The authors have experimented the application of the Failure Mode and Effect Analysis (FMEA) technique in a clinical laboratory. FMEA technique allows: a) to evaluate and measure the hazards of a process malfunction, b) to decide where to execute improvement actions, and c) to measure the outcome of those actions. A small sample of analytes has been studied: there have been determined the causes of the possible malfunctions of the analytical process, calculating the risk probability index (RPI), with a value between 1 and 1,000. Only for the cases of RPI &gt; 400, improvement actions have been implemented that allowed a reduction of IPR values between 25% to 70% with a costs increment of &lt;1%. FMEA technique can be applied to the processes of a clinical laboratory, even if of small dimensions, and offers a high potential of improvement. Nevertheless, such activity needs a thorough planning because it is complex, even if the laboratory already operates an ISO 9000 Quality Management System

BOCCIA, GIOVANNI

BRUNETTI L

CAPUNZO, Mario

CAVALLO, Pierpaolo

PIZZUTI S.

English

Archivio della Ricerca - Università di Salerno

A FMEA Clinical Laboratory Case Study:How to Make Problems and ImprovementsMeasurableMARIO CAPUNZO, PIERPAOLO CAVALLO, GIOVANNI BOCCIA, LUIGI BRUNETTI, ANDSANTE PIZZUTIThe authors have experimented the application of the Failure Mode and Effect Analysis (FMEA) technique in a clinical laboratory.FMEA technique allows: a) to evaluate and measure the hazards of a process malfunction, b) to decide where to executeimprovement actions, and c) to measure the outcome of those actions. A small sample of analytes has been studied: there havebeen determined the causes of the possible malfunctions of the analytical process, calculating the risk probability index (RPI), with avalue between 1 and 1,000.AQ1 Only for the cases of RPI > 400, improvement actions have been implemented that allowed a reductionof IPR values between 25% to 70% with a costs increment of <1%. FMEA technique can be applied to the processes of a clinicallaboratory, even if of small dimensions, and offers a high potential of improvement. Nevertheless, such activity needs a thoroughplanning because it is complex, even if the laboratory already operates an ISO 9000 Quality Management System.BACKGROUNDFailure Mode and Effect Analysis (FMEA), togetherwith other techniques, such as Fault Tree Analysis andEvent Tree Analysis, is one of the process risk evaluationtechniques that can be used to identify AQ2and measurethe malfunction hazards of a process (1). FMEA isbased on a preliminary study of safety and functionalityof a component, part, product, or productive phase,developed taking into account its possible failuremodes, the effects, the critical points, and the causesthat have generated them.FMEA is based on a preliminary study ofsafety and functionality of a component, part,product, or productive phase, developedtaking into account its possible failuremodes, the effects, the critical points, and thecauses that have generated them.The FMEA target is to measure the risk probabilityof any possible failure or nonconformity (NC), by anindex, called risk probability index (RPI), that is theproduct of:A the ‘‘severity index’’ (SI), from 1 to 10A the ‘‘probability index’’ (PI), from 1 to 10A the ‘‘detectability index’’ from 1 to 10.The RPI can go from a minimum of 1 (1  1  1) toa maximum of 1,000 (10  10  10). In general, theimprovement interventions start from the NC/failureswith RPI > 400, and for each intervention it is possible toevaluate the RPI reduction, expected and actual, and thecost for RPI unit reduction.We have evaluated an experimental application ofthe FMEA method to some analytical processes of aclinical laboratory.January/February 2004 1Mario Capunzo, M.D., is a Professor of Hygiene and Directorof the Laboratory of Hygiene and Occupational Medicine atthe University of Salerno, Italy. His research interests focus onthe epidemiology and diagnostics of occupational illnesses,and on infectious diseases prevention.Pierpaolo Cavallo, M.D., is a