Detecting, classifying, analyzing, and managing errors can help avoid the repetition of preventable mistakes and minimize harmful effects, according to a research team from Boston.
Detecting, classifying, analyzing, and managing errors can help avoid the repetition of preventable mistakes and minimize harmful effects, according to a research team from Boston.
Most radiologists have little knowledge of either their error rates or the nature of their errors, said Dr. Anna Marie O'Connell of Beth Israel Deaconess Medical Center in an education exhibit at RSNA 2007. About 4% of interpretations by radiologists in daily practice contain errors, but current reporting systems are relatively limited in their feedback.
"We all make mistakes," she said. "When analyzing errors, it is far more helpful to look at them as arising as a result of human or systems contributors, rather than seeking to place blame on individuals."
The regulatory authorities now require that peer-review systems be implemented for error detection and analysis. The emerging focus is on being proactive rather than reactionary regarding the safety of patients and employees, but no system in radiology for classifying errors is widely accepted. Most classification schemes concentrate on human error, and little attention is given to systems errors, O'Connell said.
"Humans make errors for a complex variety of reasons," she said. "Knowledge-based errors occur when you simply do not know what you are doing and do not recognize that what you are doing is wrong. Rule-based errors occur when you think you know what you are doing but apply a bad rule or do not know that a rule exists. Skill-based errors occur when the actions are okay, but the plan is inadequate to meet the intended goal."
Most errors are both latent and active. They may involve human performance and practice guidelines such as patient management, procedures, and the communication of findings. Common causes of communication errors are incomplete or inaccurate information, questionable consent and disclosure processes, questionable documentation, failure to perform preprocedure timeout, and failure to pass on critical results.
Detection and analysis of near-miss occurrences provides a useful database for implementing change aimed at minimizing errors, O'Connell said. A clinical decision is altered before harm occurs, or a clinician does not act on an erroneous radiological diagnosis. A provisional radiological report is corrected after the attending physician detects a missed finding.
The exhibit cited ways of managing errors: introduce anonymous error reporting detection systems, establish a just and blame-free culture to promote reporting or errors, implement a comprehensive root-cause analysis process, evaluate both latent and human factors, and assess outcome as well as accountability. Corrective actions must be reasonable, achievable, and measurable, and anticipating barriers to introducing change is essential.
"Monitor these corrective actions, assign responsibility, and set timelines," she said. "Continue to actively manage, analyze, and improve the error-reporting systems in your department."
AI Facilitates Nearly 83 Percent Improvement in Turnaround Time for Fracture X-Rays
December 19th 2023In addition to offering a 98.5 percent sensitivity rate in diagnosing fractures on X-ray, an emerging artificial intelligence (AI) software reportedly helped reduce mean turnaround time on X-ray fracture diagnosis from 48 hours to 8.3 hours, according to new research presented at the Radiological Society of North America (RSNA) conference.
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring
December 5th 2020Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
Can an Emerging PET Radiotracer Enhance Detection of Prostate Cancer Recurrence?
December 14th 2023The use of 68Ga-RM2 PET/MRI demonstrated a 35 percent higher sensitivity rate than MRI alone for the diagnosis of biochemical recurrence of prostate cancer, according to research recently presented at the Radiological Society of North America (RSNA) conference.
RSNA 2020: Addressing Healthcare Disparities and Access to Care
December 4th 2020Rich Heller, M.D., with Radiology Partners, and Lucy Spalluto, M.D., with Vanderbilt University School of Medicine, discuss the highlights of their RSNA 2020 session on health disparities, focusing on the underlying factors and challenges radiologists face to providing greater access to care.
Can AI Improve Detection of Extraprostatic Extension on MRI?
December 4th 2023Utilizing a deep learning-based AI algorithm to differentiate between diagnostic and non-diagnostic quality of prostate MRI facilitated a 10 percent higher specificity rate for diagnosing extraprostatic extension on multiparametric MRI, according to research presented at the recent RSNA conference.
Study: Regular Mammography Screening Reduces Breast Cancer Mortality Risk by More than 70 Percent
November 30th 2023Consistent adherence to the five most recent mammography screenings prior to a breast cancer diagnosis reduced breast cancer death risk by 72 percent in comparison to women who did not have the mammography screening, according to new research findings presented at the annual Radiological Society of North America (RSNA) conference.