此研究的目的是發展一個使護理人員能更精確篩選出住院病患中跌倒高危險者，且有良好信、效度，經臨床驗證後適用台灣住院病人的評估跌倒量表。
在第一個階段，研究者將進一步進行系統性回顧，深入了解各個評估量表，並與研究團隊進行評值，針對不同面向找出最合適之跌倒高危險性評估問項，進一步針對不適部分進行修改，擬定適用於台灣住院病人之新跌倒評估量表。並邀請三位專家進行分析，修改量表後，最終進行預試(Pilot study)求得信、效度。在研究的第二階段，共有683人次參與進行評估，收集人口基本資料和跌倒評估。其跌倒評估量表的分數與實際跌倒情況進行比較，並計算敏感性(Sensitivity, SEN)、特異性(Specificity, SPE)、陽性預測值(Positive predictive value, PPV)、陰性預測值(Negative predictive value, NPV)、及準確度(Accuracy, ACC)。
跌倒危險評估工具經專家效度與評分者間信度進行測試，其分別為1和.957，證此量表有良好之信、效度。在經臨床驗證後，以加入年齡問項之量表為最佳，其最佳切點為2，敏感性為100 ％，特異性為72.97 ％ ，陽性預測值為3.18%，陰性預測值為100%、準確度為73.21%。藉此量表，當能正確篩選出高危險性跌倒病患，並加強介入措施，以減少跌倒之意外發生。
關鍵字:跌倒、跌倒評估量表、住院病人、醫院

ABSTRACT
Development of an Evidence Based Assessment Tool for Hospitalized Patients with High Risks of Falls

Author: Pei-Ying Ho
Advisor: Miaofen Yen
Department of Nursing, College of Medicine, National Cheng Kung University

SUMMARY
The research purpose was to develop a high risk of falls assessment tool for use with inpatients in Taiwan. In the first stage the researcher undertook a systematic review of the literature, and worked with a fall team to identify the items most associated with a high risk of falls. Based on the results of this analysis, a new high risk of falls assessment tool was developed for inpatients in Taiwan. Three experts were invited to evaluate the content of each item, and then a pilot study was carried out to evaluate the tool. In the second stage of the study, 683 participants were recruited and assessed for high risks of falls. The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and accuracy (ACC) of the tool were calculated to assess its quality.
The expert validity and inter-rater reliability of the fall risk assessment tool were 1 and .957, respectively, both reaching acceptable levels. After validation, the fall risk assessment tool that included age was better than the one that did not, the best cut-off point was 2, the sensitivity was 100%, the specificity was 72.97%, the positive predictive value was 3.18, the negative predictive value was 100%, and the accuracy was 73.21%. The tool was thus able to accurately predict patients at high risk of falls, and so able to aid in the prevention of such accidents.

Key word: fall, fall assessment tool, inpatient, hospital

INTRODUCTION
Falling is the most common injury in hospitals. The Taiwan Patient Safety Reporting System reports that 34.8% of patients were not screened to assess whether they were at high-risk of falling, and that the fall assessment tools currently used by hospitals are not very sensitive or accurate. A review of the related literature and clinical projects indicates that most of the current fall assessment tools were developed and modified by the hospitals themselves. There is thus no uniform assessment scale that is used in Taiwan, and those that are now used generally have little basis in the literature, have undergone no validity or reliability testing, and have not been verified in clinical practice. Therefore, we decided to explore this issue and develop an appropriate fall risk assessment tool for use in Taiwan.

PURPOSE
The research purpose was to develop a high risk of falls assessment tool for use with inpatients in Taiwan.
METHODS
In the first stage, a systematic review of the literature on fall risk assessment tools was carried out. This was done by searching for the keywords ‘fall’, ‘assessment,’ ‘screen,’ ‘instrument,’ ‘scale,’ ‘tool,’ and ‘predict,’ in MEDLINE, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), HAPI, PubMed, The Cochrane Library, and the Chinese Electronic Periodical Services (CEPS) for the years 2000 to 2011, with the review restricted to English and Chinese language journals. Based on this analysis, a new high risk of falls assessment tool for inpatients in Taiwan was developed. Three experts were invited to evaluate the content of each item. A pilot study was done to evaluate the tool, in which three clinical nurses were invited to test the tool with five patients.
In the second stage of the study, 683 participants were recruited by stratified proportional sampling based on the hospital’s fall rates in the previous year, and then assessed for high risk of falls. The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive Value (NPV), and accuracy (ACC) of the tool were calculated to assess its quality.
RESULTS AND DISCUSSION
The expert validity and inter-rater’s reliability of the fall risk assessment tool were examined, and the results were 1 and .957, respectively, indicating that it has good validity and reliability. The best cut-off point was 1, based on the validation results, the sensitivity was 100%, the specificity was 62.92%, the positive predictive value was 2.34%, the negative predictive value was 100%, and the accuracy was 63.25%. When ‘age’ was also added to the tool, the results were improved, with the best cut-off point was 2, the sensitivity was 100%, the specificity was 72.97%, the positive predictive value was 3.18%, the negative predictive value was 100%, and the accuracy was 73.21%.
CONCLUSION
The results of this study show that the proposed assessment tool is reliable, and can accurately predict which inpatients are at high risk of falls, and thus help in preventing such accidents. 

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