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ARC’s Journey Toward EP23 Compliance

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1 ARC’s Journey Toward EP23 Compliance
Good Morning! I’ve been a Med Tech for a very long time. Early in my career I used Folin Wu tubes and a Bunsen burner to perform the glucose test. I established the calibration curve with graph paper and a serially diluted material. QC has been a thorn in my side from the beginning!! QC would morph every so often but it continued to be a thorn in my side. I used rudimentary statistical QC tools, e.g. standard deviation and bias early on. During the 90’s ARCs core lab was performing about 4,000 chemistries each day. I understood, that statistically, the lab was putting out a certain number of erroneous test results each day. The number was above my comfort level. We had been working on a fairly good quality program for years, but it was disjointed and didn’t produce the results I anticipated. So, I began looking for a structured quality management plan written by professionals that I could follow. David Martin BS MT(HEW)

2 Austin Regional Clinic (ARC)
1,000,000 patient visits 380,000 active patients 1,500 employees 320 physicians 18 locations 16 specialties 6 cities 3 counties 1,000 square miles When I started with ARC in 1982, there were 2 labs and I was the 6th lab tech. When I retired last year, there were 16 labs and 133 lab staff. The core lab was pumping out over 3 million tests per year and we performed about 5,000 chemistry tests each day on 2 Vista 1500s. QC was really a thorn in my side now!!

3 TQMP Development ARC’s journey toward an EP23 IQCP from my perspective as a Lab Director Patient Safety is my #1 priority My Patient Safety Plan is the TQMP In the late 90s I found a promising Total Quality Management Plan Key to Quality from CLSI in 1998 Lucia Berte 3rd party QC material Bio-Rad’s Unity RealTime & Westgard Advisor Lean Standardized policies, processes, & procedures Document Management System Balanced Scorecard EP23P – 2010 Discussion with Lab Support Group Quality Control Designs using data from Unity Real Time Read 1st 5 bullet points Lucy was my mentor for my roadmap toward a quality program. I attended seminars and my Quality Tech would Lucy for clarification and help as we built the 12 QSEs. The TQMP was developing; administration and the lab staff realized the plan was improving the quality of the lab system. We introduced 3rd party QC material and implemented Bio-Rad’s Unity Real Time and the Westgard Advisor. We used Lean to get rid of Muda (waste) and help streamline our processes. We standardized most of our policies, processes and procedures and purchased a document management system. We utilized the Balance Scorecard as a means to monitor the effectiveness of out TQMP. CLSI released EP23P in I read it and began discussing the plan with my support staff. Eyes rolled!! We began developing our QC plan.

4 Bio-Rad’s Quality Group to the Rescue
The Bio-Rad Discovery Group had been working with us on quality control design strategies. During one of our meetings I expressed an interest in the newly released EP23-A guideline. Dr. Parvin asked us if we would be interested in trying to “tackle” an EP23 effort and share the experience – we agreed. This is our story to date. We were treading water after several months of following the EP23 workbook. We had 2 years of Vista data in Unity Real Time and believed this data could help with our endeavor. We asked our Bio-Rad rep for help. Bio-Rad’s Quality Systems Division came in and we stripped 1 year of data from URT. We continued to meet and discuss how to follow the EP23 workbook. Dr. Parvin asked if we would be interested in their group helping design our IQCP. We agreed! This is our story to date.

5 Project Participants ARC Bio-Rad
David Martin, Administrative Director for Laboratory Services Jane Morgan, Clinical Laboratory Supervisor Leah Murphy, Laboratory Quality Technologist Tina Lam, Project Specialist Mary Tsourmas, Medical Director Clinical Risk Assessment Bio-Rad Curtis Parvin, Manager of Advanced Statistical Research John Yundt-Pacheco, Scientific Fellow Lakshmi Kuchipudi, Senior Scientist I had a fantastic support group that made this journey possible. Jane and I had worked together for over 20 years. Jane supervised the clinical lab and was my chemistry guru. Leah’s duties included URT administration. Tina put all the documents into electronic format and kept us on track. Dr. Tsourmas supported us and determined the medical risk part of the equation. The Bio-Rad Quality Systems Division team consisited of: Dr. Parvin, John, & Lakshmi would spend 4-5 hours each visit helping us understand our data and determining how to build our plan following EP23.

