Module 04 Written Assignment – Identify Tools for Developing a Plan to Reduce ED Wait Times

Module 4 Assignment Clarification

Posted on Jan 28, 2020 8:00:00 AM

Hello All! For module 4, you are asked to complete 4 things in a 3-4 page paper. Please see the following assignment clarifications:

Number 2 should be interpreted as the quality measures or data you would gather to get information about the wait times.

Number 4 should be interpreted as the tools or models you would use to collect this information such as those found in under the module 4 lesson content link – “Models for Collecting and Analyzing Data”.

The latest quality report showed that the average (median) time from emergency department (ED) arrival to transfer to the inpatient unit at your facility was above the national average of 275 minutes (4.5 hours, based on current Hospital Compare data). Your quality improvement team will review some emergency department data to help determine where there may be issues affecting wait times (Download the ED data spreadsheet here).

The length of time patients wait to be admitted to the unit or discharged from the Emergency Department (ED) exceeds the quality goal set by your hospital of 275 minutes (which is the same as the national average benchmark).

Write a 3-4 page paper (in APA format) that:

  1. Calculate the wait times for each patient and determine if they are consistently above or below the 275 minute average.
  2. Identifies the tools that could be used to gather information about the wait times (such as number of patients being registered, patient volume by time of day or staffing).
  3. Determines the departments and units that could be involved in improving this issue.
  4. Selects the tools that would be needed to collect data.

Chapter 4:

CHAPTER FOUR What to Measure—and Why

In many organizations quality is a vague concept, and one that is thought to be completely subjective and therefore unscientific. However, quality can be objectified by developing clearly defined measures, collecting data about those measures, analyzing the data, and communicating the resulting information to appropriate individuals. Quality measures, which are required by regulatory agencies, can offer health care leaders information to assess and improve patient care and to ensure that they have timely, efficient, and effective care, with expected outcomes. Included in the definition of quality care is compliance with the CMS (Centers for Medicare and Medicaid Services) evidence-based indicators (such as aspirin for acute myocardial infarction, antibiotics for pneumonia, and smoking cessation counseling at discharge). When measures are used quality can be defined objectively and scientifically.

In this chapter I will outline how measures can be developed and used to offer health care professionals, both clinicians and nonclinicians, information to improve the quality of care delivered in their institutions. I will also describe how the use of quality methodologies, such as the PDCA for performance improvement, can provide a framework for developing appropriate measures and for monitoring and improving various aspects of the delivery of care.


Leaders lead according to a value system, defining the kind of organization the institution should be. It is up to the senior leadership of the hospital to define the level of quality that is acceptable and the level that is not. Leadership defines priorities by answering such questions as these:

  • What aspects of the organization are critical to its success?
  • What expenses are most and least profitable?
  • How can excellent patient outcomes be achieved efficiently and economically?
  • What variables influence patient satisfaction?

These and many other factors need to be understood and balanced—through measures.

With objective criteria in hand, administrators have access to quality variables and can use factual information to make decisions. Becoming familiar with and using quality measures to deliver quality care helps the health care leader to do the right thing for the patient and to increase financial efficiency for the organization. The better the care, the fewer the complaints, complications, and incidents. When administrators understand how measures of quality reflect operational processes, clinical care, and patient services, as well as underlie good financial management, they become more comfortable about monitoring the delivery of care they are responsible for. Leadership and a strong quality management department should collaborate on using measures to understand the processes, procedures, and operations that have positive and negative impacts on patient care and organizational processes.


Prevention is good medicine and helps the organization maintain its financial stability. Measures should be used to establish benchmarks for preventive processes—processes such as monitoring sterilization to prevent infection, providing fall prevention, preventing skin injuries, or reducing length of stay (LOS) through appropriate and timely antibiotic administration. For example, to decrease expenses, increase efficiency, and produce good to excellent outcomes, leadership needs to control nosocomial (hospital-acquired) infection.

