Healthcare data analytics has become a vital part of healthcare, and in 2021, the projected revenue for this industry is close to $9 billion. Yes, you read that right. $9 billion. It’s only projected to increase from here on forth and could potentially rake in $20 billion by the year 2027. If this isn’t proof that it’s one of the most trending topics in healthcare, we don’t know what is!
Data analytics in healthcare is not a new phenomenon but rather years of research that has culminated in intelligent systems being used across the globe. Without healthcare data analytics, we wouldn’t be able to retrieve our records at our whims and fancies. We won’t be able to diagnose complex diseases like pancreatic cancer in its early stages, and we definitely will not be able to get personalized plans for our treatment.
I mean, let’s think about the time when the COVID-19 pandemic hit us. We wouldn’t identify and isolate people (contact tracing) unless we had data about who was infected. How do you think we identified hotspots? What do you think your e-vaccination certificate is all about? They’re all examples of the use of data analytics in healthcare! In 2020, 59% of people were happy to use AI-based devices to diagnose their health issues, and with time, it’s increased.
But how has healthcare data analytics evolved over time? Let’s break it down for you.
So, what is healthcare data analytics? Simply put, it’s a field that involves using data to make informed decisions or how we popularly know it now as data-driven decisions. Big data refers to the sheer volume of the data generated and our inability to store them physically. Data can be in any form—text, audio, video, log files, or even annotated records. If the data contributes to the decision-making process, it is considered relevant. It’s what we know as an Electronic Health Record or EHR.
It’s has a significant impact on the healthcare industry. For example, it benefits doctors by allowing them to look at a problem from various perspectives and make better decisions for their patients. From the patient’s perspective, they receive incredibly personalized plans with reduced costs rendered impossible before big data analytics. From the administrative perspective, it has simplified the entire process. Instead of wasting time making manual written entries, they can focus on actually running the healthcare center.
If you go to see, medicine is either about preventing health issues or treating health issues based on scientifically backed data. So, it’s not surprising that the next step in healthcare data analytics was predictive modeling. Doctors are humans, and there are only so many avenues they can think of at the end of the day. Predictive data analytics takes that burden off their shoulders and helps them make data-based decisions. It’s widespread to see the use of AI algorithms and the Internet of Things (IoT) to combine data from the past and present to find the best fitting solution.
A few examples of such algorithms include:
Artificial Neural Networks: Models the connections within our brain and is used for diagnostic purposes to identify patterns and develop patient-specific solutions.
Logistic Regression: Used to determine potentially risky events through a series of analysis.
Random Forest: Used to draw multiple trees or, in this case, possibilities of development of disease based on their medical history.
It has completely revolutionized the healthcare space, and now that we see it, there’s no going back.
It was not uncommon to see that every little medical record would be handwritten and stored decades ago. Even now, doctors are required to put in their ‘notes’ which are filed in the patient’s folder. But that was a dangerous way to store data. Why, you ask? Simply because it could get stolen or destroyed easily. Just imagine that there’s a fire in the hospital, and suddenly, years of data are lost in seconds. This is precisely what healthcare data analytics aims to solve.
The complete digitization of the medical workflow has allowed doctors and the administration alike to focus on much more important things. While initially, many companies were focused on creating the best technologies that can detect the most intriguing details within the shortest amount of time, something was amiss. Soon, in the 2010s, it was figured that while most of the big data in healthcare data analytics was solving a particular problem, the end user — the patient was not necessarily reaping the intended benefits.
Value-based healthcare has become a buzzword now, with many healthcare management systems focusing on the patient’s perspective. For example, there are many applications to book an appointment instead of waiting for hours on end to get one; you can voice your concerns about the treatment and service that you receive; hospitals can follow up using automated systems which saves hours on their part and the best of all, online customer relationship management (CRM) powered by Artificial Intelligence (AI) allows us to map a patient’s journey within the system,
A refreshing change, we must say!
Another critical aspect that worries most about healthcare data analytics is cybersecurity. Initially, when scientists built these systems, security measures were sub-par at best. In 2021, the going rate for one EHR is $250. That’s a massive amount to pay for one record, but it also goes to show why healthcare data is so prone to be attacked. It’s one of the reasons why most hospital systems prevent the use of third-party apps and have increased network visibility. The increasing focus on security has only helped the overall image of healthcare data analytics improve over time.
Like every other technology, big data analytics also got the much-needed push during the pandemic. The true power of data analytics in healthcare was shown when umpteen papers were published describing the various ways in which the pandemic was being fought.
From the diagnostic perspective, it was used for pre-symptomatic detection of the infection, tracking the disease in real-time also monitoring discharged patients in real-time. Epidemiologists also used it to identify hotspots and the spread of variants through the use of genetic repositories. Scientists eventually used this information to identify risks scores and conduct contact tracing activities all across the globe. It helped epidemiologists and helped hospital administrators make data-driven decisions on how and when to quarantine, what their current capacity is, what their resource capacity was like, and which patients they need to prioritize. It most definitely saved millions of lives globally by using automated software powered by big data and AI.
We must work on improving the current systems to ensure better output shortly. Some initiatives include providing more data to algorithms for precision analysis; developing error-free and flawless algorithms that prevent bias; creating tighter security networks to protect data; and focusing on using omics data to provide personalized medical recommendations.
All in all, the future of healthcare data analytics is on an upward trend and will only impact more lives soon. The implementation of big data analytics in healthcare has really given a new meaning to the phrase ‘Prevention is better than cure.’