Pre-analytical error for three point of care venous blood testing platforms in acute ambulatory settings: A mixed methods service evaluation.

INTRODUCTION: Point of care blood testing to aid diagnosis is becoming increasingly common in acute ambulatory settings and enables timely investigation of a range of diagnostic markers. However, this testing allows scope for errors in the pre-analytical phase, which depends on the operator handling and transferring specimens correctly. The extent and nature of these pre-analytical errors in clinical settings has not been widely reported.
METHODS: We carried out a convergent parallel mixed-methods service evaluation to investigate pre-analytical errors leading to a machine error reports in a large acute hospital trust in the UK. The quantitative component comprised a retrospective analysis of all recorded error codes from Abbott Point of Care i-STAT 1, i-STAT Alinity and Abbott Rapid Diagnostics Afinion devices to summarise the error frequencies and reasons for error, focusing on those attributable to the operator. The qualitative component included a prospective ethnographic study and a secondary analysis of an existing ethnographic dataset, based in hospital-based ambulatory care and community ambulatory care respectively.
RESULTS: The i-STAT had the highest usage (113,266 tests, January 2016-December 2018). As a percentage of all tests attempted, its device-recorded overall error rate was 6.8% (95% confidence interval 6.6% to 6.9%), and in the period when reliable data could be obtained, the operator-attributable error rate was 2.3% (2.2% to 2.4%). Staff identified that the most difficult step was the filling of cartridges, but that this could be improved through practice, with a perception that cartridge wastage through errors was rare.
CONCLUSIONS: In the observed settings, the rate of errors attributable to operators of the primary point of care device was less than 1 in 40. In some cases, errors may lead to a small increase in resource use or time required so adequate staff training is necessary to prevent adverse impact on patient care.

Introduction

Point of care testing (POCT) has gradually grown in popularity, both in primary care, where it is argued to be a key element of antimicrobial stewardship [1], and in secondary and tertiary care as new technologies emerge [2-4]. Although analytical performance of POCT platforms is available in manufacturers’ technical documentation, the frequency and nature of errors that may arise when using these platforms in the clinical environment is less commonly reported, and definitions of pre-analytical error are wide-ranging and inconsistently applied [5].

In practice, POCT may be performed by multiple operators with varying levels of experience [6]. This makes errors that may be attributable to the user in the pre-analytical phase of testing, such as incorrectly filling or mishandling a cartridge, especially relevant. There is a concern that the risk of error using POCT in clinical settings may be higher than in the laboratory [7], although even in the laboratory environment, rates of pre-analytical error may be non-negligible [8-10].

A study in secondary care in the UK estimated an overall error rate from a variety of POCT devices as less than 1% of total tests performed, with 32% of all errors occurring in the pre-analytical phase and the majority of these having only minor impact on patient outcomes, such as delayed diagnosis [11]. However, these rates were based on errors logged individually by the operators rather than automatically recorded.

There is therefore limited published evidence for the frequency of, and reasons for, pre-analytical errors when using POCT devices in ambulatory settings. The aims of this mixed-methods study, set in a large acute hospital trust in the UK, are to quantify error rates by error category, to identify patterns of errors by hospital department and over time, and to explore the processes involved in POCT and possible reasons underlying pre-analytical error using qualitative, ethnographic research.

Materials and methods

The mixed methods design used was convergent parallel [12]. The quantitative and qualitative components were conducted and analysed separately, and the findings from both components were then integrated, to provide a more comprehensive picture of pre-analytical error in this setting.

Quantitative

Setting

The quantitative component uses routinely collected error code information reported by POCT blood testing platforms in use at Oxford University Hospitals NHS Foundation Trust (OUHFT) between January 2016 and December 2018, namely the Abbott i-STAT [12, 13], Abbott i-STAT Alinity [14], and Afinion AS100 [15] and Afinion 2 [16] analyzers. More than 40 settings were classified by location and, where applicable, department, ward or care unit. These reflect the wide scope in which POCT is deployed, including in particular emergency departments and acute ambulatory units, but also pre-operative assessment units, departments providing medical imaging services, and community hospitals. Medical specialties in which POCT is carried out are also wide-ranging and include radiology, cardiology, gynaecology, renal medicine and paediatrics. In most locations, multiple healthcare staff performed POCT, but reliable user-level frequency data were unavailable. Tests used and relevant timeframes are shown in Table 1.

Table 1

Tests available on POCT platforms in use during study period.

Device	Cartridge	Qualitative (secondary analysis, 2014–15)	Qualitative (primary study, Jan-Feb 2019)	Quantitative
i-STAT	ACT			Jan 2016 –Dec 2018
	BNP
	CG4+	✓	✓
	CHEM8+	✓	✓
	Creatinine (Crea)
	E3+
	Glucose (G)
	β-hCG
	PT/INR	✓
	Troponin (cTnI)	✓
i-STAT Alinity	CG4+			Apr 2017 –Dec 2018
	CHEM8+
	Creatinine (Crea)
Afinion	CRP	✓		Jul–Dec 2018
	HbA1c
	Lipids

A large majority of POCT in OUHFT uses venous blood samples and so results presented here are for venous samples only. Radiometer blood gas analyzers [17] are also in widespread use in OUHFT but are not considered further here as a comprehensive report of error codes could not be obtained retrospectively.

