Big Data Integration in Healthcare Industry

The term “big data” itself is defined as a huge voluminous collection of continuously increasing complexed information which is processed via digital technologies. The evolution and usage of Internet technology nowadays has brought the ability to generate, store, and analyze these enormous volumes of data within a short period of time.

The intersection of these trends is what we calls Big Data. Volume, Velocity and Variety – are the "Three Vs" of big data, or its cornerstones. In healthcare, these three are the defining dimensions or properties of effective big data analytics.

• Volume entails the remarkable amount of data healthcare generate through their apps, portals, websites, and EHRs.
• Velocity refers to the speed at which datasets are being generated and processed.
• Variety encompasses the different number of types of data we can now generate, gather and analyze.

?Besides the three, there are two new Vs of big data: Veracity and Value.

• Value is the attribute that refers to the tangible worth of the data being generated, collected or analyzed.
• Veracity refers to the trustworthiness, integrity or quality of data generated, collected, and analyzed by healthcare institutions. Is it trustworthy?

Benefit of Big Data Adaptation

Big Data adaptation in healthcare enables us to predict epidemics, cure diseases, improve quality of life and avoid preventable deaths by comparing and analyzing existing medical records /data collection (e.g. Health Trend Analysis) which enables sophisticated predictive modelling to be applied for early illness detection and the advance medical treatment recommendation for patients.

Generally, these are the key parties that benefits the most:

1. Providers (hospitals/clinics/medical centers): Obtain insights generated by big data analytics which help healthcare providers to deliver better patient outcomes, reduce wastage, and enjoy efficient workflow and processes.
2. Payers (Insurance): Executing data analytics at large scale can benefit payers by eliminating fraud, reduction of false and improper claims, faster reconciliation, hence better service.
3. Patients: Reap countless benefits such as superior health management, predictive care, healthier lives, savings in insurance and overall healthcare.
4. Device Manufacturers: Data analytics helps manufacturers create better and more innovative products to solve general monitoring & tracking health issues and build devices relevant to patients’ needs.
5. Pharmaceutical: Better Research & Development (R&D), more effective drugs, savings on manufacturing drugs, innovative drugs.

How Big Data is Implemented in Healthcare?

Patients Registration: Big data provides patient admission trends and forecasts in hospitals. Assisting employee management and schedule the right number of staff for their operations. Overstaffing can increase labor and overhead costs while understaffing can lead to poor service, which can be detrimental to patients’ health. Big data utilizes past admission records to find patterns in admission rates, accurately predicting patient load and staff scheduling accordingly.

One of the most heavily used health applicator/system locally in Malaysia is the MySejahtera application during the Covid-19 pandemic which the application is developed to: Assists the Government in managing and mitigating the COVID-19 outbreak. It helps users in monitoring their health throughout the COVID-19 outbreak and enables users in getting treatment if they are infected with COVID-19. Locates nearest hospitals and clinics for COVID-19 screening and further treatment.

According to The Star – July 2022, “MySejahtera still app-solutely relevant for future use” – July 2022. MySejahtera still app-solutely relevant for future use | The Star.

As the features in the application has been upgraded to stay relevant and to be used in the event of a new pandemic or any emerging diseases.

Provide high-risk patient care: Big data is being used extensively in healthcare to help identify and manage both high risk and high-cost patients. Payers are leveraging the power of predictive big data analytics to zero in on high-cost patients, according to the Society of Actuaries (SOA) report. More specifically, they are looking at the patient’s gender, age, prescription drug usage and spending history as predictors of whether an individual should be considered a high-cost or not.

Opt-in Genome Registries: Verge Genomics develops drugs by automating their discovery process. They use automated data gathering and analysis to create solutions to some of the most complex diseases known today, including ALS and Alzheimer’s. Using the same technologies that power Google’s search engines, Verge has discovered ways to map out the hundreds of genes responsible for causing disease and then finding drugs that target them all at once.

