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Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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Health Information Technology (HIT)
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Data: Types and Distribution01:19

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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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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...
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Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
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Related Experiment Video

Updated: Feb 6, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Authenticating Health Activity Data Using Distributed Ledger Technologies.

James Brogan1,2, Immanuel Baskaran3, Navin Ramachandran1

  • 1University College London, Centre for Health Informatics & Multiprofessional Education, London, United Kingdom.

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Summary

Distributed ledger technologies offer secure data sharing for digital health. The IOTA protocol

Keywords:
Activity dataBlockchainDistributed ledger technologiesMedical sensorsRemote monitoringWearable deviceseHealth

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Area of Science:

  • Digital Health
  • Blockchain Technology
  • Data Security

Background:

  • The digital healthcare ecosystem is rapidly evolving, promising enhanced preventive and precision medicine through big data analytics.
  • Significant security and privacy challenges hinder the safe, large-scale mobilization of healthcare data, algorithms, and models.

Purpose of the Study:

  • To explore the role of distributed ledger technologies (DLTs) in securing electronic health data.
  • To demonstrate the application of the IOTA protocol for authenticating and ensuring the integrity of data from wearable and embedded devices.

Main Methods:

  • Investigated the potential of DLTs for advancing electronic health records.
  • Utilized the Masked Authenticated Messaging (MAM) extension module of the IOTA protocol.
  • Implemented a system for secure sharing, storage, and retrieval of encrypted activity data on a tamper-proof distributed ledger.

Main Results:

  • DLTs can ensure the authenticity and integrity of health data generated by connected devices.
  • The IOTA protocol's MAM module provides a secure method for managing sensitive health information.
  • Demonstrated a functional approach to tamper-proof data handling in digital health applications.

Conclusions:

  • Distributed ledger technologies are crucial for the secure advancement of digital healthcare.
  • The IOTA protocol offers a viable solution for protecting patient data privacy and integrity in on-demand healthcare systems.