6 Use Cases for Big Data and Analytics in Healthcare

May 12, 2020

According to Gartner’s 2019 CIO Survey, 48% of healthcare provider CIOs selected data and analytics, and AI/machine learning as the top game-changing technologies. Big data and analytics are revolutionizing many industries and the healthcare industry is one of the most prominent areas where big data is transforming processes. New sources of patient data can generate deeper insights to help healthcare providers improve the quality of care and streamline their operations.

Benefits of Big Data in Healthcare

One of the biggest challenges in healthcare is breaking down data silos to combine medical data from multiple sources and get a comprehensive view of your business data from one source, instead of various, disparate sources.

A truly data-driven healthcare analytics solution should connect all your data sources and have the ability to analyze structured, unstructured, and real-time data. Without the ability to incorporate all your patient health data including diagnostic information, doctor’s observations, and real-time data from medical equipment, your insights are not as effective as they could be.

However, once you are able to consolidate your data sources, there are many benefits of big data in healthcare.

Improve outcomes – Big data in healthcare industry can improve the quality of patient care and safety levels by providing access to real-time patient health information, reducing the possibility of human error and wrong diagnosis.

Increase operational efficiencies – Combining the patient health data from multiple sources improves visibility into resource utilization, inventory levels, and procurement, enabling you to improve cost efficiencies, minimize waste, reduce risks, and streamline the operations.

Streamline finance and accounting – Big data analytics in healthcare generate insights to help you quickly identify financial inaccuracies and rectify these issues, helping you align your KPIs to meet your financial goals.

Top Use Cases for Big Data in Healthcare

1. Analyze Electronic Health Records (EHRs)

Electronic health records are one of the most common use cases for big data in healthcare. EHRs track and record your patient’s health data like pre-existing conditions and allergies, reducing the need for unnecessary tests and the associated costs. Sharing patient data between healthcare providers as they treat patients can reduce duplicate tests and improve patient care. However, medical data is usually siloed due to security reasons, but big data and analytics for EHR data can improve the quality of care while reducing costs.

2. Deploy Evidence-Based Medicine

When a patient is admitted to a hospital, doctors usually run a battery of tests to identify the symptoms and the underlying disease. Evidence-based medicine enables healthcare providers to gather evidence of a patient’s health and compare the symptoms to a bigger patient database, enabling faster, more accurate, and effective diagnosis and treatment. Big data helps consolidate and analyze information from this large patient database generated from multiple, disparate sources.

3. Reduce Hospital Readmissions

Hospitals’ costs increase due to high patient readmission rates within a month of release. Using big data, healthcare providers can identify at-risk patients based on patient trends, medical history, diagnostic information, and real-time data from medical equipment. Hospitals can then offer these patients a lower readmission rate, allowing patients to focus on their treatment instead of their healthcare expenses.

4. Detect and Prevent Fraud

In the US alone, the National Healthcare Anti-Fraud Association estimates the loss to health care fraud to be about $80 billion annually, accounting for around 3 to 10 percent of the total annual spending on healthcare. Fraud in healthcare can range from genuine errors in billings to false claims that result in wrong payments. Health institutions have to store and navigate through massive amounts of claims, billings, and other information. Due to the volume, velocity, and variety of data, claim verification and processing could take weeks or months. Detecting fraud and collecting evidence for legal action also take a long time and could result in huge financial losses for the organization. Big data analytics can help detect anomalies much faster and notify you instantly, significantly reducing the potential for healthcare fraud.

5. Provide Real-Time Information

Physicians need access to real-time data about their patients to improve patient care – including their visits to an emergency room, length of hospital stay, new diagnoses, progress in treatment, etc. These real-time insights are derived from data collected using technologies like IoT sensors, which can optimize the hospital’s clinical, business, and administrative workflows. Using big data and advanced analytics, you can analyze real-time information to enable proactive patient care and ensure data-driven decision-making, improving the quality of healthcare, and lowering costs.

6. Optimize Supply Chain Processes

Healthcare institutions spend almost one-third of their overall operating expenses on managing their supply chains. Big data plays a major role across the healthcare supply chain from placing the order, to order fulfillment and invoicing. Real-time visibility into supply chain operations can help hospitals avoid supply round-tripping and wastage that are both expensive and affect care delivery. You can also use supply chain data combined with procedural data to improve your forecasting capabilities, ensuring that products are available at the right time, at the right place, and at the right cost. Analyzing supply chain data can also help automate routine procurement tasks to free up staff to focus on strategic initiatives.

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