A bayesian approach to laboratory utilization management

Hauser, Ronald G.; Jackson, Brian R.; Shirts, Brian H.

doi:10.4103/2153-3539.151921

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Traditionally, to evaluate lab test utilization, metrics such as those described earlier (e.g., test ordering volumes and ordering rates) are commonly analyzed ( 24–26 ). Other metrics such as test positivity rate have also been described as a useful benchmark for examining lab test utilization ( 27–30 ). While research involving this metric is still evolving, low positivity rate has been suggested as a possible signal for test overutilization, while high positivity rate has been suggested as a possible signal for test underutilization ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

The Impact of COVID-19 on Laboratory Test Utilization at a Pediatric Medical Center

Lin

Parakati

Haymond

2022

The Journal of Applied Laboratory Medicine

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Background The epidemiology and clinical manifestation of COVID-19 in the pediatric population is different from the adult population. The purpose of this study is to identify effects of COVID-19 pandemic on laboratory test utilization in a pediatric hospital. Methods We performed retrospective analysis on test utilization data from Ann & Robert H. Lurie Children’s Hospital of Chicago, an academic pediatric medical center. Data between two 100-day periods prior to (pre-pandemic) and during the pandemic (mid-pandemic) were analyzed to evaluate changes in test volume, lab utilization, and test positivity rate. We also evaluated these metrics based on in- versus out-patient testing, and performed modeling to determine what variables significantly impact the test positivity rate. Results During the pandemic period, there was an expected surge in COVID-19 testing, while over 84% of lab tests studied decreased in ordering volume. The average number of tests ordered per patient was not significantly different during the pandemic for any of the laboratories (adjusted p-value > 0.05). 33 studied tests showed significant change in positivity rate during the pandemic. Linear modeling revealed test volume and inpatient status as the key variables associated with change in test positivity rate. Conclusions Excluding SARS-CoV-2 tests, the COVID-19 pandemic has generally led to decreased test ordering volume and laboratory utilization. However, at this pediatric hospital, the average number of tests performed per patient and test positivity rates were comparable between pre- and mid-pandemic periods. These results suggest that overall, clinical test utilization at this site remained consistent during the pandemic.

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Section: Discussionmentioning

confidence: 99%

The Impact of COVID-19 on Laboratory Test Utilization at a Pediatric Medical Center

Lin

Parakati

Haymond

2022

The Journal of Applied Laboratory Medicine

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“…First, the graph is easy to understand. The method does not involve advanced mathematics (e.g., algebra, calculus) [24]. Second, although simple to understand, the graph can represent a complex process.…”

Section: Strengths Of Modeling Guideline Adherence As a Graphmentioning

confidence: 99%

Graphical analysis of guideline adherence to detect systemwide anomalies in HIV diagnostic testing

et al. 2022

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Background Analyses of electronic medical databases often compare clinical practice to guideline recommendations. These analyses have a limited ability to simultaneously evaluate many interconnected medical decisions. We aimed to overcome this limitation with an alternative method and apply it to the diagnostic workup of HIV, where misuse can contribute to HIV transmission, delay care, and incur unnecessary costs. Methods We used graph theory to assess patterns of HIV diagnostic testing in a national healthcare system. We modeled the HIV diagnostic testing guidelines as a directed graph. Each node in the graph represented a test, and the edges pointed from one test to the next in chronological order. We then graphed each patient’s HIV testing. This set of patient-level graphs was aggregated into a single graph. Finally, we compared the two graphs, the first representing the recommended approach to HIV diagnostic testing and the second representing the observed patterns of HIV testing, to assess for clinical practice deviations. Results The HIV diagnostic testing of 1.643 million patients provided 8.790 million HIV diagnostic test results for analysis. Significant deviations from recommended practice were found including the use of HIV resistance tests (n = 3,007) and HIV nucleic acid tests (n = 16,567) instead of the recommended HIV screen. Conclusions We developed a method that modeled a complex medical scenario as a directed graph. When applied to HIV diagnostic testing, we identified deviations in clinical practice from guideline recommendations. The model enabled the identification of intervention targets and prompted systemwide policy changes to enhance HIV detection.

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“…Listing questions that, if answered, could narrow the differential diagnosis can be useful. Performance measures of diagnostic tests, contraindications for their use, and complication rates could be incorporated in their knowledge bases [ 44 ]. This information can be applied in the case of an individual patient to simulate a posttest probability given a prior probability and test result, such as using likelihood ratios [ 45 ].…”

Section: Next-generation Diagnosis Support Systemsmentioning

confidence: 99%

A Learning Health Care System Using Computer-Aided Diagnosis

Cahan¹,

Cimino²

2017

J Med Internet Res

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Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners.

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A bayesian approach to laboratory utilization management

Cited by 7 publications

References 28 publications

The Impact of COVID-19 on Laboratory Test Utilization at a Pediatric Medical Center

The Impact of COVID-19 on Laboratory Test Utilization at a Pediatric Medical Center

Graphical analysis of guideline adherence to detect systemwide anomalies in HIV diagnostic testing

A Learning Health Care System Using Computer-Aided Diagnosis

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