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The Future of Clinical Interoperability

Interoperability Challenges and Recommendations 


The U.S. Department of Health and Human Services (HHS) aims to have an interoperable health IT ecosystem by 2024.

This ecosystem would make the right data available to the right people at the right time across products and organizations.

Centers for Medicare & Medicaid Services (CMS) has been regulating data exchange schematic and syntax standards using 1500, 1450 Forms, HIPAA 5010 X12 EDI Messages, and NCPDP D.0 Messages.

Clearing houses, infomediaries and plan portals require connectivity across disparate systems and organizations to exchange data.

Leveraging an interoperable health IT system, patient data would be shared seamlessly among authorized practitioners and individuals. This would help all parties make more informed decisions, improving the healthcare quality and lowering costs.

Not all types of healthcare data exchanges are as in dire need of improvement. Today, revenue cycle management (RCM) data can be shared seamlessly. It has well defined standards and connections for information exchange and interpretation across ecosystems. Overall, RCM data exchange between providers, health plans, consumers and other organizations is robust and mature.

This is especially true when compared with problematic clinical data exchanges between all authorized practitioners, consumers, and health plans. With clinical data, providers face unique challenges exchanging information outside their health system.

Obtaining this data from settings outside a network requires complex data-sharing agreements and new interfaces between systems.

According to The Office of the National Coordinator for Health Information Technology (ONC)’s 2014 data brief, less than half of providers can access clinical information from outside of their systems. The brief also states that approximately 4 in 10 hospitals can access necessary clinical information from outside providers or healthcare sources.

Providers are well-aware of these challenges in sharing clinical data. According to a recent survey by a group purchasing organization (GPO), Accountable Care Organizations (ACOs) report that lack of interoperability between their HIT systems and outside providers is their biggest challenge.

Read on to explore universal challenges with clinical data exchanges and steps healthcare leaders can take today to address these challenges.

Interoperability Challenges:

·      Lack of Universal Adoption of Standards-Based EHR Systems:

With Meaningful Use Incentives, the exchange of data between lab, pharmacy and radiology center is digitized. However, Electronic Health Record HER-to-EHR communication has yet to digitize in the same way.

The only integration between EHRs today is the

exchange of summary of care documents. This exchange is not widely adopted by EHR systems.

In addition to limited adoption, summary of care documents are hard to read and include irrelevant information. This makes necessary data difficult to find for physicians.

For EHRs, data definitions and coding standards are inconsistent across providers as well. For example, for disease definition some providers use SNOWMED codes and some use the ICD Codes. As well, each EHR system has unique software types and APIs. Therefore, when data is exchanged between providers, interpreting such data and saving it in the other provider system’s patient medical record is near impossible.

These inconsistencies require custom integration and additional development for every single exchange type.  For example, custom integrations must be made for each EHR system’s supported Health Level Seven (HL7) version.

·      Prohibitively High Data Exchange Fees: 

Implementing interoperability is costly. Each integration requires upfront capital. Some EHR vendors may claim to have the capability to send and receive patient information from other systems, but this always comes at an additional cost of $5,000 to $50,000.

·      Outdated Legacy Standalone Systems:  

Legacy systems have poor interoperability. Establishing connectivity of legacy systems to middleware creates structural misalignment within existing data layers. Remediating these structural misalignments while also establishing connectively is extremely costly.

·      Impact on Providers’ Day-to-Day Workflow:  

New technologies impact existing workflows. This is especially true for industries like healthcare with highly complex workflows. Many providers are currently operating at maximum capacity. There is no reasonable bandwidth to add additional requirements of learning new workflows or record keeping.

·      Complex & Misunderstood Privacy & Security Policies:

Privacy and security policies present a major barrier to implementing interoperable systems. In order to exchange information, EHRs must integrate varying state specific privacy and security laws.

In addition to state laws differing vastly, federal laws are also poorly understood by providers. For example, HIPAA policies and certain privacy laws addressing paper-based documents are not universally agreed upon. The differing understanding of these laws impedes streamlined data exchanges between stakeholders.

·      Lack of Incentives to Develop Interoperability: 

A key inhibitor for streamlined health information exchange is economic incentives like traditional fee-for-service payment models. These fail to encourage hospitals or health information technology (HIT) vendors to prioritize interoperability. As a result, her developers have largely ignored interoperability. They have instead opted to focused on other capabilities like improving documentation for billing purposes.

