Doctors and healthcare administrators are able to make better judgments about treatments and services thanks to the collection and analysis of big data.
For instance, clinicians with access to huge data sets may be able to recognize the precursors of a dangerous sickness before it manifests. Early disease detection and treatment can be easier and more affordable. Administrators can utilize data analytics and key performance indicators to decide how to fund and allocate resources in various areas of the healthcare sector. Critical health maps, for example, that identify underserved areas have been produced using big data gathered from health records and Google maps. But how to handle such huge volumes of data in real-time? The answer to that question is big data and healthcare IT solutions.
The Promise of 3Vs by Big Data
The three Vs—volume, velocity, and variety—are crucial to comprehending how big data may be measured and how different it is from traditional data.
- Volume: Volume is a crucial idea to understand when it comes to large data. Many corporations and industries use a lot of data, maybe because they have a lot of customers or because they feed AI with data. This includes the intelligent appliances in our homes that are always absorbing information from their environment or services like Uber, which has millions of users at any given moment and adds a ton of data to the mix. Let’s look at some real-world instances, like Facebook, which keeps pictures, to help better understand this. It’s estimated that Facebook has a staggering 250 billion photographs in its archives. This doesn’t even account for things like Facebook postings, which are estimated to number 2.5 trillion (and that is only from 2016 onwards).
- Velocity: Velocity in the context of big data refers to the rate of data inflow. Using the earlier Facebook example, 900 million photos are posted by users every single day, despite the social media giant’s 250 billion image storage capacity. This massive volume of data needs to be processed, filed, and retrieved every day. Sensor data is another instance of velocity. There will be an increasing number of connected sensors as the Internet of Things grows rapidly. Effectively, this will result in practically constant data transmission.
- Variety: Variety is the third area of big data. When talking about big data diversity, it means that the data might vary greatly from one application to another, with a large portion of it also being unstructured data. As in the past, all the data may not necessarily fit neatly into one database application. Emails provide a good illustration of big data’s diversity. Since each message has a unique destination, time stamp, potential attachments, and text that is unique, no two messages are ever the same. Emails are a type of data that, like audio recordings, films, and images, tends to be exceedingly diverse and unstructured.
Want to discover the immense possibilities of Big Data?
Where in Healthcare is Big Data used?
- Big Data healthcare application on Patient care: This is a dashboard that collects patient data in a central platform. It is designed to enhance provider service and treatment accuracy. For instance, measures on bed occupancy rates provide a window into where resources may be needed, and tracking missed or canceled appointments will provide senior executives with the information they need to lower expensive patient no-shows.
- Big Data in healthcare Applied on a Hospital Dashboard: This is a dashboard that collects Hospital data from every division like attendance, specialty bifurcation, average wait time, the total number of patients, and many more.
Application of Big Data in Healthcare
- Reducing cost: According to a report by Mckinsey, after a steady increase of 20 years, healthcare expenses now represent 17.6 percent of the US GDP, which is nearly $600 billion more than the expected benchmark. We obviously require some insightful, data-driven thinking in this regard. Additionally, current incentives are changing. Many insurance companies are moving away from fee-for-service plans, which encourage the use of expensive and occasionally unnecessary treatments as well as the quick treatment of large numbers of patients, and toward plans that place a higher priority on patient outcomes. Well, in earlier plans, healthcare providers weren’t directly encouraged to share patient information, which made it more difficult to harness the potential of analytics. They have a financial incentive to share data that can be used to improve patients’ lives while lowering costs for insurance companies now that more of them are paid based on patient outcomes.
- Predict the daily patients’ income to tailor staffing accordingly: Let’s examine a common issue that every shift manager encounters: how many employees should be on duty at any given time? Because if you hire too many people, you run the danger of incurring escalating labor expenditures. Poor customer service can result from having too few employees, which can also be disastrous for patients in that industry. Big data is helping in solving this problem. It helps you see relevant patterns in admission rates and also predicts future health trends.
- Use in Electronic Health Records (EHRs): The biggest and most widespread usage of big data in healthcare is in EHRs. These EHRs have a digital profile that includes information about their background, health history, allergies, lab test results, etc. The records are accessible to providers from every secure network. As it is a digital file providers can edit and modify the data without the pressure of increased paperwork. These EHRs come with other small but really important features like Real-Time alerting utilizing CDS(Clinical Decision Support) software.
- Help in preventing opioid abuse in the US: Interacting with those deemed to be “high risk” and keeping them from becoming addicted to drugs is a tricky task. Big data helps in strategically storing the required information from e-prescriptions into the state PDMP database and also the EHRs. Consequently, this helps in giving real-time alerts in scenarios of Opioid overuse.
- Better staffing & management: Patient care will deteriorate, service levels will decline, and errors will occur without a cohesive, motivated workforce. But you can streamline your staff administration duties in a variety of crucial areas with the help of big data solutions in the healthcare industry. Medical facilities that are already overburdened with patient care might streamline patient care by using the correct HR analytics powered by big data to estimate operating room demand and manage staffing.
Think Big (Data), Think Nalashaa
Big data in healthcare has enormous potential to improve patient outcomes and support more effective and efficient healthcare delivery.
However, the effective use of big data in healthcare requires a deep understanding of data and the ability to process, analyze, and visualize the information in meaningful ways. Nalashaa’s team of experts has extensive experience in the field of big data and healthcare IT services. We can help organizations harness the power of big data to support their goals and initiatives.
Whether you’re looking to improve population health management, enhance patient outcomes, or gain insights into disease patterns, Nalashaa can help you. Connect with us at firstname.lastname@example.org
Latest posts by Mitrajit Das (see all)
- Medicare Revolutionizing Behavioral Health Access: What Providers Need to Know - September 25, 2023