Introduction
Patient waiting time is a major issue in the provision of healthcare. This is because it affects the quality of care that patients receive in a hospital. The patient waiting time can influence the outcomes of the visit by the patient to the hospital (McHugh, Van Dyke, McClelland, & Moss, 2011). For instance, failure to attend to the patient fast enough can cause the patient’s condition to worsen. In this sense, patient waiting time can influence the outcomes of the treatment process, including patient satisfaction.
Definition of Patient Waiting Time
The term “patient waiting time” carries different meanings in different works depending on usage. Different scholars use it to express different sets of periods that relate to the time it takes patients to access health services. Sinreich and Marmor (2005) distinguished between two types of waiting periods as turnaround time and patient waiting time. In their view, turnaround time was the total time the patient spent in the hospital. On the other hand, patient waiting time referred to the time the patient spent in the waiting areas before accessing health services. The difference in the two periods was that the turnaround time included the period that the patient spent while receiving care. In a study of six major hospitals in Israel, the total patient waiting time as defined by Sinreich and Marmor (2005) constituted 51-63% of total turnaround time.
Roper St. Francis Healthcare (RSFH, 2010) looked at patient waiting time as the time it took to admit a patient after physicians made the decision to admit the patient. Their local terminology for this period was the “door to bed” duration. It referred to the total time it took between the arrival and the admission of a patient (RSFH, 2010). Sullivan (2012) on the other hand used the phrase “wait time” to refer to the duration starting with the arrival of a patient and ending with the time of discharge or admission (p. 2).
The comparison of the use of the term “patient waiting time” makes it clear that the use of the term is contextual. In broad terms, it may refer to any period where a patient is inside the door of the hospital but is still within the reception of the facility. It is possible to look at it as the dormant time spent in hospital, or the total time spent in hospital. Any work that seeks to address the delays in patient waiting time must define the term to clarify the intended meaning. Otherwise, the term is open to interpretation from various scholars.
Sullivan (2012) provided four metrics for measuring patient waiting time in Newfoundland and Labrador. These metrics form a very good set of indicators relating to the main issues for consideration when conducting an analysis of patient waiting time. The metrics include the time it takes to see a doctor after arrival at the hospital and the total time a patient spends in the ED. They also include the percentage of patients who leave without seeing a doctor, and the levels of patient satisfaction in the hospital. Each of them provides information relating to different aspects of the quality of healthcare services in any specified institution.
Ways to Reduce Patient Turnaround Time and Improve Service Quality in Emergency Departments
Summary of Article
This article examined the ways of reducing the time it took to serve patients in emergency departments. The article also explored how this reduction could improve the quality of services offered in emergency departments. The paper arose from the need to find ways of dealing with an increasing number of patients going to emergency departments. The paper was premised on the fact that patient waiting time can be used as a measure of the quality of healthcare provided in a healthcare facility, apart from clinical considerations.
Identification of Main Issues
The main issues raised in the article are as follows. First, the waiting times in hospitals located in the urban areas in Israel were on the rise (Sinreich & Marmor, 2005). Secondly, the authors felt that in order for the services offered by emergency departments to be effective, the services needed to be flexible and efficient (Sinreich & Marmor, 2005). The third issue raised by the researchers was the role that emergency departments were playing was changing. Specifically, the emergency departments were no longer used as a place for triage only. Rather, they were offering life saving services to reduce the risk of losing patients waiting for processing within hospitals.
Analysis of Major Research Variables
The researchers identified process components that contributed towards the time spent in hospital. They were able to identify and list 77 processes (Sinreich & Marmor, 2005). The purpose of this exercise was to find ways of measuring the time it took a patient to go through the processes. The researchers observed that no patient went through all the processes. Rather, the processes that a patient went through depended on the medical course needed to treat the specific patient. For instance, only patients who needed x-rays passed through this process. The data collection method used in the project depended on the measurement of the time that patients spent in each of the process components in the hospital.
Analysis of Research Hypotheses
The research hypotheses for this paper were as follows. First, the researchers felt that there was time lost in the emergency department in hospitals, which ended up lowering the quality of healthcare. The basis for this assertion was that the number of patients visiting emergency departments was on the rise and the speed of service in emergency departments was slow. Secondly, the researchers believed that it was possible to find ways of reducing the time each patient spent in the emergency department.
Analysis of Data Collection Methods
The data collection method used in the research project was a comprehensive time study in six hospitals in Israel (Sinreich & Marmor, 2005). Supervised student teams did the actual data collection at different emergency departments. During the period that the data collection took place, 1700 man-hours went into the data collection exercise.
