Fall is my favorite season, a time of change that invites us to slow down, reconnect, and nurture our wellbeing. As the air turns crisp and the leaves shift to rich hues, the season offers a unique opportunity to embrace balance and self-care.

The cooler temperatures make outdoor activities more inviting. Whether it’s a brisk morning walk or a weekend hike through the changing foliage, spending time outdoors in fall can improve mood and reduce stress. Nature’s beauty in this season also inspires mindfulness—being present in the moment, whether it’s during a walk or while enjoying a hot cup of tea.

Fall is also a season of nourishment. With harvests of pumpkins, apples, and squash, it’s a perfect time to incorporate warm, hearty meals that fuel both body and soul. Seasonal produce supports immunity and helps prepare us for the cooler months ahead. (See here for my favorite butternut squash soup recipe!)

As the days grow shorter, it’s natural to embrace rest. Fall is ideal for creating or refining evening routines that promote relaxation, such as reading, meditation, or enjoying a calming tea before bed. Prioritizing sleep and rest during this season help to restore energy and prepares us for winter.

Finally, fall encourages us to let go, just as the trees shed their leaves. It’s a time for reflection, to release stress or habits that no longer serve us, and to set new intentions as we approach the year’s end.

By aligning with the rhythm of the season, we can nurture our wellbeing and find peace in the transition that fall brings.

In “Words of Wellness” on www.radfyi.org/, members of the ARRS Wellness Subcommittee share what “wellness” and “wellbeing” mean in their own clinical practices, research focuses, and everyday lives.

Dr. Belfi’s ARRS “Sound of Wellness” Playlist Selection:

Carolina In My Mind

You may also be interested in

• ARRS Online Course: Sharpening Teamwork and Communication Skills
• AJR Live Webinar: Women’s Issues in IR, DR, and Professional Life
• ARRS Quick Bytes: Emergent Imaging: Improving CT Workflow and Image Quality

https://www.radfyi.org/2023/09/20/words-sounds-of-wellness-dr-sherry-wang/

The term “burnout” dates as far back as 1974. Coined by psychologist Herbert J. Freudenberger in a Journal of Social Issues article entitled “Staff Burnout,” he discussed job dissatisfaction precipitated by work-related stress.

Presently, burnout is included in the World Health Organization’s (WHO) 11th Revision of the International Classification of Diseases (ICD-11)—as an occupational phenomenon, however.

Burnout is not classified as a medical condition.

In the WHO’s chapter on factors influencing health status or contact with health services, the agency includes reasons for which people contact health services that are not classed as illnesses or health conditions.

And in its definition of burnout as a syndrome, the WHO identifies three key components that contribute to chronic stress associated with work:

1. Feelings of energy depletion or exhaustion;
2. Depersonalization, feelings of cynicism, negativity;
3. Reduced professional efficacy.

Burnout During Residency Training: A Literature Review

Distress during medical school and residency can lead to burnout—which, in turn, can result in negative consequences as a working physician. Prevalent in medical students (28%–45%), residents (27%–75%, though specialty dependent), and in practicing physicians (63%), burnout’s psychological distress and physical symptoms impact both work performance and patient safety. Specific contributors of said burnout include the following: time demands, lack of control, work planning and organization, as well as inherently difficult job situations and interpersonal relationships.

Fortunately, there are several workplace interventions for mentors to mitigate burnout with in-training physicians, such as wellness workshops, workload modifications (e.g., increased diversity of work duties), and better stress management education or appropriate emotional intelligence training.

As individuals, we have our own behavioral interventions to make: meditation, counseling, etc. Social interventions matter, too, especially when promoting our professional relationships. We can’t forget the importance of exercise and other physical activity either.

If not addressed, the risks of burnout are myriad. In addition to increased cardiovascular disease and inflammatory biomarkers, burnout elevates rates of depression and suicidal ideation. Thankfully, plans and attempts in burnout states do tend to decline with recovery.

Importantly, clinician depersonalization is associated with lower patient satisfaction and longer post discharge patient recovery time. So, we need to be able to identify elements of burnout—in ourselves and in others.