Data management

POCT platforms in OUHFT are connected to a POCcelerator Data Management system (Siemens Healthineers [18]), overseen by the Department of Clinical Biochemistry. Data were extracted using the Structured Query Language Report and Statistics Module. POCT information was obtained by cross-referencing a ‘log extract’ data file with a ‘workload’ data file. The log extract file contains every instance in which the device returned an error, with the machine and location ID, a time stamp recorded to the nearest minute, and the error code or code category. Information messages and messages relating to quality control were removed before analysis. Information about which analyte was being tested when errors occurred is not available. The workload data file lists every point of care (POC) test for which a valid result was returned: the machine and location ID, a time stamp, and the name of the analyte tested or cartridge used, but not the test result itself. Comparison of these files allows error rates to be estimated.

Individual POCT machines frequently moved between clinical locations and often had intermittent periods out of operation. Data from each machine were therefore restricted to periods for which both reliable workload and error data were available. For locations using more than one machine, data from each machine were aggregated so that results are presented at a per-location level, consistent with our focus being the assessment of operator errors rather than errors attributable to the particular analyzer. Duplications with identical time stamp, location and error code were removed.

Error code classification

We follow the taxonomy suggested by Meier & Jones [19], following Kost [20]. Our focus is on the ‘specimen collection’ and ‘specimen evaluation’ stages of the pre-analytical phase, while recognising that in some circumstances errors reported as occurring in the analytic phase may also have been caused by incorrect specimen collection. Error codes are subsetted to attribute failures likely to be due to user error, such as overfilling or underfilling the cartridge, and those that are outside the influence of the user, such as environmental factors and analyzer malfunction.

For the i-STAT between February 2017 and December 2018, error codes taken from the i-STAT technical manual [21] that occurred at least once in the study dataset were grouped into categories of comparable codes (S1 Appendix), and further grouped into those deemed likely to correspond to operator errors resulting in cartridge wastage. This categorisation is not definitive as some codes could plausibly correspond to either an operator error or a non-operator error. Before February 2017, the log extract record did not contain specific i-STAT error codes but instead grouped codes into eight broader categories (S1 Appendix). Descriptive labels applied to these categories were inconsistent, with a preponderance of codes being assigned to a catch-all ‘Other’ category, so error rate information for the i-STAT is reported separately for January 2016-February 2017 and February 2017-December 2018.

Numerical i-STAT Alinity error codes were categorised based on the technical manual [22] in terms of their cause and suggested resolution, and a similar procedure was followed for the Afinion [23] (S1 Appendix).

Statistical analysis

Monthly error rates were estimated as the number of recorded errors carrying the appropriate error code(s) divided by the total workload. The latter was estimated as the sum of the number of recorded errors and the number of recorded uses from which a valid test result was obtained.

For example, an attempt that produced an error code corresponding to an underfilled cartridge, followed by a second correctly-performed attempt on the same patient, would contribute one recorded error and two test attempts (workload). An attempted test that led to a device failure and no subsequent test being successfully performed would contribute one recorded error and one test attempt. It was not possible to determine with certainty whether any individual recorded error resulted in wastage of a cartridge as opposed to reinsertion of the same cartridge.

Monthly error frequencies and rates, with 95% confidence intervals (CIs), are expressed in tabular form and graphically to show trends over time. Results are also presented by error code category.

For the i-STAT, information is also presented to illustrate the between-location (e.g. hospital department or ward) variation in error rates, using numerical and graphical summaries such as stacked bar charts and forest plots. Pearson’s correlation was used to quantify the association between location-specific error rate and total workload. Details of specific locations are not disclosed. Statistical analysis used R version 3.5.2 [24].

Ethics and Information Governance

As this retrospective evaluation of routinely-collected data does not use personally-identifiable patient information, ethics committee approval was not mandated. The project was prospectively approved (November 2018) as an OUHFT service evaluation (Datix reference number CSS-BIO-4 5315).

Qualitative

There were two strands to the qualitative component of this study. The first involved a secondary analysis of a primary qualitative ethnographic dataset. It was conducted in a community-based geriatrician-led ambulatory care setting aimed at frail and older patients in Oxford Health NHS Foundation Trust (OHFT) between 2014 and 2015, to explore how POCT had become embedded in working practice [25].

The second strand comprised a prospective ethnographic study which followed on from the secondary analysis and took place in consultant-delivered acute ambulatory care at OUHFT. The POCT platforms used in these settings are shown in Table 1.

Data collection

The dataset used for the secondary analysis comprised ethnographic field notes of observations (14 sets) and transcripts of semi-structured interviews with clinical staff using POCT (six interviews). The observations encompassed 14 occasions of venous blood sampling for POCT, resulting in the use of 27 cartridges– 15 CHEM8+, 10 CRP, one troponin and one INR. Blood sampling was observed on two occasions where blood could not be obtained.

The prospective ethnographic field work was conducted in January and February 2019. It comprised three visits (and corresponding sets of field notes), encompassing seven occasions of venous blood sampling, resulting in nine cartridges being used–three CHEM8+ and six CG4+.

Data analysis

The purpose of the secondary analysis was to investigate an existing ethnographic dataset, with particular focus on workflow issues and pre-analytical errors in carrying out the POC tests in an ambulatory care setting. The data in the primary study had been analysed thematically. A coding scheme had been developed which included both a priori items and emerging ideas. The data had been systematically coded using NVivo 10 and codes had been combined into broader categories and themes.

