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EHR Interoperability

ehr interoperability
Digitally powered healthcare systems, such as patient portals, patient engagement platforms, telemedicine applications and EHRs, are among the most common. The interconnection and interoperability of all these applications make the entire system connected. However, integrating one solution with another and leading a successful system deployment is quite challenging, especially when the EHR system is concerned.

Today, healthcare organizations understand the need and benefits of interoperability and are turning rapidly towards interoperability solutions. The global market of healthcare interoperability solutions is expected to reach 4.2 billion USD with projected growth at a CAGR of 12.6%, as per reports by Gale. Even with all the benefits, interoperability is not that straightforward to implement. Health tech workers are struggling to handle the PHIs and manage the diversity of EHR systems. Moreover, some hesitations are also involved among people about the privacy of personal information and data systems’ security. Leveraging new and more secure technologies like Blockchain can ensure the protection and confidentiality of data.

This article focuses on developing an understanding of EHR interoperability, exposing the challenges involved in healthcare interoperability, and highlighting ways to overcome those challenges.

What does interoperability in healthcare imply?

Interoperability is the ability of software, information systems and devices to connect and integrate within and outside healthcare organization boundaries to facilitate the access and exchange of patient data to address health issues in general.

Interoperability in EHR refers to the seamless integration of systems for sharing vital medical information. EHRs serve as a key platform ensuring easy and secure transfer of patient records between providers and systems without obstructing data flow.

As per the National Coordinator for Health Information Technology (ONC), complete EHR interoperability requires successful integration across four key areas of technology:

  1. Application interaction with users
  2. System communication
  3. Information processing and management
  4. Consumer device integration

EHRs must effectively communicate with various stakeholders, including clinicians, pharmacists, and patients, ensuring quick access to pertinent healthcare information while safeguarding against unauthorized access.

Understanding EHR interoperability solutions at four levels

The Healthcare Information and Management Systems Society (HIMSS) defines interoperability at four levels:

Level 1: Foundational interoperability

Foundational interoperability is crucial for establishing the basic connectivity requirements between different systems or applications, enabling them to exchange data securely. However, in this form of interoperability, the receiving system cannot interpret the exchanged data and can only acknowledge receipt of the data payload.

For instance, when sharing a PDF document containing a patient’s hospital stay summary and discharge medication list, the receiving system can store the document, but it cannot analyze the data within it without manual intervention. This means that while clinicians can read the document for care delivery insights, the system cannot automatically update the patient’s Electronic Health Record (EHR) with the discharge medication list. Instead, providers must manually input this data into the EHR.

While foundational interoperability lays the groundwork for data exchange, it must improve in enabling providers to derive meaningful insights from health data. With the ability to automatically process and utilize exchanged data, providers can avoid inefficiencies and limitations in leveraging health information for informed decision-making and patient care. Therefore, while foundational interoperability is necessary, additional levels of interoperability are required to realize the full potential of Health Information Exchange (HIE) and improve healthcare outcomes.

Level 2: Structural interoperability

Structural interoperability builds upon foundational interoperability by specifying the format, syntax, and organization of data exchange, including at the data field level. This intermediate level of interoperability relies on message format standards to facilitate the exchange of health data while preserving its meaning.

For instance, in E-prescribing, the provider’s health IT system and the pharmacy’s system must adhere to the same data standards for common prescription elements to ensure accurate prescribing.

HL7 Fast Healthcare Interoperability Resources (FHIR) has emerged as a prominent standard for structural interoperability. Its adoption by major EHR vendors has solidified FHIR as a leading health information exchange (HIE) method. FHIR enables health data exchange between different systems while preserving its clinical or operational meaning.

By assigning standardized, shareable identifiers to each discrete data element, FHIR facilitates the exchange of metadata, text, or data elements while maintaining data integrity and meaning. This approach ensures seamless data sharing and interoperability across diverse health IT systems and applications.

Level 3: Semantic interoperability

As defined by HIMSS, semantic interoperability ensures that different systems can exchange data accurately using standardized models and coding vocabularies. It allows users to share an understanding and meaning of data.

This level of interoperability relies on clinical terminologies like SNOMED CT, ICD, and LOINC, which provide standardized definitions of concepts. By employing these standardized languages, data can be transferred accurately and interpreted in a clinically relevant manner.

For healthcare providers, semantic interoperability facilitates the exchange of patient summary information across different Electronic Health Record (EHR) systems. This common understanding of medical terminology enhances care delivery by ensuring consistent interpretation of patient data among caregivers.

