急診小兒科診斷群即時偵測系統：2000至2006年全國抽樣資料分析

目的：利用健保申報ICD-9-CM診斷碼，進行國內急診小兒科：(l)最不擬合模式的診斷之篩選；及(2)偵測時段內診斷特異量的挑選，示範其即時傷病的監測系統。方法：利用試題反應理論的Rasch模式，分析2000年至2006年84個月內的國內急診小兒科16個診斷特異量。以卡方檢定的Outfit均方誤及標準化殘差的Z分數，篩選最不擬合模式的傷病時段，及時段內的診斷特異量。結果：16個診斷群構築成單向度急診小兒科診斷量的測量特徵；2006年6月的非隨機性不擬合模式最嚴重；其中以心理失能就醫情形最特異，曾於2002年5月也發生過嚴重特異；肌肉與骨骼疾病在急診小兒科內最不穩定；SARS期間急診就醫人數驟減；在過去7年84個時段中，有7個月（佔8.3%）呈不擬合模式的傷病診斷量發展，SARS期間出現急診小兒科神經系統診斷特異量異常。結論：利用無母數資料分佈不拘的Rasch模式的統計特性，容易即時篩選出傷病的特異量，對於即時傷病的監測，提供追蹤可能疫情深入研究的基礎。
Objectives: Using the ICD-9-CM codes with respect to reimbursement claims data of the Bureau of National Health Insurance, we aimed to demonstrate for Taiwanese emergency pediatrics departments that it was possible to screen out, firstly, the most erratic time points for disease volume and, secondly, the most unexpected abnormalities in diagnoses.
Methods: The item response theory based Rasch model with Chi-square-like outfit mean square errors and standardized residual scores was applied to detect the volume of visits at emergency pediatric departments (EPD) by month from 2000 to 2006, and to monitor erratic stability and outbreaks in terms of timing with respect to diagnosis patterns.
Results: Sixteen disease groups classified by ICD-9-CM were used to construct a unidimensional latent trait. The month with the most outfit mean square errors was July 2006, in which patients with mental disorders had an abnormally high frequency, with the next highest being May 2002. Furthermore, diseases of the musculoskeletal system and connective tissue exhibited dramatic fluctuations in volume. Furthermore, patient load was sharply decreased during the period of the SARS outbreak in 2003. It was determined that an outfit MNSQ value greater than 2.0 denoted an unexpected abnormity using the Rasch model for diagnosis volume at the EPDs over the past seven years. This group accounted for 8.3% of total patient volume.
Conclusions: The Rasch model using a non-parametric approach with the distribution-free characteristics can be easily applied as real-time disease surveillance system to detect changes in hospital patient characteristics as well as form a foundation for further relevant and related research on patient patterns. Copyright © 2009 臺灣醫務管理學會.