Effectiveness of Practices to Support Appropriate Laboratory Test Utilization: A Laboratory Medicine Best Practices Systematic Review and Meta-Analysis.

OBJECTIVES: To evaluate the effectiveness of practices used to support appropriate clinical laboratory test utilization.
METHODS: This review followed the Centers for Disease Control and Prevention (CDC) Laboratory Medicine Best Practices A6 cycle method. Eligible studies assessed one of the following practices for effect on outcomes relating to over- or underutilization: computerized provider order entry (CPOE), clinical decision support systems/tools (CDSS/CDST), education, feedback, test review, reflex testing, laboratory test utilization (LTU) teams, and any combination of these practices. Eligible outcomes included intermediate, systems outcomes (eg, number of tests ordered/performed and cost of tests), as well as patient-related outcomes (eg, length of hospital stay, readmission rates, morbidity, and mortality).
RESULTS: Eighty-three studies met inclusion criteria. Fifty-one of these studies could be meta-analyzed. Strength of evidence ratings for each practice ranged from high to insufficient.
CONCLUSION: Practice recommendations are made for CPOE (specifically, modifications to existing CPOE), reflex testing, and combined practices. No recommendation for or against could be made for CDSS/CDST, education, feedback, test review, and LTU. Findings from this review serve to inform guidance for future studies.

Laboratory testing is integral to modern health care as a tool for screening, diagnosis, prognosis, stratification of disease risk, treatment selection, and monitoring of disease progression or treatment response. It is also a guide for hospital admissions and discharges. As rates of test utilization grow, there is increased scrutiny over the appropriateness of testing, for example to reduce potential for diagnostic error.[1,2] Additionally, with increased capitation and restrictive insurance reimbursements, clinical laboratories are under continuous pressure to improve the value and utility of laboratory investigations while operating in relation to these financial restrictions.[3,4] Within this landscape, rates of inappropriate overutilization and underutilization represent an important gap for quality improvement at one of the earliest points of clinical-laboratory interface.[1,5,6] While utilization management approaches have been described in the literature,[6-14] the effectiveness of these interventions in support of appropriate test utilization is unclear, as are current research gaps.

Quality Gap: Inappropriate Laboratory Test Ordering

Many laboratory test orders are not supported by appropriate-use protocols (ie, organizational guidelines, local consensus guidelines, algorithms, and local administrative directives on utilization) and are unnecessarily duplicative, with variations in test ordering patterns influenced by a number of factors.[4,12,15] A systematic review by Zhi et al[5] revealed that overall mean rates of inappropriate over- and underutilization of testing were 20.6% and 44.8%, respectively. In this context, a reduction in duplicate test orders and the use of test orders supported by appropriate use protocols qualify as measurable quality gaps.

Quality Improvement Practices

Eight practices impacting test utilization were determined relevant to this systematic review, and have been characterized in the literature.[7-14,16] These practices, defined in the glossary (Supplemental Table 1; all supplemental material can be found at American Journal of Clinical Pathology online), are: computerized provider order entry (CPOE), clinical decision support systems/tools (CDSS/CDST), education, feedback, reflex testing, test review, laboratory test utilization (LTU) teams, and combined practices.

These practice categories represent approaches to systematically manage the utilization of testing, and are generally preanalytical within the total testing process.[17,18] As such, each practice serves to impact the appropriateness of specific testing ordered or performed, a gap generally expressed as overutilization or underutilization of testing within this review’s a priori analytical framework and inclusion/exclusion criteria. The source of criteria informing the “appropriateness” of testing is variable, ranging from national recommendations and guidelines to local consensus and administrative protocols. This aspect of variation was not an a priori consideration for analysis and derivation of practice recommendations (ie, not an element of inclusion/exclusion nor an element of data abstraction). However, it is further discussed in the “Conclusions.”

Materials And Methods

This systematic review was guided by the Center for Disease Control (CDC) Laboratory Medicine Best Practices (LMBP) A6 Cycle, a previously validated evidence review and evaluation method for quality improvement in laboratory medicine.[19] Additional resources can be found at https://wwwn.cdc.gov/labbestpractices/.

This systematic review was conducted by a review coordinator, staff trained to apply CDC LMBP methods, and statisticians with expertise in quantitative evidence analysis. The team was advised by a multidisciplinary expert panel consisting of seven members, with subsequent approval or practice recommendations by the LMBP Work Group. Supplemental Table 2 lists the LMBP Work Group members and expert panel members and further describes their roles.

Ask: Review Questions and Analytic Framework

The question addressed through this systematic review is: “What is the effectiveness of practices used to support appropriate clinical LTU?” This review question was developed in the context of the analytic framework depicted in Figure 1.

Figure 1

Analytic framework. CPOE, computerized provider order entry.

Eligibility criteria, relevant population, intervention, comparison, outcome, and setting elements are:

• Population:

o Personnel legally authorized to order clinical laboratory tests (ie, targeted population for test utilization management interventions)

o General patient population (no restrictions, including inpatient and outpatient settings, in terms of study eligibility for the systematic review)

• Interventions (quality improvement practice interventions to manage/support appropriate clinical LTU, with quality gaps expressed as inappropriate over- or underutilization of testing): CPOE (CPOE replacement of written test orders and modifications to an existing CPOE), CDSS/CDST, education, feedback, test review, LTU teams, reflex testing, and combined practices.

• Comparison: experimental (eg, randomized controlled trials) and other comparative studies designs (eg, before and after studies) in which the effect of a utilization management intervention was compared to a group lacking it.

• Outcomes:

o Number of tests (eg, number of test orders, number of tests performed)

o Costs/charges (eg, cost of test orders, cost of tests performed, cost per diagnosis, overall health care costs)

o Turnaround time (eg, where reduction in the number of inappropriate tests may not be relevant)

o Diagnostic yield and diagnostic detection rate

o Length of hospital stay

o Other patient-related outcomes (eg, patient satisfaction, patient safety events related to delayed or incorrect diagnosis, adverse drug reactions, readmission rates, morbidity, and mortality)

• Setting:

o Facility: any health care facility in which laboratory testing is ordered/performed, including reference laboratory testing

o Targeted testing: any testing identified within eligible studies as being inappropriately utilized (ie, over- or underutilized)

For study inclusion, the intent of this review was not to focus on a single criterion for “inappropriate” utilization nor a single source of that criterion (eg, local consensus guidelines, national guidelines). Rather, focus was cast broadly on the effect of utilization management approaches. This review (particularly the “Future Research Needs” section) was informed by 2013 and 2014 reviews, which characterized the “landscape” of inappropriate testing, and identified frequent sources of criteria for identification of inappropriate utilization.[5,20]

Finally, several of the outcome types listed above (eg, number of tests, costs of tests, and turnaround time) represent intermediate outcomes and are systems/operational in nature. However, they are proximal indicators of practice effectiveness, such that direct causality in relation to an intervention is more likely. While other outcomes types (eg, length of stay, patient safety events, and morbidity) may be more patient-related outcomes, a concern (discussed in the “Limitations” section) is that they are distal to the intervention and thus influenced by other medical and nonmedical factors during the course of patient care.

Acquire: Literature Search and Request for Unpublished Studies

With involvement of the expert panel and a CDC librarian, a comprehensive electronic literature search was conducted in seven electronic databases to identify eligible studies in the current evidence base. The initial literature search was conducted April 17, 2014, with two additional searches on September 1, 2015, and January 10, 2016. Further description of the search protocol, as well as the full electronic search strategy for each searched database, are provided in Supplemental Table 3.

Appraise: Screen and Evaluate Individual Studies

Screening of search results against eligibility criteria was performed by two sets of independent reviewers, with disagreement mediated by consensus discussion or by a third reviewer. The screening process is further described in Supplemental Table 3.

Studies were categorized to specific practice category (ie, CDSS/CDST, CPOE, education, etc) independently by two reviewers, with disagreement mediated by consensus discussion or, if needed, by a third reviewer. Studies were then abstracted and quality appraised using a standard data abstraction form tailored to the topic of this systematic review (Supplemental Table 4). The final data abstraction forms—“evidence summary tables” for each study—represent consensus between two independent abstractions on content and quality appraisal, with a statistician’s review of abstracted statistical data and input of qualitative effect size ratings. Use of the data abstraction forms for generation of “evidence summary tables” is further described in Supplemental Table 4.