The analytic techniques used in the secondary analysis were similar to those applied in the primary analysis as recommended in the methodological literature [26]. This included reading and re-reading field notes and transcripts, open coding, with codes grouped into categories and themes. For the secondary analysis, a coding scheme was developed which included both the issues around workflow and pre-analytical errors and emerging ideas. The data were systematically coded using NVivo 11. The codes were combined into broader categories and themes.

The data collected in the prospective ethnographic strand of the qualitative component were also analysed thematically. A coding scheme was developed which included a priori items which had resulted from the secondary analysis of the original ethnographic dataset, areas of interest put forward by the research team for follow-up in the prospective ethnography, and emerging ideas. The data were systematically coded using NVivo 11 and codes were combined into broader categories and thematic areas.

In the primary study, written informed consent was obtained from all interview participants in which they agreed that information about them could be stored in a way that meant they could not be identified so that the information could be used for research in the future. The study was approved by the University of Oxford Central University Research Ethics Committee (reference MSD-IDREC-C1-2014-113) and the relevant NHS Trust.

The prospective qualitative component was part of the service evaluation previously described. Permissions were sought for the qualitative researcher to be given access to the OUHFT settings where POCT was practised. Staff were made aware of the reason for the observation and all involved in POCT agreed to be observed. In all the instances which brought the observer into contact with patients, verbal consent from patients was sought first.

Results

i-STAT

Across the full study period (January 2016-December 2018) and all locations, 113266 i-STAT tests were performed. The most commonly used test cartridges were Troponin, CHEM8+, Creatinine and CG4+, which together accounted for 88% of total workload (Table 2). Overall, there were 105578 returned results and 7688 errors, an error rate of 6.8% (95% CI 6.6% to 6.9%). This error rate was slightly higher in January 2016-February 2017 (2584/35113, 7.4% (7.1% to 7.6%)) than in February 2017-December 2018 (5104/78153, 6.5% (6.4% to 6.7%)).

Table 2

Total test cartridge use, i-STAT.

Cartridge	Total workload (%)
ACT	4325 (4.1%)
BNP	1713 (1.6%)
CG4+	10494 (9.9%)
CHEM8+	22695 (21%)
Creatinine (Crea)	14260 (14%)
E3+	136 (0.1%)
Glucose (G)	935 (0.9%)
β-hCG	2489 (2.4%)
PT/INR	2902 (2.7%)
Troponin (cTnI)	45269 (43%)

Table 3 shows a breakdown of errors by error category (February 2017-December 2018). Of 5104 recorded errors, 1783 were assigned to user error categories (2.3% (95% CI 2.2% to 2.4%)). Overfilling occurred on 573 occasions (0.7%) and underfilling on 709 occasions (0.9%). In January 2016-February 2017, there were 2584 recorded errors, of which 694 (2.0% (95% CI 1.8% to 2.1%)) fell into the categories of underfilling, overfilling or insufficient sample.

Table 3

Total frequencies and error rates of error code categories, i-STAT, February 2017–December 2018.

Error category	Frequency (%)	Likely user error	Total frequency (%)
Overfill	573 (0.7%)	✓	1783 (2.3%)
Underfill	709 (0.9%)	✓
Insufficient sample	207 (0.3%)	✓
Insertion error	8 (0.01%)	✓
Immunoassay–poor filling	286 (0.4%)	✓
Battery error	465 (0.6%)		3321 (4.2%)
Barcode error	41 (0.05%)
Coagulation error	177 (0.2%)
Cartridge error	1284 (1.6%)
Contact error	394 (0.5%)
Analyzer/motor error	522 (0.7%)
Immunoassay–analysis fluid	130 (0.2%)
Immunoassay–error in QC run	112 (0.1%)
Immunoassay–atypical data stream	159 (0.2%)
Field length	37 (0.05%)
TOTAL	5104 (6.5%)		5104 (6.5%)

Percentages are based on a total denominator of 78,153 tests. Refer to S1 Appendix for error category descriptors.

Fig 1 shows the number of recorded results, user errors and non-user errors by location, for the 15 locations with the highest overall usage (more than 1000 tests in total). Although details of specific locations cannot be disclosed, emergency departments and acute ambulatory settings were among those with high frequencies of POCT. Error rates accounted for only a small proportion of total uses, with some variation between locations in the numbers falling into different user error categories such as underfill and overfill.

Fig 1

i-STAT error rates in 15 locations with highest workload.

Total numbers of returned results, user errors and non-user errors (upper panel); breakdown of all errors by selected error category (middle panel); breakdown of user errors by selected error category (lower panel). In the upper panel, numbers of errors in some locations are too small to be easily visible.

Fig 2 compares the estimated error rates and user error rates by location. Estimated error rates ranged between around 4% and 10% and user error rates were similar across locations, with most lying between 1% and 3%.

Fig 2

Estimated rate of all i-STAT errors and user errors (as a proportion of total workload) by location.

Each line shows the estimate of the proportion in a particular location (blue square) and 95% confidence interval (horizontal bars). The grey vertical line shows the estimated proportion across all locations.

Over time, there was a steady increase in i-STAT use until September 2018 owing to the gradual introduction of machines into new locations, with a decline from September 2018, pending the introduction of a high sensitivity cardiac troponin assay. Error rates declined to around 7% during the second half of 2017, followed by a steeper decline to 4–5% towards the end of 2018 (S1 Graph).

