Strategies to Reduce Inappropriate Laboratory Blood Test Orders in Intensive Care Are Effective and Safe: a Before-And-After Quality Improvement Study J

Anaesth Intensive Care 2018 | 46:3 Laboratory blood tests—quality improvement study

Strategies to reduce inappropriate laboratory blood test orders in intensive care are effective and safe: a before-and-after quality improvement study J. A. Dhanani*, A. G. Barnett†, J. Lipman‡, M. C. Reade§

Summary Unnecessary pathology tests performed in intensive care units (ICU) might lead to increased costs of care and potential patient harm due to unnecessary phlebotomy. We hypothesised that a multimodal intervention program could result in a safe and effective reduction in the pathology tests ordered in our ICU. We conducted a single-centre pre- and post-study using multimodal interventions to address commonly ordered routine tests. The study was performed during the same six month period (August to February) over three years: 2012 to 2013 (pre-intervention), 2013 to 2014 (intervention) and 2014 to 2015 (post-intervention). Interventions consisted of staff education, designing new pathology forms, consultant-led pathology test ordering and intensive monitoring for a six-month period. The results of the study showed that there was a net savings of over A$213,000 in the intervention period and A$175,000 in the post-intervention period compared to the pre-intervention period. There was a 28% reduction in the tests performed in the intervention period (P <0.0001 compared to pre-intervention period) and 26% in the post-intervention period (P <0.0001 compared to pre-intervention period). There were no ICU or hospital mortality differences between the groups. There were no significant haemoglobin differences between the groups. A multimodal intervention safely reduced pathology test ordering in the ICU, resulting in substantial cost savings.

Key Words: laboratory testing, intensive care, cost savings, strategies

Worldwide, treatment in an intensive care unit (ICU) unnecessary blood loss through phlebotomy, causing clinically consumes a substantial proportion of available healthcare significant anaemia and transfusion requirement. Corwin resources1. At more than four times the cost of regular et al reported that the blood loss could be 40 to 70 ml/day hospital care, and accounting for 28% of all acute care and up to one litre per ICU stay7. Conservative phlebotomy charges, ICU treatment comes at a substantial opportunity in ICU limits blood loss8. Transfusion and associated cost to the rest of the healthcare system2. Cost-saving complications are well known and add to the burden of initiatives would have widespread benefit. healthcare expenditure. Additionally, there may be other ICU costs are multifactorial and mostly non-discretionary, costs of transfusion such as infection risk, nursing workload but pathology tests account for at least five percent of the and patient discomfort. Thus, to the public health system the total cost of care3. Patients in ICU often undergo a large panel financial burden of daily unnecessary laboratory testing is of daily routine diagnostic tests with no indication other substantial. than surveillance for occult complications. The benefit of Modifying test-ordering practices in ICU is challenging. this practice has never been established. Between 30%4 and The multidisciplinary and dynamic ICU work environment two-thirds5 of laboratory testing in hospital is likely wasteful. makes it difficult to identify which tests are unnecessary for The high frequency of testing introduces greater risk of a particular patient on a particular day9. Strategies to address false-positive test results6. Unnecessary testing also leads to unnecessary laboratory test ordering9-13 have mostly focused on standing orders prohibiting routine biochemistry and haematology blood tests9-11. However, currently there are no universally applicable guidelines for laboratory test ordering * FCICM MD MBBS, Senior Intensive Care Physician, Department of Intensive Care, in ICU. Royal Brisbane and Women's Hospital; Burns, Trauma and Critical Care Research Centre, University of Queensland; Brisbane, Queensland We hypothesised that implementing a multimodal strategy † BSc PhD, Professor, Institute of Health and Biomedical Innovation & School of Public including education, modification of the laboratory test Health, Queensland University of Technology, Brisbane, Queensland ‡ MD FCICM, Director, Intensive Care Medicine, Royal Brisbane and Women's Hospital, ordering form, senior medical staff–guided ordering, and Brisbane, Queensland implementation of a change management strategy that § DPhil DMedSc FCICM, Consultant Intensivist, Department of Intensive Care Medicine, involved monitoring and feedback would result in safe and Royal Brisbane and Women's Hospital; Burns, Trauma and Critical Care Research Centre, University of Queensland; Brisbane, Queensland; Joint Health Command, cost-effective outcomes, and that these benefits would Australian Defence Force, Canberra, Australian Capital Territory persist over time. We tested this hypothesis in a before-and- Address for correspondence: Dr Jayesh Dhanani. Email: [email protected] after study in our multidisciplinary ICU. Accepted for publication on January 24, 2018

