P30 The effects of introducing electronic prescribing for paediatric chemotherapy using aria on prescribing error rates at a primary treatment centre

2018 ◽  
Vol 103 (2) ◽  
pp. e2.33-e2
Author(s):  
Peter Cook ◽  
Andy Fox
Keyword(s):  
Acute Lymphoblastic Leukaemia ◽  
Error Rate ◽  
Lymphoblastic Leukaemia ◽  
Error Rates ◽  
Electronic Prescribing ◽  
Patient Specific ◽  
Prescribing Errors ◽  
Prescribing Error ◽  
Error Type ◽  
Before And After

IntroductionPrescribing of medication in children is a very complex process that involves an understanding of paediatric physiology, disease states, medication used and pharmacokinetics as well as patient specific details, their co-morbidities and their clinical condition. The most common medication errors have been identified as dosing, route of administration, and frequency of administration. Computerised provider order entry has been shown to reduce the number of prescribing errors related to chemotherapy as well as the likelihood of dose and calculation errors in paediatric chemotherapy prescribing. Locally, paediatric chemotherapy is prescribed on pre-printed paper prescriptions. Adaptation and implementation of ARIA electronic prescribing (EP) system for use in paediatric chemotherapy was undertaken by a Specialist Paediatric Oncology Pharmacist and was rolled out for use in January 2016 for patients with acute lymphoblastic leukaemia.MethodThe United Kingdom National Randomised Trial for Children and Young Adults with Acute Lymphoblastic Leukaemia and Lymphoma 2011 (UKALL, 2011) was developed for use on EP, with prescribing of all other chemotherapy remaining on paper. The number and type of prescribing errors were collected during a pre-implementation phase from January 2015 to June 2015. After the introduction of EP and following a 2 month acclimatisation period, a second period of data collection took place between March 2016 and July 2016. Overall prescribing error rates and the frequency of each error type were calculated both before and after implementation.ResultsBefore the introduction of EP for paediatric chemotherapy, the overall error rate was 18.4% with a total of 16 different errors seen. Post implementation, overall error rate increased to 25.7% (p<0.001) with a total of 10 different errors seen. After introduction of EP, prescribing error rates on paper were 30.6% and on EP were 7.0% (p<0.001). Only 5 different error types were seen with electronic prescribing. The most commonly seen errors in prescribing with paper, both before and after were almost eliminated with the introduction of EP.ConclusionThe introduction of EP has resulted in a significant reduction in prescribing error rates compared to paper based prescribing for paediatric chemotherapy. Overall the prescribing error rate increased after the introduction of EP but this was related to an increased rate on the paper prescriptions. One possible reason for this was the use of dual systems for prescribing. In addition there was unforeseen relocation and building work within the paediatric cancer unit, which affected prescribing time allocation. There were also several staff shortages within the prescribing team after implementation and this resulted in an increased workload on the remaining chemotherapy prescribers. All these issues could have attributed to the increase in error rates. The most common errors seen with chemotherapy prescribing have been reduced with EP as protocols have been developed with a focus on prescribing safety. Further work is needed as more prescribing takes place on EP to assess the full impact it has on paediatric chemotherapy error rates.

SP3 The effect of electronic prescribing and medicines administration systems (epmass) on paediatric medication errors

2018 ◽  
Vol 103 (2) ◽  
pp. e1.23-e1
Author(s):  
Aragon Octavio ◽  
Fayyaz Goher ◽  
Gill Andrea ◽  
Morecroft Charles
Keyword(s):  
Medication Errors ◽  
Error Rate ◽  
Error Rates ◽  
Electronic Prescribing ◽  
Prescribing Errors ◽  
Error Type ◽  
Common Error ◽  
Period 2 ◽  
The Difference ◽  
Paediatric Prescribing