Physical symptoms:

• Insomnia
• Change in appetite
• Fatigue
• Colds or flu
• Headaches
• Gastrointestinal distress

Psychological symptoms:

• Low or irritable mood
• Cynicism
• Decreased concentration
• Can negatively affect productivity and rapport

Additional elements:

• Daydreaming
• Procrastination
• Increased alcohol or drug use

Recommended Reading:

The Moral Crisis of America’s Doctors | New York Times

Back from Burnout: Confronting the Post-Pandemic Physician Turnover Crisis (mgma.com)

Addressing Health Worker Burnout: U.S. Surgeon General’s Advisory on Building a Thriving Workforce (nih.gov)

A Blueprint for Organizational Strategies To Promote the Well-being of Health Care Professionals | NEJM Catalyst

Estimating the Attributable Cost of Physician Burnout in the United States – PubMed (nih.gov)

Preventing a Parallel Pandemic — A National Strategy to Protect Clinicians’ Well-Being | New England Journal of Medicine (nejm.org)

Physician Well-being 2.0: Where Are We and Where Are We Going? – Mayo Clinic Proceedings

Dr. Drosten’s ARRS “Sound of Wellness” Playlist Selection:

Delibes

Tchaikovsky

Mendelssohn

You may also be interested in

• ARRS Online Course: Sharpening Teamwork and Communication Skills
• AJR Live Webinar: Women’s Issues in IR, DR, and Professional Life
• ARRS Quick Bytes: Emergent Imaging: Improving CT Workflow and Image Quality

https://www.radfyi.org/2023/09/20/words-sounds-of-wellness-dr-sherry-wang/

Medical errors are common and can affect overall patient care. Radiology is integral in many aspects of overall patient care, and radiologists play a critical role. As such, radiologists can affect patient morbidity and mortality as a consequence of diagnostic error. Radiologists must recognize common forms of bias and become familiar with methods (both internal and external) to minimize them. 

Diagnostic errors account for a significant cause of patient morbidity and mortality and are an understandable source of anxiety for patients, clinicians, and radiologists alike. The contribution of cognitive bias to diagnostic errors within radiology is well de- scribed, with Garland [1] first discussing differences in interpretations of chest radiographs. Since then, research has delved into the potential causes of diagnostic error and provided insight and a framework for understanding the basis of these errors and potential avenues for mitigation [2].

Cognitive Processes

Kanehman’s [3] Nobel prize-winning work first described critical concepts to understand cognition. In this framework, decision making can be divided into type 1 thinking (heuristics) and type 2 thinking (logic). Type 1 thinking is quick and involves mental shortcuts [4]; it is the muscle memory or gut reaction thinking necessary to accommodate the flood of millions of bits of sensory information processed by the brain at any given moment. Type 1 thinking allows one to make split-second decisions using limited available information, often based on experience, but it is also highly susceptible to cognitive bias. Type 2 thinking is slower and more deliberate. It is often used in completely novel situations. In radiology, an analogy would be the amount of time spent re- viewing a head CT study for the first time by a 1st-year radiology resident. The student would spend a significantly longer time reviewing the study, looking slowly and intentionally for each structure (type 2 thinking), potentially with an inefficient search pattern. Compare this to the amount of type spent by an experienced attending radiologist reviewing the same head CT study. Search patterns in this practitioner have become automatic (type 1 thinking) with attention to high-yield areas for pathologic entities and common blind spots that is based on experience. This muscle memory interpretation is what allows speed and efficiency, but it may also open the door to cognitive errors in diagnosis. A further challenge is that type 1 thinking becomes more common as an individual gets older, as more and more processes become compartmentalized [4]. Although this shift allows greater efficiency, it also creates greater opportunity for cognitive error.

Errors can occur at any time in the process, from initial perception to final image interpretation. In addition to internal fac- tors, systemic sources can also contribute to diagnostic errors in medicine [4, 5]. In this post, common errors along the path from initial perception to final interpretation will be reviewed and potential means for mitigating diagnostic errors will be discussed.

Perceptual Error

Errors in perception account for a large majority of interpretive errors in radiology. A number of factors contribute to errors in perception such as overall lesion conspicuity, including degree of contrast and border demarcation from adjacent soft tissue [5, 6].

Interpretive Error

More than 30 types of cognitive bias have been described [7]. The most commonly encountered forms of bias in diagnostic im- aging include anchoring bias, confirmation bias, framing bias, availability bias, premature closure, inattentional blindness, and hindsight bias. 

