DUCM Failed Use of Health Car

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[ Failed use of healthcare data ] Describe an example of failed use of healthcare data

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Medical vs. Health care and opioids

The United States has failed in the use of healthcare data. We have taken the data that shows where we are failing in wellness and converted it into a way to invest more in the medical infrastructure. We are no further along now, in 2021, then Chadwick and Farr in the 1800s – one arguing that the key to decreasing deaths due to poverty is to clean the environment, and the other arguing that the key is to focus on the diseases caused by that environment. (Hamlin, 2015)

When policy makers and health care professionals look at the fact that the US is one of the most obese countries in the world, we invest more money in research into treatments for obesity. No one laughs or bats an eye at that type of intervention. Yet, I can distinctly remember when New York City was no longer allowed to sell “super sized” sodas, and they were quite literally laughed at. I remember a late-night TV host doing a whole bit on how stupid the policy makers in NY were for implementing that law.

As cited in the Brookings article, “The major European countries, for instance, spend between about 9 and 12 percent of their GDP on health services. We spend more than 17 percent.” (Butler, 2016) We are spending that money based on healthcare data showing what a poor job we are doing with wellness in the country. This is an absolute failure in the use of data.

A more specific example of failed use of data, however, is the opioid crisis. Over 30 years ago, a letter to the editor in a journal declared that people with chronic pain rarely become addicted to opioids, and thus doctors are undertreating a vast majority of patients by not prescribing them. As with much in medicine, (see COVID vaccines), small voices sometimes make a big noise. Opioids became medications that were used every day in practice, and no longer reserved for the “big things”. “Governing agencies began to evaluate doctors and hospitals on their control of patients’ pain. Ultimately, reimbursement became tied to patients’ perception of pain control. As a result, increasing amounts of opioids were prescribed, which led to dependence. When this occurred, patients sought more in the form of opioid prescriptions from providers or from illegal sources. Illegal, unregulated sources of opioids are now a factor in the increasing death rate from opioid overdoses.” (Rummans, 2017)

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Healthcare data fails when the process to collect it is too complex, time consuming, or costly.

In the lecture this week from Joel Selaniko, he shows a truck full of paper data that was collected to determine what percentage of Zambian children were vaccinated in just one county. It was an entire full truckload, and each paper page would need to get it entered into the computer, so someone can analyze it, and then there can be a report from which the results can be used to better approach vaccination of children. He notes the data entry part can take 6 months to two years to input, and sometimes never gets completed. There are problems like a loss of momentum or funding.

Old data is a problem. And while advances in technology have addressed the speed of data collection and delivery many ways (Selaniko discusses palm pilot data collection), technology also needs to be able to scale. He talked about cloud computing to provide greater access

Manyazewal et al (2021) reviewed several technological approaches to collecting health technology in Ethiopia to further public health practices, and found that while digital health solutions have strong potential, there were still challenges when it comes to adoption and implementation, including infrastructure, training, and access, as well as patient and provider skepticism and outright fear of providing information. The article notes legal and regulatory challenges, including cybersecurity, with using the cloud.

The World Health Organization’s global strategy on digital health specifically targets this issue, setting a strategic objective that focuses on “strengthening the governance of digital health at national and international levels through the creation of sustainable and robust governance structures and building the capacity for digital health at global and national levels.” It includes strengthening the capabilities to scale up digital health technologies while having “standards for safety, security, privacy, interoperability, and the ethical use of data within and outside the health sector.”

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