Industry Applications of Data Analytics

How applications of Big Data drive various Industries – Big Data & Analytics within the Healthcare Sector are revolutionizing the management of tedious tasks.

Big Data has become a game-changer in most of the modern industries if not all in the past few years. While Big Data continues to permeate our day-to-day existence, there has been a significant shift of focus from the hype it has created to finding real value in its use. Therefore, understanding the value of big data continues to remain a big challenge, other practical problems such as funding, ROI (Return on Investment) and skills continue to remain at the forefront of several industries who are adopting big data. The primary goal for most organizations to adopt big data is to enhance customer experience, improved target marketing, cost reduction, and simplify existing processes and increase efficiency. However, in recent times, data breaches have made enhanced security a critical goal that big data projects focus on incorporating.

More so, where do the industries stand when it comes to big data, and you will find that they are either:

  • Trying to understand whether there is real value in big data or not
  • Evaluating the market opportunity size
  • Developing new products and services which will utilize big data
  • Already using big data solutions. Repositioning existing products and services to use big data
  • Already utilizing Big data solution

With the above in mind, having a bird’s eye view of big data and its application in different industries will help to appreciate better the future across industries. In the next few articles, I shall examine the top ten industry verticals that are using big data, industry-specific challenges, and how big data solves the problems.

Healthcare Industry: At first glance, the worlds of big data and Healthcare may not seem as though they have anything in common. Other than contributing to the increase in profits or cutting down resource overheads, Big Data has found quite a widespread of applications within Healthcare to predict epidemics, preventable deaths, cure diseases, and improve quality of life. As the global population continues to burgeon, the quality of life has grown manifold. People live longer; the healthcare and medical sectors had to transform rapidly and adapt quickly to cope with newer methods of treatments, transmission and delivery. As the technology strengthens its hold, big data solutions seek to harness the massive complex bucket of data to gain more focused insights and knowledge into the world of Healthcare.

Data now drives the decision making process. The focus now rests solely on thorough patient diagnostics at the earliest stages to pick up any signs of a potentially serious illness and to make treatments much more simpler. Experts believe that big data empowers the caregivers, management and scientists to make informed decisions, enabling them with the capability to save lives, reduce costs, and improve operational efficiency. Big data also can revolutionize laborious tasks of how the healthcare industry gather, store and transmit patients information. There are mainly seven sources in Healthcare which produce the bulk of data:

  • Electronic Health Records (EHR): Clinical records with patient details.
  • Laboratory Information Management system (LIMS): Contains lab results
  • Monitoring and diagnostic instruments: Data from instruments like MRI.
  • Pharmacy: Medication details of the patient
  • Instruments and human tracking system: Data contains location information of devices and people.
  • Insurance claim and billing: Contains insurance claims and billing details
  • Hospital Resources: Employee list and hospital supply chain details.

The above data sources are getting further enriched with newer forms of data as technology advances. E.g., Some hospitals collect Genetic Information in the EHR. Within this massive variety of data lies valuable insights, which applied judiciously can bring incredible results and make a significant impact.

  1. Disaster Planning: Both human-made and natural disasters will invoke tremendous pressure on the healthcare systems in that location. During any emergency, the demand for a particular service will increase way beyond its capacity. E.g., during an epidemic outbreak, the need for ventilators will potentially increase. Having a real-time location and availability of such facilities will be helpful to the authorities in managing the disasters seamlessly. With the help of predictive analytics, it is possible to predict such outbreaks enabling preventive measures to be taken.
  2. Patient Flow: Healthcare is a time-critical service, and data analytics plays an essential role in ensuring the smooth flow of patients while reducing the waiting time. Prediction of patient surge will help the hospital authorities take the necessary action to minimize the waiting time of patients and thereby providing timely treatment.
  3. Effective Resource Management: Location tracking technologies such as GPS or RFIDs are used in providing real-time identification, management and tracking of instruments within an organization. Along with tracking devices, now such technologies are increasingly being used to manage and track patients and staffs. Data from such services can be used to enhance patient care, staff management, and resource utilization.
  4. Cost and Effectiveness: Data Analytics is used for comparing the cost and effectiveness fo the treatments, public policies, etc. Organizations could use cost and outcome data to monitor the effectiveness of medicines and stop prescribing medications that aren’t effective enough.
    Fraud Analysis and Pre-adjudication: Hospitals receive large numbers of insurance claims daily. Big data analytics can be applied to process the vast amount of claims to reduce fraud and misuse.

