Kobid Dahal

An initiative to identify, monitor, and mitigate sepsis risks in emergency room and ICU patients using the Virginia Mason Production System (VMPS) method, based on the Toyota Production System (TPS).

ROLE
As a key member of the SMI team, I represented the ITS department in a project that significantly improved patient outcomes and enhanced hospital safety protocols. This initiative aimed to reduce patient mortality due to sepsis.

KEY ACTIVITIES
I led the technology aspects and execution, aligning with organizational goals and VMPS principles. I also developed algorithms to predict early signs of sepsis, which were integrated into the hospital's IT infrastructure. Next, I provided actionable data for clinical interventions, facilitating timely decisions, and used data to confirm sepsis diagnoses, monitor intervention, and correlate with patient improvements.

RESULTS
The patient mortality rate was reduced by 4%. The initiative won the organization's "Patient Safety Award," and the work was published in the American Journal of Nursing.

The SMI led to significant improvements in patient outcomes, demonstrating the power of technology, collaboration, and TPS principles in healthcare.

An initiative to optimize nurse staffing levels using ML to predict demand based on seasonal trends like flu and weather changes. This approach ensured efficient planning for patient care demands.

ROLE
I led an ML pilot to predict nursing staff demand, addressing fluctuating patient care needs and improving operational efficiency.

KEY ACTIVITIES
I drove the development and implementation of ML models to forecast demand and integrated data sources—including historical staffing levels, seasonal illness trends, and weather patterns—into predictive models. Next, I developed and trained algorithms to analyze data and predict future staffing needs and collaborated with customers to ensure the models met requirements. Finally, I executed the pilot to validate model effectiveness, gathering feedback and refining algorithms.

RESULTS
I achieved consistent accuracy above 85% in predicting demand. The initiative provided insights into staffing patterns for informed decision-making and established a foundation for full-scale model deployment.

This project demonstrates my ability to lead data-driven initiatives, integrate advanced technologies, and collaborate with customers to improve healthcare operations and patient care.

CUSTOMER SATISFACTION RESEARCH
I conducted market research to identify the correlation between patient satisfaction and wait times across demographic information such as race and age, providing insights to improve service delivery and customer experience.

ROLE
I led the research initiative to understand the correlation between customer wait times at the clinics and satisfaction, using the Net Promoter Score (NPS) to measure satisfaction levels.

KEY ACTIVITIES
I collected and analyzed data on patient wait times and satisfaction scores, segmented by demographic information such as geographic region, race, and age. I also developed analytical models to identify patterns and correlations and provided actionable insights to stakeholders to optimize service delivery.

RESULTS
I provided detailed insights that led to targeted improvements in service delivery, enhancing overall patient experience and satisfaction across diverse demographic groups.

These accomplishments underscore my significant contributions to data management, operational efficiency, and customer experience enhancement.