trim ref: t12/5096 mtec 2011/12 projects

Point of Care testing in ED

Integrated Point of care testing (IPoCT) Final report 24/10/2012 1

TRIM Ref: T12/5096

MTEC 2011/12 Projects Final Report

The Integrated Point of Care Testing (IPoCT) project in the ED.

Organizational Unit: St George Hospital, Emergency Department

Authors: Dr Adam Chan, Dr Stephen Asha, Allan Ajami, Liz Walter, A/Prof Roger Wilson, Dr Rachael Morton, Dr Patrick Kelly



1. Overview of the project Please provide a concise overview of the project you have undertaken with the funding. This may be the only document that is read about this project so do not make assumptions that your audience has any background information.

Reducing the amount of time that patients stay in the Emergency Department(ED) is a desirable goal to reduce over‐crowding, improve patient flow, improve patient satisfaction and possibly, reduce morbidity and mortality. Point‐of‐care (POC) testing defined as laboratory testing in or near a patient location with rapid availability of test results, has the potential to decrease ED length of stay (LOS) through reduced turn‐around times allowing clinical decisions to be made earlier.

We identified that in most metropolitan hospital Emergency Departments with 24 hour a day, in house pathology services, the utilisation of POC testing is often ad hoc. Furthermore, data integration of POC devices with the main pathology service database is rarely achieved. In addition, there is currently little information on the appropriateness and cost benefits of its application in this setting. The literature on this topic has conflicting results1‐9 and it has not been determined if POC testing in the ED translates to benefits in terms of faster decision making and shorter ED LOS.

Funding was received to conduct a project titled ‘Integrated point of care testing in the ED’. We hypothesised that reducing turn‐around times for pathology results through the use of POC devices would reduce the time to disposition decision (admission/discharge decision). The primary aim of the project was to determine if the time to make a disposition decision could be reduced with common blood tests being available by POC testing in the ED. Secondary aims were to assess for improvements in processing times on several patient subgroups, and to perform a cost‐effectiveness analysis.

To assist in evaluating the project a decision was made to conduct a randomised controlled trial. Key stakeholders were engaged including South Eastern Area Laboratory Services (SEALS) and the cardiology department to gain their support and input to help ensure the successful implementation of the project. The project team was formed to include key members from the ED, SEALS, Cardiology Department and the hospital executive.

The Project was conducted in the ED of St George Hospital, a tertiary referral and level 1 trauma centre located in Sydney, New South Wales, Australia, over a six month period from December 2011 to May 2012. The ED has approximately 65,000 presentations a year. Pathology services are available 24 hours a day.

A proportion of the grant funds allocated to the project were used to purchase the POC equipment used in the trial. The devices purchased were the Radiometer AQT90 FLEX which provided troponin, quantitative beta‐HCG and D‐Dimer results and a Roche CoaguChek XS PRO which provided INR levels. The ED had an existing Radiometer ABL 800 FLEX blood gas analyser in the department which was used in the project to provide results for electrolytes, haemoglobin, BSL, calcium and creatinine.



The results of the trial showed POC testing reduced the time to a disposition decision by 8% overall and in a sub group analysis, senior clinicians had a reduction in disposition decision of 19% with trends toward shorter decision making times in all subgroups analysed.

There was an overall reduction in ED LOS of 4.4% which was not statistically significant. However in the senior clinician sub group there was a 16% reduction in LOS.

An economic evaluation was conducted by a Health Economist. Overall the health‐care costs per ED presentation were $180 +/‐ $167 in the POC group and $172 +/‐ $133 in the central laboratory group, a net difference of $8 (95% CI ‐$13, $29) in favour of the central laboratory group. The point estimate of the incremental cost‐effectiveness ratio (ICER) was $38 per hour saved in time to disposition decision for POC compared to standard laboratory testing concluding that POC testing in the ED can be a cost effective intervention.

2. Objectives of the project Please state the objectives of the project you set out to achieve, was there any change to this during the project?

The primary aim of this study was to determine if the time to make a disposition decision could be reduced with common blood tests being available by POC testing in the ED. Secondary aims were to assess for improvements in processing times on several patient subgroups, and to perform a cost‐effectiveness analysis. Our main objectives were:

To implement a comprehensive strategy for POC testing in ED

To identify the cohort of patients for whom POC testing is appropriate and safe

To develop clinical pathways for this cohort of patients

To enhance the culture of rational pathology ordering among ED clinicians

To reduce the turnaround time (TAT) for troponin and other critical tests

To improve the percentage of patients meeting the National Emergency Access Target (NEAT) 4 hour benchmark

To integrate POC testing results with the hospital Laboratory Information System

Most of these objectives were thought to be attainable and did not require modification during the project, however the culture of pathology ordering and meeting the NEAT target were found to be more challenging for reasons discussed further on in this report.