313 J. A. Dhanani et al Anaesth Intensive Care 2018 | 46:3

Materials and methods 2) Redesigning the laboratory order form: The previous order form was redesigned to include clear tick boxes to Ethics approval facilitate limited test ordering. Boxes listed full blood count The study was approved by the hospital ethics committee (FBC), urea/electrolytes/creatinine (UEC), full biochemistry (EC00172), approval number HREC/13/QRBW/284. panel including renal and liver function tests and calcium, magnesium, phosphate (Chem 20), full coagulation panel, Study design International Normalized Ratio (INR), activated partial This was a non-randomised pre- and post-intervention thromboplastin time (APTT), and fibrinogen. These were the study. most common laboratory tests performed during the pre- 14 Study setting and patients intervention period, similar to previous reports . The form also provided guidelines for specific test indications (Figure The study was conducted in the ICU of the Royal Brisbane 1). The combination of tick boxes and guidelines with order and Women’s Hospital. This is a 22-bed tertiary academic ICU sets was deemed to be complementary and facilitated the operating a ‘closed’ model of care in which intensivists take ordering process. primary responsibility for all aspects of patient care. While 3) Medical staff education: Several educational goals were there are seasonal variations, the case-mix is approximately identified and addressed. There was poor understanding of 50% medical and 50% surgical, with all specialties referring the costs of the various test panels. Patients transferred to except cardiac surgery. A consultant intensivist is continuously our ICU from other units and other hospitals routinely had present to lead ‘on-the-floor’ patient management during tests that had already been performed earlier the same the daytime. Overnight, a senior registrar is present with a day needlessly repeated. Many tests were repeated more consultant intensivist on call. Junior medical officers (JMOs) frequently than daily. We used forums such as routine weekly with varying experience, and from a variety of subspecialties, administrative staff meetings, scheduled education sessions, manage routine care including physical ordering of laboratory email and posters. At the beginning of the intervention investigations. The six-month ICU terms for JMOs commence period, a comprehensive orientation program was conducted in February and August. where Fellows and JMOs were educated about the project. Pre-intervention 4) Consultant intensivist–led ordering practices: We had the entire intensivist group participate actively in the study. Pre-intervention, the overnight JMOs initiated laboratory During the intervention period, the intensivists planned the ordering in a self-directed manner. Overnight ordering and next day’s laboratory test ordering during their afternoon testing avoided contributing to the substantial laboratory ward rounds. The completed order forms were placed in a workload during the daytime, and facilitated availability of designated folder and handed over to the night JMO. The results during consultant rounds. The section for writing tests were performed in the early morning hours (between orders on the laboratory order forms was blank, allowing any 0400 and 0600 hours) and the results made available for the test to be ordered, with no formal guidelines available. morning handover (between 0700 and 0800 hours). The ICU To avoid any potential confounding effect of seasonal senior registrar could add tests if indicated by an evolving variations in patient population and JMO rotations, we clinical situation. conducted the study in the same six-month period (7 August 5) Intensive monitoring: At least one investigator or to 6 February) across three years. The study periods were research assistant was present at the beginning of the the pre-intervention period, 2012 to 2013—consisting of pre- afternoon ward round to facilitate the process by providing existing practices; the intervention period, 2013 to 2014— when interventions were performed as per the details given below; and the post-intervention period, 2014 to 2015—in which intensive monitoring was absent, to observe the longer-term sustainability of the intervention. Interventions During the intervention period, we introduced practice alterations with explicit participation by the medical staff. These included prescribing guidelines and new tools such as a new laboratory form. 1) Review and targeting tests: After reviewing existing literature and conducting an internal audit of the tests commonly performed, the coagulation screen and Figure 1: Laboratory test order form guidelines. FBC, full blood count; UEC, biochemistry panel were identified as the tests to be targeted urea/electrolytes/creatinine; ICU, intensive care unit; INR, International for patient-specific indications alone. Normalized Ratio; APTT, activated partial thromboplastin time.

