Industry Voices—3 ways health information exchanges have ushered in a new era by evolving their business models

Health information exchanges (HIEs) have come a long way since they rose to prominence more than a decade ago as a key component of the HITECH Act. Originally supported largely by federal and state grants, HIEs started as a means of helping providers share large amounts of disparate patient data.

In these early days, HIEs focused primarily on pushing data from one entity to another to enable providers to support care collaboration. Today, as a result of dwindling grants and evolving business models, HIEs look very different from their predecessors a decade ago. The key has been keeping up with the ever-changing healthcare industry. Unexpectedly, the pandemic has ushered in a renaissance for HIEs, with the need to bring together large amounts of data from disparate sources quickly—with meaning—more important than ever before.

For example, Indiana Health Information Exchange (IHIE) played a pivotal role in the state’s ability to reopen safely by analyzing statewide data and determining which counties to open based on the number of cases and availability of healthcare resources. IHIE included information from hospitals, health systems and providers across the state and gave the government officials an accurate picture of COVID-19 impacts.

The HIE was the conduit for getting all the data from different healthcare organizations in the state into one place quickly, to help inform a plan to reopen county by county.

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COVID-19 needs have re-emphasized the importance of HIEs, but what some have missed is that they have been evolving all along.

HIEs of today are naturally more technologically advanced than their predecessors, as a result of cloud-based services, mobile health apps and more widespread broadband connectivity. But more importantly, HIEs have changed their approach to creating value for their members, broadening the services they provide and the members they serve.

Some important aspects of HIEs’ new approach have included a greater focus on delivering high-quality clinical data, assisting users with quality improvement and reporting, clinical decision-making, and population health analytics. Further, many leading HIEs leverage automation to normalize and enrich clinical data, enabling compliance with national coding standards and delivering a consolidated longitudinal view of each patient.

Following are three examples of how HIEs have advanced and evolved their business models in recent years to transform raw clinical data into valuable information services to help customers improve care quality.

Serving payers: Clinical data is especially valuable to payers because it includes detailed patient information on vital signs, conditions, procedures and diagnoses frequently not found in claims data. Payers need clean clinical data for a number of functions, including care management, quality reporting through HEDIS (Healthcare Effectiveness Data and Information Set) measures, risk adjustment and prior authorizations.

In addition to producing standard supplemental data for use in HEDIS reporting, HIEs provide payers with the clinical data needed to reduce the administrative burden of chart-chasing, audits and data preparation. The National Committee for Quality Assurance (NCQA) recognized the importance of HIEs to payers as a clinical data source by establishing the Data Aggregation Validation (DAV) Program pilot in March 2020. Accredited HIEs and other data aggregators have become standard supplemental data sources for payers and have enhanced their role in quality reporting.

Enhancing provider services: While early HIEs concentrated on provider data-sharing, they have broadened their services to include analytics that help providers create population health dashboards and quality reporting. As opposed to delivering a view of an individual patient, these analytics enable providers to visualize their patients in aggregate—helping them determine, for example, which patients may need more preventive care, and which are over-utilizing emergency services.

By aggregating patient data across all healthcare providers into a longitudinal patient view, HIEs help providers understand the right quality metrics to emphasize for each patient.

For example, women often obtain breast cancer screenings from providers other than their primary care provider (PCP), but the PCP is impacted by the breast cancer screening quality metric. Due to their data aggregation abilities, HIEs can help PCPs avoid being penalized for not meeting the clinical quality measures that are often beyond their control. HIEs can also provide longitudinal data to support value-based care models, so clinical care teams can be evaluated as a whole rather than as individual contributors.

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Improving the usefulness of EHR data: Healthcare suffers from dual problems of not only too much data, but too much dirty data. With millions of clinicians documenting care each year in dozens of certified electronic health record (EHR) systems, the same metrics—such as HbA1c levels for patients with diabetes—are collected and reported with myriad variations, which can create challenges associated with proper procedural coding.

Already during the COVID-19 pandemic, we’ve seen evidence of inconsistent clinical documentation, with multiple ICD-10 codes used for the same diagnoses and procedures. These wide variances in coding illustrate the importance of data normalization, a process that produces a standardization of coding, display names, units and content with reference to national standards.

Once viewed as possibly headed for extinction, HIEs have persevered in the face of reduced government funding and have quietly been mounting an impressive comeback. That they have managed to accomplish this bounce-back is a testament to their ability to evolve with the broader needs of the healthcare industry by making clinical data more useful, valuable and powerful.  

John D’Amore is the president and co-founder of Diameter Health, a clinical data optimization company focused on improving the quality and quantity of actionable health data.