Anchoring Bias

Also known as focalism, anchoring bias refers to the common human tendency to place undue influence or anchor on an initial diagnostic impression, despite later information to the contrary [5, 8, 9]. A radiologist’s initial gut reaction to a case, possibly made with limited initial information, can be difficult to deviate from and can potentially lead to useful information being disregarded. 

Confirmation Bias

Conceptually related to anchoring bias is confirmation bias. In this case, data supporting an initially suspected diagnosis are sought, and contrary information is given less significance [8, 9]. As a result, diagnoses can be delayed, and potentially unnecessary procedures can be performed [10]. Further, this type of bias may also be encountered in the academic setting with attending radiologist review of preliminary reports by radiology trainees [11].  

Framing Bias

In framing bias, different final diagnostic impressions can be made with the same information depending on the presentation of initial clinical information. In clinical context, different conclusions can be drawn from the same imaging study depending on the provided clinical history [10, 12]. Preliminary clinical history can be limited and potentially misleading [13, 14]. Further, the specialty of the referring physician may also be an influencing fac- tor [10].  

Availability Bias

In cases of availability bias, recent in- formation is given undue influence in di- agnostic decision making [15]. Recently missed diagnoses may linger in the mind of a radiologist and allow him or her to attribute a rare diagnosis in a case that they may otherwise have not. For example, a radiologist labels a case as “septic arthritis with osteomyelitis” on elbow MRI, only later to find that the case was acute lymphoblastic leukemia. This error might lead the radiologist to diagnose leukemia on more routine cases of osteomyelitis, even with confirmatory laboratory and clinical findings sup- porting that diagnosis [9]. On the opposite end of the spectrum is the concept of non- availability bias; that is, diagnoses that are rarely encountered are rarely considered [9]. A variation of this bias is alliterative error, or satisfaction of report, commonly encountered in radiology as a repeat of a prior report’s impression, even if this might not have been interpreted in the same way de novo. This error has been reported as the fifth most common cause of diagnostic errors by Kim and Mansfield [16].  

Premature Closure

Premature closure, the interpretation of initial conclusions as being final, is the overall most common type of error within clinical medicine [12, 17]. This er- ror includes the concept of satisfaction of search, in which an interpretive process is considered finished once an initial abnormality or finding is identified.

Inattentional Blindness

In the case of inattentional blindness, findings may be missed owing to their un- expected nature or their location at the periphery of the image. Corner shot findings on a radiograph or findings on the final im- ages of a cine clip of an ultrasound are examples of potential causes of inattentional blindness [16, 18–20].  

Hindsight Bias

Hindsight bias is described as the tendency to de-emphasize the difficulty in making an initial diagnosis after the fact. This bias can occur in group settings including tumor boards, clinical conferences, and medicolegal settings and can prevent realistic assessment of challenges faced with complex initial diagnoses [9, 21].

External Factors

Interruptions are a common occurrence in a busy practice with visiting clinicians, telephone interruptions, and technologist requests. In the face of these interruptions it is easy for radiologists to lose their trains of thought and potentially deviate unknowingly from their typical search patterns. These interruptions have been shown to lengthen interpretation times and reduce accuracy in abnormal cases [22, 23].

Methods of Mitigation

Metacognition

A potential means of partially addressing cognitive bias is the concept of meta- cognition; that is, an individual can evaluate one’s own thought processes [22]. Metacognition involves introspection of one’s thought processes and seeking out- side perspectives.  

Minimizing Interruptions

Although radiologists must balance pro- viding high-level service to referring clinicians with efficient use of their time, methods for minimizing interruptions are critical [5, 16]. Employing reading room assistants to field and triage calls can provide a first line of screening for telephone calls to aid in reducing interruptions [24]. Further use of text messaging services can also allow radiologists to communicate findings efficiently and document exact conversations [25].

Structured Reporting

Structured reporting provides a check- list-style framework for reporting that al- lows reminders for interpreting radiologists to review all relevant anatomy. For trainees, this process also allows the development of desired interpretive search patterns [26].  

Radiologic-Pathologic Review

Follow-up on challenging cases either through quality-control conferences, tumor boards, or personal review of cases is critical for improving and expanding radiologists’ interpretive skills. Supportive and educationally oriented environments can allow meaningful discussion and review of diagnostically challenging cases.  

Computer-Aided Diagnostics

Use of increasingly powerful means of computer-aided image interpretation pro- vides another potential tool for radiologists to improve diagnostic accuracy and increase confidence. The current effective- ness of computer-aided detection within areas such as mammography has not been shown to be improved over interpretation without computer-aided detection [27]. However, there is growing potential for ap- plications in multiple other areas with use of neural network–based approaches [28].