Specifying the numerator and denominator of the measure ensures that it accurately reflects the information you want to collect. For instance, the general infection rate can be computed as the relationship between the number of patients who contract any infection within a month (the numerator, or N) divided by the number of patients admitted to the hospital per month (the denominator, or D). However, if the information you want is more specific, you define the measurement accordingly. If you are concerned about the incidence of sternal wound infections postsurgery, N becomes the number of postsurgical patients with wound infections over a specific period of time divided by the total number of surgical patients over that same time period (D). Once the measure is defined and the rate can be calculated, the information can be tracked over time. Collecting such measures allows an administrator to monitor trends, such as whether infection is rising, decreasing, spiking, or comparable to the national benchmark. Figure 4.1 illustrates the rate of surgical site infection in one hospital over a twenty-two-month period and shows that its rate is, by and large, lower than the national benchmark.

By carefully defining a measure, with the specific numerator for the objective of the study and the denominator delimiting the population of which the numerator is a subset, leaders can objectively and productively study performance, success, and opportunities for improvements. The data in Figure 4.1, for example, show that spikes in infection occur in the same months each year (January–February and September). With that information leadership can drill down in their data and attempt to analyze what might be contributing to the rise of infection during those months.


Data regarding the specifics of care help administrators make efficient financial decisions. For example, the nursing shortage in this country has resulted in staff vacancies that have had an impact on patient care. CEOs and senior administrative staff are expected to make hiring decisions, but how? Using what information? In other words, what are the criteria for evaluating long-term versus short-term investment decisions? Hiring decisions obviously have an impact on the budget, but unless administrative leadership uses objective measures to look at the specifics of operations, evaluates the effectiveness of services, and gauges the effect of staff-patient ratios, how can they understand staffing requirements and the relationship between staffing and patient outcomes?

Figure 4.1. Surgical Site Infection Rate over Time.

Many health care institutions are in financial difficulty because important decisions are being made without adequate understanding and information. Think of open-heart surgery and its huge requirements in terms of the operating room (OR), intensive care unit (ICU), specialized staff, and ancillary services and then compare those requirements to, for example, the treatment of patients with pneumonia, a far less resource-intensive hospitalization, assuming, that is, that the patient does not develop complications. Variables for both these conditions can be measured. Information (that is, data) about these variables gives administrators insights into the relationships among services, outcomes, and resource needs.

Tracking several potentially related variables can offer leadership important information. Figure 4.2 combines two variables, LOS and readmission within thirty days, across eight hospitals. If a patient requires readmittance within thirty days of discharge, it is possible that that patient was discharged prematurely or that the care was in some way deficient or inadequate. If administrators examine only LOS, they might believe that the shorter the LOS, the more efficient the hospital. However, if the hospital with a short LOS has a high rate of readmittance, as Hospital B does, then leaders may want to investigate and target improvements. Hospital D has both a long LOS and a high rate of admittance, suggesting inefficiencies of care that have financial consequences. Hospital G is providing the most efficient and effective care.

Because the government reimburses institutions according to the complexity of each case (using the case mix index, or CMI) and the procedures required to treat specific diseases, financial resources are dependent on clinical considerations and operational processes. For open-heart surgery cases, a measurable variable, such as turnaround time in the operating room, can have a financial impact for the institution. If the first procedure of the day is postponed due to operational issues, then for the rest of the day procedures are late. Late procedures have implications. It may become necessary to hire extra staff to work into an evening or night shift. Any complication during a procedure tends to cause expensive delays. Therefore good clinical supervision with clinical support can reduce such expenses. Ideally, a finance officer and a senior administrator learn enough about the delivery of care to ask intelligent questions and establish appropriate measures for data collection and analysis.

Figure 4.2. Length of Stay and Readmissions Within Thirty Days.

Tools and technology and even staff cannot be evaluated as a unidimensional financial expense. An administrator or financial officer can collect data in order to understand the complexity of services. For example, in the ICU there is usually a one-to-one patient-staff ratio. However, administrators may want to know if that ratio is crucial to the welfare of the patient, if the expense results in improved outcomes, or if it is simply a high degree of (perhaps unnecessary) monitoring. Analyzing measures helps an administrator discover the clinical as well as the financial value of a service. When leadership understands clinical care, financial decisions are not made in a void.


