In the ever-evolving field of healthcare, data plays a critical role in enhancing patient care, advancing research and informing public health decisions. The world of health data is a maze of specialized terms and acronyms that can be overwhelming. Whether you're a healthcare professional, a researcher or just curious about the inner workings of health data, it's important to understand the various concepts, standards and initiatives that shape this field.
Disclaimer: This blog post is meant to give you a glimpse into the labyrinth of healthcare data terms, not to turn you into a walking encyclopedia of health data jargon. Explanations are brief, and some terms might vary by context. So use this as a starting point, but remember: health data can get as puzzling as solving a Rubik’s cube blindfolded!
Health records & data collection
Understanding key concepts in health data is essential for grasping how information is collected, stored and analyzed. These concepts form the foundation of how data is utilized in healthcare, research and policymaking. Let’s delve into some of the most important concepts that underpin the use of health data.
EMR
Electronic medical records (EMRs) are digital versions of traditional paper medical records. EMR is an internal system within a healthcare organization, like a clinic, a clinician’s office or hospital, which allows them to organize their medical records. They include not only essential patient data but also detailed notes from clinicians, which aid in accurate diagnosis and effective treatment. While EMRs provide the benefit of quick, localized access to records, they are limited by their lack of interoperability. This means that EMRs are confined to a single provider and cannot exchange information with other organizations, making it difficult to share or receive patient records across different systems.
EHR
The electronic health record (EHR) is a digital version of a patient’s medical chart. EHRs provide comprehensive, up-to-date information about a patient’s medical history, such as diagnoses, treatments, medications and test results. While both EHRs and EMRs are digital versions of a patient’s medical record, EHRs differ by incorporating data from all clinicians and healthcare organizations involved in a patient's care. This comprehensive approach allows providers and staff to access a more complete picture of a patient’s medical history, facilitating more informed treatment decisions. Unlike EMRs, which are confined to a single provider's internal system, EHRs also support the exchange of information across different organizations, making them highly interoperable.
PHR
A personal health record (PHR) is a patient-centered system where individuals control and manage their own health information. Unlike EHRs, which are handled by healthcare providers, PHRs are owned by the patients and include details such as diagnoses, medications and family medical histories. Patients can access data from various sources, including EHRs and monitoring devices. This self-management empowers patients to be more informed and engaged in their health, ultimately leading to better health outcomes and increased personal well-being.
eCRF
An electronic case report form (eCRF) is a digital tool used to collect and manage clinical trial data. It replaces traditional paper forms with electronic systems, allowing for real-time data entry and monitoring. Unlike EMRs and EHRs, eCRFs are specifically designed for research purposes rather than routine clinical care. They improve data collection efficiency, reduce errors and streamline data analysis, while also enhancing data security and regulatory compliance.
SDC
Structured data capture (SDC) refers to the standardized collection of data within EHRs and other digital systems. By organizing data in a structured format, SDC ensures consistency and facilitates easy analysis. This is crucial in clinical research, where data accuracy and uniformity are essential for reliable results and in patient care, where it supports better data-driven decision-making.
EDC
Electronic data capture (EDC) systems have revolutionized clinical trials, leaving outdated paper methods in the dust. Unlike EHRs, which aggregate patient information for ongoing care, EDC systems focus specifically on clinical trial data, offering improved accuracy and real-time access. EDC systems enable researchers to collect, manage and store data electronically, offering increased accuracy, real-time data access and streamlined trial monitoring. They are crucial for ensuring high-quality data, meeting regulatory standards and efficiently analyzing data to support new drug approvals and medical advancements.
RWD & RWE
Real-world data (RWD) encompasses diverse health data collected outside traditional clinical trials, such as from EHRs, insurance claims, patient registries and wearable devices like phones and smartwatches. RWD reflects the realities of patient care and is used to generate real-world evidence (RWE). This evidence offers crucial insights into how treatments perform in everyday settings, beyond the controlled environment of clinical trials. RWE plays a key role in assessing treatment effectiveness and safety, guiding regulatory decisions and shaping health policies.
Learn more about how RWD can add value to randomized controlled trials (RCTs) in this blog post.
NLP
Natural language processing (NLP) is a technology that analyzes and interprets human language. In healthcare, NLP extracts meaningful information from unstructured data, like clinical notes and research articles. By converting free-text data into structured formats, NLP enhances data analysis and supports applications such as clinical decision support and patient outcome prediction.
PROM & ePRO
Patient-reported outcome measures (PROM) and electronic patient-reported outcome (ePRO) are tools used to gather direct feedback from patients regarding their health status and well-being. PROMs are designed to assess various aspects of a patient’s health, such as symptoms, functional status and quality of life. These measures help healthcare providers understand the impact of a condition or treatment from the patient's perspective. ePRO is the digital version of PROM, enabling patients to report their outcomes electronically, which can streamline data collection and improve accuracy.
