EFFECTS OF RESIDENTS ON EFFICIENCY IN AN EMERGENCY DEPARTMENT J. Silberholz, D. Anderson, E. Sze, J. Lim, E. Taneja, E. Tao, B. Kubic, K. Johnson, D. Kalowitz,

Slides:



Advertisements
Similar presentations
What are the causes and consequences of ED overcrowding? Inability to move admitted patients from the ED to appropriate inpatient units – Hospital occupancy.
Advertisements

Presented by, Matthew Rusk, D.O. Advisor: Khalid Qazi, M.D.
Does health insurance matter? Establishing insurance status as a risk factor for mortality rate Hisham Talukder, Applied Mathematics Héctor Corrada Bravo,
Acknowledgements RHH ED staff Safety and Quality Unit RHH for their participation and valuable contribution Next Steps It is envisaged over the next 12.
Irish National Acute Medicine Programme Patient Flow Model O’Reilly O, Courtney G, Casey A* Problem Patients requiring urgent care experienced long delays.
The Harris County Hospital District Program Pete Dancy, FACHE Associate Administrator Ben Taub General Hospital Houston, Texas April 3, 2008.
How Fast Are We? Throughput Times for Admissions From the Emergency Department Brian Hom; Deborah Porter RN, NM; Kathleen Chambers RN, MSN, CPN; Laura.
2014 Standard Definitions and Metric Goals. Consensus Statement Definitions for consistent emergency department metrics were introduced and signed on.
Healthcare Operations Management © 2008 Health Administration Press. All rights reserved. 1.
Results of the 2002 Emergency Pediatric Services and Equipment Supplement (EPSES) to the National Hospital Medical Care Survey (NHAMCS) Centers for Disease.
I Know I Want a Medical Career, but Which One? An Overview of Options.
OverviewOverview – Preparation – Day in the Life – Earnings – Employment – Career Path Forecast – ResourcesPreparationDay in the LifeEarningsEmploymentCareer.
August 2012 If you have an Emergency Department, you are in the Behavioral Health Business…..
Presenter: Shant Mandossian EFFECTIVE TESTING OF HEALTHCARE SIMULATION SOFTWARE.
Creating a Culture of Communication & Maximizing Efficiency in Patient Flow Daniel A. Nickerson Access & Special Projects Director, Willis Knighton Health.
OSCAR FLORES PERIOD 7 Accountant, Pharmacist, Physician.
Scorecard Tool Steve Kisiel, MS Vince Placido, BSE Jeffery K. Cochran, PhD James R. Broyles, BSE.
EMERGENCY ROOM OF THE FUTURE LEVERAGING IT AT WELLSTAR HEALTH SYSTEM: KENNESTONE EMERGENCY DEPARTMENT Jon Morris, MD, FACEP, MBA WellStar Health Systems.
Health Delivery Fundamentals
Dispensing to in and out patients or Drug distribution system
The Medical Assistant field has increased dramatically in the last decade, being able to perform many task in doctors offices and hospitals makes this.
Sophie Lanzkron, MD, MHS Associate Professor of Medicine and Oncology Johns Hopkins School of Medicine.
Component 1: Introduction to Health Care and Public Health in the U.S. Unit 3: Delivering Healthcare (Part 2) Outpatient Care (Retail, Urgent and Emergency.
Boarding Times and Patient Safety: A quantifiable and generalizable model David Wein, MD MBA Associate Facility Medical Director Tampa General Hospital.
Introduction to Healthcare and Public Health in the US Delivering Healthcare (Part 2) Lecture c This material (Comp1_Unit3c) was developed by Oregon Health.
Component 1: Introduction to Health Care and Public Health in the U.S. Unit 3: Delivering Healthcare (Part 2) Lecture 3 This material was developed by.
15: The ‘Admin’ Question Patient flow Dr Tony Kambourakis.
ED and AS Data Reporting OSHPD Healthcare Information Division Patient Data Section May 23, 2005.
REFORMING EMERGENCY CARE St. Jude's Past, present and future.
HIT Implementation Results David Anderson, Fermin Barrueto, Bruce Golden, Jon Mark Hirshon, Laura Pimentel, Ed Wasil 1 6/4/2013.
