healthcare data interoperability

How to Achieve Data Interoperability in Healthcare?

To promote healthcare data interoperability, the Department of Health and Human Services (HHS) initiated the imposition of penalties for information blocking on health IT entities, effective September 1, 2023. These penalties have the potential to reach up to $1 million per violation in cases of severe breaches.

Irrespective of the size and extent of your medical organization, it is crucial to comprehend healthcare interoperability, including the available standards and potential challenges. This understanding will be valuable when engaging with healthcare software development vendors for the implementation of new medical applications or enhancing the interoperability of existing ones.

  1. Comprehending Healthcare Data Interoperability
  2. As outlined by the Healthcare Information and Management Systems Society (HIMSS), interoperability refers to the capability of separate devices and applications to seamlessly access, integrate, and share data in a coordinated fashion, both within and beyond organizational and national borders.
  3. An alternative interpretation is provided by the Cures Act, defining interoperability as the condition where all electronically accessible health data can be retrieved "without requiring any special effort from the user."
  4. Interoperable Electronic Health Record (EHR) solutions play a fundamental role in ensuring the secure exchange of medical data.

As the initial stride toward achieving healthcare data interoperability, healthcare institutions must embrace electronic health records (EHR), given that paper-based records lack the secure and efficient sharing capabilities needed across different organizations. By 2017, a substantial 94% of US hospitals had already implemented EHR systems, and a subsequent 2019 survey indicated that nearly 90% of office-based physicians were also utilizing EHR.

However, mere adoption of any EHR system is no longer sufficient. Healthcare practices are increasingly transitioning to more robust and integrated EHR interoperability solutions. For instance, the University of Pittsburgh Medical Center, which previously relied on Oracle Cerner for inpatient records and Epic for outpatient data, is now consolidating all patient EHRs onto the Epic platform to achieve a unified view and streamlined access to health information. Another example is the collaboration between a New York-based orthopedic practice, Bone & Joint, and Athenahealth, wherein they have embraced the cloud-powered EHR solution to enhance interoperability. This strategic move facilitates seamless sharing patient data within the clinic, Albany Medical Center, and other regional facilities.

Levels of Healthcare Interoperability

The following are four levels of interoperability, ranging from basic to advanced:

  1. Foundational Level: At this level, the data is only understandable by the user and is not machine-readable. An example of this is sending a PDF file containing a patient's history through a hospital's portal, where a nurse manually enters the data into the Electronic Health Record (EHR) system.
  2. Structural Level:In structural interoperability, data is standardized, and the systems and devices involved can comprehend it at the field level. Standards like Fast Healthcare Interoperability Resources (FHIR) and Health Level 7 (HL7) ensure structural interoperability.
  3. Semantic Level:Semantic interoperability enables systems with different data structures to seamlessly exchange information. For instance, in medical imaging, various formats like Digital Imaging and Communications in Medicine (DICOM) and non-DICOM formats are used. With semantic interoperability, systems can transmit and receive images in different formats, interpret them, and integrate them into the receiving end.
  4. Organizational Level:At the organizational level, different facilities with varied requirements and objectives can exchange medical data. This level is integrated into organizational workflows and encompasses aspects like data governance, policies, consent management, and security protocols.

Why Interoperability is Important?

The significance of health data interoperability stems from the challenge posed by various medical software vendors developing products independently, creating obstacles for hospitals that do not share the same vendors to effectively communicate. The objective of healthcare data interoperability is to address this issue and provide healthcare facilities with a comprehensive perspective on patients, irrespective of the technology they employ.

Examining the support for data interoperability in healthcare from the US government, in 2016, President Obama approved the 21st Century Cures Act, which mandates Electronic Health Record (EHR) systems to furnish a patient-facing API as a requirement for obtaining or maintaining federal certification. Subsequently, in 2020, the Department of Health and Human Services issued regulations on interoperability and information blocking, outlining compliance deadlines.

Healthcare Data Standards

The world of healthcare data interoperability boasts a diverse collection of standards, each playing a crucial role in facilitating the smooth exchange of information between different systems. Here's a breakdown of some healthcare data standards:

  1. HL7 (Health Level Seven): The granddaddy of them all, HL7 provides a comprehensive framework for exchanging clinical and administrative data. Think of it as the Esperanto of healthcare data, offering a common language for diverse systems to understand each other.
    1. HL7 v2: The first iteration, still widely used for structured data like lab results and orders.
    2. HFHIR (Fast Healthcare Interoperability Resources): A modern API-based standard built on HL7 v3, offering flexibility and ease of implementation for sharing clinical data. Picture a nimble messenger delivering key information in easily digestible chunks.
    3. HCDA (Clinical Document Architecture): Focuses on exchanging narrative patient reports in a structured format, ensuring consistent documentation across systems. Think of it as a standardized format for storytelling in healthcare.
  2. We also have standard related project in our port folio- KPi-Tech built an interface eco-system using Mirth Interface engine and custom CCD(Continuity Care document) for a Healthcare company in the US that provides technology-enabled services to Chronic patients. The interface eco-system helped to reduce the risk in patients.
  3. DICOM (Digital Imaging and Communications in Medicine): The go-to standard for exchanging medical images like X-rays and MRIs. Think of it as a secure courier for high-resolution healthcare visuals.
  4. IHE (Integrating the Healthcare Enterprise): Defines profiles for specific clinical workflows, ensuring interoperability between systems within those workflows. Picture pre-built bridges designed for specific traffic flows, like lab results or medication orders.

