Health Studio Glossary

An AI healthcare platform is a software ecosystem that uses artificial intelligence to automate, enhance, and personalize healthcare delivery. These platforms integrate patient data, predictive analytics, and clinical workflows to support remote monitoring, diagnostics, and care coordination.

Algorithm bias in healthcare refers to systematic errors in AI models that lead to unfair or unequal treatment across patient populations. It can arise from skewed training data and may negatively impact outcomes for underrepresented groups, highlighting the need for ethical AI in digital health.

Ambient clinical intelligence uses voice recognition, natural language processing (NLP), and contextual AI to passively document clinical encounters and streamline workflows. It reduces clinician burnout and increases efficiency by turning ambient sounds into structured medical data.

APIs in healthcare enable secure and standardized data exchange between systems like EHRs, wearable devices, and third-party apps. They are critical for achieving healthcare interoperability and supporting patient-centric digital ecosystems.

Biometric data includes unique physiological markers such as heart rate, blood oxygen, ECG, and more, collected through wearables or medical devices. In healthcare, it supports real-time monitoring, personalized treatment, and predictive analytics for better outcomes.

Blockchain in healthcare ensures secure, decentralized data storage and transparent record-sharing across stakeholders. It improves data integrity, patient privacy, and trust in clinical trials, medical records, and pharmaceutical supply chains.

Blood pressure monitoring devices, including smart cuffs and wearables, track systolic and diastolic readings to manage hypertension and cardiovascular risk. Integrated with RPM platforms, they enable early intervention and continuous care from home.

Chronic disease management involves long-term care strategies for conditions like diabetes, heart disease, and COPD. Digital platforms use AI, wearables, and patient engagement tools to track symptoms, encourage adherence, and improve quality of life.

CDS tools deliver evidence-based insights to clinicians at the point of care using AI, rules engines, and predictive models. They improve diagnostic accuracy, flag potential risks, and guide personalized treatment pathways.

Clinical trial data capture refers to collecting and structuring patient and observational data during clinical studies. Digital platforms enhance this process through eSource, ePRO, and wearable integration for real-time, high-fidelity data.

A cloud-native healthcare platform is built to run on cloud infrastructure, offering scalability, flexibility, and security for digital health applications. These platforms accelerate development, streamline deployments, and support global interoperability.

CGM systems continuously measure glucose levels via sensors worn on the skin, providing real-time data for diabetes management. Integrated with mobile apps and AI analytics, CGMs empower patients and clinicians to optimize care.

Data privacy in digital health ensures that patient information is protected, encrypted, and used ethically under laws like HIPAA and GDPR. It builds trust in health tech platforms and is critical to safe digital transformation in healthcare.

A digital clinical trial leverages technology such as wearables, remote monitoring, and eConsent to conduct decentralized research. These trials improve accessibility, reduce costs, and generate richer real-world evidence (RWE).

The digital front door refers to the patient-facing digital tools—like apps, chatbots, and telehealth—that streamline access to care. It enhances engagement, reduces friction, and modernizes the healthcare consumer experience.

Digital mental health includes mobile apps, teletherapy, AI chatbots, and remote monitoring tools that deliver mental health support and interventions. These tools increase access to care and help personalize mental health treatment at scale.

ePRO systems capture health data directly from patients via digital interfaces, measuring symptoms, quality of life, and treatment response. Used in trials and clinical care, ePROs improve real-world data collection and patient engagement.

EHR integration connects various digital tools and platforms with core patient record systems to streamline workflows and reduce data silos. Seamless EHR interoperability is key to delivering coordinated, value-based care.

Equity in digital health ensures that health technologies are inclusive, accessible, and effective for all populations. It addresses disparities in access, outcomes, and data representation—especially in underserved or marginalized communities.

Federated learning allows machine learning models to be trained across decentralized data sources without moving the data itself. In healthcare, this supports privacy-preserving AI that learns from diverse datasets across institutions.

FHIR is a standards framework by HL7 that enables the electronic exchange of healthcare information. It supports secure, modular data sharing between EHRs, apps, and APIs to promote interoperability and innovation.

The General Data Protection Regulation (GDPR) governs personal data use and privacy for individuals in the EU. In healthcare, GDPR ensures consent, transparency, and security when processing sensitive health data.

GxP refers to regulatory guidelines like Good Clinical Practice (GCP) and Good Laboratory Practice (GLP) that ensure quality and compliance in health product development. Digital platforms must align with GxP when used in regulated environments.

Health equity in digital health promotes fair access to technology, data, and services regardless of race, income, geography, or ability. It’s critical for eliminating disparities and improving outcomes for vulnerable populations.

HIPAA compliance ensures that digital health platforms protect personal health information (PHI) in accordance with U.S. federal law. It mandates encryption, access control, and audit trails to safeguard patient data.

Hybrid care blends in-person services with digital health tools like telehealth, RPM, and mobile apps. It offers patients flexible, continuous access to care and supports providers in managing population health efficiently.

