Root Cause Analysis of Patient Samples Rejected by Laboratories: 21-Step Application Example

Year 2023, , 359 - 367, 21.09.2023

Büşra Arslan , Dilek Şahin

https://doi.org/10.33631/sabd.1188718

Abstract

Aim: Root cause analysis is widely used in healthcare services to analyze the causes of near misses and adverse events with a systematic approach. This study, it is aimed to determine the root causes of rejected samples, define corrective/preventive actions, and create an action plan that will help the implementation of the suggested remedial actions and evaluate their effectiveness.
Material and Methods: For the 21-step methodology, observation, interview, document and record review techniques were applied. The steps of the process were visualized with the flowchart technique and the reasons for rejected samples were analyzed with team members. The identified causes were visualized with the Fishbone Diagram technique, and the risk reduction strategies and improvement actions for rejected samples were determined by the Failure Mode Effect Analysis (FMEA) method. The Action Hierarchy tool was used to evaluate the power of improvement actions.
Results: The root causes of rejected samples were identified as inadequate orientation and training practices, lack of applicability of policies and procedures, lack of monitoring and evaluation, inefficient process flow and lack of equipment. A total of 11 improvement actions were determined and planned for these root causes. It was predicted that there will be an approximately 64.5% decrease in risk scores in general with the basic measures presented in the performed FMEA.
Conclusion: Overall, it was found that the 21-step methodology is suitable for determining root causes by offering detailed guidance.

Keywords

Root cause analysis, rejected samples, 21-step methodology

Laboratuvarlar Tarafından Reddedilen Hasta Numunelerinin Kök Neden Analizi: 21 Adım Uygulaması Örneği

Abstract

Amaç: Kök neden analizi, sağlık hizmetlerinde ramak kala ve istenmeyen olayların nedenlerini, sistematik bir yaklaşımla analiz etmek için yaygın olarak kullanılmaktadır. Bu çalışmada, reddedilen numunelerin kök nedenlerinin belirlemesi, düzeltici/önleyici eylemlerin tanımlaması ve önerilen iyileştirme eylemlerinin uygulanmasına ve etkililiğinin değerlendirilmesine yardımcı olacak bir eylem planının oluşturulması amaçlanmıştır.
Gereç ve Yöntemler: 21 adım uygulaması yönteminde gözlem, görüşme, doküman ve kayıt inceleme teknikleri uygulanmıştır. Akış Şeması tekniği ile süreç adımları görselleştirilmiş ve reddedilen numunelerin nedenleri ekip üyeleri ile analiz edilmiştir. Tespit edilen nedenler, Balık Kılçığı Diyagramı tekniği ile görselleştirilmiş ve reddedilen numuneler için risk azaltma stratejileri ve iyileştirme eylemleri Hata Türleri ve Etkileri Analizi yöntemi ile belirlenmiştir. İyileştirme eylemlerinin gücünü değerlendirmek için Eylem Hiyerarşisi aracı kullanılmıştır.
Bulgular: Reddedilen numunelerin kök nedenleri; oryantasyon ve eğitim uygulamaları yetersizliği, politika ve prosedürlerin uygulanabilirliği, izleme ve değerlendirme eksikliği, verimsiz süreç akışı ve ekipman eksikliği olarak belirlenmiştir. Bu kök nedenlere yönelik toplam 11 iyileştirme eylemi belirlenmiş ve planlanmıştır. Gerçekleştirilen Hata türleri ve Etkileri Analizinde sunulan temel önlemlerle genel olarak risk puanlarında yaklaşık %64,5’lik azalış olacağı öngörülmüştür.
Sonuç: Genel olarak 21 adım yönteminin ayrıntılı rehberlik sunarak kök nedenleri belirlemede uygun olduğu bulunmuştur.