6 What is EP23? Laboratory Quality Control Based on Risk Management EP23-A; Approved Oct 2011 Clinical and Laboratory Standards Institute (CLSI) Consensus produced guideline Laboratory Professionals, Govt, & Industry A guidance document for - CLSI - International standards developing and educational organization that promotes the use of consensus standards and guidelines

7 Project: Risk Assessment for a Vista 1500
Start with the EP23-A document & workbook Where necessary fill in gaps or modify to fit need Divide analytes by methodology Photometric Integrated Multisensor Technology (IMT) LOCI-chemiluminessence Nephelometric Begin with one analyte from each methodology Calcium Sodium TSH C-Reactive Protein This was our plan – We would perform the risk assessment for our Vista 1500 chemistry instruments. Read slide

8 EP23-A: Risk Assessment (Figure 5)
7.1 Hazard Identification Create a process map Identify potential failures in each process step Determine mechanisms in place to prevent or detect a failure 7.2 Risk Estimation Assess the likelihood or probability of harm for each failure Assess the severity of harm to a patient from each failure 7.3 Risk Estimation Is the residual risk of harm clinically acceptable 7.4 Risk Control Determine what control processes are needed to lower the risk to an acceptable level No How do you perform a Risk Assessment? In this instance, you start with Hazard identification. The 1st step is to have a process map. We had done a good job with our Lean Program and had process maps for all our processes. Yes 7.5 The Laboratory’s QCP Compile set of QC process into QCP Review QCP for conformance to regulatory and accreditation requirements Document and implement the set of control processes as the laboratory’s QCP

9 This is the Value Stream Map for specimens and orders going to the Vista instruments.

10 Identify Potential Failures
Identify potential stages in the process where failures could occur (fishbone diagram) List all potential failure modes at each stage Characterize the consequences of each failure mode The next step was to identify potential failures, list all potential failure modes, & characterize the consequences of each failure. FMEA had become a familiar tool and I particularly like the Ishakawa diagram.

11 FMEA for Vista instruments. HIL = hemolysis, Icteric & lipemic

12 Consequences of a Failure
The consequences of a failure which lead to a hazardous situation for a patient Incorrect result Delayed result What is an incorrect result? Define an allowable total error for each analyte, TEa If the difference between the correct result for a patient’s specimen and what the lab measures exceeds TEa then the result is incorrect. What is the “extent” of a failure? A failure that adversely effects only a single patient specimen A failure that can adversely effect many patient specimens No result Other Characterize the failure What is an incorrect result? – first you have to define the TEa for each analyte. If the difference between the correct patient result and what the lab measures exceeds TEa, the result is incorrect. What is the “Extent” of failure. Dr. Parvin brought this point up in one of our meetings. An instrument pipetting error would only effect that specimen. A QC failure of 2 2S would effect all the specimens since the last QC run.

13 EP23-A: Probability of Harm (Figure 6)
Sequence of Events Creating Risk of Harm for a Patient (Example) Hazardous Situation P1 P2 P3 P4 P5 P6 Initiating cause Testing process failure Incorrect result generated Incorrect result reported Misdiagnosis Hazardous medical action Patient harmed Probability of a failure Lab prevention/detection of failure Our next step was Probability of harm. The sequence of events creating risk of harm for a patient. Probability of harm given a delayed or incorrect patient result Number of delayed patient results, incorrect patient results not detected

14 EP23-A: Probability of Harm (Figure 6)
Sequence of Events Creating Risk of Harm for a Patient (Example) Frequent = once per week Probable = once per month Occasional = once per year Remote = once every few years Improbable = once in the lifetime of the measuring system Initiating cause Patient harmed Probability of a failure Lab prevention/detection of failure We inserted the frequency of a failure from EP23 into the chart. Our Vista corrective action logs & NCEs were good resources for determining the frequency of a problem. Probability of harm given a delayed or incorrect patient result Number of delayed patient results, incorrect patient results not detected

15 Severity of Harm Severity of harm is described in terms of the severity of the consequence to a patient Severity of harm will differ for different analytes For a given analyte, severity of harm could differ for An incorrect result A delayed result No result For a given analyte, severity of harm could differ for different patient care situations Consider the most common patient care situation for the patient population served by the laboratory For a given analyte, can the severity of harm from an incorrect result differ based on the failure mode that produced the incorrect result? Next came the severity of harm. Last bullet – yes – example – mislabeled specimen vs QC 2 2s rule

16 EP23-A: Severity of Harm Categories
Negligible = inconvenience or temporary discomfort Minor = temporary injury or impairment not requiring professional medical intervention Serious = injury or impairment requiring professional medical intervention Critical = permanent impairment or life-threatening injury Catastrophic = patient death Dr. Tsourmas was instrumental in developing our severity of harm values for each analyte.