Besides, the above Data Science is also being applied in the following areas within Healthcare and biotechnology:

  1. Medical Imaging: The first and primary use of data science in Healthcare vertical is through medical imaging. There are different kinds of imaging techniques like X-Ray, CT Scan and MRI. These techniques visualize the inner parts of the human body. Traditionally, doctors would have to inspect the images to find any irregularities within them manually. However, it was often tough to find microscopic deformities, and as a result, the doctors couldn’t advise proper diagnosis. With the advent of technologies such as deep learning in data science, it has now become possible to find any microscopic deformities in the scanned images. Through the image segmentation, it is possible to search for defects present in the scanned images. There are also other imaging processing techniques such as image recognition, edge detection, image enhancement and reconstruction, etc.
  2. Genomics: Genomics is the study of analyzing and sequencing of genomes. A genome consists of all genes and DNA of the organisms. Even since the compilation of the ‘Human Genome Project,’ the research has been rapidly advancing and has inculcated itself into the realms of data science and big data. Before the availability of computational powers, the organizations spent a lot of money and time on analyzing the sequence of genes, which was a tedious and expensive process. With the advanced data science tools, it is now made possible to analyze and derive insights from the human gene in a much shorter time and lower cost. The goal of the research scientists is to analyze the genomic strand to search for defects and irregularities. They also find the connection between the health of a person and their genetics. Researchers use data science to analyze the genetic sequences to find a correlation between the parameters contained within it and the disease. Furthermore, research in genomics also involves in finding the right drug that provides more in-depth insight int eh way particular drug reacts to the specific genetic disorders. There is the latest discipline that combines genetics and data science called ‘Bioinformatics.’ Nevertheless, there is an ocean which remains uncharted. Advanced fields such a genetic risk prediction or gene expression prediction, etc. are still being researched.
  3. Drug Discovery: Drug discovery is a highly complex discipline. Pharmaceutical industries are heavily relying on data science to solve their problems for making better drugs for the people. Drug Discovery is a time-consuming process which involves huge capital expenditure and extensive testing. Data Science and Machine Learning algorithms are revolutionizing this process and can provide deep insights to optimize and increase the accuracy of the predictions. Pharmaceutical companies use patient insights such as mutation profiles and patient metadata to build models for finding statistical relationships between the attributes. Using this method, the companies now design drugs that address the critical mutations in the genetic sequences. Deep learning algorithms can also find the probability of the development of disease in the human system. With a combination of drug-protein and genetic binding database, innovations are being explored. Using machine learning algorithms researchers can build models that compute the prediction from the given variables.
  4. Predictive Analytics: Healthcare is an essential domain for predictive analytics. Predictive Analytics plays a vital role in improving patient care, chronic disease management, increase supply chain efficiency and pharmaceutical logistics. Population Health management is increasingly a popular topic in predictive analytics. It is a data-driven approach for prevention of diseases that are commonly prevalent in society. With data science, hospitals can predict the deterioration in a patient’s health and provide preventive measures by starting the treatment early; which will assist in reducing the risk of further aggravation of the disease and patient’s health.
  5. Patient Monitoring: Data Science plays a critical role in the Internet of Things (IoT). The IoT wearable devices are that which track the temperature, heartbeat, and other medical parameters of the users. The data collected is analyzed using data science. The analytical tools help the doctors to keep track of the patient’s circadian cycle, calorie intake, and blood pressure. Besides wearable monitoring sensors, the doctor can also monitor a patient’s health through home devices. For patient’s with chronic illness, various systems track the patient’s movements, monitor their physical parameters and analyze patterns that are present in the data. It also makes use of real-time analytics to predict if the patient will face any problem based on the current condition. Furthermore, it helps the doctors to take necessary action to help the patients in distress.
  6. Tracking and Preventing Diseases: Data Science plays a pivotal role in monitoring the patient’s health and notifying the necessary action to be taken to prevent any potential diseases from taking place. Data Scientists are using advanced analytical tools to detect any chronic conditions at an early stage itself. In many extreme cases, there are instances where, due to negligence, diseases aren’t diagnosed in the early stages. This proves to be detrimental not only to the patient’s health but also the economic costs. As the disease spread, the cost of curing it also increases in proportion. Hence, data science plays a significant role in optimizing financial spending on Healthcare. There many cases where Artificial Intelligence (AI) have played a major role in the early detection of the diseases that are unheard of.
  7. Virtual Assistance: Predictive modelling has helped data scientists to develop a comprehensive virtual platform which assists the patients. The platform helps a patient to input his or her symptoms and get insights about potential diseases based on the confidence rate. Furthermore, patients who suffer from psychological problems like anxiety, depression, and neurodegenerative diseases like Alzheimer’s’ make use of virtual applications to help them in their daily activities.
  8. Telemedicine: For years, Telemedicine has been pitched as a way to democratize medicine by driving down costs, increasing access to care and making appointments more efficient. It sounds great—until you look at the data, and find that only about 10% of Americans have used Telemedicine to create a virtual visit, according to one 2019 survey. Coronavirus outbreak could finally make Telemedicine a mainstream and a safety valve for a strained Healthcare system. “Virtual visits” can be an effective way to decide who needs to be tested for COVID-19. However, remote doctors cannot diagnose or treat illness. Telemedicine is emerging as a possible filter, keeping those with moderate symptoms at home while routing more severe cases to hospitals. Telehealth giants like Amwell and Teladoc are now advertising their availability for coronavirus-related appointments, and Teladoc’s stock prices spiked in late February. XRHealth, a company that makes health-focused virtual reality applications, is this week providing Israel’s Sheba Medical Center with VR headsets that will both allow doctors to monitor COVID-19 patients remotely, and enable quarantined patients to “travel” beyond their rooms using VR, says XRHealth CEO Eran Orr. These regulatory issues, as well as a lack of patient awareness, have kept telehealth from being as widely adopted as it could be. COVID-19 could be “a good use case” for Telemedicine, but it will partially depend on lawmakers’ willingness to relax or at least streamline the regulation.

