Complementing the EHR with decision data
EHRs are a treasure trove of granular, patient-specific information. However, CDS usage data complements the EHR in three distinct ways:
- Providing potential early signals: An EHR documents a decision after it has been made—the prescription written or the test ordered. CDS usage data, on the other hand, captures information-seeking behaviors that precede the decision, offering an early look at emerging clinical trends.
- Ensuring data privacy: CDS usage data is de-identified and contains no protected health information (PHI). This allows quality and management teams to analyze practice patterns quickly and responsively without navigating the complex governance required for patient-specific data.
- Offering an enterprise-wide view: The EHR often reflects the actions of prescribers, but it doesn't always capture the contributions of the entire care team. Nurses, pharmacists, and other allied health professionals use CDS tools to inform their practice. Analyzing their usage data provides a more complete picture of how the entire healthcare team is seeking information and influencing patient care.
From common challenges to data-driven solutions
Hospitals across the APAC region face similar challenges, including the increasing complexity of patient care, staff burnout and shortages, and immense pressure to manage costs. Dr. Nguyen Hoang Dinh, Deputy Director of the University Medical Centre (UMC), Ho Chi Minh, noted how patients with multiple chronic diseases require complex, coordinated care that can often become fragmented. Dr. Jasmine Ong of Singapore General Hospital echoed these concerns, highlighting the strain of an aging population and the rising burden of chronic diseases. Technology is a key part of the solution. Leaders are implementing digital initiatives to streamline workflows and enhance patient safety. For example, UMC’s integration of a drug interaction alert system into its EMR provides real-time warnings to prevent adverse events. In Singapore, a national diabetic retinopathy screening program leverages AI to support non-specialists, democratizing access to care. These initiatives show the power of targeted digital tools. But the next step is to use the data from these tools to drive continuous improvements and broader strategy. As clinicians make decisions, they generate patterns. Aggregating this data can help identify knowledge gaps, optimize resource allocation, and guide educational efforts on a system-wide level.