BackgroundThe complex nature of paediatric prescribing makes this population more vulnerable to medication errors.1Electronic Prescribing and Medicines Administration Systems (EPMASs) have been suggested to improve paediatric medication safety by reducing prescribing errors.AimTo identify and compare the number and nature of paediatric medication errors pre and post introduction of an EPMAS at a tertiary paediatric hospital.MethodologyPharmacists collected data monthly on the number of new items prescribed and the number of errors (if any) detected in these prescriptions following methodology from the EQUIP study.2 The EPMAS Meditechv6 was introduced in June 2015. Data analysed included forms from 1st-January-2015 to 30th-June-2015 (period 1: pre-EPMAS) and 1st-January-2016 to 30th-June-2016 (period 2: post-EPMAS). The analysis aimed to investigate the rate, type and severity of errors as well as the prescriber grade, prescribing stage and drug class associated with each. Descriptive statistical methods were used to analyse the frequency and nature of errors pre and post implementation of Meditech. Statistical significance was tested using a contingency Chi-squared (χ2) test for the difference in error rates across both periods and a Mann-Whitney test for the difference between the severities of errors across both periodsResultsAn increase of 6.4% in error rate was detected post-Meditech introduction with 67 errors in 1706 items (3.9%) during period 1 and 151 errors in 1459 items (10.3%) during period 2 (p<0.001, χ2 test). FY2 doctors and ‘admission stage’ were associated with the highest error rates across both periods. Minor severity errors were the most common in both periods, with 55.2% in period 1% and 66.2% in period 2. No statistical difference was detected (p=0.403) in the severity of errors reported although the proportion of significant and serious errors decreased from 38.8% to 27.8% and 6.0% to 0.7% respectively. No errors were classed to be potentially lethal in period 1, however there was one such incident in period 2. Underdosing was the most common error type in period 1 (22.4%), falling to 4.0% in period 2. Omission on admission was the most common error type in period 2, with an error rate of 37.7% vs 20.9% in period 1. Antibacterials and analgesics were the most common classes of drugs involved in errors in both periods, although a wider range of drug classes were involved in errors post Meditech introductionConclusionA significant increase of 6.4% in error rate was found post implementation of Meditech highlighting the concept of EPMAS-facilitated errors. The positive effect of EPMASs is also apparent as the incidence of significant and serious errors decreased in period 2, although this difference was not statistically significant. Reaching definitive conclusions is difficult due to the lack of available research into the effects of EPMASs on paediatric prescribing and due to methodological limitations. However, it can be suggested that introducing functions such as comprehensive decision support and dose calculators may overcome the shortcomings of the current system3 and allow for the true benefits of EPMASs in improving paediatric medication safety to be demonstrated.ReferencesNational Patient Safety Agency. Review of patient safety for children and young people 2009. England: National Reporting and Learning Services. http://www.nrls.npsa.nhs.uk/resources/?entryid45=5986 [Accessed: 29th October 2016].Dornan T, et al. An in-depth investigation into causes of prescribing errors by foundation trainees in relation to their medical education: EQUIP study. Final Report to the General Medical Council 2009. http://www.gmcuk.org/FINAL_Report_prevalence_and_causes_of_prescribing_errors.pdf_28935150.pdf [Accessed: 9th November 2016].Johnson KB, Lehmann CU. Electronic prescribing in paediatrics: Toward safer and more effective medication management. Paediatrics 2013;131(4):e1350–e1356. doi:10.1542/peds.2013-0193

scholarly journals The Impact of Technology on Prescribing Errors in Pediatric Intensive Care: A Before and After Study

2020 ◽  
Vol 11 (02) ◽  
pp. 323-335 ◽  
Author(s):  
Moninne M. Howlett ◽  
Eileen Butler ◽  
Karen M. Lavelle ◽  
Brian J. Cleary ◽  
Cormac V. Breatnach
Keyword(s):  
Intensive Care ◽  
New Technology ◽  
Tertiary Care ◽  
Pediatric Intensive Care ◽  
Error Rates ◽  
Electronic Prescribing ◽  
Prescribing Errors ◽  
Before And After ◽  
The Impact ◽  
Before And After Study

