University of Southern California Data Evaluation Discussion

Please write a discussion

Discussion prompt:

• Prompt : In the healthcare industry, everything is measured. From drip rates to dosages, it seems as though numbers are everywhere. But how does this reflect the care given to the patients? After reviewing chapter 5, reflect on the following:
  • Which type of data (qualitative or quantitative) do you think yields the most information? Why?
  • Which type of data (qualitative or quantitative) is most effective for forecasting future trends? Why?
  • Which practices can a healthcare manager use to ensure that the data used for forecasting trends is accurate?
  • Provide examples of data evaluation in different types of facilities.
  • Provide examples of data evaluation for different types of issues, such as billing or loss of revenue.

• ALL citations and references needs to be APA 7th edition format. THANK YOU
• textbook may be used as a reference. The APA format for your text is as follows:
• Langabeer, J. R., & Helton, J. (2016). Health care operations management: A systems perspective (2nd ed.). Burlington, MA: Jones & Bartlett Learning

CHAPTER 5

Please write a discussion

Discussion prompt:Operations Research Methods

GOALS OF THIS CHAPTER

1. Describe the application of operations research methods in health care.

2. Understand how to identify and eliminate bottlenecks.

3. Use forecasting methods to estimate patient volumes and demand.

4. Understand the concept of capacity and its relationship to demand.

5. Explain why tracking systems can improve process flows.

6. Describe bar codes and radio frequency identification and their roles in operations management.

Health care facilities are busy places with hundreds of people constantly coming and going. To maintain efficient operations, organizations must optimize patient and other process flows. This entails:

Understanding patient demand.

Aligning capacity and resources with demand.

Using de-bottlenecking approaches to improve throughput.

Managing patient and asset flows through tracking systems.

The use of tools and techniques such as operations research help to incorporate quantitative methods that can improve decision making. Techniques such as wait time minimization models and forecasting algorithms help to support improvements in process and patient flows. To make informed decisions about changing processes, decisions must rely on data, not just subjective gut feel. This chapter discusses these concepts in detail.

OPERATIONS RESEARCH

Throughout the years, operations research (OR) has been defined in many ways, often using different terms to describe the same body of knowledge and methods. In England and Europe, operations research is commonly called operational research, although the terms are synonymous. Similarly, the term management science (MS) has become popular in some schools of business, though usage is mixed. Operations research is still used primarily in industrial engineering departments and other schools outside of business, but for the purposes of this research, all of these terms (i.e., operational research, management science, and operations research) are considered identical and interchangeable.

The simplest definition is what the Institute for Operations Research and the Management Sciences (INFORMS, 2014b) uses today: “a discipline that deals with the application of advanced analytical methods to help make better decisions.”

Generally, the operations management and management sciences can be combined using the term “OR/MS” and describe using a scientific view and quantitative methods to support managerial decision making (Hillier & Hillier, 2008; Anderson, Sweeney, Williams, & Loucks, 1999). The Operational Research Society (n.d.) defines OR as “the discipline of applying advanced analytical methods to help make better decisions”; it posits that “by using techniques such as problem structuring methods … and mathematical modelling [sic] to analyse [sic] complex situations, operational research gives executives power to make effective decisions and build more productive systems.”

The three key terms used or implied in most definitions are structured, decision making, and improvements. Structured implies that techniques will focus on using rigor and sophistication. Many times this also requires a reliance on data and a mathematical or quantitative basis, although this is not always the case. Traditional methods can be classified as “hard” (i.e., relatively mathematically intense) and “soft” (i.e., rigorous but qualitative, which stresses structured problem solving for complex and messy problems that cannot be solved by traditional math models). Advanced quantitative methods, such as simulations, optimization, and mathematical models incorporating probabilities and other variables, are often tools used in this scientific process.

The focus of OR relies on improving the outcomes of decision makers through use of better methods and techniques that comprehensively and systematically produce options, scenarios, and better results (Trick, 2003). Exploring data in new ways, using new techniques, or building models that can help determine the effects of decisions so that managers and other decision makers can improve the quality of their decisions is a fundamental goal of OR.

Finally, OR is about making improvements in performance (Ackoff & Sasieni, 1968). Scientific rigor and better quality decisions should result in improved operating, financial, or strategic performance. OR is not supposed to be arbitrary or exploratory for its own sake; the results need to be better through the OR if the discipline is to grow and thrive. Thus, the new slogan for INFORMS and other OR organizations is the “science of better,” focused on improving outcomes and results.

