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Decision Management Systems Combination of Rules and Analytics Ideal for Healthcare

by | Feb 13, 2013 | Business Rules, Decision Management, Platform Technologies Report, Predictive Analytics

Healthcare is an industry increasingly rich in data. Yet simply applying analytics, doing simply what the data tells you, is not really practical in an industry where peer review, published best practices and government regulation abound. Decision Management Systems, with their combination of rules and analytics, are ideal for this environment. As more of the healthcare industry is computerized and more data is collected, Decision Management Systems are playing an increasingly important role. As healthcare goes mobile, helping patients live at home and treat themselves, this is only going to increase.

Identify drug interactions and other issues

One of the most common uses of Decision Management Systems in healthcare is to identify potential problems in prescriptions. Identifying potential drug interactions and checking dosages prescribed against patient details involves large numbers of rules gathered from best practices, medical research, drug companies and more. Providing these checks in the hospital as nurses administer drugs, at the pharmacy as prescriptions are fulfilled and warning doctors about potential issues can all be driven from the same rules ensuring consistency and reach.

Determine treatment

The best practice in healthcare evolves continuously. New therapies, new suggestions, new drugs and new ways to match a patient to a therapy, using genetic matching for instance, make it hard for medical professionals to stay up on the latest treatment. Especially when multiple possible treatments can be proposed, selecting the one most likely to work for a particular patient—personalized medicine—is complex. Decision Management Systems engage medical professionals in managing their own rules, bring analytics to bear as data is gathered regarding what works, and easily stay current as best practices and guidelines change.

Target at-risk people

While we might wish we could always apply all the resources that might help to a medical problem, the reality is that we cannot. Determining which patients are most at risk and what kinds of interventions are likely to have the biggest impact is a fact of life for most healthcare organizations. Using analytics, especially predictive analytics, as well as expert rules and best practices, a Decision Management System can ensure resources are applied effectively to those most at risk.


Healthcare, like many labor intensive industries, involves complex schedules. Making sure that the relevant specialties are available at the right time and place, managing staffing to match demand, ensuring that operating rooms are prepped before they are needed—all this makes scheduling in healthcare difficult. Decision Management Systems can use rules and optimization to come up with the most effective schedules possible, given the constraints, saving money and lives at the same time.

Read more in our Decision Management Systems Platform Technologies Report.


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