簡易檢索 / 詳目顯示

研究生: 黃晶薇
Huang, Jing-Wei
論文名稱: 檢驗科血液組之人為失誤分析探討與改善
Analysis and Improvement of Human Errors Associated with Hematology in Laboratory Medicine
指導教授: 盧俊銘
Lu, Jun-Ming
口試委員: 邱銘傳
Chiu, Ming-Chuan
蕭育霖
Hsiao, Yu-Lin
學位類別: 碩士
Master
系所名稱: 工學院 - 工業工程與工程管理學系
Department of Industrial Engineering and Engineering Management
論文出版年: 2017
畢業學年度: 105
語文別: 中文
論文頁數: 110
中文關鍵詞: 病人安全人為失誤醫療照護失效模式與效應分析根本原因分析工作負荷
外文關鍵詞: patient safety, human error, Healthcare Failure Mode and Effect Analysis (HFMEA), Root Cause Analysis (RCA), workload
相關次數: 點閱:2下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 據財團法人醫院評鑑暨醫療品質策進會之報告指出,全臺灣各醫療機構常見的病人安全事件之前五名依序是藥物事件、跌倒事件、管路事件、傷害行為事件、檢查檢驗事件,由於病人因素為個人主觀意識行為,較難有效控制,而藥物事件在過去已有許多研究針對此部分進行改善與討論,因此,本研究將針對檢查檢驗事件深入討論,旨在降低人為失誤的發生、進而提升病人安全。
    研究對象為新竹某區域教學醫院檢驗科血液組的日、夜班醫事檢驗師共16名。首先透過從旁觀察並以攝影機側錄,分析其檢驗作業之操作流程,隨後根據2014年與2015年的檢驗報告之統計資料找出發生頻率較高之報告更改原因,並採取根本原因分析(Root Cause Analysis, RCA)追溯事件發生之經過,最後針對根本原因擬定改善策略。另一方面,利用醫療照護失效模式與效應分析(Healthcare Failure Mode and Effect Analysis, HFMEA)進行預應式的風險管理,根據潛在失效模式的行動優先次序擬定改善對策。此外,亦利用NASA工作負荷指標(Task Load Index)評估工作負荷以及其與作業之間的關係。
    現況分析之結果顯示:報告更改件數並未隨著月份而有特定的波動趨勢,而報告更改原因則以「醫檢人員檢驗操作步驟錯誤」、「結果輸入錯誤」與「結果判讀錯誤」依序為發生頻率最高之前三位。針對發生頻率最高之報告更改原因查閱詳細的相關資料後,再就此不良事件進行根本原因分析,調查結果為「未明確定義好更換試藥的時間點因而導致報告更改」,透過工程改善即可防止該事件發生。HFMEA之結果則指出「脂血檢體使用氣送系統傳送導致溶血」、「檢體凝固」以及「計數孔阻塞」三項失效模式必須改善,故各自提出方案並透過專家意見予以評估確認成效。
    另一方面,醫檢師自覺工作負荷之結果顯示人工閱片、體液檢驗的工作負荷最高,血液沉降速率檢驗、血色素電泳檢驗則為最低,可推論作業項目涉及手工操作時會使醫檢師之工作負荷增加。此外,男、女性醫檢師之間的工作負荷並無顯著差異,但年資較深、日班的醫檢師之工作負荷較年資淺、夜班的醫檢師高。工作負荷與風險評估的一致性分析結果則顯示:隨著操作步驟的增加,工作負荷確實會隨之提高,但風險較高的作業未必伴隨較高的工作負荷,且工作負荷高的作業項目並未反映出較多的報告更改件數,因此工作負荷可能不是造成報告更改的原因。
    藉由現況分析、HFMEA、RCA以及NASA工作負荷指標等工具,本研究發現檢驗科血液組的報告更改非與作業的風險及工作負荷直接相關,因此後續宜利用其他分析工具找出更可靠的預測因子;不過,這些分析的結果仍有助於改善檢驗流程,預防失誤的發生。此外,本研究只針對檢驗科血液組進行討論,未來可再延伸至不同的工作組別,提供臨床實驗室更廣泛的改善參考。


    According to the report of Joint Commission of Taiwan, the top five patient safety incidents in Taiwan are medication errors, patient fall events, tube incidences, injury behavior events, an inspection events. The factors of patients are mainly due to the behavior of subjective consciousness, so it is difficult to be controlled effectively. Besides, medication errors have been widely studied and improved in the past. Therefore, this study focuses on the inspection events, so as to reduce human error and enhance patient safety.
