EHR Data: Transforming Healthcare through Standardization and Innovation

Electronic Health Records (EHRs) are dynamic, integrated systems designed to support the entire healthcare continuum by providing real-time access to patient data. EHR improves decision-making and enhances care coordination.EHRs are not just data repositories, simple digital versions of paper records, or storage space for medical notes. Instead, they are dynamic, interactive tools designed to support clinical decision-making and optimize healthcare administration. Given that terms like EHR, clinical data, electronic medical records (EMRs), and clinical trial data are often used interchangeably, it's essential to distinguish among them (See table below).

Clinical Data vs. EHR Data	EHR vs. EMR	EHR vs. Clinical Trial Data
Clinical data is context-specific and fragmented, while EHR data offers a comprehensive, longitudinal patient view across different care settings.	EHRs are interoperable and span multiple care settings, whereas EMRs are limited to a single provider or practice.	EHR data reflects real-world clinical practice, while clinical trial data is standardized and controlled for research purposes.

EHR Data Management Practice Standards

The effectiveness of EHRs largely depends on the adherence to data standards that enable the consistent capture, storage, and exchange of healthcare information across systems and institutions. The healthcare industry has developed several key standards to support EHR data management:

1. openEHR

openEHR is an open standard specification in health informatics that defines an interoperable, electronic health record structure. Unlike proprietary systems, openEHR is designed to be flexible and adaptable to various healthcare settings. This standard is particularly valuable in its ability to support semantic interoperability which ensures that data is consistently interpreted across systems.

2. HL7 FHIR

Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) is a next-generation interoperability standard created by the standards development organization. It uses modern web-based technologies such as RESTful APIs and JSON/XML data formats, which makes it readily accessible for developers and facilitates easy integration with existing systems.

3. HL7 V2, V3, CDA

HL7 Version 2 (V2) and Version 3 (V3) are old standards that have been adopted in healthcare for message exchange. HL7 V2 had limitations in terms of semantic consistency and HL7 V3 was an attempt to address this using a model-driven approach, but it wasn’t adopted extensively due to its complexity. Clinical Document Architecture (CDA), another HL7 standard, defines the structure of clinical documents like discharge summaries that can be further shared between systems.

4. OHDSI OMOP CDM

The OHDSI’s Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is a standardized data model that engages in the systematic analysis of disparate observational databases. It standardizes the structure and content of observational data, and enables large-scale analytics. This model is particularly valuable in research settings where data from different sources, such as electronic health records, claims databases, and registries, need to be harmonized and analyzed collectively.

EHR Data Interoperability Standards

Why Do We Need Data Standards?

Data standards in healthcare are essential for several reasons including:

· Consistent Data Capture:
Data Standards ensure consistent data capture while ensuring its authentic interpretation and availability, as and when needed.

· Interoperability: Standards are also crucial for achieving interoperability, which facilitates the integration of data from multiple sources. It also allows different systems and organizations to work together within and across organizational boundaries.

· Comprehensive Patient View: Data standards ensure that data can be easily accessed, shared, and analyzed. This is essential for improving clinical outcomes, enhancing patient safety, and reducing costs.

Harmonization Standards in the Public Domain

Harmonization standards enable the integration and comparison of data from various sources, such as EHRs, claims databases, and public health records. Some of the most widely used harmonization standards in the public domain include:

Standards	Description
RxNorm	1. Provides normalized names for drugs and links them to many of the commonly used drug vocabularies. 2. Ensures that medication information is consistent across different systems. This reduces the risk of errors and improves patient safety.
Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT)	1. An ontological collection of medical terminologies used in clinical documentation. 2. Supports a wide range of clinical applications, including EHRs, clinical decision support systems, and research databases.
Logical Observation Identifiers Names and Codes (LOINC)	1. A universal code system used to standardize the reporting of laboratory test results and clinical observations. 2. Enables interoperability among different systems and supports large-scale data analysis and research.
National Drug Code (NDC)	1. A unique identifier for medications approved by the FDA. 2. Plays a critical role in pharmacy management, insurance claims processing, and drug interaction checking.
International Classification of Diseases (ICD)	1. The global standard for coding diseases, signs, symptoms, and other health conditions. 2. Critical for billing, reporting, and statistical analysis.

Data Standard vs. Data Model

In the context of healthcare, data models and data standards are often used interchangeably, but they serve distinct purposes. A data standard is a set of guidelines or protocols about how data should be formatted, encoded, and transmitted across different systems. On the other hand, a data model is an abstract representation of how data is organized and stored within a system. It defines the structure, relationships, and constraints of the data elements, and provides a blueprint for database design and implementation.

