頁面選擇項目多寡和點選搜尋時間的研究

醫師醫令系統,在設計一個點選網頁上,最好把所有的項目放在首頁上,醫師不用按次頁的選項,就能夠把點選他所開立的所有藥品。

而一個頁面上,多少的選項,對操作者的辦識 (Recognition)所有選項,選擇的時間最短,和正確的點選的機率最高(錯誤率最少),也就是在操作界面上,選項的數目多少會影響操作者選擇和正確率。

到底多少的選項,是比較正確的,是要在一個網頁上,放著滿滿的 Round Node 或是少少的選項?
要不要排序,還是按照使用頻率的高低來排序,或者是用英文字母的先後來排序,那樣的效果最好?

可以設計一個模式,來證明這些研究。

研究對象: 醫院內相同科的專科醫師

研究目的: 網頁介面上,藥品選擇項目多寡,對醫師選擇藥品所需的時間的影響

研究設計

總共25項藥品,以藥品英文名稱拼字,介於6-10字母(避免太短或太長的字,讓使用者能透過其它的認知..找比較短的或長的,較訊速的找到他所需要的藥品),項目的設計,以矩陣大小的方式,5,4,3 的平方,來模擬操作者的視野的大小..

點選項目多寡分為 25(5X5),16(4X4) 和 9(3X3)

1. 25項藥品,隨機出現在不同選擇格,減少學習效應對Internal Validity 的影響。

2. 沒有 High Light, 沒有不同的顏色的 Cell Background,字型大小相同,格子大小相同,格子置於螢幕正中 (Central),控制因格子大小不一,字体不同,顏色不同,所造成引導作用。

3. 25種藥品, 皆為同科的專科用藥, 為該科醫師常使用的藥品,所有研究醫師對25種藥品熟稔程度高, 控制因為醫師對拼字的不熟悉,而造成認知上的延遲。

4. 不同的 25, 16, 9 選擇項目的畫面,選擇藥品不重覆,以防止 Maturity的問 題,重覆 3×25 組畫面(25種藥品,3種界面)。

研究流程 :

1. 畫面 A,出現藥品名稱 (5 Seconds),讓醫師知道下一個畫面要點選的藥品
2. 自動轉換至畫面 B。
3. 醫師從畫面 B 中點選 A畫面中出現的藥品名稱。
4. 計算不同選擇項目的情況,醫師選擇藥品所需的時間和錯誤發生的機率。
5. 更改排序的方法,從隨機排序,到字母排序,和常用比率排序。

模擬 頁面  B 畫面

Lichen Sclerosis, Planus and Simplex

Links to DernMetNZ

    Lichen Planus   PDF
 
    Lichen Sclerosus   PDF

    Lichen Simplex    PDF
 

外陰常見的 Lichen 皮膚苔蘚病變主要的有三種,硬化性苔蘚,扁平苔蘚和簡單性苔蘚。

三種疾病的症狀可能相同,但病因不同,病理切片不同,相對的治療也不同。

以上三篇文章,是由紐西蘭皮膚科醫學會對其會員的教育資料。

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Carcinoma of the Vulva: FIGO Stages

Stage Characteristics
Stage 0 Carcinoma in situ; intraepithelial neoplasia grade III
Stage I Lesion £2 cm; confined to the vulva or perineum; no
nodal metastasis
Stage Ia Lesion £2 cm; confined to the vulva or perineum and
with stromal invasion £1 mm*; no nodal
metastasis
Stage Ib Lesion £2 cm; confined to the vulva or perineum and
with stromal invasion >1 mm*; no nodal metastasis
Stage II Tumor >2 cm in greatest dimension; confined to the vulva and/or perineum;
no nodal metastasis
Stage III Tumor of any size with adjacent spread to the lower urethra and/or vagina or
anus and/or unilateral regional lymph node metastasis
Stage IVa Tumor invasion of any of the following: upper urethra, bladder mucosa,
rectal mucosa, and/or pelvic bone and/or bilateral regional node
metastases
Stage IVb Any distant metastasis, including pelvic lymph nodes

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Models of Vulvar Carcinoma ( Young & Aged )

Models of Vulvar Cancer


Characteristic Type 1 Type 2
Age Younger (35 to 65 years old) Older (55 to 85 years old)
Cervical neoplasia High association Low association
Cofactors Age, immune status, viral integration Vulvar atypia, possibly mutated host
genes
Histopathology of tumor Intraepithelial-like (basaloid), poorly
differentiated
Keratinizing; squamous cell carcinoma, well
differentiated
HPV DNA Frequent (>60 percent) Seldom (<15 percent)
Pre-existing lesion VIN Vulvar inflammation, lichen sclerosus,
squamous cell hyperplasia
History of condyloma Strong association Rare association
History of STD Strong association Rare association
Cigarette smoking High incidence Low incidence


HPV = human papillomavirus; VIN = vulvar intraepithelial
neoplasia; STD = sexually transmitted disease.

