Leveraging technology is one of the best ways the healthcare industry will become more affordable, effectual and capable of saving more lives. Undoubtedly, big data in healthcare can be considered as one of the most significant leaps technology made towards improving human lives.
Although the healthcare industry had a slow start with big data analytics, it is making steady strides and is projected to become a market of US$67.82 billion by 2025. If you’re still skeptical about the importance of big data in healthcare, this article will fill you with enough information to make a “data-driven” acknowledgement.
Why Is Big Data Important in Healthcare?
First, the basics.
Just like its application in any other industry, for example, retail analytics or telecommunications analytics, big data in healthcare refers to the abundance and enormity of data collected from numerous sources – including but not limited to electronic health records (EHRs), medical devices, genomic sequencing, medical imagery and test results.
Although the future of big data in healthcare will undoubtedly be directed towards a shining beacon of immortality (or at the least, longevity), its current applications are promising enough.
Here are some of the applications and examples of big data in healthcare.
- Predictive medicine
- Accurate and faster diagnostics
- Reducing readmissions
- Systematic triage
- Prediction of clinical events
- Virtual care using IoT devices
- Faster drug discoveries
How Does Big Data Work in Healthcare?
To present you a clearer perspective of how big data analytics is being utilised in the healthcare industry, here are some real-world applications of big data and how they brought in notable transformations.
1. Analysing Electronic Health Records
One of the most notable benefits of big data in healthcare is its usefulness in analysing electronic health records (EHRs). They are digital versions of patients’ paper charts and offer real-time records for instant retrieval. It will contain all vital information regarding a patient, including medical history, treatment plans, radiology images, and test results.
Coupling EHRs with big data analytics can promise large-scale analysis that will aid in assigning triage (deciding the order of treatment) in case of emergencies.
It will also help in unlocking population health analytics – which is essential for formulating population health management strategies, especially during widespread outbreaks like the COVID-19.
2. IBM Watson Helps Fight Cancer
From its humble beginnings as a project developed to beat the best humans in the TV game show Jeopardy!, to scanning 15 million pages of medical data in the click of a button, the story of IBM Watson is quite awe-inspiring.
Powered by artificial intelligence (AI) and big data analytics, Watson is a question-answering machine that has enormous potential in the healthcare industry. In countries like India, where the proportion of oncologists to cancer patients is around 1:2000, Watson can augment the doctors’ intuition, capability and expertise.
It would take approximately 10,000 weeks for a doctor to read and analyse 10 million patient files. Watson does that in 15 seconds.
Also, it would take at least 160 hours of reading per week for doctors to stay up-to-date with the latest additions in medical knowledge – not to mention how long it will take to consider their relevance or application.
The IBM Watson Health Cloud can help doctors find medications that align with a patient’s lifestyle and also suggest the latest and best evidence-based treatments – as keeping up with the ever-increasing medical data is nearly impossible for doctors, but not for Watson.
Watson also allows doctors to quickly analyse treatment history from other doctors or that of the family members of the patient. Watson also makes it easier for doctors to access information from wearable devices and also learns from it.
More precisely, as the doctor and patients interact more with Watson, it acquires more knowledge, processes it and implements it back to the system.
According to a study on Clinical Trial Matching (CTM) published in the Journal of Clinical Oncology, Watson was able to reduce the time taken to screen cancer patients by 78%. And at Manipal Comprehensive Cancer Center in Bangalore, India, members of the multidisciplinary tumour board changed their treatment decisions in 13.6% of the cases with respect to the information provided by IBM Watson.
Watson is currently trained in 13 different types of cancer. Although Watson’s oncology computing system isn’t fully prepared for worldwide adoption, it is making small wins towards understanding cancer and medications associated with it.
3. Prediction of Clinical Events
When it comes to executing big data in healthcare, prediction and prevention of clinical events are the two live-saving goals the industry seeks for. The Phoenix Children’s Hospital in Arizona is extensively using big data analytics to identify new acute kidney injuries (AKIs) before they develop into life-threatening conditions.
According to the Division Chief of Nephrology, the hospital uses a combination of real-time surveillance and EHR data for detecting anomalies and sends colour-coded alerts to patients if they are entering the AKI danger zone.
For effectively performing this, the hospital scans the EHR laboratory data, every six hours, for those patients who have been prescribed medications that may have adverse effects on their kidneys. Factors such as the frequency of kidney function tests ordered by the patient are also taken into account.
With big data analytics, Phoenix Children’s Hospital was able to flag high-risk cases of AKI within hours of the initial injury. And within one year of deploying the strategy, the hospital witnessed a 34% drop in AKI cases, despite the fact that more patients were admitted during that time.
4. Predictive Analytics to Prevent Suicide and Self Harm
In the US alone, there are 132 suicides per day – making suicide the 10th most leading cause of death. The Mental Health Research Network conducted a study and found that big data analytics can accurately predict suicide risks within the 90 days following a mental health visit.
The researchers used the EHR data and results of a depression questionnaire, along with 313 clinical and demographic characteristics taken from the records of individuals, from up to 5 years before making a mental health visit.
Graph showing the validation data set for prediction of suicide attempts and deaths. Image Credit: ajp.psychiatryonline.org
The data included records on prior suicide attempts, substance use diagnoses, and psychiatric medications dispensed. The predictive model analysed and concluded that patients who made mental health speciality visits with top 5% risks scores accounted for 43% of suicide attempts and 48% of suicide deaths.
