Search results
Results From The WOW.Com Content Network
The availability heuristic, also known as availability bias, is a mental shortcut that relies on immediate examples that come to a given person's mind when evaluating a specific topic, concept, method, or decision.
The Andersen healthcare utilization model is a conceptual model aimed at demonstrating the factors that lead to the use of health services. According to the model, the usage of health services (including inpatient care, physician visits, dental care etc.) is determined by three dynamics: predisposing factors, enabling factors, and need.
The availability heuristic (also known as the availability bias) is the tendency to overestimate the likelihood of events with greater "availability" in memory, which can be influenced by how recent the memories are or how unusual or emotionally charged they may be. [20] The availability heuristic includes or involves the following:
This change can be directly measured (e.g. by rating scales used by the clinician or patient) or assumed by the use of proxy measurement (e.g. a blood test result). Interventions can be direct (e.g. medication) or indirect (e.g. change in the process of health care like integration care by different specialists).
There's more evidence of algorithms demonstrating racial bias. Researchers have determined that a "widely used" risk prediction algorithm from a major (but unnamed) healthcare provider had a ...
The Donabedian model is a conceptual model that provides a framework for examining health services and evaluating quality of health care. [1] According to the model, information about quality of care can be drawn from three categories: "structure", "process", and "outcomes". [2]
Addressing gender bias in mental health care is, first and foremost, a systemic issue. Above all, providers, researchers, and lawmakers need to raise awareness of how gender bias impacts treatment ...
Such applications outside the healthcare system raise various professional, ethical and regulatory questions. [106] Another issue is often with the validity and interpretability of the models. Small training datasets contain bias that is inherited by the models, and compromises the generalizability and stability of these models.