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 James Seevers, CIO & GM, Toyoda Gosei
By Bill Krivoshik, SVP & CIO, Time Warner Inc.
By Gregory Morrison, SVP & CIO, Cox Enterprises
By Alberto Ruocco, CIO, American Electric Power
By Bruce. D. Smith, SVP & CIO, Information Systems, Advocate...
By Adrian Mebane, VP-Global Ethics & Compliance, The Hershey...
By Graham Welch, Director-Cisco Security, Cisco
By Michael Watkins, Senior Product Director, Global Knowledge
By Bernd Schlotter, President of Services, Unify
By Patrick Hale, CIO, VITAS Healthcare
By Steve Bein, VP-GIS, Michael Baker International
By Jason Alan Snyder, CTO, Momentum Worldwide
By Mike Morris, CIO, Legends
By Louis Carr, Jr., CIO, Clark County
By Bill Dow, SVP and General Manager of Business Solutions,...
By Jim Whitehurst, CEO, Red Hat
By Darren Cockrel, CIO, Coyote Logistics, a UPS Company...
By Nathan Johnson, SVP and CIO, Werner Enterprises [NASDAQ:...
By David Tamayo, CIO, DCS Corporation
By Neil Hampshire, CIO, ModusLink Global Solutions, Inc....