The number of tests performed by location was not closely associated with the overall error rate (Pearson’s r = −0.11, Fig 3), nor with the user-attributed error rate (r = −0.17).

Fig 3

Relationship between i-STAT error rate and total workload.

All errors (upper panel) and user-attributed errors post-February 2017 (lower panel). Each point represents a different location. Locations with fewer than 200 total uses are excluded to reduce the effect of small sample variability.

i-STAT Alinity

i-STAT Alinity workload and error data were available from six locations (April 2017-December 2018). There were 1829 recorded uses, comprising 1661 returned results and 168 errors (9.2%, 95% CI 7.9% to 10.6%), of which 95 (5.2%, 95% CI 4.2% to 6.3%) were classified as likely user errors. The breakdown of these codes by error category is shown in Table 4. The largest contributing component was Cause 3, encompassing a range of possible user errors primarily relating to cartridge handling.

Table 4

Total frequencies of error code categories, i-STAT Alinity.

Error category	Frequency	Likely user error	Total frequency (%)
Cause 3 codes (Cartridge rejected)	40	✓	95 (5.2%)
Cause 6 codes (Excess blood)	18	✓
Cause 7 codes (Cartridge rejected)	14	✓
Cause 8 codes (Insufficient blood)	16	✓
Cause 9 codes (Sample rejected)	7	✓
Cause 15 codes (Cartridge rejected)	4		15 (0.8%)
Codes of unspecified cause	11
Process error codes	58		58 (3.1%)
TOTAL	168		168 (9.2%)

Refer to S1 Appendix for error category descriptors.

Afinion

Reliable data could be obtained from three locations in which the Afinion was in operation for CRP testing (July-December 2018). One of these was in ambulatory care and two in community hospitals. During this period there were 556 recorded uses, comprising 377 returned results and 179 errors (32% (95% CI 28% to 36%)), including 50 probable user errors (9.0% (95% CI 6.8% to 11.8%)).

Table 5 shows the breakdown of recorded errors by error code. The majority of probable user errors were attributed to insufficient sample volume (code 201), and more than half of these occurred at a single location.

Table 5

Total frequencies of error code categories, Afinion.

Error code	Frequency	Likely user error	TOTAL
201 (Insufficient volume)	35	✓	50
203 (Wrong sample)	6	✓
208 (Test cartridge previously used)	9	✓
101, 102 (Hematocrit error)	7		44
213, 214 (Cartridge or analyzer failure)	6
302 (Analyzer failure)	31
404 (Operator ID error)	82		85
MT_16002 (Barcode error)	3
TOTAL	179		179

Refer to S1 Appendix for error category descriptors.

From the secondary analysis we built up a step-by-step picture of the whole process involved in venous blood sampling for POCT in a community-based ambulatory care setting. In the follow-up ethnography, we examined the process in a different setting, intentionally focusing on the factors which potentially contributed to pre-analytical errors. There were no discrepancies between the findings which derived from the secondary analysis and those from the follow-up ethnography. Our findings show that, across both settings, staff were aware of ways in which errors might occur in the blood sampling and POCT process. Their main concerns were difficulties with cartridge-based testing, difficulties with obtaining blood samples and accessing POCT devices. Staff were taking active steps to minimise the impact of these difficulties.

Staff awareness of factors underlying pre-analytical error

Staff perceived that the main difficulty they encountered, and an important contributor to pre-analytical error in cartridge-based POCT, was filling the i-STAT cartridges with the right amount of blood for testing:

“I mean the hardest bit is actually just getting the blood into the actual cartridge” [Interview 4, HCA, secondary analysis]

One nurse described the process as ‘fiddly’ and sometimes ‘messy’, with the cartridges ‘temperamental’:

“…it is quite fiddly and sometimes the cartridges are quite hard to fill…they’re, sometimes they’re like a sort of little bit temperamental…you know, for example, that’s the fiddly part is filling up the cartridge and that’s what everybody gets a bit kerfuffled about and sometimes it goes everywhere and it’s a bit messy.” [Interview 3, Nurse, secondary analysis]

The ‘fiddliness’ of the i-STAT cartridges was contrasted with the CRP one:

“The CRP cartridge stops when it has the exact amount of blood it needs–this is better than the i-STAT cartridges, it would be good if all the cartridges were like that.” [Interview 2, HCA, notes, secondary analysis]

“CRP is really easy to use because it’s got like a little straw.” [Interview 3, Nurse, secondary analysis]

Staff experienced problems with both underfilling and overfilling the POCT cartridges. They have found it difficult to deliver the correct amount and are aware that this can lead to error, meaning they are unable to get a result:

“A drop at a time and it’s such a tiny, tiny, it’s the size of a pipette, it’s literally one or two drops, you can’t overfill it so you’ve also got to allow to pull the cap over, use the press stud sort of thing to close it because then it will shoot up, if you put too much in you won’t get a result, if you don’t put enough in…” [Interview 1, HCA, secondary analysis]

Patients attending these ambulatory care settings were often older people living with frailty, and staff reported that this could make the blood sampling process difficult:

“Most of our patients are really difficult to bleed anyway because they’re peripherally shut down or, you know, they’ve got really sort of scarred veins and so you’re kind of going in tiny little veins that easily blow and things and so that’s just the type of patients we tend to get unfortunately.” [Interview 3, Nurse, secondary analysis]

