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Related Concept Videos

Data Reporting and Recording01:24

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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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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Putting the data before the algorithm in big data addressing personalized healthcare.

Eli M Cahan1,2, Tina Hernandez-Boussard3,4,5, Sonoo Thadaney-Israni4

  • 11New York University School of Medicine, New York, NY USA.

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This summary is machine-generated.

Biased training data, not algorithms, causes healthcare disparities. Addressing data deficiencies is crucial for equitable big data applications in personalized medicine.

Keywords:
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Area of Science:

  • Healthcare technology
  • Data science
  • Health equity

Background:

  • Big data, predictive algorithms, and machine learning are vital in healthcare delivery.
  • These technologies risk exacerbating existing health disparities, including racial biases.
  • The root cause of biased outputs lies in the underlying training data.

Purpose of the Study:

  • To highlight the critical role of training data in algorithmic bias within healthcare.
  • To propose a shift from deductive clinical decision support to inductive clinical decision questioning.
  • To outline strategies for mitigating bias and promoting equity in big data applications.

Main Methods:

  • Analysis of the impact of training data on predictive model outputs.
  • Conceptualization of a new paradigm: clinical decision questioning.
  • Identification of key considerations for data management and correction.

Main Results:

  • Biased input data inevitably leads to biased algorithmic outputs.
  • Population-representative data with robust features is essential for model utility, equity, and generalizability.
  • A shift towards inductive reasoning can enhance efficacy and representativeness.

Conclusions:

  • Acknowledging and addressing data deficiencies is paramount to realizing the potential of big data in healthcare.
  • Strategies such as data inclusiveness, sanitation, and correction are vital.
  • Deliberate application of these considerations can mitigate the perpetuation of health inequity.