Contract Professor at theUniversity of Salerno, Italy. He works as Director of IppocraticaDiagnostica Research Center, Salerno, Italy, and is a QualityManagement Systems Lead Auditor registered by IRCA (UK).His research interests include quality management systems inhealth organizations, health economics, and quantitativemanagement methods development.Giovanni Boccia, M.D., is a Specialist in Hygiene andPreventive Medicine and a coworker of the Laboratory ofHygiene and Occupational Medicine at the University ofSalerno, Italy. His research interests focus on the epidemiologyand diagnostics of occupational diseases.Luigi Brunetti, Biologist, is the Chief Technician of theLaboratory of Hygiene and Occupational Medicine at theUniversity of Salerno, Italy. His research interests focus onlaboratory diagnostics of occupational illnesses and qualityimprovement.Sante Pizzuti, Chemist, is the Director of the ChemicalLaboratory at Ippocratica Diagnostica Research Center,Salerno, Italy, where he is attending the ISO 17025accreditation process. His research interests focus onlaboratory organization and quality improvement.Copyright # 2004 by Clinical Laboratory ManagementAssociation Inc.METHODSThe FMEA method has been implemented on threeanalytical processes of a clinical laboratory operatingin Salerno, Italy. The laboratory operates a qualitymanagement system according to the ISO 9001:2000standard, executes about 160,000 tests/year of clinicalchemistry, immunoassay, microbiology, and hematology.The laboratory team consists of 13 people (three MDs,two biologists, three technologists, and five clerical/administrative and auxiliary personnel).The study has been executed on a small sample, madeby only three clinical chemistry analytes (glucose, totalcholesterol, total bilirubin), from January to April 2002.The FMEA method has been implemented with referenceto the FMEA Manual (2), adapting the activities and thedocuments to the clinical laboratory processes.FMEA IMPLEMENTATION BY PHASESAs the results have been very similar for all of thethree analytes, we present, as an example, the glucoseAQ3(S-glucose) results. A part of the FMEA format ofS-glucose is reported in Table 1.Phase 1The first phase was made by collecting all theavailable informations about the analytical activities forthe three analytes. The analytical process only, and notthe pre-analytical and the post-analytical phases, has beendrawn, and, on its basis, have been reviewed andclassified all the failures/NC observed for the analyte.Phase 2According to the process analysis, we have identifiedthe function of each component of the analytical process,including reagent, sample, calibrator, and instrument(Table 1, column 1).Phase 3For each component of the analytical process, we haveidentified the effects of the component’s failure on thefinal result of the process (Table 1, column 2).Phase 4We assigned an SI value (1–10) to each failure modeeffect. The value has been assigned according to Table 2.In our example (Table 1, column 4), to all the failuremodes malfunction of reagent (MFR) the SI assignedwas 9, as a malfunctioning reagent not discovered in time,can produce an analytical result useless or dangerousfor the patient. AQ4For the case of malfunction of calibrator(MFC), the assigned SI value was 8, as the MFC makesimpossible to execute the analytical run and usually alertsthe operator to check the calibrator and/or repeat thecalibration run.Table 1FMEA format for S-glucose chemicals, reagent, and calibratorLOGOFMEAFormat Project leader DateFMEA name: S-Glucose Description: S-Glucoseon ILAB 500Component group: chemicalsFMEA N. Component code: 01/02 Component description: reagent/calibrator1REF. N.2FAILUREMODE3FAILUREMODEEFFECT4SI5FAILUREMODECAUSE6PI7CONTROLMEASURE8DI9RPI10PROPOSEDACTION01.01 Malfunctionof reagentMFRUselessresult9 Expired 3 Checkexpirationdate1 27 None01.02 Malfunctionof reagentMFRUselessresult9 NC storagetemperature6 Visualcheckof reagent10 540 Add temperaturemonitoring system01.03 Malfunctionof reagentMFRUselessresult9 Contaminated 8 QC beforerun10 720 Add QC after