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Big data in healthcare: six use cases

With its potential to transform care delivery, big data stands at the core of technological evolution. Yet, in its turn, it demands ever more advanced systems to generate, store, process, and analyse clinical information. Big data facilitates digital transformation in healthcare and helps establish new standards in care delivery and provider-patient relationships.

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We will explore the current use of healthcare analytics services across the entire care cycle, touching upon some technological know-how. The following are the major big data use cases facilitating digital transformation in healthcare and establishing new standards in care delivery and provider-patient relationships.

1. Disease prevention 

Big data supports a pre-emptive approach. Big data-enabled systems aggregate patient data and offer multiple prevention options based on picked-up warning signs. For example, smartphones and smart wearables, such as Fitbit and Samsung Gear Fit, help users track basic vitals, including blood pressure, weight, temperature, and blood glucose.

A range of smart devices and apps, collectively making up the internet of medical things, support healthy individuals, who want to track their well-being, and patients with chronic conditions, who need to balance their health status and prevent complications.

Healthcare institutions and businesses jump on this opportunity to improve population health. Advanced analytics services aim at aggregating and processing data from multiple sources like medical and insurance records, genetic data, wearable sensors, and social media. Clinicians use this data to create cohesive patient profiles and offer customized healthcare packages.

 2. Diagnosis at the fingertips

Healthcare providers can collect vast amounts of data to get all available information about patients' possible conditions, treatment options, and anticipated outcomes. 

However, the data is often scattered across PCP offices, hospitals, and clinics. And the issue with connecting this data to that from wearable sensors stays poignant today. 

Meanwhile, there are already some successful cases of handling multi-source big data in healthcare. For example, Johns Hopkins Hospital in Baltimore runs an AI-powered Capacity Command Centre that pulls data from dozens of streams in real-time, accessing EHR, emergency dispatch service updates, and lab results while checking hospital bed availability. 

An example of using big data in diagnostics is AI-powered software capable of recognizing patterns in patient data (for example, IBM Watson). Such systems use machine learning to identify and localize malignancies in test results.

Big data analysis in medical imaging can relieve radiologists from examining each image individually and save time. For example, Carestream, a medical imaging system, uses algorithms to read and analyse millions of images to spot specific or alarming patterns.

As the technology evolves, it will create new opportunities for early diagnosis of various conditions, allowing physicians to achieve better patient health outcomes.

3. Treatment support

Big data analytics can substantially expand health specialists’ clinical expertise when a patient's journey comes to the treatment stage. Thanks to deep learning algorithms, providers can access the bulk of existing medical research on a particular condition or medication.

As a result, providers can personalize treatment plans for patients and lower the risk of side effects or exacerbations, which can particularly alleviate cancer and chronic conditions.

Big data also helps to fight medication non-adherence, costing about $500 billion a year because of the complications developing when patients fail to follow prescribed treatment.

4. Post-discharge and follow-up counselling

A patient’s rehabilitation after an acute episode doesn’t end when they leave the hospital. That’s why providers have the 30-day readmission rate as one of the crucial benchmarks for clinical performance evaluation.

Big data combined with deep learning can point to patients needing follow-ups and long-term care. Clinicians can then provide them with a better understanding of their current health status and help them avoid complications and emergencies.

Smartphones and wearable sensors are the prevailing tools used to handle patient data at this care point. For example, GPS-enabled inhalers for asthma patients can record information about their physical location, sleep patterns, and physical activity to recognize if they might feel unwell. In case of abnormal parameters, the inhaler will alert the authorized care team members and the family about possible anxiety episodes.

5. Fraud prevention

Historically, healthcare has been prone to high levels of fraud and inaccurate claims. However, with technological advances such as EHR, big data becomes a tool that can be leveraged to curb fraud in the healthcare industry. 

It helps to analyse many claims fast and identify suspicious patterns in data, signalling potential fraud or abuse.

For example, the most common types of insurance manipulations are billing for:

As perpetrators can’t typically get the whole claim filing cycle right, some key facts get misrepresented. Repeated misrepresentations form patterns, which are then picked up by big data analysis tools, compared to approved claims, flagged as potential fraud or error, and sent for review to experts.

Specific types of fraud can also hamper clinical trials by altering information to conceal the actual effect of a drug or any other therapy or treatment.

6. Cost reduction

For individual providers, big data has more to offer in terms of cost-saving and healthcare business process management. Analyzing data about admission and staff allocation, hospitals can save costs by eliminating the problem of over- or understaffing and cutting the time patients have to spend at health facilities.

Hospitals also use big data to prevent unnecessary yet costly and time-consuming hospital visits. This benefit of big data is especially efficient when leveraged by a collective of hospitals sharing patient records. This way, hospital staff will be aware of the following:

With such data at hand, doctors won’t request repetitive tests and make patients visit the same specialists twice.

Closing thoughts

In patient-centric healthcare, big data brings an array of technologies that put control over the care cycle into patients’ hands. Some providers embrace the changes and start putting extra effort into adopting emerging technologies to gain strategic competitive advantages. Others stay cautious, waiting until long-term trends prove their viability. Although only time will tell the winning approach, you can already use big data technologies for more informed and connected care delivery.

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