Reducing Emergency Department Wait Times Essay Sample
- Word count: 2106
- Category: patient
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Reducing Emergency Department Wait Times Essay Sample
The Problem and Its Background
Waiting, an idle time, is not convenient to anyone even if it is just as simple as lining up to take an elevator. In this instance, a mounted mirror on the sides of the lobby can help divert the attention of those in line. In the case of an ailing person needing medical care, no any mirror or diversionary tactic and neither an ambulance diversion can help appease the pain or anxiety that is prolonged by an extra minute or extra mile of waiting.
Across United States, “the average emergency room wait time is now 222 minutes—that is 3 hours and 42 minutes” (Costello, 2006)1. “Hospitals in Arizona (4 hours, 57 minutes), Maryland (4 hours, 7 minutes), Utah (4 hours, 5 minutes), New York (3 hours, 58 minutes), and Florida (3 hours, 57 minutes) are among the worst, with wait time near or exceeding four hours” (Costello, 2006).
Seriously ill or injured patients or those with heart problems may not endure that waiting time. Even the less urgent care can worsen by waiting longer periods. By waiting, the agony is prolonged because it is the time spent in the queue and not the time being attended to by any medical practitioner. Chances are, patients will be irritated leading to worsening of condition while others may walk out unpleased, looking for other options.
“America’s emergency care system is in critical condition. The signs are unmistakable: Emergency Room Crowding (increase of 26% ER visits, number of ERs declined by 9% and hospitals closed 198,000 beds), Ambulance Diversion, Uncompensated Care, Fewer on-call Specialists, and Inadequate Emergency preparedness” (Kellermann, 2006)2. These are the prevalent problems that need immediate concern. Data from the American College of Emergency Physicians show that 23,000 ER doctors have to service the 114 million Emergency Room visits in 2003 while the capacity of emergency systems has decreased by 14% in 1993 (Snyder, 2006)3.
With the ailing condition stated above, alleviating the waiting time can improve the quality of health care, and avoid malpractice or errors due to work pressures. All of these are dependent on long waiting time factor which is the concern of this study.
Statement of the Problem
Emergency Care System that is in critical condition due to long queue
- Overcrowding of Emergency Rooms
- Worsening of Minor Medical Condition due to slow servicing
- Staff unavailability
- Discharge Delay (lack of Inpatient Beds)
- Use of Emergency Room as primary health care provider due to lack of insurance creating influx of more ER patients
- Most studies focus on causes and silent on the solutions
To identify the causes of Wait Times, their chances of occurrence, and the Appropriate Responses and to improve the quality of ER care and reduce current shortest recorded arrival-to-discharge time of 3.3 hours for selected care type
- Identify the types of care and their trends of happening
- Determine the skills of available attending physicians and nurses
- Revisit and evaluate the prevailing policies, guidelines, and procedures in performing ER care
Significance of the Study
This study shall :
- help practitioners in evaluating urgency, prioritization, scheduling, and the value of sound administrative systems, and quality service to patients.
- enlighten the patients in dealing with ER consultations or urgent care and fast servicing.
- enable administrators or hospitals and policy-making bodies to implement, monitor, and direct the healthcare systems towards upgrading rather than decline
Scope and Delimitation
The study shall only cover those policies, processes, and methods affecting Wait Times and shall no longer extend to the in-depth medical approaches that shall be performed on the patient, and the infrastructure or strategic plans of any hospital are not also considered.
Theoretical Framework and Review of Literature
Topics that are beneficial to the conduct of this study are:
- Statistical principles
- Scientific Management (Queuing Theory, Waiting Line Models, Inventory Models such as Weighted Moving Average, Decision Trees, PERT-CPM, Forecasting Techniques)
- Skills Inventory
- Systems and Methods Concepts
- Work Measurement and Manpower Scheduling Techniques
Those topics cited by Trout, Magnusson, & Hedges, 2000 and Ardagh, et.al, 2002
(and all other related studies affecting or related findings about ER Wait Times)
The existing shortest recorded arrival-to-discharge time of 3.3 hours in most ERs can no longer be shortened.
Definition of Terms
(at most 15 uncommon terms used in this study, alphabetically arranged)
Type of Research Methods to be Used
The method is Quasi-Experimental which shall be a combination of Historical, and Descriptive like Case Study of Queuing at ED (Turban and Meredith, 1986 p. 560, 563)  under the Qualitative type while Longitudinal Survey and Cohort Study under Quantitative. The sick requiring hospitalization at ED is a structure of a queuing system according to Turban and Meredith (1986 p.560). In this context, the arrival process shall be the patients, the Service Facility is the ED (with services like See and Treat, Clockwork ED or Surge Protection ), and the Exit from the Facility is the Discharge Time. When the Service Facility is busy due to the higher patient care demand than the care providers’ ratio, a queue or waiting time is formed.
Historical because the past records or documents shall constitute the basis of forecasting the demand or expected patient arrivals for the future periods that will be balanced with the necessary attending physicians and other ED Staff for future manpower scheduling with the consideration of the percentage growth of patients in whatever time period being forecasted..
