Traditionally, in most point-of-care testing (POCT) programmes, there has been a large focus on Quality Control (QC) analysis. Through regular monitoring of internal QC and proficiency testing reports, we can evaluate the performance of instruments. We can identify any inaccuracies and/or imprecision that may exist and take appropriate action.
For the most part, instruments provided by long-established manufacturers perform very well. Reviewing QC summary data, I rarely see problems from one month to the next. If we were to focus exclusively on the QC performance of our devices, we would be fooled into thinking that all is well in the world of POCT.
While the analytic performance of our instruments must be established and monitored, we must also focus on where the main errors of POCT are happening – at the patient’s bedside. This includes all areas of the testing pathway: pre-analytical, analytical and post-analytical. When you start to investigate, you will find an Aladdin’s cave of errors. Only by identifying and addressing these “treasures” will you start to have a real impact on the quality of your POCT programme. Then the rewards will follow in the form of safe patient care.
Most pre-analytical errors cannot be identified by the instruments during the testing process. Even if they were, frequently no hard stops exist. Incorrect results can be released that may be acted upon inappropriately. Standardisation of processes and good training, whilst very important, will only go so far. We need to come up with new ways of capturing these errors. Ultimately, the onus is on the end users to follow the correct workflow, however, we should have ways to pick up mistakes when things go wrong. The good news is there are tools at our disposal to help us.
Pre-analytical
Patient ID errors
Every POCT programme faces patient misidentification errors to one extent or another. How can we minimise or eliminate them? The incident rates you see are dependent on the type of workflow you have created and the type of technology you have at your disposal. Using a completely manual process where the operator types the patient ID into the POCT device, you will see high error rates. In a workflow where the devices are configured to read a barcode, you will see fewer. However, barcode technology is not the nirvana we all wish it were. Whilst we create workflows with an expectation that the end user will do the right thing and scan the patient wristband, there are occasions, for example, where patient labels (with barcodes) are scanned instead. This occurs because institutions are reliant on patient labels for other hospital workflows. These may find their way into POCT workflows with unintended consequences. Sometimes the wrong patient ID will be scanned.
New technologies are becoming available that will reduce these errors further, such as pre-barcoded blood gas syringes, removing the need for a patient label altogether. The technology transfers the ID electronically from the wristband to the syringe.
Another is 2D barcode technology. Most often, patient labels only have the 1D (linear) barcode present. If it were possible to create a 2D MRN barcode on the wristband (many institutions already have), then you could create a hard stop that would force the operator to scan the patient wristband instead.
Positive Patient ID (PPID) is another very useful tool where instruments can pull up specific patient details such as name and date of birth when the patient’s Medical Record Number is scanned. This information is pulled from the Admission Discharge Transfer (ADT) interface. This is a second confirmation that the nurse has the correct patient in front of them. Cleveland Clinic Abu Dhabi has this technology in place for some of our devices.
Error Detection: To identify these errors, you need to monitor patient results regularly. This can be done through manual checks on the device and/or middleware (if you have connectivity in place). You will quickly see nonsensical IDs on the instrument or results in the middleware that did not transmit to the EMR for a plethora of reasons. Share the data with nursing leadership. Use it as leverage to secure funding for IT enhancements (if there is no IT integration). At Cleveland Clinic Abu Dhabi, correct patient ID is a KPI for the POCT programme with a compliance rate of 100 per cent. We have zero tolerance for this type of error. We constantly look at new ways to improve our processes to meet our 100 per cent metric.
Blood gas errors
Blood gas sample collections are prone to many errors that are difficult to monitor. The most common error relates to the samples collected from central/venous and arterial lines. It is important that the nurse discards an adequate amount of dead volume before specimen collection. If the correct technique is not used, sample contamination with Potassium Infusions, Total Parenteral Nutrition (TPN) or saline can occur. This contamination may not be identified by the operator when running the sample on the blood gas analyser and incorrect results can be released.