For this reason, some blood samples obtained were very small, if blood could be obtained at all. Using small samples for POCT was seen as problematic by staff as they perceived that this produced error and affected accuracy:

“Or if it’s hard to bleed, if you don’t get a good flow then the results are not going to be accurate and I think that was the potassium levels, I think that can be a big one, they can be really high which is a concern and sometimes the doctors have said can you get me another specimen because I’m not convinced that that is accurate so we’ve had to do it again and sure enough, the potassium levels have been lower and so it’s obviously the way the blood was taken or the length of time before it got in to the machine so you’ve got to understand the importance of doing it properly…” [Interview 1, HCA, secondary analysis]

Similarly, they perceived that taking a long time to draw the blood could produce inaccurate results:

“I’ve learnt that if it is totally abnormal and I’ve had a difficult time getting a blood, or the tourniquet’s been on too long or something like that, you know, then it’s not going to be accurate.” [Interview 1, HCA, secondary analysis]

Staff stressed the importance of being able to process blood taken for POCT as quickly as possible, to avoid the blood sample clotting. In the community-based ambulatory care setting, the POCT devices were located close to where blood sampling took place but at busy times there could delays in using them:

“But I’ve had it where I’ve gone in to do my blood and then someone has come in with their blood and they’re like Oh I didn’t realise, and I’m like I did say I’m doing it, you know, and kind of it’s about communicating, so if it’s busy it’s difficult because you kind of, you might have three patients maybe you’re trying to do bloods and stuff on.” [Interview 3, Nurse, secondary analysis]

Staff actions to minimise pre-analytical error

Staff in both ambulatory care settings regarded the filling of the i-STAT cartridges as the trickiest part of the POCT process. To fill the cartridge, drops of blood are released from the POCT syringe into it. Staff in the community ambulatory care setting explained that when POCT was first introduced a number of cartridges had had to be discarded or were failures because they were incorrectly filled:

“…we wasted an awful lot of cartridges, we were overfilling them, underfilling them, making a real mess, you know, and not getting results, it would just say insufficient blood or whatever” [Interview 1, HCA, secondary analysis]

They have dealt with this difficulty mainly through practice and learning by doing:

“I’ve seen people overfill cartridges before and you know they’re not going to work but then people learn from that then sort of say “Oh it’s overfilled” and then you don’t fill it that much again and you kind of have to learn yourself a little bit I think, I don’t know.” [Interview 3, Nurse, secondary analysis]

“I think it’s just getting used to how much blood to put on and if you do slightly too much, it’s a bit kind of accurate how much to put on, but it was fine.” [Interview 6, Nurse, secondary analysis]

Staff have also developed their own ways of reducing the messiness of the process:

“The nurse took a piece of kitchen roll out of the dispenser on the wall, put it down on the work surface, put the cartridge on it, and put the blood in the cartridge…” [Field notes 8, secondary analysis]

and ensuring that the cartridges are correctly filled:

“…after the nurse had put some drops of blood on the cartridge, she dabbed the excess off with the corner of the kitchen roll (I’ve not seen anyone dab the excess off before)…About blotting the excess blood–she said this was not mentioned in training. If too much blood is sucked up it doesn’t work…After having some tests not work because too much blood was sucked up, she’s started blotting the excess blood. No one told her to do this. She thinks she’s seen a couple of other nurses do it; she thinks she saw another nurse do it first.” [Field notes 8, secondary analysis]

Staff explained that filling cartridges is an aspect of POCT in which new staff members are given training:

“I asked how she learned to use POCT–she said when she worked here full time previously, she had training in it when she first started. You have to have training, then someone watches you do it for the first few times to make sure you’re doing it properly.” [Field notes 8, secondary analysis]

“Mainly the procedure, mainly the getting accurate blood results, accurate blood samples and filling the cartridges, that, that was; because even now if we get new nurses, bank nurses or something like that and we show them how to use it, you’ve got to be really careful how to put those drops of blood in …” [Interview 1, HCA, secondary analysis]

During informal training in the hospital-based ambulatory care setting, staff also needed to practise to achieve the necessary control:

“The training to use POC testing is now done by staff in the setting. They get new staff to practise using water in the syringe. They then try to release one drop onto tissue, two drops and so on. They think that control gets better with practice and that practice is important.” [Field notes 2, follow-up ethnography]

In the community-based setting staff explained that expired cartridges were used for training purposes:

“There are a few cartridges that have, say, expired that they can practise putting the blood onto, but they won’t, you can’t actually put those in the machine and get the results ‘cos they’ve got the expired date and it won’t let it carry on, but at least you can practise putting the little drops of blood on, yeah.” [Interview 6, Nurse, secondary analysis]

Where staff had experienced difficulty in obtaining a sample, they reported that this introduced the possibility of getting air bubbles in the syringe which could negatively affect the POCT process. To overcome this, before filling the cartridge they discarded the first few drops of blood, where the sample was large enough to be able to do this:

“[The HCA] said you’re meant to put the first few drops in the waste bucket, before putting drops in the cartridge (I’ve seen a nurse do this before), but there wasn’t enough blood to be able to do that, so the HCA put it straight into the cartridge. The reason for putting the first drops in the bucket is to stop any air bubbles getting into the cartridge which would stop it reading.” [Field notes 6, secondary analysis]

In the community-based setting, the POCT devices were in demand and staff members worked closely together, often in pairs, with one member of staff taking the blood sample and the other staff member taking it immediately to the POCT device, to ensure that the sample was tested as quickly as possible.

