Medication errors are one of the leading causes of death in the United States, with an estimated 440,000 deaths per year. However, medication errors are not recognized as a cause of mortality. The author will analyze both qualitative and quantitative data on medication errors through patient interviews and surveys to understand the scope of the issue and identify leading causes. Predictive analytics will then be used to analyze the data and help anticipate treatment outcomes, potential risks, and ways to prevent future errors. This could help reduce medication errors in hospitals and improve population health management.

1. Kindly respond to this discussion post: 250 words, APA format,
Kindly respond to this discussion post:250 words, APA format, 2 references.Medication
errors are the topic I am choosing for this discussion. They are one of the leading causes of
death; this shows the seriousness of this issue. Multiple studies estimate the death toll to be
four hundred and forty thousand per year; a medication error is not recognized as a cause
of mortality in the United States. (Sokolove Law Team 2020). The most significant data
source will come from Qualitative and Quantitative data regarding Medication Errors.
Interviews will be conducted to discover that medication error exists in many hospitals.
When dealing with a medication error, it is necessary to consider the leading cause, how
often the error occurs, and how to prevent it. Some leading causes of medication errors are
negligence of duty by medical staff or inadequate training of medical staff, leading to
medication errors while administering medical services to a patient. Patient surveys will be
important when it comes to defining the scope. Self-reported patient ratings of many
touchpoints must be captured throughout their medical care experience. Examples are staff
response, clinician communication, technical competence, and hospital atmosphere,
depending on the component of patient satisfaction assessed. Through the patient surveys,
we will be able to know patient satisfaction while receiving treatment.I will use predictive
analytics to analyze the data. Predictive analytics can warn health care providers about
potential dangers. By studying behavioral data, we can anticipate treatment success,
potential hazards for chronic illness, and even self-harm risk. At the individual patient level,
the health data obtained can be used for risk grading, readmission prediction and
prevention, infection and deterioration prediction, and much more( Davidson et al.,
2018).On a much bigger scale, predictive modeling can be applied. Without these models,
population health management is impossible. Outbreaks can be forecast, as can outcomes,
and preventative actions can be done once we know what’s coming. Predictive modeling
can even be utilized in administrative applications to boost productivity and reduce
expenses (Davidson et al., 2018).References: Davidson, P., Rushton, C. H., Kurtz, M., Wise, B.,
Jackson, D., Beaman, A., & Broome, M. (2018). A social-ecological framework: A model for
addressing ethical practice in nursing. Journal of clinical nursing, 27(5-6), e1233-e1241.SLT
(2020). COVID-19 has surpassed medical errors to become the third-leading cause of death
in the United States. Retrieved from https://www.slw.com/blog/ COVID-19 has surpassed
medical errors to become the third-leading cause of death in the United States.