Analyze: Data Synthesis and Strength of the Body-of-Evidence (Meta-Analysis Approach)

Two analytic approaches were used in this systematic review: qualitative determinations of overall strength of evidence and quantitative meta-analysis. For the qualitative analysis, groups of studies within a practice category were classified according to the overall strength of their evidence’s effectiveness, with ratings of “high,” “moderate,” “suggestive,” or “insufficient”. These qualitative ratings take into account the number of studies within a group, their qualitative effect size ratings, and their qualitative quality ratings. Criteria in Table 1 are the minimum criteria to achieve a particular strength of evidence rating. These criteria are the basis of the body-of-evidence qualitative analyses appearing in the “Results” section and are the primary determinant of the best practice recommendation categorizations appearing in the “Conclusions” section.

Table 1

Criteria for Determining Strength of Body-of-Evidence Ratingsa

Strength of Evidence Rating	No. of Studies	Effect Size Rating	Quality Rating
High	≥3	Substantial	Good
Moderate	2	Substantial	Good
	or ≥3	Moderate	Good
Suggestive	1	Substantial	Good
	or 2	Moderate	Good
	or ≥3	Moderate	Fair
Insufficient	Too few	Minimal	Fair

aAdapted from Christenson et al.[19]

Quantitative meta-analysis was conducted on a subset of included studies meeting the following criteria: (1) have a similar outcome (eg, number of tests ordered or performed, or cost of tests), (2) have an intervention satisfying inclusion criteria, (3) have a quality rating of “fair” or “good,” and (4) have information necessary to calculate point and interval estimates. In preparation for meta-analysis, data in studies were summarized as either Cohen d or as an odds ratio (OR), representing standardized measures of effect for each included study. Continuous data were summarized using Cohen d. For continuous data, necessary information included means, standard deviations (or standard errors, confidence interval [CI], exact P value, or test statistic), and sample sizes. Nominal data, on the other hand, were summarized using ORs. For nominal data, necessary information included numerators and denominators of proportions.

Meta-analyses were conducted using Comprehensive Meta-Analysis software (version 3) from www.Meta-Analysis.com. All meta-analyses assumed random effects.[21] Unlike the fixed effect approach, the random effects approach does not assume homogeneity of effect across studies, an assumption which is not reasonable for the evidence base analyzed in this review. The I2 statistic, was used to determine consistency (homogeneity) of effects.[22] Most studies had I2 close to 100% (nearly all variation due to heterogeneity), reinforcing the decision to use a random effects approach. Results of meta-analyses were summarized numerically using the summary OR (for number of tests ordered) or Cohen d (for cost of tests ordered) and their 95% CI.

Point and interval estimates were summarized graphically using forest plots, appearing in the “Results” section. Study-specific ORs and summary ORs were classified as having minimal (OR < 2.5 or OR > 0.4), moderate (OR ≥ 2.5 or OR ≤ 0.4 and OR < 4.5 or OR > 0.2), or substantial (OR ≥ 4.5 or OR ≤ 0.2) effect sizes. Study-specific Cohen d and summary Cohen d were classified as having minimal (|d| < 0.5), moderate (|d| ≥ 0.5 and |d| < 0.8), or substantial (|d| ≥ 0.8) effect sizes.[19] These cutoffs were the criteria for qualitative effect size ratings used for the body-of-evidence qualitative analysis described above.

Results

Study Selection

A total of 23,231 bibliographic records were identified through seven electronic databases, published between the periods of January 1980 to January 2016. The bibliographic record included published studies, as well as conference abstracts and proceedings. No unpublished studies were successfully obtained for screening. After removing duplicates and non-English articles, a total of 22,207 bibliographic records were identified. Screenings of titles and abstracts independently by two reviewers resulted in 812 studies to be considered for inclusion by full-text review, with elimination of 21,395 studies (including editorials, articles unrelated to the systematic review topic). Subsequent full-text screening resulted in 95 studies determined eligible for inclusion in the systematic review, eliminating 717 studies not meeting inclusion criteria (eligible interventions and/or outcomes not present). Lastly, in accord with the LMBP methodology, 12 of these 95 studies were eliminated from final inclusion following study quality assessment and determination of a “poor” quality rating by two independent data abstractors.

This resulted in 83 studies for inclusion in this systematic review, of which 32 could not be meta-analyzed due to the absence of necessary information. The study selection flow diagram is depicted in Figure 2. Full bibliographic information for each study is provided in Supplemental Table 4.

Figure 2

Study selection flow diagram. CDSS, clinical decision support system; CPOE, computerized provider order entry; LTU, laboratory test utilization.

Study Characteristics and Risk of Bias

While studies with the aim of impacting quality gaps identified as underutilization were eligible for inclusion, the final set of studies that could be qualitatively analyzed and meta-analyzed addressed inappropriate overutilization (additional discussion on this gap in the “Limitations” section). Further, the information among the available evidence base supported statistical determinations of number of tests (representing either number of tests ordered or number of tests performed) as the measure of effect that could be analyzed across a majority of included studies.

The number of tests outcome was calculated as the difference between the number of tests pre- and postintervention, or between the intervention group and control group (for some studies, this was expressed as number of tests ordered, for others it was expressed as number of tests performed, generalized in this review as “number of tests”), with the assumption made that the testing ordered/performed postintervention was appropriate. These data permitted calculation in most cases of one of the standardized effect measures—Cohen d or OR—for inclusion in qualitative and quantitative analysis summaries (described below). As will be further described in the “Discussion” section, the quality gap addressed by all but one included study was in relation to inappropriate overutilization of testing. Each study variably defined the standard by which the quality gap of inappropriate overutilization was established, with considerable variation across studies as to criteria for determining the presence of inappropriate test utilization through utilization audit, as well as how thoroughly investigators described these criteria in their reporting. This is discussed in more detail in the “Limitations” section and in the “Future Research Need” section.

Data available in the current evidence base also supported determinations of “cost of tests” outcome, serving as this review’s secondary outcome measure analyzable across many of the included studies.

To allow for more robust synthesis of eligible studies with number of tests as the outcome, the Cohen d statistics (with CIs) were converted to ORs (with CIs).[23] Studies with cost of tests ordered as an outcome were compared using Cohen d, since subsequent conversion to an OR was unnecessary.

The available evidence base demonstrated considerable variability in terms of targeted test-ordering clinicians, targeted testing, patient setting, patient inclusion/exclusion criteria, facility type and size, study design, and the specific intervention used within a practice category. The “Applicability and Generalizability” subsection of the “Discussion” section provides details on distribution of characteristics across studies. Additionally, they varied in terms of author affiliation, with 46% of included studies having laboratory professionals in authorship (eg, pathologists, lab consultants, lab managers, and lab directors).

Supplemental Table 4 provides detailed evidence summary tables for the 83 included studies, as well as for the 12 eligible studies excluded for poor quality ratings. Supplemental Table 4 also provides criteria for study quality point deductions. With a possible score of 0 to 10, average quality score for all included studies was 6.7 (standard deviation, ±1.11) for an average “fair” quality rating across all included studies. Supplemental Table 4 lists the most common reasons for quality point deductions. Supplemental Table 5 provides consolidated intervention descriptions, targeted testing descriptions, and targeted clinical staff descriptions for each included study.

Qualitative and Quantitative Analysis

Results of quantitative analyses and meta-analysis are provided in subsequent sections, grouped by overall practice category (ie, CDSS, CPOE, etc). Subgroup analyses are also provided also for the following three practices: CDSS, CPOE, and combined practices. As described in the “Methods” section, the body-of-evidence qualitative analysis tables are the primary determinant of recommendation categorizations, with criteria for strength of evidence ratings provided in Table 1. Strength of body-of-evidence ratings are based on the outcome measure that could be meta-analyzed across a majority of studies: number of tests. These analyses are additionally summarized in the “Conclusions” section.

The source of appropriate utilization criteria (eg, national guidelines, local consensus, administrative protocols, etc) was not an element of inclusion/exclusion criteria and was not an aspect of how grouping of studies for analysis proceeded. More generally, all but one included study expressed its quality gap as inappropriate overutilization of testing. Studies were grouped, then (for qualitative and quantitative aggregation), by those that assessed a common utilization management practice (eg, education) for a gap expressed generally in the individual paper as inappropriate overutilization of the targeted testing.

Computerized Provider Order Entry

Twenty-five studies assessing the effectiveness of CPOE-alone practices (ie, not in combination with another practice type) were included in this systematic review. Interventions within the CPOE practice category were subgrouped as redundant test alerts, display of test costs, limiting test availability in the CPOE user interface, and CPOE replacement of written test orders.

