http://www.plurk.com/erebus7

就這樣；寫資格考proposal很苦悶，所以在鬼混

（老姊，你就不用加我了，我不會讓你看到我在幹麼的，哈哈哈）

Hint:ID

Appropriateness of Using a Computer Simulation Approach in Evaluating the Efficiency of a Units Dose Drug Distribution System

INTRODUCTION
An automated unit dose packaging system, Pyxis Oral Solid Packager (POSP, see Figure 1) system, was put into operation in January, 2008 at the Inpatient Pharmacy of Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, Ohio for filling the most-used oral solid doses. The CCHMC pharmacy management wondered if a computer simulation approach could be used to accurately predict the efficiency outcomes of using the POSP system. This study was conducted to determine the appropriateness of using the computer simulation approach in drug distribution system studies.
Computer simulation is a process of designing a complex model for a real or proposed working system, and it can be a powerful and flexible tool to evaluate the efficiency of a workflow system.

 OBJECTIVE
·         To determine the appropriateness of using the computer simulation approach in reengineering a Unit Dose (UD) picking process
This study compared the efficiency data (UD processing time units and queuing time) obtained from field observations (work sampling) and computer simulation to determine the appropriateness of using the computer simulation approach.

 Operational Definitions
·         Appropriateness of using the computer simulation was evaluated by comparing the variation of efficiency data obtained from field observations (work sampling) and computer simulated models
·         Efficiency Data was defined as the UD process time units (order receiving, order entry, picking, inspection, tubing, and automated UD packaging ) and queuing time.

 METHOD and APPROACH
·         This study applied work sampling and computer simulation techniques. 
·         The study design involved: (1) developing a validated computer simulation model, (2) comparing the efficiency data obtained from the computer simulation approach with a work sampling observation.
·         Prior to the installation of the POSP system, work sampling observation data, showing the time spent by pharmacists and technicians in the categories of “order receiving”, “data entry”, “filling doses”, “inspection”, and “tubing medications” was collected in October, 2007. 
·         The time spent patterns by pharmacy staff from the work measurement observation were used to develop the computer model to simulate the UD filling operations prior to installation of POSP system.
·         Arena 10.0 simulation software published by Rockwell Software, Microsoft Office 2007 was used for developing the simulation model. 
·         After the simulation model was validated, it was modified and used to estimate the efficiency outcomes of using the POSP system.  A work sampling observation was conducted in May, 2008. The performance data between work sampling after installing POSP and computer simulation was compared to determine the appropriateness of using this computer simulation approach.

 Study Site and Automated UD System:
·         UD filling area at Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH was the study site. CCHMC is a 423-bed institution and Inpatient Pharmacy opens 24-hour.
·         UD filling area filled an average of 47.12 non-batch UD orders from 7am to 7pm and an average of 275 batch UD orders at around 12 noon. 
·         The average numbers of staff per hour were 2.94 pharmacists and 2.42 technicians.
·         UD doses were delivered by cart, and tube.
·         The POSP system (see Figure 1) was installed to dispense the most-used oral solid doses for the batch UD doses. 

Figure 0. Study Design

 Figure 1.  Pyxis Oral Solid Packaging (POSP) System

 Simulation Models
The simulation models require appropriate input data (e.g., order arrival pattern, and processing time units) and logic. The patterns of hourly UD order number and UD processing time was determined by analyzing the computer data and work sampling observation. Those patterns were used in the simulation models to imitate the real-world situation. The logic of simulation models is depicted in Figure 2.
·         Hourly UD order number was determined by using the CCHMC pharmacy computer database. The data was from 7am to 7pm between October 1, 2007 and October 31, 2007. An average of 565.42 orders of accumulated UD order for non-batch UD doses determined and used. The daily batch UD orders ranged between 250 and 300 orders and this information was used in the simulation models. 
·         UD processing time was collected by using work sampling method with one minute fixed-interval observations of the activities of each pharmacy staff. The pilot test was conducted between September 24 and 28, 2007. The pre-installation POSP data collection was conducted from 7am to 7pm between October 1 and 12, 2007 (excluding weekends). The post-installation POSP data collection was conducted from 7am to 7pm between May 5 and 16, 2008 (excluding weekends). The processing unit used in the simulation models is depicted in Table 1.