3. Scope of the project

What was in and out of scope for this project? Did you stay within your original scope? Why/why not?

The trial was conducted at the St George Hospital Emergency Department (SGH‐ED) over a six month period. The project was managed by the Project Team with the direct support and assistance provided by the Clinical Group Manager for Medicine and Critical Care as the reinforcing sponsor and was guided by the Steering Committee.



A flow chart (Figure 1) was developed to assist in the selection of patients suitable for POC testing. These patients were then randomised to have their blood analysed using either POC testing devices or the central laboratory services.

Figure 1: Patient Suitability for POCT Flowchart



Patients presenting to the ED were eligible for the study if they were ≥18 years of age, and fulfilled the requirements for either of the following two groups. The first group were patients suspected of having an ACS. Those with acute ST elevation infarction were excluded. The second group (general group) were patients who only needed blood tests from the selection available by POC to complete assessment/management. The POC blood tests available were creatinine, electrolytes, glucose, calcium, haemoglobin, troponin T, D‐Dimer, beta‐HCG, and INR. The POC devices used were Radiometer ABL 800 FLEX blood gas analyser, Radiometer AQT‐90 FLEX, and a Roche CoaguChek XS PRO. Patients who presented more than once to the ED could be re‐enrolled in the study.

Participants were enrolled by nurses, nurse practitioners and doctors from intern to consultant level.

In addition to the items that are in scope, note the items that are specifically excluded from the project scope.

In Out

St George Hospital All other facilities

All patients meeting the selection criteria for IPoCT stream

All other patients

ED process of patients meeting the selection criteria for IPoCT stream

Exclude all others

LOS, time to decision to admit, disposition, cost effectiveness

All other KPI’S

Blood tests available on ED Point of care testing devices

All other blood tests

4. Methodology used in the project

The project was guided by Clinical Service Redesign Program (CSRP) methodology and used the Accelerated Implementation Methodology (AIM) and project management principles.

A multi‐disciplinary project team was nominated by the project lead Dr Adam Chan, Senior Staff Specialist St George Hospital Emergency Department

Project Team Member Role Responsibility Availability

Director of Operations‐ Cath Whitehurst

Executive Sponsor

‐ Steering Committee

Express, model & reinforce importance of the project throughout the organisation via existing reporting channels

Participation in steering committee meetings

(1 hrs/Bi‐Monthly)

CGM Medicine and Critical Care‐

Dawn Fowler

Reinforcing sponsor

‐ Steering

Provide support & assistance to manage progress to meet deliverables & milestones

Participation in steering committee meetings and Project team meetins



Committee and Project Team

(2 hrs/Month)

ED Senior Staff Specialist ( Project LEAD)‐

Dr Adam Chan

Project Team

LEAD Clinician

‐ Steering Committee and Project Team

Provide subject matter expertise in strategy development

Drive the project to meet deliverables & milestones

Facilitate and support implementation and training

Provide regular briefing for executive sponsor

Lead project team meeting and participate in Steering Committee meetings (10 hrs/fortnight)

ED NUM (Project manager)‐ Allan Ajami

ED CNS (Project Officer)‐ Liz Walter

Project Manager‐ Steering Committee and Project Team

Project Officer

‐ Steering Committee and Project Team

Drive implementation of the project

Facilitate and support implementation

Staff training and report on progress

Assist in data collection

Escalate risks and issues

Shared role (4 days a week)

ED Director of Research‐ Dr Stephen Asha

Project team Provide leadership in study design and ethics approval

Data collection and analysis

Principal author for the publication of the study result

Data collection and analysis. Participation in project team meetings (4hr/fortnight)

Cardiologist‐ Dr James Weaver

Cardiology CNC‐ Glenn Paull

Project Team

Project Team


Provide assistance to drive the project to meet deliverables

To ensure patient safety and clinical outcome in relation to ACS patients

Participation in project team meetings (1hr/fortnight)

ED Director‐ Dr Trevor Chan

ED Nurse Manager‐ Antoinette Borg

Steering Committee

Steering Committee


Provide assistance to drive the project to meet deliverables

Participation in steering committee meetings

(1 hr/Bi‐ Monthly)

Director SEALS‐ A/Prof Roger Wilson

Director Clinical Chemistry SGH‐ Vivienne Ellis

Lab Manager Clinical Chemistry STG‐ Christine Moffat

Hospital Scientist STG‐ Gina Solomos

Steering Committee and Project Team

Project Team

Project Team

Project Team


Provide assistance to meet deliverables & milestones

Provide costing data and assist in data analysis

Assist in training and accreditation of equipment

Perform quality control on equipment and attend maintenance

Participation in steering committee meetings & project team meetings

(2 hr/fortnight)



The project team lead and manager reported to a steering group on progress and worked closely with the reinforcing sponsor for project support and guidance. The project team reported to the Executive sponsor, the Director of Operations, via the steering committee on project progress. The other key stakeholders that were invited to participate in selected project team meetings were the ED CNC, St George Hospital Haematology Department representatives, Health Economist, Statistician and company representatives from Radiometer and Roche.

