Healthcare is a promising industry for implementing Data Science solutions. Years before the rise of IT departments in the hospitals, the patients’ data were collected and stored, and each client had an archive file describing his health history. This data is highly valuable for improving diagnosis, analyzing symptoms, and making it easier for medical professionals to take the right decision.
Here are seven use cases explaining more in detail the importance of Data Science in Healthcare:
- Cost Reduction:
The main goal of healthcare organizations is to provide quality treatment at a reasonable cost. Predictive analysis helps to reduce cost as hospitals can plan resources (optimal staffing) and rate of investment, thus reduction in the bill. It also allows the cash flow to the hospitals by determining the accounts that demand payment and which payments are likely to remain unpaid. Moreover, predictions allow knowing earlier about a disease, thus lower bill amount.
- Prioritizing high-risk patients:
Digitalized data is easier to analyze for having a better understanding of diseases and chronic issues about each patient, and faster to sort by the emergency of the case to be admitted.
- Preventing avoidable harm:
With real-time data available and able to be analyzed fastly, answers on questions about specific diseases become more precise, taken actionable decisions become more accurate, and the risk of prescription errors becomes very low. Data Science may allow the healthcare industry to recognize the patients who are more likely to develop specific diseases and to prepare wellness programs in which the patients are interested, for them to enjoy improved health.
- Customer Services:
Data Analytics help understand patient’s need accurately, allowing to provide faster answers to questions. Virtual assistance is also an added value for the patients who may get answers to their questions from a bot having full information about them.
- Eliminating diseases:
Predictive analysis helps to prevent the occurrence of a disease, by taking prior needed precautions. By analyzing and predicting the possibility of malaria in a specific region for ex., hospitals of this region will be able to take actions and vaccinate the people there earlier, thus prevent malaria cases.
- R&D and drug discovery:
It takes a significant amount of time, research, testing and money to develop a drug to be launched to the market. Data Science, on the other hand, can deliver a various set of biomedical data obtained from tests, treatment results and even social media to simulate the drug interaction with body and predict the rate of success. This simulation will help to speed the process, reduce the cost and time invested in the development, and mitigates the risks of failure.
- Health tracking:
Wearable technology data is an added value to the healthcare industry. Tracking sleep, heart rate, daily exercise, and distance walked helps determine the patient’s blood pressure, pulse rate, and sugar levels. Apple Watch and Fitbit as an example are helping people track their health information, and at the same time allowing hospitals to have an overview of the patient’s lifestyle and diseases he has or may have.
The importance of the contribution of Data Science in the healthcare industry is very high, from predicting treatment risk to curing diseases. Big Data fed into machine learning algorithms is already helping physicians improve patient care: IBM Health collaboration with Mayo Clinic is a real-life example.
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