Technology-enabled clinical decision support (CDS) tools, in their most basic form, are nothing new. Just walk through a hospital corridor and you will likely hear any number of familiar alerts and alarms coming from the nurses? station prompting the replacement of an IV bag or warning of a change in vitals status. But a new generation of tech-enabled CDS tools is upon us. Thanks to advances in health IT and data analytics, a better (i.e. safer, more effective, more efficient) machine-assisted medical future is closer than you may think.

According to the Office of the National Coordinator for Health Information Technology, a CDS is a system that ?provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and healthcare.? In short, it is the capacity to get the right information to the right people at the right time to inform diagnosis, treatment and follow-up care.

CDS tools use data from a variety of sources (EHRs, clinical knowledge databases, medical devices, etc.) to provide real-time guidance using established and proven guidelines. In other words, the days of physicians making recommendations for individual patients based on standardized (sometimes stagnant) guidelines alone are numbered. In the very near future, using machine learning methods to extract subtle patterns in massive data sets and create new recommendations (informed by both cumulative knowledge and statistical analysis) will be the norm.

But challenges exist in developing and implementing effective CDS tools: tools that not only enable better patient experiences and outcomes, but are also embraced by physicians, nurses, and other clinicians. Whether you are a hospital system seeking to build your own, or a HIT vendor serving the provider space(from physician practices and ambulatory centers to pharmacies and skilled nursing centers), four critical challenges must be addressed:

1. Interoperability
Your CDS must allow for the easy and ongoing exchange of health information among various internal and external systems, rather than forcing time-consuming, frustrating workarounds. This can be particularly challenging due to the lack of standardization across commercial EHR and other health IT solutions.

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2. Usability
Clinical decision support tools must fit within existing clinical workflows, and they must be easy to use and easy to understand, even in complex, fast-paced environments. Your CDS? user interface and user experience (UI / UX) design will be critical in this respect.

3. Reliability
Static guidelines may not be very advanced technology, but the decisions resulting from them are easily traced back to accepted standards. Decisions resulting from mountains of statistical information in a machine learning model almost never result from an easily identified rationale. To ensure your CDS remains reliable now and into the future, it?s critical for your CDS to ?close the loop? and continue to provide new data for model improvement over time.

4. Security
Machine learning can provide amazing results given sufficient data, but the balance between the benefits of making decisions based on all available data and protecting the privacy rights of patients is not always easy to spot. Compiling enormous amounts of data for statistical analysis creates an attractive target for malicious actors looking for weaknesses in technology safeguards. Security in your CDS will never be?done?; protecting sensitive data on an ongoing basis will require constant attention and investment.

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