Statistical Quality Control in Nursing Homes: Assessment and Management of Chronic Urinary Incontinence John F Schnelle, Daniel R. Newman, and Toni Fogarty This artick describes a statistical quality control system that allows nurse managers to monitor staff performance of a critical patient care function. Descriptive data concerning patients' incontinence frequencies were colkcted during a two-day assessment periodfor 87 patients under conditions that guaranteed that the patients' protective garments were changed on a one- or two-hour basis. The average and expected norms ofpatient wetness were calculatedfor a sampk of patients in four different nursing homes. Periodic monitoring ofpatient wetness in each sampk and the use of statistical quality control charts permitted nurse managers to determine if nursing aides were changing patients on either a one- or a two-hour scheduk. 7he implications of the research for meetingfederal incontinence care standards andfor assuring high-quality patient care are discussed.

The supervision of patient care in nursing homes is of critical importance since the majority of direct care activities are performed by relatively untrained nursing aides (National Academy of Sciences 1986; Brown 1988). Difficulties in managing performance of this work have led to criticism of the quality of care delivered by nursing homes Research for this article was supported by National Institute of Aging and The National Center for Nursing Research Grant #5 UO1 AG05270-2. Address correspondence and requests for reprints to John F. Schnelle, Ph.D., Professor, Department of Psychology, Box 438, Middle Tennessee State University, Murfreesboro, TN 37132. Daniel R. Newman, B.A. is Project Manager, and Toni Fogarty, M.A. is Research Assistant, Middle Tennessee State University.

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(National Academy of Sciences 1986; Nyman 1989; U.S. Congress 1974, 1986; U.S. General Accounting Office 1987). This article describes a statistical quality control system that can be used to manage nursing aide performance as it relates to incontinence care. Urinary incontinence affects approximately 50 percent of the 1.6 million patients who reside in nursing homes (American Health Care Association 1988; Ouslander and Kane 1984). Two recent studies (Schnelle, Traughber, Sowell, et al. 1989; Hu, Kaltreider, Yu, et al. 1989) have documented that approximately 40 percent to 50 percent of the patients in nursing homes can benefit significantly from being offered regular toileting assistance. Unfortunately, approximately 50 percent of incontinent nursing home patients will not respond to toileting programs with significant increases in appropriate toileting or correlated decreases in wet episodes. The hope remains that at least some of the unresponsive patients can be brought to the point where they will be able to initiate voiding successfully when offered toileting assistance. However, current therapies to produce this change have not been validated. Until therapy is improved, the incontinence of the unresponsive group of patients must be managed with timely changing programs. The goal of changing programs is to keep the patient maximally dry and to reduce the potentially deleterious effects of incontinence. To manage incontinent patients on a changing program with any success, staff must check patients on a consistent basis (at least every two hours) and change the patients' protective garments when wet. Thus, a high rate of repetitive behavior is required of nursing staff. Studies and surveys (Health Care Financing Administration 1988; Schnelle et al. 1988) of nursing care have documented that nursing homes often do not change patients on a timely basis. It is clear that this patient care area requires attention and must be dosely monitored by nurse managers. It is also clear that the incontinence care area has characteristics that make this management possible with statistical quality control technology. Statistical quality control is a technology widely used in manufacturing to improve product quality and worker productivity (Wetherill 1977). The technology involves first describing the average and the normal variability of work performance outcomes. Repeated samples of work outcomes are then taken and compared to these established standards. Normal and abnormal variation among samples is visually determined with a control chart. The expected performance standard and ranges of expected variation in samples are illustrated on this control

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chart. Sample data are graphed on the chart and evaluations made on whether the process is in control (normal variation) or out of control (abnormal variation). If the work sample is out of control (i.e., three standard deviations from the average), the variation is abnormal and steps should be taken to identify and solve the problem at the sources of the abnormal variation. The rationale of statistical quality control and control chart procedures are well described in several sources (Wetherill 1977; Mainstone and Levi 1987; Gidow and Gitlow 1987; Fogarty, Schnelle, and Newman 1989; Burr 1979). Statistical quality control is most applicable in situations in which a high-rate work activity produces a measurable outcome, for instance, in assembly line work. This situation enables managers to frequently sample work quality. One advantage of frequent sampling is that the average and the variability of the work performance can be quickly established and, another, that worker productivity can be easily sampled. Thus, problems with the quality of the work system can be identified on a timely basis and before the system has been out of control for too long. These latter advantages have led statistical quality control to be more widely used in manufacturing than in the service or health care industries (Gitlow and Gitlow 1987; Gilliem 1988). Although one article (Fogarty, Schnelle, and Newman 1989) has discussed the application of statistical quality control technology in developing and interpreting key indicators of patient care, no other examples of statistical quality control applications in the long-term health care field have been published. Furthermore, there appear to be several areas of patient care in nursing homes that could be monitored with a powerful statistical quality control technology. If a patient care area that requires nursing staff to engage in a repetitive work activity has established clear outcome measures, then statistical quality control can be used to determine the quality of this work activity. This article applies statistical quality control to the care of patients whose incontinence is managed by consistent changing procedures.