    A total of 16 medical technologists in a regional teaching hospital in Hsinchu were surveyed for their inspection work associated with Hematology in laboratory medicine. The operating processes were observed and recorded by a digital video recorder. After that, according to the records of test reports issued in 2014 and 2015, the most frequent causes of report correction were identified, followed by Root Cause Analysis (RCA) for potential strategies of improvement. On the other hand, the Healthcare Failure Mode and Effect Analysis (HFMEA) was carried out for prospective risk management, which helps formulate improvements according to the action priority of the potential failure modes. Moreover, the relationship between medical technologists’ workload and operations was evaluated by using NASA Task Load Index.
    The results of current status analysis showed that there was no monthly trend found in numbers of report correction. Besides, the major reasons for report correction are "inspection procedure error," "result input error," and "result identification error." After that, the most frequent cause of report correction was analyzed by Root Cause Analysis. The result of RCA indicated that unclear instructions of when to change the reagent may lead to report correction, which needs engineering improvement. In addition, the result of HFMEA showed that there were three failure modes requiring improvement, including" lipaemic samples being delivered by pneumatic tube system," " sample clot," and " the aperture of coulter counter being blocked." Thus, the improvement for each failure mode was proposed and evaluated through expert opinions.
    According to the results of medical technologists’ subjective workload, "Blood film" and "Body fluid" had the highest workload, while "Erythrocyte sedimentation rate" and "Hemoglobin electrophoresis" had the lowest workload. It can be inferred that medical technologists’ workload increases with manual tasks. In addition, there was no significant difference in workload between male and female medical technologists. But when it comes to work experience and work shift, senior medical technologists’ workload are higher than junior ones, while those on day shift are with higher workload than those on night shift. Further, it was found that as the number of operating steps increases, the workload becomes higher. However, work processes with higher risks are not always accompanied with a higher workload. Moreover, larger number of report correction was not found in work processes with higher workload. Thus workload may not be a direct cause of the report correction.
    The overall results of current status analysis, HFMEA, RCA and NASA Task Load Index showed that the reason for report correction is not directly related to the risks and workload of work processes. In order to effectively respond to the frequent causes of report correction, further studies applying other analyzing tools is therefore suggested. However, the analysis results of this study did help identify improvement strategies to prevent human errors. Besides, since this study was limited to Hematology in laboratory medicine, further studies regarding other work groups would be worthwhile for a broader range of clinical improvement.