17 EP23-A Risk Acceptability Matrix
This is the Risk Acceptability Matrix taken from ISO “Application of Risk Management to Medical Devices” Probability of harm paired with severity of harm determine if the risk is acceptable or unacceptable.

18 EP23-A Risk Assessment Table
OK – this was getting complicated Probability of harm and severity of harm are assessed for each targeted failure mode Acceptability of residual risk is based on the risk acceptability matrix Probability of harm and severity of harm are assessed for each targeted failure mode Acceptability of residual risk based on risk acceptability matrix

19 ARC Experience Struggled to make effective use of the EP23-A risk assessment table in its original form Replaced 4 columns Measuring system feature or recommended action Known limitations of feature or recommended action Control process effective? QCP actions required to address known limitations With 2 columns Engineering controls/Internal QC – manufacturer Lab implemented monitors/External QC – ARC TQMP Added 3 additional columns Causes of failure Methodology effected Extent of failure We struggled to make effective use of the EP23-a risk assessment table in its original form We replaced 4 columns with 2 columns and added 3 new columns

20 EP23 Workshop in Houston, TX
Tools for Tackling EP23TM: Laboratory Quality Control Based on Risk Management; Approved Guideline September 29, 2012 Presented by the Clinical and Laboratory Standards Institute Just in time Jane attended

21 ARC Progress Decided to include additional columns in our risk assessment worksheet for Frequency Severity Detectability Criticality Easier to think in terms of probability of occurrence of a failure and the ability to detect the failure Rather than probability of occurrence of patient harm When Jane returned she debriefed the lab support group on her new insight and we trudged forward with new ideas We had difficulty thinking in terms of patient harm. I had built a culture of producing a product with as few errors as possible. We thought like lab techs. We added columns for Frequency, Severity, Detectability & Criticality It was easier to think in terms of probability of failure and the ability to detect failure. Rather than probability of occurrence of patient harm

22 EP23 Workshop Risk Assessment Worksheet
New Columns New columns added to the worksheet Frequency Severity Detectability Criticality We added the new columns to the risk assessment worksheet

23 Risk Assessment Worksheet: Frequency
Frequency (or probability) of occurrence of a failure We were modifying EP23 using Jane’s newfound knowledge. Now we needed a way to quantitate the data We used a table for frequency of failures from ISO 14971 Note, “probability of occurrence of harm” (EP23-A) is not the same as “probability of occurrence of a failure” (above)

24 Risk Assessment Worksheet: Severity
Severity of patient harm The Severity table idea came from the Houston workshop and was also from ISO 14971

25 Risk Assessment Worksheet: Detection
Probability of detecting a failure mode that has occurred Detectability had been brought up by some of our QC statistical gurus. But it was not explicitly addressed in ep23 We thought the probability of detecting a failure was an important piece in assessing risk FMECA – failure mode effects and criticality analysis Not explicitly addressed in EP23-A

26 Risk Assessment Worksheet: Criticality
Criticality = Frequency X Severity X Detectability We were on a roll - It was finally coming together We had a rating range for criticality from the statistical tool box Criticality = The amount of risidual risk and the priority to address failures

27 ARC Final Worksheet Design
This is ARC ‘s final worksheet design Note the risk ratings

28 ARC Final Worksheet Design: Left Columns
Read columns

29 ARC Final Worksheet Design: Right Columns
Read column headers Criticality – The amount of residual risk and priority to address a failure. Shipping Storage

30 Quality Control Plan for Siemens Vista Chemistry Analyzer
External QC: Liquid Unassayed QC: Multiqual QC is run at the beginning and end of each shift (3x/day) Immunoassay, Cardiac, and Immunology QC are run at the beginning of each shift only due to lower patient volume for those tests TDM and Direct LDL QC is run once per day that patient is run due to extremely low volumes QC is run following each assay calibration, or when recommended by service Refer to QC - 2 level procedures for interpretation of results including Westgard rules Proficiency/Competency testing: Participate in API proficiency testing 3x per year. PT is rotated among personnel Annual competency reviewed and documented by technical supervisor Techs are required to complete 10 hours of Continuing Education credits per year, 2-3 hours mandatory specific to ARC Lab Assistants are required to complete 2-3 hours of Continuing Education per year depending upon position Training Lab Assistants: all laboratory assistants must complete phlebotomy and specimen process training. Refer to specimen collection procedures/manuals Techs: Operators must complete online training for Vista chemistry analyzer combined with hands on training before operating instrument without supervision Materials Manager: Follows all procedures for receipt and storage of reagents This is the IQCP for the Vista instruments.