Keywords

kök neden analizi, reddedilen numuneler, 21 adım yöntemi

References

• Rosier AS, Tibor LC, Turner MA, Phillips CJ, Kurup AN. Improving root cause analysis of patient safety events in radiology. RadioGraphics. 2020; 40(5): 1434-40.
• Eom SH, Lee SK. A study on analysis of laboratory accident with root cause analysis. J Korean Inst Gas. 2010; 14(4): 4-8.
• Dolansky MA, Druschel K, Helba M, Courtney K. Nursing student medication errors: a case study using root cause analysis. J Prof Nurs. 2013; 29(2): 102-8.
• Choi KR, Ragnoni JA, Bickmann JD, Saarinen HA, Gosselin AK. Health behavior theory for pressure ulcer prevention root-cause analysis project in critical care nursing. J Nurs Care Qual. 2016; 31(1).
• Hampe H. Online simulation of a root cause analysis for graduate health administration students. Clin Simul Nurs. 2017; 13(8): 398-404.
• Sluggett JK, Lalic S, Hosking SM, Ilomӓki J, Shortt T, McLoughlin J, et al. Root cause analysis of fall-related hospitalisations among residents of aged care services. Aging Clin Exp Res. 2020; 32(10): 1947-57.
• Sluggett JK, Lalic S, Hosking SM, Ritchie B, McLoughlin J, Shortt T, et al. Root cause analysis to identify medication and non-medication strategies to prevent infection-related hospitalizations from Australian Residential Aged Care Services. Int J Environ Res Public Health. 2020; 17(9): 3282.
• Sturgill B, Patolia H, Gushiken A, Gomez dela Espriella M, Baffoe-Bonnie AW. Braden score may be associated with time to onset of catheter-associated urinary tract infection in high-risk patients: lessons learned from a root cause analysis tool. Am J Infect Control. 2019; 47(3): 343-5.
• Hagley GW, Mills PD, Shiner B, Hemphill RR. An analysis of adverse events in the rehabilitation department: using the veterans affairs root cause analysis system. Phys Ther. 2018; 98(4): 223-30.
• Green SF. The cost of poor blood specimen quality and errors in preanalytical processes. Clin Biochem. 2013; 46(13-14): 1175-9.
• Snydman LK, Harubin B, Kumar S, Chen J, Lopez RE, Salem DN. Voluntary electronic rreporting of laboratory errors. Am J Med Qual. 2012; 27(2): 147-53.
• Grecu DS, Vlad DC, Dumitrascu V. Quality indicators in the preanalytical phase of testing in a stat laboratory. Lab Med. 2014; 45(1): 74-81.
• Plebani M. Quality indicators to detect pre-analytical errors in laboratory testing. Clin Biochem Rev. 2012; 33(3): 85-8.
• Lippi G, Guidi GC. Risk management in the preanalytical phase of laboratory testing. Clin Chem Lab Med. 2007; 45(6): 720-7.
• Celik S, Seyrekel T, Arpa M. How to decrease the rejection rates: reasons of sample rejection and solutions. Turkish J Biochem. 2018; 43(1): 98-100.
• Cadamuro J, Simundic AM, Ajzner E, Sandberg S. A pragmatic approach to sample acceptance and rejection. Clin Biochem. 2017; 50(10-11): 579-81.
• Zeytinli Akşit M, Yalçın H, Tonbaklar Bilgi P, Avcı R, Karademirci İ, Buzkan E, et al. Evaluation of rejection causes based from preanalytic origin in our emergency laboratory. J Tepecik Educ Res Hosp. 2016; 26(1): 41-5.
• Güvenç Y. Sample rejection in outpatient, inpatient and emergency bloods: training and new approaches. Türk Klin Biyokim Derg. 2017; 15(3): 119-28.
• The Joint Commission. Root cause analysis in health care: tools and techniques. 6th ed. Joint Commission Resources (JCR); 2017.
• Kaewlai R, Abujudeh HH. Root cause analysis (RCA) and health care failure mode and effect analysis (HFMEA). In: Abujudeh HH, Bruno MA, editors. Quality and Safety in Radiology. Oxford University Press; 2012. p. 39.
• Jensen EB. Root cause analysis: compendium for use by patient safety officers and others responsible for conducting root cause analysis of adverse events. Copenhagen: Dansk Selskab for Patientsikkerhed; 2004.
• National Patient Safety Foundation. RCA 2 improving root cause analyses and actions to prevent harm. Boston: National Patient Safety Foundation; 2016.
• The Joint Commission. Most commonly reviewed sentinel event types. 2020. https://www.jointcommission.org/-/media/tjc/documents/resources/patient-safety-topics/sentinel-event/most-frequently-reviewed-event-types-2020.pdf