METHOD Eighty-seven subjects were recruited from four intermediate and skilled nursing bed facilities -labeled A, B, C, and D -that ranged in size from 60-100 beds. These nursing homes maintained identical staffing patterns: one nursing aide per 10 patients on the day shift, and one nursing aide per 16 patients on the night shift. The patients had

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reached an average of 84.2 years of age and had lived in the nursing home an average of 3.5 years. Consents were obtained for the subjects from a total pool of 229 incontinent patients. The subjects were severely impaired. Seventy-five percent were unable to ambulate independently, and their average score on the Folstein Mini Mental Status Examination (MMSE) was 7.2 (range 0-24). Seventeen percent of the patients had an indwelling-catheter history of longer than six months. A disproportionate number of patients in nursing homes C and D had a longer catheterization history. The majority of these patients had had their catheters removed within six months prior to this study. The number of patients in each nursing home and their average length of time catheterized are: nursing home A - 27 patients (129 days), B = 30 (8 days), C = 15 (435 days), and D = 15 (419 days). INCONTINENCE ASSESSMENT

Incontinence severity was assessed by physically checking the patients on a hourly basis for two days from 7:00 AM to 7:00 PM. The patients, who were changed each hour when they were wet, were assisted to the toilet only if they spontaneously requested assistance. The average frequency of patient wet checks was 5.7 for 12 hours. Two observers independently checked the patient on 11 percent of the checks in order to assess the accuracy of the measures. The two observers agreed on the incontinence status of the patients 99 percent of the time. Previous studies had documented that voiding frequency measured during the first two days of monitoring is highly correlated with data collected over longer baseline periods (Schnelle, Traughber, Sowell, et al. 1989). CONTROL CHART DEVELOPMENT

Two control charts were developed that reflected how often the patients were wet, under conditions in which they would be checked and changed on a one-hour and a two-hour basis, respectively. The average percentage of patients found wet each hour was calculated to determine the hourly patient wetness standards. To determine a two-hour wetness standard, the hourly incontinence data were collapsed into six twohour blocks, for example, 8:00 AM to 9:00 AM and 10:00 AM to 11:00 AM, and so on. If a patient was wet on either of the hours in a two-hour block, the patient was counted wet for that inspection. An inspection was counted as dry only if a patient was dry during both hours within the two-hour block. The conventional statistical formula for binomial data was utilized to determine the normal variation, the warning limit, and the upper limit in the one-hour and two-hour percent wet data.

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Thus, the average (X) and the normal expected variation (standard deviation) in the percent of patients who would be wet if they were changed on either a one-hour or a two-hour basis was described. The data were based on 24 hourly sample checks or 12 two-hour sample checks. A warning limit was defined as an area two standard deviations (SDs) above the average according to conventional statistical quality control standards (Burr 1979). If sample patient wet percents fell into this warning area, then a potential wetness problem was developing. The upper limit (three SDs from the mean) signified percent wet samples that were out of control (Mainstone and Levi 1987; Fogarty, Schnelle, and Newman 1989; Burr 1979). In short, if patient wetness in a sample was three SDs above the expected mean for either the onehour or two-hour data, then the patients were wetter than one would expect if they were being changed on a consistent basis. CONTROL CHART MONITORING

Samples of patient wetness data were taken from all patients, for all hours between 8:00 AM and 7:00 PM, and for all days of the week. A computer-generated program randomly selected the hours and days the checks would be made. Research staff entered the nursing home and began spot-checking patients at the beginning of the selected hour. Patients were checked but not changed. To complete a round of ten patients, it took approximately 29.3 minutes, induding travel time. Two people independendy checked patients and recorded incontinence data on 8 percent of the rounds. The agreement between these two staff members was calculated to assess the reliability of the data collection. The two raters agreed on their recordings of patient incontinence status on 99 percent of the checks. A total of two checks for each hour between 8:00 AM and 7:00 PM for each nursing home is reported in this article.

