Journal of Genetic Counseling https://doi.org/10.1007/s10897-018-0226-8

ORIGINAL RESEARCH

Reduction of Health Care Costs and Improved Appropriateness of Incoming Test Orders: the Impact of Genetic Counselor Review in an Academic Genetic Testing Laboratory Emily Wakefield 1

&

Haley Keller 1 & Hannah Mianzo 1 & Chinmayee B. Nagaraj 1 & Sanjukta Tawde 1 & Elizabeth Ulm 1

Received: 13 November 2017 / Accepted: 29 January 2018 # National Society of Genetic Counselors, Inc. 2018

Abstract The goal of this study was to evaluate the impact of genetic counselor (GC) review of incoming test orders received in an academic diagnostic molecular genetics laboratory. The GC team measured the proportion of orders that could be modified to improve efficiency or sensitivity, tracked provider uptake of GC proposed testing changes, and calculated the health care dollar savings resulting from GC intervention. During this 6-month study, the GC team reviewed 2367 incoming test orders. Of these, 109 orders (4.6%) were flagged for review for potentially inefficient or inappropriate test ordering. These flagged orders corresponded to a total of 51 cases (1–5 orders for each patient), representing 54 individuals and including 3 sibling pairs. The GC team proposed a modification for each flagged case and the ordering providers approved the proposed change for 49 of 51 cases (96.08%). For the 49 modifications, the cost savings totaled $98,750.64, for an average of $2015.32 saved per modification. This study provides evidence of the significant contribution of genetic counselors in a laboratory setting and demonstrates the benefit of laboratories working with ordering providers to identify the best test for their patients. The review of test orders by a genetic counselor both improves genetic test ordering strategies and decreases the amount of health care dollars spent on genetic testing. Keywords Utilization management . Genetic counselor . Laboratory . Genetic testing . Cost reduction

Introduction Laboratory tests make up a large portion of health care costs. Laboratory utilization management, the practice of actively assessing test orders for appropriateness and efficiency, has been recognized to reduce unnecessary testing and associated costs while improving timely and appropriate patient care (Dickerson et al. 2014). Utilization management programs have been implemented in hospital send-out laboratories as well as in referral and diagnostic laboratories to ensure appropriate genetic testing, with order review taking place either at the time of the test order or upon the receipt of the sample by

* Emily Wakefield [email protected] 1

Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, MLC 7016, Cincinnati, OH 45229, USA

the testing laboratory. Recent studies have measured the impact of such utilization management programs in different settings and highlighted the role of genetic counselors (GCs) in this process (Dickerson et al. 2014; Kotzer et al. 2014; Londre et al. 2017; Miller et al. 2014; Riley et al. 2015). A retrospective review of GC-facilitated order changes over a period of 21 months at ARUP laboratories revealed that 26% of all genetic test orders were changed, resulting in a cost reduction of an average of $48,000 per month (Miller et al. 2014). GC review of a subset of outgoing genetic test orders at Seattle Children’s Hospital over a period of 8 months led to modification of 24% of the 250 test orders, corresponding to a savings of $118,952 (Dickerson et al. 2014). A third study of GC review of incoming test orders at PreventionGenetics Laboratory over a period of 6 months resulted in modification of 6.6% of all cases and a cost savings of more than $47,000 each month (Londre et al. 2017). Because ordering a genetic test requires an understanding of the possible genetic heterogeneity of a condition, an appreciation for the limitations of testing methodologies, and familiarity with the ever-changing testing options,

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additional studies highlight the suitability of a GC skill set for laboratory utilization management, and how test review and provision of guidance can improve effectiveness of genetic test orders and indirectly impact medical decision making and patient care (Kotzer et al. 2014; Riley et al. 2015). GC review of incoming orders is an established process in the Molecular Genetics Laboratory at Cincinnati Children’s Hospital Medical Center, but had not been previously quantified or tracked by the team. A pilot study was performed from July 2015 through June 2016 to ascertain and categorize the types of order errors and to evaluate the impact of collecting this data. The data collected during the year-long pilot study was not included in this current study. The aim of this study was to determine the impact of GC review of incoming test orders received for a subset of tests in this academic diagnostic molecular genetics laboratory, with three specific measures: (1) identify the proportion of incoming test orders that would benefit from modification to a more appropriate test based on the patient’s indication, family history provided, or the nature of the test ordered; (2) determine the percent of test orders that were actually modified by health care provider based on the recommendations of the GC following their review; and (3) quantify the health care dollar savings from order changes following the recommendation of a GC.