5. Better Patient Utilisation Rates
Along with helping hospitals get ahead of no-show appointments, predictive analytics can also be used for better patient utilisation rates by giving a heads-up that things are going to get busier.
Before utilising big data analytics, the Wake Forest Baptist Health in North Carolina had an uneven utilisation of the haematology-oncology clinic. Improper utilisation caused headaches to both the nurses and pharmacists, which further made it difficult for patients.
During peak hours, that is 10 am to 2 pm, the resources were stressed to the maximum and appointments were fully-packed – even though many of the 43 treatment chairs would sit idle for the rest of the day.
The over-crowded atmosphere during peak hours also raised concerns about patients’ safety in the light of infectious diseases. Although the hospital tried manual scheduling to reduce the rush, they saw little difference and finally looked into a big data analytics solution.
Since the clinic serves patients with varying oncology needs, modifying the schedules by taking into account a large variety of treatment protocols was an arduous task. With analytics, the clinic was able to flatten the curve and observed a 10% increase in overall chair utilisation, not to mention how relieved the nurses were relieved.
6. Prevent Healthcare Fraud and Abuse
In the fiscal years 2013 and 2014, Centers for Medicare & Medicaid Services (CMS, the federal agency that administers the major healthcare programs in the US) saved nearly US$42 billion with the help of predictive analytics.
Before utilising analytics, new rules were established, and experts were hired, each time there were known and suspected fraudulent issues. But there were numerous flaws with this system. Firstly, as rules kept changing frequently, several clerical errors arose as physicians weren’t trained to perform billing.
Secondly, the illicit benefactors of the system who stole millions of dollars were sophisticated players and were aware that the industry is watching them – giving them the advantage of being a step ahead.
With advanced big data analytics, payers (the insurance companies) were capable of digesting multiple data sets such as demographics, provider (doctor) credentials, geographic location and past claims data.
Analytics was also capable of eliminating false positives, enabling investigation teams to differentiate between real cases of repeated clerical errors and the fraudsters. For CMS, 68% and 74% of the total savings in the fiscal years 2013 and 2014, respectively came from prevention activities using big data.
7. Personalized Medicines
Most senior medical researchers (jokingly) feel that a biologist nowadays must be a programmer and a statistician first, before ever considering clinical research.
It is intriguing to note that the DNA, which is essentially a set of instructions to reproduce cells is being studied by high-end computers, which in essence, are also sets of instructions called as algorithms.
Although humans as a species are 99.9% the same (DNA-wise), there will be at least 3 million differences between your genome and that of anyone else you pick in random. This means that the efficacy of a specific medicine isn’t the same for everyone.
Thus arose the need for personalized medicine (aka precision medicine). As a matter of fact, one of the primary intentions behind the Human Genome Project was to empower personalized medicine. And it closely did and has given the industry a considerable boost to break free from the “one-size-fits-all” formula when it comes to medication.
Research studies in genomics (the study of genomes of organisms) generates humongous volumes of data (big data), which requires advanced analytics to decode and make useful. Although precision medicine is still in its infancy, big data has the potential to deliver systematic ways to achieve it.
According to Gil McVean, a professor at the University of Oxford’s Big Data Institute, 90% of a biomedical research centre today will be composed of computers and just a 10% will be a wet lab.
One of the easiest ways he explains the application of big data in genomics is by stating that if we’re to look at 10,000 genomes of people with a disease and 10,000 without, an algorithm can be used to compare them – which would help in finding the differences between them.
This will help in identifying the genes that are linked to a particular disease, even without having a hint of which ones they might be, beforehand.
Challenges of Big Data in Healthcare
Methods for big data analysis and management are being continually developed. Yet, there are some challenges associated with data collection and storage. Let’s take a look at those.
Storing large volumes of data is one of the primary challenges of big data in healthcare. Although numerous organisations have the capability to do so, several small to medium-sized clinics find it expensive to rely on cloud-based storage, or let alone maintain an on-site server network.
Even if organisations succeed in acquiring high volumes of storage spaces at low cost, there will still be issues regarding security, up-time and ease of access.
Several researchers observed that the data collected and stored in the EHRs are not entirely accurate. But this can be tracked down to the individuals responsible for data collection or the inaccuracies of the devices used.
Since big data analytics is only as good as the data it uses, discrepancies can lead to a broken understanding of data and will question the veracity of data-driven decisions.
As big data in healthcare is still in its nascent stage, numerous physicians and administrators are still sceptical about its effectiveness. Additionally, big data analytics will also require organisations to have in-house data scientists – which can be a bummer for small to medium-sized clinics.
Data sets can vary in type, format and volatility. While some data sets of patients such as their date of birth, name and gender remain the same, there are numerous other sets of data such as marital status and address that may rarely change and others like vital signs that continually change.
Considering such differences, organisations must have a clear understanding of which datasets can be manually updated, which requires automation and how data sets can be updated without damaging their integrity.
Unnecessary duplication of data can also make it difficult for physicians and lab technicians to rightly perform their duties.
From phishing attacks to accidentally misplacing devices, healthcare data is subjected to vulnerabilities, just like in any other industry. Although numerous safety reforms such as encryption and multi-factor authentication can reduce the risks associated, the system is still fallible to physical breach or ransomware episodes.
Within a few years, big data analytics will enable the healthcare industry to break free from the “one-size-fits-all” approach towards treatment and medication. Researchers estimate that big data analytics can help in saving more than US$300 billion per year in the US alone.
Although the applications of big data analytics in the healthcare industry are still in its infancy, rapid advancements in the tools and methods used for data collection can significantly accelerate their maturing process.