Moreover, semantic interoperability enables providers to share patient data to avoid duplicate testing, utilize clinical decision support tools, and prevent adverse health events. Facilitating effective data sharing supports improved care coordination, reduced hospital readmissions, and cost savings for healthcare institutions.

Level 4: Organizational interoperability

As defined by HIMSS, organizational interoperability encompasses governance, policy, social, legal, and organizational considerations to facilitate secure and seamless communication and data utilization within and between entities. This form of interoperability involves addressing non-technical aspects of Health Information Exchange (HIE) due to the involvement of diverse organizations with varying aims, regulations, and requirements.

To achieve organizational interoperability, stakeholders must navigate policy, legal, social, and organizational factors alongside technical considerations. Some HIE organizations are adopting the health data utility (HDU) model described by Civitas Networks for Health.

HDUs, typically established as statewide entities, consolidate, enhance, and exchange electronic health data for various purposes, including treatment, care coordination, quality improvement, and public health initiatives. These entities prioritize multi-stakeholder organizational and data governance, often operating as non-profit organizations or independent state agencies. HDUs commonly leverage existing infrastructure, such as regional and statewide HIE networks, to facilitate data exchange and interoperability.

EHR Interoperability standards

The actions that could significantly speed up the progress toward achieving interoperability in healthcare systems. Common interoperability standards include:
FHIR: It stands for Fast Health Interoperability Resources. HL7 International develops this standard for exchanging information electronically. The “resources” that describe exchangeable health data format and elements are the basic building blocks for FHIR. It also provides the standard for implementing APIs. FHIR supports various transport mechanisms, including HTTP-based transfer and MLLP (Minimum Lower Layer Protocol). It is a universal standard for cloud-based systems, apps, server-based communication and EHR data sharing.
Direct standard: It is a set of standards and protocols that allows users to authenticate and encrypt healthcare information directly to known and trusted recipients over the web. This standard contributed to the transition from paper-based to EHR records.
DICOM: This is a standard for managing and exchanging medical images and related information. DICOM facilitates the seamless exchange of medical images across various systems, including Picture Archiving and Communication Systems (PACS).

Challenges in EHR interoperability solutions

Sharing information across products and organizations is possible only by introducing interoperability in the HIT (Healthcare Information Technology) ecosystem. EHR interoperability is achieved by integrating disparate EHR systems, enabling access to clinical information from within and outside the health system.
The HHS has demanded interoperable health IT ecosystem by 2024 that delivers the correct data at the right time to the concerned people across products and organizations.

However, various technical and administrative challenges are involved in integrating an EHR system with other EHR systems and even with other healthcare software like patient portals, telemedicine applications, etc. Let’s discuss these challenges in detail:

  • Standardization of information

Different healthcare organizations follow other standard formats. This difference in standards makes the exchanging of data a difficult task. Therefore, it is necessary to maintain similar standards amongst organizations. HL7 and FHIR standards must be used as conventional standards for information exchange in EHR systems to maintain integration standards.
Fast Healthcare Interoperability Resources (FHIR) is a standard known to provide high bandwidth, efficiency, speed and security to data, which is beneficial for real-time data exchange. Moreover, when real-time data exchange is not required, Health Level Seven International (HL7) v2 and v3 are accepted standards.

  • Unique patient identifier

Organizations use multiple information systems to gather, track, and analyze patient data, which further creates interoperability challenges. The absence of a unique patient identifier increases the difficulty of efficiently exchanging medical information and patient health records. Therefore, whenever planning for the healthcare data exchange system, make it a priority to standardize patient identification.

  • High integration cost

The cost associated with the integration process is reasonably high, which becomes another challenge for interoperability. Traditional integration models are generally expensive, which only large organizations can afford. At the same time, new affordable integration models are also available that a mid-or-small-sized organization can implement.

  • Privacy and security challenges

To ensure data security, healthcare organizations compliantly implement their systems with HIPAA standards and limit who and how the data is shared. To ensure that the security standards do not create any further challenges in data sharing, a relevant HIPAA Business Agreement is implemented to enable third-party integration.

  • Malformed or missing data

The system may be unable to locate or intercept the data in a sender-specific format, leading to malformed data or missing data fields. This issue is caused by a lack of compliance with standards. Therefore, organizations need to comply with international standards to handle data exchange between variant systems correctly.

