Health care organizations are well underway in adopting and implementing electronic health records in order to improve the quality and safety of patient care. By making clinical patient data electronically available, opportunities exist to analyze the patient data in new meaningful ways. Clinical analytics is an emerging concept dedicated to applying the science of analytics to the clinical setting.  It starts by aggregating relevant clinical data, then applying certain pre-defined analytics and algorithms to extract actionable patient data.  The goal is provide personalized efficient and effective quality care to patients and populations.

Through clinical analytics, health care organizations can become more proactive in the real-time surveillance and longitudinal monitoring of individual patients. For example, by analyzing patient data according to clinical algorithms, we can detect early signals and prevent adverse patient events and hospital acquired infections that affect every healthcare organization.

Aggregated clinical data may cross over several patient encounters in an organization. Therefore, we can support clinicians’ longitudinal view of patient health status over time for enhanced decision-making across visits. Through real-time surveillance and clinical monitoring, clinicians can use Diagnosoft FUSION as knowledge and support tool for improved patient outcomes and reduced length of stay.

Clinical analytics also address a gap in population-level patient management. By aggregating clinical patient data according to diagnostic groups, such as Heart Failure or Diabetes, clinicians can identify trends in the outcomes of these patient populations. With more robust algorithms, the opportunity exists to conduct predictive modeling analyses and proactively identify points in time where a group of patients are likely to experience a change in their condition or may benefit from an adjustment in their treatment approach.