RESULTS Table 1 illustrates the one-hour and two-hour patient wetness data for each nursing home. The average percent of patients expected to be wet, the warning limit, and the upper control limit are listed for both the one-hour and two-hour changing conditions. The two nursing homes in which a large percentage of the patients had a longer catheterization history (C and D) have a higher average wetness frequency than nursing homes A and B. In fact, patients in

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Table 1: Patient Wetness Data Nursing Homw Aveage Percent Wd, and

Qudisy Control

Standards One-hour checks Average (%) Warning limit (%) Upper limit (%) Two-hour checks Average (%) Warning limit(%) Upper limit (%)

A

N-27

B N-30

C N-15

D N -15

37 55 64

45 63 72

60 86 99

62 86 98

56 76 86

66 84 93

78 100 111

79 99 109

nursing homes C and D had such high incontinence frequencies that the upper limits signifying that patients are not being changed on a two-hour basis are above 100 percent. A one-way analysis of variance (ANOVA) of the wetness frequency F showed significant differences among the four nursing homes [1P3,84) = 3.26, p < .03]. Additional analyses by Fisher's LSD (lump-sum distribution) test revealed significant differences between nursing home A and nursing home C, and also between nursing home A and nursing home D (CD = 2.81, p < .05) (SPSSX User's Guide 1983). In both cases, nursing home A had a lower wetness frequency. Figure 1 illustrates the two-hour control charts for each nursing home. The patient wet percentage data collected for 24 samples in each nursing home are graphed on each chart. All nursing homes are in control according to two-hour changing standards. Nursing homes A, B, and C have no wet samples above the warning limit, while nursing home D has three samples that border on the warning limit. Nursing home C and D patients have such high incontinence frequencies that sample percentages above the two-hour warning limit cannot be determined. The average and variability of patient wetness in the latter facilities were so high that the warning limit exceeded 100 percent. This means that even if 100 percent of the patients were found wet in a work sample, one could still not condude that the patients were not being changed on a two-hour schedule. However, fewer than one-half of the samples in nursing homes C and D are above the expected twohour average. This low number of samples above the average suggests that the nursing homes are adhering to a two-hour changing schedule. In regard to the one-hour changing standard and control limits, nursing homes A and B show 10 and 11 samples, respectively, or approxi-

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mately 50 percent of all samples above the one-hour warning limit. Nursing homes C and D show three (12 percent) and six (25 percent) samples, respectively, over the one-hour warning limit. All of these sample percentages are more than one would expect if staff were changing on a one-hour schedule.

DISCUSSION This article has outlined a technology to describe and set work performance standards related to urinary incontinence care. This quality control technology has immediate applied value for nursing home managers. The development of control charts such as those shown here would require a two-day assessment period in which patients are checked and changed hourly. These assessment data would permit work standards to be set relating to patient wetness if staff were changing on a one- or two-hour basis. Periodic random samples taken two or three times a week should allow managers to know whether or not nursing aides are changing patients within the expected range of either a one-hour or two-hour changing schedule. The time involved in the control checks is not prohibitive. If the aide's work performance has become inadequate, the sample wet percents will consistently be above the warning zone or above the average expected wet. In this case, immediate steps can be taken to remedy the deteriorating work situation. The nurse manager can increase the frequency of the work sampling until there is evidence that the work situation is back in control. Such preventive management would make intervention into the problem by state and federal regulation agencies unnecessary. The same statistical quality control methodology that assures consistent changing performance could also be used to assure consistent toileting of those patients responsive to toileting programs. The data described in this article reveal several important points about the nature of urinary incontinence in nursing home patients. The major point is that its frequency is exceptionally high and significantly different across different samples of patients. Most notably, patients with long histories of catheterization may have excessively high frequencies. These data suggest that samples of patients within nursing homes should be individually assessed to determine expected levels of wetness. The application of the wetness standards set on the patient data for nursing homes A and B would be unacceptably low if applied to nursing homes C and D. If one simply were to sample

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Figure 1: Control Chart for Each Nursing Home