POCT, however, is less in demand in the hospital-based ambulatory care setting as clinicians have access to laboratory results within about an hour. At particularly busy times staff can use alternative POCT devices located elsewhere in the hospital but still close by.

Staff perceptions of error rates

Staff in both settings were aware of the potential for error in the process and tried to actively reduce the possibility of these occurring. In both settings, blood sampling and POCT was carried out mainly by health care assistants (HCA) but also by nurses. All staff who were involved were familiar with the processes involved and were conducting them multiple times during their working day.

During the field work, there was only one instance observed of an error recorded by a POCT device:

“One of the nurses called me to the clinical room as (s)he had taken some blood from a patient and was about to use the POCT device. (S)he was preparing to do a CHEM8. After discarding the first few drops of blood into the empty cartridge packet (s)he filled a CHEM8 cartridge. (S)he said she was getting rid of air as this might prevent the test working. About a minute or so after (s)he had put the cartridge in, the POCT device gave the error message ‘Unable to position sample’. (S)he immediately repeated the process with another cartridge using the same blood sample, which worked second time around. (S)he said she did not know what might have gone wrong with the test and did not say anything about what (s)he thought the error code might mean. (S)he said that the cartridge seldom needed to be done again.” [Field notes 3, follow-up ethnography]

Among other staff, there was also a perception that the amount of error occurring was low:

“While observing a POC test being processed in the clinical room, a senior member of staff began to talk about the wastage of cartridges. (S)he estimated that only about one in 50 cartridges was wasted and that this was sometimes due to the wrong amount of blood being inserted in the cartridge. (S)he also mentioned that cartridges were sometimes wasted if the POCT device was not functioning properly.” [Field notes 2, follow-up ethnography]

Discussion

The rate and nature of pre-analytical error when performing POC tests in acute clinical settings has not previously been widely reported. In the period for which the most reliable retrospective data were available, our study estimates the user-attributable error rate using the i-STAT device in a large acute hospital trust to be 2.3%, roughly stable over time, and with overfilling or underfilling the cartridge being the most commonly identified reasons for device-reported error messages. Our conclusions relating to the i-STAT Alinity (estimated user error rate 5.2%) and the Afinion (9.0%) are less certain because of the considerably lower usage of these devices.

These results are consistent with those from the qualitative component of our study, which found that hospital staff saw filling the cartridge as the most difficult aspect of POCT, but that this rarely resulted in machine error and errors could often be resolved simply by repeating the test with a new cartridge. In our prospective ethnographic field work, we observed only one instance of POC device-recorded error, and this was of a non-serious nature and was promptly resolved. The ethnographic project did however highlight the importance of training, both formal and informal, in ensuring that cartridges were filled correctly to minimise the possibility of pre-analytical errors occurring.

As the focus of our work was on the assessment of pre-analytical error, it is important to realise that these errors do not necessarily directly impact on test results, treatment decisions and patient outcomes, and nor does our estimated error rate cover all types of error that may occur in the ‘Total Testing Process’ [27]. In most cases, we would expect the types of error our study assesses to be resolved by repeating the test, although there may be impact on resource use through the wastage of a cartridge or the time required to obtain a result. For any particular patient, these factors are unlikely to be substantial as the rate of pre-analytical errors is low, but they may accumulate in busy clinical environments, where hundreds of POC tests are performed daily.

This study has some limitations. Although the quantitative analysis used a complete sample of data that had been recorded automatically by POCT devices at the time of operation, some data had to be excluded because of concerns over inaccurate recording during some periods. Our classification of error codes directly uses the descriptions given in the manufacturers’ manuals, but some codes allow for multiple causes and could not definitively be assigned as user errors. This was particularly relevant to the first thirteen months of the study period, when i-STAT error data were recorded as broad categories rather than as specific numerical codes. Our results only relate to the POCT devices already used in the clinical environment considered in our study, and cannot be extrapolated to other analyzers.

More generally, our estimate of error rates only considers test attempts that provoked an error message from the POCT device. It was not possible to assess whether an incorrectly filled cartridge or poor quality sample might have given an inaccurate and therefore clinically misleading test result if the device did not initiate an error message, and this may be affected by the site of sampling or the timing when the test was performed [28]. Additionally, failed attempts in which the operator discarded a cartridge without inserting it into the machine would not be recorded, and this may have introduced some inaccuracy into our estimate of error rates. We had intended to investigate this by cross-classifying ordered stock, test results, and recorded errors, but decided this would be unreliable as departments might vary in the amount of stock retained in reserve at any one time. Additionally, there may be situations in which a cartridge might be discarded without an error having occurred, for example if it were used for practice or training.

The qualitative work was constrained by the time required to observe a sufficient number of test attempts to draw meaningful conclusions. It was not possible to observe all test cartridges and devices, although for most of these the steps required to take blood and fill the cartridge would be similar as i-STAT and Alinity cartridges are identical in design. As the overall error rate was low, we are reliant on staff interviews for information about errors with potentially more damaging consequences. These suggested that these errors would be uncommon, even in care settings such as those used in this study, where difficult or complex cases are frequently encountered. Interviews did however suggest that some difficulties may arise in taking blood from certain patient groups and that filling cartridges was not always easy and required practice. The dependability of these interviews is supported by the fact that one participant estimated the rate of cartridge wastage to be one in fifty, very close to our quantitative estimate. This highlights the importance of appropriate training before undertaking to use blood POCT devices, and this may also include ongoing peer support and additional practice in transferring blood to the cartridge. Error rates in different locations did not appear to be strongly associated with total test use. This was unexpected but could be explained by location specific factors that we were unable to measure, such as the experience of individual operators. An interesting extra research question would be to investigate whether new operators have higher error rates or improve with practice.

