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急性护理医院中两种患者分类工具的比较。

A comparison of two patient classification instruments in an acute care hospital.

作者信息

Seago Jean Ann

机构信息

Department of Community Health, Center for the Health Professions, University of California, San Francisco School of Nursing, San Francisco, 94143-0608, USA.

出版信息

J Nurs Adm. 2002 May;32(5):243-9. doi: 10.1097/00005110-200205000-00004.

Abstract

OBJECTIVE

Patient classification systems are alternately praised and vilified by staff nurses, nurse managers, and nurse executives. Most nurses agree that substantial resources are used to create or find, implement, manage, and maintain the systems, and that the predictive ability of the instruments is intermittent. The purpose of this study is to compare the predictive validity of two types of patient classification instruments commonly used in acute care hospitals in California.

BACKGROUND

Acute care hospitals in California are required by both the Joint Commission on Accreditation of Healthcare Organizations and California Title 22 to have a reliable and valid patient classification system (PCS). The two general types of systems commonly used are the summative task type PCS and the critical incident or criterion type PCS. There is little to assist nurse executives in deciding which type of PCS to choose. There is modest research demonstrating the validity and reliability of different PCSs but no published data comparing the predictive validity of the different types of systems. The unit of analysis is one patient shift called the study shift. The study shift is defined as the first day shift after the patient has been in the hospital for a full 24 hours. Data were collected using medical record review only. Both types, criterion and summative, of PCS data collection instruments were completed for all patients at both collection points. Each patient had a before and after score for each type of instrument. Three hundred forty-nine medical records for inpatients meeting the inclusion criteria were examined.

RESULTS

The average patient age was 76 years, the average length of stay was 6.6 days with an average of 6.7 secondary diagnoses recorded. Fifty-five percent of the sample was female and the most common primary diagnosis was CHF, followed by COPD, CVA, and pneumonia. There was a difference in mean summative predictor score and the mean summative actual score of 1.57 points with the predictor score higher (P =.001; CI =.62--2.5). For the criterion instrument, 68.4% of the predictor criterion scores were in category 2 compared to 65.5% of the actual criterion scores. The criterion predictor agreed with the criterion actual score 45% of the time for category 1 patients, 87.3% of the time for category 2 patients, 77.1% of the time for category 3 patients and 72.7% of the time for category 4 patients, with an overall agreement between predictor and actual criterion scores of 79.9% (Kappa P <.001, indicating agreement is not by chance).

CONCLUSIONS

The most significant finding of this study is that there are virtually no differences in the predictive ability of summative versus criterion patient classification instruments. Using the same patients, both types of instruments predicted the actual score over 78% of the time.

摘要

目的

患者分类系统受到 staff nurses、护士经理和护士主管的褒贬不一。大多数护士认为,创建或查找、实施、管理和维护这些系统需要大量资源,而且这些工具的预测能力时断时续。本研究的目的是比较加利福尼亚州急性护理医院常用的两种患者分类工具的预测效度。

背景

医疗保健组织认证联合委员会和加利福尼亚州第22号法规都要求加利福尼亚州的急性护理医院拥有可靠且有效的患者分类系统(PCS)。常用的两种一般类型的系统是汇总任务型PCS和关键事件或标准型PCS。几乎没有什么能帮助护士主管决定选择哪种类型的PCS。有适度的研究证明了不同PCS的有效性和可靠性,但没有已发表的数据比较不同类型系统的预测效度。分析单位是一个称为研究班次的患者班次。研究班次定义为患者住院满24小时后的第一个白班。仅通过病历审查收集数据。在两个收集点,为所有患者完成了标准型和汇总型两种PCS数据收集工具。每个患者每种工具都有前后得分。检查了349份符合纳入标准的住院患者病历。

结果

患者平均年龄为76岁,平均住院时间为6.6天,平均记录有6.7个次要诊断。样本中55%为女性,最常见的主要诊断是充血性心力衰竭(CHF),其次是慢性阻塞性肺疾病(COPD)、脑血管意外(CVA)和肺炎。汇总预测得分与汇总实际得分的平均差值为1.57分,预测得分更高(P = 0.001;置信区间 = 0.62 - 2.5)。对于标准工具,68.4%的预测标准得分属于2类,而实际标准得分中这一比例为65.5%。对于1类患者,标准预测与标准实际得分在45%的时间一致,对于2类患者在87.3%的时间一致,对于3类患者在77.1%的时间一致,对于4类患者在72.7%的时间一致,预测与实际标准得分的总体一致性为79.9%(Kappa P < 0.001,表明一致性并非偶然)。

结论

本研究最显著的发现是,汇总型与标准型患者分类工具的预测能力几乎没有差异。对于相同的患者,两种类型的工具在超过78%的时间里都能预测实际得分。

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