Thank you for Subscribing to Life Science Review Weekly Brief
Healthcare analytics allows hospitals to track patient records, histories, and to ensure the right doctors to them. Deploying a prominent healthcare suite also offers improved areas of several operations in the healthcare sector.
FREMONT, CA: Analytics has made a significant impact on the healthcare industry. Starting from fitness bands to robotic surgeries, technology has transformed the healthcare industry. The private sector, as well as the public sector, is hugely benefitting with the healthcare data at their disposal. It represents an excellent opportunity for professionals in the healthcare domain to learn and apply enhanced and improved analytics to overcome business challenges and convert them into success. Below given are some of the instances of how analytics helps in resolving major healthcare problems.
Emergency Rooms and Cost-Efficiency
The high cost of maintenance and restricted availability of the Emergency Rooms (ER) are under intense scrutiny by the payers, government, and employers. Avoidable ER visits emerge from a lack of coordinated medical attention, which push toward a higher cost of care, longer wait times, and the sub-standard outcomes. As a transformative solution, analytics, with consideration of the healthcare sector rules, was used to identify the low-intensity conditions where ER episodes could be avoided.
External Data to Enhance Pricing
A health insurer leverages the predicted claims by external data, in addition to internal factors like age, gender, and the region to decide on the pricing of the policies. Fractal analytics suggested a rating modifier be built the expected claims experience that is informed based on external data. As a transformative solution, the movement explored a lot of traditional and improved machine learning techniques to predict the claims experience at the member level.
Patients Prediction for Staffing
Big data is helping solve staffing problems. Data scientists crunch ten years' worth of hospital admission records using the time series analysis steps. The processed information permits the researchers to see relevant patterns in admission rates and can use the machine learning algorithms to predict future trends. The approach helps the hospital management to draft extra staff only when a high number of visitors are arriving. Application of the big data minimizes the patient wait times and improve the hospital workforce to serve better quality of care and attention.