Utilizing Big Data for Healthcare Sector

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Introduction Utilizing Big Data for Healthcare Sector

This Big data has proven its value by making its presence known across all industries. The healthcare sector has not been left behind. The need to facilitate quick and efficient patient care has increased in recent times. Technology and medical care seem to be going hand in hand towards this change, with the rise in robot-assisted surgeries, augmented and virtual reality used to view the complexities of the human body…the list goes on. Big data is all set to revolutionalize the healthcare industry! Patient records, hospital data, test results – all form a part of big data. Domain experts and data scientists have only recently tapped what could be the vast potential of big data. Who could imagine a few decades ago that big data could actually save lives!

Let’s discuss some applications of Big Data for Healthcare Sector that have already been implemented in the medical domain.

1. Biometrics Tracking

Wearables and smartwatches have become increasingly common these days with the need and raising awareness of exercising and health tracking. A large amount of data is collected by such devices, including but not limited to heart rate, sleeping patterns, SpO2 readings, glucose, etc.

Classifiers and detection systems are constantly being tweaked and developed with more precise machine learning algorithms. These are extremely useful in identifying and tracing common ailments in regards to heart and respiratory problems. These wearable devices have the potential to replace oximeters and glucose monitors in the near future! The early detection of potential health issues by continuous monitoring of such data can help hospitals minimize high-risk situations that could arise. Possible health issues are also easily detectable. This kind of data enables medical organizations to provide sufficient care before the condition gets out of hand. It also increases the user’s awareness of what their body is going through without having to depend on a doctor all the time.

Popular smartwatch brands like FitBit and Apple Watch are researching ways to detect atrial fibrillation. If successful, wearers can immediately be notified to seek medical attention.

2. Aiding high-risk patients

This is probably one of the best use-cases of data science in the medical domain. The ability to detect risks and provide a diagnosis in the early stages is seen as data science’s best way to modernize healthcare. By recognizing patterns and patient behavior, it is possible to deliver precise medications and personalized care. For example, if a hospital has completely digitized its records, it can be easier to detect patients who are admitted frequently and identify potential threats. In addition to this, high-risk patients with a similar pattern can also be treated and an efficient prognosis can be provided.

Thus, big data analysis in healthcare can help in detecting patients who opt for medical services more than the required threshold.

3. Improving Diagnosis and Efficiency

Humans are all but prone to errors. But these errors prove to be far too costly and bleak especially in the healthcare sector where human lives are at stake. How many times have you heard about wrongful deaths due to misdiagnosis?

Data science has been employed to increase the efficiency and accuracy of diagnosis. With records of patient allergies and symptoms, big data systems can be employed to choose the effective treatment and right medication. Thus, we can avoid incorrect prescriptions in this manner. This helps physicians up to a great extent from being prone to human errors. This in turn also reduces the overall cost of treatment by providing a patient with precision medicine. Big data technology coupled with physician’s insight has proven to result in improved patient care, lesser frequent readmissions, and shorter hospital stay.

4. Genome Mapping

The study of sequencing and genes and DNA constitutes the science of genomics. Genome mapping specializes in finding characteristics, abnormalities, and irregularities in DNA. This further facilitates the detection of diseases, certain symptoms and health conditions. Genome mapping can also be used to test drug and vaccine reactions and antibody generation. Without data analysis and machine learning techniques, genome mapping proved to be an excruciatingly time-consuming process. With the help of data analysis and big data tools, analyzing human genes has been made efficient.

5. Cancer Diagnosis

The early detection and treatment of cancer can be vital in increasing survival rates and avoiding its lethal later stages. Cancer detection applications have been developed. Such applications are fed specific data in order to detect the cancer and its variants. Deep learning neural networks have also been implemented for brain tumor segmentation.

Challenges in implementing Big Data For Healthcare Sector

  • Data privacy and security

    The importance of data privacy has gained momentum these days especially when Whatsapp announced its security changes. Now imagine having to keep secure critical patient records and user data! User consent and authorization must be implemented before healthcare organizations use their data. Even while implementing a centralized data warehouse, special care must be taken to avoid privacy breaches and ensure safety of patient’s data.
  • Data aggregation

    Records of patients can be across hospitals. Sometimes, they could even be across different districts and countries in some scenarios. Even for a single hospital to aggregate all the records from the past few decades will pose to be a challenge. Information of patients is spread across different administrators, servers, and often in arcane file cabinets as well. Systematic planning must be done in order to place all these records in a specified format. Data redundancy and noise should be removed from health records in order to maintain data quality and consistency. Such a process may take years to fulfill. But the outcomes that can be generated from aggregating patient records are more than fruitful both for patients as well as medical workers.
  • Data management

    After implementing privacy and security strategies, the healthcare organization needs to finalize how they wish to manage their data. There are various cloud infrastructure as well as on-premises data warehouses available to serve this purpose. Improper data storage and management can result in unnecessary issues and struggles.

What the future looks like Big Data For Healthcare Sector…

Big Data For Healthcare Sector is leading the front line of technological innovations that are changing the face of healthcare. The advances in medicine along with big data technology offer solutions that enhance patient care and drive excellence in healthcare organizations. Big data has also made us understand the present global pandemic COVID-19 and help us develop measures to thwart it during subsequent waves. The number of hospitals that are implementing such data-driven patient care is still scarce in India. But with the rise in demand of data scientists, this too will soon make steady headway. Although most big data applications are yet to be utilized and discovered, it is exciting to see what the future has in store for us!

In this article we discussed about Utilizing Big Data For Healthcare Sector, also you can read how data science application in healthcare sector

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