Analysis of Data Analysis Methods
The method used to analyze the data was straightforward. The researchers used a set of measures to determine the main variables that they needed to quantify. These measures included the “average waiting time before examination by the first physician, and the average duration of examination by the first physician” (Sinreich & Marmor, 2005, p. 102). The researchers developed a comprehensive list of measures based on similar reasoning to determine how much time it took for a patient to access a service and the time the patient spent consuming the service.
Summary of Research Findings and Contributions
The main findings made by this project were as follows. First, the researchers found that the average waiting time for patients comprised 52-63 percent of total patient turnaround time in the emergency department (Santibanez, Chow, French, Puterman, & Tylesley, 2008). The services that contributed the most to wait time were accessing x-ray facilities, waiting for a physician to see a patient for the first time, and the time it took to carry out tests on blood samples.
Reducing Patient Wait Times and Improving Resource Utilization at BCAA’s Ambulatory Care Unit through Simulation
Summary of Article
This article resulted from the work of researchers at the Vancouver center of the British Columbia Cancer Agency (BCAA). The institution provides care for cancer patients. The research project focused on the Ambulatory Care Unit (ACU), which cares for non-hospitalized patients. The research project sought ways of reducing the patient waiting time in the ACU. To do this, the researchers developed a computer simulation model to determine the viable options in reducing the waiting time in the hospital. As a result, the researchers identified ways of reducing the waiting time by 70% and reducing space requirements by 25% (Santibanez, Chow, French, Puterman, & Tylesley, 2008).
Identification of Main Issues
The central concerns of the researchers were whether there was a way to reduce the patient waiting time in the ACU. One of the main issues that the researchers felt contributed to this problem was variable patient volumes. This arose because there was no system of rationalizing the appointments made by the physicians. The result was that there were more patients in the unit than the caregivers could handle, because the patients all arrived at the same time. The second concern for the researchers was that the unit had limited capacity for expansion. This meant that the unit could not increase the staff to take care of the extra patients. The third area of concern for the researchers was the inflexible physical layout of the hospital.
Analysis of Major Research Variables
The three main variables that the researchers used to model the operations of the unit were patient wait time, clinic overtime, and resource utilization (Santibanez, Chow, French, Puterman, & Tylesley, 2008). The patient wait time was the main issue that the researchers wanted to measure. They were trying to understand what leads to long waiting time in the hospital. The clinic overtime in this case referred to the time spent by physicians beyond the allotted working time. The third issue was resource utilization in terms of physical capacity and human resource.
Analysis of Research Hypothesis
The researchers used three propositions in this research project. First, they felt that there was a possibility of reducing patient waiting time by changing the physical layout of the facility. This arose from the observation that the layout made it necessary for patients and caregivers to move around unnecessarily. This led to the wastage of time. Secondly, the researchers felt that there was a possibility of reducing congestion and patient waiting time by adopting better scheduling policies. Finally, the researchers felt that there was an opportunity to reduce patient waiting time by rationalizing the allocation of caregivers to patients.
Analysis of Data Collection Methods
The main methods used to collect data were a comprehensive process and data analysis. The researchers compared the performance of the ACU with other BCAA centers to generate information regarding its performance. This helped them to identify potential areas for improvement. The researchers also collected primary data from the appointment booking system in use in the ACU. This system carried all information regarding the booking of appointments in the ACU. Other important data in the system included “patient volumes by time of the day, day of the week, and month” (Santibanez, Chow, French, Puterman, & Tylesley, 2008).
Analysis of Data Analysis Methods
The main method used for data analysis was by computer simulation models. The researchers developed a series of scenarios based on the data collected in the previous exercises. The researchers tested more than one hundred scenarios. The researchers used the scenarios to analyze issues relating to the operations and the processes of the system such as the time that the clinic opens for business. They also examined the appointment scheduling system, and the resource allocation processes in the ACU.
Summary of Research Findings and Contributions
The researchers found several ways in which the ACU could resolve the issues it was experiencing. First, the ACU should have considered redistribution of clinic workload. This would call for adjustments in the schedules of the physicians. Secondly, the researchers advised that the ACU should find ways of allocating examination rooms more flexibly to make it easy for the available physicians to see patients without having to use their personal workspaces. The researchers also advised the ACU to promote clinic punctuality to eliminate delays caused by late commencement of operations. Finally, the researchers recommended that the ACU should reevaluate its scheduling practices to reduce the number of patients waiting for services in the facility at any time.