Materials and Methods This research study was conducted by the GC team in the Molecular Genetics Laboratory at Cincinnati Children’s Hospital Medical Center. This laboratory is a non-profit, academic, CAP-approved, and CLIA-certified diagnostic laboratory. The GC team consists of five genetic counselors and one research assistant. The Institutional Review Board at Cincinnati Children’s Hospital Medical Center determined that this project was exempt from IRB review. As part of regular day-to-day operations of the laboratory, numerous groups in the laboratory including the login staff, technologists, office staff, and GCs’ review incoming test orders. The order review process is outlined in Fig. 1. Initially, the institution receives the samples and requisitions and brings them to the laboratory log in area for review. If the test orders selected on the requisition are unclear, the login group contacts the GC team to clarify the test orders with the ordering provider. If the login group does not perceive any issues with the requested test, they enter the ordered test into the system and laboratory staff initiates the DNA extraction. The office staff then reviews the completed requisitions to resolve any clerical issues, such as missing billing information. If the office staff identifies a duplicate test order, such as repeat order for a test that has been previously performed for the patient, they contact the ordering provider to initiate test cancelation. This office staff resolves these orders without GC

Fig. 1 Requisition review process. Orders that could benefit from modification were either ascertained by the GC team or brought to the attention of the GC team by the office staff, login staff, or laboratory technologists

involvement, so these duplicate orders are not included in this study. Once DNA is extracted, the technologists are given the sample and the test orders to initiate genetic testing. If the technologists identify a possible order error, such as full sequencing for a relative of a proband previously tested by the laboratory, they contact the GCs to follow up with the ordering provider. Approximately 1 day after the sample and requisition has been received by the laboratory, one rotating member of the GC team reviews all incoming constitutional test orders for sequencing-based tests and gene-specific deletion/ duplication analysis by array comparative genomic hybridization, including any orders that may have been previously received and acted upon by the team the prior day. The GC team member carefully evaluates the completed requisition to check for order appropriateness and completeness based on the clinical information provided, the concurrent test orders received for the patient, or ongoing or previous testing performed for the patient in the laboratory. Requisitions received with no perceived issues are deemed appropriate and the testing is performed as ordered. This study took place between July 1, 2016 and December 31, 2016. In order to quantify the first measure for this study, the daily number of received test orders was tracked and the orders that could benefit from modification were flagged. For orders ascertained for potential modification, a GC team member contacted the ordering provider to clarify the testing strategy or to recommend a more appropriate alternative test based on clinical or family history noted on the test requisition. These recommendations were provided through phone correspondence, e-mail correspondence, or both. The GC team manually entered each flagged case into a Microsoft Excel spreadsheet, including the initial test order, the final test order,

Reduction of Health Care Costs and Improved Appropriateness of Incoming Test Orders: the Impact of Genetic...

the type of order error, how the problem order was ascertained, and the specialty of the ordering provider. Test results of modified orders were tracked to identify abnormal test results that would have been missed without GC intervention. The second measure was assessed by tracking whether the ordering providers accepted or rejected the proposed modifications. The final measure was calculated by determining the cost difference between the cost of the initial order and the billed cost of the final test order. Billed costs were provided by the billing department for the laboratory. The net savings for order changes was calculated based on the price that was billed for each case, which often represented the new test. Microsoft Excel was used for all data analysis.

Results A summary of the reviewed and modified orders and the overall and average cost differences are outlined in Table 1. During the 6-month study window, 2367 test orders were reviewed by the GC team (approximately 19 per day). Of these, 109 individual orders (4.60%) were flagged for review as potentially inefficient, inappropriate, or erroneous, and were classified according to pre-determined error categories. These represented 51 total cases, representing 54 individuals including 3 sibling pairs. For each case, a modification was proposed to resolve the 109 flagged orders. The majority of proposed modifications (49/51) were approved by the ordering provider and the recommended test was performed. Two proposed modifications, which represented eight original orders from two different patients, were declined by the client. These comprised single-gene Sanger sequencing of five genes for one patient and three genes for a second patient. For both cases, a multigene next-generation sequencing panel which included each of the individually ordered genes was suggested as a costsaving and more comprehensive alternative, with proposed cancelation of the eight individual orders. Both providers declined this modification, citing prior insurance authorization and prior New York state approval, respectively, for the original order strategy as their reasons for declining the suggested modifications. Nineteen modifications involved a reduction in the total number of tests performed for the patient. Table 1