The impact of bias in radiologic interpretation can be substantial, with potential implications in patient outcomes. Better understanding the forms of bias, related to both internal and external pressures, can allow radiologists to implement methods for mitigating these biases.

REFERENCES

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2. Degnan AJ, Ghobadi EH, Hardy P, et al. Percep- tual and interpretive error in diagnostic radiology: causes and potential solutions. Acad Radiol 2019; 26:833–845
3. Tversky A, Kahneman D. Judgment under un- certainty: heuristics and biases. Science 1974; 185:1124–1131
4. Durr T. Thinking, fast and slow by Daniel Kahneman. (book review) Am J Educ 2014; 120:287–291
5. Waite S, Scott J, Gale B, Fuchs T, Kolla S, Reede D. Interpretive error in radiology. AJR 2017; 208:739–749
6. Patel SH, Stanton CL, Miller SG, Patrie JT, Itri JN, Shepherd TM. Risk factors for perceptual-versus- interpretative errors in diagnostic neuroradiology. AJNR 2019; 40:1252–1256
7. Rosenkrantz AB, Bansal NK. Diagnostic errors in abdominopelvic CT interpretation: characteriza- tion based on report addenda. Abdom Radiol (NY) 2016; 41:1793–1799
8. Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med 2003; 78:775–780
9. Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med 2002; 9:1184–1204
10. Busby LP, Courtier JL, Glastonbury CM. Bias in radi- ology: the how and why of misses and misinterpre- tations. RadioGraphics 2018; 38:236–247
11. Nanapragasam A, Bhatnagar P, Birchall D. Trainee radiologist reports as a source of confirmation bias in radiology. Clin Radiol 2018; 73:1052–1055
12. Lee CS, Nagy PG, Weaver SJ, Newman-Toker DE. Cognitive and system factors contributing to diag- nostic errors in radiology. AJR 2013; 201:611–617
13. Gunderman RB, Phillips MD, Cohen MD. Improving clinical histories on radiology requisitions. Acad Radiol 2001; 8:299–303
14. Loy CT, Irwig L. Accuracy of diagnostic tests read with and without clinical information. JAMA 2004; 292:1602–1609
15. Dumitrescu A, Ryan CA. Addressing the taboo of medical error through IGBOs: I got burnt once! Eur J Pediatr 2014; 173:503–508
16. Kim YW, Mansfield LT. Fool me twice: delayed diag- noses in radiology with emphasis on perpetuated errors. AJR 2014; 202:465–470
17. Graber ML, Franklin N, Gordon R. Diagnostic er- ror in internal medicine. Arch Intern Med 2005; 65:1493–1499
18. Drew TH, Võ ML, Wolfe JM. The invisible gorilla strikes again. Psychol Sci 2013; 24:1848–1853
19. Beanland V, Pammer K. Gorilla watching: effects of exposure and expectations on inattentional blind- ness. In: 9th Conference of the Australasian Society for Cognitive Science. Sydney, Australia: Macquarie Centre for Cognitive Science, 2010:12–20
20. Drew TH, Vo ML, Olwal A, Jacobson F, Seltzer SE, Wolfe JM. Scanners and drillers: characterizing ex- pert visual search through volumetric images. J Vis 2013; 13:3
21. Gunderman RB. Biases in radiologic reasoning. AJR 2009; 192:561–564
22. Flavell JH. Metacognition and cognitive monitoring: a new area of cognitive-developmental inqui- ry. Am Psychol 1979; 34:906–911
23. Wynn RM, Howe JL, Kelahan LC, Fong A, Filice RW, Ratwani RM. The impact of interruptions on chest radiograph interpretation: effects on reading time and accuracy. Acad Radiol 2018; 25:1515–1520
24. Ngo  JS,  Maxfield  CM,  Schooler  GR.  The  current state of radiology call assistant triage programs among US radiology residency programs. Acad Radiol 2018; 25:250–254
25. Torres A, Milov DE, Melendez D, Negron J, Zhao JJ, Lawless ST. A new approach to alarm manage- ment: mitigating failure-prone systems. J Hosp Adm 2014; 3:79–83
26. Marcovici PA, Taylor GA. Structured radiology re- ports are more complete and more effective than unstructured reports. AJR 2014; 203:1265–1271
27. Lehman CD, Wellman RD, Buist DSM, Kerlikowske K, Tosteson ANA, Miglioretti DL. Diagnostic accuracy of digital screening mammography with and with- out computer-aided detection. JAMA Intern Med 2015; 175:1828
28. Taylor AG, Mielke C, Mongan J. Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: a retrospective study. PLoS Med 2018; 15:e1002697

Artificial intelligence (AI) has been likened to a new species, “Auto Sapiens.” I know this is wild, but bear with me—it may actually help us “get along,” “collaborate,” and “lead” AI to improve radiology practice.

