5 Reasons Why Healthcare Data Is Still NOT Interoperable In 2022

Alexander Skalabanov, Co-founder and Chief Executive Officer of Intellectsoft, a software development partner of Fortune 500 companies, explains data interoperability in healthcare, in line with discussing top related challenges and possible solutions. Ultimately, to help service providers and IT developers across the industry make long-term progress through better patient experience, improved delivery, and more consistent error-free workflow.

By definition, data interoperability (otherwise, data “liquidity”) refers to the ability of electronically-stored health information to be easily shared through network-connected, enterprise-wide electronic health record systems. Building efficient infrastructure for transferred and exchanged data ensures seamless communication, consistent patient experience, and measurable gains in the company’s efficiency, after all. So, it’s not surprising that for business owners and executives, the topic of interoperable data in healthcare is more relevant than ever. Based on the latest healthcare interoperability market research, we can expect a stable CAGR of over 13% in the next seven years (the forecast period of 2019-2026). As there is much work to do to achieve a fully interoperable system, below we’re deep diving into the basics to figure out why data is still NOT interoperable in healthcare in 2022.

What Is Healthcare Interoperability?

Healthcare organizations have got plenty of data for patient care and patient use, which means sensitive information should at least be capable of securely moving from one place to another. EHRs typically include different types of data, such as medical history, test results, images, allergies, immunization, other personal data, such as billing information.

This means that patient data is always available to stakeholders. If a practice has data that is not liquid, it could be labeled a data blocker under the Cures Act. The civil penalties for data blocking may inspire some software vendors to inch toward compliance. At the same time, the others may decide NOT to share data at all and forget about interoperability in a desperate hope to get a leg up on the heating market.

True data interoperability implies both syntactic and semantic interoperability. Simply put, syntactic interoperability refers to the ability to move data from one EHR to another, for example, using a common transport method.

Why Is Data Interoperability So Important In Healthcare

First and foremost, healthcare providers are pushed towards accelerating their infrastructure updates by Legal acts enacted during recent years. As a result, they adopt EHR and get their privacy of sensitive data transfer improved to a higher level.

In the wake of global consolidation and the recent growth of the health information exchanges (HIE), healthcare providers are facing the following four core challenges with data interoperability, information exchange, and data sharing across different electronic health record systems:

• Lack of communication standards across different electronic health record systems
• Lack of patient identification across different health information exchanges
• Poor data sharing due to the lack of payer participation
• Integration costs that may easily become prohibitive

Of course, the scope of this challenge is not limited to communication standards, patient identification, payer participation in data sharing, and a significant cost that only well-established healthcare providers can afford. At the same time, however, understanding the most critical concerns is the first step on the way to achieving true interoperability.

Also, the following four benefits make interoperability of healthcare systems absolutely critical for companies:

• Potential for enhancing the patient experience
• Improved service delivery at a lower cost
• Effective infrastructure for transferring and sharing sensitive data
• Reduced risk of medical error and/or data leakage

Why Data Is Still NOT Interoperable In Healthcare And How To Make It Work For Business In 2022

#1 Industry-wide measurement standards

Enforcing industry-wide measurement standards across the industry is the first thing to consider. Simply put, true interoperability is impossible when organizations can not measure outcomes and, therefore, don’t have clear visibility of the areas that need improvement most.

Federal agencies need to be consistent when measuring and assessing the state of data interoperability to provide a realistic evaluation of the achieved initiative progress. Simply put, the challenge is all about the lack of a standardized approach to measuring delays and identifying issues across the whole healthcare chain. When the industry-wide standards are in place, the quality of care and, even more importantly, patient outcomes, can be drastically improved via easy analysis and timely action-taking.

#2 Health IT interoperability standards

Next comes the compatibility and consistency issue that calls for enforcing standards for health IT interoperability. Modern healthcare technology has difficulties sharing data between different types of EHR software or healthcare technology. At Intellectsoft, we face the common situation when the information needs adjusting before it can be successfully imported elsewhere, let’s say, in the case that there are external data fields or mismatched fonts detected.