Based on its focus and intent, it is important to evaluate the scope of OR for the health care industry, both currently and its future potential.

MANAGEMENT DECISION MAKING

In a completely rational model explaining how managers “do” (descriptive models) or “should” (normative models) behave in organizations, the emphasis is placed on maximizing outcomes of the decision process. Management of any organization would identify the goals of a specific problem or situation, generate alternatives, and select the one that is optimal. In this environment, OR methods would appear to be highly complementary. OR techniques allow managers to seek alternatives; evaluate these choices using probabilities, risks, and other variables as key criteria; and then model potential outcomes. Unfortunately, managers in organizations do not always behave rationally, which has opened the decision sciences field to a much less rational approach to decision making. Due to behaviors, politics, and other potential influences, the rational model is not the norm.

OR methods play a vital role in the management decision-making process. For these purposes, decisions are defined as a choice between two or more alternatives, and management decision making is the process in an organization by which decisions are made.

Because managerial decision making occurs at higher levels of an organization and typically involves major commitments of resources or changes in strategic direction, this research seeks to understand how decision processes work in health care organizations. Understanding the unique aspects of this industry is important because they have been described as service intensive and goal ambiguous in many respects. Management theorists, such as Harrison (1987), have suggested that as the organization’s environment becomes more complex, there is a higher use of “judgment” in decision making and less procedural computation, as in a rational model of decision making. Better understanding of the health care industry’s organizational environment and the specifics of the decision-making process can offer greater insight into how decisions are made, which criteria are used, how the search for alternatives occurs, and the role analytical or quantitative methods can play in the evaluation of alternatives in decision making.

A BRIEF HISTORY OF OPERATIONS RESEARCH

OR seeks to apply structured analytical techniques to improve decisions made by managers. These can come in the form of qualitative (i.e., soft) techniques or the more commonly cited quantitative techniques. For this reason, it is typically described today by management theorists as being its own “school” but as a derivative of the scientific or classical school of management thought (George, 1968; Salveson, 2003), which evolved from the work of Frank and Lillian Gilbreth, Frederick Taylor, and others.

Based on most accounts, the OR discipline can be traced back to the pre–World War II 1930s and 1940s. The British government brought together several interdisciplinary teams to apply science to investigate military tactics. OR groups were used to develop the first radar system around 1941 to help the British military track and identify aircraft. This led to the use of OR for improving other communication systems, and it became instrumental in the Royal Air Force, Army, and Navy (McCloskey & Trefethen, 1954). It was due to these efforts to incorporate scientific and mathematical information into military activities that OR found its niche. Subsequently, operations researchers were deployed to numerous projects throughout all of the British armed forces. With success in England, OR began to move into U.S. military operations during the early 1940s.

During the latter part of that decade, the Massachusetts Institute of Technology developed courses in OR, and in the early 1950s a complete curriculum was developed in OR/MS by Columbia University, Case Western Reserve University, and others. Many universities in England followed suit and developed OR short courses during this time frame as well. The Operational Research Society of the UK (previously the OR Club) was formed in 1950 and is considered to be the world’s oldest OR society (Symonds, 1962). The Operational Research Quarterly began publication in 1950, and the journal Management Science was launched in the United States in 1952, both providing avenues for OR in which to publish and expand. Annual conferences soon began uniting academic researchers worldwide, and since this time the OR discipline has continued to thrive (Schrady, 2001).

Based on its military beginnings, OR quickly became known for incorporating scientific processes into decision making, and it is sometimes called a systems approach (Ackoff, 1971; Riggs & Inoue, 1975). A systems approach refers to how OR attempts to study the underlying behavior and structure of the systems—or interrelated set of processes, events, and activities—that define most problems and decision realms.

This systems approach recognizes that forces and relationships exist between the environment and the internal processes, and that they can be analyzed closely, modeled, and then used for predicting or simulating results. Systems can be defined formally as the “collection of activities that share in their transformation to achieve a defined purpose” (Riggs & Inoue, 1975, p. 70). When systems are modeled, they then can be manipulated in various ways to estimate the effects of changing policies or decisions. Therefore, when applied to management, OR has shown that through a variety of methods (e.g., linear programming, optimization) better or improved results can be identified.

