Predictive Analytics Use Cases in Healthcare
Predictive analytics is showing great promise in helping the health sector by improving their efficiency and productivity. Organizations in the healthcare sector are increasingly recognizing the use of machine learning for predictive analytics to envision and enhance the future outcome. Predictive analytics is poised to grow further, and in research, it is expected to reach $33 billion by 2022. Following are the few cases where the predictive analytics knowledge got transferred into action and improved business operations:
Productivity and Strategic Planning
Using historical data, hospitals are training machine learning models that can predict the future peak in patient volume at the hospitals; this insight assists in optimizing the staff counts to handle the surge. For instance, machine learning models can predict the increase in the number of emergency cases on a particular day of a month. By adopting such insights, administrators can increase the headcount of the employees on that day, and in turn, the organization can cut on unnecessary spending.
Most of the time decision makers are faced with critical situations when there is a dilemma in choosing the type of treatment for a disease. But, with the availability of vast quantities of data points due to Electronic Health Records (EHR), the statistical tests are now more accurate, and this increased precision assists the doctors in choosing the best treatment. Moreover, drug manufacturers are also using these similar techniques to improve and manufacture new drugs.
In recent times data security is one of the most important things for every organization, and keeping it secure has had its fair share of challenges. Hospitals are setting up warehouses to collect and share data for collaborative efforts. However, facilitating easy data sharing may also give the intruder an opportunity to access the confidential data. Monitoring the data access pattern with analytics tools provides the administrator with a warning when something changes abruptly, signifying an intruder’s presence. Moreover, machine learning bots can also flag abnormal data access and can restrain the sharing of information. The approach is similar to what ML bots do in the finance sector where it detects and restricts suspicious transaction.
According to research by HITECH in the USA, 94% of hospitals have adopted EHRs; this helps the organization in collecting large health data, which assists in building predictive models on top of it to get well-informed insights. By using such big data prognostication, the healthcare sector has the potential to reduce the costs of treatment, and predict epidemics.
By Phil Jarvis, VP, IT, Thirty-One Gifts
By Dr.Chris Ewell, CISO, Seattle Children
By Eloise Young, CIO, Philadelphia Gas Works
By Phil Stevens, CIO, The Exchange
By Herman Nell, SVP & CIO, Rent-A-Center
By John Honeycutt, CTO, Discovery Communications
By Mark Wead, Chief Enterprise Architect– North America...
By Federico Flórez, Chief Information & Innovation Officer,...
By David Berry, CIO, Daymon Worldwide
By Douglas Turk, Chief Marketing Officer, JLT Speciality
By Tekin Gulsen, CIO, Global IT & Corporate Planning...
By John Sprague, Deputy CTO, IT and the End User Architect,...
By Craig C Shrader, CIO Engagement Partner, Tatum, a...
By Bill Schimikowski, VP, Customer Experience, Fidelity...
By Tom Bressie, Vice President, Oracle Cloud
By Jeff Katz, CTO, Energy & Utilities, IBM [NYSE:IBM]
By Dr Dirk E Mahling, VP, Technology, Alliant Energy
By Steven John, CIO, AmeriPride Services
By Leon Ravenna, CISO, KAR Auction Services, Inc.