Details of reviewed orders

Orders reviewed during 6 months Average number of orders reviewed per day Orders flagged upon review Number of suggested modifications Uptake of suggested modifications Difference in billed cost to payers Average cost difference per order modification

2367 18.6 109 (4.60%) 51 49/51 (96.08%) − $98,750.64 − $2015.32

The error categories with category-specific modifications and the cost savings per category are described in Table 2. Category A included orders for full gene analysis when a mutation had previously been identified in the family. The proposed modification for this order category was to cancel the full gene sequencing or deletion/duplication analysis and instead perform known mutation analysis. Category B included orders for an incorrect methodology for a known familial mutation. The proposed modification for this order category was to cancel the original order and switch to the correct methodology. Category C included concurrent orders for full gene sequencing for multiple at-risk family members, or concurrent orders for an affected proband and one or more relative. The proposed modification for this order category was to prioritize testing for the most informative family member and store the additional samples for potential future targeted testing. Category D included concurrent orders for multiple subpanels that are included in a larger panel, or orders for multiple individual genes that are available as a panel. The proposed modification for this order category was to cancel the subpanels or individual orders, and to perform the panel that encompassed the originally requested genes. Category E included suboptimal testing strategies based on the types of mutations previously reported in the requested gene(s). The proposed modification for this order category was to cancel the original order and switch to the more sensitive methodology. Category F included suboptimal testing strategies based on clinical features or previous genetic testing performed on the patient. The proposed modification for this category was to cancel the order or switch to a more sensitive test. For each order change, the difference between the billed cost for the new order and the price for the original order was calculated in order to assess savings to the client that resulted from the order modification. For categories E and F, the cost calculations are not as straightforward as the other categories. To keep the calculation strategies consistent across the categories, we only calculated the difference between the canceled and the new test. Adding the cost of the original canceled test to the cost difference could have given a more comprehensive cost savings for the provider. The order change category with the largest cost difference between the new and original price was category C (full panel/gene sequencing on multiple relatives reduced to one panel pending a positive result). This modification was applied to three cases, resulting in an average of $3972.01 in savings per case. The order change category that was seen most frequently and resulted in the biggest cost savings per case was category D (multiple subpanels ordered simultaneously or multiple individual genes ordered instead of a panel). This modification was applied 21 times, resulting in a total cost savings of $51,691.88. The order change category with the smallest cost difference between the new and original billing price was category E (deletion/ duplication analysis changed to sequencing or vice versa).

Wakefield et al. Table 2

Category-specific test modifications and cost impacts

Order category

Modification

A. Full gene analysis for family member of diagnosed proband B. Wrong methodology or gene for known testing C. Full sequencing for multiple family members or suboptimal testing strategy when familial mutation(s) unknown

Cancel order, perform known 7 (14.3%) mutation analysis Cancel order, switch to correct 2 (4.1%) methodology/gene Confirm relationship; prioritize testing 3 (6.1%) for most informative family member, store other sample(s) for possible future targeted testing Cancel subpanel or individual 21 (42.9%) orders, switch to larger panel

D. Multiple subpanels of a large panel; or multiple individual genes that are available as a panel E. Suboptimal test sensitivity based on types of previously reported mutations F. Suboptimal test strategy based on clinical features or previous testing (including complex duplicate orders)

Number of times modification was applied (percent of total modifications)

Cancel order, switch to more sensitive methodology Cancel order, switch to more sensitive strategy

This modification was applied four times, resulting in an average of $69.17 in savings per case.

Discussion Previous studies have demonstrated the benefits of having a laboratory utilization management program to identify and correct order errors in order to provide better care for patients and reduce health care costs (Mathias et al. 2016; Miller et al. 2014; Suarez et al. 2017). Consistent with these studies, our results illustrate cost savings and order improvement when incoming orders are reviewed by a GC team. Of the 2367 orders reviewed during our study period, 109 (4.6%) of orders were flagged for further GC review. This is a lower percentage than demonstrated in similar studies, which have identified a 6.6% (Londre et al. 2017), 22% (Suarez et al. 2017), and 26% error rate (Miller et al. 2014). This difference may partially be explained by the fact that our percentage does not include straightforward duplicate test orders as an error category, as these errors are resolved prior to GC review. Additionally, we did not collect information about whether external incoming orders may have been reviewed and corrected by additional utilization management programs before they were sent to our laboratory. In this study, we observed some patterns among erroneous orders. Category D, the most common type of error, included inefficient orders such as concurrent subpanels of or with a larger panel, or orders for multiple individual genes that are available as a panel. For example, one set of flagged orders requested sequencing of four single stand-alone genes that are associated with hemophagocytic lymphohistiocytosis (HLH).