This study was an open, parallel arm, randomised trial. Permission for the study was granted by the Human Research Ethics Committee of St George Hospital and registered with the ANZCTR (12611001228976).

Participants were randomly allocated to either the intervention or the control arm of the study. For participants allocated to the intervention arm of the study, if they were in the general group all blood tests were processed in the ED using the POC devices. For participants in the ACS group, only the troponin was processed using the POC device, and other blood tests required were sent to the central laboratory for processing. This was because we considered troponin to be the critical blood test for making a disposition decision in patients with an ACS, while other tests often are requested for ‘baseline’ measurement and rarely influence management and disposition.

For participants allocated to the control arm of the study (ACS and general groups) the clinician would send all blood tests to the central hospital pathology service for processing as per normal practice.

Following this initial set of testing if there were abnormalities or the clinical situation changed dictating the need for further testing this was allowed but all subsequent pathology testing was performed in the central laboratory.

Blood results from POC testing devices are integrated with the all other pathology test results and are available for access in the hospital Electronic Medical Record (EMR). This was achieved using Radiometer’s RADIANCE analyser and data management system interfaced to the central hospital pathology Laboratory Information System for the Radiometer analysers and retrospective entering of INR results from the Roche CoaguChek XS PRO.

Turn‐around times for the POC devices (time from specimen insertion into the POC device to availability of the result) ranged from 2 minutes to 21 minutes with troponin available in 12 minutes, D‐Dimer and b‐HCG available within 21 minutes and INR and VBG results available within 2 minutes.

Turn‐around times for laboratory tests (time from sending a specimen to the laboratory to availability of the result) can range between 30 minutes and 2 hours.



5. Measures of success of the project This should include analysis of data to demonstrate that objectives were met and impact of the implementation of the project. This should also include a narrative on other qualitative and quantitative measures which demonstrate the impact of the project. What was your communication strategy and how effective was that? What has been put in place to ensure sustainability of the project?

The primary outcome was the time from ED arrival to disposition decision. This was chosen as the primary outcome (rather than ED LOS) as delays in accessing in‐patient ward beds may mask a benefit in patient processing time.

The secondary outcomes were ED LOS for the whole study population; time to disposition decision and ED LOS for the following subgroups: diagnostic group (ACS or general), disposition (discharged home, admitted to the ward, admitted to the emergency medicine unit which is an ED short stay ward), seniority of enrolling staff. We also determined the number of pathology tests ordered while in ED. A cost‐effectiveness analysis of POC testing compared to central laboratory testing was conducted.

Methods and Measurements

The staff member enrolling a participant entered diagnostic information on a data collection form. For those in the general group this was the provisional diagnosis, while those in the ACS group were stratified to a low, intermediate or high risk category. Demographic data and times for the primary and secondary outcomes were obtained from the ED computer management system in which the times of all significant events in the patient journey are entered. The time of the admission decision was defined as the time that the clinician notified the nurse in charge to book a bed following patient acceptance by an admitting team. For patients sent home, the discharge decision time was determined by the departure ready time as entered by the clinician into the ED computer management system. This was often the same as the departure time but may be earlier for patients awaiting transportation home.

All pathology tests ordered from the time of arrival to the time of disposition decision were obtained from the pathology database. When calculating the number of tests, any test routinely ordered and billed as a standard order set was considered to be one test, for example a full blood count consists of 21 different parameters and liver function tests consist of 7 parameters but each was recorded as 1 test.

Analysis

The required sample size was determined using the mean and standard deviation of the disposition decision time for the study population estimated from a pilot study conducted over 2 months before the start of the randomised trial. Clinicians completed a survey for each adult patient seen to identify patients fulfilling inclusion/exclusion criteria. Disposition decision times were obtained from the ED computer management system. We considered a 15% reduction in disposition decision time to be the minimum clinically important



reduction. Using a power of 80%, α level of 0.05, 450 participants were required. It was necessary for the study to be adequately powered for subgroup analysis, in particular the ACS group. The pilot study demonstrated the ACS group and the study group as a whole to have a similar mean and standard deviation, so we determined to stop the study once 450 participants had been enrolled in the ACS group.

