Davis G E, Lowell W E, Davis G L
Augusta Mental Institute, ME 04332, USA.
Am J Med Qual. 1997 Spring;12(1):33-43. doi: 10.1177/0885713X9701200107.
This study investigates a new method of measuring quality of care. Taking place at a tertiary psychiatric hospital with 5,128 admissions from January 1989 through December 1995, this study uses artificial neural networks (ANNs) to predict hospital length-of-stay (LOS) and uses the standard deviation of LOS in a formula to measure quality of care, Q. ANNs are trained with data using unique patient identifiers and are compared with identical ANNs trained without these identifiers. These two types of ANNs make a LOS prediction, P, with a slightly different accuracies, and this fact is exploited in measuring Q. The authors defined U as the standard deviation of the difference between the actual and the predicted LOS of the ANNs with unique patient identifiers, and defined G as the standard deviation of the difference between the actual and the predicted LOS of the ANNs without using these unique identifiers. Dividing U, the variation of individual LOS patterns intertwined with systemic LOS patterns, by G, the variation of predominately systemic LOS patterns, yields the ratio U/G, in which systemic effects are factored out leaving a measure of the average severity of patient illness. Ratios that exceed unity are seen in the patients who are more severely ill. The formula for quality of care, Q, divides the best LOS prediction accuracy, P, which is inversely proportional to overall variation in the delivery system, by U/G, which is inversely proportional to quality of care, written as: Q = P/(U/G). Q reflects the patients' perspective because LOS is concrete and tangible to patients. The study took place during hospital downsizing (political change), a consent decree (policy change), new administrative and medical personnel (staffing change), and the introduction of clozapine and risperidone for schizophrenia (therapeutic change). These events had a predominantly positive impact on Q. The value of Q correlated well with the Joint Commission on Accreditation of Health Care Organizations (JCAHO) triennial evaluations. Some conclusions that emerged from this study: 1) System variation, reflected in the standard deviation of LOS, increased with frequent changes in top management. 2) There was a clear-cut beneficial effect of clozapine, and to a lesser extent of risperidone in schizophrenia, allowing more community placement. 3) With a dedicated professional staff quality of care can prevail despite increasing variation in LOS (systemic problems). 4) The number of hospital employees per Q unit halved when the overall hospital Q ranged from low to high values as a result of policy and staffing improvements, suggesting an increased efficiency of operation. 5) Q can be an objective outcome measure of quality care from the patients' perspective.
本研究调查了一种衡量医疗质量的新方法。该研究在一家三级精神病医院开展，研究时段为1989年1月至1995年12月，期间共有5128例入院病例。本研究使用人工神经网络（ANNs）来预测住院时长（LOS），并在一个公式中使用LOS的标准差来衡量医疗质量Q。ANNs使用独特的患者标识符对数据进行训练，并与未使用这些标识符训练的相同ANNs进行比较。这两种类型的ANNs做出的LOS预测P，准确率略有不同，而在衡量Q时利用了这一事实。作者将U定义为带有独特患者标识符的ANNs实际LOS与预测LOS之间差异的标准差，将G定义为未使用这些独特标识符的ANNs实际LOS与预测LOS之间差异的标准差。用U（个体LOS模式与系统LOS模式交织的变化）除以G（主要是系统LOS模式的变化），得到比率U/G，其中系统效应被排除，留下患者疾病平均严重程度的一种度量。病情更严重的患者的比率超过1。医疗质量Q的公式将最佳LOS预测准确率P（与医疗服务系统的总体变化成反比）除以U/G（与医疗质量成反比），写成：Q = P/(U/G)。Q反映了患者的视角，因为LOS对患者来说是具体且可感知的。该研究是在医院规模缩减（政策变化）、同意令（政策变化）、新的行政和医务人员（人员配置变化）以及引入氯氮平和利培酮治疗精神分裂症（治疗变化）期间进行的。这些事件对Q产生了主要为积极的影响。Q的值与医疗保健组织认证联合委员会（JCAHO）的三年期评估相关性良好。这项研究得出的一些结论：1）LOS标准差所反映的系统变化随着高层管理人员的频繁变动而增加。2）氯氮平有明显的有益效果，利培酮对精神分裂症的效果较小，能使更多患者安置到社区。3）尽管LOS变化（系统问题）增加，但有一支敬业的专业人员队伍，医疗质量仍能得以维持。4）由于政策和人员配置的改善，当医院总体Q值从低到高时，每Q单位的医院员工数量减半，这表明运营效率提高。5）从患者的角度来看，Q可以是医疗质量的一个客观结果指标。
