Big Data

Photo By Alexander Korzh via 123RF

 

Electronic health records are digital records of patients that contain essential information, such as the history of medical procedures and current treatments. EHRs are a part of the growing industry of big data which can help the future of healthcare industry.

While the use of EHRs remains messy, the system has great potential in the medical field. Information from patients may be used by researchers in studying deadly infections and hereditary infections, and without the help of big data, the analysis of overflowing information would take longer to finish.

“It’s not about the data, it’s about what you do with the data in terms of making sense of it,” said Dr. Anil Jain, Vice President and Chief Health Information Officer at IBM Watson Health.

How do patients and hospital staff benefit from big data?

1. It reduces the number of medication errors. Without digital influence, the details in the patient’s chart are handwritten, including their prescription. But some patients still get the wrong medication that may cause harm or death. EHRs can be analyzed by tools in big data to determine if there is something odd. In a system developed by MedAware, more than 15,000 out of 747,985 patients have been flagged after being analyzed. Big data can help spot any errors that may potentially cause adverse effects on patients.

2. It separates high-risk patients from low-risk ones. Emergency departments in hospitals can experience chaos from time to time, which affects care and outcome of patients. Big data can help analyze the patients who are at high-risk using collected information to alert hospital staff. The system can create a priority based on the medical data of patients admitted to the ER.

3. It helps deal with hospital costs. In any region, hospital costs are always a problem to deal with. In Paris, a hospital applied predictive analytics to help with manpower. Using big data filled with the history of admission rates, the system gave a forecast of how many admissions were likely to occur for the next two weeks. The predictive analysis can help in scheduling of hospital staff for the next two weeks that reduces hospital costs.