The outcome measures of time to disposition decision and ED LOS were positively skewed. Therefore, the data was first transformed using the natural logarithm and the analysis was conducted by comparing the means of the natural logarithm of these outcomes using linear regression. The differences in time between study groups are presented as percentage reductions in the means of the logarithmically transformed data, while the average times presented are the geometric means, which are the means of the logarithmically transformed data back‐transformed using the exponential.

The number of tests conducted from arrival to decision to discharge/admit was analysed using Poisson regression. A random effect model was included to adjust for repeated presentations over the period of the study. All analyses were conducted in Stata 12.

The cost‐effectiveness analysis calculated total costs and mean costs per ED presentation, as well as total and mean benefits in time saved to disposition decision. Unit costs from hospital casemix and pathology data were obtained for radiology and pathology diagnostic tests from time of arrival in the ED to time of disposition decision. Indirect costs for capital equipment (i.e. POC analysers) were calculated using the equivalent annual cost method10. A weighted average clinical staff time for POC and standard blood test processing was derived from a time‐in‐motion study with 25 consecutive ED presentations. The differences between the costs in the two groups, and the 95% confidence intervals were then calculated. Volumes of resources and costs are reported as mean values with standard deviations and as mean differences with 95% confidence intervals. Discounting was not applied. The arithmetic mean of the disposition decision time (rather than the geometric mean as described above) was used in the calculation of an incremental cost‐effectiveness ratio (ICER) for POC compared to central laboratory testing. Non‐parametric bootstrapping was employed for a 95% confidence interval around the ICER.

Results

Characteristics of study subjects

There were 881 enrolments into the study, but 66 enrolment forms were not returned preventing identification of the participant. Two participants (4 observations) were excluded as they were enrolled in both arms of the study for the same presentation, giving a total of 811 enrolments. There were 410 participants randomised to POC and 401 to the control arm of the study (figure 2). The trial was balanced with respect to baseline characteristics (Table 1).



Figure 2: Participants Table 1: Baseline characteristics by randomization group

Characteristic

Control POC

Mean or n (SD or %) Mean or n (SD or %)

Age 57.8 (20.4) 57.7 (20.1) Sex Female 223 (56) 219 (55)Male 178 (44) 191 (46) Arrival mode Private Car 247 (62) 264 (63)Ambulance 152 (38) 144 (37)Police 2 (1) 2 (<1) Enrolling Staff Consultant 36 (9) 48 (10)Registrar 77 (19) 83 (20)Medical Officer (Junior, Career, Senior Resident) 205 (51) 194 (49)Nurse (Registered or Practitioner) 83 (21) 85 (21) Australasia Triage scale 1 1 (<1) 1 (<1)2 207 (52) 215 (52)3 127 (32) 103 (28)



4 63 (16) 83 (18)5 3 (1) 8 (1) Insurance Yes 157 (39) 157 (39)No 238 (59) 252 (60)Missing 6 (2) 1 (1) Diagnosis category ACS group 233 (58) 235 (58)

Low risk 77 (19) 65 (18)Intermediate risk 103 (26) 119 (27)High risk 33 (8) 41 (9)ACS risk stratification not specified 20 (5) 10 (4)

General group 168 (42) 175 (42)

Non‐cardiac chest pain 19 (5) 21 (5)Bleeding (nose/GI/respiratory/urine/wound) 18 (4) 16 (4)PV bleeding in pregnancy 25 (6) 28 (7)Trauma/falls/head injury 15 (4) 12 (3)Syncope/vertigo/dizziness 13 (3) 17 (4)Palpitations/arrhythmia 11 (3) 7 (2)Abdominal pain/flank 10 (2) 15 (3)Gastroenteritis/dehydration 9 (2) 7 (2)Vomiting 8 (2) 11 (2)Anaemia 3 (1) 9 (1)Other 37 (9) 32 (9)

Laboratory troponin in ACS subgroup (n = 458)

≤ 14 171 (75) 175 (76)> 14 58 (25) 54 (24)

Total 401 410

Main results

Tables 2 and 3 present the results of the time to disposition decision and ED LOS respectively. For the primary outcome, POC testing reduced the time to a disposition decision from a mean of 3.50 hours to 3.24 hours, a difference of 7.6% (95%CI 0.4‐14.3, p=0.04), with trends toward shorter decision making times in all subgroups analysed. There was a reduction in ED LOS of 4.4%, from 4.52 to 4.32 hours, but this difference was not statistically significant (95%CI ‐2.7‐11.0, p=0.21). There were trends toward shorter decision making times in all but one of subgroups analysed (JMO group). The improvement in patient processing times was greatest for those patients enrolled by senior staff (consultants and registrars), with a reduction in the time to a disposition decision of 19.1% (95%CI 7.3‐29.4, p<0.01) and a reduction in ED LOS of 15.6% (95%CI 4.9‐25.2, p=0.01). The number of blood tests ordered from the time of arrival to the time of disposition decision was very similar in both groups, with a mean of 4.44 tests in the POC group and 4.38 tests in the control group, a difference of 0.05 (95%CI 0.2‐0.9, p=0.02). There were also similar numbers of tests ordered in all subgroups analysed (table 4).



