500 words public health discussion “biosurveillance agorithms” of a
There are many covariates to consider when developing an algorithm. These include demographic factors such as age, race/ethnicity, gender and socio-economic status; lifestyle factors such as diet, exercise habits and smoking status; medical history including chronic illness diagnoses and past hospitalizations; personal or family history of other diseases or conditions; laboratory results such as cholesterol levels or blood glucose levels; use of any prescribed medications; psychosocial/behavioral factors such as stress level, access to quality healthcare services or substance abuse history. The model can identify the people at greatest risk and provide them with preventative measures to lower their chances of experiencing the adverse health outcome.
While algorithms are useful tools for predicting outcomes based on this information – there are still limitations that must be acknowledged. Algorithms often use data from electronic records that may have been entered incorrectly due to human error. In addition, data reported by patients themselves is not always reliable because of recall bias or social desirableness bias. In addition, the use of algorithms to make decisions regarding patient care can result in unequal treatment based on racial or ethnic differences. This is because some models are biased towards certain groups due to a lack of data.
In addition, algorithms used to measure risk in a community must take into account the implications for public health. In addition to improving community health by identifying individuals in greatest need, algorithms could also lead to unintended outcomes if they are not properly used or have biases within them. It is therefore essential that algorithms be regularly updated based on the latest research evidence to ensure they are valid measures of disease risk and avoid any discriminatory practices.