Towards high reliability in national pathology education: Evaluating the United States and Canadian Academy of Pathology educational product

Harris, Clair; Chen, Yigu; Jensen, Kristin C.; Hornick, Jason L.; Kilfoyle, Claire; Lamps, Laura W.; Heher, Yael K.

doi:10.1016/j.acpath.2022.100048

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…National organizations like the United States and Canadian Academy of Pathology (USCAP) struggle to teach patient safety. 24 Do pathology training programs struggle to teach these critical concepts or are they satisfied with their existing curricula? To answer this question, and with the support of the Association of Pathology Chairs Residency Program Directors Section (APC-PRODS) and the American Society for Clinical Pathology, two of the authors (RLH and YKH) established the APC-PRODS Training Residents in Patient Safety (TRIPS) workgroup to perform a needs assessment to determine the current state of patient safety education in pathology.…”

Section: Introductionmentioning

confidence: 99%

The next phase in patient safety education: Towards a standardized, tools-based pathology patient safety curriculum

Harris,

Chen,

Alston

et al. 2023

Academic Pathology

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Section: Introductionmentioning

confidence: 99%

The next phase in patient safety education: Towards a standardized, tools-based pathology patient safety curriculum

Harris,

Chen,

Alston

et al. 2023

Academic Pathology

Self Cite

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Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML

Lennerz

Salgado

Kim

et al. 2023

Clinical Chemistry and Laboratory Medicine (CCLM)

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Background Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality – for the specific purpose of assessing AI/ML improvements – is currently missing. Methods A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on “AI in the Laboratory of the Future” prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. Results The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. Conclusions A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

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Towards high reliability in national pathology education: Evaluating the United States and Canadian Academy of Pathology educational product

Cited by 2 publications

References 19 publications

The next phase in patient safety education: Towards a standardized, tools-based pathology patient safety curriculum

The next phase in patient safety education: Towards a standardized, tools-based pathology patient safety curriculum

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML

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