The Effect of Emergency Waiting Time on Patient Satisfaction
Bibliographic record
Abstract
INTRODUCTIONHealthcare issues have been a problem for many Americans for years. A number of presidents have tried to reform Healthcare system. Today more people than ever are financially and medically are at risk because they might not be able to effort health insurance and to be treated at earlier stage of their sickness (Glied, 2010). Perhaps, this is one of many reasons that on March 23, 2010, president Obama signed Affordable Care Act which allows for a complete health insurance reforms (The White House, 2010). The promise of this reform is that poor and those who cannot afford medical expenses can get proper health care. However, these law changes do not affect problem that Hospital emergency departments as they are faced today.Without a doubt, when looking at Healthcare reform, we cannot overlook problem of overcrowding and lengthy waiting times in most emergency rooms (Horwitz, 2009). In 2008, a study was conducted by Ontario researchers where they stated, long waiting time not only affects patient satisfaction, and they increase risk of death and hospital readmission for patients who have been discharged from emergency department (Laupacis, 2011). According to American College of Emergency Physicians, emergency room visits will continue to rise, regardless of Healthcare ruling (Cheung-Larivee, 2012). Many Americans today are living without healthcare insurance, so they are using emergency room doctors as their primary physician (The White House, 2010).Statement of ProblemIn 2007, a nationwide Emergency Room Pulse Report was done to examine more than 1.5 million patients treated at 1656 emergency rooms. The experiment findings concluded that:* The average waiting time in emergency rooms was 4 hours, 5 minutes, which has increased by 5 minutes from previous year.* Unpredictability, state by state average waiting times were between 2 hours to 6 hours.* Another important point was that geographic location showed a distinguishing difference in overall satisfaction. In top 10 patient satisfaction report, highest levels were from emergency room in Milwaukee, WI, with New Orleans, LA coming in 5th.* The study showed that a shift in waiting times was main overall issue when dealing with patient satisfaction. It was revealed that Emergency Department could alleviate patient satisfaction even when waiting times were high, by updating patient with information while they were waiting (Emergency Department Resources, 2008).These facts and observation support idea that quality of care and waiting times patients are receiving, should be taken into consideration when looking at patient satisfaction. Indeed, long waiting times have increased risk of mortalities. Today we must consider idea that some people may not mind waiting for care if quality of care is satisfactory.Many hospitals measure waiting times as average time from arrival and check-in, to time when patient is placed in a room and care is started (Shelton, 2013). Nevertheless, there is not a set rule when it comes to how hospitals actually handle emergency room waiting times. According to a new report from 2010 Emergency Department Pulse Report, from moment patients walk into a hospital emergency room until time they are discharged from emergency department, average time spent 4 hours and 5 minutes (Emergency Department Resources, 2009). Many researchers have found that waiting times are different depending on number of patients to be seen, triage procedures, staffing, and availability of beds (Shaikh, 2012). The Press Ganey Association states that to improve patient experience, health care providers must first be able to see and understand the complex relationships between satisfaction, clinical, safety and financial measures (Emergency Department Resources, 2010).Statement of ObjectiveAccording to Board of Health Care Services, one out of every three Americans is visiting hospital emergency department a year, which account for more than 114 million people (HCS, 2007). …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".