The applicability of number crunching in sensitive fields like medicine is also discussed in this book. It recommends doctors to have less wariness like the pilots and that the treatment should be based on statistical evidence which in turn depends on data or doctor’s dispositions. Though this process is time-consuming and challenging in terms of evidence collection, it would make the job of doctors simpler while at the same time assure better safety for the
Some kids that failed the test could be more successful in the future than the kid that past the test and told him that his future is gonna be good. These test are basically are just ruining kids future, so if they know they failed the test then they are gonna feel that what’s the point of trying to reach their goals if their future isn’t gonna be successful later
Concerns about AI also encompass whether it does create the most accurate diagnoses for patient care. Since AI uses large data banks to make decisions for what treatments may be best for a patient or for a diagnosis of an illness, people may be led to believe that this option is less personalized. Some people have made this belief since AI takes information from the general public to make a decision about an individual. However, research has shown that the accuracy of diagnoses when using data banks to make medical decisions is far more accurate than a diagnosis from a typical doctor. Also, doctors normally do not have the same opinions so that creates inconsistency for diagnoses, whereas AI would be consistent (Sissons, Ben. “Using Artificial Intelligence to Bring Evidence-Based Medicine a Step Closer to Making a Difference”). The average doctor gives correct diagnoses less than half the time. The accuracy for different diagnoses depend on the field of study. For example, the accuracy of AI for correctly diagnosing dermatological diseases was 97.55%. The accuracy for diagnosing a group of people with a certain type of diabetes came to be 79.37%, which is still higher than the average for a typical doctor (Lekkas, Stavros. “Evolving Fuzzy Medical Diagnosis of Pima Indians Diabetes and of Dermatological Diseases”). This research shows that AI is capable of making accurate diagnoses. Ethical concerns were
To sum up, any exam is not a failure if it proves the hypothesis wrong or if scientist’s prediction is not accurate. An exam is only a failure if its design is flawed. A flawed experiment is one that, first, does not keep its variables under control, and, second, does not sufficiently
This article from The Washington Post speaks about the surgeries having a high rate of drug-related error, which led me to think about the point estimates and margins of error of surgery in general and the effectiveness of drugs. For example, medical professionals can claim that a random drug, x, will allow a patient to recover from an illness 50% faster than they naturally would, with a margin error of 40%. This means that the patient could recover 10% faster or 90% faster. The actual result of their healing time is misinterpreted because of the uncertainty of many unknown factors that are specific to the patient that may speed up or slow down healing, such as, age, weight, severity of the disease, other medications, etc.
You must be extremely careful. For example; if the calculation was .5 (.05) it may be mistaken with 5 mg instead of 0.5 mg (.5mg). The above calculation can result in a miss calculation. A little miscalculation can harm the patient.
According to a 2002 Agency for Healthcare Research and Quality report, about 7,000 people were estimated to die each year from medication errors - about 16 percent more deaths than the number attributable to work-related injuries (6,000 deaths). Medical errors affect one in 10 patients worldwide. One extrapolation suggests that 180,000 people die each year partly as a result of iatrogenic injury. One in five Americans (22%) report that they or a family member have experienced a medical error of some
Milo works in a bioengineering firm that develops new medical devices that diagnose for prostate cancer. He is tasked with performing numerous tests to verify the accurateness of the medical devices detecting prostate cancer since some medical devices are prone to have questionable results. During his research, he notices an alarming change in a specific patient results and is unsure of how to interpret the results. His uncertainty is due to the patients’ results changing from being consistently negative to being consistently positive for early stage prostate cancer. Milo monitors the results and believes that either the medical devices have a technical issue or the patient has developed prostate cancer. He talks to his boss about the issue
Quality Assessment of Diagnostic Accuracy Studies (QUADAS) is a tool to assess the quality of diagnostic accuracy studies included in systematic reviews . Included studies are further evaluated with QUADAS criteria. To perform accuracy analyses, we take fully into account the following items: patients enrollment criteria clearly described; patients receive the same reference standard regardless of the index test result; the positive index test results interpreted by cytopathological analyses, histopathological method, surgery and/or other reference standards; patients with suspected lung cancer were enrolled in consecutive method or not-defined; blinded judgment of needle aspiration biopsy results; reporting of uninterpretable/ indeterminate/
Although medical assistants are not the ones you perform the tests and compute the results, another way math is widely used in the medical field is through the use of percentages when discussing the rate of survival, the risk of disease, and the probability of your chance of contracting such as disease or genetic trait. They also used percentages when performing tests for certain viruses and diseases such as tuberculosis, strep throat, and other infections. For example, physicians utilize mathematics when determining the risk of genetic mutations and the probability of a patient contraction a genetic disease such as Huntington’s disease.
The diagnostic tools that I have seen used at the clinic where I do my practicum includes incision and drainage (I & D) of the abscesses, rapid strep throat testing, influenza swab, urine dip-stick testing, and slit lamp examination.
Of note, 2 caveats are in order: First, survey results are almost always based only on those respondents who use tests or based on official clinical records;hence, many studies report data on less than 50% of sample surveyed due to a high percentage of non-responders. Thus, in the aggregate, individual tests may be ‘popular’, but only within the parameters of professionals who rely on tests in assessment. Second, an individual test’s ranking is