PREM
Patient-reported experience measures (PREMs) focus on gathering feedback about patients' experiences with healthcare services. It evaluates aspects like the quality of communication, accessibility of services and overall satisfaction with the care they received. While PROMs measure health outcomes, PREMs assess the patient’s experience of the healthcare process itself. Both PROMs and PREMs are crucial for improving patient-centered care, as they provide valuable insights into how treatments and services affect patients. These measures also support research and quality improvement efforts by highlighting areas for enhancement in healthcare delivery.
COA
Clinical outcome assessment (COA) refers to methods used to evaluate the effectiveness of medical treatments from various perspectives. It includes patient-reported outcomes (PROs), observer-reported outcomes (ObsROs) and clinician-reported outcomes (ClinROs). PROs capture patients' self-reported health status, ObsROs involve feedback from caregivers about the patient’s condition and ClinROs are assessments by healthcare professionals. COAs help determine the impact of treatments on patients' health and quality of life. They are vital for both clinical trials and routine practice to ensure that interventions meet patients' needs effectively.
Standards for data interoperability
Standards in healthcare are crucial for ensuring that data from different sources can be shared, understood and utilized consistently. These standards enable interoperability, allowing different systems and organizations to work together seamlessly. Let's dive into some of the most important standards that are shaping the future of health data management!
HL7
Health level seven (HL7) is an international set of standards for the exchange, integration and retrieval of electronic health information. The HL7 standards facilitate communication between healthcare systems by providing a framework for the consistent transmission of health data. HL7 standards support a wide range of healthcare activities, including patient management, clinical documentation and data exchange between systems.
FHIR
Fast healthcare interoperability resources (FHIR) is a standard within the HL7 suite that redefines how health information is exchanged. FHIR uses modern web technologies to simplify communication between healthcare applications. It offers a flexible framework for sharing EHRs, enabling both healthcare providers and patients to access and exchange data securely and efficiently.
Uncover the essentials of FHIR in this blog post.
OMOP CDM
The observational medical outcomes partnership (OMOP) common data model (CDM) is another standard designed to harmonize health data across diverse sources. Unlike FHIR, which is designed for the real-time exchange of healthcare data, OMOP CDM is more suited for long-term data storage and analysis within relational databases, as it is specifically tailored for research purposes and large-scale observational studies. OMOP CDM allows researchers to transform disparate datasets into a uniform structure, making it easier to perform large-scale observational studies.
Uncover the essentials of OMOP CDM in this blog post.
CDISC
In the realm of clinical research, data quality and consistency are paramount. Clinical data interchange standards consortium (CDISC) standards are specifically designed to address these needs by providing a standardized framework for clinical trial data. While OMOP CDM standardizes observational data, focusing on facilitating research and analysis, CDISC covers the entire data lifecycle: collection, storage, exchange and analysis. This framework is specifically tailored to meet the needs of clinical trials and is often mandated by regulatory authorities for data submissions. CDISC is also widely utilized in the pharmaceutical industry for its robust approach to ensuring data reliability and compliance.
OpenEHR
OpenEHR is a standard designed for the long-term storage and exchange of EHRs. It uses archetypes and templates to create a flexible and consistent framework for health data across different systems. OpenEHR stands out with its detailed and flexible archetype-based model for structuring health data, offering long-term adaptability and consistency, whereas HL7 and FHIR focus on interoperability with more fixed formats and use cases. Unlike OMOP and CDISC, which are tailored for research and specific trials, OpenEHR supports a broader range of clinical and operational scenarios with its dynamic data modeling approach.
SNOMED CT
Systematized nomenclature of medicine (SNOMED) clinical terms (CT) is one of the most comprehensive healthcare terminology standards available today. It provides a systematically organized collection of medical terms, including codes, definitions and synonyms, which are used in clinical documentation and reporting. By standardizing medical language across different healthcare settings, SNOMED ensures that health information is accurately recorded and easily shared. This standard is crucial for supporting clinical decision-making, improving patient care and facilitating research.
LOINC
Logical observation identifiers names and codes (LOINC) is a widely adopted standard for the consistent identification of laboratory tests, measurements and clinical observations. It complements other standards like SNOMED CT, which focuses on clinical terminology, by providing specific codes for lab and diagnostic data. This standard is crucial for integrating lab results into EHRs and supports interoperability, efficient data sharing and analysis across various healthcare settings.