Table 2: Time from arrival to disposition decision

Geometric mean (hours)

Arithmetic Mean (hours)

% reduction (95%CI)

P-value

Control IPOCT Control IPOCT

Overall 3.5 3.24 3.99 3.78 7.6 (0.4,14.3) 0.04

Enrolling staff 0.06#

Consultant/registrar 3.63 2.94 4.21 3.39 19.1 (7.3,29.4) <0.01 Junior Medical officer (JMO)

3.54 3.51 3.98 4.08 0.9 (-9.6,10.4) 0.85

Nurse 3.24 3.12 3.74 3.69 3.8 (-15.7,20.1) 0.68 Diagnosis 0.86#

ACS 3.43 3.15 3.91 3.58 8.2 (-0.9,16.5) 0.08

General 3.61 3.36 4.11 4.05 6.9 (-5.2,17.6) 0.25

Decision 0.60#

Discharge 3.68 3.5 4.11 3.96 4.9 (-5.9,14.5) 0.36 Admit to Ward 3.66 3.22 4.33 3.87 12.1 (-0.8,23.3) 0.06 Admit to EMU* 2.94 2.81 3.21 3.27 4.4 (-11.1,17.8) 0.56

# testing for an interaction between treatment and subgroup *EMU Emergency Medicine Unit

Table 3: Length of Stay in the Emergency Department

Geometric mean (hours)% reduction (95%CI)

P‐value Control IPOCT

Overall 4.52 4.32 4.4 (‐2.7,11) 0.21

Diagnostic category 0.70#

ACS 4.65 4.5 3.1 (‐5.5,10.9) 0.47

General 4.34 4.09 5.7 (‐6.6,16.6) 0.35

Disposition 0.62#

Discharge 4.15 3.78 8.9 (‐0.9,17.7) 0.08

Admit to ward 5.86 5.52 5.8 (‐5.6,16) 0.31

Admit to EMU 3.59 3.49 2.8 (‐11.2,15.1) 0.68


Consultant or registrar 4.96 4.19 15.6 (4.9,25.2) 0.01

Junior medical officer (JMO) 4.51 4.59 ‐1.7 (‐12.4,7.9) 0.74

Nurse 4.31 3.7 14.1 (‐1.6,27.5) 0.08

# testing for an interaction between treatment and subgroup



Table 4: Number of tests ordered from arrival to admission/discharge decision

Control IPOCT Mean Difference(IPOCT‐Control) (95%CI)

P‐value

Overall 4.38 4.44 0.05 (‐0.26,0.38) 0.75

Diagnosis 0.01#

ACS 4.99 5.39 0.4 (‐0.02,0.84) 0.06

General 3.53 3.17 ‐0.37 (‐0.77,0.09) 0.11


Consultant or registrar 4.87 4.75 ‐0.12 (‐0.73,0.59) 0.73

Medical officer 4.4 4.41 0.01 (‐0.4,0.47) 0.96

Nurse 3.65 4.01 0.36 (‐0.22,1.04) 0.24

# testing for an interaction between treatment and subgroup

Resource utilisation

Table 5 shows the utilisation of health‐care resources per ED presentation according to the study group allocation. The number of pathology, radiology and cardiology tests per presentation did not significantly differ between the groups. The ED staff time for hands‐on pathology processing was significantly shorter in the POC group compared to the central laboratory group (1.34 minutes, 95%CI 1.22‐1.46).

Health‐care costs

Health‐care costs per ED presentation are reported in Table 5. Although the mean volume of pathology tests was slightly lower in the central laboratory group, the mean costs were slightly higher than the POC group. This result was not statistically significant.

Overall the health‐care costs per ED presentation were $180 +/‐ $167 in the POC group and $172 +/‐ $133 in the central laboratory group, a non‐significant net difference of $8 (95%CI ‐$13, $29) in favour of the central laboratory group.