Body-of-Evidence Qualitative Analysis

Twenty-four studies in the CPOE practice category were qualitatively analyzed using the LMBP rating criteria, with results summarized in Table 2. They have a “high” rating for the overall strength of evidence of effectiveness in relation to the primary outcome measured: number of tests. Tierney et al (1993) (available in Supplemental Table 2) supported derivation only of the secondary outcome assessed by this review—the cost of test orders—and the study is therefore not included in this qualitative analysis. Twenty-one of these 24 studies assessed the impact of modifications to the CPOE, while three studies (Georgiou et al,[30] Hwang et al,[32] and Westbrook et al[47]) assessed the impact of CPOE systems as replacement for written test orders.

Table 2

Body-of-Evidence Qualitative Analysis for Computerized Provider Order Entry Practicea

Study	Quality Rating	Effect Size Rating
Bansal et al, 2001[24]	Fair	Minimal
Bates et al, 1999[25]	Fair	Moderateb
Bates et al, 1997[26]	Fair	Minimal
Bridges et al, 2014[27]	Good	Moderateb
Fang et al, 2014[28]	Good	Substantialb
Feldman et al, 2013[29]	Good	Substantialb
Georgiou et al, 2011[30]	Fair	Minimal
Horn et al, 2014[31]	Fair	Cannot be determined
Hwang et al, 2002[32]	Good	Substantial
Kahan et al, 2009[33]	Good	Substantialb
Le et al, 2015[34]	Good	Substantialb
Li et al, 2014[35]	Fair	Moderateb
Lippi et al, 2015[36]	Good	Substantialb
Love et al, 2015[37]	Fair	Minimalb
May et al, 2006[38]	Good	Minimalb
Olson et al, 2015[39]	Good	Substantialb
Pageler et al, 2013[40]	Good	Substantialb
Probst et al, 2013[41]	Fair	Minimalb
Procop et al, 2015[42]	Fair	Cannot be determined
Shalev et al, 2009[43]	Good	Minimalb
Solis et al, 2015[44]	Fair	Substantial
Vardy et al, 2005[45]	Fair	Minimal
Waldron et al, 2014[46]	Good	Substantialb
Westbrook et al, 2006[47]	Good	Minimal

aOverall strength of evidence of effectiveness rating is “high”: 9 studies were good/substantial, 1 study was good/moderate, 1 study was fair/substantial, 2 studies were fair/moderate, 3 studies were good/minimal, 6 studies were fair/minimal, 2 studies were standard effect measure cannot be determined, and 5 studies were excluded.

b P < .05.

For the 21 studies assessing the impact of modifications to the CPOE, standard effect measures (OR or Cohen d with standard error) could not be determined for two of them (Horn et al[31] and Procop et al[42]). Thus, 19 CPOE modification studies were meta-analyzed in relation to the primary outcome measure. Eight of these studies (Bates et al,[26] Bridges et al,[27] Fang et al,[28] Feldman et al,[29] Le et al,[34] Lippi et al,[36] Probst et al,[41] and Waldron et al[46]) additionally supported derivation of the secondary outcome—the cost of test orders—and (along with Tierney et al 1993 [available in Supplemental Table 2]) are further discussed in the “Additional Outcomes Data” subsection of the “Results” section.

An additional five studies were excluded from the review due to poor quality ratings (Manka et al 2013, McArdle et al 2014, Mekhjian et al 2003, Procop et al 2014, and Stair et al 1998 [available in Supplemental Table 2]).

Meta-Analysis

Nineteen of the 25 studies examining CPOE modifications contained sufficient information to be included in a meta-analysis, with the primary outcome of number of tests. The forest plot in Figure 3 presents the meta-analysis results.

Figure 3

Forest plot for computerized provider order entry modification studies. CI, confidence interval.

The summary OR (95% CI) is 0.125 (0.081-0.194) indicating a substantial and statistically significant reduction in the number of tests associated with the intervention. The results are heterogeneous (I2 = 99%, P < .001).

The 19 studies included in the meta-analysis had one of three types of CPOE modification. These are an alert for or a block of redundant tests within a specified time interval, a display of cost of a test at the time of ordering, or a limit of test availability in the CPOE user interface.

Nine of the 19 studies had an alert for, or a block of, tests repeated within a specified time interval (Bansal et al,[24] Bates et al,[25] Bridges et al,[27] Li et al,[35] Lippi et al,[36] Love et al,[37] May et al,[38] Pageler et al,[40] and Waldron et al[46]). The subgroup forest plot for these nine studies is in Supplemental Figure 1. The summary OR (95% CI) for those studies is 0.241 (0.148-0.391), which is a moderate effect that is statistically significant. These articles are heterogeneous (I2 = 98.4%, P < .001).

Three of the 19 studies included in the meta-analysis described the effect of displaying the cost of a test when it was ordered (Bates et al,[26] Fang et al,[28] and Feldman et al[29]). The subgroup forest plot for these studies is in Supplemental Figure 2. The summary OR (95% CI) is 0.028 (<0.001-5.573), which is a substantial effect that is not statistically significant. These studies are heterogeneous (I2 = 99.5%, P < .001), but in the same direction favoring the intervention. Because Bates et al[26] was identified as having significant study design flaws that would result in an underestimate of intervention effect, it was identified as an effect outlier. Summary results for this subgroup are also provided after removing data from this study from meta-analysis. Without Bates et al,[26] the summary OR (95% CI) is 0.0041 (0.0002-0.0827). This is a substantial effect that is statistically significant. The studies are heterogeneous (I2 = 88.0%, P = .004), but in the same direction favoring the intervention.

Seven of the 19 studies included in the meta-analysis addressed the effect of changing test availability on a CPOE interface (Kahan et al,[33] Le et al,[34] Olson et al,[39] Probst et al,[41] Shalev et al,[43] Solis et al,[44] and Vardy et al[45]). The subgroup forest plot for these studies are in Supplemental Figure 3. The summary OR (95% CI) is 0.080 (0.032-0.199), which is a substantial effect that is statistically significant. These studies are heterogeneous (I2 > 99.9%, P < .001).

Clinical Decision Support Systems/Tools

Nine studies assessing the effectiveness of CDSS/CDST-alone practices (ie, not in combination with another practice type) were included in this systematic review. Interventions within the CDSS/CDST practice category were subgrouped as reminders of guidelines and proposed testing.

Eight studies in the CDSS/CDST practice category were qualitatively analyzed using the LMBP rating criteria, with results summarized in Table 3. They have a “suggestive” rating for the overall strength of the evidence’s effectiveness in relation to the primary outcome measure assessed and the number of tests. Tierney et al 1988 (available in Supplemental Table 2) supported derivation only of the secondary outcome measure assessed by this review—the cost of test orders—and is therefore not included in this qualitative analysis.

Table 3

Body-of-Evidence Qualitative Analysis for Clinical Decision Support Systems/Tools Practicea

Study	Quality Rating	Effect Size Rating
Bindels et al, 2003[48]	Fair	Minimal
Collins et al, 2014[49]	Good	Minimalb
Howell et al, 2014[50]	Fair	Cannot be determined
McKinney et al, 2015[51]	Good	Moderateb
Nightingale et al, 1994[52]	Fair	Substantialb
Poley et al, 2007[53]	Good	Minimalb
Roukema et al, 2008[54]	Fair	Cannot be determined
vanWijk et al, 2001[55]	Good	Substantialb

aOverall strength of evidence of effectiveness rating is “suggestive”: 1 study was good/substantial, 1 study was good/moderate, 1 study was fair/substantial, 2 studies were good/minimal, 1 study was fair/minimal, and 2 studies were standard effect measure cannot be determined.

b P < .05.

Among the eight studies assessing the impact of CDSS/CDST, standard effect measures (OR or Cohen d with its standard error) could not be determined for two of them (Howell et al[50] and Roukema et al[54]). Thus, six CDSS/CDST practice studies were meta-analyzed in relation to the primary outcome measure assessed. Two of these studies (Nightingale et al[52] and Poley et al[53]) additionally supported derivation of the secondary outcome—the cost of test orders—and (along with Tierney et al 1988 [available in Supplemental Table 2]) are further discussed in the “Additional Outcomes Data” subsection of the “Results” section.

Six of the nine studies examining CDSS/CDST contained sufficient information to be included in a meta-analysis with the primary outcome of number of tests. The forest plot in Figure 4 presents the meta-analysis results. The summary OR (95% CI) for the CDSS practice is 0.310 (0.141-0.681). This is a moderate effect that is statistically significant. These studies are heterogeneous (I2 = 99.9%, P < .001), but in the same direction favoring the intervention.

Figure 4

Forest plot for clinical decision support systems/tools studies. CI, confidence interval.

The six studies included in the meta-analysis had one of two types of intervention. Three of the studies (Bindels et al, Collins et al, and McKinney et al) had the CDSS/CDST remind the individual ordering a test of guidelines for the utilization of the test for individual patients. The other three studies (Nightingale et al, Poley et al, and vanWijk et al) involved the CDSS/CDST proposing appropriate testing for individual patients.