Adapted from Crum CP. Carcinoma of the vulva: epidemiology and
pathogenesis. Obstet Gynecol 1992; 79:448-54.

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DRG 和 Risk Premium 的關係

International lessons in high complexity and capitation
期刊 The European Journal of Health Economics
出版社 Springer Berlin / Heidelberg
ISSN 1618-7598 (Print) 1618-7601 (Online)
Volume 5, Number 2 / 2004年5月
文章類目 Original Papers
DOI 10.1007/s10198-003-0208-z
95-109
Subject Collection 商業和經濟
SpringerLink Date 2004年2月19日

Kathryn M. Antioch1, 2, 3 Contact Information and Michael K. Walsh1

(1)  Bayside Health, The Alfred Hospital, Melbourne, Australia
(2)  Department of Epidemiology and Preventive Medicine, Faculty of Medicine, Monash University, Melbourne, Australia
(3)  Bayside Health, The Alfred Hospital, Commercial Rd Prahran, 3181 Melbourne, Victoria, Australia

Published online: 10 December 2003

Abstract  Hospitals
throughout the world using funding based on diagnosis-related groups
(DRG) have incurred substantial budgetary deficits, despite high
efficiency. We identify the limitations of DRG funding that lack risk
(severity) adjustment for State-wide referral services. Methods to risk
adjust DRGs are instructive. The average price in casemix funding in
the Australian State of Victoria is policy based, not benchmarked.
Average cost weights are too low for high-complexity DRGs relating to
State-wide referral services such as heart and lung transplantation and
trauma. Risk-adjusted specified grants (RASG) are required for five
high-complexity respiratory, cardiology and stroke DRGs incurring
annual deficits of $3.6 million due to high casemix complexity and
government under-funding despite high efficiency. Five stepwise linear
regressions for each DRG excluded non-significant variables and
assessed heteroskedasticity and multicollinearlity. Cost per patient
was the dependent variable. Significant independent variables were age,
length-of-stay outliers, number of disease types, diagnoses, procedures
and emergency status. Diagnosis and procedure severity markers were
identified. The methodology and the work of the State-wide Risk
Adjustment Working Group can facilitate risk adjustment of DRGs
State-wide and for Treasury negotiations for expenditure growth. The
Alfred Hospital previously negotiated RASG of $14 million over 5 years
for three trauma and chronic DRGs. Some chronic diseases require
risk-adjusted capitation funding models for Australian Health
Maintenance Organizations as an alternative to casemix funding. The use
of Diagnostic Cost Groups can facilitate State and Federal government
reform via new population-based risk adjusted funding models that
measure health need.

Keywords  Hospital funding – Risk adjustment – Diagnosis-Related Groups – Diagnostic Cost Groups – Casemix funding

——————————————————————————————————————————————-
DRG 在台灣只是一種壓縮費用的方式,還是一個具有避險工具。
如果是一個避險的工具,如何在 合理的成本,合理的利潤,合理的風險溢差的時候,醫院願意投入這項服務。

當風險因成本的壓縮而增加時,因風險而造成的損失加大,風險損失大於成本壓縮所得的獲利時,醫院的經營者或醫師的行為會如何。

DRG 的價格如何定,合理定價是否要加入風險溢酬,低估時會不會倒致醫院或醫師退出這個市場,多少的溢酬才是合理。

醫院內部服務的價格訂定,如何轉移,如何設定 Minimal Service Requirement.

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辯證是研究的主軸

Debate & Prove

研究的過程,是個辯證的過程,提出你的推論 (Propositions) 或 假說 (Hypotheses),用任可的研究方法,來證實你的推論或假說,或資料分析,或實驗證明,或模形推導,在過程中重要的是你的證實,是否有足夠的強度,證實的構念和衡量所使用的變數,是右可以符合你的推論和假說的要求。

辯證中,最常見的問題是,你所舉出的事證偏離了你的推論,事證和推論無關,也可以說你的衡量的變數和須證實的構念無關。
內部效度的問題,是研究初學者常犯的錯誤。

另一種問題是推論和驗證相依強度夠,但是研究者過度解釋其推論,某些模型,資料無法解釋的部份,研究者做了過度的推導,就是研究者常見到的問題,外推性的效度不足,或選擇的架構過於簡單,無法包含相關的變數,過於自信的研究者,在導論和討論中,常犯此類錯誤。

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