Abstract Background Increased use of health information technology (HIT) has been advocated as a medication error reduction strategy. Evidence of its benefits in the pediatric setting remains limited. In 2012, electronic prescribing (ICCA, Philips, United Kingdom) and standard concentration infusions (SCIs)—facilitated by smart-pump technology—were introduced into the pediatric intensive care unit (PICU) of an Irish tertiary-care pediatric hospital. Objective The aim of this study is to assess the impact of the new technology on the rate and severity of PICU prescribing errors and identify technology-generated errors. Methods A retrospective, before and after study design, was employed. Medication orders were reviewed over 24 weeks distributed across four time periods: preimplementation (Epoch 1); postimplementation of SCIs (Epoch 2); immediate postimplementation of electronic prescribing (Epoch 3); and 1 year postimplementation (Epoch 4). Only orders reviewed by a clinical pharmacist were included. Prespecified definitions, multidisciplinary consensus and validated grading methods were utilized. Results A total of 3,356 medication orders for 288 patients were included. Overall error rates were similar in Epoch 1 and 4 (10.2 vs. 9.8%; p = 0.8), but error types differed (p < 0.001). Incomplete and wrong unit errors were eradicated; duplicate orders increased. Dosing errors remained most common. A total of 27% of postimplementation errors were technology-generated. Implementation of SCIs alone was associated with significant reductions in infusion-related prescribing errors (29.0% [Epoch 1] to 14.6% [Epoch 2]; p < 0.001). Further reductions (8.4% [Epoch 4]) were identified after implementation of electronically generated infusion orders. Non-infusion error severity was unchanged (p = 0.13); fewer infusion errors reached the patient (p < 0.01). No errors causing harm were identified. Conclusion The limitations of electronic prescribing in reducing overall prescribing errors in PICU have been demonstrated. The replacement of weight-based infusions with SCIs was associated with significant reductions in infusion prescribing errors. Technology-generated errors were common, highlighting the need for on-going research on HIT implementation in pediatric settings.

scholarly journals Effects of Two Commercial Electronic Prescribing Systems on Prescribing Error Rates in Hospital In-Patients: A Before and After Study

PLoS Medicine ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. e1001164 ◽  
Author(s):  
Johanna I. Westbrook ◽  
Margaret Reckmann ◽  
Ling Li ◽  
William B. Runciman ◽  
Rosemary Burke ◽  
...  
Keyword(s):  
Error Rates ◽  
Electronic Prescribing ◽  
Prescribing Error ◽  
Before And After ◽  
Before And After Study

scholarly journals P029 Audit of aciclovir prescribing to assess whether changing the order of drop down box options in an electronic prescribing system can reduce prescribing errors

2019 ◽  
Vol 104 (7) ◽  
pp. e2.33-e2
Author(s):  
Clarissa Gunning ◽  
Jennifer Gray
Keyword(s):  
Patient Safety ◽  
Intensive Care ◽  
Improve Patient Safety ◽  
Error Rates ◽  
Electronic Prescribing ◽  
Contributory Factor ◽  
List Type ◽  
Prescribing Errors ◽  
Prescribing Error ◽  
Dosing Error