While data standards focus on ensuring that data can be shared and understood across different systems, data models are centered on the internal organization of data within a system. Both are essential for achieving interoperability in healthcare by warranting that data is both well-structured and consistently communicated.

Key Features of Clinical Data Models

Clinical data models are designed to capture the complexity and diversity of healthcare data. These models also ensure effective use of data for patient care, research, and administration.The key features of clinical data models include:

· Interoperability: The ability to share and use data across different systems and organizations is a critical feature of clinical data models. Interoperability is achieved through the use of standardized terminologies, data formats, and exchange protocols.

· Data Integrity: Clinical data models must ensure accuracy, consistency, and completeness of data. Data integrity is essential to maintain information trustworthiness and support clinical decision-making.

· Scalability: Clinical data models must be able to accommodate large volumes of data and support the growing needs of healthcare organizations It implies the ability to handle diverse types of data including structured, unstructured, and semi-structured data.

· Security and Privacy: Protecting patient data is paramount in healthcare. Clinical data models must include mechanisms for ensuring data security, confidentiality, and compliance with regulatory requirements like s HIPAA.

· Flexibility: Clinical data models must be adaptable to changing clinical practices, regulations, and technologies. Consequently, it would entail the ability to support new data types, workflows, and care models without requiring significant changes to the underlying system.

Data Models in Clinical Domain

Data models like the BRIDG Initiative, i2B2/TranSMART, Sentinel, and PCoRNet are utilized to standardize and integrate health data for research and clinical care in the clinical domain. The Biomedical Research Integrated Domain Group (BRIDG) Initiative provides a standardized data model specifically for the clinical research domain to enhance interoperability and data exchange among stakeholders involved in clinical trials, regulatory submissions, and healthcare delivery. i2B2/TranSMART focuses on integrating clinical, translational and genomic data. Sentinel supports active safety surveillance of FDA-regulated products, and PCoRNet facilitates patient-centered research by harmonizing data across multiple healthcare systems. These models collectively enhance data interoperability, research, and patient outcomes.

Data Models	PCORNet, i2b2, BRIDG (Model visualization), Sentinel
Features	1. Open source data models for clinical data standardization. 2. Not governed by any global standards 3. Efforts are made to map these models to standards such as FHIR.

Regulatory Compliance- EHR Data Management

The healthcare industry necessitates regulatory compliance to ensure that patient data is handled securely, ethically and in sync with legal requirements.

Several key regulations and standards govern EHR data management including the following:

IRB

An Institutional Review Board (IRB) is a committee whose primary purpose is to protect the rights and welfare of the participants. It mandates the de-identification of EHR data to ensure that patient privacy is maintained.

FISMA

Federal Information Security Management Act (FISMA) defines a comprehensive framework for ensuring the security of federal information systems. Compliance with FISMA involves implementing security controls to protect EHR data from unauthorized access, breaches, and other cyber threats.

HIPAA

Health Insurance Portability and Accountability Act (HIPAA) is perhaps the most well-known regulation governing the use and exchange of healthcare information in the U.S. HIPAA sets guidelines for how EHR data can be shared and transmitted among entities, and ensures that patient information remains confidential and secure.

21 CFR Part 11

The Title 21, Code of Federal Regulations Part 11 (21 CFR Part 11) sets the requirements for the use of electronic records and signatures which ensure their trustworthiness, reliability and equivalence to paper records. . Compliance with 21 CFR Part 11 is essential for organizations which use EHR data in regulated activities like clinical trials or drug manufacturing.

Role of EHR in Clinical Informatics

Electronic Health Records (EHRs) play a critical role in clinical informatics, as they serve as central repositories for healthcare data across the care continuum. EHRs facilitate real-time access to comprehensive patient information, which supports clinical decision-making, improves care coordination, and streamlines research processes. The key contributions of EHRs include:

· Data-Driven Research: EHR data supports population health research by providing real-time and comprehensive patient information. It reduces the need for manual data collection.

· Population Health Management. Healthcare organizations can identify disease patterns , monitor the effectiveness of interventions, and address health disparities by aggregating and analyzing data from EHRs across a population.

· Clinical Trial Recruitment: EHR systems streamline participant identification and recruitment, especially for underrepresented populations, which improves the efficiency of clinical trials.

EHR Data - Public Domain

The burgeoning demand for data-driven insights in healthcare, has ensured greater availability of EHR data in the public domain, which can be utilized for research and analysis.

