[[alternative]]Utilizing Data in Hospital Information System and Spatial Analysis Technique to Assist the Control of Healthcare-Associated Infections

[[abstract]]每年有兩百萬的美國人發生醫療照護相關感染(Healthcare-Associated Infections, HAI)，並導致約8萬8千人死亡。許多研究在利用醫院資訊系統(Hospital Information System, HIS)的數據解決HAI的問題上已經花了相當大的努力。然而，HIS的原始資料相當複雜而且很難找出與HAI的關係。傳統的人工監測是依賴於疑似病例的審核和製表的基本概要統計。感染管制人員從原始資料中識別出處於高HAI的病人是不容易的。本研究目的是建置一個醫療照護相關感染專用的資料庫和一套視覺化醫療照護相關感染的決策系統。本研究採用空間分析技術(Spatial Analysis Technique)為核心，並以泌尿道感染和血流感染為例，發展四階段的系統開發流程。根據台灣疾病管制局對HAI的定義與HAI資料庫數據，本研究提供視覺化的HAI資訊於空間分析和儀表板上。感染管制運用空間分析能夠幫助感染管制人員即時找出醫療照護相關感染，進而增加感染識別的效用、提高病人安全和減少感染管制人員的工作量和醫療照護相關感染的費用。相較於傳統的人工監測，自動化監測提供更高的判斷準確性和能夠快速識別感染病人。視覺化空間分析介面於地圖上呈現關鍵資訊能夠幫助感染管制人員決策以及預防爆發群突發的感染。[[abstract]]Two million Americans acquired healthcare-associated infections (HAIs) in hospital, and resulting about 88,000 patient deaths per year. Many studies have spent considerable efforts on utilizing data in hospital information system (HIS) to resolve HAI problem. However, the raw data in HIS is quite complex and hard to find the relationships with respect to HAI. Traditional HAI surveillance relies on the manual review of suspected cases and the tabulation of basic summary statistics. The infection control staff are not readily apparent from the raw data that directly identify patients under high risk of HAI. The objective of this study is building a dedicated HAI database and a visualized HAI decision-making system. We adopt Spatial Analysis as the core, to develop a four-stage system development procedure. Following the definitions of HAI from Centers for Disease Control, Taiwan, and data in HAI database, we offered visualized observation of HAI information on the dashboard. Using Spatial Analysis for infection control can aid infection control staff to be able to find HAIs in a timely manner, that can enhance the effectiveness of infection recognition, improve patient safety, and reduce the workload of the infection control staff and the cost spent on HAIs. Compare with the traditional manual monitoring, automated system monitoring provides more judgment accuracy than manual monitoring, and be able to quickly identify infected patients. Visualization Spatial Analysis interface presents critical information on map to help the infection control staff to make decisions, and prevent the cluster of nosocomial outbreaks