By centralizing key functions and leveraging a modern foundational architecture that prioritizes security and reliability, such as those designed to meet stringent U.S. government security requirements, the SYaaS model can deliver better performance and reliability to eliminate both scheduled and unscheduled downtime. In addition, health systems using a SYaaS model avoid large up-front costs and have predictable ongoing costs, including all current and future interfaces, system enhancements and upgrades, ongoing IT and implementation support, and training and system optimization, which assures return on investment, reduces labor costs and significantly lowers total cost of ownership.

Lean into integration and interoperability standards
While the U.S. government has been instrumental in pushing several initiatives to address the issue, a lack of interoperability persists, from the inability to easily share patient data to suspected data blocking. A recent report from IDC indicated that 40% of clinical leaders identify integration and middleware as infrastructure technologies that have challenged their organizations the most, demonstrating the negative impact that fragmented data has on daily workflows and care outcomes.


With a SYaaS approach, health systems can work with vendors that have a proven track record for embracing data sharing and interoperability using standards, such as the Fast Healthcare Interoperability Resources (FHIR) Application Programming Interface (API) as the foundation. For example, a recent report by McKinsey quantifies the potential to free up to 15% of nurses’ time on a typical shift by using technology to automate tasks, such as documenting data from bedside devices, and support better delegation among care teams.

By taking advantage of standard integration protocols, data from a full range of bedside physiological monitors, pumps, ventilators, and any other devices that export data can be automatically integrated within the EHR. Once the data is in the EHR, it’s available for real-time analysis, including predictive scores and scales to identify at-risk patients. Pulling all this data together through integration dramatically reduces charting time, increases accuracy and makes data available to clinicians wherever and whenever it’s needed.

Focus on continuous improvement
When it comes to ongoing support, according to KLAS, healthcare organizations often report that problem resolution isn’t what they expected or that they lack access to the support resources they were promised during the sales process. For many, it means that the support package they purchased isn’t sufficient, leading to a feeling of being “nickeled and dimed” for support requests.

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