Establishing quality indicators for point of care glucose testing: recommendations from the Canadian Society for Clinical Chemists Point of Care Testing and Quality Indicators Special Interest Groups

Introduction

Monitoring quality indicators (QIs) is an important part of laboratory quality assurance (QA) and essential to patient safety. QIs are useful tools to aid in priority setting and for improving performance [1], [2], [3]. Monitoring of QIs is also an important aspect of the quality improvement process and is included as an accreditation standard requirement for laboratories as well as for point of care testing (POCT) programs [4]. POCT refers to diagnostic testing performed outside the laboratory environment, closer to the patient, often at the patient bedside. POCT is typically not performed by laboratory staff and instead by clinical staff, such as physicians or nurses, thus increasing the need for clear guidance and standardization for process monitoring.

The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) and the Working Group, Laboratory Errors and Patient Safety (WG-LEPS) published multiple guidelines on QI monitoring, including a recommended set of 53 QI covering the entire testing process [2, 3, 5]. Oriented toward central laboratory processes, these recommendations do not include any POCT specific QIs. As the establishment of benchmarks is essential for improvement of quality, standardization of QIs is essential for the development of quality specifications [3, 5]. Studies have demonstrated that QI monitoring is an important component of quality assurance for POCT, specifically to identify areas for training and process improvement [6]. Error rates in the analytical phase are relatively low compared to the pre and post-analytical phases in Laboratory medicine, partly because of the implementation of QI monitoring and quality specifications [7]. POCT remains vulnerable to quality issues, especially where process checks are lacking, which can negatively affect patient care [8], particularly as POCT use expands.

Materials and methods

Results

Field validation of high priority QIs

Positive patient identification quality indicator (PPID QI)

Preliminary validation of this QI was performed within the working group, where the data submission criteria were determined as the number of POCT glucose performed with invalid patient ID as a percentage of the total number of POCT glucose performed in one month. Members of the working group, representing seventeen sites, submitted these data. The percentage of POCT glucose performed with invalid patient ID ranged from 0.1 to 72.6% with an average of 12% and median of 6%. The Program for Quality Indicators Comparison of the Société Québécoise de Biologie Clinique (SQBC) was then used to implement the PPID QI developed by the CSCC SIG POCT QIs to facilitate data submission from laboratories across Canada. This QI comparison program was developed by the SQBC who collaborates with the CSCC to promote participation from laboratories across Canada as well as with the WG LEPS of the IFCC to participate in the international QI standardization effort [10]. To further validate the QI, institutions across Canada with POCT glucose programs were invited to submit data for one month in 2022. Fifty-seven sites across Canada submitted data for the PPID QI. The percentage of glucose tests performed with an invalid patient ID ranged from 0 to 87%. The percentage ranged from 26 to 87% in sites without an ADT connection to the POCT meters and 0–67% in sites with an ADT connection (Table 5, Figure 1), which reached statistical significance, demonstrating the importance of connectivity. The 25th and 75th percentiles for all submitted data were calculated, based on the recommendation by the WG-LEPS of the IFCC. Data were also separated by sites with ADT vs. without ADT connectivity as shown in Table 5. Sites with the percentage of POCT glucose tests performed without valid PPID less than the calculated 25th percentile are considered high performing sites, moderate performing with a percentage between the calculated 25th and 75th percentiles and low performing if the percentage of POCT glucose tests performed without valid PPID exceeds the calculated 75th percentile.

Table 5:

The 25th, 50th and 75th percentiles of invalid PPID percentage calculated from the PPID data submitted from 57 sites across Canada.

	Percentile
	25th	Median	75th	No. labs
Overall	0.16%	4.31%	32.29%	57
With ADT	0.12%	1.40%	12.20%	48
Without ADT	51.03%	54.55%	67.44%	9

1. ADT, admission, discharge, transfer; PPID, positive patient identification. Low performing sites are those with percentage of POCT glucose tests performed without valid PPID >75th percentile, moderate between the 25th–75th percentile and high performing if the percent POCT glucose performed without valid PPID is <25th percentile.

Figure 1:

Box and Whisker plot showing the percentage of POCT glucose tests performed with invalid PPID in sites without (blue) and with an ADT connection (grey) to the glucose meters. Averages shown with standard deviation. *p<9.63E-06 with a two tailed heteroscedastic t-test. Edges of the boxes represent the 25th and 75th percentiles. PPID, positive patient identification; ADT, admission, discharge, transfer; POCT, point of care testing.