4 (8.2%) 12 (24.5%)

Cost savings Cost savings (percent of total per modification savings)

$12,839.10 (13.0%) $2,834.78 (2.9%) $11,916.02 (12.1%)

$1,834.16 $1,417.39 $3,972.01

$51,691.88 (52.3%)

$2,461.52

$276.68 (0.3%)

$69.17

$19,192.18 (19.4%)

$1,599.35

A 14-gene HLH panel, which includes these four single genes, was a more cost-effective option for this patient. The proposed order change of four single HLH genes to the HLH panel was implemented, resulting in a cost savings of $2318. The second most common error category identified in this study was category F, which included complex duplicate testing or concurrent testing. For example, the laboratory received orders for three concurrent tests (18-gene cholestasis panel, 2gene cystic disease panel, and UGT1A1 sequencing) for a patient with suspected liver disease. A tiered approach was suggested to the provider, starting with the cholestasis panel as it was identified as the most sensitive test based on the patient’s symptoms. In this instance, the patient received abnormal results for the cholestasis panel, eliminating the need for the subsequent two tests and saving $3500 in health care costs. Category F also included complex duplicate testing. Because repeat orders of the same gene or panel are typically canceled by the office staff prior to GC order review, the duplicate tests identified in our study include only complex duplicate orders that were acted on by the GC team. For example, the laboratory received an order for PSMB8 sequencing. Upon review of clinical history provided on the requisition, it was identified that the patient already had whole exome sequencing which was positive for one sequence change in PSMB8. Deletion/duplication of the PSMB8 gene was suggested to the provider, rather than sequencing, which prevented the duplicate sequencing test from being performed. Duplicate test orders could be attributed to lack of awareness of an ordering provider of previous testing that was ordered by a different provider, or tests ordered at a different institution prior to transfer of care. Some duplicate test orders occurred because the provider ordered single-gene sequencing for a

Reduction of Health Care Costs and Improved Appropriateness of Incoming Test Orders: the Impact of Genetic...

gene that was previously included in a NGS panel. For example, GATA2 sequencing was ordered by a hematologist for a patient with suspected bone marrow failure. A few months prior to ordering GATA2 sequencing, this same provider ordered the 59-gene Bone Marrow Failure (BMF) Syndromes Panel in our laboratory, which already included GATA2. The provider was unaware that the gene had been included and agreed to cancel the duplicate test, resulting in cost savings of $1117. Although not encountered during the study period, some providers may have used this testing strategy deliberately if a previous test, such as exome sequencing, had low coverage for a particular gene of interest. We commonly encountered order errors surrounding testing for an at-risk relative, encompassed by categories A, B, and C. Common issues included orders for full sequencing instead of targeted mutation analysis, targeted testing for the inappropriate methodology or gene, and orders for a NGS panel on a sibling before testing had been completed in the proband. For example, a neonatologist ordered the 59-gene BMF panel for a set of symptomatic dizygotic twins. The most efficient testing strategy in this case was to perform the BMF panel on one of the patients and perform targeted mutation analysis in the sibling once the genetic etiology was identified. Canceling one of the BMF orders resulted in cost savings of $4500 for this case. Because the BMF panel was negative, no additional genetic testing was indicated or performed for the sibling. Fullsequencing orders might be placed for a relative if the provider is unaware of the previous testing performed on the proband, or does not have access to the proband’s test report. If the proband’s report is available, the order can be switched to targeted mutation analysis, which is the most cost-effective testing strategy. If the proband’s report is not available, full gene analysis is the appropriate test for an at-risk relative. It is important to note that concurrent testing in both a proband and siblings, or multiple concurrent tests in a proband, may be selected deliberately if testing is extremely time-sensitive. This strategy was not utilized for any cases within our study window but is most often used when a bone marrow transplant for a proband is pending positive genetic test results and testing is being ordered in the sibling to assess for bone marrow donor suitability. Lastly, category E included orders for inappropriate test methodologies. One of these was an order for deletion/ duplication analysis of the FMR1 gene. Because the most sensitive test for Fragile X syndrome is FMR1 trinucleotide expansion testing, the GC team successfully proposed switching the order to trinucleotide expansion testing upon learning that no testing had been previously performed for the patient. This order modification resulted in cost savings of $1351.68. In addition to cost savings, GC involvement in order review can yield positive testing in a patient that would have been missed by the initial test order. For example, the laboratory received an order for deletion/duplication analysis