The lack of a consistently-applied taxonomy for describing errors in POCT is a potential barrier to work in this area. We chose to follow that used by Meier & Jones [19] to categorise errors falling into the pre-analytical phase of blood sample collection and suggest that future published reports use a similar classification to allow direct comparisons of error rates in different settings to be made.

In summary, future evaluations of the effect of introducing POCT devices into clinical practice should recognise the need to assess pre-analytical error. Although in this study we conclude that the effect of pre-analytical error is unlikely to be large, this may depend on the device, the setting and the level of expertise of the operators, and so should not be automatically assumed in general. In particular, future work would benefit from an assessment of the effectiveness and impact of training.

Tables of i-STAT, i-STAT Alinity and Afinion error codes and categories.

(DOCX)

Click here for additional data file.

i-STAT total monthly workload and error count.

Monthly workload (solid line) and error count (dotted line) over time (upper panel); enlarged view of error count over time (middle panel); error rate (error count divided by workload) over time (lower panel).

(EPS)

Click here for additional data file.

Data used in quantitative analysis.

(XLSX)

Click here for additional data file.

24 Dec 2019

PONE-D-19-29304

Pre-analytical error for point of care venous blood testing in acute ambulatory settings : a mixed methods service evaluation

PLOS ONE

Dear Dr Fanshawe,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

In addition to the few issues raised by the reviewers, please reconsider revising the title to clearly state that the paper is on the effect of pre-analytic handling  on three POCT instrument errors. One of the most common pro-analytic errors insufficient patient ID.

We would appreciate receiving your revised manuscript by Feb 07 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Pal Bela Szecsi, M.D. D.M.Sci.

Academic Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2.  Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

3. Thank you for stating the following in the Financial Disclosure section:

This research was funded by the National Institute for Health Research (NIHR) Community Healthcare MedTech and In Vitro Diagnostics Co-operative at Oxford Health NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The project was also supported by a grant from Becton, Dickinson and Company. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We note that you received funding from a commercial source: Becton, Dickinson and Company.

Please provide an amended Competing Interests Statement that explicitly states this commercial funder, along with any other relevant declarations relating to employment, consultancy, patents, products in development, marketed products, etc.

Within this Competing Interests Statement, please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests).  If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include your amended Competing Interests Statement within your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: N/A

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The study consider the pre—analytical errors recorded with the use of three specific devices, both in an hospital setting and community in ambulatory care. The authors consider the errors registered by the instruments and in particular the errors attributable to the operators.

The errors in the pre-analytical phase include many aspects, in addition to those that can be registered by the analytical system: the choice of the right timing with respect to the clinical conditions (for example for blood gas, you could quote Auvet doi: 10.1186/s13613-016-0152-6 ), the site of the sampling, the instruments used for venipuncture, the any dilution or contamination of the blood drawn, the sample hemolysis. Despite this, the study provides new data on the safety of POCT systems. This limitation must be discussed.

Another limitation of the study consists in the typology of errors that are strictly related to the specific type of instrument considered. These results cannot be reported to other types of analyzers.

In the qualitative study the secondary analysis is based on limited number of cases.

The "Staff awareness of factors", “staff actions” and “staff perception” chapters (lines 329 - 512) present individual anecdotal cases that could be placed in a section of additional materials rather than in the text of the paper. Alternatively, the proposed reports could be summarized.

In the conclusions a positive judgment is expressed regarding the frequency of errors attributable to the operators. But it is not known what the impact of these errors on care was, as the authors themselves claim, therefore the statement appears arbitrary and must be more prudent. In absolute terms the frequency is actually high, for example if we estimate it with the sigma metric.

Figure 3 shows little relevant data that can be briefly presented in the text.

Reviewer #2: This is a well written manuscript with focus on a topic for which there are very few published studies.

I only have one comment. The authors found that there was no difference in error rates between locations performing high and low volume POCT. I would have expected the error rate to be lower in areas performing high volume testing. Could the authors speculate or comment on why they saw no difference?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

9 Jan 2020

Response to reviewers

*Editor:

*In addition to the few issues raised by the reviewers, please reconsider revising the title to clearly state that the paper is on the effect of pre-analytic handling on three POCT instrument errors.

We agree and have modified the title to reflect this:

Pre-analytical error for three point of care venous blood testing platforms in acute ambulatory settings : a mixed methods service evaluation

*One of the most common pro-analytic errors insufficient patient ID.

This was true of the Afinion but this was likely to be only a technical result in accessing the device (quickly resolved by the operator) and so not likely to be an operator error. As such, although we thought it appropriate to report this in results tables we do not feel that it warrants more prominence than currently.

*Reviewer #1: The study consider the pre—analytical errors recorded with the use of three specific devices, both in an hospital setting and community in ambulatory care. The authors consider the errors registered by the instruments and in particular the errors attributable to the operators.