The financial implications of purchasing decisions are entwined with various aspects of patient care, and intelligent decisions cannot be made without an understanding of other expenditures and the impact on patient outcomes.

Administrators should consider using their quality management departments to mediate information between finance and the medical requirements of care. Quality indicators can help administrators determine the value of specific services, such as whether an elaborate (and expensive) CAT scan will result in better patient outcomes. Without data there is no way to assess whether more sophisticated technology should be purchased. With data, leadership can expect answers to such reasonable questions as what are the financial and clinical implications of a 64-slice CAT scan, and how will it be better for patient care than a 34-slice scan? The medical staff may request new equipment, but it is up to leadership to understand that equipment’s relative value to the organization. Measures improve administrative understanding by providing detailed information.

Some decisions regarding expenses may have far-reaching implications, others may be of less consequence. Purchasing improved cardiac stents, for example, may reduce bleeding and complications from the stent procedure, so although this purchase is expensive it may result in fewer complications, a shorter LOS, and therefore a better financial situation than the hospital would have if the purchase were not made. Data collected over time would reveal the value, and leadership would be able to intelligently monitor costs and benefits. Likewise, robotics technology is very costly. Without objective data it would be difficult to determine if such an expense is of worth to the patients and to the hospital. Information can be collected about the volume of patients who might be attracted to the institution if robotic surgical procedures were in place and the outcomes were excellent. A financial assessment could be projected based on those numbers. Obviously, numbers provide a great deal of crucial information for decision making.

An example of a quality variable that reveals a great deal about operational and financial efficiency is mortality. Administrators should collect these measures monthly in order to monitor the delivery of care and the services being offered. If there are problems, for example, if there were three unexpected mortalities in the OR, there may be a problem that requires addressing. Mortalities cost money. Reports have to be filed with appropriate agencies; malpractice suits can occur; peer reviews have to be conducted. If the source of the mortality is infection, then corrective actions have to be put in place. If the source of the mortality is clinical incompetence, intervention or reeducation can be conducted.

But it is most important to know that the mistakes occurred and then to ascertain the causes in order to develop appropriate improvements. Administrators look at mortality reports and often go looking for someone to blame, rather than considering the situation as an opportunity to improve the delivery of care. If the hospital reports a high mortality rate for a specific procedure, such as cardiac bypass surgery, or for a particular patient population, such as heart failure patients, there might be a financial impact associated with that report because patients with these conditions or who need these procedures may be less attracted to the hospital. The public understands mortality data. (Physicians may say the data are flawed or not risk adjusted, but if the data are out there and the public is afraid, people won’t come to the hospital for treatment.) Operationally, it may be important to understand why the rate is high so that specific processes can be targeted for improvement.

Quality issues and operational issues are interdependent. If data reveal that patients with certain conditions, such as elderly patients with AMI, have a higher incidence of mortality than others, then the care of that patient population has to be carefully reviewed. If patients from certain nursing homes die at a higher rate than others because those patients have comorbidities that are having an impact on mortality, then improving risk assessment might increase safety for those patients. These questions can be empirically tested through developing measures, collecting data, and analyzing trends.


Quality management data are required by agencies for accreditation and for compliance with regulations, but data are also collected as part of various national programs to assess and improve the quality of care, such as the CMS core measures, the Institute for Healthcare Improvement (IHI) 100,000 Lives Campaign, and the National Patient Safety Goals initiative of the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) (see Figure 4.3). JCAHO mandates that each of its goals be implemented; the individual organization determines how to implement each goal. For example, to improve accuracy of patient identification, an organization is required to check two patient identifiers before administering medication, blood products, or performing clinical testing, treatments, or procedures. The hospital determines which two identifiers it will use. Improving communication involves ensuring that phone and verbal orders are properly understood; JCAHO recommends that hospitals require a read-back by the person receiving the order. Medication safety involves several improvements: limit drug concentrations, review look-alike and sound-alike drugs to prevent interchanges, and label all medications. For infections, comply with CDC guidelines for hand hygiene. These goals and their implementation recommendations can be found at the JCAHO Web site (

Figure 4.3. JCAHO’s 2006 Hospital National Patient Safety Goals.

