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首页> 外文期刊>Proceedings of Singapore Healthcare >Development of predictive scoring model for risk stratification of no-show at a public hospital specialist outpatient clinic
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Development of predictive scoring model for risk stratification of no-show at a public hospital specialist outpatient clinic

机译：公共医院专家门诊诊所风险分层预测评分模型的发展

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Aim: No-shows are patients who miss scheduled specialist outpatient clinic (SOC) appointments. A predictive scoring model for the risk stratification of no-shows was developed to improve the utilisation of resources. Method: The administrative records of new SOC appointments for subsidised patients in 2013 were analysed. Univariate analysis was performed on 16 variables comprising patient demographics, appointment/visit records and historical outpatient records. Multiple logistic regression (MLR) was applied to determine independent risk factors of no-shows. The adjusted parameter estimates from MLR were used to develop a predictive model for risk stratification of no-show. Model validation was performed using 2014 data. Result: Out of 75,677 appointments in 2013, 28.6% were no-shows. Univariate analysis showed that 11 variables were associated with no-shows. Six variables (age, race, specialty, lead time, referral source, previous visit status) remained independently associated with no-shows in the MLR model, and their odds ratios were used to develop the weighted predictive scoring model. Weighted scores were 0 to 19, and five levels of no-show risk were derived: extremely low (score: 0–4; odds ratio (OR): 1.0); low (5–6; OR: 2.5); medium (7–8; OR: 5.6); high (9–10; OR: 9.2); and extremely high (11–19; OR: 16.7). The predictive ability of the model was tested using receiver operation curve analysis, where the area under curve (AUC) was 72%. AUC remained at 72% upon validation with 2014 data. Conclusion: The prediction model developed using only administrative data was robust and can be used for the risk stratification of SOC no-show for better resource utilisation to improve access to care.
机译：目的：没有节目是小姐定期的专科门诊诊所（SOC）约会的患者。开发了一种预测的缺口风险分层的评分模型，以提高资源利用。方法：分析了2013年补贴患者新SOC任命的行政记录。在包含患者人口统计数据，约会/访问记录和历史门诊记录的16个变量上进行单变量分析。应用多个逻辑回归（MLR）以确定无节目的独立风险因素。来自MLR的调整后的参数估计用于开发禁止展示风险分层的预测模型。使用2014数据执行模型验证。结果：2013年的约75,677名约会，28.6％是没有节目。单变量分析表明，11个变量与无节目相关。六个变量（年龄，种族，特种，提前期，推荐源，先前的访问状态）仍然与MLR模型中的无节目独立相关，并且它们用于开发加权预测评分模型。加权分数为0〜19，衍生出五种水平的风险：极低（得分：0-4;赔率比（或）：1.0）;低（5-6;或：2.5）;媒介（7-8;或：5.6）;高（9-10;或：9.2）;非常高（11-19;或：16.7）。使用接收器操作曲线分析测试模型的预测能力，其中曲线（AUC）的区域为72％。在2014年数据验证时，AUC仍保持72％。结论：使用仅管理数据开发的预测模型是强大的，可用于SOC NO-SHOW的风险分层，以便更好地利用，以改善对护理的获取。

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