Descriptive Method shall make use of Flow Process Charts or Narrative Procedures and employ the existing systems review and the proposed flow of the processes involved in ED like Caring for Seriously Ill or Injured Patient (Emergency Care), Caring for Escalating Symptoms (Urgent Care), Laboratory Testing Procedures, Performing Safety Net Care, Admission Procedures, See and Treat Procedures, On-call Scheduling and Requisition, Check-out or Discharging Procedures, Housekeeping Procedures, Facility Maintenance or Inventory and Requisition of Supplies. All of these systems interface may directly or indirectly affect the upstream and downstream flow of patients and materials, supplies, manpower, or facility. Looking at the critical points and problem areas along the activities in each of those processes will pinpoint the areas for improvement. The documentation and observation does not necessarily mean the same group of samples being continuously observed. This method is process-based and not person-based. The standard time of processing a certain sub-process is established regardless of patient being observed. Upon identification of the areas for improvement, that is the time to devise a control set-up, one hospital where systems or processing improvement have been practiced, and the existing set-up, where the same, old system is still practiced. Then, the performance is observed, recorded, and compared. The purpose of this is to improve the system by combining some activities, eliminating unnecessary steps, or modifying the existing steps.
The Cohort Study, person-based, shall tackle the same batch or group of patients wherein their every stage of care received are being followed through up to the dispatch or discharge time. This is to identify the arrival-doctor-discharge time of a particular care type of a specific patient (with emphasis on the degree of severity and not solely on the care type needed). The performance of the department on the arrival-to-doctor or doctor-laboratory results can be better established by focusing on a Cohort Study.
Sources of Data
- Patients of ED of __________ Hospital (past and present records)
- All Staff (Regular, On-Float, On-Call from other Departments)
- Records of Personnel (Skills, Disciplinary Notices, Absences whether planned or unplanned)
- Admission and Discharge Coordinator
- Other Thesis
“In research, the entire population is seldom used because of the cost and time involved. Most researches use only a small representative of a population called the sample. The sample is used to know and/or describe the characteristics of a population.” ( Summation Webpage, 2006)
“To determine the ideal sample size for a population, the Slovin’s formula is used:
n = sample size
N = population size
e = margin of error
* When only a sample (instead of the entire population) is observed or studied, we do not get the actual value but just an estimate of the actual value of the parameter. Therefore, it is important to consider the margin of error when using the sample.
A group of students want to know the age of students in a high school but do not have the resources to survey an entire population of 2,500. If they want to use a sample with a 5% margin of error, what should their sample size be? “ (Summation Webpage, 2006)
N = 2,500
e = 5% = 0.05
Required: n = ?
Type of Researc
- Random for survey of waiting Time regardless of Patient and Type of Care Needed (as they occur on the actual queue)
- Purposive for certain Type of Care that do not occur in the hospital on study (selective of location and care type or cases)
- Stratified if the sample is to be drawn from various periods or seasons
- Cluster if sampling is focused on the degree or level of treatment affecting the processing time.
Tally Sheets for the survey of records wherein the Arrival Date & Time of the Patient are specified, Reason for Emergency Care, Degree of Cure Needed, Laboratory Tests Done, Doctor Name, Diagnosis, Transfer of Care, Date/Time (Start/End) admitted to next stage care, and Date/Time discharged. If all of these data are not available, even just the statistics such as the Date arrived, Reason for Emergency Care, Diagnosis, Date Discharged, and Assessment or Reason for Discharged will do.
Observation Sheets (Time Study Sheets), Interview Records, Personnel Schedule, Tally of Materials and Supplies during the conduction of Care (if they caused delay or prolonged the stay of the patient, etc.)
After getting the data for the number of patients with respect to type of care, group all the data for the same type. Do the same for all those with same care type and degree. Then sum up all the number of patients that flocked in that period of say one year (100%). This is to establish the sharing or percentage of data distribution per care type. Get the percent share and establish the probability of occurrence. With that probability distribution or chances of happening, rank the type of care that is prevalent or just consider all care types if significant in number. Use that percentage (Weighted Average) to get the share of sample size per are type from the value of n = (certain quantity). That same sharing can be used to estimate the distribution or type of doctor specialist needed.
Conduct Time Study or Record Estimates (if records are acceptable and reliable). Otherwise, perform actual time study. Compute the Standard Time of Processing Each Care Type at a given scenario or assumption. Measure the Care Provider time to be consumed. Ratios of doctors to nurses or nurses to a given number of patients must also be studied as actual utilization. Consider a certain level of growth or decline on the data for each care type and compare or compute the proportionate care provider complement.
The ratio of demand or arrival to the doctor diagnosed to the discharged patients must be 1:1:1 or less than one to the next indicator. The diagnosis must be faster or commensurate to the pacing of the arrival rate. While the admission for clockwork must not be faster than the discharge rate.
The total man-hours consumed by each doctor per Care Type must be compared with the total care type demanded against the number of doctors booked or deployed against the predicted patient influx to check if inpatient facility is fully, shortly, or overly utilized.
On the Systems Study, whatever flaws or loopholes in any of the processes, the recommendations must be validated or tested of its acceptability and adaptability before citing the differential benefits brought about the supposedly change.
3 Don Snyder, Fox News, 2006, www.foxnews.com
 Turban and Meredith, Fundamentals of Management Science, 10th Ed, Business Publications, USA 1986
 http://www.textcentral.com/statistics/2006/01/21/22-determining-the-sample-size/ posted on Saturday,
January 21st, 2006 at 9:01 am