Preanalytical mistakes in samples from primary care patients

Costs analysis of a training intervention for the reduction of preanalytical errors in primary care samples

BACKGROUND

To perform a cost-error analysis based on a quasi-experimental pre-post study of the preanalytical errors in 2 hospital laboratories. The real cost and theoretical cost are defined as the cost resulting from errors with or without the training intervention. The real impact associated to the training program was estimated, calculated as the total associated to the preanalytical errors cost difference. The costs were measured using Andalusian Public Health Service fees. Cost analysis of an educational intervention presented in a previous study from 2017. Preanalytical errors were detected in the laboratories of the University Hospital Virgen de la Victoria (Málaga, Spain) and in the University Hospital Juan Ramón Jiménez (Huelva, Spain).

METHODS

The founded errors were divided into blood and urine samples. Univariate sensitivity analysis was used to assess how parameter uncertainty impacted on overall results. Variations of parameters between 0% and 5% were substituted into the base case.

RESULTS

The real impact associated with educational intervention in LAB1 was an increase of &OV0556;16,961.378, and the expected impact was an increase &OV0556;78,745.27 (difference of &OV0556;61,783.9). In LAB2, the real impact in the same period amounted to &OV0556;260,195.37, and the expected impact was &OV0556;193,905.83 (difference of -&OV0556;66,289.54). The results were different in the 2 laboratories, proving the intervention in only one of them to be more effective.

CONCLUSIONS

Costs analysis determined that this training intervention can provide saves in the costs, as the effectiveness of the educational sessions in reducing preanalytical errors currently results in a significant decrease of the costs associated with these errors.

Transcultural Adaptation and Piloting of the "Regarding Blood-Sampling Practices at Primary Health Care Centres" Questionnaire

Preanalytical errors account for up to 70% of the total potentially detectable errors in the laboratory. The main problems detected are related with procedures associated with Primary Care nursing practices that are directly involved in the preanalytical phase. The objective of this study is to carry out a transcultural adaptation and piloting of the "Regarding Blood-Sampling Practices at Primary Health Care Centres" questionnaire as regards blood sampling in Primary Care. For this, a cross-sectional descriptive study has been developed within the Primary Care area of the Andalusian Public Health System. The venous blood sampling questionnaire was translated into and adapted to Spanish by qualified professionals and expert translators. The questionnaires were then delivered to all staff nurses from the health districts involved. The total sample consisted of 224 primary care nursing professionals. The factors that showed statistically significant relationships were identification and sample collection, management with information search, storage and labelling of samples, and reporting of errors. A lack of global relationship between factors makes it impossible to find a global quality factor in the sampling process. The process of translation, adaptation, and piloting of the questionnaire from its original version to the Spanish one has proven to be understandable by professionals in its entirety and to offer data similar to the original version.

Reduction of pre-analytical errors in the clinical laboratory at the University Hospital of Korea through quality improvement activities

BACKGROUND

The clinical laboratory is responsible for reporting accurate and expeditious results. However, the pre-analytical phase is directly related to the procedure of specimen collection and is mostly out of the direct control of the laboratory; further, most pre-analytical errors are related to human factors. Therefore, education and training programs for the phlebotomy teams are considered the most significant and necessary measures to reduce these errors.

METHODS

A cross-sectional study was conducted to investigate the types and frequencies of pre-analytical errors in the hospital laboratory. Pre-analytical errors were categorized into four main categories: rejected sample, error related to test ordering, misidentification, and others. Several activities were performed for quality improvement in order to reduce the rates of these errors. The data were analyzed by comparing the pre-intervention and post-intervention results along with the results of questionnaires to assess knowledge to investigate the effects of the activities.

RESULTS

The rates of pre-analytical errors decreased from 0.42% in the pre-intervention period to 0.32% in the post-intervention period. The rejected sample category accounted for the highest rates in the pre- and post-intervention periods. In the questionnaires, the overall average score after the intervention was 71.5, which was a significant increase from 46.0 in the pre-intervention period.

CONCLUSIONS

Each clinical laboratory has various types of pre-analytical errors due to the complexity of the healthcare environment. Therefore, targeted intervention including a quality improvement program and its continuous maintenance should be conducted to reduce pre-analytical errors and to improve patient safety.

Utilization of a healthcare failure mode and effects analysis to identify error sources in the preanalytical phase in two tertiary hospital laboratories

Introduction

The presence of errors in the preanalytical phase is a thoroughly studied problem. A strategy to increase their source detection might be the use of the Healthcare Failure Mode and Effects Analysis (HFMEA). The aim of this study is improving the capacity of identifying sources of error during the preanalytical period in samples provided by primary care clinics (PCC) with the use of the HFMEA as a tool in the laboratories of two tertiary hospitals.

Materials and methods

A HFMEA was carried out in each laboratory, by means of the creation of groups of experts with similar characteristics (doctors and nurses from PCC and laboratory, support staff, and laboratory technicians). The Risk Priority Number (RPN) was calculated.