    摘要 6 Abstract 7 一、緒論 9 1.1. 研究背景與動機 9 1.2. 研究目的與範圍 11 1.3. 研究架構 12 二、文獻探討 15 2.1. 檢驗科血液組作業中的人為失誤 15 2.2. 檢驗科血液組中人為失誤的可能原因 17 2.3. 風險評估與管理 19 2.3.1. 根本原因分析(RCA) 19 2.3.2. 失效模式與效應分析(FMEA) 21 2.3.3. 醫療照護失效模式與效應分析(HFMEA) 21 2.3.4. 失誤樹分析(FTA) 22 2.3.5. 小結 23 2.4. 人員之工作負荷 23 2.5. 小結 24 三、研究方法 26 3.1. 現況分析 28 3.2. 醫療照護失效模式與效應分析(HFMEA) 29 3.2.1. 組成團隊 29 3.2.2. 繪製流程圖 30 3.2.3. 執行危害分析 30 3.2.4. 擬定改善策略 32 3.3. 根本原因分析(RCA) 32 3.3.1. 組成團隊 33 3.3.2. 蒐集資料 33 3.3.3. 檢視近端原因 34 3.3.4. 找出根本原因 34 3.3.5. 擬定改善策略 35 3.4. 工作負荷分析 35 3.5. 改善對策之評估 37 四、研究結果 41 4.1. 現況分析結果 41 4.1.1. 作業項目 41 4.1.2. 報告更改資料 47 4.2. HFMEA結果 50 4.2.1. 組成團隊與繪製流程圖 50 4.2.2. 發想失效模式 53 4.2.3. 執行危害分析評分 53 4.2.4. 決策樹分析 64 4.2.5. 擬定改善策略 66 4.3. RCA結果 67 4.4. 醫檢師自覺工作負荷 71 4.4.1. 單因子變異數分析 71 4.4.2. 工作負荷來源 75 4.4.3. 群體之間的比較 76 五、討論 81 5.1. 報告更改資料總數變化趨勢 81 5.2. 風險評估工具改善建議討論 81 5.2.1. HFMEA評估的指標 81 5.2.2. RCA改善 83 5.3. 工作負荷結果討論 86 5.4. 工作負荷與風險評估的一致性分析 87 六、結論 88 6.1. 研究發現 88 6.2. 貢獻與應用 89 6.3. 研究限制與未來方向 89 參考文獻 90 中文文獻 90 英文文獻 90 附錄 93 附錄一 人體研究倫理審查委員會同意臨床試驗證明書 93 附錄二 工作負荷問卷 94

    中文文獻
    1.三軍總醫院松山分院急診室:降低急診檢體退檢率(2013)。取自:http://health.gov.taipei/Portals/0/醫護管理處/品質管理股/08降低急診檢體退檢率.pdf
    2.財團法人醫院評鑑暨醫療品質策進會:台灣病人安全通報系統2014年年報(2015)。臺北,醫策會,61-65。取自:http://www.patientsafety.mohw.gov.tw/Content/Downloads/List01.aspx?SiteID=1&MmmID=621273303702500244
    3.張錦標、祝年豐、董潔、顏瓊姿 (2000)。台灣地區某系統醫院醫事檢驗人員工作壓力及其因應之研究. 中華職業醫學雜誌, 7(2), 77-83。
    4.張曉鳳、林育秀、王素美、饒育華、吳芯霈、梁亞文 (2010)。醫療服務人員工作壓力源與離職意願之探討. 澄清醫護管理雜誌, 6(1), 21-31。
    5.林麗珍、陳淑嬌、李麗雲、蘇麗惠、畢耜春、李小凰、林綽娟 (2007)。護理人員對給藥錯誤原因看法之初探. 中台灣醫學科學雜誌, 12(3), 157-165。
    6.謝孟真 (2014)。美國國家航空暨太空總署工作心智負荷指標之中文化與信效度初探,長庚大學護理學系碩士論文。
    英文文獻
    1.Astion, Michael L., Shojania, Kaveh G., Hamill, Tim R., Kim, Sara. and Ng, Valerie L. (2003) Classifying laboratory incident reports to identify problems that jeopardize patient safety. American Journal of Clinical Pathology, 120(1): 18-26.
    2.Bagian, James. P., Gosbee, John, Lee, Caryl. Z., Williams, Linda, McKnight, Scott. D., and Mannos, Dea. M. (2002) The Veterans Affairs root cause analysis system in action. The Joint Commission Journal on Quality and Patient Safety, 28(10): 531-545.
    3.Barrett, Linda, and Yates, Patsy (2002) Oncology/haematology nurses: a study of job satisfaction, burnout, and intention to leave the specialty. Australian Health Review, 25(3): 109-121.
    4.Bonini, Pierangelo, Plebani, Mario, Ceriotti, Ferruccio, and Rubboli, Francesca (2002) Errors in laboratory medicine. Clinical chemistry, 48(5): 691-698.
    5.Boone, D. J. (1990) Comment on Dr Houwen's paper Random errors in haematology tests. Clinical and laboratory haematology, 12: 169-170.
    6.Chambers, Anne. M., Elder, Jennifer and D ST J, O. Reilly (1986) The blunder-rate in a clinical biochemistry service. Annals of Clinical Biochemistry: An international journal of biochemistry in medicine, 23(4): 470-473.
    7.Chiozza, Maria L. and Ponzetti, Clemente (2009) FMEA: a model for reducing medical errors. Clinica Chimica Acta, 404(1): 75-78.
    8.Coutinho, John. S. (1964) DIVISION OF ENGINEERING: FAILURE‐EFFECT ANALYSIS. Transactions of the New York Academy of Sciences, 26(5 Series II): 564-584.
    9.DeRosier, Joseph, Stalhandske, Erik, Bagian, James. P., and Nudell, Tina (2002) Using health care failure mode and effect analysis™: the VA National Center for Patient Safety's prospective risk analysis system. The Joint Commission Journal on Quality and Patient Safety, 28(5): 248-267.
    10.Ekaette, Edidiong, Lee, Robert C., Cooke, David L., Iftody, Sandra, and Craighead, Peter (2007) Probabilistic fault tree analysis of a radiation treatment system. Risk analysis,27(6): 1395-1410.
    11.Goodman, Eric. A., and Boss, R. Wayne (2002) The phase model of burnout and employee turnover. Journal of health and human services administration, 33-47.
    12.Gorman, Jamie C., Cooke, Nancy J. and Winner, Jennifer L. (2006) Measuring team situation awareness in decentralized command and control environments. Ergonomics, 49(12-13): 1312-1325.
    13.Hyman, William. A., and Johnson, Erin (2008) Fault tree analysis of clinical alarms.Journal of Clinical Engineering, 33(2): 85-94.