Besides, with the Coronavirus Pandemic, the massive epidemiological and scientific Big Data is enabling health workers, epidemiologists, scientists, and policymakers make more informed decisions in fighting COVID-19. The near real-time COVID-19 trackers that continuously pull data from sources around the world are helping healthcare workers, scientists, epidemiologists and policymakers aggregate and synthesize incident data on a global basis. There has been some new data resulting from GPS analyses of population movement by region, city, etc., which ultimately helps provide a view of the population’s compliance — or lack of compliance — with social-distancing mandates.

These are some of the key areas of how data science applications within the Healthcare sector. Big Data & Analytics in Healthcare has empowered doctors to fight against horrifying diseases such as AIDS and Cancer. Data Science has an immense impact on the Healthcare Industry. Data Science in Healthcare can address many health issues, save lives, and provide us enough time for taking precautions. Ideas such as creating large scale COVID-19 Real World Evidence (RWE) studies that pull data from a variety of real-world sources — including patients now be treated in the hospital setting — could help accelerate the development of treatments in a more patient-centric and patient-friendly way. We are just starting to see movement among advanced data aggregation service companies and virtual studies platforms in serving the life sciences sector. The most common aim is to connect assay results to clinical status in near real-time.

The data sets from the COVID-19 pandemic will likely form part of the evidence package that will be presented to regulatory authorities once a therapy or therapies have been identified that appear to be effective. This will potentially set a precedent for how data can be used in similar situations in the future. What we learn from approaches like synthetic control in the absence of randomized control populations will be effective in mitigating challenges for future epidemiological outbreaks.

To Summarise, Data Science has become the backbone of the Healthcare vertical. The healthcare and medicine sector has humungous utilization — Data Science for bettering the patient’s lifestyle and prediction of disease right in the beginning itself. There are many opportunities to make use of Big Data which would be highly valuable in situations like these as a society and as an industry. However, we have not yet been able to effectively leverage the power of Big Data in the search of a cure for Coronavirus. With the advancements in medical image analysis, doctors find it possible to identify microscopic tumors that are otherwise hard to find. Therefore, Data Science has disrupted the Healthcare and Medical Industry in a colossal way and will further transform the face of the medical industry when it can find a cure for COVID-19.

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