Burnout and resiliency in Mohs surgeons: A survey study

Lam, Charlene; Kim, Yesul; Cruz, Michael J.; Vidimos, Allison; Billingsley, Elizabeth M.; Miller, Jeffrey J.

doi:10.1016/j.ijwd.2021.01.011

Cited by 12 publications

(12 citation statements)

References 12 publications

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“… 44 The other found that community-based surgeons were more likely to experience burnout compared with surgeons in academic settings. 53 …”

Section: Resultsmentioning

confidence: 99%

“…44 The other found that community-based surgeons were more likely to experience burnout compared with surgeons in academic settings. 53 Twenty studies addressed support from management, organisational leadership or mentors, and of these, 15 found an effect. One study found that feeling unsupported by leadership was linked with higher burnout.…”

Section: Professional and Clinical Practice Characteristicsmentioning

confidence: 99%

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Predictors of burnout among US healthcare providers: a systematic review

et al. 2022

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ObjectiveOne potential barrier to optimal healthcare may be provider burnout or occupational-related stress in the workplace. The objective of this study is to conduct a systematic review to identify the predictors of burnout among US. healthcare providers.DesignSystematic review using in-depth critical appraisal to assess risk of bias and present the quality of evidence in synthesised results from the prognostic studies.Data sourcesWe searched 11 databases, registries, existing reviews and contacted experts through 4 October 2021.Eligibility criteria for selecting studiesWe included all studies evaluating potential predictors and documenting the presence and absence of associations with burnout assessed as a multidimensional construct. We excluded studies that relied solely on a single continuous subscale of burnout. Data were abstracted from eligible studies and checked for accuracy by a content expert and a methodologist.Data extraction and synthesisTwo reviewers independently screened citations and full-text publications using predetermined eligibility criteria.ResultsThe 141 identified studies evaluated a range of burnout predictors. Findings for demographic characteristics were conflicting or show no association. Workplace factors, such as workload, work/life balance, job autonomy and perceived support from leadership, had stronger associations with risk for burnout. Mental health factors, such as anxiety, and physical health risks may increase the risk, although the direction of these associations is unclear as few prospective studies exist to address this question. Factors such as social support appear to have a protective effect.ConclusionWe found the most evidence for workplace, mental health and psychosocial factors in predicting burnout but limited evidence for other potential predictors. However, more prospective studies are needed to improve our understanding about how to prevent provider burnout.PROSPERO registration numberCRD4202014836.

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“… 44 The other found that community-based surgeons were more likely to experience burnout compared with surgeons in academic settings. 53 …”

Section: Resultsmentioning

confidence: 99%

Section: Professional and Clinical Practice Characteristicsmentioning

confidence: 99%

Predictors of burnout among US healthcare providers: a systematic review

et al. 2022

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“…The increasing rise in NMSC translates to an increasing demand for MMS, and although healthcare systems perform differently in each country, optimizing treatment management is a necessity worldwide, as is reducing physician burnout rate. 33 As a community of researchers, we should always strive towards excellence, and even a slight improvement could serve as a stepping stone for future progress. The WAR score was the first to classify complicated MMS procedures by measuring procedure duration.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting Mohs surgery complexity by applying machine learning to patient demographics and tumor characteristics

Shoham

Berl

Shir-Az

et al. 2022

Experimental Dermatology

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Mohs micrographic surgery (MMS) is considered the gold standard for difficult‐to‐treat malignant skin tumors, whose incidence is on the rise. Currently, there are no agreed upon classifiers to predict complex MMS procedures. Such classifiers could enable better patient scheduling, reduce staff burnout and improve patient education. Our goal was to create an accessible and interpretable classifier(s) that would predict complex MMS procedures. A retrospective study applying machine learning models to a dataset of 8644 MMS procedures to predict complex wound reconstruction and number of MMS procedure stages. Each procedure record contained preoperative data on patient demographics, estimated clinical tumor size prior to surgery (mean diameter), tumor characteristics and tumor location, and postoperative procedure outcomes included the wound reconstruction technique and the number of MMS stages performed in order to achieve tumor‐free margins. For the number of stages complexity classification model, the area under the receiver operating characteristic curve (AUROC) was 0.79 (good performance), with model accuracy of 77%, sensitivity of 71%, specificity of 77%, positive prediction value (PPV) of 14% and negative prediction value (NPV) of 98%. The results for the wound reconstruction complexity classification model were 0.84 for the AUROC (excellent performance), with model accuracy of 75%, sensitivity of 72%, specificity of 76%, PPV of 39% and NPV of 93%. The ML models we created predict the complexity of the components that comprise the MMS procedure. Using the accessible and interpretable tool we provide online, clinicians can improve the management and well‐being of their patients. Study limitation is that models are based on data generated from a single surgeon.

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“…Integration of the vast amount of data present in a pathology report through automation will decrease the amount of work on the back end and also provide both written and visual outputs that can be used either in real-time or post-operatively. Excessive charting and documentation result in pathologist burnout, limiting the amount of manual documentation will both increase productivity and decrease administrative burden [52][53][54] .…”

Section: Discussionmentioning

confidence: 99%

ArcticAI: A Deep Learning Platform for Rapid and Accurate Histological Assessment of Intraoperative Tumor Margins

Levy

Davis

Chacko

et al. 2022

Preprint

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Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Radial sectioning of the resected tumor and surrounding tissue is the most common form of intra-operative and post-operative margin assessment. However, this technique samples only a tiny fraction of the available tissue and therefore may result in incomplete excision of the tumor, increasing the risk of recurrence and distant metastasis and decreasing survival. Repeat procedures, chemotherapy, and other resulting treatments pose significant morbidity, mortality, and fiscal costs for our healthcare system. Mohs Micrographic Surgery (MMS) is used for the removal of basal cell and squamous cell carcinoma utilizing frozen sections for real-time margin assessment while assessing 100% of the peripheral and deep margins, resulting in a recurrence rate of less than one percent. Real-time assessment in many tumor types is constrained by tissue size and complexity and the time to process tissue and evaluate slides while a patient is under general anesthesia. In this study, we developed an artificial intelligence (AI) platform, ArcticAI, which augments the surgical workflow to improve efficiency by reducing rate-limiting steps in tissue preprocessing and histological assessment through automated mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma (BCC) as a model system, the results demonstrate that ArcticAI can provide effective grossing recommendations, accurately identify tumor on histological sections, map tumor back onto the surgical resection map, and automate pathology report generation resulting in seamless communication between the surgical pathology laboratory and surgeon. AI-augmented-surgical excision workflows may make real-time margin assessment for the excision of more complex and challenging tumor types more accessible, leading to more streamlined and accurate tumor removal while increasing healthcare delivery efficiency.

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Burnout and resiliency in Mohs surgeons: A survey study

Cited by 12 publications

References 12 publications

Predictors of burnout among US healthcare providers: a systematic review

Predictors of burnout among US healthcare providers: a systematic review

Predicting Mohs surgery complexity by applying machine learning to patient demographics and tumor characteristics

ArcticAI: A Deep Learning Platform for Rapid and Accurate Histological Assessment of Intraoperative Tumor Margins

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