  • Use of outdated legacy systems

When used to connect the middleware for exchanging data, old and outdated systems often cause structural damage to the existing data. This issue leads to high repair costs.

  • Interface discrepancies

Systems at the sending and receiving ends must exchange EHRs correctly and clearly without the data getting lost in the translation. An integration engine must exist in an interoperable system dedicated to correctly routing the information and transforming it from one format to another.

  • Existence of multiple medical records

Multiple medical records for one patient with a single condition may exist. This situation arises because whenever a patient visits a hospital or clinic, a new patient entry is created. Each individual and independent task must be combined in one record to make the system interoperable.

  • Patient ID errors

Patients with similar first and last names, birthdates or zip codes are highly likely to be misidentified. In some cases, their PHI, demographic data and even their insurance and claims may be merged. To avoid such errors, the data management staff must closely monitor the data and prioritize using the Master Patient Index (MPI) for patient identification.

  • Human errors

Errors may arise from improper and incomplete data collection during inpatient registrations or outpatient referrals.

How can AI overcome the interoperability challenges?

There are several ways in which AI can overcome interoperability challenges; these include:

Data integration and standardization

  • Natural Language Processing (NLP): AI-driven NLP algorithms extract information from unstructured clinical notes, converting it into structured formats for easy integration into Electronic Health Records (EHRs).
  • Code standardization: ML algorithms standardize medical codes (ICD-10, CPT, SNOMED) across systems, ensuring consistent and compatible data for interoperability.
  • Fast Healthcare Interoperability Resources (FHIR): AI and ML assist in FHIR implementation, translating data between formats for seamless exchange.

Unique patient identifier

  • Patient matching and deduplication: AI algorithms can analyze various data points from different sources to accurately match and deduplicate patient records. By identifying patterns and similarities in patient demographics, medical history, and other relevant data, AI can improve patient matching accuracy, reducing the risk of duplicate records.
  • Biometric identification: AI-powered biometric identification systems, such as facial recognition or fingerprint scanning, can provide unique patient identifiers based on biometric characteristics. These identifiers can be securely linked to patient records, ensuring accurate identification and reducing the need for manual input of patient information.
  • Natural Language Processing (NLP): NLP algorithms can analyze unstructured data sources, such as clinical notes and medical records, to extract relevant patient information. By processing textual data, NLP can identify patient identifiers, medical conditions, and other relevant information, creating unique patient identifiers.
  • Machine learning for data linkage: Machine learning algorithms can learn from historical data patterns to link patient records across different systems. ML models can accurately link patient records by analyzing data from various sources and identifying common attributes, even without unique identifiers.

Cost of integration

  • Automated integration processes: AI algorithms can automate various integration tasks, reducing the need for manual intervention and the associated labor costs. This automation can speed up the integration process and minimize errors, ultimately lowering the overall cost.
  • Data mapping and transformation: AI technologies, such as machine learning, can assist in mapping and transforming data from one format to another. By automating these tasks, AI can ensure that data is correctly interpreted and translated between different systems, reducing the need for costly manual data manipulation.
  • Predictive analytics for resource allocation: AI-powered predictive analytics can forecast resource requirements for integration projects, allowing organizations to allocate resources more efficiently. AI can help prevent cost overruns and delays by accurately predicting project timelines and resource needs.
  • Identifying cost-effective solutions: AI algorithms can analyze various integration options and recommend the most cost-effective solutions based on organizational needs, budget constraints, and scalability requirements. By identifying cost-effective integration approaches, AI can help organizations minimize expenses while achieving interoperability goals.

Privacy and security

AI can significantly address privacy and security challenges in healthcare data sharing, particularly in ensuring compliance with HIPAA standards and facilitating third-party integration. Here’s how AI can aid in this:

  • Anomaly detection: AI algorithms can continuously monitor healthcare systems’ data access and usage patterns. By analyzing these patterns, AI can detect anomalies or suspicious activities that may indicate potential security breaches or unauthorized access to patient data.
  • Data encryption: AI-powered encryption techniques can enhance data security by encrypting sensitive patient information at rest and in transit. This ensures that even if unauthorized individuals gain access to the data, they can only decipher it with the proper encryption keys.
  • Access control: AI-based access control mechanisms can dynamically adjust access permissions based on user roles, responsibilities, and behavior. This helps enforce the principle of least privilege, ensuring that only authorized individuals have access to specific patient data.
  • Threat intelligence: AI can analyze large volumes of data to identify potential security threats and vulnerabilities in healthcare systems. By leveraging machine learning algorithms, AI can detect emerging threats and proactively implement security measures to mitigate risks.

