Applications of big data and predictive analytics in the global healthcare industry

Big data and predictive analytics in healthcare

Big data has been driving monumental advancements across industries optimizing business operations and expediting supply chain and logistics processes, to say the least. In healthcare, it’s simplifying strenuous tasks – from consolidating medical records for a disease cause, treatment, and prevention discovery, to offering near-real-time patient information through smart devices and more. Big data and predictive analytics is increasingly turning into the cornerstone of a gamut of healthcare applications, especially in telemedicine and remote patient monitoring. 

big data in healthcare

It comes as no surprise that investments in this space have reached a remarkable USD 7.0 billion worldwide. Success for any business today relies heavily on maximizing the outcomes from available data. That’s exactly where big data and predictive analytics are helping the healthcare sector. Estimates suggest the global predictive healthcare analytics market is projected to witness a CAGR of 28.9% by 2025. Here’s a quick look at the top healthcare big data sources:

To give savvy players a glimpse of how these big data and predictive analytics can notch up their existing offerings for a competitive market edge, we dive into the top 9 segments benefiting from them:

1. Diagnostics and imaging

Analyzing and manually storing scores of medical images is expensive both in terms of time and money, as radiologists need to examine each image individually, while hospitals need to store them for several years. Big data is helping overcome this challenge by digitizing records for easier accessibility at lowered costs.

Within diagnostics and imaging, algorithms developed analyzing hundreds of thousands of digital images could identify specific patterns in the pixels and convert them into a number to help physicians with rapid diagnosis. The future could also have radiologists no longer looking at images, but instead simply analyzing the outcomes of the algorithms that will inevitably study and memorize more images than they could in a lifetime.

2. Telemedicine

Telemedicine has been extremely instrumental during the pandemic in providing personalized treatment plans and preventing hospitalization or re-admission. Combining big data acquired through such instances with predictive analytics can facilitate accurate predictions of serious medical events in advance and prevent the deterioration of patients’ conditions.

3. EMRs

According to the CDC, 85% of office-based physicians in the US used electronic medical record (EMR) systems in 2021. EMRs are a crucial part of the shift toward evidence-based medicine. By providing accurate and up-to-date information on patients’ health through big data, EMRs enable doctors to make better decisions about diagnosis and treatment. 

This in turn helps to improve patient outcomes and reduces costs across the healthcare system. According to a McKinsey report on big-data healthcare integrated medical records improved outcomes in cardiovascular disease and achieved an estimated USD 1 billion in savings from reduced office visits and lab tests.

Related reading: Four ways AI is revolutionizing EHR

4. Patient monitoring 

Within this space, doctors can monitor vital signs and get timely alerts in case of emergencies using interconnected patient monitoring systems. For insurers, big data in the form of a digital library helps them restructure value-based, data-driven payments and even reduce healthcare fraud.

5. Critical care monitoring

Predictive analytics for health monitoring is seeing a host of applications from smart wearables, to remote monitoring medical devices and more. Using predictive analytics models can reduce the risk of emergencies for patients who must visit hospitals regularly due to chronic or critical conditions.

6. Hospital management

Data-driven analytics can help forecast times when you would require staff in specific departments during peak times while moving qualified personnel to other sections of the hospital during quieter periods.

Also using big data and predictive analytics can help streamline care processes. Bed occupancy rate indicators, for example, provide a window into where resources may be needed, whereas monitoring canceled or missed appointments provide senior staff with the data they need to avoid expensive patient no-shows.

7. Modeling and forecasting

Massive databases of DNA records, health records, research papers, and other relevant domains, typically containing fully anonymized patient data, can be used as AI material. This can uncover previously unknown links and connections, as well as the development of completely new drugs.

Predictive modeling in research and development can be used to create a leaner, quicker, and more focused R&D pipeline for pharmaceuticals and devices. To match therapies to individual patients, statistical methods and algorithms can be used to enhance clinical trial design and patient recruitment, minimizing trial failures and expediting novel treatments to market. Analyzing clinical trials and patient data, on the other hand, can uncover follow-on indications and detect side effects before medicines hit the market.

8. Preventive healthcare

Big data information provides healthcare practitioners with greater insights than they would otherwise have at their disposal. Precise preventive care powered by predictive analytics can lower the number of required expensive treatments or hospital visits, ensuring direct savings in healthcare expenses.

9. Population health

Sources of data in the healthcare industry include hospital records, medical records of patients, medical examination results, and internet-connected devices. The collection, storage, and analysis in more streamlined ways can help a wide range of public and private sector businesses can improve the quality of their services. 

Eager to maximize their market potential and drive first-mover advantage over a gamut of innovations healthcare players are joining hands to bring the best of big data and predictive analytics to the table. Here are some of the top mergers and collaborations in the space:

  • Happify Health and Zuellig Pharma’s out-licensing and distribution deal to commercialize prescription digital therapeutics in Asia.
  • Zuellig Pharma’s licensing of ‘Ensemble,’ Happify Health’s transdiagnostic prescription digital therapeutics. This system aids individuals with both major depressive disorder (MDD) and generalized anxiety disorder (GAD).
  • Hello Heart, a cardiac health management company, acquired USD 70 Mn in a Series D funding round led by the growth equity firm Stripes.
  • Patient journey automation company, Gyant (Europe), announced their automated patient intake and charting tool called ‘Intake,’ with Walmart Health Virtual Care.
  • Gyant (Europe) follows this with the launch of its asynchronous, AI-powered e-Visit care platform Async with Intermountain Healthcare to automate patient intake and EHR note generation, enabling low-touch and time-effective virtual visits.

The future of healthcare delivery is promising owing to the rapid adoption of predictive analytics and big data and real-time analytics. To uncover how your organization can harness the potential of these technologies to boost business success, you can request our latest report Integration of Big Data and Predictive Analytics into Global Healthcare Industry. 

Our verdict is that big data analytics solutions are considered a landmark in managerial studies applied to healthcare organizations. However, the need of the hour is focusing on standardization, integration of devices, and data analysis protocols to increase healthcare organization performance. Enterprises and government agencies should find advanced analytics solutions that can fully utilize their data to improve treatments and create a platform that can deliver personalized care. 

The application of big data and predictive technology have enhanced the performance of primary healthcare service providers and companies. It has also brought about improvement in the macro factors such as time efficiency, cost reduction, and resource allocation which determine the growth of the healthcare industry.

Netscribes delivers comprehensive market and innovation information to help healthcare providers and technology companies stay on top of industry trends and better respond to customer requirements through successful solutions. To know how we can help you drive strategic growth by harnessing the power of big data and predictive analytics, contact us. 

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