The errors in the pre-analytical phase include many aspects, in addition to those that can be registered by the analytical system: the choice of the right timing with respect to the clinical conditions (for example for blood gas, you could quote Auvet doi: 10.1186/s13613-016-0152-6 ), the site of the sampling, the instruments used for venipuncture, the any dilution or contamination of the blood drawn, the sample hemolysis. Despite this, the study provides new data on the safety of POCT systems. This limitation must be discussed.

Thank you – we agree that this should be mentioned and we have added to the relevant part of the Discussion: “It was not possible to assess whether an incorrectly filled cartridge or poor quality sample might have given an inaccurate and therefore clinically misleading test result if the device did not initiate an error message, and this may be affected by the site of sampling or the timing when the test was performed [28]” (citing the paper suggested).

*Another limitation of the study consists in the typology of errors that are strictly related to the specific type of instrument considered. These results cannot be reported to other types of analyzers.

We have added a sentence to the limitations in the Discussion: “Our results only relate to the POCT devices already used in the clinical environment considered in our study, and cannot be extrapolated to other analyzers.”

*In the qualitative study the secondary analysis is based on limited number of cases. The "Staff awareness of factors", “staff actions” and “staff perception” chapters (lines 329 - 512) present individual anecdotal cases that could be placed in a section of additional materials rather than in the text of the paper. Alternatively, the proposed reports could be summarized.

In qualitative research, emphasis is placed on gaining rich, deep information from a relatively small number of cases, where each has substantial experience of, and involvement in, the phenomenon under investigation (in this case, POC testing). The secondary analysis strand is based on data collected in a qualitative case study of one ambulatory care unit published elsewhere (Jones C, Glogowska M, Locock L, Lasserson D. Embedding new technologies in practice – a normalization process theory study of point of care testing. BMC Health Surv Res. 2016;16:591) which encompassed 14 episodes of ethnographic observation (approximately 60 hours) and 7 semi-structured interviews with staff (6 of which were audio-recorded and transcribed). In a similar way, the prospective ethnographic strand (which took place in another ambulatory care unit), represented 15 hours of observation.

For a qualitative study, this represents a considerable dataset, which was able to provide both a description of the context in which the POC testing had been implemented and a detailed interpretation of how usual practice around POC testing took place.

As regards the “Staff awareness of factors”, “staff actions” and “staff perception” sections, the material here are rigorously collected and robustly analysed data from the secondary analysis and the prospective ethnographic strands of the qualitative component. The qualitative component was part of the original mixed methods design which aimed at producing a more comprehensive assessment of preanalytical error in POC testing in this setting. Where quotations are included, it is because they are reflective of the views of more than just the one participant to whom they are attributed and are not simply ‘anecdotal cases’. Thus, we regard the qualitative material as playing an important role in aiding our understanding, and providing some explanation of, the quantitative findings and do not think it should be moved into additional materials.

*In the conclusions a positive judgment is expressed regarding the frequency of errors attributable to the operators. But it is not known what the impact of these errors on care was, as the authors themselves claim, therefore the statement appears arbitrary and must be more prudent. In absolute terms the frequency is actually high, for example if we estimate it with the sigma metric.

We agree that our study was unable to assess the impact on patient care (this was not the objective). We have amended the Conclusions of the Abstract as a result: “In the observed settings, the rate of errors attributable to operators of the primary point of care device was less than 1 in 40. In some cases, errors may lead to a small increase in resource use or time required so adequate staff training is necessary to prevent adverse impact on patient care.”

*Figure 3 shows little relevant data that can be briefly presented in the text.

We have moved this figure to a Supporting Information file (S2 Graph) and shortened the text.

*Reviewer #2: This is a well written manuscript with focus on a topic for which there are very few published studies.

I only have one comment. The authors found that there was no difference in error rates between locations performing high and low volume POCT. I would have expected the error rate to be lower in areas performing high volume testing. Could the authors speculate or comment on why they saw no difference?

Thank you – we had also anticipated that the error rate might be lower in areas performing high volume testing but were limited in the amount of location-specific information that could be collected to determine factors that might influence error rates at different locations. We have amended the relevant part of the Discussion to now read: “Error rates in different locations did not appear to be strongly associated with total test use. This was unexpected but could be explained by location specific factors that we were unable to measure, such as the experience of individual operators. An interesting extra research question would be to investigate whether new operators have higher error rates or improve with practice.”

Submitted filename: Response to reviewers.docx

Click here for additional data file.

22 Jan 2020

Pre-analytical error for three point of care venous blood testing platforms in acute ambulatory settings : a mixed methods service evaluation

PONE-D-19-29304R1

Dear Dr. Fanshawe,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Pal Bela Szecsi, M.D. D.M.Sci.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: N/A

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper was corrected as requested. However, I believe that research can be deepened with a new study with respect to clinical risk (for example through interviews, questionnaires, event analysis) and with respect to patient safety. Also the process of patient identification sometimes is a critical issue. But this is only a my suggestion.

Reviewer #2: The authors have sufficiently addressed the concerns I raised. This manuscript is well written and addresses an important topic.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

27 Jan 2020

PONE-D-19-29304R1

Pre-analytical error for three point of care venous blood testing platforms in acute ambulatory settings : a mixed methods service evaluation

Dear Dr. Fanshawe:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Pal Bela Szecsi

Academic Editor

PLOS ONE