ICD-10
The international classification of diseases, 10th revision (ICD-10) is a global standard for diagnosing and classifying diseases and health conditions. This system provides codes for a wide range of conditions, symptoms and procedures, facilitating consistent documentation and reporting of health data. While SNOMED is a detailed clinical terminology that captures a wide range of medical concepts for use in EHRs, ICD-10 is a classification system designed specifically to categorize diseases and health conditions into standardized codes for statistical analysis, billing and reporting. SNOMED offers a higher level of clinical detail, whereas ICD-10 focuses on simplifying health data categorization and coding.
Smart technologies
Imagine a universe where your devices are not just smart, but practically clairvoyant. In this section, we’ll delve into the marvels of modern tech that are reshaping how we interact with our world. We’ll take a peek into how these advanced systems are making life more interactive, intuitive and, dare we say, a bit magical. Hang tight as we delve into a world where technology truly seems to have a mind of its own!
AI
Artificial intelligence (AI) refers to the simulation of human intelligence in machines designed to think and learn like humans. It involves creating algorithms that allow computers to perform tasks that typically require human intelligence, such as decision-making and problem-solving. AI finds applications in various areas, such as voice assistants, recommendation engines and self-driving cars.
ML
Machine learning (ML) is a subset of AI focused on developing algorithms that enable computers to make predictions based on data. Instead of being explicitly programmed for each task, ML systems improve their performance as they are exposed to more data. This involves training models on large datasets to recognize patterns and make informed decisions. Common uses include spam filtering, image recognition and personalized recommendations.
DL
Deep learning (DL) is a specialized area within ML that uses neural networks with many layers (hence "deep") to analyze various types of data. These complex networks can automatically extract features and patterns from raw data, such as images or text. Deep learning outperforms in tasks like speech recognition, natural language processing and image classification. It requires large amounts of data and computational power to train effectively.
CV
Computer vision (CV) is a field of AI that enables machines to interpret and understand visual information from the world. It involves processing and analyzing images and videos to extract meaningful data, such as recognizing objects or tracking movement. Computer vision technologies are used in applications like facial recognition, autonomous driving and medical imaging. It combines elements of ML and pattern recognition.
VR
Virtual reality (VR) creates a fully immersive digital environment that completely replaces the user's real-world surroundings. Users interact with this virtual world through specialized headsets and controllers, which simulate a 3D space. VR is often used in gaming, simulations and virtual tours to provide an immersive experience. It requires significant computational power and high-quality graphics to create a convincing virtual environment.
AR
Augmented reality (AR)overlays digital information onto the real world, enhancing the user's perception of their environment. This technology uses devices like smartphones or AR glasses to blend virtual elements with physical surroundings in real-time. Examples include interactive maps, educational apps and games that integrate virtual objects into the real world. AR is used in various fields, such as gaming, retail and industrial training.
XR
Extended reality (XR) is an umbrella term that encompasses all immersive technologies, including AR and VR. XR refers to the combination of real and virtual environments created through computer technology, in varying degrees of immersion. It is designed to enhance or replace reality by blending physical and digital worlds. Applications range from gaming and entertainment to training and simulation.
Health data governance
Effective governance and regulatory frameworks are essential for maintaining the integrity and security of health data. These frameworks establish the guidelines and requirements that organizations must follow to ensure compliance with legal and ethical standards. By setting clear rules and enforcing best practices, they help protect patient privacy, ensure data quality and promote transparency. While they might not be the most thrilling aspect of data management, they are vital for maintaining order and efficiency. So, let’s acknowledge these unsung heroes, keeping the data world well organized and under control!
FAIR
FAIR stands for findable, accessible, interoperable and reusable, a set of principles designed to enhance the usability and management of data. Findable means that data should be easily located through clear metadata and identifiers. Accessible ensures that data is available to users with appropriate permissions and in usable formats. Interoperable refers to the ability of data to be integrated and used across different systems and platforms. Reusable implies that data can be effectively used for multiple purposes, with clear documentation supporting its future use.
ISO 27001
ISO 27001 is a specific standard within the international organization for standardization (ISO) family that focuses on information security management systems (ISMS). It provides a framework for establishing, implementing, maintaining and continually improving information security within an organization. The standard focuses on protecting sensitive information by assessing and managing security risks. Achieving ISO 27001 certification demonstrates a commitment to safeguarding sensitive information against a wide range of security threats.
HIPAA & GDPR
Health insurance portability and accountability act (HIPAA) is a U.S. federal law designed to protect the privacy and security of individuals' health information. It establishes national standards for the handling and safeguarding of protected health information (PHI) by healthcare providers, insurers and their business associates. HIPAA ensures that individuals have access to their own health records and controls how their data is shared. While HIPAA focuses on health information and is applicable mainly in the U.S., GDPR covers a broader scope of personal data protection in Europe, including health data, with an emphasis on transparency, consent and data subject rights.
