Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
Flow Sheet01:17

Flow Sheet

Flowsheets are valuable tools in nursing documentation. They enable healthcare professionals to efficiently record and monitor various patient assessments and measurements in a consolidated format.
Here's a closer look at the examples of flowsheets commonly used by nurses:
Graphic Sheet Documentation:
Integrated Healthcare System01:20

Integrated Healthcare System

An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cultryx: Precision Diagnostic Stewardship for Blood Cultures Using Machine Learning.

medRxiv : the preprint server for health sciences·2026
Same author

Predictors of Response to Cardiac Resynchronization Therapy in Pediatric Patients and Patients With Congenital Heart Disease.

Circulation. Arrhythmia and electrophysiology·2025
Same author

Enhancing Distress Tolerance Skills in Adolescents With Anorexia Nervosa Through the BALANCE Mobile App: Feasibility and Acceptability Study.

JMIR formative research·2025
Same author

Toward Interoperable Digital Medication Records on Fast Healthcare Interoperability Resources: Development and Technical Validation of a Minimal Core Dataset.

JMIR medical informatics·2025
Same author

Multicenter Results of a Novel Pediatric Pacemaker in Neonates and Infants.

Circulation. Arrhythmia and electrophysiology·2025
Same author

Single cell RNA sequencing of haematopoietic cells in fresh and frozen human atheroma tissue.

Cardiovascular research·2025
Same journal

A multimodal instruction dataset and benchmark for ultrasound understanding.

NPJ digital medicine·2026
Same journal

Evaluating the shift in psychiatric care: Associations between remote consultation use and clinical outcomes in a large longitudinal cohort.

NPJ digital medicine·2026
Same journal

Identifying suicide-related language in smartphone keyboard entries among high-risk adolescents.

NPJ digital medicine·2026
Same journal

Impact of a virtual nurse-led Early paLlIative Care IntervenTion (ELICIT) randomized controlled trial.

NPJ digital medicine·2026
Same journal

Towards trustworthy AI-driven cuffless blood pressure monitoring.

NPJ digital medicine·2026
Same journal

Spatially identifying regions of tumor recurrence in patients with suspected recurrent glioma using physiologic MRI and machine learning.

NPJ digital medicine·2026
See all related articles

Related Experiment Video

Updated: May 14, 2026

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
03:47

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients

Published on: July 12, 2024

Spezi Data Pipeline: Streamlining FHIR-based interoperable digital dealth data workflows.

Vasiliki Bikia1,2,3, Paul Schmiedmayer4, Aydin Zahedivash5

  • 1Stanford University, Department of Biomedical Data Science, Stanford, CA, USA. bikia@stanford.edu.

NPJ Digital Medicine
|May 12, 2026
PubMed
Summary
This summary is machine-generated.

The Spezi Data Pipeline is an open-source toolkit for analyzing digital health data. It streamlines data handling from access to visualization, supporting interoperable clinical research with tools like ECG analysis.

More Related Videos

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Related Experiment Videos

Last Updated: May 14, 2026

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
03:47

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients

Published on: July 12, 2024

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Area of Science:

  • Digital Health
  • Health Informatics
  • Biomedical Data Science

Background:

  • Digital health technologies are increasingly adopted, necessitating robust solutions for managing complex healthcare data.
  • Existing systems often lack interoperability, hindering efficient analysis and research.
  • The Stanford Spezi ecosystem aims to support the development of research and translational digital health software.

Purpose of the Study:

  • To introduce the Spezi Data Pipeline, an open-source Python toolkit.
  • To streamline the end-to-end analysis of digital health data, including secure access, processing, visualization, and export.
  • To enable standardized handling of diverse digital health data types using Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR).

Main Methods:

  • Developed an open-source Python toolkit, the Spezi Data Pipeline.
  • Integrated the Pipeline into the Stanford Spezi ecosystem.
  • Leveraged HL7 FHIR standards for data representation.
  • Demonstrated application using real-world data from the Pediatric Apple Watch Study (PAWS).

Main Results:

  • The Pipeline facilitated efficient extraction, transformation, and clinician-driven review of Apple Watch ECG data.
  • Supported annotation and comparative analysis of ECG data alongside traditional monitors.
  • Enabled standardized handling of sensor-derived observations, ECG recordings, and clinical questionnaires.
  • Reduced the need for bespoke data engineering in digital health research.

Conclusions:

  • The Spezi Data Pipeline supports reproducible and interoperable clinical research.
  • It enables prospective, clinician-in-the-loop analysis within standardized workflows.
  • The toolkit is valuable for utilizing routinely collected digital health data in research and clinical settings.