Benefits of Interoperability

Healthcare data interoperability has the potential to revolutionize the way we deliver and experience healthcare. By breaking down data silos and enabling seamless information exchange, it offers a plethora of benefits for patients, providers, and the healthcare system as a whole. Let's delve into the key advantages:

For Patients:

  1. Improved care coordination: Access to complete medical history across different providers ensures smoother transitions between care settings, reducing the risk of duplicate tests and medication errors. Imagine doctors having a full picture of your health journey, leading to more informed treatment decisions.
  2. Empowered decision-making: Accessing your own health data in a centralized location fosters patient engagement and allows you to actively participate in decisions about your care. Think of yourself as an active player in your own health story.
  3. Personalized medicine: Integrated data from various sources, including wearables and other devices, allows for personalized treatment plans tailored to your specific needs and health markers. Picture medication doses or therapy sessions adjusted based on real-time data from your body.
  4. Enhanced research and innovation: With anonymized data readily available, researchers can gain valuable insights into disease patterns and develop new treatments faster. This paves the way for personalized medicine advancements and proactive intervention strategies.

For Providers:

  1. Improved clinical efficiency: Accessing patients' complete medical history from different providers saves time and reduces the need for repeated questions or tests. Imagine doctors spending more time providing actual care instead of chasing missing information.
  2. Reduced errors and improved safety: Real-time access to medication allergies, lab results, and other critical data minimizes the risk of medication errors and adverse events. Picture a future where safety nets catch potential issues before they harm patients.
  3. Data-driven decision-making: Analyzing integrated data empowers providers to make informed clinical decisions based on evidence and best practices. Imagine treatment plans guided by real-time data analyses, not just intuition.
  4. Enhanced collaboration and communication: Interoperability facilitates seamless communication between healthcare professionals, regardless of their location or organization. Think of specialists easily consulting with each other to create a unified care plan for complex cases.

For the Healthcare System:

  1. Reduced costs: Efficient data exchange minimizes redundant tests, administrative tasks, and errors, leading to significant cost savings for healthcare providers and insurers. Imagine streamlined processes saving precious resources that can be reinvested in patient care.
  2. Improved public health: Real-time data sharing facilitates better disease surveillance and outbreak management. Imagine quickly identifying and containing outbreaks by having immediate access to relevant patient information.
  3. Strengthened research and development: Access to large, anonymized datasets enables accelerated research on new drugs, treatments, and healthcare technologies. Think of faster innovations fueled by a collaborative data pool.
  4. Empowered population health management: Interoperability enables proactive identification and intervention for individuals at risk of chronic diseases. Imagine tailored preventive measures based on population-level data insights.

Methods for Data Integration

Achieving data interoperability in healthcare isn't about choosing one magical technique, but rather embracing a blend of strategies and tools. Here are some key approaches to break down those data silos and create a connected healthcare ecosystem:

  1. API-based Integration:

    1. FHIR (Fast Healthcare Interoperability Resources): An API-based standard rapidly gaining traction for its flexibility and ease of use. Systems expose data via FHIR APIs, allowing seamless retrieval and exchange. Think of it as building bridges with convenient toll booths for data flow.
    2. Real-life example of healthcare data interoperability from KPi-tech

      A US-based ambulatory Electronic Medical Record (EMR) provider partnered with KPi-Tech to address challenges posed by insurance claim denials. To enhance cash flow and reduce operational costs, the solution integrated EMR with a Patient Access platform using Mirth Integration and FHIR standards. This facilitated the seamless transmission of admission data, overcoming challenges of data standardization. The technology stack included Mirth Connect, FHIR R4, and JavaScript, resulting in improved reimbursements, enhanced patient access, and better satisfaction. The integration streamlined administrative processes, offering a standardized and efficient solution for the client's healthcare delivery.

    3. RESTful APIs: Lightweight and widely adopted, RESTful APIs offer a standardized approach for data exchange between diverse systems. Imagine standardized protocols for exchanging information, regardless of the messenger.
  2. Data Warehousing and ETL (Extract, Transform, Load):

    1. Data Warehouses: Centralized repositories designed for storing and analyzing large volumes of data from various sources. Think of it as a secure library for all your health data, organized for easy access and analysis.
    2. ETL Tools: Automate the process of extracting data from different sources, transforming it into a unified format, and loading it into the data warehouse. Picture efficient data janitors cleaning and organizing information before adding it to the library.
  3. Healthcare Integration Engines:

    These software platforms specifically designed for healthcare data exchange bridge disparate systems and facilitate seamless communication. Healthcare Integration engines are like complex data highways with toll booths ensuring secure information flow.

