Data is everywhere. From small businesses to large multinational organizations, data is used in almost every area of study and work. The small mathematical problems solved by a child, to the complex functions executed in large organizations, data is used almost everywhere.
Data is one of the most important components of any organization, because it assists leaders in making decisions based on absolute certainty, comprising of facts, statistical results and trends. Any result based on correct and concise data tends to be correct. Data can reveal a lot about an organization, and organizations rely heavily on this data.
Due to the growing relevance and importance of data, data science came into the picture. Data science is a multidisciplinary field. It uses algorithms, scientific procedures and approaches to derive conclusions from massive amounts of data. This data can be either structured or unstructured.
In this article, we shall be looking at data science in the healthcare industry.
Medicine and healthcare are two of the most important components of our lives. Traditionally, medicine and medical advice was given solely by the doctors based on the patient’s symptoms. However, this was not always accurate and was prone to errors. With the advancements in the field of data science, it is now possible to obtain a more accurate diagnosis.
The new health data science perspective allows applying data analytics, that are collected from various fields, to augment the healthcare sector. There are several areas in healthcare, such as drug discovery, medical imaging, genetics, predictive diagnosis and others which make full use of the results derived at through data science techniques. With ERM’s, clinical trials and internet research, there is so much data being accumulated every day. With the majority of people seeking healthcare advice online, gathering data has become increasingly convenient.
Let us now try to derive an insight into how data science and healthcare can become mutually beneficial.
1. Data management and Data Governance: The opportunities derived from managing data efficiently are extensive. When data is managed effectively, it makes information easily accessible to all those in the healthcare industry. When data is analysed and shared effectively among doctors and healthcare providers, it will enable them to be more personal and humane in their approach towards treatment. Since the healthcare sector has its fair share of risks, data analytics should always be at the top of its game; it should be up-to-date and acute.
· Each patient’s medical records can be combined into one dataset, and then analysed and utilised when needed, to derive at the required conclusions.
· Data management also involves data sharing. Data can be shared across several datasets, eliminating the need for excessive office work.
· When data is analysed repeatedly, it will bring out any and all errors in clinical data.
· Cloud-based clinical software enables faster processing of data, leading to time saved when deciding on treatment or obtaining test results.
· Machine learning assists in shortening the process of drug discovery.
While data governance has been recognized as crucial to healthcare, there are opportunities to expedite the prioritization of data governance, so that data is accurate, complete, structured, precise and available. Data governance plays a pivotal role in patient engagement, care coordination, and looking after the overall health of the community. If data is not governed properly, different healthcare companies will release inconsistent data which will prove to be a major hindrance. Healthcare data science apps exist in order to avoid such inconveniences.
2. Workflow Optimization and Process Improvements: Big data analytics is not as profound in healthcare. Hence, certain decisions are taken based on the ‘gut instinct’. Data science improves the workflow in the following ways:
· Lesser time taken and more precise outcomes lead to more effective work processes.
· Healthcare providers and other staff get the chance to perform more tasks in limited time.
· More effective work processes lead to higher recovery rates, faster reactions to crises and, in turn, less fatal results.
· Patients get more personalized treatments.
3. Medical Imaging: Medical imaging refers to the process of creating a visual representation of the body for medical analysis and treatment. If is a non-invasive method for doctors to look inside the human body and decide on the required treatment plan. With the swift growth of healthcare and artificial intelligence, this process of medical imaging becomes easier. Some of the types of medical imaging include tomography, longitudinal tomography etc. The primary methods of medical imaging are X-ray computer tomography (CT), PET, and MRI. Medical imaging needs the images to be absolutely accurate. Even minor discrepancies might lead to disastrous results, which can be catastrophic to the patients. The images need to be precisely viewed and interpreted. Data analysis refines these images by enhancing their characteristics like
· Modality difference
· Image size
· Resolution.
Doctors need not worry about the accuracy of the images at all.
4. Genetics/Genomics – Treatment personalization: With the introduction of new technologies, including new forms of genomic profiling or sequencing, it provides a new look at the world of genomics. The massive amounts of data today produce genetic data faster than ever. This is partly because the techniques of structuring data, lag behind the ability to actually get the data. Healthcare data science produces copious amounts of data, but that data needs to be made sense of. Some of the challenges in the field of genomics are:
· Studying human genetic variation and its impact on patients
· Identifying genetic risk factors for drug response
DNA Nanopore Sequencer is a tool that helps patients before they suffer from septic shock. It provides genetic sequences mapping, which abbreviates the time span of the information preparing activity. Moreover, this tool recovers genomic information, BAM document controls, and provides calculations.
5. Predictive Analysis: Predictive analytics refers to a technology that learns from experience, i.e. data, to predict a patient’s behaviour. It builds a connection between the data and the consequent actions which need to be taken based on that data. Predictive analytics allows healthcare to use predictive models or models found specifically in health data science. This allows identification of risks even before they occur. However, there are some drawbacks to predictive analytics.
· Lack of coherent healthcare information exchange between the systems
· Shortage of skilled workers to fill knowledge gaps
These hurdles can be eliminated using the following types of databases:
· Medical records
· Ongoing condition charts of patients
· Medication databases
· Genetic research and other uses
6. Drug Research: If we look back to the time of another major pandemic, the Spanish Flu, we see that drugs and vaccines took a considerable amount of time. But now, with the help of data science, data from millions of test cases can be processed within weeks. Development of vaccines and other drugs has become easier and less time-consuming.
Healthcare has a vast amount of data being generated every day. This data needs to be made sense of, it needs to be structured and organized so that meaningful conclusions can be derived at from the data. The healthcare industry needs to heavily utilize this data so that patients’ lifestyle can improve, diseases can be predicted before their inception. Moreover, with medical imaging analysis, it is now possible for doctors to find even the most microscopic tumours. Doctors can also monitor the conditions of their patients from remote locations.
Data science is already doing wonders for the healthcare industry. It is only a matter of time before it proves itself to be invaluable.