The Relationship between Patient’s Perceived Waiting Time and Office-Based Practice Satisfaction
Summary of Article
The article relates to the research findings of a project designed to compare the relationship between waiting time and patient satisfaction in clinics belonging to the Wake Forest University Baptist Medical Center. The researchers were also trying to find the impact of the time spent with caregivers on the satisfaction of the patients. The analysis of the data involved examining the relationship between the levels of satisfaction associated with the time spent waiting for services and the time spent with a caregiver.
Identification of Main Issues
The main issue that was of interest to the researchers was the impact of waiting time on patient satisfaction. The researchers wanted to find out whether the time spent waiting for services had an impact on patient satisfaction at the end of the process. In addition, the researchers sought to find out whether this would affect the patient’s decision to come back to the facility on another visit.
Analysis of Major Research Variables
The main research variables for this project were patient waiting time, and the time spent with the caregiver. The first variable formed the basis for data collection. The researchers needed to know how this variable affected patient satisfaction. The second variable was time spent with caregivers. The researchers measured the impact this variable had on satisfaction. The researchers wanted to cross-reference the two durations to determine whether there was a correlation between the two durations.
Analysis of Research Hypothesis
The hypothesis the researchers used were as follows. First, the researchers expected to find a correlation between patient satisfaction and the waiting time in the hospital before seeing the caregivers. There was sufficient reference to this phenomenon in the literature the researchers reviewed. The researchers also expected to find a correlation between patient satisfaction and the time spent with a caregiver. The third hypothesis made by the researchers was that the overall level of satisfaction of a patient resulted from the correlation of these two variables. In other words, the satisfaction of a patient depended on both the time a patient spent waiting to see a caregiver, and the time spent with the caregiver.
Analysis of Data Collection Methods
The researchers used quantitative data collection methods. The first tool they used was the Consumer Assessment of Health Plans Study (CAHPS) global item, which has a rating scale of 0-10 (Camacho, Anderson, Safrit, Jones, & Hoffmann, 2006). A rating of 10 is given to the best healthcare provider, while the worst healthcare provides is rated zero. The researchers also asked the patients whether they were willing to return or not, based on their experience. This gave the researchers data on whether the patient was willing to return, or whether the patient was not willing to return (Camacho, Anderson, Safrit, Jones, & Hoffmann, 2006).
Analysis of Data Analysis Methods
The researchers used multivariate regression and logistic regression models to predict the three satisfaction ratings (Camacho, Anderson, Safrit, Jones, & Hoffmann, 2006). They made the actual estimates using the General Estimations Equations (GEE) methods (Camacho, Anderson, Safrit, Jones, & Hoffmann, 2006). The researchers also used an exchangeable working correlation matrix to adjust for clustering (Camacho, Anderson, Safrit, Jones, & Hoffmann, 2006).
Summary of Research Findings and Contributions
The main findings made by the researchers were as follows. First, the researchers found that patient satisfaction levels were lower than expected whenever a patient waited more than 20 minutes to see a caregiver and their visit time did not exceed 5 minutes. The researchers also found that the satisfaction level dropped by -0.10 rating points for every 10 minutes of waiting when the time spent with caregivers was more than 5 minutes. Satisfaction dropped by a wider margin (-0.30 rating points) whenever the visit time was less than 5 minutes
References
Camacho, F., Anderson, R., Safrit, A., Jones, A. S., & Hoffmann, P. (2006). The Relationship between Patient’s Perceived WaitingTime and Office-Based Practice Satisfaction. North Carolina Medical Journal , 409-413.
McHugh, M., Van Dyke, K., McClelland, M., & Moss, D. (2011). Improving Patient Flow and Reducing Emergency Department Crowding: A Guide for Hospitals. Rockville, MD: AHRQ.
RSFH. (2010). Quality Improvement Initiatives: Reducing Emergency Room Wait Times. The Consult , pp. 3-7.
Santibanez, P., Chow, V., French, J., Puterman, M., & Tylesley, S. (2008). Reducing Patient Wait Times and Improving Resource Utilization at BCAA’s Abulatory Care Unit through Simulation. Vancouver: Canadian Institute of Health Reseach.
Sinreich, D., & Marmor, Y. (2005). Ways to Reduce Patient Turnaround Time and Improve Service Quality in. Journal of Health Organization and Management , 88-105.