Informed consent in digital trials uses eConsent platforms to educate participants, collect signatures, and ensure regulatory compliance. It enhances understanding, streamlines onboarding, and increases enrollment in decentralized studies.

Healthcare interoperability is the ability of different systems, devices, and apps to access, exchange, and use patient data seamlessly. It’s foundational to coordinated care, digital transformation, and AI implementation.

The Internet of Medical Things includes connected health devices like wearables, monitors, and sensors that collect and transmit health data. IoMT enables real-time tracking, remote diagnostics, and smarter clinical workflows.

JITAI uses contextual data and real-time analytics to deliver personalized health interventions at the most effective moment. Often deployed via mobile apps or wearables, JITAI improves behavioral health and chronic disease outcomes.

Knowledge graphs map relationships between medical concepts, patients, and clinical data to power AI applications and decision-making. In healthcare, they enhance search, diagnostics, and drug discovery through semantic understanding.

Longitudinal health records compile patient data over time across various care settings, creating a complete health history. They support predictive analytics, population health, and continuity of care.

Low-code and no-code platforms allow healthcare teams to build apps and workflows with minimal programming. These tools accelerate innovation, reduce IT burdens, and empower clinicians and researchers to prototype digital solutions quickly.

Machine learning uses algorithms to analyze health data, detect patterns, and make predictions that support diagnostics and treatment planning. It powers many AI tools across radiology, RPM, clinical trials, and population health.

Smart pill bottles and medication adherence devices track when medication is taken and send reminders or alerts to patients and providers. These tools help improve treatment adherence, especially for chronic conditions and remote care.

Modular digital health platforms are built with interchangeable components that allow healthcare systems to customize and scale digital tools easily. This approach improves flexibility, shortens implementation times, and enables tailored solutions for various clinical workflows.

NLP in healthcare enables computers to understand, interpret, and generate human language from clinical documents, notes, and patient interactions. It powers AI tools like ambient documentation, sentiment analysis, and virtual assistants to reduce administrative burden and extract insights from unstructured data.

The NIH Cloud Smart Initiative supports secure, scalable cloud adoption across federal health research institutions. It promotes interoperability, AI-readiness, and cost-effective infrastructure modernization for public health, life sciences, and digital clinical trials.

Omics data refers to large-scale datasets in genomics, proteomics, metabolomics, and other biological fields that provide insights into patient health at the molecular level. In digital health, AI-powered tools use omics to enable precision medicine, predictive analytics, and population-level research.

Outcomes-based healthcare shifts the focus from volume to value, rewarding providers for delivering high-quality care that improves patient outcomes. It relies on real-world data, digital tracking, and continuous monitoring to measure success and reduce unnecessary costs.

Patient engagement tools include apps, portals, and personalized education platforms that encourage individuals to take an active role in their care. In digital health, engagement drives better adherence, satisfaction, and health outcomes.

PGHD includes biometric, lifestyle, and symptom data captured directly by patients via wearables, apps, or home monitoring devices. This real-time, contextual information complements clinical data and supports personalized, remote, and preventive care.

Population health analytics uses aggregated clinical and social data to identify trends, predict risks, and optimize outcomes across communities. It supports care coordination, resource allocation, and health equity strategies.

Post-surgical recovery monitoring combines remote patient monitoring, symptom tracking, and predictive alerts to support healing after procedures. It reduces readmissions, enhances patient safety, and improves care continuity from hospital to home.

Predictive healthcare leverages machine learning and data analytics to anticipate disease progression, hospital admissions, and other clinical events. It enables proactive interventions and risk mitigation strategies at both individual and population levels.

Preventive care programs use screenings, education, and early intervention to reduce the onset of chronic disease and promote long-term wellness. Digital tools like wearables and mobile health platforms enhance accessibility and adherence.

Quality metrics in digital health evaluate performance across clinical, operational, and patient experience domains. These standardized indicators help healthcare organizations track success, meet regulatory goals, and improve digital transformation initiatives.

The quantified self movement involves individuals tracking their own health data—such as sleep, activity, heart rate—via smart devices. In healthcare, this empowers patients to make informed decisions and enables personalized preventive care.

Real-world evidence is data collected outside controlled clinical trials—from EHRs, claims, devices, and registries—to assess the effectiveness of interventions. RWE supports regulatory decisions, value-based care, and post-market surveillance.

RPM involves the collection and transmission of patient health data—like heart rate, blood pressure, or glucose levels—from home to healthcare providers. It improves chronic disease management, reduces hospitalizations, and enables real-time intervention.

Rural health connectivity addresses digital infrastructure gaps that limit access to care in underserved areas. Solutions include telehealth, mobile health apps, and satellite-powered broadband to support equitable healthcare delivery.

Sleep tracking devices use sensors to monitor sleep duration, stages, and quality, often integrating with health apps and wearable platforms. These tools support wellness, identify sleep disorders, and offer actionable insights for clinical care.

SDOH are the non-clinical factors—like income, housing, education, and environment—that influence health outcomes. Digital health platforms increasingly integrate SDOH data to design equitable care strategies and reduce disparities.