run02.01 Malfunctionof calibratorMFCCalibrationfailure8 Expired 3 Checkexpirationdate2 48 None02.02 Malfunctionof calibratorMFCCalibrationfailure8 NC storagetemperature6 Visualcheck ofcalibrator2 96 None02.03 Malfunctionof calibratorMFCCalibrationfailure8 Contaminated 8 Visualcheck ofcalibrator2 128 Freeze sigle dosesand use once2 Clinical Leadership & Management ReviewA FMEA Clinical Laboratory Case StudyPhase 5In this phase, to each failure mode an effect has beenlinked to its possible causes. In our example, to MFR andMFC have been coupled the causes (Table 1, column 5),to obtain the couples ‘‘failure-cause.’’Phase 6The PI (1–10) has been assigned to each couple. Thecriteria to assign PI are reported in Table 3.The PI has been determined on the basis of the frequencyof each failure/NC as reviewed in Phase 1. In our example(Table 1), the PI of expiration of the reagent or the calibratorwas 3 because the internal procedure was to eliminate thereagent/calibrator the week before its expiration, so therewas a very low likelihood to find an expired reagent orcalibrator on board an analyzer, whereas the PI ofcontamination of the reagent was 8 because its frequency infailure/NC review was approximately 1%.Phase 7In this phase, the control measures actually usedwere reported, for each couple ‘‘cause-failure.’’ In ourexample (Table 1, column 7), for the item 02.03,corresponding to the MFC due to contamination of thecalibrator, there was only a visual inspection measureactivated at the moment of the FMEA.Phase 8In this phase, each couple ‘‘failure-cause,’’ withreference to its actual control measure, has been assigneda value of detectability index (1–10). This index isassigned according to:a) the capability to keep the user from receiving anonconforming productb) the perception of the defect from the user.The values range from 1 (the user does not receive anonconforming product or does not notice the effect)to 10 (the failure/NC is not detectable, so the user willsuffer all its consequences).Phase 9In this phase, the RPI (1–1,000) has been calculated,by multiplying SIPIRI and obtaining a value between 1(1  1  1) and 1,000 (10  10  10). In our example,the RPI values (Table 1, column 9) ranged between 27 and720. AQ5The improvement actions have been designed andimplemented as preventive actions, according to the QMSapplicable procedure, but their impact has been analyzed interms of RPI point reduction, and the relative cost has beenanalyzed in terms of cost for RPI point reduction.In our example, improvement actions have beendesigned for the following items:a) 01.02—MFR/nonconforming storage temperature(RPI = 540), by adopting a continuous temperaturemonitor for the reagents/calibrators refrigeratorb) 01.03—MFR/contaminated reagent (RPI = 720),by changing the frequency of quality controlexecution, introducing a second quality control testafter the end of the execution of unknown samplesand before their approvalc) 02.03—MFC/contaminated calibrator (RPI = 128), bychanging the procedure of preparation, storage anduse of the calibrator, by reconstituting the calibrator,dividing it in aliquots, freezing them, and chilling andusing one aliquot for each calibration run.Phase 10The improvement actions have been implemented andthe results have been analyzed (see Results).RESULTSThe improvement action carried out on item 01.02showed an RPI = 180. The reduction of RPI was 360(540  180), with a percentage value of 66% (360/540).This result was obtained by the FMEA reassessmentshown in Table 4.Table 3Probability indexProbabilityindex Value Description/explanationRemote 1 It’s not reasonable to expectthat the cause will show itselfLow 2–3 The rate of failure/NCfor that cause goes from1/20,000 to 1/10,000Mild 4–6 Rate of failure/NCin parts/1,000High 7–8 Rate of failure/NCin parts/100Very high 9–10 Rate of failure/NCworse than 10%Table 2Severity indexSeveritylevel Value DescriptionMinimal 1 User is not able to know theeffect, it does not affect theproduct performancesLow 2–3 Slight disturbance to theuser, which can identifya reduction in performanceMild 