Malformed or missing data

  • Data parsing and cleaning: AI algorithms can parse through incoming data streams and identify any irregularities or inconsistencies. By employing natural language processing (NLP) and pattern recognition techniques, AI can clean and standardize data, ensuring it adheres to predefined standards.
  • Error detection and correction: AI systems can detect errors in incoming data, such as missing fields or incorrectly formatted values. Through machine learning algorithms, AI can learn from past data discrepancies and develop models to correct common errors, automatically improving data quality.

Human error

AI can play a significant role in reducing human errors in data collection during patient registrations or outpatient referrals in several ways:

  • Automated data entry: AI-powered systems can automate data entry processes during patient registrations, reducing the reliance on manual input and minimizing the risk of errors caused by human data entry.
  • Data validation: AI algorithms can validate data entered during registrations or referrals in real-time, flagging potential errors or inconsistencies for review by healthcare staff before they become problematic.
  • Quality assurance: AI-powered quality assurance tools can continuously monitor data entry processes, identifying patterns of errors and providing feedback to staff for improvement.
  • Predictive analytics: AI and machine learning algorithms can analyze historical data on errors during registrations or referrals to identify potential risk factors and predict where errors are likely to occur, enabling proactive measures to prevent them.

AI and ML technologies break down barriers to healthcare interoperability by enhancing data integration, standardization, and seamless information exchange across diverse platforms.

How to implement EHR interoperability solutions?

Here are the ways to implement EHR interoperability solutions in healthcare:

  • Determine use cases: To tackle interoperability, specific clinical use cases, the type of data, and the parties involved must be identified. This will enable users to determine the most appropriate technical approach for data sharing.
  • Strategize execution approach: Together with all the interoperability project stakeholders, develop a strategy to achieve interoperability based on your specific use cases. Develop a plan and prepare a written document on it. Ensure everyone is on the same page about the requirements, implementation timeframe and the value expected from the project. This document will help identify the steps to be taken, outline the measurable intermediate goals and assign priorities.
  • Perform gap analysis: Performing a gap analysis in EHR interoperability involves assessing current systems, identifying gaps in data exchange capabilities compared to desired standards, and prioritizing actions to bridge these gaps. It helps establish a clear vision and roadmap for achieving seamless data exchange and integration across healthcare systems and stakeholders.
  • Communicate ROI: Always document ROI expectations and their value for every participant, including patients, doctors and the organization. It helps to identify short-term and long-term expectations.

What are the benefits of EHR interoperability?

The benefits of EHR interoperability include:

  • Secure patient data: Interoperability helps enhance patient data security and privacy by limiting access grants and continuously tracking and assessing PHIs. EHR interoperability provides healthcare organizations with the power to assess and manage access rights, identify authorized users and track PHIs. The fewer people who touch and update patients’ data, the lesser the chances of data going into criminal hackers’ hands.
  • Supports research: The data gathered during the ordinary course of events while diagnosing and treating patients can prove to be a goldmine for scientists and researchers. Interoperable systems allow you to share and make the patients’ data accessible to scientists to carry out their research. In this way, you use patients’ data within the organization and share the data more readily with the researchers for a good cause because of interoperability.
  • Improved productivity: It has been known that working with digital documents alone saves time and helps improve productivity. And now, with interoperability, productivity tends to increase manifolds. A reduced amount of the same data being entered redundantly can accelerate document processing and provide a noticeable increase in productivity.
  • Inter-organization communication: EHR interoperability solutions facilitate seamless communication between healthcare organizations, enhancing productivity by eliminating gaps in patient data. Hospitals and clinics can effortlessly exchange vital information, including test results, prescriptions, clarification requests, and billing details, with pharmacies and lab testing centers, streamlining clinical and administrative processes for all stakeholders involved.
  • Better patient experience: Any healthcare organization’s main focus is patient satisfaction. Interoperability helps to achieve this goal efficiently. With interoperability, patients do not need to perform repetitive paperwork or explain their health situation to every doctor repeatedly. Thus, EHR interoperability aims to provide patient satisfaction.

How to improve EHR interoperability?

