Data analytics involves looking at raw data for patterns and insights to help in decision-making. It’s applicable to any and all businesses, but notably those in the medical field. It is crucial to use data analytics in the medical field. According to Harvard Business School, it aids Data Analytics in Healthcare Industry in doing tasks such as assessing and training practitioners, identifying scan abnormalities, and forecasting disease epidemics.
Data Analytics in Healthcare Industry has the potential to improve business intelligence while also reducing expenses for Healthcare Industry. Most significantly, it aids medical institutions in making more informed choices about patient treatment.
The following are seven important applications of Data Analytics in Healthcare Industry.
1. Medical Cost Reduction
The use of Data Analytics in Healthcare Industry within commercial applications is possible. In this particular instance, one specific example that I came across was the use of data analytics to cut down on medical expenses.
Opioid-Induced Respiratory Depression was discovered to be more expensive in research conducted by Medtronic, and it was revealed that data analytics would be able to greatly cut that cost, by an amount equal to $535,000 per year!
2. Clinical Risk Prediction
In a research that was carried out in Singapore, patient similarity analytics was used in order to uncover common patient traits. This is analogous to the example that was just shown. Because of this, it will be possible for you to determine if a person is more or less likely to be sensitive to a certain ailment!
Common machine learning methods such as K-nearest neighbors, logistic regression, random forest, and support vector machine models were used in this data analytics research. Python is the name of the programming language that was used in the development of a tool to help clinical decision-making that was based on these models.
3. Drug Discovery
One of the most important uses of data analytics that has been around for some time in Data Analytics in Healthcare Industry is the development of new drugs. A good illustration of this may be seen in the pharmaceutical industry with the company AstraZeneca, which uses data science and artificial intelligence (AI) to make predictions about prospective new medications.
Data Analytics in Healthcare Industry uses conventional methods of drug discovery that are exceedingly time-consuming and rely only on a process of trial and error to find potentially useful medicines. On the other hand, in this particular instance, AstraZeneca has formed a partnership with BenevolentAI in order to find a unique AI-generated target for chronic kidney disease!
4. Genomic Studies
There is a growth in the number of people interested in gaining insight from their healthcare data, which may be attributed to the vast quantity of data that is created by healthcare apps in Data Analytics in Healthcare Industry.
There are genetic studies being conducted right now with the goal of gaining a deeper understanding of our bodies by analyzing the data. One of these startups is DNAnexus, which uses artificial intelligence to assist humans in better comprehending the workings of our bodies by analyzing genetic data. Their flagship products are known as Titan and Apollo, and both of these products include the use of healthcare data in some capacity.
5. Patient Safety Improvement
According to a poll of patients that was conducted in the United States in 2017, more than 21 percent of individuals in the country have experienced a medical blunder for themselves, and 31 percent had seen one in the medical care of a friend or family. Because every error has the potential to cost someone their life, this activity carries a very high level of danger. Consider the following illustration to see how data analytics has contributed to the improvement of that!
6. Fraud Prevention
According to Data Analytics in Healthcare Industry estimates provided by the National Healthcare Anti-Fraud Association, fraud costs the United States healthcare system up to $68 billion per year. Fraud prevention, a less well-known aspect of the Healthcare Industry, is equally essential to ensuring that a hospital makes effective use of all of its available resources.
The following is an example of a genuine use case for data analytics, which involves the prevention of fraud committed by healthcare practitioners.
In Dataiku, methods from the field of machine learning are used to assist in the identification of fraudulent activity in Data Analytics in Healthcare Industry. Their technologies are able to identify interesting trends as well as oddities like this. These irregularities serve as a warning sign for any potential scam. Find out more about their unusual behavior detection here.
7. Prediction of Mortality During Hospitalization
COVID-19 has had an effect on hospital mortality, and the field of data science has had an effect by contributing to the ability to forecast in-hospital mortality via the use of machine learning models.
COVID-19 patient data were reviewed as part of a research that took place at the Robert Wood Johnson University Hospital (RWJUH) in New Jersey, United States. The purpose of the study was to identify possibly increased death rates.