4–6 Disturbance to the user,with clear evidence ofreduction in performanceHigh 7–8 High level of unsatisfactiondue to high reductionof performanceVery high 9 Defect can damagesafety and makesthe product uselessCatastrophic 10 Defect causes damage topeople and/or propertiesA FMEA Clinical Laboratory Case StudyJanuary/February 2004 3The calculated costs for RPI point are as follows:A cost of the temperature monitor: 500 Euro,duration 5 years = 100 Euro/yearA cost of the emergency batteries for the monitor +12 paper recorder discs/year:10 Euro/yearA working time to change discs, review data: 20hours/year, value 240 Euro/year (at 2002 workingtime costs).Total cost for year was 340 Euro, reduction 360 RPIpoints, cost for point 0.94 (340/360) Euro/point (about0.95 $U.S. at end-2002 change rate). The cost incrementestimated for the clinical chemistry laboratory and forthe workstation on which the S-glucose is executed wasabout 0.3% per year.The improvement action carried out on item 01.03showed an RPI of 189. The reduction of RPI was 531(720  189) RPI points, with a percentage value of73% (531/720). This result was obtained by the FMEAreassessment shown in Table 5.The calculated costs for RPI points are only the costs ofthe additional dose of quality control serum, theadditional dose of reagent plus instrument time, and theadditional working time to evaluate the result.We estimated the full cost in about 300 Euro/year, and,with a reduction of 531 RPI points, cost for point = 0.56(300/531) Euro/point (about 0.57 $U.S. at end-2002change rate). The cost increment estimated for the clinicalchemistry laboratory and for the workstation on which theS-glucose is executed was about 0.5% per year. Theimprovement action carried out on item 02.03 showed anRPI of 96. The reduction of RPI was 32 (128  96 =), witha percentage value of 25% (32/128).In this case, the only item that changed with theimprovement action was the PI, that changed from 8before to 6 after the action, because freezing single dosesof the calibrator and thawing one dose for eachcalibration, to be used once, reduces probability indexfrom high (level 8, Table 3) to mild (level 6, Table 3).The 25% reduction in RPI was obtained at a cost peryear of <100 Euro, with an increase of costs for singleanalyte practically not measurable because the AQ6frozensingle dose calibrator is used to calibrate all the analytesexecuted on the same instrument, and so the cost ofthe operation of preparation of doses and freezing isdivided by the 38 analytes executed together.DISCUSSIONFMEA is a preventive technique already experimentedwith and applied in some areas of health organizations.The first applications have been made in the field ofclinical engineering (3), but the FMEA also has beenrecognized as a valuable instrument to forecast and riskassessment for health products and processes, to correlatethem also to legal liability (4). More generally, FMEA hasbeen used as an instrument of risk assessment in the casesin which the human intervention is involved, consideringthat the phases in which the human intervention isinvolved are the riskiest AQ7points of a process (5).FMEA is a preventive technique alreadyexperimented with and applied in some areasof health organizations.The technique also has been evaluated as capable ofidentifying and preventing the potential problems intherapeutical systems, with reference to the patient safety(6, 7). FMEA also has been used in the transfusionlaboratory/blood bank, in which it allowed to eliminatethe process errors (8); in the transfusion/blood bankarea, the concept of the error frequency measurementTable 5Improvement actions effects for malfunctioningreagent/contaminated reagentIndex Before After CommentSI 9 9 No possible modificationof severity: if amalfunctioning reagententers the process thepossible damageremains high.PI 8 7 The probability, even if inthe same group (high,see Table 3), is reasonablyreduced to the minimumof the group.DI 10 2 The adoption of a morepressing QC plan makesmalfunctions revelation mucheasier: DI lowers 70%(from 10 to 3).Table 