Reflective testing – A randomized controlled trial in primary care patients

Oosterhuis, Wytze P.; Venne, Wilhelmine P.H.G. Verboeket-van de; Deursen, Cees T. Bm van; Stoffers, Jelle; Acker, B.A.C. van; Bossuyt, Patrick M.

doi:10.1177/0004563220968373

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…This service provides diagnostic guidance by the addition of interpretative comments, and moreover additional tests can be added by the specialist in laboratory medicine to complete the diagnostic workup. This saves time and prevents additional phlebotomy for the patient, and has been proven to be highly appreciated by general practitioners [7]. As the evaluation of changes in patterns across numerous laboratory parameters can be complex or subtle the algorithm developed in this study can assist specialists to predict iron deficiency more accurately.…”

Section: Discussionmentioning

confidence: 98%

“…Decision support tools provide a welcome opportunity to lower the work pressure of physicians in an aging population with multimorbidity [14,15]. In several regions of the Netherlands, general practitioners are supported by specialists in laboratory medicine with a service called 'reflective testing' [7,16,17]. This service provides diagnostic guidance by the addition of interpretative comments, and moreover additional tests can be added by the specialist in laboratory medicine to complete the diagnostic workup.…”

Section: Discussionmentioning

confidence: 99%

“…In many anemic primary care patients ferritin is not included in the laboratory order, resulting in underdiagnosis of iron deficiency. Importantly, specialists in laboratory medicine can provide assistance to physicians in diagnostics of anemia in the form of a 'laboratory anemia workup' or 'reflective testing' [7]. Computational assistance in the interpretation of laboratory results can provide a key automated diagnostic support tool for physicians as well as specialists in laboratory medicine, reducing the number of overlooked and/or undiagnosed iron deficiencies.…”

Section: Introductionmentioning

confidence: 99%

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Automated prediction of low ferritin concentrations using a machine learning algorithm

Kurstjens

Bel

Horst

et al. 2022

Clinical Chemistry and Laboratory Medicine (CCLM)

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Objectives Computational algorithms for the interpretation of laboratory test results can support physicians and specialists in laboratory medicine. The aim of this study was to develop, implement and evaluate a machine learning algorithm that automatically assesses the risk of low body iron storage, reflected by low ferritin plasma levels, in anemic primary care patients using a minimal set of basic laboratory tests, namely complete blood count and C-reactive protein (CRP). Methods Laboratory measurements of anemic primary care patients were used to develop and validate a machine learning algorithm. The performance of the algorithm was compared to twelve specialists in laboratory medicine from three large teaching hospitals, who predicted if patients with anemia have low ferritin levels based on laboratory test reports (complete blood count and CRP). In a second round of assessments the algorithm outcome was provided to the specialists in laboratory medicine as a decision support tool. Results Two separate algorithms to predict low ferritin concentrations were developed based on two different chemistry analyzers, with an area under the curve of the ROC of 0.92 (Siemens) and 0.90 (Roche). The specialists in laboratory medicine were less accurate in predicting low ferritin concentrations compared to the algorithms, even when knowing the output of the algorithms as support tool. Implementation of the algorithm in the laboratory system resulted in one new iron deficiency diagnosis on average per day. Conclusions Low ferritin levels in anemic patients can be accurately predicted using a machine learning algorithm based on routine laboratory test results. Moreover, implementation of the algorithm in the laboratory system reduces the number of otherwise unrecognized iron deficiencies.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated prediction of low ferritin concentrations using a machine learning algorithm

Kurstjens

Bel

Horst

et al. 2022

Clinical Chemistry and Laboratory Medicine (CCLM)

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“…Oosterhuis et al [ 44 ] conducted a randomized controlled trial on reflective testing. The laboratory specialist performed reflective testing according to a routine procedure.…”

Section: Possible Strategies To Avoid Inappropriate Test Utilizationmentioning

confidence: 99%

Laboratory Demand Management Strategies—An Overview

et al. 2021

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Inappropriate laboratory test selection in the form of overutilization as well as underutilization frequently occurs despite available guidelines. There is broad approval among laboratory specialists as well as clinicians that demand management strategies are useful tools to avoid this issue. Most of these tools are based on automated algorithms or other types of machine learning. This review summarizes the available demand management strategies that may be adopted to local settings. We believe that artificial intelligence may help to further improve these available tools.

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“…This issue sees the print publication of a second important paper in this area: a randomised controlled trial (RCT) of reflective testing in primary care patients. 8 In this study, patients were randomly allocated to an intervention group, where requesting clinicians received reflectively added test results and interpretive comments as appropriate, in addition to the originally requested tests, and a control group where they did not. The medical records of patients in each group were followed up six months after the reports/comments were issued.…”

mentioning

confidence: 99%