OPERATIONS RESEARCH APPLIED TO HEALTH CARE

OR was applied to health care as early as the 1950s, with one of the first OR articles related to medicine published in the Operational Research Quarterly (Bailey, 1952). This early work was sponsored by a trust of the British National Health Service and led to a small collection of articles. Around the same time in the United States, the Johns Hopkins Hospital assigned a contract position (joint with the Army Operations Research) for a full-time director to assist in hospital management decisions (Flagle, 2002). From the 1960s through the early 1970s, there appeared to be a growing interest in OR, with the field gaining significant momentum around 1970.

It was then that the Operations Research Society of America (now part of INFORMS) held its first symposium on health services delivery (Young, 1969). The Health Applications Section of INFORMS was created in the early 1970s and currently has more than 500 members (INFORMS, 2014b). Subsequently, in 1975, the European Working Group on Operational Research Applied to Health Services was formed and now claims 242 members in more than 30 countries (Operational Research Applied to Health Services, 2014). The result of these societies is a much broader, global effort to apply OR to health care delivery processes. Both of these groups have conducted annual meetings and conferences to continue to encourage innovation in and research on OR topics in health care. In addition, in the late 1970s, the Society for Medical Decision Making was formed to help introduce more quantitative and sophisticated methods into health care decision processes.

Prior to this time, there were several articles published on decision methods and quantitative techniques in health care administration, but they were less focused on the unifying themes, which center on building quantitative models of systems that are stochastic in nature and ultimately patient focused.

Since this time OR has developed some momentum, although not as much as might be expected considering the size and complexity of the health delivery system. Carter (2002) described the lack of OR focus in health care when he stated in his research that only two members of the entire INFORMS membership community were professionals working in hospitals or health organizations and that fewer than 2% of the entire membership body was involved in the Health Applications Section. Figure 5–1 shows a brief time line of the significant early events in health care OR.

About 15 books have been written that focus exclusively on health care and OR. The most significant include Operational Research Applied to Health Services (Boldy, 1981), Application of Operations Research to Health Care Delivery Systems (Fries, 1981), and Operations Research in Health Care (Shuman, Speas, & Young, 1975). More recently, the edited collection from Brandeau, Sainfort, and Pierskalla (2004), Operations Research and Health Care, provides detailed application of OR methods to health operations and clinical processes. Health Operations Management, edited by Vissers and Beech (2005), focuses on using OR and operations management to improve patient flows and logistics in health care organizations. It was the first book to concentrate specifically on this area and discusses basic concepts and frameworks for classifying processes in health, provides methods for analyzing supply and value chains, and offers multiple case studies on outpatient clinic scheduling, master planning, and admissions planning, among others. Other works include Blumenfield (1985), Kessler (1981), and Koza (1973). Nearly all of these books include a summary of key applications and methods used in health care, and most have focused on either clinical decisions or patient logistics.

FIGURE 5–1 Time Line of Significant OR Events

Pierskalla and Brailer (1994) developed a bibliographic survey of OR applications in health care and describe numerous applications for OR methods. Carter (2002) maintains a database of OR research articles focused on health care, and there were more than 800 in 2002. A simple Google search shows about two million hits for the combination of “operations research” and “health care,” although most of these are likely related to clinical or patient care uses of OR. It does appear, however, that OR has started to penetrate at least part of the health care field in certain areas.

OPERATIONS RESEARCH APPLICATIONS

Given the political and community concerns about health care access and costs, it is critical to use more sophisticated tools for solving problems involving variability, uncertainty, and risk. One of the key areas where OR methods can contribute is in the modeling of patient volumes and flow through organizations and health systems. Patient flow means the movement of patients from initial point of entry or service to the point when the patient exits the system. This entails understanding the key processes and transactions that patients must experience in multiple departments (such as admissions, triage, treatment room, laboratory, pharmacy, and finance) and through the network of providers. This process perspective in health care management modeling is extremely important.

Linear programming has been somewhat widely used to minimize labor costs in health care settings. Linear programming is a mathematical technique designed to make decisions that optimize the trade-offs necessary for resource allocation. Linear programming problems focus on maximizing (usually revenue) or minimizing (usually costs). This represents the objective function of the problem. Constraints are the restrictions that are inherent in the problem that limit the degree of change. For example, if a hospital chooses to minimize nurse labor costs but must ensure that at least one nurse is on shift at all times, this represents a constraint.

Simulation models have also been applied to labor staffing problems. A simulation model is a computer application that predicts the behavior or performance of a process or how something may perform in the real world. Discrete event simulation models allow for changes in resources and inputs. For instance, a model of the emergency department can show patient flow and movement if resources are changed, tasks are modified or realigned, or variability in demand occurs. Commercial software for simulation is widely available.