4Improvement actions effects for malfunctioningreagent/nonconforming storage temperatureIndex Before After CommentSI 9 9 No possible modificationof severity: if amalfunctioningreagent enters the process,the possible damageremains high.PI 6 4 The probability, even if inthe same group (mild,see Table 3), is reasonablyreduced to the minimumof the group.DI 10 5 The adoption of amonitoring systemmakes the detectabilitymuch better: estimatedDI lowers 50%(from 10 to 5).4 Clinical Leadership & Management ReviewA FMEA Clinical Laboratory Case Studyalready has been used, reducing error frequencies from20–30 to 6–21 per 10,000 procedures (9), even if there hasbeen identified a certain difficulty to compare the errorfrequency results between different institutions (10).Perhaps FMEA could become a common standard tomeasure and compare.Other applications of FMEA have occurred inbioethics, as a support instrument to improve the accuracyof information to patients who participated in clinicaltrials (11) and as an instrument to improve a drugdistribution system in a large teaching hospital (12).In our experience, FMEA showed some problems in itsapplication. These have been related to:a) the necessity of a detailed analysis of the processesand of the failure/NC datab) the lack of preceding experiences, that made morecomplex the attribution of the indexesc) the difficulty in making all the work manually,AQ8 witha large consumption of time.The above difficulties have been, however, overcome,and we can draw the following conclusions that mustbe considered only a suggestion for future research.FMEA is a technique that can be applied to a clinicallaboratory, even if of small dimensions. Problems in itsimplementation are resoluble by a thorough planningand, if necessary, using a FMEA management software.The improvement potentially obtainable by FMEA in aclinical laboratory is high, and this fact should suggestfurther experiences in this field.REFERENCES1. Gressel MG, Gideon JA. An overview of process hazard evaluationtechniques. Am Ind Hyg Assoc J, 1991;52:158–163.2. Werwega S. QSE-FMEA APQP Manual, QSE Inc., 2002 (withreferences from the FMEA Manual, edited by the DaimlerChrysler,Ford, General Motors Supplier Quality Requirements Task Force).3. Willis G. Failure modes and effects analysis in clinical engineering.J Clin Eng, 1992;17:59–63.4. Passey RD. Foresight begins with FMEA: delivering accurate riskassessments. Med Device Technol, 1999;10:88–92.5. Kieffer RG. Validation and the human element. PDA J Pharm SciTechnol, 1998;52:52–54.6. An introduction to FMEA: using failure mode and effects AQ9analysisto meet JCAHO’s proactive risk assessment requirement. HealthDevices, 2002;31:223–226.7. Grissinger M, Rich D. JCAHO: meeting the standards for patientsafety. Joint Commission on Accreditation of Healthcare Organ-izations. J Am Pharm Assoc (Wash), 2002;42(suppl 1):54–55.8. Burgmeier J. Failure mode and effect analysis: an application inreducing risk in blood transfusion. Jt Comm J Qual Improv,2002;28:331–339.9. Taswell HF, Galbreath JL, Harmsen WS. Errors in transfusionmedicine: detection, analysis, frequency, and prevention. ArchPathol Lab Med, 1994;118:405–410.10. Galloway M, Woods R, Whitehead S, et al. An audit of error ratesin a UK district hospital transfusion laboratory. Transfus Med,1999;9:199–203.11. Bramstedt KA. Failure mode and effects analysis as an informedconsent tool for investigational cardiothoracic devices. ASAIO J,2002;48:293–295.12. McNally KM, Page MA, Sunderland VB. Failure-mode and effectsanalysis in improving a drug distribution system. Am J Health SystPharm, 1997;54:171–177.A FMEA Clinical Laboratory Case StudyJanuary/February 2004 5

A FMEA clinical laboratory case study: how to make problems and improvements measurable

https://www.iris.unisa.it/bitstream/11386/1002587/3/2004%20CLMA%20-%20A%20FMEA%20clinical%20laboratory%20case%20study.pdf

A FMEA clinical laboratory case study: how to make problems and improvements measurable

Abstract

Similar works

Full text

Available Versions

Archivio della Ricerca - Università di Salerno