Why healthcare needs big data analytics
13 min read
13 min read
Healthcare is among the few industries -- alongside aviation, defense, space research, and law -- where precision in decision-making and certainty in action are 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, healthcare organizations and professionals need accurate and reliable data.
And they need a lot of such data. Hence, it is no surprise that healthcare has taken well to big data analytics as a key enabler in recent years. After all, the “3 Vs” that characterize big data also apply to the data generated in the healthcare industry, as follows:
By leveraging all this data using analytics, healthcare institutions are able to provide better outcomes for patients as well as healthcare providers, such as:
And how exactly is big data leading to these outcomes?
Healthcare institutions are utilizing the evergrowing cornucopia of data at their disposal to find trends and patterns that can help them identify problems in patients with increasing accuracy. In addition to helping them solve existing problems, big data along with other techniques like data mining, statistics, modeling, machine learning, and artificial intelligence is also enabling 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 aiding the healthcare sector:
Big data analytics is 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. 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, 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.
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. Big data analytics 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.
Using wearables and other biosensing devices, doctors can 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’ personal 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.
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 doctors.
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, doctors can easily make sense of the analytics reports and make decisions quickly. As a result, doctors will be able to treat more patients and minimize the average waiting time for patients. They can also further expedite treatments and potentially save more lives.