Note: Because JCAHO has retired some goals over the years as it has added new ones, the numbering of current goal sets is no longer consecutive.

The data about safety are collected, and administrators should use the information to understand their operations; furthermore, because quality management data are benchmarked against national standards, administrative and other leaders can evaluate how their operations compare to other institutions. Through measures and benchmarks the data provide relevant information about daily performance and about areas where improvements should be instituted.

The IHI 100,000 Lives Campaign is the first national initiative to prevent avoidable deaths in hospitals and to implement change to improve patient care. The goal is to save 100,000 lives as of June 14, 2006. Highlights of the prevention program include the creation of rapid responses teams, using evidence-based care for AMI, preventing ventilator-acquired pneumonia, preventing indwelling venous catheter infections, preventing surgical site infections, and preventing severe drug events.


Collecting data on an operational variable, such as blood administration, waiting time in the ED, turnaround time in the OR, or time to receive consultations or laboratory reports, reveals information about efficiency; efficiency has an impact on the financial success of the institution. In addition to using the quality management department to establish databases and benchmarks for best practices, the organization can use quality methodologies, such as PDCA and Six Sigma, that help analysts to inform administrators about services and to improve the delivery of care.

Using quality methodologies may enhance the assumption that excellent care is equal to a sound business plan and economic success. However, a simple economic model might even be in opposition to the mission of a hospital, which may be to serve the poor and the underserved. Such patients may not have the luxury of focusing on health prevention in the way that individuals with economic means and health insurance do. This lack of prevention might result in more sickness, which might in turn burden the hospital because it will be providing expensive care without reimbursement. Such expense can be anticipated, however. Therefore those expenses within the organization’s control should be maximally efficient.

As long as the CEO is using a methodology that is based on data and statistical analysis, measures help employees and managers and administrators and members of the governance committees to share clearly defined goals that stem from a specific philosophical position and to share a commitment to excellence and improvement. Using any deliberate methodology creates a focus for addressing the process of care or product or service. With numbers, administrators can suggest, for example, improving the volume (that is, raising the numbers), eliminating wasteful services (as measured through volume and finance), improving productive services, and targeting specific goals.

Six Sigma is a methodological tool designed to reduce the negative economic impact of inefficient services. Based on the concept of the normal curve, Six Sigma was initially used as a measurement standard in product variation. In the 1920s, Walter Shewhart showed that three sigma from the mean is the point where a process requires correction. As a quality management tool for health care, Six Sigma is useful for analyzing and improving operational processes through measuring how far specific data vary from the mean.

For example, to understand turnaround time in the OR, data can be gathered about timeliness of patient preparation, OR readiness, equipment reliability, surgeon start time, readiness of appropriate ancillary staff, availability of required documentation, causes of delays, if any, and analysis of morbidity that might require extra OR time or an unanticipated return for repair. All of these variables can and should be measured, and each has a financial analogue. Once the inefficient process is identified, improvements can be developed.

The Plan Do Check Act (PDCA) cycle is a robust performance improvement methodology, and one that works particularly well in a health care setting. This model was also developed for monitoring quality improvement in industrial settings and is designed to standardize processes and minimize variation, that is, eliminate mistakes and rework. The PDCA cycle, by breaking function and role into variables that can be measured, helps leadership understand the clinical and medical environment and the method of providing care.

Using the PDCA cycle to continuously improve quality allows current performance to be measured, processes to be analyzed, and improvement actions to be identified (Plan). Improvement actions are then implemented (Do), and the benefits of the actions are measured (Check). Once measured, improvements can be standardized and communicated and reassessed (Act). The PDCA cycle provides for the systematic acquisition of knowledge through focused data collection and, through measurements, validates that improvements are effective (see Figure 4.4).

There are many advantages to using an industrial performance improvement model, such as PDCA, to continuously evaluate improvement and determine variation from the standard. The PDCA cycle provides a continuous loop of quality monitoring, based on data from measures. By defining the numerator and denominator of a measure, leadership can objectively understand the product being delivered, and by holding staff accountable to these measures, leadership clearly anticipates a uniform standard of excellence.