 Figure 2. Logic of the Simulation Models

Table 1. Simulation Input Data: Processing Time

*Normal Distribution; **Uniform Distribution

 Analysis of the Appropriateness of Using Computer Simulation Approach
 Two internal validations and one external validation were conducted to examine the appropriateness of using the computer simulation approach in reengineering the UD picking process.
·            Pre-POSP and post-POSP model internal validation: compare observed data and simulation results. Internal validation is to validate the logic of the simulation models.  (Figure 3)
·            External Validation: compare post-phase simulation result and predicted post-phase simulation result. External validation is to validate the appropriateness of using computer simulation to predict a changed system. (Figure 3) 

RESULTS
·         There was no difference between observed data and simulated result in the order processing time:
w  Using the pre-POSP data. (see Table 2: Non-batch orders: 5.528 min versus 5.571 min; Batch orders: 1.895 min versus 1.894 min). 
w  Using the post-POSP data. (see Table 2: Non-batch orders: 5.773 min versus 5.573 min; Batch orders: 2.111 min versus 1.928 min).
·         For the queuing time analysis of non-batch orders, there was no significant difference between simulated post-POSP versus post-POSP observed data and pre-POSP versus post-POSP observed data, but there was a significant difference between these two groups after POSP was installed. (Table 3)
·         For the queuing time analysis and processing time of the batch orders, there were both significant differences between simulated post-phase versus post-POSP observed data and pre-POSP phase versus post-POSP observed data after using POSP system. (Table 3)

Table 2.  Internal Validation of Simulation Model

嗯....

敝人的blog已經雜草叢生

不對，是變熱帶雨林了

所以來寫點東西吧

原本預計暑假要修課的

沒想到學校獎學金不包含暑假的部份

所以就整個空下來了

但是為了賺生活費，還是得留在這邊去children hospital pharmacy作project

不過老闆才剛回美國，現在還不確定要做什麼主題....

總之呢，暑假就是要留在cincinnati啦

所以先來計畫一下想做什麼事吧（ "計畫" XD）

1. 生活規律，改掉懶散的習慣  <=這超難，我承認 :p

2. 解決awa

3. 念統計；尤其是迴歸之後的東西

4. 背單字；我的字彙實在有夠少....

5. 把kroger的東西寫成paper

6. 多看些文獻，該開始想想以後的研究主題了

7. 規律運動

8. 聽力

9. 閱讀

目前想到的是這些

其他想到以後再加

突然發現blog已經雜草叢生了
也許再過一陣子就會發現香菇園出來了....Orz
好吧， 最近努力擠點東西出來

Hint:給自己看的

Hint:給自己看的

昨天Cincinnati發佈了暴風雪警報
也是我來美國第一次碰到這種情形
短短的幾個小時，積雪積到高達10幾公分
學校也發佈說晚上學校停課
整個就是很恐怖的感覺

從下午2點多開始
學校外面就開始一直塞車
我還跟lab的同學開玩笑說今天大概要住學校了
後來到了5點多，發現情況越來越不對
真的開始擔心不用回家了orz

後來撐到7點半，看到窗外的馬路似乎沒塞車
就決定趕快衝去開車
沒想到惡夢才正要開始....

剛開始的前一小段路，雖車速很慢，但至少還有動
沒想到接下來的路程才恐怖
車子幾乎都不動，完全塞住
而且還會打滑！
這種狀況我完全沒碰過
所以整個人開車就呈現非常警戒的狀態

原本我回家的車程最多20分鐘
沒想到，我竟然開的1個半小時才到家！！！
竟然讓我開到有點火大
真是難忘的經驗

後來終於回去以後
聽到一件事後，心情就好了許多
原來我不是開最久的啊～～
聽說我老闆的太太從下午3點開到晚上9點！（正常車程40分鐘）
而我老闆則開了2個半鐘頭才到家
聽完讓我愉快多了，哈哈哈

說真的
這次的經驗讓我真的開始不喜歡下雪了
讓很多事情變得很麻煩
希望以後不要再碰到這種狀況
就算碰到，也拜託讓我躲在家裏吧～

就是留言板歡迎留言，話題不忌唯一要求就是請保持禮貌，謝謝

Columbus今天晚上下雪啦
哈哈哈，終於看雪了
不過有點失望
很像白色的毛毛雨
(Read More...)

雖然文章跟這張圖片完全無關
但我還是要說"歡迎回來，賢治！"
(Read More...)

現在是凌晨2點
眼皮很沈重
(Read More...)