A recent Harvard Business Review article by Jeremy Heimans and Henry Timms explained Auto Sapiens: AI is able to act autonomously, make decisions, learn from experience, and operate without continuous human supervision, hence “Auto.” Also, AI possesses knowledge and the ability to make judgments in context, hence “Sapiens.” It is hard to think of Auto Sapiens when it’s software running on your data, but it is valid considering this terminology when thinking of AI used in humanoid robots.

As a thought exercise, let’s think of radiology AI as an Auto Sapiens, and a coworker in the role of an “assistant.” Is this assistant going to take our jobs? Will it (they?) help us in our jobs, and could it make radiology practice more profitable? I say, no, to taking our jobs. And, yes, to helping us change our practice for the better.

https://www.radfyi.org/2023/09/08/the-workplace-revolution

Here is how: I believe that AI will not displace radiologists. Somebody needs to be liable for mistakes made by AI. Medical malpractice can be established when physicians deviate from the profession’s standard of patient care. If a radiologist uses an AI-enabled medical device for diagnosis or treatment of a patient, and their use deviates from an established standard of care, the physician could be liable for improper use of that AI medical device. As of now, the radiologist must independently review the AI’s recommendations, applying the standard of care in treating the patient regardless of the AI’s output. After all, AI is an assistant needing supervision, right?

Holding AI developers liable is quite difficult. One would have to prove that the AI was defective at the time of product purchase by the user and did not become corrupt as it continued to train itself on user data. There are currently no sufficient industry standards to address this.

I doubt that insurers would take liability; they lack the expertise to minimize liability should an AI application go awry. Radiologists are the ones assuring AI performs consistently in accordance with their intended purpose and scope, as well as at the desired level of precision. Insurers do not have the expertise to check on radiology AI applications’ correctness, relevance, robustness, or interpretability. Radiologists will be the stewards of quality assurance for AI.

I do, however, wonder about a threat to reimbursements. It is conceivable that AI can evolve to perform better than radiologists—faster and with fewer errors. In that event, insurers could cut physician fees. We need to think about reimbursements in the AI era. Will there be a new component to the fee schedule, such as “AI supervision,” which entails auditing and supervising AI? We will need to continuously “invest” in our AI assistants to make sure they are trained up to the latest technological standard.

OK, now that I’ve argued how AI will not replace radiologists, let’s see how our new assistants will help us. First, Auto Sapiens, like a good assistant, will happily do all the stuff many of us like less about our jobs, such as reading endless chest radiographs, scrutinizing CT images for lung nodules, measuring lesions and transcribing measurements into reports, and so many other things. Yes, I want this assistant, like, now!

Additionally, Auto Sapiens will also help us decrease errors of perception and interpretation and delays for reporting incidental critical results, such unexpected intracranial hemorrhage on a nonemergent head CT. What is not to like about this type of assistant? Maybe liability insurance payments will even come down?

And all of this can result in a more profitable business? Sure, as soon as AI enables radiologists, technologists, and imaging equipment to handle larger volumes, there could be a massive increase of imaging orders. Dream on, though, if you think that decision support will help us control imaging utilization. Imaging is already being used in lieu of a thorough clinical exam. In fact, Dr. Joseph Alpert called the physical exam “an ancient ritual” in 2019.

So, the AI assistant can help us grow our business and focus on work we enjoy, like making a diagnosis and providing excellent services to physicians and patients. However, this model relies on us being proper supervisors to our AI assistants. As Curtis Langlotz, MD, PhD, once put it: “AI won’t replace radiologists, but radiologists who use AI will replace those who don’t.”

The time to learn about AI is now, and I am excited about it!

https://www.radfyi.org/2023/01/06/bye-bye-work-life-balance-welcome-work-life-integration