31 Quality Control Plan for Siemens Vista Chemistry Analyzer
Environmental Control Reagent storage refrigerators/freezers monitored 2x per day Room temperature and humidity monitored and recorded daily Instrument Maintenance/Verification Instrument maintenance is performed as required by manufacturer Calibration/Verification of all assays with less than 3 point calibration is performed every 6 months. Instrument to instrument correlations are run on all Vista assays every 6 months Monitor the IQCP on an ongoing basis for effectiveness NCEs are submitted and monitored by Lab Supervisor to document failures Review any complaints the laboratory may receive from providers LJ graphs are reviewed at least weekly to detect possible trends and shifts Balanced Scorecard prepared quarterly to monitor quality indicators ARC Patient Population is very healthy – 95% of our chemistries are within the reference range. We usually see about 2-3% above and 2-3& below the reference range.

32 Laboratory Environment
Steps Failure Mode Causes Extent Operator Training/External Control Engineering Control General Comments of other Laboratory-Implemented Monitor Frequency (F) Severity (S) Detectability (D) Criticality (FxSxD) Residual Risk Acceptable? Yes/No List the stage or aspect of the test system’s process under investigation. List all manners in which failure could occur in this step. List all causes of the failure mode that have the potential to produce incorrect test results. Does the failure affect a single patient or have the potential to affect multiple patients? Can external controls and/or operator training increase the probablility to detect failure? Are there manufacturer checks to reduce the probability of failure? What other processes can the laboratory implement to detect failure? 1- Improbable Remote Occasional Probable Frequent 1-Negligible Minor Serious Critical Catastrophic What is the likelihood that the control process detects or prevents the failure? (1-5)* <10 Low Mid >20 High If no, how will QCP address? Laboratory Environment Temperature Temperature out of range (too high or too low) A temperature failure can affect multiple patients. Whether it be in a freezer, refrigerator, ambient air, or if the analyzer's temperature goes out of range. The techs are trained to monitor the temperatures of ambient air as well as the refrigerators and freezers around the lab. There is a 24 hour temperature monitor in the walk-in regrigerator. The Vista is programmed to alarm if the temperature falls out of acceptable range. 24 hour monitoring system for walk-in refrigerator and each zone of the Laboratory. Techs record the temperatures for the freezers/refrigerators and ambient air at the beginning and end of work day. Velcro closures placed on the freezer doors. Installed a monitor in the walk-in refrigerator which records the temperature every 15 minutes, this information will be reviewed every Monday. Occasional Negligible 2 <10 Low Yes Humidity Humidity (too high or too low) A humidity failure can affect multiple tests and therefore multiple patients. Techs are trained to record humidity on a daily basis No Procedures/logs Remote Power Source Power Failure A power failure can affect multiple patients. Multiple specimens can be lost due to a power failure changing the temperature in the refrigerators, freezers, incubators. Tests can also be lost due to the power source of the analyzer being compromised. There is a power source backup on the vista that will last for 20 minutes. Possibly a back up generator. Ocassional * Where 1 represents the control can detect the failure and 5 represents the control is ineffective.

33 Specimen Ordering Steps Failure Mode Causes Extent
Operator Training/External Control Engineering Control General Comments of other Laboratory-Implemented Monitor Frequency (F) Severity (S) Detectability (D) Criticality (FxSxD) Residual Risk Acceptable? Yes/No List the stage or aspect of the test system’s process under investigation. List all manners in which failure could occur in this step. List all causes of the failure mode that have the potential to produce incorrect test results. Does the failure affect a single patient or have the potential to affect multiple patients? Can external controls and/or operator training increase the probablility to detect failure? Are there manufacturer checks to reduce the probability of failure? What other processes can the laboratory implement to detect failure? 1- Improbable Remote Occasional Probable Frequent 1-Negligible Minor Serious Critical Catastrophic What is the likelihood that the control process detects or prevents the failure? (1-5)* <10 Low Mid >20 High If no, how will QCP address? Specimen Ordering Ordered by provider Test entered incorrectly Single Yes, training the providers to correctly order lab tests. No Training not done with laboratory procedures. NCEs are written up and discussed with the center managers and providers Frequent Negligible 1 <10 Low Yes, but this can result in a delay in testing. Dx code incorrect or missing No orders Ordered by lab staff Transcription errors Refer to procedures: Processing Lab Orders in Copia, Processing Add-on Lab Tests, Processing CPL Specimens and Reports, DSHS Specimen Processing Probable §Epic àCopia §paper ordersàCopia * Where 1 represents the control can detect the failure and 5 represents the control is ineffective.