314 Anaesth Intensive Care 2018 | 46:3 Laboratory blood tests—quality improvement study

the laboratory forms. The investigator and the research Results that spanned the patient’s stay (e.g. administration assistant used this opportunity for education around the of blood products) were standardised by their LOS and the purpose of the intervention as well as the processes involved. results are presented on a scale of per patient per day. All Another visit in the evening was made to ensure that the analyses were made in R version 3.3.016. laboratory forms were completed and kept in the designated place. The tests ordered were at the discretion of the treating Results intensivist and no attempts were made to interfere or influence this practice. Demographic characteristics Data collection There were 1,141 patients in the pre-intervention period, 1,067 in the intervention period and 1,042 in the post- We collected demographic characteristics and clinical and intervention period (Table 1). Data for all the patients was laboratory parameters by retrospective audit of the electronic available for analyses. The patient groups were comparable patient record. Clinical parameters consisted of diagnosis, in their age and gender. The APACHE III score was statistically Acute Physiology and Chronic Health Evaluation (APACHE) significantly higher in the intervention group than the pre- III severity of illness score, length of stay (LOS) in ICU, and intervention group; the difference in APACHE III between the ICU and hospital outcome. Laboratory data included the pre- and post-intervention groups did not reach statistical number and type of tests ordered, and the number of tests significance. There was a significantly lower mean bed with results outside the laboratory reference range. We also occupancy during the post-intervention period, so more collected data on the use of invasive organ-support measures beds were available in this period compared to the pre- such as duration of mechanical ventilation and dialysis, along intervention period. The lower number of occupied bed days with the number of packed red blood cell transfusions for the could lead to fewer tests ordered, however we adjusted for ICU admission. The costs of individual tests were obtained this by standardisation to per patient per day. from Pathology Queensland, Queensland Health, Australia. Primary aims Statistical analyses There was a statistically significant reduction of A$28.26 Numbers of tests, costs, and outcomes of ICU patients (95% confidence interval (CI) A$31.64 to A$24.88) per patient in the two follow-up periods, intervention and post- per day in the cost of pathology tests during the intervention intervention, were compared with the pre-intervention period. This decreased in the post-intervention period to a period. saving of A$24.45 (95% CI A$27.86 to A$21.04) per patient The study was non-randomised and is therefore vulnerable per day. There was an overall net saving of A$213,326 in the to the potential confounding effect of differences in patient intervention period and A$175,267 in the post-intervention characteristics and intercurrent care amongst the three time period, compared to the pre-intervention period. periods. We assessed the likelihood of this confounding In the intervention period, there was a significant reduction by comparing baseline patient characteristics including in FBC (12%), Chem 20 (44%), and coagulation panel tests age, gender, source of admission (medical/surgical) and APACHE III score using Student’s t-tests or chi-square tests as Table 1 appropriate. No correction was made for multiple testing15. Characteristics of the study population We used plots to compare the three time periods together with tables of summary statistics. We estimated changes Parameter Pre-intervention Intervention Post-intervention between the three time periods using multiple regression Patients, n 1,141 1,067 1,042 analysis with a dependent variable of the key outcome (e.g. Age, years, mean 57 (18.7) 56 (18.5), 56 (17.9), LOS) and independent variables of time period and the (SD) P=0.275 P=0.104 potential confounders of APACHE III score, age, gender, ICU Female gender, 445 (39%) 447 (42%), 430 (41%), admission source, dialysis and ventilation. n (%) P=0.18 P=0.30 To understand the association between the study period Admitting diagnosis, n (%) and outcome (ICU and hospital mortality), logistic regression Medical 466 (42%) 474 (46%) 482 (48%) models were built using all available predictor variables (APACHE III score, age, surgical/non-surgical category, Surgical 645 (58%) 548 (54%) 516 (52%) 95% CI 0.1% to 95% CI 2.0% to duration of mechanical ventilation or continuous renal 8.7%, P=0.046 10.7%, P <0.001 replacement therapy (CRRT), chronic respiratory failure, liver APACHE III score, 47.7 (25.8) 50.5 (29.3), 45.9 (25.4), failure, and immunosuppression). More parsimonious models mean (SD) P=0.03 P=0.12 (in which the study period remained forced into the model) did not improve model fit, and so only the full models are SD, standard deviation; CI, confidence intervals; APACHE, Acute Physiology reported. and Chronic Health Evaluation. P-values are in comparison to the pre- intervention period.