    14.Kalra, Jawahar (2004) Medical errors: impact on clinical laboratories and other critical areas. Clinical biochemistry, 37(12): 1052-1062.
    15.Kara, Hasan, Bayir, Aysegul, Ak, Ahmet, Degirmenci, Selim, Akinci, Murat, Agacayak, Ahmet, Marcil, Emine, and Azap, Melih (2014) Hemolysis associated with pneumatic tube system transport for blood samples. Pakistan journal of medical sciences, 30(1): 50-53.
    16.Lapworth, Ruth and Teal, Teresa K. (1994) Laboratory blunders revisited. Annals of Clinical Biochemistry: An international journal of biochemistry in medicine, 31(1): 78-84.
    17.Nagamine, Janet and Williams, Mark (2005) Quality tools: root cause analysis (RCA) and failure modes and effects analysis (FMEA). Hospitalist, 9: 19-22.
    18.NASA Human Performance Research Group (1987) Task Load Index (NASA-TLX) v1. 0. NASA Ames Research Centre.
    19.Norman, Donald. A. (1981) Categorization of action slips. Psychological Review, 88, 1-15.
    20.Norman, Donald. A. (1988) The Psychological of everyday things. New York: Basic.
    21.Nutting, Paul. A., Main, Deborah. S., Fischer, Paul. M., Stull, Tina. M., Pontious, Mike, Seifert, Milton, Boone, Joe and Holcomb, Sherry (1996) Problems in laboratory testing in primary care. Jama,275(8): 635-639.
    22.Parker, Donald F. and DeCotiis, Thomas A. (1983) Organizational determinants of job stress. Organizational behavior and human performance, 32(2): 160-177.
    23.Plebani, Mario and Carraro, Paolo (1997) Mistakes in a stat laboratory: types and frequency. Clinical Chemistry, 43(8): 1348-1351.
    24.Plebani, Mario (2006) Errors in clinical laboratories or errors in laboratory medicine? Clinical Chemical Laboratory Medicine, 44(6): 750-759.
    25.Plebani, Mario (2007) Errors in laboratory medicine and patient safety: the road ahead. Clinical Chemical Laboratory Medicine, 45(6): 700-707.
    26.Reason, James (1984) Lapses of attention. In R. Parasuraman and R. Davies (Eds.), Varieties of attention. New York: Academic Press.
    27.Reason, James (1990) Human error. Cambridge, England: Cambridge University Press.
    28.Reason, James (2000) Human error: models and management. Western Journal of Medicine, 172(6), 393.
    29.Reason, James (2008) The human contribution: Unsafe acts, accidents and heroic recoveries. Burlington VT: Ashgate.
    30.Rooney, James J. and Heuvel, Lee N. Vanden (2004) Root cause analysis for beginners.Quality progress, 37(7): 45-56.
    31.Salas, Eduardo, Prince, Carolyn, Baker, David P. and Shrestha, Lisa (1995) Situation awareness in team performance: Implications for measurement and training. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1): 123-136.
    32.Shaqdan, Khalid, Aran, Shima, Besheli, Laleh D., and Abujudeh, Hani (2014) Root-cause analysis and health failure mode and effect analysis: two leading techniques in health care quality assessment. Journal of the American College of Radiology, 11(6): 572-579.
    33.Sönmez, Betül, Oğuz, Zeynep, Kutlu, Leman, and Yıldırım, Aytolan (2016) Determination of nurses' mental workloads using subjective methods. Journal of Clinical Nursing. DOI: 10.1111/jocn.13476
    34.Vaughan, Sophie, Bate, Tate, and Round, Jonathan (2012) Must we get it wrong again? A simple intervention to reduce medical error. Trends in Anaesthesia and Critical Care, 2(3): 104-108.
    35.Weigl, M., Müller, A., Vincent, C., Angerer, P., and Sevdalis, N. (2012) The association of workflow interruptions and hospital doctors' workload: a prospective observational study. BMJ quality & safety, 21(5): 399-407.
    36.Weinger, Matthew. B., Herndon, Oliver. W., Zornow, Mark. H., Paulus, Martin. P., Gaba, David. M., and Dallen, Larry. T. (1994) An objective methodology for task analysis and workload assessment in anesthesia providers. Anesthesiology, 80(1): 77-92.
    37.Wickens, Christopher. D. (1984) Engineering psychology & human performance. Columbus, OH: Merrill.
    38.Young, Gloria, Zavelina, Lyubov, and Hooper, Vallire (2008) Assessment of workload using NASA Task Load Index in perianesthesia nursing. Journal of PeriAnesthesia Nursing, 23(2): 102-110.

    QR CODE