Data science has completely revolutionized sectors across the global economy. Medicine and healthcare is a promising industry to implement data science applications as the industry is generating a copious amount of data every day. Data from wearables, electronic medical records, billing clinical systems, all of such aspects churn massive volumes of information which can be leveraged to enhance the healthcare sector. Such information is a pool of actionable insights and intelligence which will further optimize healthcare operations and unlock the potential of data. Data science is furthering medical science to a whole new level, from drug discovery to computerized health records. And this is just the beginning of the journey of data science. According to Stanford Medicine’s 2018 Health Trends Report, AI could help reduce health care costs by $150 billion by 2026 in the U.S. alone
There are innumerable use cases of data science applications in the healthcare industry which is opening doors for future development in medicine.
Medical Image Analysis
Data science applications have been of immense help in the healthcare sector. A lot of research has been undertaken in this area. A study published in BioMed Research International on big data analytics in healthcare suggests that known imaging techniques include mammography, magnetic resonance imaging (MRI), x-ray and others. Various methods are used to handle the difference in resolution, modality, and dimension of these images. Other techniques are being developed to enhance the image quality which will further help to extract data from images in an efficient manner to come up with accurate interpretation. Image processing techniques focus more on segmentation, and enhancement which allows for a deep analysis of an organ and aid in the detection of disease conditions. Applications aim to detect organ delineation, tumor, artery stenosis etc.
Drug discovery isn’t a one day process neither a flash in a pan success. The process is highly complex involving various disciplines, more of a trial and error approach with heavy financial and time expenditure. Data science and machine learning algorithms simplify the process and shorten the time for drug creation. A step by step process allows for screening the initial drug compounds and takes up to the prediction of its success rate based on certain biological factors. These algorithms working in the back end uses advanced mathematical modeling which forecasts the reactions of the drug in a body instead of the traditional ‘lab experiments’. The techniques are competent enough to predict the side effects of certain chemical combinations as well. It further allows for testing of certain chemical compounds against all possible combination of cell types, genetic mutation, and other conditions.
Diagnosis forms a critical part of the patient care cycle since diagnostics determine the nature and duration of treatment to be provided. A story on BBC revealed that diagnostic errors resulted in about 80,000 deaths in the United States. Using data science, analysts can apply deep learning techniques to provide an extensive clinical report and aid in quicker and accurate diagnostics. It allows detecting early signs of a symptom or disease to enable doctors to provide a preventive care treatment, rather than treating the disease after it has seeped in. In the near future, early diagnose of fatal diseases -like cancer and diabetes- can help to save a lot of lives.
Adoption of smart health devices are quickly becoming ubiquitous. Apart from being a trending accessory, it also encourages self-health management in its users. The device records essential readings like heart rate, blood pressure, pulse, sleep pattern etc. These devices are connected with a mobile device, and the amount of data generated is massive in its scale which is stored in the cloud. Using applications of big data, data scientists can analyze raw information from the data provided by the wearables to deduce meaningful insights. Such data can act as an early detection mechanism, making it possible for medical practitioners to provide preventive healthcare and also act as an early warning system.
Managing Patient Data
In the data management area, application of machine learning techniques allows for the creation of comprehensive storage of medical data. This eliminates the need for paperwork and is transferred to a more promising digital platform. The ever-evolving machine learning algorithms are making it possible to use and exchange information to help in diagnostics and treatment decisions. All these are possible due to data captured on the digital front. Analysts can leverage big data where they can identify certain diseases, help in further research.
Data science solutions and applications are reshaping the face of the medical industry. It is helping to uncover deeper and hidden insights. The possibilities of data sciences are expanding as the data is increasing at an unprecedented scale, the future is promising.