315 J. A. Dhanani et al Anaesth Intensive Care 2018 | 46:3

Table 2 Number of individual tests per patient days a. Analysis with day one of admission included

Pre-intervention period Intervention period Post-intervention period Number of tests Number of tests Number of tests Test P-value P-value per patient days per patient days per patient days FBC, mean (SD) 0.7795 (0.36) 0.7115 (0.29) 0.00276 0.7043 (0.32) 0.00129 UEC, mean (SD) 0.0009 (0.01) 0.3022 (0.27) 0.0001 0.1483 (0.22) 0.0001 Chem20, mean (SD) 0.70 (0.32) 0.3671 (0.32) 0.0001 0.5541 (0.34) 0.0001 INR, mean (SD) 0.0115 (0.06) 0.0163 (0.07) 0.29888 0.0101 (0.06) 0.74280 APTT, mean (SD) 0.0779 (0.34) 0.1054 (0.36) 0.25694 0.0794 (0.36) 0.95097 Coags, mean (SD) 0.6561 (0.34) 0.1567 (0.25) 0.0001 0.1715 (0.27) 0.0001 FBC, full blood count; UEC, urea/electrolytes/creatinine; Chem20, full biochemistry panel; INR, International Normalized Ratio; APTT, activated partial thromboplastin time; Coags, full coagulation panel. The P-values compare the number of individual tests for the intervention period and post-intervention period compared to the pre-intervention period.

(70%) ordered (Table 2). The ordering of UEC (400%) and despite the increased severity of illness in patients during individual coagulation tests (INR [180%] and APTT [142%]) the intervention period, as reflected in the APACHE III increased. In the post-intervention period, differences in the scores. The differences in abnormal tests results between test ordering persisted. Overall there were 28% fewer tests the groups was small. Table 5 shows patient outcome data performed in the intervention period and 26% fewer in the using multiple regression models adjusted for APACHE III post-intervention period. To test the effect of longer ICU stay, score, age, gender, and ICU admission source. Median LOS the data was analysed with and without including day one in ICU had clinically and statistically insignificant differences of the admission as more tests are likely to be performed on day one. This showed that the differences in the INR Table 3 and APTT were not as pronounced with day one excluded, Change in blood product usage per patient per day but remained significantly less. Other differences were maintained. Intervention period Post-intervention period Compared to the pre-intervention group, there was a Blood Mean % Mean % 17.9% reduction in the packed red blood cell usage in the Product change 95% CI P-value change 95% CI P-value intervention group, but this apparent reduction did not in use in use reach statistical significance (P=0.26). Similarly, apparent RBC -17.9% -42.0% 0.26 18.7% -15.2% to 0.31 to 16.2% 66.1% reductions in fresh frozen plasma and platelet transfusions in the intervention and post-intervention periods were not FFP -37.5% -73.7% 0.28 -27.2% -69.6% to 0.47 to 48.8% 74.2% statistically significant (Table 3). Platelets -31.7% -75.4% 0.46 -8.54% -67.0% to 0.86 Secondary aims to 89.8% 154.0% The percentages of abnormal test results are shown Results are adjusted for APACHE III, age, gender, admission source, dialysis, in Table 4, with fewer abnormal tests results in the and ventilation. All comparisons made with the pre-intervention period. APACHE III, Acute Physiology and Chronic Health Evaluation; RBC, red blood intervention and the post-intervention period. This is cells; FFP, fresh frozen plasma; CI, confidence intervals.

316 Anaesth Intensive Care 2018 | 46:3 Laboratory blood tests—quality improvement study

Table 4 Test data for the three time periods showing the number of tests and abnormal results

Pre-intervention Intervention Post-intervention Test Total number Total number Total number % abnormal % abnormal P-value % abnormal P-value of tests of tests of tests ALB 2,712 92.3% 2,672 90.3% 0.010 3,343 90.0% 0.003 ALP 2,282 31.2% 2,366 27.8% 0.014 3,210 27.9% 0.010 ALT 2,257 49.9% 2,350 41.4% <0.001 3,159 40.0% <0.001 APTT 354 51.4% 276 53.6% 0.638 234 37.6% 0.001 AST 2,263 65.0% 2,353 64.3% 0.663 3,176 55.8% <0.001 BILI 2,276 40.8% 2,365 35.6% <0.001 3,210 36.5% 0.001 CRE 4,220 60.1% 4,003 61.6% 0.183 3,763 52.6% <0.001 GGT 2,282 45.1% 2,361 44.8% 0.825 3,209 47.1% 0.148 HGB 4,343 3.2% 3,993 2.8% 0.259 3,775 3.7% 0.259 INR 298 44.3% 196 40.8% 0.502 200 26.0% <0.001 K 4,233 11.6% 4,019 8.9% <0.001 3,781 7.9% <0.001 NA 4,272 32.5% 4,069 33.4% 0.390 3,818 32.7% 0.907 PLT 4,273 18.2% 3,903 13.2% <0.001 3,699 15.1% <0.001 UREA 4,271 47.2% 4,044 49.3% 0.050 3,800 46.6% 0.637 WBC 4,293 55.4% 3,943 56.1% 0.547 3,771 58.7% 0.003 ALB, albumin; ALP, alkaline phosphatase; ALT, alanine transaminase; APTT, activated partial thromboplastin time; AST, aspartate transaminase; BILI, bilirubin; CRE, creatinine; GGT, gamma-glutamyl transferase; HGB, haemoglobin; INR, International Normalized Ratio; K, potassium; NA, sodium; PLT, platelet count; UREA, urea; WBC, white blood cell count. The P-values compare the percentage of abnormal tests for the intervention period and post-intervention period to the pre-intervention period.