Table 5: Mean use of health‐care resources and mean total health‐care costs per presentation for time to decision according to random allocation

Item IPOCT (n=410) Central laboratory (n=401) Difference

Mean (SD) Mean (SD) Mean (95%CI)

Volume Cost ($) Volume Cost ($) Volume Cost ($) Pathology tests 4.41 (2.59) 90 (55) 4.38 (2.08) 94 (39)

0.03 (‐0.30, 0.36) ‐4 (‐11, 3)

Radiology tests 0.76 (0.71) 85 (145) 0.77 (0.59) 75 (119) ‐0.01 (‐0.10, 0.08) 10 (‐8, 28)

Cardiology tests 0.02 (0.13) 4 (32) 0.01 (0.09) 1 (17)

0.01 (‐0.01, 0.03) 3 (‐1, 7)

ED staff time pathology (mins) 1.28 (0.78) 1 (0.1) 2.63 (0.92) 2 (0.1)

‐1.34 (‐1.46, ‐1.22) ‐1 (‐1, ‐1)



Total cost 180 (167) 172 (133) 8 (‐13, 29)

Cost‐effectiveness

The incremental cost‐effectiveness ratio (ICER) was $38 per hour saved in time to disposition decision, for POC compared to central laboratory testing. Considerable uncertainty was observed around the ICER and this is plotted in a cost‐effectiveness plane and cost‐effectiveness acceptability curve in the appendix (Figures 3 and 4).

Communication Strategy

An imperative for the successful implementation of the project was the engagement of key stakeholders, primarily SEALS and the Cardiology department. We developed a communication plan to facilitate communication between the executive steering committee, project team and all stakeholders. We used email, the Emergency Department newsletter and meetings to disseminate information about the project as well as a launch party to commence the project (appendix: figure 5). Staff were informed about the project aims and methodology via flyers and power point presentations. The development of pathways throughout the project including the development of advanced nursing practice clinical pathways (appendix: figure 6) to be used in conjunction with existing advanced standing orders. This allowed a large cohort of staff to participate in the study.

The involvement of company representatives to aid in education on equipment and attend staff training was also beneficial and allowed the majority of the department to be trained and accredited in the use POC equipment.

To help ensure sustainability several ED staff specialists were recruited to continue championing the use of POC tests for certain cohort of patients. Further to this the POC training session has been incorporated into all the JMO, Registrar, Staff Specialist, and Extended Practice Nurse Orientation schedules.

6. Discussion Was the project successful, why or why not? What are the generic principles of this model of care or new way of operating that would be transferrable to other hospitals/health services? What were the lessons learnt during this project? What would you do differently next time and why?

In this randomised trial we were able to demonstrate a small reduction (7.6%) in the time to reach a disposition decision and in the ED LOS (4.4%) amongst participants randomised to POC testing. While the improvement in the primary outcome was statistically significant, we had pre‐specified that we considered the minimum clinically important reduction to be 15%. In the subgroup analysis, there were trends of similar small improvements in processing times with the exception of participants enrolled by senior staff where the outcomes were considerably better (19%) and exceeded our minimum clinically important reduction.



There are probably a number of reasons why we were only able to demonstrate small improvements from POC testing.

There may be inaccuracies in the measurement of the actual disposition decision times as this relied on staff entering the time accurately on the computer management system. The Decision to Admit (DTA) parameter with which we based our disposition decision time in both groups, is manually entered by staff. In the discharged home cohort, this time may not accurately reflect the actual decision time as staff often activate the DTA time concurrently with the actual discharge time once all of the online paperwork is completed, even though the decision has been made at times significantly prior.

Physicians seeing several patients simultaneously may have got caught up in clinical care delaying action on an available result.

Participants enrolled by nurses were having tests ‘fast‐tracked’ prior to being seen by a doctor, so the benefit of POC testing may have been reduced by prolonged waiting times to see a doctor during peaks in activity. Despite this possibility, patients fast‐tracked and enrolled by nursing staff had a greater reduction in disposition decision (3.8%) and LOS (14.1%) times when compared to those enrolled by JMO’s (0.9%;‐1.7%), highlighting the value of advanced nursing interventions in conjunction with sufficient senior medical assessment models. ED’s with prolonged wait to be seen times may have a limited benefit from POC testing attended by nursing staff without implementation of early senior medical assessment models and availability of senior medical staff.

Possibly the most important factor was that a junior doctor’s ability to make decisions could be influenced more by the time taken to obtain a history, examination, and consultation with a senior, rather than the turn‐around time of a test. The policy at St George ED is that JMO’s must discuss their patients with the senior consultant or registrar on duty. The limited number of senior staff and lack of accessibility of senior staff due to high work loads, may be a factor in the lengthened decision making for junior medical officers where there was only a 1% improvement in DTA. As the majority of patients were enrolled by junior doctors this would have had a strong influence towards a reduced effect. This is supported by the subgroup analysis of processing times according to the seniority of the clinician as evidenced by the reduction in the time to a disposition decision of 19.1% and a reduction in ED LOS of 15.6% for senior clinicians. The reduction in disposition decision making time from 3.63 to 2.94 hours for senior clinicians indicates that POC testing could align well with the 2:1:1 model of care11, leaving an hour for departure and aiding the potential for meeting the NEAT target, particularly if inpatient beds are available.