For the three studies with a reminder of guidelines, the subgroup forest plot is in Supplemental Figure 4. The summary OR (95% CI) for these studies is 0.457 (0.196-1.070). This indicates a minimal effect that is not statistically significant. These studies are heterogeneous (I2 = 95.7%, P < .001), but in the same direction favoring the intervention.

For the three studies with proposed testing for a patient, the subgroup forest plot is in Supplemental Figure 5. The summary OR (95% CI) for these studies is 0.216 (0.069-0.672). This indicates a substantial effect that is statistically significant. These studies are heterogeneous (I2 = 99.9%, P < .001), but in the same direction favoring the intervention.

Education

Seven studies assessing the effectiveness of education-alone practices (ie, not in combination with another practice type) were included in this systematic review.

Seven studies in the education practice category were qualitatively analyzed using the LMBP rating criteria, with results summarized in Table 4. They have a “suggestive” rating for the overall strength of evidence of effectiveness in relation to the primary outcome measure assessed and the number of tests.

Table 4

Body-of-Evidence Qualitative Analysis for Education Practicea

Study	Study Quality	Effect Size Rating
Abio-Valvete et al, 2015[56]	Poor (study excluded)	Cannot be determined
Baral et al, 2001[57]	Fair	Substantialb
Chonfhaola et al, 2013[58]	Fair	Cannot be determined
Dawes et al, 2015[59]	Fair	Moderateb
DellaVolpe et al, 2014[60]	Fair	Minimalb
Eisenberg, 1977[61]	Good	Substantialb
Gardezi, 2015[62]	Fair	Minimalb
Kotecha et al, 2015[63]	Poor (study excluded)	Cannot be determined
Thakkar et al, 2015[64]	Good	Minimalb

aOverall strength of evidence of effectiveness rating is “suggestive”: 1 study was good/substantial, 1 study was fair/substantial, 1 study was fair/moderate, 1 study was good/minimal, 2 studies were fair/minimal, 1 study was standard effect measure cannot be determined, and 2 studies were excluded.

b P < .05.

For the seven studies assessing the impact of education, a standard effect measure (OR or Cohen d with its standard error) could not be determined for one of them (Chonfhaola et al[58]). Thus, six education studies were meta-analyzed in relation to the primary outcome measure. Two of these studies (Baral et al[57] and DellaVolpe et al[60]) additionally supported derivation of the secondary outcome—the cost of test orders—are further discussed in the “Additional Outcomes Data” subsection of the “Results” section.

Six of the seven studies examining education contained sufficient information to be included in a meta-analysis with the primary outcome of number of tests. The forest plot in Figure 5 presents the meta-analysis results.

Figure 5

Forest plot for education studies. CI, confidence interval.

The summary OR (95% CI) are 0.224 (0.127-0.393). This is a moderate effect that is statistically significant. The results are heterogeneous (I2 = 94.8%, P < .001), but in the same direction favoring the intervention.

Feedback

Seven studies assessing the effectiveness of feedback-alone practices (ie, not in combination with another practice type) were included in this systematic review.

Seven studies in the feedback practice category were qualitatively analyzed using the LMBP rating criteria, with results summarized in Table 5. They have a “suggestive” rating for the overall strength of evidence of effectiveness in relation to the primary outcome measure assessed and the number of tests.

Table 5

Body-of-Evidence Qualitative Analysis for Feedback Practicea

Study	Quality Rating	Effect Size Rating
Baker et al, 2003[65]	Good	Minimal
Bunting et al, 2004[66]	Good	Substantialb
Gama et al, 1992[67]	Fair	Cannot be determined
Miyakis et al, 2006[68]	Good	Minimalb
Verstappen et al, 2004[69]	Fair	Cannot be determined
Verstappen et al, 2004[70]	Fair	Cannot be determined
Winkens et al, 1992[71]	Fair	Cannot be determined

aOverall strength of evidence of effectiveness rating is “suggestive”: 1 study was good/substantial, 2 studies were good/minimal, and 4 studies were standard effect measure cannot be determined.

b P < .05.

For the seven studies assessing the impact of feedback, standard effect measures (OR or Cohen d with its standard error) could not be determined for four of them (Gama et al,[67] Verstappen et al,[69] Verstappen et al,[70] and Winkens et al[71]). Thus, three feedback studies were meta-analyzed in relation to the primary outcome.

Three of the seven studies examining feedback provided sufficient information to be included in the meta-analysis, with the primary outcome of number of tests. The forest plot in Figure 6 presents the meta-analysis results.

Figure 6

Forest plot for feedback studies. CI, confidence interval.

The summary OR (95% CI) is 0.116 (0.003-4.599). This indicates a substantial effect that is not statistically significant. The results are heterogeneous (I2 = 98.9%, P < .001), but in the same direction favoring the intervention.

Reflex Testing

Five studies assessing the effectiveness of reflex testing-alone practices (ie, not in combination with another practice type) were included in this systematic review.

Five studies in the reflex testing practice category were qualitatively analyzed using the LMBP rating criteria, with results summarized in Table 6. They have a “moderate” rating for the overall strength of evidence of effectiveness in relation to the primary outcome measure assessed and the number of tests.

Table 6

Body-of-Evidence Qualitative Analysis for Reflex Testing Practicea

Study	Quality Rating	Effect Size Rating
Baird et al, 2009[72]	Good	Substantialb
Bonaguri et al, 2011[73]	Fair	Cannot be determined
Cernich et al, 2014[74]	Poor (study excluded)	Cannot be determined
Froom et al, 2012[75]	Good	Substantialb
Tampoia et al, 2007[76]	Fair	Cannot be determined
VanWalraven et al, 2002[77]	Poor (study excluded)	Cannot be determined
Wu et al, 1999[78]	Fair	Cannot be determined

aOverall strength of evidence of effectiveness rating is “moderate”: 2 studies were good/substantial, 3 studies were standard effect measure cannot be determined, and 2 studies were excluded.

b P < .05.

For the five studies assessing the impact of reflex testing, standard effect measures (OR or Cohen d with its standard error) could not be determined for three of them (Bonaguri et al,[73] Tampoia et al,[76] and Wu et al[78]). Thus, two reflex testing studies were meta-analyzed in relation to the primary outcome measure.

Two of the five eligible studies had sufficient information to be included in the meta-analysis, with the primary outcome measure of number of tests. The forest plot in Figure 7 presents the meta-analysis results.

Figure 7

Forest plot for reflex testing studies. CI, confidence interval.

The summary OR (95% CI) is 0.008 (0.003-0.022). This indicates a substantial effect that is statistically significant. The results are homogeneous (I2 < 0.001%, P = .513).

Test Review

Five studies assessing the effectiveness of test review-alone practices (ie, not in combination with another practice type) were included in this systematic review.

Five studies in the test review practice category were qualitatively analyzed using the LMBP rating criteria, with results summarized in Table 7. They have an “insufficient” rating for the overall strength of the evidence's effectiveness in relation to the primary outcome measure assessed and the number of tests.

Table 7

Body-of-Evidence Qualitative Analysis for Test Review Practicea

Study	Quality Rating	Effect Size Rating
Aesif et al, 2015[79]	Fair	Minimalb
Barazzoni et al, 2002[80]	Fair	Moderateb
Chu et al, 2013[81]	Fair	Cannot be determined
Dickerson et al, 2014[82]	Fair	Minimalb
Dolazel et al, 2015[83]	Poor (study excluded)	Cannot be determined
Miller et al, 2014[84]	Fair	Cannot be determined

aOverall strength of evidence of effectiveness rating is “insufficient”: 1 study was fair/moderate, 2 studies were fair/minimal, 2 studies were standard effect measure cannot be determined, and 1 study was excluded.

b P < .05.

For the five studies assessing the impact of test review, standard effect measures (OR or Cohen d with its standard error) could not be determined for two of them (Chu et al[81] and Miller et al[84]). Thus, three test review studies were meta-analyzed in relation to the primary outcome measure.

Three of the five eligible studies in this practice category had sufficient information to be included in the meta-analysis, with the primary outcome of number of tests. The forest plot in Figure 8 presents the meta-analysis results.

Figure 8

Forest plot for test review studies. CI, confidence interval.

The summary OR (95% CI) is 0.388 (0.314-0.480). This indicates a moderate effect that is statistically significant. The results are homogeneous (I2 < 0.001%, P = .851).

