Mistakes and Complexity in Health Care
John R. Grout, Campbell School of Business, Berry College, Mt. Berry, Georgia 30149-5024, phone :(706) 238-7877
Draft: This paper is work in progress. Suggestions that would improve the paper gratefully received.
ABSTRACT
Hinckley and Barkan’s conformance model predicts defect rates using three sources of defects: variance, mistakes, and complexity. Variance (and statistical process control) has been studied extensively generally and for health care. Mistakes and complexity have not been as widely addressed. This paper 1) presents evidence that demonstrates the importance of mistakes in health care processes, 2) discusses an approach to remediate mistakes, and 3) proposes an approach to assessing changes to a health care providers’ process based on its relative priority and impact on complexity.
INTRODUCTION
In the last decade, many firms including health care organizations have mounted substantial efforts to improve quality and customer satisfaction. These efforts have centered on a corporate culture of employee empowerment and involvement, decision making based on data, and statistical process control (SPC). More recently, some of these firms’ efforts have been focused on documenting their processes in compliance with ISO 9000-based quality standards.
While these efforts have been important and effective in many cases, the framework for thinking about quality has been incomplete. This is particularly true when the definition of quality is the conformance- or manufacturing-based definition [Garvin 1988].
Recent work by Hinckley and Barkan [1995] identify 3 causes of non-conformities, or defects: variance, mistakes, and complexity. Add to this the possibility of reducing defects through cultural means (incentives, awareness, driving out fear, etc.) and there are four distinct areas that must be addressed to achieve the single digit defects per million opportunities that are sought in today’s highly competitive business environment. Of these only two are widely addressed in current quality practice: culture and variance. Variance as used here is the statistical variance that is usually managed using statistical tools like SPC, DOE, acceptance sampling, etc.
MISTAKES
Hinckley & Barkan [1995], and Chase & Stewart [1994] both argue that statistical variance based tools for controlling the process are not well suited to detecting mistakes caused by human error. Human error will often be classified as a common cause not a special cause. This is because human error tends to rare and intermittent. The impact of human errors on estimators of the process average and variance is likely to be small and undetected by sampling. However, their impact is substantial when quality goals are in the range of single digit defects per million. Rook [1962] found that human errors in experimental settings are likely to reach nearly 300 defects per million for relatively simple operations. Leap [1994] found that errors are a much larger problem than that in the health care industry: approximately two percent (20,000 errors per million) of patient days involve an adverse drug reaction of some kind. McClelland, McMenamin, Moores, and Barbara [1996] report that individuals are 30 times more likely to die from human errors in the transfusion process than the more highly publicized risk of receiving HIV tainted blood.
The approach to error prevention in health care (and elsewhere) has relied on individuals to not make errors [1]. The presumption has been that if errors occur, it indicates a lack of vigilance and determination by the individual. Similar approaches were, and often still are typical in industrial settings. The reflex among managers to exhort workers to "be more careful" is still common.
In quality management, it is often asserted that 85% of problems are attributable to "systems" outside the workers' control and that only 15% are attributable to workers. This has led managers to focus on improving systems instead of blaming workers for results that are out of their control. This approach is also appropriate for human errors. Donald Norman urges us to "change the attitude toward error. Think of an object's user as attempting to do a task, getting there by imperfect approximations. Don't think of the user as making errors; think of the actions as approximations of what is desired" [1989].
A set of strategies for reducing mistakes and human error was developed at Toyota Motor Company by an industrial engineer named Shigeo Shingo [1986]. These strategies rely heavily on the use of poka-yoke (pronounced POH-kah YOH-kay) devices. Poka-yoke is Japanese for mistake-proofing. Poka-yoke devices are simple mechanisms that either prevent errors from occurring or make errors obvious before serious consequences result.
Poka-yoke Framework
Poka-yoke devices have three attributes: an inspection method, a setting function, and a regulatory function. Each attribute is discussed in detail below.
Inspection methods. Shingo identified three types of inspection: judgment inspection, informative inspection, and source inspection. Judgment inspection sorts out defects. There is relative consensus that this type of inspection is discouraged.
Informative inspection is an inspection of the products produced by the process. Information from these product inspections is used as feedback to control the process and prevent defects. Control charts are one form of informative inspection. Shingo’s successive checks and self-checks are alternative forms. These involve having each operation inspect the work of the prior operation, successive checks, or having workers assess the quality of their own work, self-checks. Informative inspections occur "after the fact."
Source inspection creates and uses feed-forward information to determine "before the fact" that conditions for error-free production exist. Norman [1989] refers to this type of device as a "forcing function" because these devices are often designed to prevent erroneous actions from occurring. Source inspection is preferred to informative inspection.
Source inspection, self-checks, and successive checks each involve inspecting 100 percent of the process output. In this sense, zero quality control is a misnomer. These inspection techniques are intended to increase the speed with which quality feedback is received. Although every item is inspected, Shingo was emphatic that the purpose of the inspection is to improve the process and prevent defects, and therefore is not intended to sort out defects (although in some cases that may also be an outcome) [Shingo, 1986, p. 57]. Shingo believed that source inspection is the ideal method of quality control since conditions for quality production are assured before the process step is performed. Self-checks and successive checks should be used when source inspection cannot be done or when the process is not yet well enough understood to develop source inspection techniques.
Setting Functions. A setting function is the method used to perform an inspection. Chase and Stewart [1995] identify four setting functions 1) physical 2) grouping and counting 3) sequencing and 4) information enhancement. Physical methods determine whether defects or problems exist based on the presence or absence of physical contact with a sensing device. The small bevel on one corner of 3.5 inch diskettes combined with a stop in the computer’s disk drive eliminate the possibility of disks being inserted incorrectly into the computer. The grouping and counting method uses counting or measuring methods to insure no errors have occurred. L.L. Bean uses product weight information and an electronic scale to insure that orders are complete and correct. Sequencing methods check that a standard sequence of actions occurs. In a car, the key must be switched on before the car is shifted out of park and must be shifted back to park before the keys can be removed. Information enhancement methods provide or preserve information that would not be available otherwise. Restaurants use pagers to allow patrons to stroll and shop without fear of not hearing that their table is ready.
Regulatory Functions. There are two regulatory functions: 1) warning functions and 2) control functions. The bells, buzzers, and warning lights in automobiles are warning functions. Their purpose is to warn that an error has occurred or is about to occur. Control functions are more restrictive than warning functions. They actually keep errors from occurring by stopping the process or in some cases correcting the process automatically. A car's gearshift mechanism is an example of a control function. The car cannot be shifted out of park unless the ignition key is inserted and turned to the on position.
IMPORTANCE OF MISTAKE-PROOFING IN HEALTH CARE
The fact that a patient is 30 time more likely to die as a result of a human error than to die HIV tainted blood, along with the
startling number of medication errors that occur in hospitals, indicates that mistake prevention is critical.
To further demonstrate the importance of mistakes in the transfusion process, consider Figure 1. It shows a flowchart of the blood transfusion process labeled with the types of process errors that are possible. This chart and process failure modes were provided by doctors studying transfusion medicine at the University of Texas Southwestern Medical school. The majority of these errors can be grouped in to 4 categories.
Table 1.
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