(median difference within 0.3 days) between study periods. blood product usage in this period would have resulted in Mortality was comparable between the study periods. There additional cost savings, both direct and indirect. Predictably, was no difference in the number of patients mechanically as the coagulation panel tests decreased significantly, the ventilated or the duration of mechanical ventilation between number of individual coagulation tests increased in the the pre-intervention and intervention periods, but there was intervention and the post-intervention group. Being a tertiary a significant reduction (compared to the pre-intervention referral hospital, patients may have had tests done elsewhere period) in the duration of mechanical ventilation and number prior to transfer. Hence, as per the guidelines, they may of patients ventilated in the post-intervention period. have had individual tests done at the time of ICU admission Requirement for, and duration of, CRRT was similar in the three study periods. Multivariable regression analysis of Table 5 potential predictors of ICU and hospital mortality with all the study periods (Tables 6a and 6b) showed that the study ICU outcomes comparing the intervention and post-intervention periods period was not associated with mortality. When comparing with pre-intervention period the admission-to-discharge haemoglobin difference, the Outcome Pre-intervention Intervention Post-intervention groups were comparable (Table 7). ICU mortality, n (%) 44 (4.9%) 49 (5.7%) 46 (5.5%) (P=0.35) (P=0.21) Discussion Length of stay in ICU, 1.92 2.01 (1.84– 2.18) 1.89 (1.74–2.06) Our interventions were associated with a significant days, median (IQR) (1.77–2.08) P=0.43 P=0.81 reduction in the number of blood tests ordered. The Mechanical 367 (40.8%) 351 (41%) 321 (38.6%) apparent reduction in blood product transfusion rates ventilation, number (0.31) (0.17) of patients (%) was not statistically significant. The study groups were comparable with the important exception that the APACHE Renal dialysis, number 39 (4.08%) 42, 4.1% 29 (3.86%) of patients (%) P=0.36 P=0.49 III score was higher in the intervention group than in the pre-intervention group. This difference only accentuates the APACHE III, Acute Physiology and Chronic Health Evaluation; IQR, interquartile range; ICU, intensive care unit. The P-values are from multiple effect of our intervention, as the benefits persisted despite regression models adjusted for APACHE III score, age, gender, and ICU the greater severity of illness in this group. The reduced admission source.

317 J. A. Dhanani et al Anaesth Intensive Care 2018 | 46:3

Table 6a Table 6b Multivariable regression analysis of the potential predictors of Multivariable regression analysis of the potential predictors of hospital intensive care unit survival, with study period (pre-intervention, intervention survival, with study period (pre-intervention, intervention and post- and post-intervention) forced into the model intervention) forced into the model

Odds Odds Variable ratio of P-value 95% CI Variable ratio of P-value 95% CI survival survival Period (compared to Period (compared to pre- pre-intervention) intervention) Intervention 1.32 0.236 0.83 to 2.10 Intervention 1.29 0.165 0.90 to 1.86 Post-intervention 0.96 0.864 0.60 to 1.53 Post-intervention 1.23 0.260 0.86 to 1.77 APACHE III score APACHE III score (per score increment) 0.96 <0.001 0.95 to 0.97 (per score increment) 0.96 <0.001 0.96 to 0.97 Age (per year increment) 0.99 0.039 0.98 to 1.0 Age (per year increment) 0.97 <0.001 0.96 to 0.98 Non-surgical 0.29 <0.001 0.17 to 0.50 Non-surgical 0.56 0.001 0.40 to 0.79 Mechanical ventilation 0.15 <0.001 0.08 to 0.27 Mechanical ventilation 0.27 <0.001 0.19 to 0.38 CRRT 1.27 0.387 0.74 to 2.20 CRRT 1.34 0.265 0.80 to 2.26 Chronic respiratory failure 0.97 0.941 0.45 to 2.11 Chronic respiratory failure 0.82 0.532 0.44 to 1.52 Chronic liver failure 1.25 0.669 0.44 to 3.55 Chronic liver failure 0.80 0.610 0.34 to 1.89 Chronic immunosuppression 0.58 0.021 0.37 to 0.92 Chronic immunosuppression 0.55 0.001 0.39 to 0.79 P-values compare the intervention and post-intervention period to the P-values compare the intervention and post-intervention period to the pre-intervention period. APACHE III, Acute Physiology and Chronic Health pre-intervention period. APACHE III, Acute Physiology and Chronic Health Evaluation; CRRT, continuous renal replacement therapy; CI, confidence Evaluation; CRRT, continuous renal replacement therapy; CI, confidence intervals. intervals.

