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Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.
If attempts are made to purchase or commission health services using outcomes data, bias may be introduced that will negate the benefits, especially in the service provider produces the outcomes measurement. See Goodhart's Law; Inadequate attention may be paid to the analysis of context data, such as case mix, leading to dubious conclusions. [27]
The field of statistics, where the interpretation of measurements plays a central role, prefers to use the terms bias and variability instead of accuracy and precision: bias is the amount of inaccuracy and variability is the amount of imprecision. A measurement system can be accurate but not precise, precise but not accurate, neither, or both.
Observer bias can be seen as a significant issue in medical research and treatment. There is greater potential for variance in observations made where subjective judgement is required, when compared with observation of objective data where there is a much lower risk of observer bias.
Information bias is also referred to as observational bias and misclassification. A Dictionary of Epidemiology , sponsored by the International Epidemiological Association , defines this as the following:
Sampling bias is problematic because it is possible that a statistic computed of the sample is systematically erroneous. Sampling bias can lead to a systematic over- or under-estimation of the corresponding parameter in the population. Sampling bias occurs in practice as it is practically impossible to ensure perfect randomness in sampling.
In statistics, verification bias is a type of measurement bias in which the results of a diagnostic test affect whether the gold standard procedure is used to verify the test result. This type of bias is also known as "work-up bias" or "referral bias".
Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare: (1) claims and cost data, (2) pharmaceutical and research and development (R&D) data, (3) clinical data (such as collected from electronic medical records (EHRs)), and (4) patient behaviors and preferences data (e.g. patient satisfaction or retail ...