Results

Items with elevated RPN were presented in both centers. The highest RPN were in LAB1: "two request notes for a patient" and "the segregation of oncology urgent samples" (both with 384), while in LAB2 was "the lack of information in patients with oral glucose overload test" (RPN 576). Considering the different steps in the preanalytical phase, LAB1 paid attention in sampling, samples reception and the programming in the Laboratory Information System, while LAB2 paid attention in the request form, the appointment system, sampling procedures, transport and reception.

Conclusion

The laboratories prioritized the problems differently. However, both centers offer solutions to these possible sources of error. We proposed improvement actions that can be resolved easily, with a low cost for the system, mainly to schedule a specific formative programme and a deep revision of the existing protocols.

Making sense of a haemolysis monitoring and reporting system: a nationwide longitudinal multimethod study of 68 Australian laboratory participant organisations

Background:

The key incident monitoring and management systems (KIMMS) quality assurance program monitors incidents in the pre- and postanalytical phases of testing in medical laboratories. Haemolysed specimens have been found to be the most frequent preanalytical error and have major implications for patient care. The aims of this study were to assess the suitability of KIMMS for quality reporting of haemolysis and to devise a meaningful method for reporting and monitoring haemolysis.

Methods:

A structured survey of 68 Australian KIMMS laboratory participant organisations was undertaken. Quarterly haemolysis reports (2011–2014) were analysed.

Results:

Among 110 million accessions reported, haemolysis rates varied according to the reporting methods that participants used for assigning accessions (16% of participants reported haemolysis by specimen and 83% reported by episode) and counting haemolysis rejections (61% by specimen, 35% by episode and 3% by test). More than half of the participants (56%) assigned accessions by episode and counted rejections by specimen. For this group, the average haemolysis rate per 100,000 episodes was 177 rejected specimens with the average rate varying from 100 to 233 over time. The majority of participants (91%) determined rejections using the haemolysis index. Two thirds of participants (66%) recorded the haemolysis manually in laboratory information systems.

Conclusions:

KIMMS maintains the largest longitudinal haemolysis database in the world. However, as a means of advancing improvements in the quality of the preanalytical laboratory process, there is a need to standardise reporting methods to enable robust comparison of haemolysis rejection rates across participant laboratories.

Potential cost savings by minimisation of blood sample delays on care decision making in urgent care services

Background

Timely availability of blood sample results for interpretation affects planning and delivery of patient care from initial assessment in Accident and Emergency (A&E) departments.

Materials and methods

Rates of, and reasons for, rejected blood samples submitted from all clinical areas over one month were evaluated. Haemoglobin (Hb) represented haematology and potassium (K⁺), biochemistry. A prospective observational study evaluated the methodology of sample collection and impact on utility.

Results

16,061 haematology and 16,209 biochemistry samples were evaluated; 1.4% (n = 229, range 0.5–7.3%) and 4.7% (n = 762, range 0.9–14%) respectively were rejected, with 14% (n = 248/1808) K⁺ rejection rate in A&E. Patients with rejected K⁺ and Hb had a longer median in-hospital stay of 9 and 76 h respectively and additional stay fixed costs of £26,824.74 excluding treatment. The rejection rate with Vacutainer and butterfly (4.0%) was lower than Vacutainer and cannula (28%).

Conclusion

Sample rejection rate is high and is associated with increased in-hospital stay and cost. Blood sampling technique impacts on rejection rates. Reduction in sample rejection rates in emergency care areas in acute hospitals has the potential to impact on patient flow and reduce cost.

Conclusion

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Blood sample rejection rate in a single large NHS Foundation Trust is high.

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The highest rate of blood sample rejection is in the Accident and Emergency department.

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Blood sample rejection is associated with increased in-hospital stay.

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Blood sampling technique impacts on rejection rates.

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Reduction in sample rejection rates in emergency care areas in acute hospitals has the potential to impact on patient flow and cost.

Test result communication in primary care: a survey of current practice

Background

The number of blood tests ordered in primary care continues to increase and the timely and appropriate communication of results remains essential. However, the testing and result communication process includes a number of participants in a variety of settings and is both complicated to manage and vulnerable to human error. In the UK, guidelines for the process are absent and research in this area is surprisingly scarce; so before we can begin to address potential areas of weakness there is a need to more precisely understand the strengths and weaknesses of current systems used by general practices and testing facilities.

Methods

We conducted a telephone survey of practices across England to determine the methods of managing the testing and result communication process. In order to gain insight into the perspectives from staff at a large hospital laboratory we conducted paired interviews with senior managers, which we used to inform a service blueprint demonstrating the interaction between practices and laboratories and identifying potential sources of delay and failure.

Results

Staff at 80% of practices reported that the default method for communicating normal results required patients to telephone the practice and 40% of practices required that patients also call for abnormal results. Over 80% had no fail-safe system for ensuring that results had been returned to the practice from laboratories; practices would otherwise only be aware that results were missing or delayed when patients requested results. Persistent sources of missing results were identified by laboratory staff and included sample handling, misidentification of samples and the inefficient system for collating and resending misdirected results.

Conclusions

The success of the current system relies on patients both to retrieve results and in so doing alert staff to missing and delayed results. Practices appear slow to adopt available technological solutions despite their potential for reducing the impact of recurring errors in the handling of samples and the reporting of results. Our findings will inform our continuing work with patients and staff to develop, implement and evaluate improvements to existing systems of managing the testing and result communication process.