Laboratory Test Utilization Team

No studies were found for inclusion assessing the effectiveness of an LTU team as the only practice assessed, in isolation. LTU was observed only in combination with other practices (eg, education and LTU). The combined practices section provides reference to a comparative plot assessing the impact of combined practices with, and those without, an LTU component.

Combined Practices

Twenty-five studies assessing the effectiveness of combinations of practices were included in this systematic review. While not all possible combinations of practices were observed in the available evidence base, Table 8 provides detail on the combinations that were observed.

Table 8

Body-of-Evidence Qualitative Analysis for Combined Practicea

Study	Practice Combination	Quality Rating	Effect Size Rating
Bareford et al, 1990[85]	Education, feedback	Poor (study excluded)	Cannot be determined
Baricchi et al, 2012[86]	Education, LTU	Fair	Minimalb
Calderon-Margalit et al, 2005[87]	Education, feedback, LTU	Good	Substantialb
Dowling et al, 1989[88]	Education, feedback	Good	Minimalb
Gilmour et al, 2015[89]	Education, reflex	Fair	Cannot be determined
Isofina et al, 2013[90]	CDSS, education	Poor (study excluded)	Cannot be determined
Hutton et al, 2009[91]	CPOE modification, education, LTU	Fair	Cannot be determined
Janssens et al, 2015[92]	CPOE modification, LTU	Good	Minimal
Kroenke et al, 1987[93]	Education, feedback	Good	Minimalb
Larochelle et al, 2014[94]	CPOE modification, CDSS, education	Good	Cannot be determined
Lum, 2006[95]	Education, test review, LTU	Fair	Cannot be determined
MacPherson et al, 2005[96]	Education, LTU	Fair	Cannot be determined
McNicoll et al, 2015[97]	Education, feedback	Good	Moderateb
Minerowicz et al, 2015[98]	Education, feedback	Good	Substantialb
Newman et al, 2015[99]	Education, feedback	Good	Moderate
Riley et al, 2015[100]	CPOE modification, test review	Fair	Cannot be determined
Roggeman et al, 2014[101]	CDSS, education	Fair	Cannot be determined
Rosenbloom et al, 2005[102]	CPOE modification, CDSS	Fair	Cannot be determined
Samuelson et al, 2015[103]	CDSS, reflex	Good	Minimal
Spiegel et al, 1989[104]	Feedback, LTU	Fair	Cannot be determined
Thomas et al, 2006[105]	Education, feedback	Fair	Minimalb
Tomlin et al, 2011[106]	Education, feedback	Good	Minimalb
Vegting et al, 2012[107]	CPOE modification, eduaction, feedback	Fair	Cannot be determined
Vidyarthi et al, 2015[108]	Education, feedback	Good	Cannot be determined
Wang et al, 2002[109]	CDSS, education, LTU	Good	Cannot be determined
Warren, 2013[110]	CDSS, LTU	Fair	Cannot be determined
White et al, 2013[111]	CPOE modification, CDSS	Good	Minimal

CDSS, clinical decision support systems; CPOE, computerized provider order entry; LTU, laboratory test utilization.

aOverall strength of evidence of effectiveness rating is “moderate”: 2 studies were good/substantial, 2 studies were good/moderate, 6 studies were good/minimal, 2 studies were fair/minimal, 13 studies were standard effect measure cannot be determined, and 2 studies were excluded.

b P < .05.

Twenty-five studies in the combined practices category were qualitatively analyzed using the LMBP rating criteria, with results summarized in Table 8. They have a “moderate” rating for the overall strength of evidence of effectiveness in relation to the primary outcome measure assessed: number of tests.

For the 25 studies assessing the impact of combined practices, standard effect measures (OR or Cohen d with its standard error) could not be determined for 13 of them (Gilmour et al,[89] Hutton et al,[91] Larochelle et al,[94] Lum,[95] MacPherson et al,[96] Riley et al,[100] Roggeman et al,[101] Rosenbloom et al,[102] Spiegel et al,[104] Vegting et al,[107] Vidyarthi et al,[108] Wang et al,[109] and Warren[110]). Thus, 12 combined practices studies were meta-analyzed in relation to the primary outcome measure.

Twelve of the 25 studies examining combined practices had sufficient information to be included in the meta-analysis, with the primary outcome of number of tests. The forest plot in Figure 9 presents the meta-analysis results.

Figure 9

Forest plot for combined practice studies. CI, confidence interval.

The summary OR (95% CI) is 0.411 (0.149-1.131). This indicates a minimal effect that is not statistically significant. These studies are heterogeneous (I2 > 99.9%, P < .001), but in the same direction favoring the intervention.

Three additional forest plots and three comparison plots are now provided. These serve to indicate the effect of select groupings of combined practice studies (inclusion of education and feedback, inclusion of CDSS and/or CPOE modifications, and inclusion of LTU), and to compare their effects to single practice studies.

Of the 12 combined practices studies in the meta-analysis, eight included both education and feedback. The subgroup forest plot for those studies is in Supplemental Figure 6. The summary OR (95% CI) is 0.292 (0.097-0.880). This indicates a moderate effect that is statistically significant. These studies are heterogeneous (I2 = 99.9%, P < .001), but in the same direction favoring the intervention.

These eight studies with an education and feedback component were compared to the studies in the education practice (studies assessing the effect of education alone) and to studies in the feedback practice (feedback alone). That comparison is illustrated in Supplemental Figure 7. There is no statistically significant difference between the OR for the combined practices studies containing both and education and feedback component, and the education alone studies (P = .775) or the feedback alone studies (P = .847).

Of the 12 studies in the combined practice meta-analysis, three included a CPOE modification and/or CDSS/CDST component. The forest plot for these studies is in Supplemental Figure 8. The summary OR (95% CI) is 0.750 (0.552-1.018). This indicates a minimal effect that is not statistically significant. These studies are homogeneous (I2 < 0.001%, P = .546).

These three studies were compared to studies in the CPOE practice (CPOE alone) and to studies in the CDSS/CDST practice (CDSS alone). That comparison is illustrated in Supplemental Figure 9, with CDSS and/or CPOE combinations referred to in the forest plot as “health IT.” There is no statistically significant difference between the ORs for the health IT studies and the CDSS alone studies (P = .225). The difference between the ORs for the combined practice studies and the CPOE alone studies, however, is statistically significant (P < .001).

Of the 12 studies in the combined practice meta-analysis, three included an LTU component. The forest plot for these studies is in Supplemental Figure 10. The summary OR (95% CI) is 0.412 (0.555-3.046). This indicates a moderate effect that is statistically significant. These studies are heterogeneous (I2 > 99.9%, P < .001), but in the same direction favoring the intervention.

These three were compared to the nine combined practices studies that did not include LTU. That comparison is illustrated in Supplemental Figure 11. There is no statistically significant difference between the ORs for studies that include LTU and those that do not include LTU (P = .953).

Additional Outcomes Data

Data available in the current evidence base supported determinations of cost of tests outcome, this review’s secondary outcome measure. Meta-analysis forest-plots, as well as the strength of evidence ratings, for cost data are provided in Supplemental Figures 12 to 15. Sixteen of 83 studies included in this review could be meta-analyzed for the secondary outcome, cost of tests. Fourteen of these 16 studies were also meta-analyzed for the number of tests outcome. Tierney et al 1988 and Tierney et al 1993 were meta-analyzed only for cost of tests. Cost outcome definitions for these 16 studies are provided in Supplemental Table 6. Limitations of the cost of tests outcome measure are provided in the “Limitations” section.

Supplemental Table 6 conveys other outcomes- related data present in the eligible evidence base and conveys P values (when reported) for the 30 studies that could not be meta-analyzed in relation to the review’s primary or secondary outcome measure. Of these 30 studies, 19 report P values; of these 19 studies, 18 indicated a statistically significant favorable impact of the utilization management practice intervention.

Applicability and Feasibility Data

Two pie charts and three cross-attribute tables support more detailed discussion in the “Applicability and Generalizability” subsection of the “Discussion” section. Figure 10 and Figure 11 indicate the distribution of facility setting and practice category across all included studies, while Table 9, Table 10, and Table 11 provide additionally detail on the landscape of two study-level attributes—facility setting and patient setting—relative to practice categories (CPOE, CDSS, etc). Definitions for facility setting types are provided in the glossary (Supplemental Table 1). For a consolidated list of interventions (including practice composition of “combined practice” studies), targeted testing, and targeted clinical staff, readers are referred to Supplemental Table 5. Finally, Table 12 provides additional information as to distribution of study-level characteristics within the assessed evidence base.

Figure 10

Pie chart for facility setting across all included studies. VA, Veterans Affairs.