Studying Drug Efficacy: Drug discovery often takes a long time to test compounds against samples of diseased cells. Finding compounds that are biologically active and are worth investigating further requires even more analysis. As computers are far quicker compared to traditional human analysis and laboratory experiments in uncovering new data sets, new and effective drugs can be made available sooner, while also reducing the operational costs associated with the manual investigation of each compound.

Mobile Data & Wearables: This is where big data, when combined with other health technologies, can help track and identify diseases long before it happened and therefore boost preventive care. Devices such as smart watches, smart phones and health mobile applications enables the users to enjoy the pedometers functions in the devices to measure for instance walking/running distance, calorie counters, heart rate etc. Data gathered from wearable devices would give the medical practitioner information and a complete picture of the patients’ health and advise preventive care which could act as the preliminary assessment on the general health condition of the patient’s lifestyle before recommending and scheduling medical treatments or prevention remedies.

Many have heard about the STRAVA fitness apps which enables its users to track their running and cycling performance (GPS-Supported) in click of button on your smartphones.

Apart for mapping, recording and creating routes, STRAVA apps offers a lot of data analysis derived from any logged activities of the user input in which it records the heart rate, speed, distance, elevation change, power and etc. The Fitness & Freshness chart uses?Training Load and/or Relative Effort?to quantify the user’s daily training routine. Training Load is then calculated using power data and Relative Effort is calculated using either heart rate data or Perceived Exertion input which at the end of the day will help create a trend for overall health tracking and performance monitoring of its users.

Telemedicine/Tele-health: With modern communications technology, the medical professionals (e.g doctors) and patients communicates over the phone, video conferencing, the internet, email, text messages or any other form of non-face-to-face communication. Refers to as receiving medical treatment remotely, usually in your own home with the aid of a computer and internet connection for self-diagnosing, communication with doctor-on-call via voice call/message on computer/laptop. Doctors can also use wearable devices to attend to emergency cases without bringing patients to the hospital, saving hospital cost on in-house treatment.

Prevents human error: 10% percent of total spending on healthcare is wasted due to human error or fraud. In fact, human error alone accounts for about 6 percent of healthcare provider’s expenses. When companies leverage big data and predictive analytics in the healthcare industry, fraud and errors can easily be detected and prevented, saving healthcare organizations huge amounts of money in the process.

Pharmacy, Medical and Insurance Claim: Often by partnerships between medical and data professionals, with the potential to peek into the future and identify problems before they happen. Which aims to take data from various sources (such as medical and insurance records, wearable sensors, genetic data and even social media use) to draw a comprehensive picture of the patient as an individual, in order to offer a tailored healthcare package.

Challenges in Big Data in Healthcare

The medical sector appears to be behind other industry in the adoption and application of big data, mainly due to resistance from healthcare providers. Experts in the industry would rather make treatment decisions based on their experience and skills rather than insights from big data.

Organizations are trying to keep up with the rapid growth and changing technology. The vast sea of data often means that people struggle to know and catch up with the latest advanced technology and applications.

Not enough healthcare organizations possess systems and databases to handle the mass inflow of data.

?Of course, no data is more personal than our medical data, so extremely secure safeguards have to be put in place to make sure the information only gets to those who we meant to see it. Despite that, cyber thieves routinely target medical records, and reportedly earn more money from stolen health data than pilfering credit card details.

Conclusion

Despite those challenges, the growing trend towards centralization of medical data will cause concern, but as long as privacy and security can be maintained, it is certain to play a big part in the development of new treatments and add to our growing understanding of how our bodies work.

?However, access to new information is beginning to see big data gaining a foothold in the healthcare industry. Advances in medical technology are also improving the ability of healthcare personnel to interact with and utilize big data.

More crucially, big data will help clinicians and hospitals provide more targeted healthcare and see better results. For pharmaceutical companies, big data is a driving force that’ll help the design and build more innovative drugs and products.

On the overall, healthcare stakeholders can rely on big data and predictive analytics to tackle major issues like admission rates, high-risk patient care, staffing issues, dosage errors, and much more.

Article written by Mdm. Shahira Mustaffa , Big Data Trainer at AntsBees Sdn. Bhd.