Improving Diagnosis in Health Care

Balogh, Erin; Miller, Bryan T.; Ball, Jessica

doi:10.17226/21794

Cited by 684 publications

(155 citation statements)

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“…The costs associated with harms of a FN and FP test result (B and H, respectively) and medical misdiagnosis have been the subject of growing number of articles ( 19 ). The Institute of Medicine (IOM), an American non-profit, non-governmental organization, reports that about 30% of health care costs spending in the US, around US$ 750 billion, is wasted on unnecessary services ( 20 ).…”

Section: Bayesian Approach In Determining the Cut-off Valuementioning

confidence: 99%

On determining the most appropriate test cut-off value: the case of tests with continuous results

Habibzadeh

Yadollahie³

2016

Biochem Med

520

406

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There are several criteria for determination of the most appropriate cut-off value in a diagnostic test with continuous results. Mostly based on receiver operating characteristic (ROC) analysis, there are various methods to determine the test cut-off value. The most common criteria are the point on ROC curve where the sensitivity and specificity of the test are equal; the point on the curve with minimum distance from the left-upper corner of the unit square; and the point where the Youden’s index is maximum. There are also methods mainly based on Bayesian decision analysis. Herein, we show that a proposed method that maximizes the weighted number needed to misdiagnose, an index of diagnostic test effectiveness we previously proposed, is the most appropriate technique compared to the aforementioned ones. For determination of the cut-off value, we need to know the pretest probability of the disease of interest as well as the costs incurred by misdiagnosis. This means that even for a certain diagnostic test, the cut-off value is not universal and should be determined for each region and for each disease condition.

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Section: Bayesian Approach In Determining the Cut-off Valuementioning

confidence: 99%

On determining the most appropriate test cut-off value: the case of tests with continuous results

Habibzadeh

Yadollahie³

2016

Biochem Med

520

406

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“…9 Generally the improved collaboration in the form of MDT result into emphasize that health-care providers have primary responsibility for checking/verifying a patient’s identity, development of standardized organization policies and protocols to emphasize the importance of positive patient identification that are compatible with the values and needs of the medical facilities, e.g., requirement of unique patient identifiers on specimen labels, implementation of zero tolerance policy, staff performance assessment, availability of adequate number of qualified personnel to perform specimen collection, reinforcement of specimen labeling at the bed side, delta checks etc.…”

Section: Description Of Evaluated Practicesmentioning

confidence: 99%

Effectiveness of Laboratory Practices to Reduce Specimen Labeling Errors at the Time of Specimen Collection in Healthcare Settings: A Laboratory Medicine Best Practices (LMBP) Systematic Review

Sandhu

Bandyopadhyay

Hunt

et al. 2017

The Journal of Applied Laboratory Medicine

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Background Specimen labeling errors have long plagued the laboratory industry putting patients at risk of transfusion-related death, medication errors, misdiagnosis, and patient mismanagement. Many interventions have been implemented and deemed to be effective in reducing sample error rates. The objective of this review was to identify and evaluate the effectiveness of laboratory practices/ interventions to develop evidence based recommendations for the best laboratory practices to reduce labeling errors. Content The standardized LMBP™ A-6 methods were used to conduct this systematic review. Total evidence included 12 studies published during the time periods of 1980 to September 2015. Combined data from seven studies found that the interventions developed as a result of improved communication and collaboration between the laboratory and clinical staff resulted in substantial decrease in specimen labeling errors (Median relative percent change in labeling errors: −75.86; IQI: −84.77, −58.00). Further data from subset of four studies showed a significant decrease in specimen labeling errors after the institution of the standardized specimen labeling protocols (Median relative percent decrease in specimen labeling errors: −72.45; IQI: −83.25, −46.50). Summary Based on the evidence included in this review, the interventions that enhance the communication and collaboration between laboratory and healthcare professionals can decrease the specimen identification errors in healthcare settings. However, more research is needed to make the conclusion on the effectiveness of other evaluated practices in this review including training and education of the specimen collection staff, audit and feedback of labeling errors, and implementation of new technology (other than barcoding).

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“…In Improving Diagnosis in Health Care, the Institute of Medicine noted that many diagnostic errors result from failures in information gathering, integration, and interpretation [4]. These types of errors can result from "work-arounds" to avoid entering useful data when rigid software requirements result in real or perceived increased work by end users [1].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Effect of Unstructured Clinical Information on Clinical Decision Support Appropriateness Ratings

Moriarity

Green

Klochko

et al. 2017

Journal of the American College of Radiology

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Improving Diagnosis in Health Care

Cited by 684 publications

References 0 publications

On determining the most appropriate test cut-off value: the case of tests with continuous results

On determining the most appropriate test cut-off value: the case of tests with continuous results

Effectiveness of Laboratory Practices to Reduce Specimen Labeling Errors at the Time of Specimen Collection in Healthcare Settings: A Laboratory Medicine Best Practices (LMBP) Systematic Review

Evaluating the Effect of Unstructured Clinical Information on Clinical Decision Support Appropriateness Ratings

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