AimIn December 2016 it was identified that there had been multiple reports of prescribing errors with intravenous aciclovir on the paediatric intensive care unit (PICU). After investigation it was concluded that prescribers choosing incorrectly from a drop down menu of drug and dosing options on the electronic prescribing (EP) system was the main contributory factor. From 01/02/17 the aciclovir drop down options were prioritised, with the most frequently used option appearing first, to encourage prescribers to pick the correct regimen.MethodsThe trust has been using the Phillips ICCA EP system across all intensive care units since 2016. Picking errors when prescribing are known to be a potential risk within EP systems, however tailoring these systems to guide choice also has the potential to improve patient safety by reducing the risk of prescribing errors.1 Aciclovir has a complex range of dosing recommendations, especially in paediatrics, and incorrect prescribing increases the likelihood of subtherapeutic treatment or adverse effects. The aim of this audit is to assess whether changing the order of prescription choices on the drop down menu in the EP system reduced prescribing error rates for intravenous aciclovir. All prescriptions for aciclovir on PICU were identified during the 6 months before and after implementing the change, from 01/08/16 to 31/07/17. 65 prescriptions were included in the audit and were reviewed retrospectively using the EP system and electronic medical notes to assess whether the prescribed aciclovir dose and route was correct for the patient’s age, weight and indication as well as whether the appropriate drop down option had been selected by the prescriber. Dosing was assessed against recommendations in the British National Formulary for children and trust empirical antibiotic guidelines.ResultsDosing errors were found in 22% (14/65) of prescriptions overall during the review period. Before the change was implemented 26% (9/35) of aciclovir prescription doses were incorrect, reducing to 17% (5/30) after the change. The overall dosing error rate was 14% (7/50) in prescriptions where the correct drop down option was chosen, in comparison to 47% (7/15) in cases where the wrong option had been selected, suggesting the importance of choosing the correct pre-set option to minimise prescribing error rates. In cases where doses were incorrect, the prescriber had chosen the incorrect pre-set drop down option for the patient’s age and indication in 78% (7/9) of prescriptions before the order change compared to 0% (0/5) afterwards.ConclusionThese results suggest that prescribing error rates were reduced after making alterations to the order of prescription choices on the drop down menu in the EP system and that prioritising the order of these options may positively influence prescribing. Errors were not completely eliminated suggesting more work is required to further minimise risk.ReferenceAhmed Z, Garfield S, Jani Y, et al. Impact of electronic prescribing on patient safety in hospitals: implications for the UK. Pharm J 2016;8:1–11.

scholarly journals 1 Developing consensus on hospital prescribing indicators of potential harm for infants and children

2018 ◽  
Vol 103 (2) ◽  
pp. e2.1-e2
Author(s):  
Andy Fox
Keyword(s):  
Expert Panel ◽  
Clinical Decision ◽  
Error Rates ◽  
Electronic Prescribing ◽  
Prescribing Errors ◽  
Prescribing Error ◽  
Prescribing Indicators ◽  
Potential Harm ◽  
The Uk ◽  
Paediatric Prescribing

AimsTo develop a list of hospital based paediatric prescribing indicators that can be used to assess the impact of electronic prescribing or clinical decision support tools on paediatric prescribing errors.BackgroundMedication errors are a major cause for concern in the NHS. Prescribing is part of the medication use process and is a complex task requiring an understanding of medicines, disease processes, and patient parameters. Systematic reviews have reported that medication errors occur in as many as 50% of hospital admissions and prescribing error rates in the UK hospitals vary between 9% and 15%.Prescribing for children is further complicated by the need to take into account weight, altered physiology and pharmacokinetics. Prescribing error rates of 13.1% have been reported in children with a potentially greater impact due to the nature of the patients.Electronic prescribing (EP) while relatively uncommon in UK hospitals is an important tool in reducing prescribing errors. EP systems have been shown to have a positive impact on prescribing errors, however methodologies vary and the reduction in harm is rarely investigated. A standard tool to allow an evaluation of the harm reduction is desirable and currently does not exist for the paediatric setting.MethodsTwo rounds of an electronic consensus method (eDelphi) were carried out with 21 expert panellists from the UK. Panellists were asked to score each prescribing indicator for its likelihood of occurrence and severity of outcome should the error occur. The scores were combined to produce a risk score and a median score for each indicator calculated. The degree of consensus between panellists was defined as the proportion that gave a risk score in the same category as the median. Indicators were included if a consensus of 80% or higher was achieved and were in the high risk categories.ResultsAn expert panel consisting of 8 pharmacists and 13 paediatricians with a total of 437 years of clinical experience completed an exploratory round and two rounds of scoring. This identified 41 paediatric prescribing indicators with a high risk rating and greater than 80% consensus. The most common error type within the indicators was wrong dose (n=19) and the most common drug classes were antimicrobials (n=10) and cardiovascular (n=7).ConclusionsA set of 41 paediatric prescribing indicators describing potential harm for the hospital setting have been identified by an expert panel. The indicators provide a standardised method of evaluation of prescribing data on both paper and electronic systems. They can also be used to assess implementation of clinical decision support systems or other quality improvement initiatives.

