Healthcare is among the few industries -- alongside aviation, defense, space research, and law, where precision in decision-making and certainty in action is of the highest importance. That’s because every major decision and action in this field can directly impact human life and health. To gain certainty and precision, and to improve the quality of healthcare delivery, healthcare organizations and professionals need to adapt to the latest advancements in technology.
It is no surprise that healthcare has taken well to big data analytics as a key enabler in recent years. Thanks to digital transformation in the healthcare industry, data collection has become more relevant, accurate, and faster.
So, what is big data in healthcare?
A large amount of data is being generated, consolidated, and analyzed continuously in the healthcare space. Healthcare data analytics solutions draw specific statistics from the data to research new advancements, simplify processes, reduce costs and make healthcare more affordable and efficient.
The “3 Vs” that characterize big data also apply to the data generated in the healthcare industry, as follows:
- Volume: The healthcare industry generates massive volumes of data through endless sources ranging from electronic health records to sensor-embedded wearables.
- Variety: Every operation in healthcare and medicine involves different types of data such as health parameters, drug proportions, behavioral data, among others.
- Velocity: A lot of healthcare data, such as that from intensive care units and wearable devices, is constantly generated in real-time, leading to data generation at an unprecedented velocity.
By leveraging all this data using analytics, healthcare institutions can provide better outcomes for patients as well as healthcare providers, such as:
- quicker, more accurate diagnosis of medical conditions,
- timely treatment to prevent terminal diseases,
- high success rates of surgeries and medications,
- increased productivity of physicians, and
- improved health in the general population.
And how exactly is big data leading to these outcomes?
Big Data Analytics and Data Science- How it is Aiding Healthcare
The demand and benefits of big data in healthcare are at an all-time high. In addition to helping them solve existing problems, big data along with other techniques like data mining, statistics, modelling, machine learning, and artificial intelligence also enables healthcare professionals to perform predictive analysis. Predictive analysis allows the professionals to identify emerging issues in patients and preemptively take actions that lead to the best patient outcomes.
Following are a few ways in which big data is revolutionizing the healthcare sector.
1. Aiding Diagnosis for Prediction and Prevention of Illnesses
Big data technologies in healthcare are being used to track a large number of indicative and causative factors of diseases and report the findings to doctors. The doctors can then use this information to perform tests that can confirm the presence of a medical condition and initiate appropriate treatment if needed.
It helps in improving healthcare by predicting which patients are at higher risks and arranging early intervention before the conditions worsen, thereby cutting costs to a great extent.
An example of this is the use of big data to identify patients with a high likelihood of developing cancer. By analyzing the large volumes of data from Electronic Health Records (EHR), scan reports like MRI and CT, as well as genetic data such as lab values and medical history, analytics can highlight data points and patterns that characterize a typical cancer patient.
The doctors can then investigate the information further to identify high-risk patients and monitor them for early signs of cancer. As a result, doctors can enable patients to avoid the worst effects of cancer, since it is easier to treat the earlier it is detected.
2. Expediting Clinical Research to Develop New Treatments
Clinical trials help in the development and testing of new treatments such as medicines and procedures. These procedures require the researchers to monitor a large number of test subjects, their individual EHR information, medical histories, their habits, allergies, genetic details, and other data that may be relevant to the study being performed.
Keeping track of all this data manually is virtually impossible, let alone analyzing it to find hidden correlations. Data analytics in healthcare can be used to analyze such data and find patterns and correlations that can help in assessing the effectiveness of different medicines with greater accuracy.
Researchers and analysts can explore the data from the analytics reports to verify and test their hypotheses before confirming the effectiveness of a drug or a medical procedure. As a result, new treatment methods can be commercialized quickly, benefiting both the patients and healthcare institutions.
3. Providing Real-time Care with the Help of IoT
The Internet of Things (IoT) has resulted in doctors and healthcare providers using wearables and other biosensing devices to monitor patient health after surgeries and other treatments. These wearables can constantly collect data on key health parameters of patients after they’ve been discharged from hospitals and record the same in the patients’ health records.
Any anomalies in the health parameters that indicate a potential health complication can be reported to both the patients and relevant doctors in real-time. By receiving health alerts in real-time, patients and doctors can quickly schedule consultations to investigate and spot signs of relapse or complications. As a result, any potential negative side-effects of treatments and surgeries can be mitigated in time.
4. Focusing on Value-based Care by Removing Possibilities of Error
Medical error is one of the leading causes of death and a core issue that every healthcare focus on. Healthcare data analytics can be used to combat any human error occurring in patient care, be it those occurring at the doctor’s table or the back office. It can analyze the prescribed medications and validate it with the user data to flag potential discrepancies or errors, thereby saving lives.
In addition to the precision and certainty offered by big data analytics, the healthcare profession also has another critical factor -- time. Interpreting analytics reports, which are often in the form of large quantitative tables, take time and effort to understand and interpret.
In the absence of data scientists and analysts, converting these numerical tables into medical inferences may not be a straightforward process for healthcare providers.
Thus, it is essential to deliver the analytics reports to doctors in a language they can understand. To overcome this hurdle, healthcare organizations can leverage automated report writing with Natural Language Generation (NLG), an advanced AI technology to convert this big data into simplified text summaries.
Using these plain text reports, healthcare providers can easily make sense of the analytics reports and fasten decision making. As a result, they will be able to treat more patients and minimize the average waiting time, while further expediting treatments and potentially saving more lives.