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patient wetness percentages within each nursing home without first setting individual patient standards, one would condude that nursing homes C and D are not changing patients as frequently as nursing homes A and B. The reality is that all nursing homes are in control according to two-hour changing standards and all nursing homes are out of control according to one-hour changing standards. State and federal survey teams must also be cautious in sampling patient wetness in nursing homes and in drawing condusions without developing individual assessment data concerning the specific patients sampled. It is doubtful that nursing homes have the staff resources to check and change patients on a one-hour basis and, hence, to be in control according to a one-hour work standard. Alternatively, patient wetness is predicted to be exceptionally high if changes occur on a two-hour basis. In the case of nursing homes C and D, for example, a two-hour changing schedule would mean that 42 percent of all patients would be wet 90 percent of the time, and 23 percent of all patients would be wet 100 percent of the time at each check. These high incontinence rates suggest that frequent "changing" is not an effective management strategy for many patients in nursing homes. The use of absorbent pads and diapers may be an acceptable treatment for those patients who are unresponsive to toileting or other medical intervention programs and who would be frequently wet if changed on a one- or two-hour basis. Such padding devices would be particularly effective if they were sufficiently absorbent so that they would not have to be changed after every voiding episode.

REFERENCES American Health Care Association. Long Term Care Data Book. Washington, DC: American Health Care Association, 1988. Brown, M. "Nursing Assistants' Behavior Toward the Institutionalized Elderly." Qpality Review Bulltin 14, no. 1 (1988):15-17. Burr, I. W. Elmentary Statistical Quality Control. New York: Marcel Dekker, Inc., 1979. Fogarty, T. E., J. F. Schnelle, and D. R. Newman. "Statistical Quality Control in Nursing Homes: A Key Indicator to Evaluate Patient Incontinence Care." Quality Review Bulktin 9, no. 2 (1989):273-78. Gilliem, T. R. 'Deming's 14 Points and Hospital Quality: Responding to the Consumer's Demand for the Best Value Health Care."Journal of Nursing Quaity Assurance 2, no. 3 (1988):70-78. Gidow, H. S., and S. F. Gitlow. The Deming Guide to Quality and Competitive Position. Englewood Cliffs, NJ: Prentice-Hall, 1987. Health Care Financing Administration. Medicare/Medicaid Nursing Home Infor-

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mation: 1987-1988. Washington, DC: U.S. Government Printing Office, 1988. Hu, T. W., D. L. Kaltreider, L. C. Yu, T. J. Rohner, P. J. Dennis, W. E. Craighead, E. C. Hadley, and M. Ory. "A Clinical Trial of a Behavioral Therapy to Reduce Urinary Incontinence in Nursing Homes: Outcomes and Implications." Journal of the American Medical Association 261, no. 18 (12 May 1989):2656-62. Mainstone, L. E., and A. S. Levi. "Fundamentals of Statistical Process Control."Journal of Organizational Behavior Management 9, no. 1 (1987):5-21. National Academy of Sciences, Institute of Medicine. Improving The Quality Of Care In Nursing Homes. Washington, DC: National Academy Press, 1986. Nyman, J. A. "Excess Demand, Consumer Rationality, and the Quality of Care in Regulated Nursing Homes." Health Services Research 24, no. 1 (1989): 105-27. Ouslander, J. G., and R. Kane. "The Costs of Urinary Incontinence in Nursing Homes." Medical Care 22, no. 1 (1984):69-79. Schnelle, J. F., V. A. Sowell, T. W. Hu, and B. Traughber. "Reduction of Urinary Incontinence in Nursing Homes: Does It Reduce or Increase Costs?"Journal ofAmerican Geriatrics Society 36, no. 1 (1988):34-39. Schnelle, J. F., B. Traughber, V. A. Sowell, D. R. Newman, C. 0. Petrilli, and M. Ory. "Prompted Voiding Treatment of Urinary Incontinence in Nursing Home Patients: A Behavior Management Approach for Nursing Home Staff." Journal of American Geriatrics Society 37, no. 11 (1989): 1051-57. SPSSX User's Guide. Chicago, IL: McGraw-Hill, 1983, 456-58. U.S. Congress. Senate. Special Committee on Aging. Nursing Home Care: The Unfinished Agenda. Washington, DC: Staff Report, 1986. . Subcommittee on Long-Term Care. Nursing Home Care in the United States: Failure of Public Policy. Washington, DC: U.S. Government Printing Office, 1974. U.S. General Accounting Office. Medicare and Medicaid: Stronger Enforcement of Nursing Home Requirements Needed. Washington, DC: General Accounting Office, 1987. Wetherill, G. B. Sampling Inspection and Quality Control. 2d ed. New York: John Wiley & Sons, Inc., 1977.

Statistical quality control in nursing homes: assessment and management of chronic urinary incontinence.

This article describes a statistical quality control system that allows nurse managers to monitor staff performance of a critical patient care functio...
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