• Kaya S, Aydan M. Failure mode and effects analysis (FMEA): an application in a university hospital. Hacettepe J Heal Adm. 2017; 20(4): 475-502.
• Kurutkan MN. Ölümcül hataları engelleme programı: sentinel olaylara yaklaşım modelleri. Sageya; 2008.
• Rooper L, Carter J, Hargrove J, Hoffmann S, Riedel S. Targeting rejection: analysis of specimen acceptability and rejection, and framework for identifying interventions in a single tertiary healthcare facility. J Clin Lab Anal. 2017; 31(3): 1-8.
• Jones BA, Calam RR, Howanitz PJ. Chemistry specimen acceptability: a College of american pathologists Q- Probes study of 453 laboratories. Arch Pathol Lab Med. 1997; 121(1): 19.
• Zarbo RJ, Jones BA, Friedberg RC, Valenstein PN, Renner SW, Schifman RB, et al. Q-tracks: a college of american pathologists program of continuous laboratory monitoring and longitudinal performance tracking. Arch Pathol Lab Med. 2002; 126(9): 1036-44.
• Sağlık Hizmetleri Genel Müdürlüğü (SHGM). Gösterge Yönetimi Rehberi. Ankara: Sağlık Hizmetleri Genel Müdürlüğü (SHGM); 2023.
• Erkan I, İlik Y, Ulutin HC. Evaluation of rejected samples from emergency laboratories with a focus on patient rights. Turan-CSR Int Sci Peer-Reviewed Ref J. 2017; 9(33): 63-7.
• Ekinci A. The analysis of laboratory sample rejections and the effect of training on the rejection rate. Van Med J. 2019; 26(1): 79-84.
• Arıkan Z, Aksu M, Madenci ÖÇ. Preanalytical errors of specimens sent from primary health care centers to public health laboratories. Mersin Univ J Heal Sci. 2016; 9(1): 1-8.
• Öz L, Buldu S, Koçer D, Karakükcü Ç. Analysis of pre-preanalytical errors in the clinical biochemistry laboratory of Kayseri Training and Research Hospital. J Turkish Clin Biochem. 2016; 14(1): 6-11.
• Atay A, Demir L, Cuhadar S, Saglam G, Unal H, Aksun S, et al. Clinical biochemistry laboratory rejection rates due to various types of preanalytical errors. Biochem Medica. 2014; 24(3): 376-82.
• Aksun S, Erbak Yılmaz H. Accurate and timely medical biochemistry laboratory results and pre-analytical errors. J Contin Med Educ. 2019; 353-8.
• Çokluk E, Şekeroğlu MR, Tuncer FB. Determination of six sigma level with pareto analysis of sample rejection reasons. J Turkish Clin Biochem. 2020; 18(1): 33-41.
• Korkmaz Ş. Evaluation of rejected sample rates using six sigma method. J Turkish Clin Biochem. 2020; 18(1): 17-25.
• Aydın Ö, Göl G, Gönen Dede F, Akın D. Investigation of blood collection errors in the preanalytical process. Turkish J Clin Lab. 2017; 8(4): 146-51.
• Sinici Lay I, Pınar A, Akbıyık F. Classification of reasons for rejection of biological specimens based on pre-preanalytical processes to identify quality indicators at a university hospital clinical laboratory in Turkey. Clin Biochem. 2014; 47(12): 1002-5.
• Simundic A-M, Nikolac N, Vukasovic I, Vrkic N. The prevalence of preanalytical errors in a croatian ISO 15189 accredited laboratory. Clin Chem Lab Med. 2010; 48(7): 1009-14.
• Ercan Ş. The evaluation of rejected samples prevalence using six sigma. J Turkish Clin Biochem. 2016; 14(1): 32-9.
• Kulkarni S, Ramesh R, Srinivasan AR, Silvia Crwd. Evaluation of preanalytical quality indicators by six sigma and pareto`s principle. Indian J Clin Biochem. 2018; 33(1): 102-7.
• Carlson RO, Amirahmadi F, Hernandez JS. A primer on the cost of quality for improvement of laboratory and pathology specimen processes. Am J Clin Pathol. 2012; 138(3): 347-54.
• Lippi G, Salvagno GL, Montagnana M, Franchini M, Guidi GC. Phlebotomy issues and quality improvement in results of laboratory testing. Clin Lab. 2006; 52(5-6): 217-30.
• Da Rin G. Pre-analytical workstations: a tool for reducing laboratory errors. Clin Chim Acta. 2009; 404(1): 68-74.
• Aykal G, Yeğin A, Aydın Ö, Yılmaz N, Ellidağ HY. The impact of educational interventions on reducing the rejection rates in the preanalytical phase. Turkish J Biochem. 2014; 39(4): 562-6.
• Aboumrad M, Fuld A, Soncrant C, Neily J, Paull D, Watts B V. Root cause analysis of oncology adverse events in the Veterans Health Administration. J Oncol Pract. 2018; 14(9): 579-90.
• Shojania KG, Duncan BW, McDonald KM, Wachter RM, Markowitz AJ. Making health care safe: a critical analysis of patient safety practices. Am J Cosmet Surg. 2001; 18(4): 215-24.