Revenue Cycle Management

Given today’s reimbursement models, operations management in the United States is largely focused on maximizing revenues (and not just minimizing resources or expenses). Financial decisions arise from a contracting perspective with third-party payers and insurers, and it is necessary to ensure that the reimbursement from payers exceeds the operational cost in each service line. This process is called revenue management, or revenue cycle management. Revenue cycle management is the process of managing claims processing, setting payment practices, and generating revenue. It should be an analytical method for determining prices to achieve specific objectives, such as greater demand, higher utilization, or maximizing margins. Price (payer reimbursement) optimization models can be built that minimize risk (the variance in net profitability of a payer contract), which results in a formula such as:

where p_j is equal to the price of an input or patient service j, d_j is the demand, and c_j is the cost for service j. Several constraints are used (such as an equation to define minimal net margin requirements) as well as a variety of other parameters.

Risk and Financial Simulation Models

Financial simulation models were described in the early 1970s as potential OR tools for improving planning outcomes. Many large Fortune 500 corporations constructed formal models that used mathematical programming to dynamically explore changing financial policies, debt leverage, or changes in operational conditions. In essence, these tools help to create pro forma financial statements given certain assumptions and historical relationships. The models range from simple, deterministic, and top down to more complex stochastic, multivariable simulation models. Simulation models allow managers to play “what if” using many different assumptions and scenarios.

Most simulations in health care utilize Monte Carlo simulation analysis, which combines probability theory with random number generation and defined distribution patterns to iteratively simulate outcomes. Monte Carlo methods have been incorporated into spreadsheet solution solvers and programs such as @RISK, RiskAMP, and Crystal Ball. Software tools that incorporate Monte Carlo’s statistical powers allow managers to simulate budgets and plans.

DE-BOTTLENECKING

Assume that a hospital admissions department has two full-time employees who admit patients into the hospital during the 8-hour day shift. Each employee has a computer and monitor with access to the admission system, which takes approximately 30 minutes to complete for an average new patient admission. Therefore, the maximum capacity of this process is 32 new patient admissions daily (2 employees × 8 hours × 2 patients per hour). This 400-bed hospital has a 72% occupancy rate and frees up approximately 40 rooms daily. The challenge for this hospital has always been to get more patients into the process earlier.

As described in this example, only 32 patients can be admitted based on current capacity at the entry point of the process, even though 40 is the actual demand or theoretical capacity further downstream in the process. Therefore, if more than 32 patients arrive, a bottleneck would exist (Demand > Capacity). A bottleneck is a choke point, or a point in a process where demand exceeds available capacity. In other words, a bottleneck can occur at any point where capacity is insufficient to meet demand due to physical or logical constraints. A bottleneck can also be a person, role, or any other barrier or obstacle to cooperation and work performance among departments.

One of the keys to increasing throughput or capacity is to remove these obstacles or bottlenecks, which is called de-bottlenecking. In the preceding example, potential solutions for reducing the bottleneck might be to add labor (recruit additional employees), reduce the process time below 30 minutes (invest in systems and procedures that allow for faster processing), or remove forms or tasks that are redundant. All of these should be considered. Figure 5–2 provides an example of a bottleneck, shown visually as a funnel. In a funnel, the neck of the funnel limits volume throughput. In other words, the narrowest part of the funnel determines how quickly volume can be moved through the process, thus creating a bottleneck.

FIGURE 5–2 Process De-Bottlenecking

The key to being able to de-bottleneck is to thoroughly analyze both demand and capacity to determine where the bottleneck exists. To be successful in improving processes, it is important to determine if the bottleneck is the result of an inability to handle demand at all times, or just at a specific point in time, as well as to discover if other barriers to throughput exist.

Bottlenecks can occur at any point in the process: where a patient enters the hospital, at registration, during transition time of equipment, and at time of discharge. The earlier the bottleneck exists in the process, the fewer the number of patients (or throughput) that can be pushed through the system. Alternatively, a bottleneck at the end of the process typically results in wait times and inefficiency that can eventually affect the entire system. Eliminating a bottleneck at the beginning, only to discover that more exist in the middle or end of the system, will not help increase throughput. That is why it is important to study all processes systematically and to identify those obstacles that really limit capacity.

FORECASTING PATIENT DEMAND AND VOLUMES

Forecasting patient demand is the first step to thoroughly understanding changes in activity levels over time. Comprehensively defining patient logistic flow involves tracking volumes intraday, as well as throughout the week, using time-series data. If a hospital does not exhaustively know patient volumes and traffic levels, it cannot project volumes for individual departments and services throughout the day. Without understanding demand, it is nearly impossible to align resources and capacity with demand.