·      Standards Not Adequate to Deliver Relevant Data: 

Lack of interoperability hinders comprehensive data on your patients’ health. Currently practitioner notes do not have to be written in a shareable format to share with other providers. Without a clear understanding from all specialists and health systems serving your patient, you cannot offer the most accurate diagnoses and ideal treatment options.  


·      Payment Incentives for Adapting Interoperability: 

High-value interoperability measures targeting both providers and vendors will help streamline data exchanges. New payer payment models and CMS’s introduction of Valued Based Reimbursements can realign incentives to prioritize interoperability for all stakeholders. In addition to rewarding high quality of care, information blocking activities should be penalized. Overall, to create an interoperable healthcare ecosystem there must be clear and specific incentives, defined measures and an actionable timeline with deadlines.

·      Interoperability Standards Definition:

The US federal government is the largest healthcare insurance payer (CMS’s Medicare & Medicaid) and provider (DoD, VA, IHS, etc.). These organizations will have a large impact on shaping the future of nationwide health information exchanges. Federal agencies have strongly supported HL7, Consolidated Clinical Document Architecture (C-CDA) and Fast Healthcare Interoperability Resources (FHIR) through Meaningful Use (MU) incentives. However, these agencies have not achieved interoperability due to lack of a defined structure and process. Without this clear structure, there is no way to drive development, adoption, and self-regulation of industry-wide standards.

In private sector dominated industries like banking, firms come together to provide necessary standards for interoperability. The US federal government should study these standards from the private sector and implement them within the healthcare ecosystem. Health systems and HIT vendors should ally with the government to support these adoption efforts to achieve interoperability.

·      Enterprise Master Patient Index: 

To enable data exchange across the continuum of care, patient identity must be reconciled accurately across organizations. Currently these patient-confirming capabilities are inferior to those delivered by an embedded Enterprise Master Patient Index (EMPI).

The core function of this technology-agnostic index is to aggregate data, including identity data, between applications regardless of data type or format. They usually employ a probabilistic matching engine. This engine leverages statistics and data analytics to pinpoint variation and establish more accurate forecasting. system can be problematic depending on the environment’s size and complexity. It is well suited for complex organizations with numerous disparate systems and databases.

·      Connecting Private Electronic Health Information Exchanges (HIEs): 

Black Book Research in April 2016 reported growing HIE user frustration over lack of standardization and preparation of providers and payers.

Today HIE’s pose additional challenges for data exchange. These challenges include added costs and resources to achieve interoperability goals, as well as needed governance and trust among entities to facilitate sharing health information.

To address this, healthcare vendors are turning to middleware solutions employed by other industries like retail, banking, and transportation. Middleware platforms facilitate transparent, yet secure access of patient health data. They do so by translating information directly from disparate systems including EHRs and HIEs. They create a business intelligence layer providing data to all stakeholders in real-time.

·      HIT Alliances Collaboration:  

The Sequoia Project and the CommonWell Health Alliance are advocating for a nationwide health data exchange and interoperability.

Sequoia supports Carequality, a public-private collaboration developing common interoperability frameworks for data exchange.

CommonWell launched in 2013 and has grown to 40 HIT organizations. CommonWell supports secure access to and exchange of health data nationwide. Its members are committed to implementation of initiatives person enrollment, record location, patient identification, linking, data query and retrieval.

Alliances like CommonWell and Sequoia should further collaborate amongst themselves to implement a common interoperability standard across various healthcare sectors.

·      Regulations to Drive Necessary Clinical Data Exchange:

Regulatory mandates don’t enforce exchange of non-standard data like notes between health systems.

As well, the adaption of FHIR, that supports non-standard clinical data, is largely limited to influence CMS’s interoperability rule.

CMS’s interoperability rule should be expanded to mandate the exchange of needed non-standard clinical data between the health systems.  The regulation should also focus on building trust across health systems to improve data exchanges.

Benefits of Interoperability:

·      A unified standard implementation enables all disparate systems to interpret data accurately.

·      Middleware enables secure exchange of data across various source systems

·      An interoperable EMPI helps identify and locate the right patient record

To achieve healthcare interoperability, synchronous collective action is needed among multiple stakeholders. It also requires consensus among all healthcare participants on an actionable roadmap, timeline, and standards for interoperability.

Interoperability Implementation at Your Health Plan

Although it takes an entire healthcare ecosystem to establish nation-wide interoperability, there are some action items your health plan can implement today to create a more interoperable environment within your company and patient and provider networks.

The most effective way to streamline interoperability is to partner with a healthcare SaaS professional, like Source, to incorporate information exchanges within a single API. To get started book a demo for your team today.