RATES AND TYPES OF PRESCRIBING ERRORS AND RELATED INTERVENTIONS IN PAEDIATRIC ONCOLOGY

2016 ◽  
Vol 101 (9) ◽  
pp. e2.32-e2 ◽  
Author(s):  
Claire Fosbrook
Keyword(s):  
Error Rate ◽  
Paediatric Oncology ◽  
Electronic Prescribing ◽  
Clear Correlation ◽  
Prescribing Errors ◽  
Prescribing Error ◽  
Start Date ◽  
Pharmacy Intervention ◽  
Type Data ◽  
The Impact

AimThe aim of the pharmacy intervention audit was to prospectively record the number and type of interventions made to paediatric oncology chemotherapy prescriptions. This baseline data will be used in the future to assess the impact of electronic prescribing (EP) on prescribing error or intervention rates.Independently from the EP project research, I interrogated the data to establish if there was a correlation between prescribing workload and rate of errors or interventions. I predicted that an ‘overworked’ prescriber would make more mistakes due to the volume of the workload and a less frequent prescriber would make more mistakes due to scarce use of these skills.Intervention rates have been found to be as high as 66% for chemotherapy prescriptions, including interventions for missed information, wrong doses and protocol breach1. This intervention rate demonstrates the importance of pharmacy verification. Another source demonstrated that 80% of errors were due to poor prescription writing.2 MethodAn audit tool was created to collect the data, included fields for date, prescriber type, number of drugs prescribed, number of interventions made and intervention type. Data was collected from 5 Jan 2015 to 12 Jun 15.ResultsThe most common interventions required were addition of diluent volume, addition of start date and dose amendments to ensure doses could be accurately measured.The staff grade doctor prescribed on average 75% of the prescriptions each week, with an intervention rate of 19%. The registrar was responsible for 23% each week and had an error rate pf 24%. Consultants were responsible for only 2% of the weekly prescription workload and had the lowest rate of interventions at 7%. There was no clear correlation between percentage of chemotherapy prescribed per week and rate of errors.ConclusionThe most common types of errors expected from the background reading are demonstrated by this audit, as the three common interventions are related to poor prescribing. EP should eliminate all three of these interventions as all these are either mandatory fields for a prescription to be ordered or measurable dose rounding will be in inbuilt into each drug field, and therefore calculated automatically by our prescribing system.There was no clear correlation between error rate and proportion of prescribing. Errors are therefore independent of prescribing workload. Alternative reasons for errors could include external factors such as environment or bad habits of the prescriber. I believe the low rate of errors from the consultants is due to the types of prescriptions they often prescribe. Which were more frequently for single agents such as intrathecals. This suggests further data interrogation could identify whether there is a relationship between prescription complexity (or length) and error rate.

ASSESSING THE IMPACT OF A NEWLY INTRODUCED ELECTRONIC PRESCRIBING SYSTEM ACROSS A PAEDIATRIC DEPARTMENT – LESSONS LEARNED

2016 ◽  
Vol 101 (9) ◽  
pp. e2.13-e2 ◽  
Author(s):  
Anastasia Tsyben ◽  
Nigel Gooding ◽  
Wilf Kelsall
Keyword(s):  
Good Practice ◽  
Electronic System ◽  
Daily Basis ◽  
Error Rates ◽  
Lessons Learned ◽  
Electronic Prescribing ◽  
Prescribing Errors ◽  
Prescribing Error ◽  
New Challenges ◽  
The Impact

