days

Track the ACO Enrollment of Patients Graph database technology enables healthcare organizations participating in an ACO to easily identify whether a patient is enrolled in the ACO or not. When a patient arrives at a hospital or doctor's office to be checked in for a service — be it the emergency department, the hospital, or an outpatient procedure — the registration clerk would be able to see if the patient is enrolled in an ACO and, if so, which ACO. With this knowledge, the provider can make sure the ACO is notified of any clinical events for that patient, helping the ACO maintain an updated understanding of what's going on with their members.

Conclusion Each healthcare organization measures success in valuebased care differently. Some focus more on quality, others on cost, and still others on patient experience. However, one fact remains the same for all organizations: success in a valuebased care environment requires that accurate, comprehensive, and properly identified data be available for clinical and operational use. This data must be interoperable not just within a single enterprise but across the entire continuum. Relying solely on current EMRs and HIEs, interoperability remains an elusive goal even as achieving it becomes increasingly urgent. Fortunately, vendor-agnostic solutions are available today that can solve the interoperability challenge and liberate data to inform clinical and operational workflows. Based on a foundation of accurate identification and secure clinical-document sharing, such solutions use innovative graph database technology to reveal the relationships between people, places, and events in healthcare. Such unprecedented understanding of the complex web of healthcare relationships enables better and more timely decision making, fewer medical errors, more collaboration across care teams and facilities, a better understanding of the patient journey, and ultimately the ability to transcend a narrow focus on discrete care episodes and to put effective strategies in place to manage and improve overall health.

ABOUT NextGate uses its market leading expertise in patient and provider identification to connect the healthcare ecosystem by accurately identifying and linking patient and provider data. NextGate’s EMPI technology, rated No. 1 by KLAS, is deployed by the nation’s most successful healthcare systems and health information exchanges to manage more than 175 million lives. This technology helps organizations deliver higher quality, better care coordination and greater business and clinical efficiencies. To learn more, visit NextGate.com

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Intelligent Data Aggregation Server

INTELLIGENT DATA AGGREGATION SERVER Sources 1. http://www.cdc.gov/nchs/data/databriefs/db143.htm#x2013;2013