7 Best Advantages of Data Science in Healthcare Industry
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Technology has come to dominate and disrupt almost all aspects of our life, and so is also good for healthcare. Many healthcare companies and concerns are increasingly spending on use of data science. It is the latter which will actually spell complete industry transformation. There is a huge growth of data in terms of clinical documents annually, which implies that there is a massive database which is constantly building to help doctors carry out their research. In addition to this, very high volumes of health-related information can be accessed via widespread adoption of wearable technology. Thus, the opportunities in the field of more informed healthcare are rising. Thus, the scope of data science for healthcare cannot be ignored or neglected.
This is no secret that despite the amount of data and technology at hand, there are still incidences of failed or faulty diagnosis. The alarming rates of diagnosis failure have pressed the need for using data science which can help push up better diagnosis rates. Data science algorithms can help reading of imaging data from X-rays, CT-scans, etc. It furthers helps in data analysis by checking the results with the huge database of clinical reports and other research studies. Significant improvement can be seen in diagnosis and at a faster rate.
The way scientists collect and analyze the healthcare data for locating the exacts symptoms and thereby identification of diseases, it is now possible that doctors can track the clinical history of the patients by precise diagnosis. Data science helps doctors to give more informed care, and the treatments become more personalized. This helps in bringing down the death rate, which means much more predictable outcomes. Health-care has become more precise due to the adoption of Electronic Health Records to rapid progress seen in the genome sequencing; there is enough information available with the physicians to be able to extrapolate their diagnosis to identify consistent symptoms of disease and creation of accurate profiles of patients. Thus, there has been an apparent boost in precision medicine.
Data science helps the healthcare personnel by optimizing various operations of the hospital. It is making use of machine-learning for various business processes in industries like finance, retail, and even healthcare that better data interpretation is being done which in turn leads to intelligent engagement of users and apparently improved business performance. It is a known fact that almost 80 percent of the data we collect remains unexplored, so the data science helps bridge this gap and gives you deeper insights into the hitherto ‘dark data.’ It is also useful for tasks like scheduling of clinical staff, reduction in wait times, supply-chain management, and even building effective programs for epidemics.
Data Science helps by checking the prescriptions against all the similar cases which have occurred before and are in the database and lets the doctor know about any deviations from the typical plan of treatment. This is helpful to the patients as they able to get the most accurate prescription and for the hospitals, this means a reduction in risk of lethal results, and also the elimination of unnecessary expenses which arise from unwanted readmissions or extended stays in the hospitals.
As in other businesses, even in healthcare, the increase in digitization and technical transformations bring down the costs. It is a growing trend that analytics-backed preventive medicine also contributes to the overall reduction in healthcare costs. It is by the help of algorithms that hospitals can identify patients who are most at-risk and thereby help to coordinate the much-needed care. Thus, both hospital admissions and re-admissions are reduced. Also, the analytics help doctors to make better decisions which help in significant savings.
The primary aim of every healthcare organization is to be able to give quality treatment at affordable costs. This is achievable only if the healthcare providers can maintain high standards of diagnosis and services to the patients. It has been noticed that a huge amount of data in healthcare often leads to complications in making decisions. All the data in the form of call center records, doctor’s notes, prescriptions, reports, results of lab tests, etc. have to be stored quickly and stored safely so that it can be used effectively. All this raises the need to have data scientists with basic medical knowledge and deep understanding of the needs of the healthcare, on board.
Every medicine is the result of years of investment of time and resources in research. Despite this, many medicines fail to achieve the desired results. Data science can help the laboratories to reduce this effort significantly by combining inputs of case studies, results of lab tests, records of previous treatments, medical history, allergic reactions, etc. by various machine-learning algorithms. These help in the creation of simulations which tell us beforehand that the drug is not going to have desired results on the human body. The response of various genes to the drug can also be predicted. This tremendously increases the speed of the research and also brings down wastage of resources in fruitless research.
Thus, Data Science has limitless possibilities in healthcare and preventive medicine as there is a humongous growth in data which needs to be collected, managed, and analyzed for better diagnosis and prescription. Data science is also helpful in reduction in wastage of resources in research of drug discovery, which will not give fruitful results. Overhead costs of patient admissions and readmission are also checked as the doctors can focus on patients who need more care. Prediction of outcomes of various treatments, more effective care of patients, curing of deadly diseases like Ebola, cancer, etc. data science has proven itself as an invaluable contribution to the healthcare industry. It has made the healthcare from being an unstructured profession to a highly streamlined, efficient, and more personalized industry.
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