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Greenberg P, Ranum D, Siegal D. Patient Saf Qual Healthc. October 2015;12:18-20,22-24.
Patients diagnosed with breast cancer face complex health care processes. This magazine article discusses a detailed analysis of malpractice claims associated with breast cancer diagnosis and treatment, highlighting delays in diagnosis and treatment issues as primary concerns.
Diagnostic Error in Medicine 12th International Conference.
Society to Improve Diagnosis in Medicine. November 10-14, 2019; Hyatt Regency Washington, Washington DC.
Reducing Diagnostic Error: Measurement Considerations.
National Quality Forum.
Missed diagnosis of cancer in primary care: insights from malpractice claims data.
Aaronson EL, Quinn GR, Wong CI, et al. J Healthc Risk Manag. 2019 Jul 23; [Epub ahead of print].
Mark Graber Diagnostic Quality & Safety Award.
Society to Improve Diagnosis in Medicine.
Serious misdiagnosis-related harms in malpractice claims: the "Big Three"—vascular events, infections, and cancers.
Newman-Toker DE, Schaffer AC, Yu-Moe CW, et al. Diagnosis (Berl). 2019;227-240.
Ambulatory safety nets to reduce missed and delayed diagnoses of cancer.
Emani S, Sequist TD, Lacson R, et al. Jt Comm J Qual Patient Saf. 2019;45:552-557.
Emerging Safety Issues in Artificial Intelligence
Robert Challen, MA, MBBS
Accuracy of computer-aided diagnosis of melanoma: a meta-analysis.
Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. JAMA Dermatol. 2019 Jun 19; [Epub ahead of print].
Miro's dots and lines.
Taran S, Detsky AS. JAMA Intern Med. 2019 Jun 17; [Epub ahead of print].
Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.
Tschandl P, Codella N, Akay BN, et al. Lancet Oncol. 2019 Jun 11; [Epub ahead of print].
Health Professions Education.
Dhaliwal G, Olson APJ, Singhal G, eds. Diagnosis (Berl). 2019;6:75-185.
Dangers of diagnostic overshadowing.
Iezzoni LI. N Engl J Med. 2019;380:2092-2093.
Controversies in diagnosis: contemporary debates in the diagnostic safety literature.
Bergl PA, Wijesekera TP, Nassery N, Cosby KS. Diagnosis (Berl). 2019 May 27; [Epub ahead of print].
What interventions could reduce diagnostic error in emergency departments? A review of evidence, practice and consumer perspectives.
Wright B, Faulkner N, Bragge P, Graber M. Diagnosis (Berl). 2019 May 22; [Epub ahead of print].
Patients as diagnostic collaborators: sharing visit notes to promote accuracy and safety.
Blease CR, Bell SK. Diagnosis (Berl). 2019;6:213-222.
Virtual patients designed for training against medical error: exploring the impact of decision-making on learner motivation.
Woodham LA, Round J, Stenfors T, et al. PLoS One. 2019;14:e0215597.
Inpatient notes: just what the doctor ordered—checklists to improve diagnosis.
Gupta A, Graber ML. Ann Intern Med. 2019;170:HO2-HO3.
Governing the safety of artificial intelligence in healthcare.
Macrae C. BMJ Qual Saf. 2019;28:495-498.
Machine learning in medicine.
Rajkomar A, Dean J, Kohane I. N Engl J Med. 2019;380:1347-1358.
Diagnostic accuracy of physician-staffed emergency medical teams: a retrospective observational cohort study of prehospital versus hospital diagnosis in a 10-year interval.
Schewe JC, Kappler J, Dovermann K, et al. Scand J Trauma Resusc Emerg Med. 2019;27:36.
Deny, Dismiss, Dehumanise: What Happened When I Went to Hospital.
Cullen A. Uitgeverij van Brug: The Hague, The Netherlands; 2019. ISBN: 9789065232236.
Systematic error and cognitive bias in obstetric ultrasound.
Sotiriadis A, Odibo AO. Ultrasound Obstet Gynecol. 2019;53:431-435.
Adversarial attacks on medical machine learning.
Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS. Science. 2019;363:1287-1289.
AHRQ Health Services Research Project: Partners Enabling Diagnostic Excellence (R01).
US Department of Health and Human Services. Program Announcement No. RFA-HS-19-003.
Teaching novice clinicians how to reduce diagnostic waste and errors by applying the Toyota Production System.
Radhakrishnan NS, Singh H, Southwick FS. Diagnosis (Berl). 2019;6:179-185.
PSNET: Patient Safety Network
PSNet is produced for the Agency for Healthcare Research and Quality by a team of editors at the University of California, San Francisco with guidance from a prominent Technical Expert/Advisory Panel. The AHRQ PSNet site was designed and implemented by Silverchair.
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