Date: 22/12/23

Healthcare Customer Outcomes

Clinical Program – Sepsis

Septic shock occurs when organ injury from infection leads to dangerously low blood pressure and abnormalities in cellular metabolism. Severe sepsis and septic shock have one of the highest rates of hospital mortality, with estimates ranging from 25 to over 50%.

This use case focuses on how Fusion Consulting guided Loma Linda University Health Systems to successfully utilize analytics to actually affect patient quality of care and clinical outcomes. Enablement was priority for the business and clinicians to access appropriate information at appropriate times to accurately identify those clinical areas with the greatest opportunity for improvement. We did this in part by developing work groups where quality clinicians worked with the business intelligence team to develop analytics for each targeted clinical program such as

Affected Job Capacities

• Chief Medical Officer (CMO)
• Chief Nursing Officer
• VP of Patient Care
• Director of Clinical Quality
• Inpatient Clinical Services Director

Use Case Example

Fusion utilized their expertise by implementing Loma Linda’s EHR enterprise data warehouse and business intelligence tools to analyze and improve clinical outcomes for patients.

We developed work groups where quality clinicians worked with the business intelligence team to develop analytics for each targeted clinical program. Clinical programs identify a specific patient population based on acute or chronic diagnoses, physical hospital location, and performed hospital procedures. Sepsis was one of the targeted programs that had applicable clinical KPI’s to track.

With severe sepsis and septic shock having one of the highest rates of hospital mortality, it was imperative that we identified key metrics for it which are antibiotic administration, lactate collection, blood culture collection, central venous pressure (CVP), and fluid resuscitation. Sepsis treatment is extremely time-sensitive so the most important metric is the number of minutes from arrival and/or instances of an EHR decision support alert to these clinical events. Additionally, fluid resuscitation requires a specific amount of fluid administration per kg of the patient’s weight.

To enable our short-term lessons learned analytics we created were custom Tableau [19] dashboards for each clinical program and included the data points specific to that clinical program such as Sepsis, but also included our clinical outcomes including length of stay, mortality, readmission and critical care length of stay in each dashboard.


We have evaluated early progress of the analytics in improving clinician behavior and patient outcomes. In the last 7 months, we have observed the following results vs patient encounters prior to the analytics rollout:
• Sepsis: 27% reduction in mortality rate
• 25% reduction in readmissions
• 16- hour reduction in critical care length of stay
• 14.6% improvement in antibiotic administration in first 3 hours
• 14.8% improvement in timely lactate measurement
• Reduction in average time to each important clinical action

Primary Information Workflow

Reuse Opportunity of Use cases

CHF analytics can be reused in all inpatient hospital settings. CHF is a common chronic medical condition that is associated with complications and high mortality rates. Improving the quality of
timely medical interventions associated with CHF exacerbation can improve patient outcomes. This CHF analytic clinical program will provide the necessary data elements clinically recommended to treat CHF exacerbation. The program tracks the times of which medical interventions are performed. This data will help to identify potential barriers and trends in treatment plans once CHF exacerbation treatment begins. The improvement of CHF exacerbation treatment workflows impacts notable hospital KPI’s such as length of stay, readmissions, and mortality rates.


Use Case Model Data Flow Diagram


Technology Architecture



“Improving Patient Care Through Analytics” – Paper Publication Conference Paper· September 2016

DOI: 10.1109/ISCBI.2016.7743265
Conference: 2016 4th International Symposium on Computational and Business Intelligence