AimPrescribing audits have shown that the Women's and Children's Directorate reported higher number of prescription errors on the paediatric and neonatal wards compared to other areas in the Trust. Over the last three years a multidisciplinary prescribing team (PT), which included senior clinicians, pharmacists and trainees introduced a number of initiatives to improve the quality of prescribing. Strategies included structured departmental inductions, setting up of designated prescribing areas and reviewing errors with the prescriber. Year on year there were fewer prescribing errors.1 With the introduction of a new electronic prescribing system in October 2014 prescribing error rates were expected to decrease further, eradicating omissions around allergy recording, ward location and drug names. The aim of this abstract is to highlight the impact of the new system and describe lessons learned.MethodIn the summer of 2014, all inpatient drug charts across the department were reviewed on three non-consecutive days over a period of three weeks. Prescribing errors were identified by the ward pharmacist. Errors were grouped according to type and further analyzed by the PT. Errors deemed to have no clinical significance were excluded. Error rates were compared to the previous audits performed with identical methodology. Following the introduction of the electronic prescribing system, the ward pharmacists continued to review prescription charts on daily basis and generate regular error reports to notify the staff of new challenges.ResultsThere were 174 (14%) errors out of 1225 prescriptions on 181 drug charts. The most commonly made mistakes included drug name errors, strength of preparation, allergies and ward documentation, prescriber's signature omissions, and antibiotic review and end dates. The introduction of an electronic system has eliminated drug name, strength of preparation, allergy recording and ward errors. However, serious challenges have been identified: entering of an incorrect weight resulted in all drug dosages being inaccurate; the timing of drug levels for Vancomycin and Gentamicin and the administration of subsequent doses have been problematic. Communication difficulties between all staff groups has led to dosage omission, duplicate administration and confusion around start and stop dates. The ability to prescribe away from the bedside and indeed the ward has compounded some of these problems.ConclusionThe implementation of a new electronic system has reduced prescribing errors but has also resulted in new challenges, some with significant patient safety implications. The lessons learned and good practice introduced following previous audits of “traditional paper based” prescribing are equally important with electronic prescribing. Communication between staff groups is crucial. It is likely that the full benefits of the system will be realized a year after its introduction. On-going audit is required to assess the impact and safety of the electronic prescribing and lessons learned.

Perceptual Characteristics of Consonant Production in Apraxia of Speech and Aphasia

2019 ◽  
Vol 28 (4) ◽  
pp. 1411-1431 ◽  
Author(s):  
Lauren Bislick ◽  
William D. Hula
Keyword(s):  
Error Rate ◽  
Error Rates ◽  
Group Differences ◽  
Diagnostic Process ◽  
Apraxia Of Speech ◽  
Contextual Variables ◽  
Error Type ◽  
Type Distribution ◽  
Phonetic Features ◽  
Syllable Position

Purpose This retrospective analysis examined group differences in error rate across 4 contextual variables (clusters vs. singletons, syllable position, number of syllables, and articulatory phonetic features) in adults with apraxia of speech (AOS) and adults with aphasia only. Group differences in the distribution of error type across contextual variables were also examined. Method Ten individuals with acquired AOS and aphasia and 11 individuals with aphasia participated in this study. In the context of a 2-group experimental design, the influence of 4 contextual variables on error rate and error type distribution was examined via repetition of 29 multisyllabic words. Error rates were analyzed using Bayesian methods, whereas distribution of error type was examined via descriptive statistics. Results There were 4 findings of robust differences between the 2 groups. These differences were found for syllable position, number of syllables, manner of articulation, and voicing. Group differences were less robust for clusters versus singletons and place of articulation. Results of error type distribution show a high proportion of distortion and substitution errors in speakers with AOS and a high proportion of substitution and omission errors in speakers with aphasia. Conclusion Findings add to the continued effort to improve the understanding and assessment of AOS and aphasia. Several contextual variables more consistently influenced breakdown in participants with AOS compared to participants with aphasia and should be considered during the diagnostic process. Supplemental Material https://doi.org/10.23641/asha.9701690

scholarly journals How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis

2020 ◽  
Author(s):  
Peter J Gates ◽  
Rae-Anne Hardie ◽  
Magdalena Z Raban ◽  
Ling Li ◽  
Johanna I Westbrook
Keyword(s):  
Systematic Review ◽  
Medication Error ◽  
Medication Errors ◽  
Meta Analysis ◽  
Error Rates ◽  
Prescribing Error ◽  
Before And After ◽  
Actual Harm ◽  
Computer Based ◽  
Wide Scale

