Team # 819 -- Healthcare Engineering Team
College of Engineering / College of Information Sciences and Technology / College of Medicine
ObjectiveTo enact process changes that significantly impacted patient care in our Emergency Department by physician directed queuing: efficient function under crowded conditions. We strove first to examine our current facility using a traditional model of care and our current volume and space parameters. Then we applied healthcare engineering methods to modify our treatment model to match capacity to demand. The team involved members of the Department of Emergency Medicine at PSU-HMC and Industrial Engineering and Information Technology at PSU.
- Chris DeFlitch, Leader
- Joanna Abraham, Member
- Jodi Armold, Member
- Ravi Cherukuri, Member
- Steve Davis, Member
- Stan Duffendack, Member
- Mark Escott, Member
- James Fenush, Member
- William Fisher, Member
- Glenn Geeting, Member
- Lori Gettle, Member
- Lisa Haas, Member
- Catherine Harmonsky, Member
- Frank Kapper, Member
- John Litell, Member
- Deb Medeiros, Member
- Kathleen Moyer, Member
- Sharoda Paul, Member
- Victor Pilewski, Member
- Madhu Reddy, Member
- Nathaniel Sheetz, Member
- Justin Smith, Member
- Eric Swenson, Member
- Thomas Terndrup, Member
- Molly Wood, Member
Results Achieved to Date
- Objective: Physician Directed Queuing:
Efficient Function Under Crowded Conditions
Emergency healthcare in the United States is at a crossroads. Decreasing hospital and Emergency Departmet (ED) capacity coupled with a steady rise in patient volume have created conditions of crowding. We see long waits for care, caring for patients in hallway beds, and the practice of boarding admitted patients in the ED which any facilities and providers have accepted as normal. A commonly held belief is that hospitals cannot survive financially unless they operate at or near capacity.
Queuing simply means forming a line while waiting for something. Whether we acknowledge it or not, healthcare is full of queues: waits for registration, triage, procedures or testing, admission, bed placement, discharge, etc. Effective management of these waits results in more safe, effective, efficient, personalized, timely, and equitable care.
We strove first to examine our current facility using a traditional model of care and our current volume and space parameters. Then we applied healthcare engineering methods to modify our treatment model to match capacity to demand.
The setting is one of a suburban academic Emergency Department caring for 50,000 patients annually in a 29-bed facility originally designed to care for about 33,000 annual patients. We examined our department based on a common industry target of 1,350 annual patient visits per bed, an average of 10 admitted patients in the ED nightly, and expectations of patient volume continuing to increase in the future. We calculated the current and projected patient visits per clinical station and the number of stations required to accommodate them.
We applied queuing methods to the process of our ED through flowchart analysis, clinical value streaming, the use of information technology to measure time periods involved in care, and testing original ideas. A primary tenet of queuing theory is that as a system nears full capacity, waiting time exponentially increases, especially with stochastic (random) customer arrivals. Simple flowchart analysis was used to evaluate our current “traditional” model of care and alternative models were then developed, scrutinized and refined.
The current function of this department is above industry standards for patient visits per year per clinical station. Imposing boarded patients significantly stresses a system that is already at full capacity.
Continued function under a traditional model in this current physical plant is unsustainable.
Flowchart analysis demonstrated significant inefficiencies, exposing the imposition of steps with little clinical or perceived value that obstructed the patient from receiving the care they sought. Applying queuing methodology to rethink our current care model led to restructuring the system to reduce “door-to-doctor” time and permit the initiation of diagnostic and therapeutic actions early in a patients visit. We present a different model of care that could be performed within our current department without significant changes in staffing or physical plant. It is our hope that application of basic queuing principles should prove effective in improving patient safety and satisfaction throughout the hospital.
Pilot data showed the following:
PDQ methodology - 131 patients
PDQ primary for 29.3%
36 Discharged, 3 Observation
PDQ LOS- 2hrs (including OBS)
Complex diagnostic/therapeutic & room 70.7%
68 (74%) were discharged
24 (26%) were admitted
For all patients
Door to doctor time was 2 minutes
Door to room-14 minutes
0 patients who left without treatment
0 patients in the waiting room at 6pm
Satisfied PROVIDERS & Patients
Ongoing results over the next months showed:
Patients left prior to treatment decreased from 5.6% to 2.7% (52% improvement) Door-to-doctor time from 93 min to 60 min (33% improvement) Door-to-room time from 71 min to 45 min (37% improvement) Total ED length of stay for all patients from 8:06 to 6:16 (23% improvement). This modification affected the treatment of patients of all acuity levels.
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