34 Specimen Collection Steps Failure Mode Causes Extent
Operator Training/External Control Engineering Control General Comments of other Laboratory-Implemented Monitor Frequency (F) Severity (S) Detectability (D) Criticality (FxSxD) Residual Risk Acceptable? Yes/No List the stage or aspect of the test system’s process under investigation. List all manners in which failure could occur in this step. List all causes of the failure mode that have the potential to produce incorrect test results. Does the failure affect a single patient or have the potential to affect multiple patients? Can external controls and/or operator training increase the probablility to detect failure? Are there manufacturer checks to reduce the probability of failure? What other processes can the laboratory implement to detect failure? 1- Improbable Remote Occasional Probable Frequent 1-Negligible Minor Serious Critical Catastrophic What is the likelihood that the control process detects or prevents the failure? (1-5)* <10 Low Mid >20 High If no, how will QCP address? Specimen Collection Patient ID Incorrect Patient ID Can affect two patients Yes, training all staff collecting specimens with proper specimen processing criteria. No Refer to Blood Specimen Collection procedure and NCE Management Program. Occasional Serious or Critical 2 Mid to High Collection Technique Wrong Tube Single Probable Negligible 1 <10 Low Yes, all of these can result in a delay in testing. Contamination: Incorrect Order of Draw, Improper cleansing of draw site Site Selection Improper use of tourniquet Improperly labeling specimen (provider) Improperly labeling specimen (lab) Improperly mixing the specimen Yes, training all lab staff with proper specimen processing criteria. Refer to procedures: Blood Specimen Collection, Criteria for Specimen Rejection, Centrifuge Instructions, and NCE Specimen Processing Not allowing SST to clot completely Not centrifuging specimen within allotted 2 hour period or not centrifuging at the correct RPM for the correct amount of time Pouring over specimen into the incorrect container Improper storage of the specimen. * Where 1 represents the control can detect the failure and 5 represents the control is ineffective.

35 Specimen Transport and Receiving
Steps Failure Mode Causes Extent Operator Training/External Control Engineering Control General Comments of other Laboratory-Implemented Monitor Frequency (F) Severity (S) Detectability (D) Criticality (FxSxD) Residual Risk Acceptable? Yes/No List the stage or aspect of the test system’s process under investigation. List all manners in which failure could occur in this step. List all causes of the failure mode that have the potential to produce incorrect test results. Does the failure affect a single patient or have the potential to affect multiple patients? Can external controls and/or operator training increase the probablility to detect failure? Are there manufacturer checks to reduce the probability of failure? What other processes can the laboratory implement to detect failure? 1- Improbable Remote Occasional Probable Frequent 1-Negligible Minor Serious Critical Catastrophic What is the likelihood that the control process detects or prevents the failure? (1-5)* <10 Low Mid >20 High If no, how will QCP address? Specimen Transport Incorrect Temperature Specimen shipped under incorrect conditions ex. No ice pack in cooler Potential for multiple since samples are shipped in batches Training staff to properly prepare coolers for transport. Some analytes can give clues as to whether tubes were centrifuged within 2 hours. Ex. Glucose No Currently there are procedures for Criteria for Specimen Rejection, Centrifuge Instructions, and Processing Specimens in Copia LIS. Occasional Negligible 1 <10 Low Yes Centrifuging Tubes Specimens not centrifuged w/in 2 hours can yield incorrect results Potential for multiple if samples are batched before centrifuging Specimen Receiving Manifest Incomplete/Incorrect Single Properly training staff to check-in coolers that arriving at the FW lab. Probable RSO (Release Stored Orders) Orders released in RSO but no sample received in lab Specimen received in lab but orders not released in RSO Vistas will give a "No Test Ordered" alarm for specimens that have not been released in the RSO. Sample Integrity Lipemia Staff are instructed to let the Vista determine whether the sample can(not) be run based on it's measurements of the severity of HIL Vista will measure HIL and reject specimens not suitable to run. Hemolysis Frequent Icteric * Where 1 represents the control can detect the failure and 5 represents the control is ineffective.