EFFECTS OF RESIDENTS ON EFFICIENCY IN AN EMERGENCY DEPARTMENT J. Silberholz, D. Anderson, M. Harrington, Dr. Jon Mark Hirshon, Dr. Bruce Golden 1.
Evaluating a new Approach for Improving Care in an Accident and Emergency Department The NU-Care project The 2004 Healthcare Conference April 2004,
Exploratory Analysis of Observation Stay Pamela Owens, Ph.D. Ryan Mutter, Ph.D. September, 2009 AHRQ Annual Meeting.
Cleveland Clinic Science Internship Program How Fast Are We? Throughput Times for Admissions from the Emergency Department Brian Hom; Deborah Porter RN,
Emergency Department November 8, 2005 ”Wall time” No data collected –Current systems do not allow for collection of meaningful metrics –In process of.
Nicole Sutherlin Brianna Mays Eliza Guthorn John McDonough.
Symposium sponsors:  Mount Sinai Department of Emergency Medicine  Emergency Medicine Associates (EMA)
Empirical Analysis of the Effect Residents Have on Treatment Times in an Emergency Department David Anderson, John Silberholz, Bruce Golden, Mike Harrington,
BROUGHT TO YOU BY LEADING EDGE GROUP Welcome Using Simulation Modelling to improve the performance of Healthcare Facilities.
Marian Conde University of Central Florida Leadership and Management
 To identify the Emergency Department efficiency measures for Inpatient admissions.  To demonstrate an understanding of the process of determining median.
The Medical Assistant field has increased dramatically in the last decade, being able to perform many task in doctors offices and hospitals makes this.
Using Self-Serve Predictive Analytics to Align Staffing with Forecasted Demand Yvette Porter-Lee, BS, MSJ Manager, Staffing/ Budget Nursing Administration.
 Capacity Management seeks to improve organizational effectiveness by increasing operational efficiency and reducing patient congestion.  To include.
Preceptorship Teaching Project Jennifer Nagy Auburn University School of Nursing.
Hospital inpatient data James Hebblethwaite. Acknowledgements This presentation has been adapted from the original presentation provided by the following.
‘Environment’ Glossary Administrative categories from UK National Health Service.
References Methods Introduction Results Dicussion The Effect of Resident Physicians on Press Ganey Scores in the Emergency Department The patient’s experience.
Chapter 13 Physician Assistant. PA Work Description A Physician assistant (PA) is formally trained to provide routine diagnostic, therapeutic, and preventive.
Emergency Department Admission Refusals Requiring Readmission at an Academic Medical Center David R. Kumar MD, Adam E. Nevel MD/MBA, John P. Riordan MD.
Title Slide Alternative 1 Subtitle Downtown Louisville Medical Campus.
Physicians- Health science Abigale
Pennsylvania Hospital Trends,
Lean Six Sigma Black Belt Project Improving Throughput to Provider
Primary Care Expansion Enhance Urgent Medical Advice
Telepsychiatry Consultation Program Achieving Tomorrow, Today
System Dynamics Dr Jennifer Morgan.
Optimizing Emergency Department Utilization
Evaluating Effectiveness of a Chair Unit in a Tertiary Academic Medical Center Yash Chathampally MD, MS.
Facility & Hospital Patient Types
PIMC Patient Experience Update January-June 2016
NEUROSURGICAL RESIDENCY PROGRAM: APPRAISAL AFTER 25 YEARS
Noa Zychlinski* Avishai Mandelbaum*, Petar Momcilovic**, Izack Cohen*
Lecturer: Yariv Marmor, Industrial Engineering, Technion
Canada Needs PAs.
Harper University Hospital Orientation
Canada Needs PAs.
Canada Needs PAs.
Canada Needs PAs.
Harper University Hospital Orientation
Presentation transcript:

EFFECTS OF RESIDENTS ON EFFICIENCY IN AN EMERGENCY DEPARTMENT J. Silberholz, D. Anderson, E. Sze, J. Lim, E. Taneja, E. Tao, B. Kubic, K. Johnson, D. Kalowitz, J. Kellegrew, A. Simpson, M. Harrington, Dr. Jon Mark Hirshon, Dr. Bruce Golden 1

