With the introduction of big data analytics, the healthcare sector experienced a paradigm shift in the age of digitization. When paired with strong analytics tools, the enormous amount of data produced by Electronic Health Records (EHRs) has the potential to completely transform patient care and clinical decision-making.

Since there are so many new data records created every day, it is getting more and harder to efficiently gather and analyze all of the available data.

Electronic Health Records (EHRs) and every other type of digital data pertaining to the healthcare industry have accumulated in enormous numbers, necessitating the use of technology and its tools in order to benefit from the data that is now available. Big Data can be useful in this situation.

This blog explores the role of big data analytics in healthcare, focusing on key areas such as EHRs, predictive modeling, and data mining.

Electronic Health Records (EHRs):

The way healthcare professionals handle patient information has changed as a result of electronic health records. EHRs store a variety of data, including demographic data, medication records, lab findings, and medical history. Healthcare organizations can use big data analytics to extract insightful information from EHRs, which has a number of advantages:

• Better patient outcomes: Big data analytics can find correlations and patterns in EHR data that help with early disease diagnosis, more precise diagnoses, and individualized treatment strategies.
• Improved care coordination: By analyzing EHR data, healthcare professionals may smoothly share information, which enhances care coordination among various providers and lowers medical errors.

Predictive Modeling:

Statistical methods and historical data are combined in predictive modeling to forecast future results. Predictive modeling in healthcare can have a significant impact on patient care and resource allocation. Big data analytics strengthens predictive modeling in healthcare in the following ways:

• Early disease detection: Predictive algorithms can locate early warning signals and risk factors linked to diseases by examining enormous volumes of patient data, including EHRs. This enables medical professionals to take proactive measures and intervene.
• Resource allocation optimization: Because predictive algorithms can predict the number of new patients, healthcare organizations may allocate resources more effectively. Hospitals can better manage manpower, resources, and bed capacity by anticipating patient demands.

Data Mining:

The extraction of useful patterns and insights from huge databases is known as data mining. Data mining is essential in the healthcare industry for spotting trends, patterns, and possible correlations that may affect patient treatment. The following are some essential uses of data mining in healthcare:

• Drug development and discovery: Data mining methods assist researchers in the analysis of substantial amounts of genetic data, clinical trial data, and biological data. As a result, treatment plans are made more effective, possible therapeutic targets are found, and the drug discovery process is sped up.
• Fraud detection: Data mining tools can find patterns suggestive of fraudulent activity by examining claims and billing data. This lessens financial losses for healthcare payers and providers by preventing healthcare fraud.

How does Big Data analytics assist in Follow-up care?

Big data is particularly crucial when it comes to follow-up, long-term care, and preventative healthcare. To assist minimize hospital readmissions in the most vulnerable patients, big data technology has been used to forecast which patients are most likely to heed their doctor’s recommendations and which ones aren’t.

GPS-enabled inhalers for asthmatics are now being created to track when a patient is taking their prescriptions using location-tracking data. The doctor can use this knowledge to effectively avoid further disease or harm and create better, more individualized treatment strategies for specific patients.

Companies like Ginger.io are creating healthcare mobile applications for tracking patient improvement—or lack thereof—in order to make use of the advantages data has to offer. The app can help notify doctors or family members if a patient is likely feeling ill or in danger of an anxiety attack or other psychological attack by recording data discovered in areas such as calls, geographic location, physical movement, or sleep patterns with the patient’s consent. A mobile application like this needs data to be successful, but the technology it provides is undeniably starting to open new doors for patient follow-up solutions.

Does Big Data Analytics in Healthcare aid in preventing errors?

There will inevitably be mistakes made by doctors and other healthcare workers throughout their careers, but frequently lives are at stake. Unfortunately, medication mistakes in hospitals happen much more frequently than individuals may realize. Big data can assist prevent or further reduce the occurrence of such significant errors, though.

Big data technologies may examine each patient’s unique medical records as well as any other issues, such as allergies, previously prescribed medications, doses, and other pertinent data. Data analysis tools can finally identify any missing data or highlight anything that might appear dubious after examining all records.

One of these software systems for big data was created in 2012. The electronic health record (EHR) system of a hospital can be connected to the MedAware firm, which was developed by Dr. Gidi Stein. This allows for the early detection of prescription errors.

The system highlights a selected drug that doesn’t correctly match the patient’s records or needs, and it pauses the drug order until it is re-evaluated and confirmed. This prevents potential errors. When MedAware was being developed, it was tested on 747,985 patient records as part of a clinical study, and 15,693 of those records were flagged for potential medication errors. This further demonstrated the effectiveness of the data platform and demonstrated just how helpful it is for hospitals and medical professionals.

Final Thoughts

Big data analytics has created new opportunities for the healthcare sector, empowering professionals to revolutionize patient care through data-driven decisions. Data mining, predictive modeling, and electronic health records are just a few examples of how big data analytics is revolutionizing the healthcare industry. Healthcare organizations may improve patient outcomes, better allocate resources, and promote medical research by utilizing the power of data. The potential for big data analytics to revolutionize healthcare is limitless as technology advances, ultimately resulting in a more effective and healthy healthcare ecosystem.