Figure 11

Pie chart for practice category across all included studies. CDSS, clinical decision support systems; CPOE, computerized provider order entry.

Table 9

Practice to Facility Setting Cross-Attribute Table

Facility Setting				Practice Category
	CDSS	CPOE	Combined	Education	Feedback	Reflex Testing	Test Review	Totala
University/academic/	3	17	19	5	1	4	3	52
teaching hospital
Children’s hospital	1	3	0	0	0	0	0	4
Community hospital	1	1	0	0	0	1	1	4
Public hospital	1	3	2	0	0	0	0	6
VA hospital	1	0	0	1	0	0	0	2
Primary care	4	4	4	0	5	1	0	18
office/outpatient
clinic
Reference	0	0	0	0	2	1	1	4
laboratory
International	5	8	9	1	6	3	2	34
(outside US)
Not otherwise	1	1	2	1	1	1	0	7
specified
Totala	17	37	36	8	15	11	7	131

CDSS, clinical decision support systems; CPOE, computerized provider order entry; VA, Veterans Affairs.

aThe totals may (and do) include the same study more than once if the study authors reported more than one setting.

Table 10

Practice to Patient Setting Cross-Attribute Table

Patient Setting				Practice Category
	CDSS	CPOE	Combined	Education	Feedback	Reflex Testing	Test Review	Totala
Emergency	4	3	1	2	0	1	2	13
department
Hospital inpatient	4	4	7	2	2	3	2	24
and outpatient
Hospital inpatient only	3	23	16	3	2	3	2	52
Hospital outpatient only	0	1	5	1	0	2	0	9
Primary care/clinic	6	6	7	0	11	2	1	33
outpatient
Totala	17	37	36	8	15	11	7	131

CDSS, clinical decision support systems; CPOE, computerized provider order entry.

aThe totals may (and do) include the same study more than once if the study authors reported more than one setting.

Table 11

Facility Setting to Patient Setting Cross-Attribute Table

Facility Setting			Patient Setting
	Emergency Department	Hospital Inpatient and Outpatient	Hospital Inpatient Only	Hospital Outpatient Only	Primary Care/ Outpa tient Clinic	Totala
University/academic/	4	12	32	4	0	52
teaching hospital
Children’s hospital	1	0	3	0	0	4
Community hospital	0	2	1	1	0	4
Public hospital	2	1	3	0	0	6
VA hospital	1	0	1	0	0	2
Primary care	0	0	0	0	18	18
office/outpatient
clinic
Reference	0	0	1	0	3	4
laboratory
International	4	6	9	3	12	34
(outside US)
Not otherwise	1	3	2	1	0	7
specified
Totala	13	24	52	9	33	131

VA, Veterans Affairs.

aThe totals may (and do) include the same study more than once if the study authors reported more than one setting.

Table 12

Distribution of Attributes Across All Included Studies

Attribute	Value	Percentage
Article type	Abstract only	7.2% (6/83)
	Full article	92.8% (77/83)
Article year	≥2006	68.7% (57/83)
	<2006	31.3% (26/83)
Country	US	59% (49/83)
	Non-US	41% (34/83)
Study design	Postintervention only	8.4% (7/83)
	Retrospective before and after	8.4% (7/83)
	Retrospective before and after,  with concurrent control	4.8% (4/83)
	Prospective postintervention  group, with historical control	13.3% (11/83)
	Before and after without  concurrent control	36.2% (30/83)
	Before and after with  concurrent control	2.4% (2/83)
	Other quasi-experimental	15.7% (13/83)
	randomized trial	10.8% (9/83)
Facility size	≥300 beds	37.4% (31/83)
	<300 beds	3.6% (3/83)
	Unclear/not reported	59% (49/83)
Funding	Funded	33.7% (28/83)
	Not funded	13.3% (11/83)
	Unclear/not reported	53% (44/83)
Investigator	Pathology/laboratory only	9.6% (8/83)
background	Nonpathology/ laboratory only	45.8% (38/83)
	Both	36.2% (30/83)
	Unclear/not reported	8.4% (7/83)

Discussion

Additional Benefits and Economic Evaluation

Several subsections of this section serve to further inform implementation decisions, while indicating limitations of the current evidence base. Frameworks guiding decision-making exist, such as GRADE (Grading of Recommendations Assessment, Development, and Evaluation Working Group) Evidence to Decision frameworks, and may be applied by health care decision-makers when choosing to adopt (or adapt) recommendations in new contexts.[112]

To this end, this first subsection refers readers to the data provided in the “Additional Outcomes Data” of the “Results” section, suggesting benefit beyond this review’s primary outcome (“number of tests”). These data suggests statistically significant favorable impact (via P values) for studies that could not be meta-analyzed. While the patient-related outcomes provided by some of the studies are distal outcomes, impacted by many factors during the course of care, the following pattern arises: the rates of the patient-important outcomes assessed (eg, morbidity, mortality, and length of stay) were not significantly different pre- or postintervention, therefore interventions apparently were not associated with adverse impact on these outcomes. Additional discussion on limitations associated of the patient-related outcome measures encountered in the current evidence base appears in the “Limitations” section.

Additionally, while concerns associated with the economic evaluation supported by the current evidence base are also noted in the “Limitations” section, what the meta-analyzed “costs of tests” outcome does provide is complementary indication of the proximal effects of these interventions, expressed as cost-savings.

Applicability and Generalizability

From the pie charts in the “Applicability and Feasibility Data” subsection of the “Results” section, the following patterns emerge: (1) academic/university/teaching as well as primary care/outpatient clinic facility settings are strongly represented, while community hospitals, children’s hospitals, and public hospitals (including Veterans Affairs [VA] hospitals) are less represented, and (2) education, feedback, reflex testing, and test review interventions are less represented as single-practice interventions compared to CPOE and CDSS. From the cross-attribute tables, observable in Table 9 is that within university/academic/teaching hospitals CPOE and combined practice interventions are more strongly represented relative to other intervention types. Because CPOE interventions most commonly appeared as modifications to an existing CPOE system (see “Results” section), this finding is perhaps reflective of increased resource availability in such settings in support of improvement efforts, a point perhaps also true of combined practice interventions (which may require more resources to plan and implement relative to single-practice interventions). A pattern observable in Table 10 is that most interventions occurred in hospital inpatient settings, perhaps reflecting that (1) most testing occurs in hospital labs (a point supported by basic Centers for Medicare and Medicaid Services data), and (2) inpatients may experience increased testing in terms of scope or frequency. Finally, a pattern emerging from Table 11 is that there is greater focus on primary care/outpatient clinic patient settings among studies outside the US relative to other patient setting types, possibly a reflection of intervention planning occurring in different reimbursement environments (eg, national health care systems) than in the US.

Further, a few studies were identified from the meta-analysis forest plots as representing effect outliers, such that the desired effect of the study was not only “substantial,” but was also stronger relative to other studies quantitatively combined within the same practice category. Characteristics of these studies may be generalizable. Three CPOE modification studies were effect outliers: Pageler et al[40] (a “good” quality study) exhibited strong administrative support for the intervention; Waldron et al[46] (a “good” quality study) utilized a hard stop (while hard stops may not be appropriate in many cases, it may be appropriate for some types of esoteric testing, for example); and Solis et al[44] (a “fair” quality study) sent multiple emails and reminders to test ordering clinicians, informing about the change to the CPOE system. One education study was an effect outlier: Eisenberg[61] (a “good” quality study) exhibited an education intervention of considerable duration.

Finally, two combined practice studies were effect outliers. Calderon-Margalit et al[87] (a “good” quality study, combining education, feedback, and LTU) exhibited strong administrative restrictions and control within the intervention. Minerowicz et al[98] (a “good” quality study, combining education and feedback) exhibited continued repetition of the feedback component. These findings are consistent with existing claims that the impact of an intervention may benefits by strength (eg, repetition, substantial administrative support, etc) of intervention components,[11,113,114] although whether this was a direct cause of these two studies having stronger effect is unclear, as many organizational and process variables may impact magnitude of effect.

Feasibility of Implementation

The practices evaluated in this systematic review are generally feasible for implementation in most health care settings, though ease of implementation may vary from setting to setting. Drivers of implementation feasibility may include organizational and management structures, the business environment and the laboratory’s business process needs (including financial targets and budget considerations), the patient population being served through the provision of laboratory testing, and staff competencies. Implementation of any new practice in a hospital setting may therefore encounter challenges due to budgets, programming resources, training needs, and commitment/follow up from key stakeholders. Ultimately, the successful implementation of practices depends on demonstrating meaningful impact on the quality of care and patient outcomes, as well as potential cost effectiveness, in a way that complements administrative and clinical objectives. Examples of implementation challenges encountered within the included evidence base are listed in Supplemental Table 7.