Reduction of preanalytical errors in laboratory by establishment and application of training system

OBJECTIVES

Errors in preanalytical phase occupied for almost half of total errors in clinical laboratory, and the causes are related to medical staff's quality awareness and behaviors. In order to reduce the preanalytical errors in our hospital, we established and applied a training system to improve the situation.

METHODS

The disqualified sample types and major causes of errors in the preanalytical phase were investigated in clinical laboratory department from September 2008 to August 2009. In the following year, we established and applied a training system to affect the quality awareness and behaviors of medical staff. Questionnaire investigation was analyzed to illustrate the changes of respondents' quality awareness and behavior, and the preanalytical errors were reanalyzed according to different departments to evaluate the effects of the intervention measures.

RESULTS

The total disqualification rate in the preanalytical phase obtained from September 2008 to August 2009 was 1.36%, and the major types of disqualified samples were coagulation of anticoagulant sample, sample inadequacy, sample container error, sample information error, and sample type error. After application of established training system, respondents' quality awareness on preanalytical samples changed dominantly, and respondents' own behavior and behavior to others also changed notably. The total disqualification rate in preanalytical phase dropped to 0.94%, among 33 clinical departments, the preanalytical errors in 25 departments decreased to various degrees, and 10 departments had overall decreasing amplitude over 50%.

CONCLUSIONS

The overall effect of the application of established training system is very good, and the disqualification rate of the major departments decrease to various degrees.

Reduction of preanalytical errors in clinical laboratory through multiple aspects and whole course intervention measures

OBJECTIVE

Errors in preanalytical phase decrease the accuracy of reports from clinical laboratory department. Considering the disqualified rate of preanalytical sample in our hospital, we performed several intervention measures to improve the situation.

METHODS

The disqualified sample types and major causes of errors in the preanalytical phase were investigated in clinical laboratory department from September 2008 to August 2009. In the following year, we utilized multiple measures to properly intervene the key points of whole sample collection process, and the preanalytical errors were reanalyzed trimonthly, then the disqualification rate of total, major disqualified sample types and different test groups were calculated to evaluate the effects of the intervention measures.

RESULTS

The total disqualification rate in the preanalytical phase obtained from September 2008 to August 2009 was 1.36%, and the major types of disqualified samples were coagulation of anticoagulant sample, sample inadequacy, sample container error, sample information error and sample type error. After one year intervention through key points of whole preanalytical sample collection process, the total disqualification rate dropped to 0.94%, and the disqualification rate of coagulation of anticoagulant sample, sample inadequacy, sample container error, sample information error, and sample type error decreased by 20.45%, 28.00%, 25.00%, 76.92%, and 66.66%, respectively. As for test groups, the decreasing amplitude of biochemical, routine, immunological, microbiological and emergency test group was 47.36%, 33.33%, 20.00%, 50.00%, and 21.43%, respectively.

CONCLUSIONS

The overall effect of the interventions is very good, and the disqualification rate of the main causes decreases to various degrees.

Role of training activities for the reduction of pre-analytical errors in laboratory samples from primary care

BACKGROUND

The presence of pre-analytical errors (PE) is a usual contingency in laboratories. The incidence may increase where it is difficult to control that period, as it is the case with samples sent from primary care (PC) to clinical reference laboratory. Detection of a large number of PE in PC samples in our Institution led to the development and implementation of preventive strategies. The first of these has been the realization of a cycle of educational sessions for PC nurses, followed by the evaluation of their impact on PE number.

METHODS

The incidence of PE was assessed in two periods, before (October-November 2007) and after (October-November, 2009) the implementation of educational sessions. Eleven PC centers in the urban area and 17 in the rural area participated. In the urban area, samples were withdrawn by any PC nurse; in the rural area, samples were obtained by the patient's reference nurse. The types of analyzed PE included missed sample (MS), hemolyzed sample (HS), coagulated sample (CS), incorrect sample (ISV) and others (OPE), such as lipemic or icteric serum or plasma.

RESULTS

In the former period, we received 52,669 blood samples and 18,852 urine samples, detecting 3885 (7.5%) and 1567 (8.3%) PEs, respectively. After the educational intervention, there were 52,659 and 19,048 samples with 5057 (9.6%) and 1.256 (6.5%) PEs, respectively (p<0.001). According to the type of PE, the incidents compared before and after compared incidences were: MS, 4.8% vs. 3.8%, p<0.001; HS, 1.97% vs. 3.9%, p<0.001; CS, 0.54% vs. 0.25%, p<0.001; ISV, 0.15% vs. 0.19% p=0.08; and OPE, 0.3% vs. 0.42%, p<0.001.

CONCLUSIONS

Surprisingly the PE incidence increased after the educational intervention, although it should be noted that it was primarily due to the increase of HS, as the other EP incidence decreased (MS and CS) or remained unchanged (ISV). This seems to indicate the need for a comprehensive approach to reduce the incidence of errors in the pre-analytical period, as one stage interventions do not seem to be effective enough.