GxP
Good practice (GxP) refers to a collection of quality guidelines and regulations, where the "x" can stand for various aspects such as manufacturing (GMP), clinical practices (GCP) or laboratory practices (GLP). These regulations ensure that products are consistently produced, controlled and documented to meet quality standards. GxP principles are essential for ensuring that products are safe, effective and reliable. Compliance with GxP standards is crucial for gaining regulatory approval and maintaining public trust.
Leading initiatives in health data
Beyond the standards and concepts, several key initiatives are driving the advancement of health data management and utilization. These initiatives bring together stakeholders from both healthcare and research communities to address challenges, promote collaboration and harness the power of data to improve health outcomes.
FDA
Food and drug administration (FDA) is a U.S. federal agency responsible for regulating and ensuring the safety of food, drugs, medical devices and cosmetics. It oversees the approval and monitoring of pharmaceuticals, vaccines and medical treatments to ensure they meet safety and efficacy standards before reaching the public. The FDA also sets guidelines for manufacturing practices and conducts inspections to enforce compliance. Beyond health products, it regulates food safety and labeling to protect public health. The agency plays a critical role in ensuring that products are safe and effective for consumer use.
EMA
The European medicines agency (EMA) is a regulatory agency of the European Union responsible for the evaluation and supervision of medicinal products. It assesses the safety, efficacy and quality of medicines before they can be approved for use across EU member states. The EMA also monitors ongoing drug safety and provides scientific advice to support the development of new treatments. By harmonizing drug regulations and facilitating access to innovative therapies, the EMA ensures that medicines meet high standards across Europe.
CRO & CRA
A contract research organization (CRO) is a company that provides outsourced research services to the pharmaceutical, biotechnology and medical device industries. CROs manage various aspects of clinical trials, including study design, data collection and regulatory compliance, allowing sponsors to focus on their core activities. Clinical research associates (CRAs) are professionals employed by CROs or other organizations who oversee and monitor clinical trials to ensure they are conducted according to protocols, regulatory requirements and ethical standards. CRAs are responsible for site management, data integrity and resolving issues that arise during the trial. Together, CROs and CRAs play a critical role in facilitating the efficient and compliant execution of clinical research.
EHDEN
The European health data & evidence network (EHDEN) is a pioneering initiative that aims to harmonize health data across Europe to support research and innovation. By standardizing data through the OMOP CDM, EHDEN enables researchers to access and analyze consistent, comparable datasets from multiple European countries. This initiative plays a critical role in accelerating medical research, improving patient care and fostering collaboration across borders.
Explore the story of EHDEN with a mystical twist in this blog post.
OHDSI
While EHDEN focuses on harmonizing data within Europe, observational health data sciences and informatics (OHDSI) operates with a similar goal on a global scale. OHDSI uses the OMOP CDM to standardize and analyze data from a wide range of sources, including hospitals and patient registries. This global standardization allows for the large-scale analysis of health trends, treatment effectiveness and safety profiles. OHDSI’s mission is to improve health outcomes through collaborative research and its open-science approach has led to numerous advancements in the understanding of diseases and treatments.
DARWIN
The data analysis and real-world interrogation network (DARWIN) is an initiative of the EMA that plays a crucial role in generating RWE. By leveraging RWD from diverse sources like EHRs, DARWIN evaluates the safety and effectiveness of medicines after they have been introduced to the market. This analysis provides crucial insights into how medicines perform in everyday clinical practice and supports post-market surveillance, ensuring that treatments remain safe and effective for patients. The distinguishing feature of DARWIN is its concentrated effort on monitoring drug safety and effectiveness in regular clinical practice.
i~HD
The European institute for innovation through health data (i~HD) is a non-profit organization dedicated to using health data effectively and transparently to enhance care and research. The institute collaborates with a diverse range of stakeholders, including healthcare providers, researchers and policymakers, to foster innovation and improve patient outcomes. By promoting best practices, developing tools and setting quality standards, i~HD aims to maximize the value of health data. They are actively involved in national and international projects, facilitating collaboration through education and events. Their core activities include data quality improvement, governance, interoperability and quality certifications.
Conclusion
As healthcare continues to advance, the realm of health data evolves with it, offering both new opportunities and challenges. A solid understanding of the core concepts, standards and initiatives in this field helps navigate this complexity. By becoming familiar with these aspects, you can play a meaningful role in the ongoing transformation of healthcare and support innovations that enhance overall health outcomes.
-compressed.jpg)