of the USB1 gene for a patient whose requisition form noted a Bsister with known poikiloderma neutropenia.^ Upon further investigation, the GC team determined that the proband has a homozygous frameshift mutation comprised of a single-base deletion. Thus, the patient’s order was modified to targeted sequencing for the familial mutation and the patient was found to be a carrier. The familial mutation would not have been identifiable through array comparative genomic hybridization (aCGH), so had the order not been switched the patient’s carrier status would have been missed by this test and the patient may have been inaccurately classified as a non-carrier. Barriers to efficient test ordering may be present when insurance companies or institutional send-out laboratories are involved in the test ordering process. Many ordering providers utilize a hospital send-out laboratory to coordinate outgoing testing, and paperwork may be completed by send-out laboratory staff who may not have genetics expertise and knowledge about efficient ordering practices. Additionally, send-out laboratories may not have copies of the most up-todate laboratory test requisition form readily available. Subsequently, staff may not be aware of recent test launches including next-generation sequencing (NGS) panels, which could result in an order for sequencing of several single genes when a NGS panel may be available. Testing strategies utilized by an ordering provider may also be influenced by insurance considerations. For example, one provider in our study ordered single-gene sequencing for BIRC4, ITK, and SH2D1A, which are three genes included in the 14-gene HLH panel. The cost of the HLH panel is $870 less than the cost of Sanger sequencing of these three genes individually. Because single-gene sequencing but not panel testing had been authorized by the patient’s insurance company, this testing strategy was selected purposefully by the ordering provider, even though it was the more expensive testing strategy. Laboratory genetic counselors are well suited to engage ordering providers in discussions about testing and to offer alternative testing strategies that may not have been initially considered. GC skills such as building rapport, maintaining interdisciplinary professional relationships, and navigating confrontation are especially important when pointing out potential ordering errors and sharing test recommendations with ordering providers (Goodenberger et al. 2015; Kotzer et al. 2014). Often, the phone number provided on the requisition is not a direct line to the provider, but rather to an office staff member or a nurse line. As a result, the person who answers the phone may not be able to answer GC questions regarding this case, which can be frustrating and overwhelming for an individual who is not familiar with genetic testing. In this scenario, the GC team can utilize learned interpersonal skills by employing empathy to normalize frustration with the situation and facilitate problem resolution. Beyond utilization management, communication between the GC team and ordering providers has numerous additional

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benefits. Although not tracked during our study, we hypothesize that ordering providers that were contacted by a GC may have improved ordering practices in the future as a result of our intervention, resulting in continued reduction of health care costs. In addition, these conversations may encourage the provider to communicate with a GC prior to ordering genetic testing and raise awareness about testing considerations that were previously overlooked (Miller et al. 2014). GC involvement can increase provider and patient satisfaction with the laboratory because their commitment to patient care is observed as the priority, rather than the revenue generated from costly inappropriate testing (Dickerson et al. 2014; Riley et al. 2015). Together, these studies reinforce the positive impact that laboratory GCs have in utilization management programs. Utilization management programs reduce health care costs by eliminating unnecessary or duplicate testing and by implementing more cost-efficient testing strategies (Dickerson et al. 2014; Miller et al. 2014; Riley et al. 2015). GC review of test orders results in cost savings when orders are switched to a less expensive test or when a more efficient test is utilized. Cancelation of duplicate or unnecessary test orders eliminates costs to the patient and also protects valuable laboratory resources such as technologist time and laboratory reagents and materials. Without GC intervention, unnecessary or inefficient testing proceeds as ordered, and in one case during our study, a positive carrier result would have been missed without intervention.

Limitations Several factors limited the scope of this study. Only orders received within a 6-month window were assessed, so our results may not account for the full breadth of potential orders that could be sent to the laboratory. As this study was intended to measure the impact of GC review of incoming test orders, it does not directly incorporate the impact of other laboratory staff in ascertaining unnecessary or inefficient orders (Fig. 1). Straightforward duplicate test orders are typically canceled prior to GC review of the incoming orders. Outside of this, we do not expect that laboratory review as a first-line step has a substantial impact in the number of orders ascertained because our study captures both errors identified solely by the GC team and errors brought to the GC team by laboratory staff for resolution. The GC team is responsible for reviewing only sequencingbased tests and array comparative genomic hybridization deletion/duplication analysis for order appropriateness and completeness. Other non-sequencing (e.g., genotyping, methylation-based tests) orders are not reviewed during this process and thus are not included in this study. Order errors of other test types could partially account for the difference in