The lack of standards often leads to poor performance of health data exchange by creating additional barriers to achieving a seamless data flow. That’s why the adoption of standards should become a reliable foundation for enabling interoperability across different sectors of the industry and between EHRs.

The number of different standards development organizations (SDOs) create, define, update, and maintain health data standards through collaborative processes that involve health IT users, but there is no single standard. Today, the U.S healthcare system started moving from a fee-for-service to a value-based health care system focusing on outcomes and populations. As more cost goes to EHR implementation, the healthcare system must find efficient ways to connect the patient data accurately. This lack of a common standard for capturing, transferring, receiving, storing, and managing patient health data causes inaccuracies and delays – a major disadvantage to interoperability efforts.

#3 Patient identification standards

Validating electronic requests for patient information across HIEs is a matter of identification. Most commonly, patient identification goes through the name, birth date, and social security number. Easier said than done, as today there’s still no unified approach to identifying a patient both accurately and without putting in excessive time and effort.

Too often, different pieces of data are stored across multiple systems in different formats, and here the only adequate solution is, perhaps, creating a national individual patient identifier system. Eventually, a standard patient identifier based on a corresponding social security number can make things much easier for all, both for patients during care and for medical organizations. For healthcare providers, the global benefit is eliminating potential human error (for example, in the case of mismatched patient records in EHRs). The need for a national uniform set of patient identification standards drives growing healthcare facility costs, misaligned health data exchange, and patient safety threats.

#4 Organizational resistance to data sharing

Imagine the situation when hospital systems will compete for patients, let’s say, with urgent care clinics. It may happen that certain players in the healthcare industry stop sharing data with other providers. Although the availability and accessibility of health data are determined by law, overcoming organizational resistance to sharing data still poses a problem and a severe challenge to achieving true interoperability.

Currently, information blocking is still a rather common state of affairs for many organizations — including the Intellectsoft clients in the healthcare sector — which means that policies introduced to date have failed to curb resistance to data sharing. According to industry research, this year’s survey taking 60 HIE leaders reconfirmed this fact. A quarter of respondents admitted engaging with hospitals and health providers guilty of the information blocking, and nearly half of respondents engage with health IT organizations who repeatedly resisted data sharing.

#5 High cost of interoperability

Achieving truly interoperable EHRs will not only need a lot of time and specific qualifications. The thing is that to keep up with tracking and analyzing healthcare PHI data, organizations need end-to-end connectivity and seamless integration across a number of standalone EHRs and other systems (for example, HITs). Eventually, that drives a significant cost of integration that may become prohibitive for many healthcare enterprises of small and mid caliber.

Here is the scope of the challenge: modern healthcare networks commonly engage with something around 15-18 different EHR platforms. For hospitals, healthcare, and diagnostic providers that need to exchange, receive, and analyze that information, everything ends up with costly, error-prone, and too slow workarounds. Therefore, achieving interoperable EHRs in most healthcare settings is just a matter of choice where to get started, either with extra full-time hiring, contracting fees for a dedicated specialist, or the cost of a base platform license.

Summary

In a nutshell, EHR interoperability means having one system work with another. Easier said than done — in the eyes of the entire health system, the task involves an infinitely complex and confusing assemblage of EHR systems. Unfortunately, this inevitably calls for potential bottlenecks and inefficiencies.

The question of how to improve interoperability in healthcare revolves mainly around level-based complexity. Although there are plenty of implementation practices, the following simple yet critical tips are must-haves for achieving interoperability, be it a hospital or other healthcare provider’s environment.

1. We recommend that you outline clinical cases of data usage. It will determine what data is needed and what kind of structuring it involves.
2. Make sure to define the rules and protocols, follow them, and plan the actual implementation for the future.
3. Keep in mind that implementation goes through the following phases: Business Analysis, Project Performance and Testing, and ending with Deployment in a corresponding environment.