Error detection: Ensure weekly or monthly reviews of reports of patient results that exceed the Analytical Measuring Range (AMR) of the Instrument. Whilst you do see results like this occasionally for very sick patients, these are the exceptions. For example, a patient result with sodium and chloride results exceeding the AMR is highly indicative of saline contamination. A protocol could be introduced for any high glucose values for those patients on TPN. If TPN contamination is suspected, a repeat sample should be taken to confirm.
Capillary sample collection errors
The most common pre-analytical errors related to the capillary sampling include “milking”. Excessive massage and squeezing around the puncture site are often done when capillary blood flow is not adequate in order to obtain the necessary blood volume to fill the strip. Excessive massage may cause falsely decreased concentrations of some parameters due to the dilution of the blood sample with tissue fluid. To avoid this, a lancet of adequate size should be used to ensure satisfactory skin incision.
Error Detection: Witness audits are a useful tool but will not pick up all errors. Some instruments report “underfilled” if there is not enough sample detected on the strip. These could be pulled manually from the device, or perhaps through middleware. Some strips also provide a number of internal checks before a patient result is reported, including humidity checks for example. It may be possible to retrieve this data too. Cartridge-based systems also give errors that can be captured and traced back to the operator. Cleveland Clinic Abu Dhabi has this in place for some of our cartridge-based systems. It is a great way to monitor competency and address poor practice.
Analytic phase
One great way to detect errors with your instrument is to perform periodic method comparisons with the laboratory. Nothing is more powerful than performing these comparisons and sharing good correlations data with your end users. This will instil confidence in our POCT devices. It may also identify errors that may not be picked up with QC. The relationship between POCT and the laboratory needs to be fully understood at the outset, and throughout the life cycle of the POCT method.
For example, split-sample analysis between the POCT and the laboratory will inform us if the electrolytes and metabolites reported on our blood gas analysers are accurate.
If your POCT Urinalysis instruments are different from the main laboratory, physicians may ask why they are seeing “discrepancies” between the two technologies. Sometimes the resulting cut-offs for the semi quantitative tests are different. A 1+ on the POCT (less than 25mg/dl) is not equivalent to a 1+ on the laboratory analyser (less than 50 mg/dl). It’s not that the POCT is an inferior method, but that the instruments are reporting at different concentrations. This information needs to be shared and understood by end users.
PT/INR is another area where there is confusion between the POCT and what the laboratory reports. Periodic comparisons are vital to ensure the relationship between both is understood. We need to know to what extent there is an agreement between the two methods and where the relationship is lost.
Post-Analytical
Once the result has been generated, how do we ensure that it is entered into the right Medical Record (MR)? If you are lucky enough to have your POCT interfaced to the MR, then this is not so much of an issue. But for many POCT programmes, this integration is lacking, and the hospital is reliant on the end user to either scan the reports or manually enter them. This is a high-risk activity prone to many errors. The consequences for a patient being treated based on a wrong result are very serious.
Another area where there is poor compliance is critical result documentation. This is for POCT values that are deemed critical by the organisation. As per Joint Commission International (JCI) , the requirements are documentation in the medical record with the date and time that the end user informed the physician. There must be evidence of critical result read back by the physician also.
Error detection: Audit, Audit, Audit! Share the compliance data of correct result entry with hospital leadership. Monitor over time to assess if non-compliance is an issue. In the acute care setting, this is a powerful tool to justify IT integration.
For critical result documentation, we must audit as well. Some instruments do have features that allow us to capture the JCI requirements. Use whatever tools you have at your disposal.
Conclusion
The traditional focus of POCT coordinators on the analytical performance of instruments needs to change. I encourage those of you who work in this challenging discipline to be creative. There is a lot we can do to pick up these “treasures” and share with our nursing colleagues to address. Nursing leadership will be happy and willing to assist with any non-conformities found. We can add real value to our clinical colleagues and be more impactful. By identifying and addressing patient-testing errors, future mistakes can be prevented.