36 Operator Training and Competency
Steps Failure Mode Causes Extent Operator Training/External Control Engineering Control General Comments of other Laboratory-Implemented Monitor Frequency (F) Severity (S) Detectability (D) Criticality (FxSxD) Residual Risk Acceptable? Yes/No List the stage or aspect of the test system’s process under investigation. List all manners in which failure could occur in this step. List all causes of the failure mode that have the potential to produce incorrect test results. Does the failure affect a single patient or have the potential to affect multiple patients? Can external controls and/or operator training increase the probability to detect failure? Are there manufacturer checks to reduce the probability of failure? What other processes can the laboratory implement to detect failure? 1- Improbable Remote Occasional Probable Frequent 1-Negligible Minor Serious Critical Catastrophic What is the likelihood that the control process detects or prevents the failure? (1-5)* <10 Low Mid >20 High If no, how will QCP address? Operator Training and Competency Capacity Training A tech has the potential to affect many patients if they are not properly trained. Training by staff/supervisors, staff must be signed off before operating the Vista on their own.They are not given a log-in to approve results until they are signed off by a supervisor. Making sure maintenance is completed on the Vista at the appropriate times. Competencies are given on a yearly basis. Controls can also detect correct preparation of controls/reagents. No, there is an option to enter individual user IDs on the Vista. ARC does not utilize this because results are approved in the LIS and tech ID can be tracked through the LIS system. The controls are approved in Unity Real Time which also requires a unique log-in for each user. Ensure that only staff that have been adequately trained and signed off are the sole operators of the Analyzer. If API surveys come back unsatisfactory, council the staff and figure out why the test was inaccurate, whether it be machine failure or staff incompetency. Remote Serious 1 <10 Low Yes Competency A tech has the potential to affect many patients if they do not have the knoweledge/competency to perform testing and interpret the results. API Proficiency Since API testing mimics patient testing in the laboratory, any false results reported to API can mirror false results that are being reported on patients, and could affect multiple patients API Samples are run 3x per year and rotated among all Vista operators and between both analyzers to test the competency of laboratory personnel and the proficiency of the Vista Analyzer No Negligible Staffing Short Staffing A tech who is overly stressed out has a greater chance of making a mistake and reporting inaccurate results which can affect multiple patients. Even the best trained staff can make mistakes when under stress or when rushed. Limited, the vista will detect a QNS control, but only review of the QC values will determine if a control/reagent was prepared improperly. Ensure that there is as much staffing as is required for the lab to run efficiently. Understand the limitations of staff, different staff members have different work capacities. Probable 10-20 Mid * Where 1 represents the control can detect the failure and 5 represents the control is ineffective.

37 Specimen Storage and Archiving
Steps Failure Mode Causes Extent Operator Training/External Control Engineering Control General Comments of other Laboratory-Implemented Monitor Frequency (F) Severity (S) Detectability (D) Criticality (FxSxD) Residual Risk Acceptable? Yes/No List the stage or aspect of the test system’s process under investigation. List all manners in which failure could occur in this step. List all causes of the failure mode that have the potential to produce incorrect test results. Does the failure affect a single patient or have the potential to affect multiple patients? Can external controls and/or operator training increase the probability to detect failure? Are there manufacturer checks to reduce the probability of failure? What other processes can the laboratory implement to detect failure? 1- Improbable Remote Occasional Probable Frequent 1-Negligible Minor Serious Critical Catastrophic What is the likelihood that the control process detects or prevents the failure? (1-5)* <10 Low Mid >20 High If no, how will QCP address? Storage and Archiving Specimen Storage Specimen stored improperly after testing Multiple Yes, training of techs to properly store specimens that have already been tested and to adhere to a disposal schedule for specimens that are stored in the clinical laboratory refrigerator No Write a procedure pertaining to the storage and disposal of chemistry specimens. Remote Negligible 1 <10 Low Yes Specimen disposed of too soon Archiving Archiving too soon Yes, lab staff are trained to properly archive documents. Refer to "Archiving Laboratory Reports" procedure Archiving the wrong reports * Where 1 represents the control can detect the failure and 5 represents the control is ineffective.

