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Measuring the quality of health care has become a major concern for advocates and providers of health services in recent decades. One of the ways in which quality of care is currently assessed is by taking routinely collected data and analyzing them quantitatively. The use of comparative data has many advantages but there are also some important pitfalls. Collating numerical data in this way means that comparisons can be made—whether over time, with benchmarks, or with other healthcare providers (at individual or institutional levels of aggregation). Inevitably, such comparisons reveal variations. The natural inclination is then to assume that such variations imply rankings: that the measures reflect quality and that variations in the measures reflect variations in quality.Do you believe this tool would be helpful?
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FlagUnit 3 - Discussion Board Flag Post
Bias is skewed results typically not based on accurate evidence. When we conduct a study we carry our 3 main steps; we gather the sample of the population, perform the study and then analyze the results. However, there are opportunitiesduring each step where we could unintentionally shift the results to lean in favor or against the question at hand.
When we want to study how a particular group would react/respond to a situation we collect a small group of subjects that fits that description because there is no way to ask every single person to partake. Selection bias occurs when the sample group does not represent the target population(Morgan, XXXXXXXXXXThis will distort the data that is collected because it doesn't reflect that population accurately. For example if my study was based on teenagers and their preference to music it wouldn't be a good idea to survey middle-aged adults because their answer would not provide an accurate depiction of a teenagers preferences.
Another type of bias that can occur is during the execution of the study. Measurement bias is when the instrument being used to measure or collect data is not calibrated, leveled etc. if I was conducting a study about blood pressure and men over the age of 50 and the sphygmomanometer was defected, the readings that were reported will not be true.
The final step to a study is analyzing the data. There can be unintentional bias at this point too. When one draws the conclusion of a cause and effect between two variable but there was another factor behind the outcome, this is called a confounding bias(Morgan, XXXXXXXXXXFor example the results in my study concluded that increased ice cream sales leads to higher report of sunburns. It is more likely that it was the high temperature is what caused both actions. The warmer weather resulted in more people buying ice cream and it was also the warm weather that caused more sunburns. This type of bias will distort data because another variable was the real reason for the effect of the variable being studied.
Morgan, C. J XXXXXXXXXXReducing bias using propensity score matching.Journal of Nuclear Cardiology,25(2), 404–406.https://doi.org/10.1007/s XXXXXXXXXXy
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