As with most complex activities, doing something according to a plan is more productive than simply reacting to some stimulus on the spur of the moment. In health care, planning involves collecting information and analyzing current processes, identifying gaps in care, establishing improvements, and monitoring their effectiveness. Making improvements or changing processes is often met with resistance and confusion over accountability (who is in charge) and details of the process changes (who is doing what).

Figure 4.4. A Quality Improvement Methodology: PDCA.

My experience shows that to improve a process, adopt new information, and actually change the delivery of care, the unit manager and the clinicians benefit by working within a methodology, such as the PDCA, that continuously and objectively reviews and evaluates their actions. The PDCA method allows the professionals to pause and consider the workload with a critical eye. Working with many patients, with multiple diagnoses and treatment plans, caregivers require a method that directs and prioritizes activity. Daily planning must be continuously communicated, from the beginning to end of shift, through the changes in shift, and to the end of the shift to maximize efficiency and reduce potential for errors.


Every aspect of the PDCA cycle depends on measurements, not of an individual’s experience but of a population of patients. The first stage, Plan, requires that stakeholders, who have similar goals, formulate an assumption about care, in other words, develop a hypothesis. The hypothesis may be derived from external or internal sources. For example, because the CMS requires smoking cessation counseling for pneumonia patients, administrators may assume that most of the patients are receiving the recommended counseling. Their assumption may be that clinicians are incorporating patient education about smoking into their practice.

Data can be collected to confirm that assumption. Quality management can develop a methodology for chart review and determine the percentages of patients who have had the counseling and of those who haven’t. With this information in hand, further analysis can drill down in the data and examine the records of those patients who did not receive counseling to see if they have any areas in common, such as physician, unit, secondary diagnoses, and so forth. However, without quantifying the process, it is hard to convince anyone that there is a problem, let alone that it should be fixed.

The assumption or hypothesis should reflect areas of concern to the investigating team. Another assumption might be that patients who are given antibiotics before surgery have fewer infections than patients who are not given this medication. This is a testable assumption. Other testable assumptions are that patients who develop pneumonia on ventilators were not properly weaned off the ventilators, and that patients who fall do so because of a desire to be mobile when there are insufficient staff to assist them. Administrators and staff should meet together to determine which assumption to measure and which care process to improve.

In the planning stage organizational culture should be evaluated to determine whether there are possibilities for change and whether a structure exists to implement changed practices. Leadership chooses which battles deserve to be fought; not every process needs to be changed, and different stakeholders may be interested in different issues. Physicians may be concerned with high mortality, surgeons with infections, nurses with falls, and respiratory therapists with ventilator-associated pneumonias. It is up to the administrative leadership to determine priorities, perhaps based on the goals, mission, and vision of the institution or derived from external pressures from the public and the media or revealed on some scalar dimension by such questions as which problem poses the highest risk, where can the impact of improvement efforts be greatest, or where can financial gains be seen? The senior staff of the organization decides priorities for improvement, what outcomes to look at, what processes to change, which measures to use, and what process to develop to monitor, assess, analyze, and communicate the results of the data collection activities.

Before you determine your measures it is essential to define your clinical or operational goals, establish priorities, and understand the patients (the organization’s customers) and their concerns and priorities. The quality management department at our health system developed a prioritization matrix to help decision makers evaluate competing issues for performance improvement (see Table 4.1). Competing issues for improvement are listed across the top of the matrix. Each issue is evaluated by the criteria listed along the side of the matrix—such as alignment with leadership goals and vision, impact on the delivery of care, or outcomes showing a negative trend. Different organizations will define their criteria differently, but it is useful to think about prioritization in terms of structure, process, and outcome. For each issue a value is entered in each cell of the matrix, from 0 to 3 (no application to maximum concern) and these values are totaled. A comparison of the totals defines the most pressing priorities. By objectifying and quantifying priority options, stakeholders have an opportunity to evaluate and consider how to allocate resources.

Table 4.1. Prioritization Matrix.

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