Associated Harms

Some of the practices evaluated in this review may have unintended impacts on patient care or practitioner diagnostic workflows. In addition, a concern for implementation of LTU teams and test review resulting is the possible delay of timely patient care causing patient dissatisfaction and potential increase in hospital stay. Further, the ability to display test cost, definitions of test cost, and capacity to keep this information up to date, may vary from setting to setting, and in relation to local practice standards. A comprehensive LTU approach should evaluate the merit of planned test utilization practice interventions and evaluate the impact of any change in laboratory test ordering process or procedures, including the possibility of unintended systems and/or patient outcomes, prior to implementation. Some of the examples of implementation challenges listed in Supplemental Table 7 may also inform potential associated harms.

Conclusion

Practice Recommendations

Recommendations are categorized as “recommend,” “not recommended,” and “no recommendation for or against due to insufficient evidence.” Recommendation categorizations in this review are a function of the current available evidence base, and of the LMBP methodology, including a priori analysis criteria (eg, selected effect measure rating cutoffs, the LMBP quality assessment tool, and the LMBP strength of body of evidence matrix). Recommendations are made for single practice categories, as well as for the combined practice category. The approach for recommendation categorization is described in the “Materials and Methods” section, with criteria indicated in Table 1.

For practices with a “no recommendation for or against” categorization, this finding does not rule out the potential current value of these practices; rather, it indicates a need for additional studies evaluating the effect of these practices, as well as a need for greater access to relevant unpublished studies. Despite there being no firm “best practice” recommendations for most practices, the data do suggest the practices have the potential to promote appropriate test utilization, such that additional research should be pursued.

Given the current evidence base, and the data available to support the LMBP qualitative analyses and the meta-analyses, recommendations arising from analyses are made in relation to evidence-based assessing practices targeting quality issues that are locally determined (ie, within the health care setting of the individual study) to represent inappropriate test “overutilization” and are made in relation to this review’s primary outcome measure, “number of tests.” Recommendations arising from this systematic review do not serve to endorse specific appropriate use protocols guiding individual investigators in auditing appropriateness of test utilization (ie, organizational guidelines, local consensus guidelines, algorithms, pathways, appropriateness criteria, and local administrative directives on utilization). Rather, recommendations arising from this systematic review relate to the means (CDSS, CPOE, education, feedback, etc) of supporting/promoting appropriate test utilization; in other words, recommendations relate to utilization management practices.

In instances where health care organizations choose to implement a practice (or a combination of practices) with the goal of test utilization management, we recommend the quality and applicability of available guidelines (local and national) and protocols guiding utilization appropriateness be carefully assessed and validated in the local setting, to further evaluate potential patient harms relative to benefits. Decision to implement should include review by the institution’s medical executive committee (or equivalent), involve a use-case (eg, practice impact modeling) within the institution to assess impact before implementation, and involve feedback informing continuous quality improvement.

Practice recommendations are summarized in Table 13, with additional detail provided in the remainder of this section.

Table 13

Summary of Practice Recommendations

Practice Category	Practice Recommendation
CPOE	Use of CPOE is recommended as a best practice to support appropriate clinical LTU
CDSS/CDST	No recommendation for or against due to insufficient evidence is made for CDSS/CDST as a best practice to support appropriate clinical LTU
Education	No recommendation for or against due to insufficient evidence is made for education as a best practice to support appropriate clinical LTU
Feedback	No recommendation for or against due to insufficient evidence is made for feedback as a best practice to support appropriate clinical LTU
Reflex testing	Use of reflex testing practices is recommended as a best practice to support appropriate clinical LTU
Test review	No recommendation for or against due to insufficient evidence is made for test review as a best practice to support appropriate clinical LTU
LTU team	No recommendation for or against due to insufficient evidence is made for LTU team as a best practice to support appropriate clinical LTU
Combined  practices	Use of combined practices is a recommended as a best practice to support appropriate clinical LTU

CDSS/CDST, clinical decision support systems/tools; CPOE, computerized provider order entry; LTU, laboratory test utilization.

Recommendation for Computerized Provider Order Entry Practices

Use of CPOE is recommended as a best practice to support appropriate clinical LTU.

The overall strength of the evidence’s effectiveness of the practice for utilization management is rated as high. The evidence base analyzed to arrive at this recommendation examined modifications to existing CPOE systems (not CPOE replacement of written test orders). The pooled effect size rating (OR = 0.125, 95% CI = 0.081-0.194, P < .001) for 19 meta-analyzed studies is substantial for the number of tests outcome. Effects across studies were not consistent (I2 = 99.3 %, P < .001); however, all studies demonstrated a favorable effect for the number of tests outcome.

Recommendation for Clinical Decision Support Systems/Tools Practices

No recommendation for or against due to insufficient evidence is made for CDSS/CDST as a best practice to support appropriate clinical LTU.

The overall strength of evidence of effectiveness of the practice for utilization management is rated as suggestive. The pooled effect size rating (OR = 0.310, 95% CI = 0.141-0.681, P = .004) for six meta-analyzed studies is moderate for the number of tests outcome. Effects across studies were not consistent (I2 = 99.9%, P < .001); however, all studies demonstrated a favorable effect for the number of tests outcome.

Recommendation for Education Practices

No recommendation for or against due to insufficient evidence is made for education as a best practice to support appropriate clinical LTU.

The overall strength of the evidence’s effectiveness of the practice for utilization management is rated as suggestive. The pooled effect size rating (OR = 0.224, 95% CI = 0.127-0.393, P < .001) for six meta-analyzed studies is moderate for the number of tests outcome. Effects across studies were not consistent (I2 = 94.8%, P < .001); however, all studies demonstrated a favorable effect for the number of tests outcome.

Recommendation for Feedback Practices

No recommendation for or against due to insufficient evidence is made for feedback as a best practice to support appropriate clinical LTU.

The overall strength of evidence of effectiveness of the practice for utilization management is rated as suggestive. The pooled effect size rating (OR = 0.116, 95% CI = 0.003-4.599, P = .252) for three meta-analyzed studies is substantial for the number of tests outcome. Effects across studies were not consistent (I2 = 98.9%, P < .001), however all studies demonstrated a favorable effect for the number of tests outcome.

Recommendation for Reflex Testing Practices

Use of reflex testing practices is recommended as a best practice to support appropriate clinical LTU.

The overall strength of evidence of effectiveness of the practice for utilization management is rated as moderate. The pooled effect size rating (OR = 0.008, 95% CI = 0.003-0.022, P < .001) for two meta-analyzed studies is substantial for number of tests outcome. Effects across studies were consistent (I2 < 0.001%, P = .513), with all studies demonstrating a favorable effect for the number of tests outcome.

Recommendation for Test Review Practices

No recommendation for or against due to insufficient evidence is made for test review as a best practice to support appropriate clinical LTU.

The overall strength of evidence of effectiveness of the practice for utilization management is rated as insufficient. The pooled effect size rating (OR = 0.388, 95% CI = 0.314-0.480, P < .001) for three meta-analyzed studies is moderate for number of tests outcome. Effects across studies were consistent (I2 < 0.001%, P = .851), with all studies demonstrating a favorable effect for the number of tests outcome.

Recommendation for Laboratory Test Utilization Team

No recommendation for or against due to insufficient evidence is made for LTU teams as a best practice to support appropriate clinical LTU.

No studies were found for inclusion assessing the effectiveness of an LTU team as the only practice assessed in isolation. LTU was observed only in combination with other practices (eg, education and LTU; refer to Table 8). No recommendation for or against due to insufficient evidence is made for LTU—apart from combination with other practices—as a best practice to support appropriate clinical LTU. Subgroup analyses for practice interventions with an LTU component appear in the “Results” section for combined practices.

Recommendation for Combined Practices

Use of combined practices is recommended as a best practice to support appropriate clinical LTU.

The overall strength of the evidence’s effectiveness of the practice for utilization management is rated as moderate. The evidence base analyzed to arrive at recommendation examined was not inclusive of all possible practice combinations (combinations observed are indicated in Table 8). The pooled effect size rating (OR = 0.411, 95% CI = 0.149-1.131, P = .085) for 12 meta-analyzed studies is minimal for number of tests outcome. Effects across studies were not consistent (I2 > 99.9%, P < .001); however, all studies demonstrated a favorable effect for the number of tests outcome.

Limitations

An important limitation of this study was the inability to obtain unpublished data from relevant quality improvement/research efforts. This may have limited this systematic review’s ability to (1) achieve greater strength of body-of-evidence ratings for specific practices, (2) evaluate practices in more diverse settings, and (3) better avoid potential for publication bias.

