Large volumes of data are created by healthcare facilities in the form of electronic medical records, clinical trial data, billing, customer records management databases, etc. Every second, a tonne of new knowledge is published in scientific journals.
Such information is not standard and may be organised or unstructured. It’s time to identify, process, and use data that can be mined to provide priceless insights into disease, prevention, and therapy.
In order to identify patterns and determine what causes them, data can be gathered and evaluated in the healthcare industry. This allows for the necessary preventive or therapeutic action to be taken. This can lead to increased life expectancy, the development of early disease detection and identification tests, and the provision of treatment at a reasonable cost.
Health Information Exchange (HIE) is a way of using data that combines a patient’s clinical data from several medical facilities’ servers into a single record that can be accessed by all of the patient’s healthcare providers. The healthcare company needs have access to high-end computers, sophisticated software programmes, and AI-based algorithms in order to use and contribute to Big Data and fully realise the promise of this strategy.
The Rise of Big Data in Healthcare
The introduction of new technologies, advanced testing equipment, mobile applications, and wearables that offer some sort of healthcare, whether in the diagnosis or monitoring of disease, is mostly to blame for the increase in the availability of healthcare data. Big Data, defined as “data that is so huge, quick, or complex that it’s difficult or impossible to process using traditional methods,” has emerged as a result of this.
EMR, EHR, Web Services, Sensor Data, and Biometric Data are all sources of healthcare data. Research studies, general databases, government and patient portals, and others all play significant roles.
EHR stands for electronic health records, which are digital files used to store medical data. With the help of natural language processing (NLP) technology, which reads and analyses handwriting, this may now be done without the need for manual data entry. This can be combined with biometrics software to ensure that patient data is kept secure and that authorised staff can access it whenever they need it, wherever they are. The potential for using such technologies to quickly and accurately diagnose and track specific diseases is also being explored.
Using Big Data to Draw Inferences
Big Data in the healthcare industry is currently valued at over $15 billion and is expected to rise by about 20% over the next ten years. To get important insights into disease, new techniques incorporating machine learning (ML) and artificial intelligence (AI) are being used.
ML refers to computer programmes that use certain techniques, such as fuzzy logic and neural networks, to analyse data. Without human input, these algorithms assist the system in self-adapting to shifting data inputs. As a result, the system develops the ability to evaluate large amounts of data using precise approaches to provide the necessary output.
Data science is already gaining traction in the healthcare industry to improve the efficient and individualised delivery of medical treatment. Data mining describes the numerous procedures that are used to glean practical conclusions from unprocessed user data. In addition to regression and data storage, this might also comprise data grouping, clustering, correlation, and searching for patterns in the data.
One of the most promising subfields of data science in healthcare is the analysis of medical pictures with the help of ML. The enhanced identification of illnesses utilising mammograms, X-rays, and other diagnostic imaging technology has considerably benefited from the ability to detect patterns and identify anomalies via deep learning techniques, making them less dependent on observer error and inexperience.
Diagnosis and Monitoring
Once more, similar technologies are highly helpful in creating advanced microbiologic diagnosis techniques combined with genomic information about bacteria. Software tools that can distinguish between the distinct antigenic patterns and sequences of a microbe or a portion of a microbe can make it possible to diagnose infections quickly and safely without putting humans in danger. Additionally, this automated procedure may lessen the chance of human error.
Wearables make it possible to gather information on the user’s vital signs as well as numerous other factors, such as their sleep patterns, fluid consumption, and mood swings. This large amount of data may be evaluated in real-time to keep track of patients who are being followed up on and to spot deviations from the usual as soon as they are noticed so that urgent reporting and appropriate action can be taken.
Applications of Data Analysis in Healthcare
Providers and Payers
One area relates to healthcare providers, where data can be analyzed to help improve the efficiency of care and how well patients are shifted to their areas of care, in addition to ensuring that personalized care is obtained.
For healthcare payers like health insurance companies, data can drive applications to detect fraudulent claims but provide timely payments for genuine claims.
As for patients, they are the group that benefits the most from the proper use of data. Understanding disease and its causes, prevention, and treatment that comes from looking for patterns in medical data can improve the kind of treatment that is given, help tailor it for individual needs, and provide increased accuracy of diagnosis.
Using past data, combined with wearables, it is possible to track patients and identify rising biomarkers before a clinical event occurs. Analytic software can identify high-risk habits, predict the risk of specific health events, and suggest preventive plans. In addition, Big Data can help prevent and predict larger outbreaks.
Again, pharmaceuticals could benefit immensely from the use of data. Considering the mean cost of successful drug development, at over $2.5 billion, and the timeline of at least a decade, the advantages of using Big Data are obvious. The analysis of Big Data helps identify the drugs that are needed most.
The ability to search for and examine previous papers, patents and trials can uncover mechanisms of drug action on the body and may help correlate different events. These insights could speed up the development of new drugs built on previous research, using algorithms to, for instance, simulate the drug action in the human body or using real-world data to simulate a clinical trial.
Marketing and sales strategizing could also gain much from analyzing earlier data. And importantly, pharmacovigilance, which involves tracking and identifying adverse drug reactions post-marketing, can be carried out much better using adverse event reports (AERs).