commence the same number of weeks after the change of instead of the full coagulation screen, potentially explaining JMO’s terms, so that the familiarity of the medical officers the differences seen in Table 2a and 2b. with the ICU practice would not be a confounding factor in The significantly decreased proportion of abnormal test the relationship between study period and the measured results in the intervention group compared to the pre- outcomes. intervention group was despite the increased severity of Medical reversal18—the phenomenon of abandoning illness. Thus, contrary to conventional wisdom, higher current practice—takes a long time19, longer than introducing severity of illness need not correlate to more laboratory tests. a new practice20. One study reported the large variations in Most of the benefits persisted in the post-intervention the physician-attributable costs in ICU21. Whilst we did not group, showing the longer-term sustainability of the study this variable, it emphasises the importance of a uniform intervention. buy-in by all intensivists, as was the case in our study. An intervention that results in fewer diagnostic tests Ordering patterns are also shown to correlate with duration might plausibly be associated with overall patient harm. of clinical exposure, with those taught to rely on clinical Although there was no evidence of harm in the outcomes examination and judgement early in training likely to order we studied, it is possible that other adverse effects were less laboratory tests22. Thus, by setting an example by direct not observed. Moreover, the crude retrospectively analysed demonstration of appropriate test ordering, the trainees data in the study might not detect subtle harm. Whilst there may learn the right habits thus ensuring future compliance. is a focus on reducing tests, a 15-year meta-analysis of the It has also been shown that the menu of choices for test use of laboratory tests found both over- and under-use is ordering has a direct impact on the ordering patterns23. By widespread4. It is therefore deemed important to incorporate designing the pathology order forms to limit choice as in our the principle of the ‘right test in the right patient at the right study, it may be possible to reduce test ordering. For data time’. In our study, the mortality rate, ICU LOS, duration of analysis, it is seen that adjusting for severity of illness scores mechanical ventilation, and dialysis duration was comparable enables comparison of data between units24. Hence, we have between groups. adjusted the important outcome data to APACHE III scores. Seasonal variations in the pathology and demography Several other investigators have studied different strategies of critically ill patients have been reported in literature17. to reduce the pathology cost of critical care9,11,25-28. Rachakonda Similar variations are also observed in our ICU. We et al reported a significant reduction of tests ordered by conducted our study of sustained intensive interventions instituting consultant-led high-volume test ordering26. We over a six-month period. The study periods were chosen to adopted this strategy in our study. Musca et al introduced a

