Healthcare Technology Featured Article

January 31, 2017

Predict Admission Rates with Machine Learning

Big data analysis has been used to improve the healthcare industry in several ways, such as developing personalized medicines, working to fight cancer and making pharmaceutical trials more efficient. Another exciting area that big data is ameliorating is the ability to forecast admission rates. With the help of machine learning, big data is able to predict the number of admissions a healthcare facility will have at any given time. This predicted number will allow for these facilities to better prepare for their anticipated number of patients, either by having enough staff ready to work, surgeries scheduled at the most opportune times, and the right type and amount of supplies stocked.

This new application of big data analysis and machine learning to predict admission rates is based on both internal and external data. In addition to a wide variety of resources, one form of internal data that this technology studies includes ten years of past admission records. With these records and other data, machine learning programs try to detect possible patterns and trends. This combined analysis of resources that allows for predicted admission rates is built on the open source Trusted Analytics Platform (TAP). TAP was specifically chosen for this technology because of it’s capacity to handle and interpret large amounts of data. While this application of big data analysis to predict patient numbers is currently in an experimental stage, here are three ways healthcare facilities can best take advantage of this technology.

Better Staffing Hospitals

With the help of this patient admission prediction technology, healthcare facilities can now base their staff’s work schedules on these predicted numbers. By ensuring that healthcare facility staff are prepared for high numbers of admissions will help both to lower the amount of time patients spend waiting for treatment and increase the efficiency of the facility. These predicted numbers can also help facilities schedule less staff when trends show lower admission numbers. This will allow for less staff to be scheduled with nothing to do and give them more time to catch up on their certifications, such as ACLS renewal or receiving medical transcriptionist training.

Scheduling Surgeries At Ideal Times


Healthcare facilities can also use patient admission prediction technology as they schedule patient surgeries. By understanding what days and times are expected to bring in more admissions, healthcare facilities can schedule surgeries around these trends. Healthcare facilities can use the data presented and schedule surgeries on days when low admissions are expected. Understanding and using these predicted numbers can help these facilities ensure they have enough staff to aid with both the surgeries scheduled for that day and any patient admissions.

Stocking The Right Type and Amount of Supplies

From medicine to rags to needles and surgery kits, healthcare facilities use and store a lot of supplies. However, balancing inventory numbers can be challenging. It’s important that healthcare facilities have enough supplies on hand, but also have the right amount and the space to store it. By using patient admission prediction technology to understand upcoming admission trends, healthcare facilities can purchase the right supplies at an appropriate amount. This will help ensure that these facilities have the proper supplies on hand without purchasing too much.

Although patient admission prediction technology is currently in experimental stages, it shows promising ways that it can increase the efficiency of healthcare facilities and improve the overall experience that admitted patients endure. Healthcare facilities will be able to take full advantage of this platform by using it when they create staff work schedules, schedule patient surgeries, and order supplies for their facility. By understanding the trends that this technology predicts, healthcare facilities and professionals can be better prepared to handle any number of patient admissions.

Edited by Stefania Viscusi
By Special Guest
Rick Delgado, Freelance Writer ,

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