As in other fields, medical sciences are subject to different sources of bias. While understanding the sources of bias is a key element in drawing valid conclusions, bias in health research remains a very sensitive topic that can affect the approach and outcome of research. Information bias, also known as misclassification, is one of the most common sources of bias affecting the validity of health research. It originates from the approach used to obtain or confirm study measurements.
This article seeks to raise awareness of information bias in observational and experimental research study designs, as well as to enrich discussions about bias problems. Specifying the types of bias may be essential to limit their effects, and the use of adjustment methods could serve to improve clinical evaluation and healthcare practice. Bias can be defined as any systematic error in the design, conduct, or analysis of a study. In health studies, bias can arise from two different sources: the approach taken to select the subjects of a study or the approach taken to collect or measure data from a study.
These biases are called, respectively, selection bias and information bias. 1 Bias can have different effects on the validity of medical research findings. In epidemiological studies, bias can lead to inaccurate estimates of the association or to an overestimation or underestimation of risk parameters. The allocation of sources of bias and their impacts on the final results are key elements for drawing valid conclusions.
These measurements can be obtained through experimentation (for example, bioassays) or observation (for example, questionnaires or surveys). Good data will train good algorithms in healthcare. But what if the data used to train an algorithm doesn't tell the whole story? What happens when these data provide erroneous or biased decisions for use by health systems, insurance companies, or government agencies to predict risk or determine treatment protocols in managing the health of the population? Both data scientists and doctors talk about the problem? of data bias that has enormous consequences on health care and human lives. Often, unconscious attitudes and stereotypes can adversely affect the care that some patients receive.
Studies have found that these implicit biases are associated with lower compliance with treatment plans and lower trust in providers, and may cause patients to avoid or delay necessary care, worsening outcomes. Solving the problem will involve holding medical institutions accountable for addressing implicit bias among providers. In addition, most noise models assume randomness, while physician and patient behaviors intentionally contribute to health care processes. Conclusions: Health processes must be addressed and taken into account in the analysis of observational health data.
Physicians could also use health care processes as part of the movement toward precision medicine by identifying subpopulations that have different patterns of health care processes after a new diagnosis or a change in treatment strategy. However, they also show that explicitly modeling the dimension of the healthcare process can address some of the limitations of data and increase the predictive value of data. Department of Basic Medical Sciences, Faculty of Medicine, King Saud bin Abdulaziz University of Health Sciences, Riyadh, Saudi Arabia. In an article published in the journal The Lancet Digital Health, a team of data researchers recommended organizational action to address low diversity in health data science.
However, this should be done with caution, since changes in the dimension of the health care process, such as the increase in the order of laboratory tests, could be an early sign that certain patients are responding poorly to treatment. The point is that the time of day of the white blood cell count test does not affect the patient's health, but it is a readily available variable that summarizes a wealth of information about the patient's interaction with the health system. As a result, “subsequent providers can read, be affected and perpetuate negative descriptors, reinforcing the stigma for other healthcare teams,” the authors wrote. The Milbank Memorial Fund is a nonpartisan foundation that focuses on improving the health of communities and entire populations.
Figure 1 shows that five years of observational electronic health record (EHR) data (July 28, 2001 to July 27, 2000) for these patients were extracted from a single clinical data repository, the Partners Healthcare Research Patient Data Registry, which combines data from the two hospitals. For EHRs to be able to do this, it is necessary to continue working to develop measures that assess bias in structure, process and outcomes, as well as policies to persuade providers and health systems to prioritize systemic equity as the central objective of EHRs. . .