  4. Health Information Exchanges (HIEs): 

    These regional or statewide HIEs establish data-sharing agreements and implement infrastructure for broader data exchange within a defined geographical area. Imagine regional data managers setting up the pathways for information flow across a healthcare neighborhood.

  5. Cloud Computing:

    Migrating data to the cloud creates a central hub accessible from anywhere, breaking down physical barriers and fostering collaboration. Imagine all your health data living in a secure, online penthouse accessible by authorized users.

How to choose the best method for healthcare data integration

Choosing the best method for healthcare data integration can be a complex task, as there's no one-size-fits-all approach. The ideal method for you will depend on several factors, including:

Your specific needs and goals:

  1. What data sources do you need to integrate? Is it primarily EHRs, labs, pharmacies, or a mix of others?
  2. What do you want to achieve with data integration? Is it improved care coordination, enhanced research, or population health management?
  3. What is your budget and timeline? Different methods can vary significantly in cost and implementation time.

Your existing IT infrastructure:

  1. What systems are you currently using? Do they have built-in integration capabilities or require additional tools?
  2. What is your data volume and security requirements? Does your infrastructure have the capacity and security measures to handle the integrated data?

Your organizational context:

  1. Are you a single hospital, a regional network, or a statewide initiative? The scale of your organization will influence the scope and complexity of data integration.
  2. What is your data governance policy? Do you have established processes for data quality, privacy, and security?

Here are some key considerations for choosing the best method:

  1. Scalability: Can the method handle your current and future data volume needs?
  2. Flexibility: Can the method adapt to integrate with new data sources and systems in the future?
  3. Interoperability: Does the method rely on open standards and protocols to ensure seamless communication between different systems?
  4. Cost-effectiveness: Does the method fit your budget and offer a good return on investment?
  5. Ease of implementation: Can the method be implemented and maintained with your existing resources and expertise?

Based on these considerations, here are some approaches you might consider:

  1. For smaller-scale integration within a single organization: API-based integration or data warehousing with ETL might be sufficient.
  2. For larger-scale regional or statewide data exchange: An HIE or a combination of methods like data federation and HIE technology might be necessary.
  3. For research and analytics: Data lakes and big data analytics tools can be helpful for exploring large, diverse datasets.
  4. For microservice-based architectures: Data mesh principles and microservices-based integration methods can offer agility and flexibility.

It's important to involve stakeholders from both clinical and IT teams in the decision-making process. Conducting a feasibility study and evaluating different options based on your specific context will help you choose the best method for your healthcare data integration needs.

Remember, there's no perfect solution, and the best approach might involve a combination of methods. Don't hesitate to seek guidance from healthcare data integration experts to ensure you make an informed decision for your organization.

5 tips for improving data interoperability in healthcare

Here are five recommendations from KPi-Tech co-founder and CEO, Kishore Pendyala, for healthcare organizations aiming to enhance data interoperability:

  1. Prioritize Patient Anonymity: Emphasize the importance of patient privacy by ensuring that unauthorized third parties, including external vendors and researchers, cannot access patient information without complete anonymization. This precaution extends to those not directly involved in a specific patient's care.
  2. Establish Transparent Patient Consent Processes: Develop a clear and transparent consent protocol to encourage user participation in data interoperability efforts. This protocol should include policies preventing unauthorized data access and transmission. Educate patients on how their information can contribute to innovative treatments and emphasize collaboration with reputable patient advocacy organizations.
  3. Manage IT Integration: Focus on two critical aspects when building a data ecosystem: implement a data layer with compliant APIs for partners to integrate their repositories, and develop a data management strategy to ensure consistency and privacy. Explore additional applications of organized data in healthcare.
  4. Utilize Health Information Exchange (HIE) Services: Leverage HIE servicesh to facilitate the seamless exchange of clinical data across various health information systems while preserving its meaning. Connect to available HIE services, such as New York's Statewide Health Information Network for New York (SHIN-NY), to enhance patient care.
  5. Transition to Cloud-Based Data Storage: If not already done, consider moving healthcare data to the cloud. Studies indicate that a significant percentage of small practices in urban areas have adopted cloud-based Electronic Health Records (EHRs) and express satisfaction with the system. Embracing cloud technology offers numerous benefits, including improved data interoperability in healthcare.


Healthcare data integration plays a crucial role in enhancing healthcare delivery, bringing about long-term savings and invaluable rewards. Despite the initial costs, the benefits of data integration are substantial. It is important to note that healthcare data integration is not a universal solution, as different organizations have unique needs. At KPi-tech, we specialize in assisting organizations in implementing tailored strategies to achieve their specific goals. Contact us today to explore how we can support your organization in optimizing medical data integration for improved healthcare outcomes.

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