These are important findings for departments considering the implementation of POC devices, particularly for tertiary ED’s with large numbers of junior staff, and laboratory services available 24 hour a day. Our results would indicate that in such a setting only small benefits could be expected if there were not enough senior staff to oversee practice. However, if the use was targeted to senior staff with the experience and ability to make rapid decisions, clinically relevant benefits could be realised. It is also important to emphasise the importance of system improvements to ensure the flow of patients out of the ED, as any improvements in the efficiency within the ED will be quickly lost, an effect



echoed in our results with smaller improvements seen in ED LOS compared to making a disposition decision. Whilst there was little difference in disposition decision for those admitted to the EMU (4.4% reduction), the geometric mean was already below 3 hours for both groups highlighting our departments robust model for early identification and streaming to EMU regardless of diagnostic results.

As part of Health funding reforms being implemented nationally, activity based funding (ABF) has been used to fund ED services as of July 1st 2012. The St George ED has been allocated $505 per patient treated in the ED. Our study demonstrated that for all participants enrolled, LOS, on average, was close to 4 hours. At face value this would equate to $126 per patient per hour of their stay. In an attempt to quantify the cost effectiveness based on the point estimate of the ICER ($38 per hour saved in time to disposition decision), the data suggests that POC testing is a cost effective intervention, when time saved in decision making translates to time saved in the ED.

Previous research in this area has had mixed results, although if only the randomised studies are considered each has failed to demonstrate a benefit from POC devices1‐9. In contrast, in this study small benefits were able to be demonstrated, although these may not be large enough to impact significantly at a systems level. Importantly, this study has identified a niche amongst senior clinicians for the rational use of POC devices.

We also considered that POC may lead to more rational test ordering. There may be a tendency for clinicians to over‐order tests when they know it will take 1‐2 hours to obtain a result: better to order everything now than have to add‐on more tests and be further delayed. With POC testing, clinicians need only order the tests they require and have a result in a few minutes. If more need to be done there has only been a few minutes delay. However, our results do not support this notion, with very similar numbers of tests ordered each group as a whole and in subgroup analysis. This is more likely attributed to a workplace culture effect, which the limitations of this study could not overcome.

7. Conclusion Where to from here? Please include details of the generic documents/project implementation guides you have developed for state‐wide use.

The results of the project demonstrated that POC testing can have a significant impact however gains are reduced when there is a lack of senior clinician oversight. Based on this outcome the ED has already commenced changes to the current clinical model of care to increase supervision and support to junior staff in the department. The implementation of geographical team based medical review as well as the implementation of early senior clinician assessment models, will aim to improve senior clinician oversight of JMO’s and disposition decision making.

The restriction of POC testing to the discretion of senior staff will also align well with these changes to work practice. With this new practice, having a senior clinician readily available for discussion and guidance on pathology testing, the use of POC testing may be streamlined and utilised in a more beneficial way.



Based on these new models we are considering a further project to identify if isolating POC testing to the direction of senior clinicians will lead to better outcomes than achieved through the initial project.

The project has successfully integrated POC testing results with the hospital Laboratory Information System. Therefore all blood test results, whether the sample was analysed by POC or central laboratory analysers, are integrated and easily identified in the hospital Electronic Medical Record (EMR). In addition, the study has highlighted the benefit of close collaboration with the SEALS staff who provide the maintenance, quality control and technical support of the POCT devices in ED.

The study also led to the development of a sub‐study of the integrated point of care testing. In the randomisation process of the primary study patients were stratified according to those with suspected ACS and all other patients suitable for POC testing. It is the subgroup of patients with ACS that this study is based on.

When a sample is run on two different types of analyser, it is impossible to guarantee that both machines will give the same results. Both the POC and central laboratory analysers used in this study perform high sensitivity troponin tests with similar accuracy, but as with any tests there is always a certain amount of scatter of tests results (imprecision). If the same sample from the same patient was tested on the same machine multiple times it is unlikely that you would always get the exact same result. This scatter is exaggerated if this testing is performed on two different analysers of the same model, and greater again if done on two different models of analyser. These differences are due to differing technologies and sample matrices (eg. whole blood used in POC testing and serum in lab based assay). It is inevitable that there will be a small number of discrepant results, and these discrepant results between duplicates tests have greatest clinical importance when a result is near the cut‐off value for a positive/negative result i.e. one machine gives a result just below the cut‐off while the other machine gives a result just above. This may then lead to different management decisions being made, for example a discharge rather than an admission, stress test rather than an angiogram. However, while a discrepant result may lead to different decisions being made about further testing or management, this does not necessarily mean that there would be any difference in terms of clinical outcome (further angina, AMI, need for revascularisation, death).