318 Anaesth Intensive Care 2018 | 46:3 Laboratory blood tests—quality improvement study

clinical guideline and an education program to successfully Table 7 reduce coagulation tests and associated laboratory costs in a Comparison of haemoglobin (Hb) difference from admission to discharge tertiary ICU29. Our study included coagulation tests along with between the study groups other high-volume, frequently performed tests, as well as quantifying some measures of the safety of the interventions. Period Hb difference, g/dl, mean, (SD) Read et al substituted a daily routine testing practice by Pre-intervention -10.7 (19.3) on-demand testing which led to a substantial reduction in the Intervention -10.7 (17.5) number of tests ordered and cost savings27. Due to an already Post-intervention -9.2 (16.8) significant daytime workload in our laboratories and hence There was no significant difference in the haemoglobin changes amongst the potential delayed results, we did not apply this practice in three study groups (F=1.74, DF=2, P=0.18) our study. Applying a 24-hour limit for repetition of the test led to reduction in the tests in a paediatric intensive care could have offset the savings we observed. However, the 11 setting in one study . The study did not report on the safety comprehensive list of tests we studied makes this unlikely. outcomes of the interventions. In our setting, it was deemed Our study did not include point-of-care tests such as by the intensivists that patient diagnoses might necessitate arterial blood gases and viscoelastic coagulation tests, which repetition of the tests, hence we did not implement this have specific indications and should be studied separately. intervention. Introducing a laboratory test order chart filled in Although the reduction in our coagulation tests could for tests to be performed the following day led to a reduction possibly lead to increase in the ordering of viscoelastic in the tests and the associated costs in the study by Goddard coagulation tests, our ICU at the time of the study did 30 and Austin . We mimicked this intervention in our study. not have this technology. There might have been other Guidelines for the ordering of routine laboratory tests and unaccounted-for interventions or events that could have chest X-rays resulted in reduction of tests and costs in Prat influenced the results, including awareness and education 28 et al’s study . In one study, incorporating guidelines and from other departments. Although the study realised the routine practice of discussing laboratory tests on ward cost savings for the ICU, there could be other costs to the 9 rounds led to fewer tests . Our study included guidelines pathology services that may affect the overall cost to the as one of the interventions, although this was limited to health service. pathology tests only. Having a list of acceptable indications The study sought to evaluate only a period of six months for tests such as complete blood count, renal function tests in the year. Whilst it is possible that the results could be and electrolytes and incorporating ordering in the afternoon different in the rest of year, this is unlikely; the periods ward rounds led to reduction in the tests and cost savings in studied covered summer and winter months to the same 10 the study by Iosfina et al . We did not prescribe indications degree. We have studied the effects of the intervention for for the tests and instead relied on continuous education as a period of one-year post-implementation. It is not certain a strategy. Interestingly, providing information about the if the effects would last over a longer period and a follow- costs of individual tests resulted in less test ordering by the up study at the five-year and ten-year intervals will be 25 clinicians in Seguin et al’s study . The consultant intensivists important. We used a pragmatic pre–post design and whilst in our ICU felt that while this intervention may reduce test we controlled for known confounders there may have been ordering, it could be a disincentive to performing otherwise other changes that we were unaware of. appropriate tests thus resulting in patient harm. Thus, although the results are variable, it is apparent from the studies that a variety of interventions could result in cost Conclusion savings. Many factors contribute to test ordering and most Our study shows that successful implementation of a test-reducing interventional studies have been performed multimodal strategy consisting of a consultant-led ordering in different settings as well as using different endpoints, practice, education and a menu-based ordering aimed rendering meta-analysis for cost-reducing interventions at rationalisation of commonly ordered pathology tests inappropriate or impossible. Although the majority of ICU was associated with significant cost savings. There was no expenditure is non-discretionary3, abnormal results from a associated increase in patient morbidity or mortality. The screening test performed without a clinical indication could benefits of the interventions were sustained one year after potentially lead to further testing and treatments that are not the intervention period. indicated. Hence, cost implications may still be important. Acknowledgements Limitations The study was funded by the The Australian Centre For We studied only the pre-specified tests that we knew were Health Services Innovation stimulus grant number SG0009- commonly ordered in our ICU. It is possible that other tests 000435. might have been substituted for those we targeted, which We wish to acknowledge Tony Ghent (Supervising Scientist,