percentage of erroneous orders ascertained between our study and others. The data collected in this study additionally had qualitative limitations. This data was not evaluated to measure ordering trends or potential changes in order quality among clinicians after initial education. Thus, it does not capture potential education and intervention provided prior to receipt of a sample, such as through phone calls from providers who contacted the laboratory with questions about planned orders. Ordering practices by repeat providers may have improved after GC education provided at the time of an intervention early in the study window or in the pilot study. Additionally, incoming orders were reviewed by a rotating group of six people. This study did not examine the possibility of biases or subjectivity within the review process among the team. Lastly, the laboratory relies on ordering providers to provide accurate clinical information to assess the appropriateness of the order. Since providing clinical information is not mandatory for most tests, lack of clinical information for some cases may have potentially impacted the number of order inefficiencies or errors ascertained in the present study.

Future Directions This study supports and adds to the existing and growing literature on utilization management by GCs, and emphasizes the financial and patient care impact of this practice. Future studies could assess the overall financial impact of GC intervention on test orders which could take into account the potential time reduction and resources required for performing more efficient tests. Testing laboratories could simplify their requisitions and create algorithms and ordering guides to aid the appropriate testing process. Laboratories could periodically reach out to send-out laboratories to ensure their requisition forms are up-to-date and to provide education about new test launches. In addition, laboratory staff could request clinical information for every requisition received in order to best determine the most appropriate and cost-effective testing strategy for each patient. Acknowledgements The authors thank the log-in staff, administrative staff, and molecular technologists for their continued attention to potential order errors. They would also like to thank the directors of the Molecular Genetics Laboratory as well as the Division of Human Genetics for their ongoing support of our GC team projects.

Compliance with Ethical Standards Conflict of Interest Emily Wakefield, Haley Keller, Hannah Mianzo, Chinmayee B. Nagaraj, Sanjukta Tawde, and Elizabeth Ulm declare that they have no conflict of interest. Human Studies and Informed Consent In accordance with federal regulations as determined by the Cincinnati Children’s Hospital Medical Center Internal Review Board, this study (2016–1499) does not meet

Reduction of Health Care Costs and Improved Appropriateness of Incoming Test Orders: the Impact of Genetic... regulatory criteria for research involving human subjects as defined under 45 CFR 46.102(d) and 45 CFR 46.120(f). Animal Studies No animal studies were carried out by the authors for this article.

References Dickerson, J. A., Cole, B., Conta, J., Wellner, M., Wallace, S. E., Jack, R., et al. (2014). Improving the value of costly genetic reference laboratory testing with active utilization management. Archives of Pathology and Laboratory Medicine, 138(1), 110–113. Goodenberger, M. L., Thomas, B. C., & Wain, K. E. (2015). The utilization of counseling skills by the laboratory genetic counselor. Jornal of Genetic Counseling, 24, 6–17. Kotzer, K. E., Riley, J. D., Conta, J. H., Anderson, C. M., Schahl, K. A., & Goodenberger, M. L. (2014). Genetic testing utilization and the role of the laboratory genetic counselor. Clinica Chimica Acta, 427, 193–195.

Londre, G. K., Zaleski, C. A., & Conta, J. H. (2017). Adding value to genetic testing through utilization management: commercial laboratory’s experience. American Journal of Genetic Part A, 173A(5), 1433–1455. Mathias, P. C., Conta, J. H., Konnick, E. Q., Sternen, D. L., Stasi, S. M., Cole, B. L., et al. (2016). Preventing genetic testing order errors with a laboratory utilization management program. American Journal of Clinical Pathology, 146(2), 221–226. Miller, C. E., Krautscheid, P., Baldwin, E. E., Tvrdik, T., Openshaw, A. S., Hart, K., & LaGrave, D. (2014). Genetic counselor review of genetic test orders in a reference laboratory reduces unnecessary testing. American Journal of Genetic Part A, 164A(5), 1094–1101. Riley, J. D., Procop, G. W., Kottke-Marchant, K., Wyllie, R., & Lacbawan, F. L. (2015). Improving molecular genetic test utilization through order restriction, test review, and guidance. The Journal of Molecular Diagnostics, 17(3), 225–229. Suarez, C. J., Yu, L., Downs, N., Costa, H. A., & Stevenson, D. A. (2017). Promoting appropriate genetic testing: the impact of a combined test review and consultative service. Genetics in Medicine, 19, 1049–1054.