Implementing EHR interoperability solutions is pivotal for enhancing healthcare delivery and patient outcomes. Here are some effective strategies:

  • Transition to cloud-based solutions: Embracing cloud-based EHR systems streamlines integration with diverse data sources like clinics and labs. This fosters seamless interoperability and elevates the quality of patient care. Cloud platforms offer decentralized storage, ensuring data accessibility across geographical locations while mitigating security risks through robust data encryption.
  • Integration of blockchain EHR: Leveraging blockchain technology enhances the accessibility and security of EHRs. Its decentralized ledger system ensures tamper-proof transaction records, eliminating the need for costly integrations between disparate EHR systems. With blockchain-based EHRs, healthcare providers can securely access patient records using valid credentials from any location.
  • Adoption of open APIs: Open Application Programming Interfaces (APIs) enable seamless communication and data exchange among diverse healthcare systems and applications, fostering seamless interoperability within the healthcare ecosystem. These standardized gateways enable efficient information flow irrespective of each application’s design. Open APIs empower patients to access their health records easily, encouraging active engagement with their healthcare data and enabling clinicians to exchange specific patient information efficiently.
  • AI integration: Incorporating AI and ML technologies within EHR systems streamlines data analysis, integration, and decision-making processes, enhancing interoperability. By discerning correlations, patterns, and insights from vast health datasets, these technologies empower healthcare practitioners to diagnose conditions and discern patient health trends with greater precision. Many healthcare practices have already adopted AI to augment physicians’ diagnostic capabilities and uncover emerging health patterns.

By implementing these practices, healthcare organizations can overcome interoperability challenges, optimize data exchange, and deliver enhanced patient-centric care.

Scope of EHR interoperability solutions

EHR interoperability solutions encompass a broad spectrum of functionalities that seamlessly integrate, exchange, access, and derive insights from patient data. These solutions enhance patient care and pave the way for transformative advancements in healthcare delivery.

  • Integration: EHR interoperability prioritizes the seamless integration of internal data, processes, and personnel within healthcare organizations. By unifying disparate systems and workflows, interoperability ensures data becomes actionable, facilitating more efficient and effective clinical decision-making.
  • Exchange: Timely and accurate exchange of patient information between healthcare entities is a cornerstone of EHR interoperability. This external exchange enables early detection of diseases, fosters collaboration among healthcare providers, and promotes transparency in healthcare costs, ultimately leading to improved patient outcomes.
  • Access: EHR interoperability solutions aim to streamline and secure access to patient data, ensuring that healthcare providers have the information they need to deliver high-quality care. By providing authorized access to relevant patient information, interoperability enhances service quality, patient satisfaction, and overall consumer experience.
  • Insights: EHR interoperability extends beyond clinical data to encompass insights derived from various health data sources, including clinical records, claims data, device monitoring data, and social determinants of health. By analyzing this comprehensive dataset, healthcare stakeholders can gain valuable insights into patient health trends, identify risk factors, and tailor interventions to address individual needs.

The evolution of EHR interoperability is poised to transform healthcare by merging disparate health data streams and creating highly personalized views of patient well-being. Healthcare organizations can open new opportunities for innovation, collaboration, and improved patient care by harnessing the power of interoperability.


Electronic Health Record (EHR) interoperability solutions hold immense potential to transform healthcare delivery by facilitating seamless data exchange, standardization, and integration across disparate systems and platforms. By breaking down silos and enabling the free flow of information, interoperability enhances care coordination, improves patient safety, and empowers healthcare providers with timely access to comprehensive patient data. However, achieving true interoperability requires concerted efforts from healthcare organizations, technology vendors, policymakers, and regulatory bodies to address technical, administrative, and regulatory challenges. As we advance towards a more interconnected healthcare ecosystem, we must prioritize interoperability initiatives and leverage emerging technologies such as artificial intelligence to unlock the full benefits of interoperable EHR systems. With collaborative efforts and ongoing innovation, interoperability will play a pivotal role in driving healthcare transformation and improving patient outcomes in the years to come.

If you’re looking for a company to integrate your healthcare software systems or develop healthcare software from scratch, we are ready to help you. Get in touch with our health-tech experts and get a free consultation.

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Author’s Bio

Akash Takyar
Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO of LeewayHertz. With a proven track record of conceptualizing and architecting 100+ user-centric and scalable solutions for startups and enterprises, he brings a deep understanding of both technical and user experience aspects.
Akash's ability to build enterprise-grade technology solutions has garnered the trust of over 30 Fortune 500 companies, including Siemens, 3M, P&G, and Hershey's. Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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