Healthcare is often a matter of life and death and the ability to analyze data quickly and accurately for use in decision making is critical. Using data analytics, this task that rests so heavily on the shoulders of administrators and healthcare providers can be lifted.
Data analytics refers to the interpretation of quantitative data to provide insights, identify trends and answer questions. When applied to healthcare, easier and more impactful decision-making can positively impact the health outcomes of individuals and the general populace.
The use of data analysis in healthcare
You will recall from our previous article that there are four (4) main types of data analysis that can be used in healthcare in the following ways:
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Descriptive analysis: examines past events, for example looking at the spread of a virus in a population by examining the positive test rate in a specific time.
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Diagnostic analysis: tries to understand why something happened and can be used to diagnose a patient’s injury or illness
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Predictive analysis: uses historical data, past trends, and assumptions to try and forecast the future, for example using case data from the past to try and forecast the spread of a disease
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Prescriptive analysis: identifies actions to achieve a particular outcome. This could mean looking at a patient’s medical history to determine their risk for developing conditions in the future and using targeted preventative treatment to lower that risk.
Applications of data analytics in healthcare
The uses of data analytics in healthcare are deep and varied, so today, we will just be touching on a few of them:
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Medical Research: when used in research, data analytics techniques can be used to gather and analyze clinical data from many different sources including electronic health records, electronic medical records, public health records, and personal health records. This can help researchers to identify ways to improve the efficiency of clinical processes and healthcare operations, lead to more accurate diagnosis and personalized treatment plans for patients and can provide insight into disease causation by linking risk factors and health outcomes.
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Using patient data to improve health outcomes: data analytics can collect and analyze data in real-time, thereby reducing errors and admission rates while simultaneously identifying systematic ways to improve patient health. Patient data used in these types of analyses include their blood pressure, the results of blood tests and the patient’s wishes.
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Gaining operational insights: the efficiency and quality of healthcare can be greatly improved using data analytics to assess current conditions and enhance organizational efficiencies such as charting, financial management, emergency preparation, and compliance.
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Predicting outbreaks: a very relevant example is the use of data analytics to predict the possible spread of illnesses so that schools and hospitals can be properly prepared. With the coronavirus (COVID-19) pandemic by analyzing data, future spikes can be predicted so that hospitals can get sufficient personal protective equipment and patient beds, or school administrators can decide whether in-person learning is advisable.
We cannot wait to see the continued use of data analytics in making transformative changes to the healthcare industry to improve the lives of patients and providers alike.
References
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Cote, C. (2021, February 18). 3 APPLICATIONS OF DATA ANALYTICS IN HEALTH CARE. Harvard Business School Online.
https://online.hbs.edu/blog/post/data-analytics-in-healthcare
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Maryville University. (n.d.). 4 Benefits of Data Analytics in Healthcare.