Next, this systematic review highlights the presence in the current evidence base of the limited number of “good” quality studies, largely due to incomplete reporting. In addition to the data collection form discussed in the next section (and provided in Supplemental Table 8), investigators are referred to a centralized repository of available reporting standards at the Enhancing the Quality and Transparency of Health Research Network’s website (http://www.equator-network.org/).

Third, a current trend in laboratory practices suggests the important role of LTU teams and somewhat relatedly diagnostic management teams (refer to the Supplemental Table 1 glossary for definitions of LTU teams and diagnostic management teams). However, a limited number of studies were available to determine effectiveness of LTU teams, as this practice invariably appeared in the included evidence base in combination with another utilization management practice.

Fourth, these studies shared the common aim of introducing utilization management practices in order to support rational use of diagnostic resources by clinicians ordering testing, and reduce variability in test ordering behaviors. However, there was considerable variation across studies as to criteria for determining the presence of inappropriate test utilization through utilization audit, as well as how thoroughly investigators described this criteria (and its source), along with efforts to validate criteria in the local setting. This review did not seek to document the source of criteria used nor assess the validity of the criteria used. Rather, it focused on utilization management approaches in local settings where inappropriate test utilization was determined to be occurring. Future systematic review updates may incorporate a component to assess the quality/validity of inappropriate utilization criteria utilized within a study.

Fifth, the primary outcome measure supported by the current evidence base (such that an outcome could be meta-analyzed across a majority of studies) was number of tests, which has limitations as an outcome. While it is a reflection of resource utilization, and is a measure proximal to the interventions assessed, it neglects to include information reflecting the proportion of testing that was concordant with appropriate utilization criteria. Furthermore, there is not a formal assessment on whether this reduction resulted from elimination of only inappropriate testing or also included testing that would have been appropriate.

Sixth, the analysis of cost outcomes supported by the current evidence base has limitations. While “costs of tests” may complement the “number of tests” outcomes, it was variably defined and derived by investigators (see Supplemental Table 6), and often represented an ad hoc analysis within the studies. As another expression of resource use, it supports proximal cost-minimization or cost-consequence analyses, neglecting cost-effectiveness. However, as is, it is assumed to be an important measure to decision-makers (a recent study estimated in vitro diagnostic testing to represent 2.3% of all health care spending in the US, or about US$73 billion annually),[4,115] while providing a point of reference for future economic evaluations. While detailed discussion on health care payment models, health care economic evidence, and evaluation approaches (eg, to more clearly depict possible health economic benefits) is beyond the scope of this review, investigators are encouraged to review available resources.[4,115-117]

Seventh, convincingly establishing the impact of test utilization practices on patient-related outcomes, especially those as distal as patient length of stay, morbidity, and mortality, is challenging within the primary evidence base, given such outcomes are influenced by many factors in health care.[2,118] Nevertheless, as supported by the current evidence base, the observation that these interventions are apparently not associated with adverse impact on these outcomes is an important one. While establishing causality, through study designs such as randomized controlled trials, is often unrealistic in such investigations, improved incorporation of “big data” (ie, large data sets and analytics from health information technology/systems, including medical data warehouses) may support more robust outcomes associations, as well as more robust economic evaluations in the context of test utilization management practices.[119]

Alternatively, investigators may focus on surrogate or intermediate patient outcomes more proximal to the interventions; for example, outcomes or metrics relating to effect on patient management.[120-125] While such measures have limitations of their own, they may include diagnostic yield, time to treatment, etc, as may better support claims that use of such interventions will lead to improved patient health outcomes. Further, increased use of intermediate outcomes, such as rates of guideline-concordant testing among the primary evidence base, would also benefit the evidence base, as would other discrepant analyses demonstrated, for example, by Larochelle et al[94] and Le et al.[34] Additional discussion on laboratory-related outcomes can be found in the literature.[118,126]

Eighth, for the combined practice category, all possible combinations of practices were obviously not observed in the eligible evidence base, impacting generalizability of combined practice recommendations. Further, while the intensity/frequency of individual components within a combined practice was often not explicitly clear, in general there appeared to be a pattern that frequency/intensity of practice components may have been less than that of individual practice studies, potentially contributing to lower effect sizes among the combined practice studies. This does not rule out the potential increased value of combining practices to impact test ordering practices but is a reflection of the current evidence base and the way in which combined practices were planned and/or implemented. A prior systematic review suggests benefit in combining practices (expressed in the review as “multidimensional interventions”) to impact test ordering behaviors.[12] Further, it is reasonable to assume there is some overlap of practices otherwise depicted by investigators as using a single practice (eg, CDSS alerts might be assumed to have an educational influence, while not explicitly/reproducibly involving an educational intervention).

Finally, there was only one study (Roukema et al[54]) that attempted to impact a utilization quality gap expressed as underutilization of testing, reflecting a substantial gap in the evidence given that overall mean rates of inappropriate underutilization appear to exceed those of inappropriate overutilization (20.6% and 44.8%, respectively).[5,16] Relatedly, no studies were found for inclusion where the intervention directly targeted patients, eg, through text or email reminders of necessary testing.

Future Research Needs

In guiding future quality improvement studies, investigators may consider the following definition of appropriate test utilization, which is informed by several resources: utilization that is consistent with current subject-specific expert knowledge or evidence-based standards for usage, matching the patient with the correct test(s) at the right time and in the correct order, and performed with expectation of informing patient-management decisions for the benefit of health outcomes, in a cost-effective manner.[16,20,120,124,127-133] Further, it has defined criteria for identifying occurrences of inappropriate utilization through test utilization audits (criteria may, for example, relate to testing frequency and timing, test choice in relation to alternative tests, clinical indications for tests, or probability a test result will provide clinically actionable information). Inappropriate utilization may involve underutilization, overutilization, or misuse (eg, misinterpretation) of testing in relation to specific patient populations and clinical settings.

This emphasizes criteria-based assessments during utilization audits, as well as the link from testing to clinical decision-making and patient-relevant outcomes, with consideration of cost-effectiveness. This definition should be assessed in light of the fact that “appropriateness” in testing remains a complex, multidimensional, multiperspective concept.[127,128] Additionally, it should be assessed with consideration that some element of subjectivity may be unavoidable in the provision of testing for a specific patient in a specific context.[128]

Specific needs for future research as suggested by this review include (1) assessment of test utilization management practice intervention in additional settings/contexts (ie, the underrepresented settings/contexts indicated in the “Applicability and Generalizability” section: community hospitals, children’s hospitals, and public hospitals including VA hospitals); (2) better description of the source of criteria applied in tests utilization audits for determinations of inappropriate test utilization, as well as description of efforts to validate the criteria in the local setting, as well as better description of the test utilization audit data collection process (and how it reliably applied the criteria)[5,20,132]; (3) incorporation of patient-related outcomes (surrogate or health outcomes) affected by inappropriate test utilization; (4) inclusion of cost data as permits more robust economic evaluations, such as cost-effectiveness, allowing for comparative analysis of an intervention in terms of both cost and effect; (5) assessment of alternate utilization management interventions, head-to-head, within individual studies; (6) assessment of practice intervention not examined in this review, for example diagnostic management teams (defined and distinguished from LTU teams in the glossary); and (7) assessment of quality gaps representing inappropriate underutilization of testing.

Additionally, while laboratory professionals were frequently involved as investigators and authors within the included evidence base (46% of included studies), we recommend more robust representation of laboratory professionals on teams investigating the impact of practices to manage appropriate test utilization. Recent articles provide guidance on the role of the laboratory at this point of clinical-laboratory interface, as well as on relevant lab-related performance measures laboratory professionals may monitor, since labs should be “continuously engaged in auditing, monitoring, and improving the appropriateness of test requests….”[6,16,115,131,134,135] This should include efforts to further evolve how appropriate test utilization is benchmarked and monitored though lab-related performance measures or indicators (ideally standardized or “harmonized” across laboratories and health care organizations),[134,136] as may involve increased use of health information technology, web technology, and new approaches to integrating and analyzing available, relevant data.[6,119]

Available in Supplemental Table 8 is a data collection form, as can assist in preparing existing data or performing prospective studies for publication, or for submission to the CDC LMBP initiative as unpublished studies.

Funding source

Work conducted by Kakali Bandyopadhyay and Millie Linville was supported by CDC funding for the Laboratory Medicine Best Practices Initiative to Westat-Deloitte under contract (HHSD2002013M53968B/2002013F57569). Work conducted by Robert Hirsch was supported under contract to Carter Consulting (200-2010-37203).

Disclaimer

The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

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