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Related Concept Videos

Review and Preview01:10

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Review and Preview01:13

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.
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Digital health app development standards: a systematic review protocol.

Michelle Helena Van Velthoven1, James Smith2,3, Glenn Wells4

  • 1Department of Paediatrics, Healthcare Translation Research Group, University of Oxford, Oxford, UK.

BMJ Open
|August 20, 2018
PubMed
Summary
This summary is machine-generated.

Developing safe digital health apps requires clear standards. This review overviews current guidelines to mitigate risks for patients and providers, paving the way for future app development standards.

Keywords:
Health PolicyQuality In Health CareTelemedicine

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Area of Science:

  • Digital Health
  • Software Engineering
  • Regulatory Science

Background:

  • Lack of standardized development processes for medical and healthcare apps creates risks for users and developers.
  • Existing standards for medical devices and clinical information systems offer potential lessons for health app development.
  • Current landscape lacks clear, accepted guidelines for the entire lifecycle of digital health applications.

Purpose of the Study:

  • To provide a comprehensive overview of existing standards, frameworks, best practices, and guidelines for digital health app development.
  • To identify criteria from relevant international, US, European, and UK standards applicable to health app creation.
  • To serve as a foundational step towards establishing robust standards that minimize clinical, privacy, and economic risks.

Main Methods:

  • Systematic review of applicable standards, guidelines, frameworks, and best practices for health app development.
  • Inclusion of standards related to medical device software, clinical information systems, and medicine.
  • Searches of regulatory organization websites, electronic databases (MEDLINE, Embase, SCOPUS), and reference lists.

Main Results:

  • Identification and synthesis of criteria from diverse sources for health app development.
  • Narrative overview and tabular summaries of extracted data from international, US, EU, and UK standards.
  • Examination of interrelationships between standards and comparison of US and EU approaches.

Conclusions:

  • The review highlights the need for standardized development practices to ensure the safety and efficacy of digital health apps.
  • Findings will inform the creation of new standards to mitigate risks associated with health applications.
  • Dissemination through publications and presentations aims to improve the quality and reliability of digital health tools.