Repeat of critical glucose results

Participants were asked to submit the total number of critically high POCT glucose results at their site for one month in 2022, as well as the number of critically high results that were repeated within 10 min (n=12 sites provided data). Repeat tests could be either measured by a POCT or a central laboratory method. The percentage of discordant repeat results were also submitted (Table 6). The percentage of repeated results ranged from 0 to 50% (target 100%) and the percentage of discordant repeat results ranged from 0 to 56% (target 0%). Discordant results were defined as ≥20% different from the initial result obtained.

Table 6:

Summary of critically high glucose results repeated and the number discordant results upon repeat. Green indicates “high performing”, yellow “moderate” and red “low” for repeat of critical results.

1. ^aSite data not included in calculations as this site does not have a critical POCT glucose repeat policy. Discordant repeat results were defined as repeats ≥20% different than the original result. n/a means no data were submitted from this participant.

The 25th and 75th percentiles of performance were calculated for the percentage of repeated results. Sites were considered low performing for repeat of critical results when the percent repeated was less than 7% (25th percentile), moderate between 7 and 37% and high if more than 37% (75th percentile) of results were repeated.

Discussion

In this report, we demonstrate an approach to establishment and monitoring of QI for POCT. The process begins with mapping of the testing process, to identify error prone steps, followed by risk assessment of each step, to prioritize steps for QI monitoring, based on risk and ability to monitor. The data presented here illustrate the value and importance of establishing, monitoring and follow-up of QIs, specifically for POCT using blood glucose meters as an example. Sites with the poorest compliance with the use of a valid patient identification for POCT glucose testing were those that do not have ADT connectivity to the POCT meters with results transmission to the electronic medical record. These findings demonstrate the importance of investments in these capabilities by healthcare facilities. The non-compliance data presented here include instances where an unregistered, or “dummy” patient identifier was used for testing with no indication that a result was documented in the correct patient chart. Many sites employ unregistered patient identifiers as a safeguard, to be used only in emergency situations when the patient identification is unknown. Generally, there are follow-up processes in place for results to be documented in the correct patient chart once the patient’s medical record number is reconciled and the emergency has been dealt with. Sites participating in this study reported low compliance with the follow-up charting processes and frequent inappropriate use of unregistered patient identifiers for testing. Invalid PPID suggests that these results are often not documented in the patient chart and are therefore missing from the patient’s medical record.

We found generally low operator compliance with repeat testing of critically high POCT glucose results. Even in sites deemed “high performing” based on the 25th and 75th percentiles calculated, only up to half of all critically high results were repeated. Low compliance exists despite all but one of the participating institutions having a recommendation in place that repeat testing should be performed or a formal policy stating that all critically high results must be repeated. This also demonstrates the importance of professional judgment on what constitutes acceptable performance along with interpreting results within the context of the clinical picture.

The low rate of compliance with repeating critically high glucose measurements is concerning given the high rates of discordant repeat results, up to 56% in one site. These data demonstrate the importance of repeating critically high POCT glucose results before dosing insulin. Our findings are not dissimilar to previous studies, which found insufficient handwashing as the primary cause of falsely elevated POCT glucose results [7].

The approach taken here to establish QI for POCT has been used by others [11]. A recent study used a Failure Mode and Effects Analysis (FEMA) to perform a risk assessment for potential pre-analytical errors when performing POCT blood gas analysis [11]. The authors identified twelve quality assurance or process steps in the pre-analytical phase and recommended key performance indicators (KPI) to monitor for several of the steps. Positive patient identification was among the steps with highest risk of failure and identified with the recommended calculated metric as the percentage of unidentified samples, consistent with QI recommended in the current study.

A strength of our study is the inclusion of field validation of QI, with data gathered from several institutions across health jurisdictions and located throughout Canada. Gathering of these data was challenging for sites that do not employ connectivity for POCT instruments, resulting in manual data extraction and analysis. Data extraction from the laboratory information system of middleware was reported as relatively straightforward for sites with connectivity.

The main limitation of the current study is that the process map and risk assessment are based on the self-assessment and experiences of the authors, who are largely Clinical Biochemists and Laboratory Directors who oversee POCT in their institutions within Canada. The process map and risk assessment do not include the views of the individuals who perform POCT, meaning there may be factors or situations that have been overlooked. Another limitation is that the error rates identified for these two QIs are a combination of non-compliance with the procedure as well as the ‘true error’.

In conclusion, monitoring QIs for POCT are important to ensure quality and patient safety. Here a Canadian consensus process for establishing these QIs is described. The findings demonstrate the utility of QIs for identification and comparative performance to improve the quality of POCT.