Healthcare usually demands the ability to collect and analyze data from patients, which then allows physicians to make a choice on the individual’s mode of treatment, needed medications and so on. Medical practitioners can use data analytics to enhance decision making. This refers to the process of interpreting quantitative data to identify trends and answer questions.
Data can be analyzed using algorithms and software or manually, with the visual representation of data through charts and graphs also being useful in demonstrating trends and designing presentations. The main types of data analysis include prescriptive analysis, predictive analysis, diagnostic analysis and descriptive analysis.
Healthcare can make use of each of these in different ways. For instance, prescriptive analytics can help assess an individual’s pre-existing conditions and calculate their risk of developing conditions in the future. Below are a few more applications of data analytics in the healthcare sector.
Forecasting disease outbreaks
Data analytics can be used to forecast trends in the spread of ailments, which will enable individuals, hospitals and physicians to sufficiently prepare. For example, the CDC has been gathering data about flu cases for more than 10 years. This data can be used to predict the severity of flu seasons in the future, which may be helpful in decision making.
The coronavirus pandemic is another good example of how data can be analyzed to forecast future increases in cases, which will help hospitals ensure that they have enough patients’ beds and staff have enough PPEs. Analyzing data from the pandemic can also allow school administrators to decide if in-person learning is a safe option for all.
Identifying anomalies in scans
The healthcare sector can also use machine-learning algorithms to analyze data efficiently. For example, Massachusetts Institute of Technology researchers developed an algorithm in 2018 that could identify differences between medical images such as MRI scans. This algorithm can detect differences in scans much faster than a human physician can.
However, Dustin Tingleyat Harvard warns against relying on algorithms too much, noting that pairing algorithms with knowledge and critical thinking may help save time as well as individuals’ lives.
Patients may be asked questions regarding their experiences with doctors, and such data is later analyzed to find areas for improvement.
For example, a study that involved patients rating their physicians on how empathetic they were during visits found that the physicians from the group that received empathy training also receive higher ratings in comparison with the control group. Analyzing the data gathered from the patients led to the discovery that training could increase doctors’ empathy, which has been linked to fewer malpractice claims and higher patient safety.
Many companies, including RYAH Group Inc. (CSE: RYAH), have created an ecosystem of IoT devices and software aimed at making remote/virtual health service provision possible for as many people around the globe as possible.
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