319 J. A. Dhanani et al Anaesth Intensive Care 2018 | 46:3

Pathology Queensland), Pierre Clement (Manager, Clinical 17. Danai PA, Sinha S, Moss M, Haber MJ, Martin GS. Seasonal Information System, ICU, Royal Brisbane and Women’s variation in the epidemiology of sepsis. Crit Care Med 2007; Hospital), and the pathology department staff involved in this 35:410-415. study. 18. Prasad V, Cifu A. Medical reversal: why we must raise the bar before adopting new technologies. Yale J Biol Med 2011; References 84:471-478. 19. Tatsioni A, Bonitsis NG, Ioannidis JP. Persistence of contradicted 1. Halpern NA, Bettes L, Greenstein R. Federal and nationwide claims in the literature. JAMA 2007; 298:2517-2526. intensive care units and healthcare costs. Crit Care Med 1994; 20. Moscucci M. Medical reversal, clinical trials, and the “late” open 22:2001-2007. artery hypothesis in acute myocardial infarction. Arch Intern 2. Emanuel E, Tanden N, Altman S. A systemic approach to Med 2011; 171:1643-1644. containing healthcare spending. N Engl J Med 2012; 367: 21. Garland A, Shaman Z, Baron J, Connors AF Jr. Physician- 949-954. attributable differences in intensive care unit costs: a single- 3. Rechner IJ, Lipman J. The costs of caring for patients in a tertiary center study. Am J Respir Crit Care Med 2006; 174:1206-1210. referral Australian intensive care unit. Anaesth Intensive Care 22. Harendza S, Alofs L, Huiskes J, Wijnen-Meijer M. Ordering 2005; 33:477-482. patterns for laboratory and radiology tests by students from 4. Zhi M, Ding EL, Theisen-Toupal J, Whelan J, Arnaout R. The different undergraduate medical curricula. BMC Med Educ 2013; landscape of inappropriate laboratory testing: a 15-year 13:109. meta-analysis. PLoS One 2013; 8:78962. 23. Krasowski MD, Chudzik D, Dolezal A, Steussy B, Gailey MP, 5. Miyakis S, Karamanof G, Liontos M, Mountokalakis TD. Factors Koch B et al. Promoting improved utilization of laboratory testing contributing to inappropriate ordering of tests in an academic through changes in an electronic medical record: experience at medical department and the effect of an educational feedback an academic medical center. BMC Med Inform Decis Mak 2015; strategy. Postgrad Med J 2006; 82:823-829. 15:11. 6. Cismondi F, Celi LA, Fialho AS, Vieira SM, Reti SR, Sousa JMC, 24. Slonim AD, Pollack MM. An approach to costs in critical care: Finkelstein SN. Reducing unnecessary lab testing in the ICU with macro- versus microeconomics. Crit Care Med 1999; 27: artificial intelligence. Int J Med Inform 2013; 82:345-358. 2286-2287. 7. Corwin HL, Parsonnet KC, Gettinger A. RBC transfusion in the 25. Seguin P, Bleichner JP, Grolier J, Guillou YM, Malledant Y. Effects ICU. Is there a reason? Chest 1995; 108:767-771. of price information on test ordering in an intensive care unit. 8. Harber CR, Sosnowski KJ, Hegde RM. Highly conservative Intensive Care Med 2002; 28:332-335. phlebotomy in adult intensive care—a prospective randomized 26. Rachakonda KS, Parr M, Aneman A, Bhonagiri S, Micallef S. controlled trial. Anaesth Intensive Care 2006; 34:434-437. Rational clinical pathology assessment in the intensive care unit. 9. Kumwilaisak K, Noto A, Schmidt UH, Beck CI, Crimi C, Anaesth Intensive Care 2017; 45:503-510. Lewandrowski K, Bigatello LM. Effect of laboratory testing 27. Raad S, Elliott R, Dickerson E, Khan B, Diab K. Reduction of guidelines on the utilization of tests and order entries in a laboratory utilization in the intensive care unit. J Intensive Care surgical intensive care unit. Crit Care Med 2008; 36:2993-2999. Med 2017; 32:500-507. 10. Iosfina I, Merkeley H, Cessford T, Geller G, Amiri N, Baradaran N 28. Prat G, Lefevre M, Nowak E, Tonnelier J-M, Renault A, et al. Implementation of an on-demand strategy for routine L’Her E, Boles J-M. Impact of clinical guidelines to improve blood testing in ICU patients. Am J Respir Crit Care Med 2013; appropriateness of laboratory tests and chest radiographs. 187:A5322. Intensive Care Med 2009; 35:1047-1053. 11. Pageler NM, Franzon D, Longhurst CA, Wood M, Shin AY, 29. Musca S, Desai S, Roberts B, Paterson T, Anstey M. Routine Adams ES et al. Embedding time-limited laboratory orders coagulation testing in intensive care. Crit Care Resusc 2016; within computerized provider order entry reduces laboratory 18:213-217. utilization. Pediatr Crit Care Med 2013; 14:413-419. 30. Goddard K, Austin S. Appropriate regulation of routine 12. Peixoto A, Meneses F, Barbosa B, Pessoa L, Melo R, Fideles G. laboratory testing can reduce the costs associated with patient Laboratory routine in the ICU: a practice to be abolished? stay in intensive care. Crit Care 2011; 15:1-190. Crit Care 2013; 17:1-25. 13. See KC, Phua J, Mukhopadhyay A. Monitoring of extubated patients: are routine arterial blood gas measurements useful and how long should patients be monitored in the intensive care unit? Anaesth Intensive Care 2010; 38:96-101. 14. Frassica JJ. Frequency of laboratory test utilization in the intensive care unit and its implications for large-scale data collection efforts. J Am Med Inform Assoc 2005; 12:229-233. 15. Perneger TV. What's wrong with Bonferroni adjustments. BMJ 1998; 316:1236-1238. 16. R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2016.

320