Abstract Objective To conduct a systematic review and meta-analysis to assess: 1) changes in medication error rates and associated patient harm following electronic medication system (EMS) implementation; and 2) evidence of system-related medication errors facilitated by the use of an EMS. Materials and Methods We searched Medline, Scopus, Embase, and CINAHL for studies published between January 2005 and March 2019, comparing medication errors rates with or without assessments of related harm (actual or potential) before and after EMS implementation. EMS was defined as a computer-based system enabling the prescribing, supply, and/or administration of medicines. Study quality was assessed. Results There was substantial heterogeneity in outcomes of the 18 included studies. Only 2 were strong quality. Meta-analysis of 5 studies reporting change in actual harm post-EMS showed no reduced risk (RR: 1.22, 95% CI: 0.18–8.38, P = .8) and meta-analysis of 3 studies reporting change in administration errors found a significant reduction in error rates (RR: 0.77, 95% CI: 0.72–0.83, P = .004). Of 10 studies of prescribing error rates, 9 reported a reduction but variable denominators precluded meta-analysis. Twelve studies provided specific examples of system-related medication errors; 5 quantified their occurrence. Discussion and Conclusion Despite the wide-scale adoption of EMS in hospitals around the world, the quality of evidence about their effectiveness in medication error and associated harm reduction is variable. Some confidence can be placed in the ability of systems to reduce prescribing error rates. However, much is still unknown about mechanisms which may be most effective in improving medication safety and design features which facilitate new error risks.

Reducing Culture Reporting Errors in the Microbiology Laboratory

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S131-S132
Author(s):  
Kathryn Hogan ◽  
Beena Umar ◽  
Mohamed Alhamar ◽  
Kathleen Callahan ◽  
Linoj Samuel
Keyword(s):  
Real Time ◽  
Error Rate ◽  
Reporting System ◽  
Error Rates ◽  
Tracking Errors ◽  
Corrective Measures ◽  
Safety Awareness ◽  
Before And After ◽  
Set Up ◽  
System Errors

Abstract Objectives There are few papers that characterize types of errors in microbiology laboratories and scant research demonstrating the effects of interventions on microbiology lab errors. This study aims to categorize types of culture reporting errors found in microbiology labs and to document the error rates before and after interventions designed to reduce errors and improve overall laboratory quality. Methods To improve documentation of error incidence, a self-reporting system was changed to an automatic reporting system. Errors were categorized into five types Gram stain (misinterpretations), identification (incorrect analysis), set up labeling (incorrect patient labels), procedures (not followed), and miscellaneous. Error rates were tracked according to technologist, and technologists were given real-time feedback by a manager. Error rates were also monitored in the daily quality meeting and frequently detected errors were discussed at staff meetings. Technologists attended a year-end review with a manager to improve their performance. To maintain these changes, policies were developed to monitor technologist error rate and to define corrective measures. If a certain number of errors per month was reached, technologists were required to undergo retraining by a manager. If a technologist failed to correct any error according to protocol, they were also potentially subject to corrective measures. Results In 2013, we recorded 0.5 errors per 1,000 tests. By 2018, we recorded only 0.1 errors per 1,000 tests, an 80% decrease. The yearly culture volume from 2013 to 2018 increased by 32%, while the yearly error rate went from 0.05% per year to 0.01% per year, a statistically significant decrease (P = .0007). Conclusion This study supports the effectiveness of the changes implemented to decrease errors in culture reporting. By tracking errors in real time and using a standardized process that involved timely follow-up, technologists were educated on error prevention. This practice increased safety awareness in our micro lab.

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