38 What Did We Change/Learn
QC Frequency From 2x/day to 3x/day – added QC at end of day run Type and number of QC samples 2 levels of 3rd party QC unassayed Statistical QC limits used to evaluate the result – added the 10x rule Compared our QC results with target results weekly Frequency of periodic review for trending shifts and trends Weekly or more often if we have a failure QC Frequency change from 2x/d to 3x/d We added QC at the end of the day run to assess those results before we released the patient reports We changed to 2 levels of 3rd party QC unassayed We added the 10x rule for shift to our statistical QC rules We started comparing our QC results with target results weekly We started reviewing our statistical QC and LJ graphs weekly or more often if we had a failure We stopped repeating QC when it was out of control and educated the operators to use RCA to determine the cause of the problem We understood some of the internal detection/prevention mechanisms built into the instrument, but discovered more, e.g. the IMT self calibrates every 2 hrs The photometer check and the reagent check We learned the Allowable Total Error needs scrutiny and should be easier to fine for each analyte

39 What Did We Change/Learn (cont’d)
Actions taken when results exceed acceptable limits Education of operators Stop repeating QC – perform RCA Controls built into the measuring system IMT self calibrates every 2 hours Photometer check Checks reagent specs before adding sample Performs HIL interference TEa needs scrutiny and should be easier to find for each analyte QC Frequency change from 2x/d to 3x/d We added QC at the end of the day run to assess those results before we released the patient reports We changed to 2 levels of 3rd party QC unassayed We added the 10x rule for shift to our statistical QC rules We started comparing our QC results with target results weekly We started reviewing our statistical QC and LJ graphs weekly or more often if we had a failure We stopped repeating QC when it was out of control and educated the operators to use RCA to determine the cause of the problem We understood some of the internal detection/prevention mechanisms built into the instrument, but discovered more, e.g. the IMT self calibrates every 2 hrs The photometer check and the reagent check We learned the Allowable Total Error needs scrutiny and should be easier to fine for each analyte

40 Lessons Learned Challenges in performing a risk assessment for QC
Daunting and Tedious – make templates Resources – internal, e.g. HR and external, e.g. vendors and seminars Support Staff – motivation & knowledge Front Line Staff – how to push down the info – educate and train Establish Allowable total error (TEa) for each analyte – CLIA, API, CAP, etc. Methods to minimize the challenges Staff Education and Training Start before you begin the journey CLSI EP23 workbook - imperative Bio-Rad Quality series - Dr. Westgard’s web site - CLSI Docs on quality GP26-A4 Utilize Industry periodicals, gurus, manufacturers, Involve front line staff in development Attend seminars Read instrument manuals Some of our challenges in performing a risk assessment for QC Daunting and Tedious – make templates Resources – internal, e.g. HR and external, e.g. vendors and seminars Support Staff – motivation & knowledge Front Line Staff – how to push down the info – educate and train Establish Allowable total error (TEa) for each analyte – CLIA, API, CAP, etc. Methods to minimize the challenges Staff Education and Training Start before you begin the journey CLSI EP23 workbook - imperative Bio-Rad Quality series Dr. Westgard’s web site CLSI Docs on quality – GP26-A4 Utilize Industry periodicals, gurus, manufacturers, Involve front line staff in development Attend seminars Read instrument manuals and package inserts

41 Lessons Learned cont. Which test methods might be prioritized for risk assessment High volume quantitative tests Chemistry tests with high probability of harm Tests with most problematic history Sodium, Potassium, Glucose, Calcium, TSH EP23 IQCP can be modified or scaled for quantitative, semi- quantitative and qualitative tests Does risk assessment approach align with institutional quality goals ARC has a mature Patient Centered Medical Home and is part of a Pioneer Accountable Care Organization Quality & Value and demonstrating Quality & Value are priorities Which test methods might be prioritized for risk assessment High volume quantitative tests Chemistry tests with high probability of harm Tests with most problematic history – corrective action reports/NCEs Sodium Potassium Glucose Calcium TSH EP23 IQCP can be modified or scaled for quantitative, semi-quantitative and qualitative tests Does the risk assessment approach align with your institutional quality goals ARC has a mature Patient Centered Medical Home and is part of a Pioneer Accountable Care Organization Value and demonstrating Value are priorities You could design an instrument IQCP and attach an attributes table for each method or analyte.

42 The Journey Continues ARC implemented their new EMR in July at the first clinic. The lab’s resources are supporting this endeavor I know the new Lab director will continue to refine and implement the IQCP Thank you


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