Several notable datasets that have been made available for public use serve different purposes and provide valuable resources for researchers, policymakers, and healthcare providers alike. Some of them are mentioned below:


MIMIC	1. Large ICU datasets from Beth Israel Deaconess Medical Center. 2. The latest version MIMIC IV contains data from 364,627 patients covering granular details on ICU stays, medications, and lab results, among others.
Synthea	1. Synthetic dataset that simulates patient records across a virtual population, and is ideal for testing healthcare applications. 2. Generated over a million patient records across various demographics and conditions.
eICU	1. De-identified ICU data from over 200,000 ICU stays from more than 139,000 patients across 208 hospitals in the United States.
HiRiD	1. Comprehensive ICU dataset with over 33,000 admissions from an ICU in Switzerland focusing on high-resolution temporal data.

Key Challenges: Transforming Patient Data for Effective Care

The remodeling of unstructured patient data into structured formats is critical for accurate medical record-keeping and effective patient care.

‘Data is stored in both structured and unstructured forms until they are either “pushed” (to users) or requested for use (by government agencies and others).’ ~HIMSS, 2016 [Source]

However, this process presents several significant challenges which must be addressed to ensure data integrity, interoperability, and regulatory compliance. Some of these challenges are:

· Standardization of Unstructured Data
Clinical notes and medication orders often include free-text entries and inconsistent formats. It complicates its integration into standardized EHR systems. Converting this unstructured data into formats like HL7 FHIR is crucial but requires advanced tools and algorithms.

· Complex Data Integration
Healthcare providers generate data across various departments with differing protocols. Integrating this diverse data into a unified system calls for data accuracy and resolving inconsistencies during the standardization.

· Handling Diverse Data Attributes
Patient data includes demographics, medical history, and medication details. Mapping this information to structured formats, such as FHIR resources, requires both technical expertise and clinical knowledge.

· Ensuring Interoperability
Interoperability allows seamless data exchange but can be arduous while working with unstructured data. Integrating standardized codes like RxNorm requires careful alignment of both technical and clinical factors.

· Data Quality and Validation
Ensuring the accuracy of transformed data is critical in healthcare. Tools like HL7 FHIR Validator attest that such data meets schema and business rules. However validation is time-consuming and may reveal issues.

· Regulatory Compliance
Compliance with regulatory standards like FHIR R5 is essential for patient safety and legal requirements. Ensuring that the transformed data adheres to these standards, requires a deep understanding of the regulatory landscape.

‍

Elucidata’s Expertise-FHIR Case Study

In the healthcare settings, medication orders are generated across various departments like inpatient, outpatient, and emergency services. These orders, often captured in unstructured formats, must be standardized and integrated into the EHR system. This ensures accurate medication administration and tracking.

Elucidata leveraged its expertise in data harmonization to build an end- to-end pipeline to transform unstructured clinical notes into structured data which could be seamlessly integrated into the EHR system, adhering to the HL7 FHIR R5 standards.

The key steps included :

Step 1: Data Transformation of Clinical Notes: Converted unstructured clinical notes into structured format, aligned with FHIR MedicationRequest attributes.

Step 2: Standardizing and Structuring Medication Data: Refined medication data, linked with RxNorm codes, and organized dosage details into distinct columns.

Step 3: Patient Dataset Creation: Generated patient dataset, and mapped attributes to FHIR Patient resource with accurately derived fields from unstructured data.

Step 4: Data Profiling: Performed data profiling using Pandas library to ensure quality and completeness of medication and patient datasets.

Step 5: Defining The Mapping Rules: Defined rules for mapping structured dataset fields to FHIR MedicationRequest and Patient resources, ensuring FHIR compliance.

Step 6: Resource Mapping and Serialization: Mapped and serialized MedicationRequest and Patient datasets into JSON format, grouped for FHIR server submission.

Step 7: Validation of Resources: Validated JSON resources using HL7 FHIR Validator to ensure compliance with FHIR standards and error-free readiness.

Step 8: Submission to HAPI FHIR Test Server: Submitted validated FHIR resources to the HAPI FHIR Test Server for further testing before deployment.

Through the implementation of HL7 FHIR standards and RxNorm coding, Elucidata successfully transformed unstructured medication data into structured FHIR resources, and materialized the following outcomes:

· Standardization and Accuracy: Ensured consistent and accurate representation of medication orders, and reduced data variability and errors.

· Improved Data Integration: Facilitated better integration and interoperability across healthcare systems, enhancing data sharing and coordination.

· Enhanced Data Processing: Enabled efficient processing, analysis, and tracking of medication orders, resulting in improved patient care.

· Regulatory Compliance: Adhered to HL7 FHIR R5 standards, promoting best practices in healthcare delivery.

This case study underscores how Elucidata has spearheaded innovation in healthcare by transforming unstructured data into actionable, structured formats, and ultimately contributed to better patient outcomes and operational efficiencies.