Forecasting is a collaborative process that estimates the volume of patients who will be served over a specific time period. More precisely, it is a projection of demand that will occur along three dimensions: service type, location, and time dimensions. Service type includes the specific procedures performed or the staff involved in the effort. Location includes the specific department, unit, floor, or other geographical location that performs the service types. Time refers to the hour, day, week, and month that the demand was met. Forecasts are based on time-series data. Time series refers to a set of values or observations at successive points in time.

Forecasting, by definition, is the practice of making a prediction or estimation about the future (Makridakis, 1996). It involves modeling the past to define the future. Demand forecasting, then, is the practice of predicting future demand to accomplish specific business goals, such as more accurately planning how many beds or clinics are needed or how much staff to hire. Performing forecasting really well allows managers to minimize unproductive wait time, maximize customer service, and in general improve operational efficiencies—the goal of operations management.

There are two major types of forecasts: qualitative and quantitative (Armstrong, 2001). Qualitative methods include mainly market research, executive opinion, or Delphi methods to make subjective or judgmental decisions about the future without relating demand to historical performance quantitatively. Qualitative methods for demand forecasting may be useful for gauging potential demand of entirely new products that have no relationship with other products and cannot be reasonably estimated statistically. Qualitative forecasts of new products that a surgeon or specialty area requires may be the best use of these types of forecasts.

In health care, forecasting should primarily be based on quantitative methods. Quantitative forecasts can be broken down into two major types: univariate and multivariate methods. Univariate can be defined as dependence on a single variable; univariate methods attempt to forecast demand by exploring historical data relative to a single variable, such as number of patients, procedures, or items. In standard hospital environments, all of the transactional details for patient volume are captured in the clinical scheduling or information system, such as the number of admissions or the number of surgeries. In addition to this, clinical systems also capture the date patients are admitted and discharged, which procedures were given, the drugs and supplies administered, and prices charged. Reliance on any one of these transactional data elements is a univariate method, which reflects the single variable that will be analyzed to assess historical usage levels and then, based on this analysis, used to make a projection about future values.

With univariate forecasting, there are several different statistical models that are often called on to assess patterns in the data. These include such methods as Box-Jenkins, linear trend analysis, exponential smoothing, moving averages, least squares, and many others. These models all have specific advantages and disadvantages that make them useful for single-variable forecasts. Some of these methods will be discussed in the rest of this section.

Moving Average Forecast

A moving average calculates an average historical figure for a specific time period, such as the last 3 rolling months, and then extrapolates this average forward. This is a very imprecise type of forecast because it actually lags the relevant time period. In a constantly growing environment, moving average can be too conservative, and it is underbiased in its predictions. The mathematical calculation of a moving average forecast is:

The term moving indicates that as a new data point becomes available, the oldest data value drops off and is replaced. In other words, if you were calculating a 3-month moving average, the calculation would sum the last 3 months’ actual historical data values and divide the total by 3. For example, if historical data values were 10, 20, and 30, the moving average forecast would be 20, calculated as follows:

Trend Forecasting

Another type of forecasting algorithm is based on simple trend analysis. Trend analysis looks for linear upward or downward movements in data and then extrapolates them going forward. Trend models are effective when demand for a product exhibits fairly consistent demand over time. The basic formula for calculating trend forecasts uses the initial starting point or intercept and adjusts for slope (or angle of the trend) over time. This is often called rise over run, and it is mathematically calculated as follows, where y is the forecasted value, a is the y-axis intercept, b is the slope of the regression line, and x is the independent variable.

Other Methods

Smoothing methods in demand forecasting are useful because they use a factor to weight the most recent demand observations more than in previous periods, and they help account for errors in previous periods. Smoothing, whether it is exponential (i.e., discounts previous periods with a higher magnitude as the observations age), double exponential, or third order, focuses on improving forecast accuracy by giving more weight to the most relevant historical periods.

Box-Jenkins is a slightly more complex model that uses regression or curve-fitting techniques at predefined time intervals for the single variable being analyzed. It combines single-variable linear regression with a moving average technique to achieve good results from univariate methods.

A much more comprehensive set of forecasting methods falls within the category called multivariate. Multivariate methods attempt to use more than one variable to help better explain or model the past to make more accurate forward projections about the future. Although factors such as seasonality and cyclicality (i.e., business cycles that repeat s