The design and evaluation of a graphical display for laboratory data

Bauer, David T.; Guerlain, Stephanie; Brown, Patrick J.

doi:10.1136/jamia.2009.000505

Cited by 89 publications

(62 citation statements)

References 33 publications

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“…One logical direction to pursue is the graphical representation of numerical data 45. One usability study found that graphical laboratory value displays led to reduced review times and that graphical and tabular representations were each more effective for answering different clinical questions 46. However, different clinical settings may benefit from differing data summary formats.…”

Section: Resultsmentioning

confidence: 99%

Use of health information technology to reduce diagnostic errors

2013

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BackgroundHealth information technology (HIT) systems have the potential to reduce delayed, missed or incorrect diagnoses. We describe and classify the current state of diagnostic HIT and identify future research directions.Methods A multi-pronged literature search was conducted using PubMed, Web of Science, backwards and forwards reference searches and contributions from domain experts. We included HIT systems evaluated in clinical and experimental settings as well as previous reviews, and excluded radiology computer-aided diagnosis, monitor alerts and alarms, and studies focused on disease staging and prognosis. Articles were organised within a conceptual framework of the diagnostic process and areas requiring further investigation were identified.ResultsHIT approaches, tools and algorithms were identified and organised into 10 categories related to those assisting: (1) information gathering; (2) information organisation and display; (3) differential diagnosis generation; (4) weighing of diagnoses; (5) generation of diagnostic plan; (6) access to diagnostic reference information; (7) facilitating follow-up; (8) screening for early detection in asymptomatic patients; (9) collaborative diagnosis; and (10) facilitating diagnostic feedback to clinicians. We found many studies characterising potential interventions, but relatively few evaluating the interventions in actual clinical settings and even fewer demonstrating clinical impact.ConclusionsDiagnostic HIT research is still in its early stages with few demonstrations of measurable clinical impact. Future efforts need to focus on: (1) improving methods and criteria for measurement of the diagnostic process using electronic data; (2) better usability and interfaces in electronic health records; (3) more meaningful incorporation of evidence-based diagnostic protocols within clinical workflows; and (4) systematic feedback of diagnostic performance.

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Section: Resultsmentioning

confidence: 99%

Use of health information technology to reduce diagnostic errors

2013

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“…There were efforts made by Bauer et al [3] to enhance a physician's throughput by the means of an alternative visualization of the laboratory reports. In a study done by Torsvik et al [4], it was observed that alternative visualizations of laboratory test reports helped clinicians in few special cases.…”

Section: Introductionmentioning

confidence: 99%

Estimating personalized risk ranking using laboratory test and medical knowledge (UMLS)

Patil

Bhaumik

Paul

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In this paper, we introduce a Concept Graph Engine (CG-Engine) that generates patient specific personalized disease ranking based on the laboratory test data. CG-Engine uses the Unified Medical Language System database as medical knowledge base. The CG-Engine consists of two concepts namely, a concept graph and its attributes. The concept graph is a two level tree that starts at a laboratory test root node and ends at a disease node. The attributes of concept graph are: Relation types, Semantic types, Number of Sources and Symmetric Information between nodes. These attributes are used to compute the weight between laboratory tests and diseases. The personalized disease ranking is created by aggregating the weights of all the paths connecting between a particular disease and contributing abnormal laboratory tests. The clinical application of CG-Engine improves physician's throughput as it provides the snapshot view of abnormal laboratory tests as well as a personalized disease ranking.

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“…There is a need to present quality data in a more effective way as the risk of it being misunderstood or not used at all is well recognised 3. Graphic display may make interpretation easier as presenting data visually is known to amplify cognition by capitalising on human perceptual capabilities 4. Graphic display, with several quality domains to capture, is not easy to design.…”

Section: Introductionmentioning

confidence: 99%

Making GRADE accessible: a proposal for graphic display of evidence quality assessments

Khan

Borowiack²,

Roos

et al. 2011

Evid Based Med

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When generating guidelines, quality of evidence is frequently reported in tabulated form capturing several domains, for example, study design, risk of bias and heterogeneity. Increasingly, this is done using the Grading of Recommendations Assessment, Development and Evaluation approach. As assimilating large amount of tabulated data across several comparisons and outcomes spread over many pages (sometimes hundreds) is not easy, there is a need to present evidence summaries in a more effective way. A graphic display plotting the several domains used in evidence grading on equiangular spokes starting from the same point, the data length of each spoke proportional to the magnitude of the quality, succinctly captures tabulated information. These plots allow easy identification of deficiencies, outliers and similarities in evidence quality for individual and multiple comparisons and outcomes, paving the way for their routine use alongside tabulated information.

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The design and evaluation of a graphical display for laboratory data

Abstract: Data presentation format can affect how physicians interpret laboratory data. Graphic displays have several advantages over numeric displays but are not always optimal. User, task and data characteristics should be considered when designing information displays.

Cited by 89 publications

References 33 publications

Use of health information technology to reduce diagnostic errors

Use of health information technology to reduce diagnostic errors

Estimating personalized risk ranking using laboratory test and medical knowledge (UMLS)

Making GRADE accessible: a proposal for graphic display of evidence quality assessments

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