A case study conducted by the NORC showed challenges and successes that six states had in creating HIE security and interoperability.
The Office of the National Coordinator for Health Information Technology (ONC) recently released the results of a case study on six states that was done to better understand the overall impact of the State Health Information Exchange (HIE) Cooperative Agreement Program (the State HIE Program). Created in 2009, the State HIE Program was designed to encourage the secure exchange of health information. Data needs to be moved securely to ensure that patients receive the best care possible.
The study was conducted by the NORC at the University of Chicago, and took place over the course of two years. Iowa, Mississippi, New Hampshire, Utah, Vermont and Wyoming were all investigated to see how HIE security and interoperability were working.
Moreover, the study wanted to determine what the key enablers, challenges and lessons learned from the State HIE Program were.
“First and foremost, stakeholders believe the program raised awareness of HIE and helped potential exchange partners gain an appreciation for benefits and its relevance to overarching health care reform and health care delivery goals,” the report read. “Stakeholders also reported HIE facilitated the breaking down of silos — of information and between entities for whom the program fostered conversation and collaboration.”
While stakeholders said it was too early to truly know the long-term impact of the State HIE program, they added that the value would definitely increase over time. This is because Accountable Care Organizations (ACOs), Patient Centered Medical Homes and new payment models would also increase in use.
“Critically, the program established the foundational elements necessary for exchange, including governance and technical structure, privacy and security policies, and stakeholder collaboration,” explained the report.
The study also compared states’ experiences with direct exchange and query-based exchange. Direct exchange is when information is sent electronically from an authorized user, while query-based exchange requires providers to find data and then request that it be sent.
Direct exchange is increasing in popularity, but there are still workflow challenges, according to the case study. A lack of integration into existing EHR systems and clinical workflows was one of the main barriers listed.
“It requires providers to log into separate, standalone portals to send or receive data, which providers find onerous and therefore avoid,” the report stated, in reference to Direct exchange.
However, a centralized infrastructure was reported as having numerous advantages. Those interviewed for the case study said that this option gives them quicker access to data because its centrally maintained.
“The aggregation of data centrally also facilitates community-wide data analytics, population health management, and other value-add functions and offers a consolidated view of the patient,” the report stated.
The case study also found that changes to privacy legislation helped facilitate HIE progress in some instances. For example, some stakeholders said that the opt-in consent model was an impediment to the program. In this type of HIE model, patients must give their consent so providers can exchange their health data.
“Grantees spent time and financial resources on education campaigns and other efforts to convince patients to opt-in, yet the build-up of a participant base was slow,” the report said.
Overall, setting intermediate goals that can help create ongoing process toward HIE interoperability was cited as one of the key lessons learned. Moreover, this approach can help healthcare organizations make corrections along the way.
“For interoperability, many look to the ongoing development and adoption of data standards as the long-term solution,” the report stated. “In the short term, grantees are encouraging the use of certain standards among their participants and building capability in data translation and in-house teams responsible for data cleaning.”
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