The aims of this study are:

1) Quantify the proportion of patients with discrepant results based upon the Roche lab based troponin assay and the Radiometer point of care assay.

2) Determine 3 month clinical outcome of all patients enrolled in the acute coronary syndrome arm of the integrated point of care study.

The hypothesis is that there is no difference in clinical outcome of patients based upon the method of troponin testing. We hope to complete this study in the next few months.



References

[1] Singer AJ, Ardise J, Gulla J, Cangro J. Point‐of‐Care Testing Reduces Length of Stay in Emergency Department Chest Pain Patients. Ann Emerg Med. 2005; 45: 587‐91. [2] Loten C, Attia J, Hullick C, Marley J, McElduff P. Point of care troponin decreases time in the emergency department for patients with possible acute coronary syndrome: a randomised controlled trial. Emerg Med J. 2010 27: 194‐8. [3] Ryan RJ, Lindsell CJ, Hollander JE, et al. A Multicenter Randomized Controlled Trial Comparing Central Laboratory and Point‐of‐Care Cardiac Marker Testing Strategies: The Disposition Impacted by Serial Point of Care Markers in Acute Coronary Syndromes (DISPO‐ACS) Trial. Ann Emerg Med. 2009; 53: 321‐28. [4] Renaud B, Maison P, Ngako A, et al. Impact of point‐of‐care testing in the emergency department evaluation and treatment of patients with suspected acute coronary syndromes. Academic Emergency Medicine. 2008 15: 216‐24. [5] Lee‐Lewandrowski E, Corboy D, Lewandrowski K, Sinclair J, McDermot S, Benzer TI. Implementation of a point‐of‐care satellite laboratory in the emergency department of an academic medical center. Impact on test turnaround time and patient emergency department length of stay. Archives of Pathology & Laboratory Medicine. 2003 127: 456‐60. [6] Singer AJ, Viccellio P, Thode Jr HC, Bock JL, Henry MC. Introduction of a stat laboratory reduces emergency department length of stay. Academic Emergency Medicine. 2008 15: 324‐8. [7] Parvin CA, Lo SF, Deuser SM, Weaver LG, Lewis LM, Scott MG. Impact of point‐of‐care testing on patients' length of stay in a large emergency department. Clinical Chemistry. 1996 42: 711‐7. [8] Murray RP, Leroux M, Sabga E, Palatnick W, Ludwig L. Effect of point of care testing on length of stay in an adult emergency department. J Emerg Med. 1999 17: 811‐4. [9] Kendall J, Reeves B, Clancy M. Point of care testing: randomised controlled trial of clinical outcome. BMJ. 1998; 316 1052–7. [10] Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL. Methods for the economic evaluation of health care programmes. 3rd ed. Oxford University Press 2005. pp74‐75

[11] NSW Health. Emergency Department Models of Care 2012. 2012. NSW Ministry of Health.



Tables and Graphs In Text: Table 1: Baseline characteristics by randomization group Table 2: Time from arrival to disposition decision Table 3: Length of Stay in the Emergency Department Table 4: Number of tests ordered from arrival to admission/discharge decision Table 5: Mean use of health‐care resources and mean total health‐care costs per presentation for time to decision according to random allocation Figure 1: Patient Suitability for POCT Flowchart

Figure 2: Participant breakdown Appendix: Figure 3: Cost‐effectiveness plane showing 1000 bootstrap replicates of incremental cost per hour saved (time to disposition decision) for POC vs central laboratory testing. Figure 4: A cost‐effectiveness acceptability curve of POC testing at different willingness to pay levels for 1 hour of time saved to disposition decision. Figure 5: Photo’s from the IPOCT Trial launch party Figure 6: SESLHD Advanced Nurse Standing Order: Chest Pain‐ Adult



Figure 3: Cost‐effectiveness plane showing 1000 bootstrap replicates of incremental cost per hour saved (time to disposition decision) for POC vs central laboratory testing. Seventy percent of replicates were in the north‐east quadrant of the plane, showing that in the majority of cases POC had both slightly higher costs and higher effects than the control group.

NE= north‐east quadrant where interventions are more expensive, but more effective. SE= south‐east quadrant where interventions are less expensive and more effective. SW= south west quadrant where interventions are less expensive but less effective. NW= north‐west quadrant where interventions are more expensive and less effective.

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Figure 4: A cost‐effectiveness acceptability curve of POC testing at different willingness to pay levels for 1 hour of time saved to disposition decision. The ICER point estimate of $38 is observed at the 50% probability mark. At $120 per hour saved, POC testing has an 80% probability of being cost‐effective.

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Figure 6: SESLHD Advanced Nurse Standing Chest Pain‐ Adult

trim ref: t12/5096 mtec 2011/12 projects

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