Pathology trainees rarely report safety incidents: A review of 13,722 safety reports and a call to action

Harris, Clair; Chen, Yigu; Yarsky, Benjamin; Haspel, Richard L.; Heher, Yael K.

doi:10.1016/j.acpath.2022.100049

Cited by 3 publications

(1 citation statement)

References 17 publications

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“… 11 , 12 , 13 Pathology residents specifically have been found to report even less frequently than that, with one study finding that pathology residents report safety events at one tenth the rate of trainees overall. 14 Although only one component of patient safety, physicians’ lack of engagement in safety reporting is viewed as emblematic of the broader challenge of engaging physicians in patient safety.…”

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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Assessment of Quality Improvement and Patient Safety Resources, including Event Reporting Systems, in Graduate Medical Education Training Programs - An Institutional National Survey

Atkins,

Akrawe,

Flynn

et al. 2024

Michigan Medical Education and Health Bulletin

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Background Quality improvement (QI) and patient safety (PS) education were requirements implemented in 2017 by the Accreditation Council for Graduate Medical Education (ACGME) for residents and fellows. Trainees must receive training and experience in QI processes, including an understanding of healthcare disparities, but how training and experience occur remains largely unknown. Our study aimed to determine how institutions have integrated these requirements into medical education. Methods A Google Forms survey was emailed to the Designated Institutional Officials (DIOs) of 650 United States’ institutions with ACGME accredited programs in September 2021. The survey consisted of 34 multiple choice questions related to QI and PS parameters and the event reporting system (ERS); a Likert scale was used. Results More than half (n=392) of the emails were undeliverable, leaving only 258 possible responders; 51 responses were received, and all but one met the inclusion criteria of having at least one training program, which translated to a response rate of 19% (=50/257). The survey found that while 90% of institutions had an institutional QI/PS Committee, only 30% of these institutions also had a Graduate Medical Education-specific QI/PS Committee as well. Most institutions provided QI (88%) or PS training (94%), but only 71% and 83%, respectively, had mandatory training. Most institutions utilized an ERS (96%). While one of the less common methods was education on the ERS, approximately two-thirds of responding DIOs believed that by providing education on the ERS, resident/fellow participation in event reporting would improve, as well as contribute to their total education. Conclusions Further ERS education is warranted to encourage resident/fellow participation to develop various protocols related to QI and PS parameters.

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Pathology trainees rarely report safety incidents: A review of 13,722 safety reports and a call to action

Cited by 3 publications

References 17 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

Assessment of Quality Improvement and Patient Safety Resources, including Event Reporting Systems, in Graduate Medical Education Training Programs - An Institutional National Survey

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