Overview 2 Broad Healthcare Landscape -Health Care Reform Bill, Americans spent $2.3 trillion on health care in Hospitals are one of the least efficient sectors University of Maryland Medical Center (UMMC) UMMCUMMC ED 800 beds 1,182 doctors 742 residents 55 beds 20% admission rate 46,000 patients/year

Residency Model Medical School Four years Classes, clinical rotations Residency First year: Internship, general medicine Next 2-6 years: Specialty Designed for teaching Attending Physician Private practice or hospital 3

Research Question What effects do residents have on the efficiency of the emergency department? Total throughput Patient waiting time Residents are in the hospital to learn One conjecture is that the teaching of residents takes time away from patient care and negatively affects efficiency 4

Resident Seminars Residents absent every Wednesday morning for a seminar No replacement workers hired Wednesday mornings provide a representative sample of all emergency department activity Wide range of arrival rates All types of patients and severities Congestion levels vary as well 5

Simulation Model Overview 6 Patients Generated Poisson process with varying rates Triage Nurse Severity and treatment parameters assigned Waiting Room Patients held until called back Bed and Treatment Patients called back according to Analytic Hierarchy Process If the patient has not left before being seen, they are taken to a bed to be treated Treatment time drawn from empirical distribution for this patient’s category Discharge Patient either discharged or admitted as an inpatient Once the bed has been cleaned, a new patient is chosen by the AHP

Patient Arrivals Nonhomogenous Poisson Process 7

Patient Attributes Sent to ambulatory zone? Severity Score: 1(high) to 5(low) Admitted to inpatient ward? Triage time Labs needed (Yes/No)? Drawn from historical data (database data Oct 2009 – Jan 2010) 8 Patient12 Sent to AZ?No Severity?24 Admitted?YesNo Triage Time?4 min.8 min. Labs Needed?YesNo

Patient Bed Selection Analytic Hierarchy Process (AHP) used to determine which patient is chosen for next available bed Pairwise comparisons between all combinations of solutions Each admission from database examined Decision made based on severity level of patient and number of times passed over 9 0 Losses1 Loss2-3 Losses4+ Losses Severity N/A Severity Severity Severity AHP scores from 1/9 (lowest priority) to 9 (highest) based on severity

Abandonment Patients sometimes leave before they are called back Simulation determines abandonment probability based on a function of severity score and time in waiting room 10

Patient Categories for Treatment Time 11 Yes No Yes No

Model Validation Compared patients per bed per day, abandonment rate, average time until first bed and total time in system statistics from simulation to those from historical hospital data Also Kolmogorov-Smirnov test comparing total time in system distributions found no difference (p =.18) 12 MetricHistorical ValueOur Value Patients Per Bed Per Day Abandonment Rate8.02%7.76% Time to First Bed80.3 minutes81.1 minutes

Experimental Design Alter percent of both high and low priority patients seen by residents to test how decreasing resident activity affects the efficiency of the system Performance metrics used Time to Bed Total Time in System Throughput 13

Effect on Throughput 14 Increasing resident presence from 0 to 100% increases throughput by 5%

Effect on Total Time In System 15 Increasing resident presence from 0 to 100% decreases total time in system by 11%

Effect on Waiting Time 16 Increasing resident presence from 0 to 100% decreases waiting time by 25%

High Severity vs. Low Severity 17 Vertical contour lines imply that percent of high severity treated is the driving factor in efficiency gains – most of the patients treated are high severity

Effects on Time in System 18 Again, the vertical contour lines imply percent of high severity patients seen is the driver for gains in efficiency

Results Residents do have an impact on the efficiency of the ED Contrary to our original intuition, residents add efficiency to the system Strong linear relationships found between percent of patients seen by residents and throughput metrics 19

Discussion Strong linear trends showing increasing efficiency with residents present Most important for high severity patients to be seen by residents Decreases patient service times slightly, which leads to significant decrease in time to bed for low priority patients, as there is more slack in the system Contradicts the notion that residents themselves are a source of inefficiency – we do not compare them to other healthcare workers 20

Future Work Quantify effect of each additional healthcare worker and compare nurses and nurse practitioners to residents Model doctor decisions explicitly, show how they move through ED Identify bottlenecks in the system Include data gathered in person to help model doctor movement 21