Efforts to reduce seclusion and restraint use in a state psychiatric hospital: a ten-year perspective.

OBJECTIVE: The authors previously demonstrated an 82.3% reduction in seclusion and restraint use at an inpatient psychiatric facility, largely attributable to changes to the physical environment. This study investigated whether the reduction was sustained over time. METHODS: This follow-up study examined archival data by using a longer preintervention baseline phase and examined the sustainability of intervention gains in the absence of a research agenda. Over ten years, 3,040 seclusion and restraint incidents were analyzed across 254,491 patient-days. RESULTS: The extended baseline phase (N=38 months) exhibited a linear trend upward in seclusion and restraint use, and the formal intervention period and subsequent follow-up periods (N=82 months) showed a stabilization effect (p<.001). CONCLUSIONS: The findings suggest that reduction in seclusion and restraint use is sustainable, and judicious use of seclusion and restraint can become the new normative practice-even in the face of potentially disruptive administrative and environmental changes.

Systematic investigation of initiatives to reduce seclusion and restraint in a state psychiatric hospital.

OBJECTIVES: This study used an experimental design to examine the effect of systematic implementation of behavioral interventions on the rate of seclusion and restraint in an inpatient psychiatric hospital. METHODS: With a variant of the multiple-baseline design, a model designed to reduce seclusion and restraint was implemented at a large state-funded hospital in the southeastern United States. The implementation schedule was established such that each of five inpatient units was randomly assigned to implement the intervention components in a different order, and each unit served as its own control. Participants were patients and staff, for a total of 89,783 patient-days over a 3.5-year period from January 2005 through June 2008. The components included trauma-informed care training, changes to unit rules and language, changes to the physical characteristics of the therapeutic environment, and involvement of patients in treatment planning. The rate of inpatient psychiatric seclusion and restraint (per patient day) was tracked continuously during the 3.5-year period. RESULTS: A significant reduction of 82.3% (p=.008) in the rate of seclusion and restraint was observed between the baseline phase (January 2005 through February 2006) and the follow-up, postintervention phase (April 2008 through June 2008). After control for illness severity and nonspecific effects associated with an observation-only phase, changes to the physical environment were uniquely associated with a significant reduction in rate of seclusion and restraint during the intervention rollout period. CONCLUSIONS: These data suggest that substantial reductions in use of seclusion and restraint are possible in inpatient psychiatric settings and that changes to the physical characteristics of the therapeutic environment may have a significant effect on use of seclusion and restraint.

A new approach to tracking the harmfulness of medical errors in health care systems.

A variety of approaches are available to monitor and quantify the frequency of medical errors and the harm associated with them. The University HealthSystem Consortium Patient Safety Net provides a categorical, rank-ordered approach for designating harm associated with medical errors but does not lend itself to routine monitoring of change over time. A brief questionnaire was developed and given to hospital administrators to collect ratings about the harmfulness of events occurring in each of the 10 University HealthSystem Consortium harm-score categories. Results revealed a high degree of agreement among raters for harmfulness ratings for each category. A cubic model fit the data best and provided weights for each of the harm-score categories. This preliminary study proposes a methodology for quantifying harm scores.

Routine assessment of patient-reported outcomes in behavioral health: room for improvement.

Outcomes assessment has become an important tool in assessing the quality of health care. To date, most quality initiatives have focused on adverse events, clinical processes, and/or cost variables. Considerably less attention has been paid to indices of clinical improvement, especially from a patient's perspective and in behavioral health settings. The relative inattention given to clinical improvement is attributable to a number of reasons, including (but not limited to) a lack of consensus regarding measures of improvement, few simple methods for data collection and analysis, and an inability to provide timely feedback. In this article, the authors describe a Web-based system designed to routinely collect quality-of-life ratings from patients in outpatient behavioral health clinics, allowing for real-time feedback at the patient levels regarding clinical improvement. The system also allows for administrative evaluation of overall clinic performance. The costs and benefits of this system are discussed.

Clinical outcomes assessment in behavioral healthcare: searching for practical solutions.

Most quality management initiatives in behavioral healthcare have focused on adverse events, clinical processes, or cost variables. Considerably less attention has been paid to indices of clinical improvement, likely because of a lack of agreed-upon measures of improvement, a lack of availability of simple methods for data collection and analysis, and an inability to provide timely feedback regarding clinical improvement, among other reasons. A computerized system was designed to collect relevant psychiatric symptom ratings in a manner that allows for real-time feedback at the patient and clinician Levels regarding clinical improvement and for higher-level, administrative evaluation of overall clinic performance. The costs and benefits of this system are discussed.

How unusual are the "unusual events" detected by control chart techniques in healthcare settings?

Statistical process control (SPC) charts have become widely implemented tools for quality monitoring and assurance in healthcare settings across the United States. SPC methods have been successfully used in industrial settings to track the quality of products manufactured by machines and to detect deviations from acceptable Levels of product quality. However, problems may arise when SPC methods are used to evaluate human behavior. Specifically, when human behavior is tracked over time, the data stream generated usually exhibits periodicity and gradualism with respect to behavioral changes over time. These tendencies can be quantified and are recognized in the statistical field as autocorrelation. When autocorrelation is present, conventional SPC methods too often identify events as "unusuaL" when they really should be understood as products of random fluctuation. This article discusses the concept of autocorrelation and demonstrates the negative impact of autocorrelation on traditional SPC methods, with a specific focus on the use of SPC charts to detect unusual events.

An autocorrelation-corrected nonparametric control chart technique for health care quality applications.

Recently, Alemi proposed a nonparametric control chart technique (Tukey's control chart) for quality management applications when few data points are available and when data do not conform to the assumptions of traditional control chart techniques. Borckardt et al then published an empirical evaluation of the technique and concluded that the presence of autocorrelation in control-chart data negatively impacted the technique's ability to help managers make accurate decisions about the presence of special-cause variation in their data. Thus, there is still a need for control chart techniques that appropriately handle short data streams that do not necessarily conform to the assumptions of traditional control chart techniques but are not negatively impacted by autocorrelation in the data. In this article, the authors empirically evaluate a modified version of the technique presented by Alemi that is designed to account for autocorrelation. Empirical analyses indicate that the modified technique demonstrates superior false-positive performance with very little degradation of power compared with the original technique proposed by Alemi.

An empirical evaluation of Tukey's control chart for use in health care and quality management applications.

Recently in this journal, F. Alemi (Qual Manage Health Care. 2004;13(4):216-221) proposed the use of Tukey's Control Chart because of the unique benefits associated with nonparametric inferential statistics including robust performance with (1) low N sizes, (2) nonnormal data, and (3) the presence of outliers, However, an assumption that applies to virtually all inferential statistical procedures (parametric and nonparametric) is that the observations in question are independent from each other. Unfortunately, there is good reason to suspect violation of this assumption when evaluating quality processes over time (as is often the case in health care settings). In this article, the power (ability to detect real changes in a data process) and type I error (probability of false positives) performance of Tukey's Control Chart technique is empirically evaluated. When observations are not independent, Tukey's Control Chart technique demonstrates unacceptable type I error performance. Additionally, regardless of whether observations are independent, the technique demonstrates low power to detect real effects unless the effects are quite large.