Implementation of a Business of Healthcare Curriculum for General Surgery Residents.

OBJECTIVE: To better prepare general surgery residents for handling the business aspects of healthcare, this project evaluation reports on the implementation of a business of healthcare curriculum (BHC) in a general surgery residency program. We evaluated (pre and post curriculum) self-perceived knowledge and attitudes toward common business topics. DESIGN: General surgery residents were administered a 13-item survey (7 Likert-type and 3 open-ended items assessing self-perceived knowledge and attitudes toward BHC, and 3 demographic questions) prior to the start of the curriculum. The curriculum was comprised of four core sessions, which included didactic lectures and group projects, including the creation of a business plan. At the conclusion of the curriculum, a post-test with the same items was administered. A total of 21 residents completed both the pre and post-tests. SETTING: The BHC was a mandatory part of the general surgery residency program and was conducted in Honolulu, Hawaii (University of Hawaii at Manoa). PARTICIPANTS: All general surgery residents, PGY-1 to PGY-5, were required to participate in the curriculum. RESULTS: Statistically significant increases in resident knowledge were found overall and specifically for healthcare reform legislation, differences between practice settings, financial matters, contracting and coding and billing for services. Additionally, responses to open-ended questions showed that residents had a positive attitude toward the curriculum and found it useful. CONCLUSIONS: General surgery residency programs can successfully create an impactful business of healthcare curriculum with minimal cost if volunteers and existing resources are utilized.

A preoperative radiomics model for the identification of lymph node metastasis in patients with early-stage cervical squamous cell carcinoma.

OBJECTIVES: To develop and validate a radiomics model for preoperative identification of lymph node metastasis (LNM) in patients with early-stage cervical squamous cell carcinoma (CSCC). METHODS: Total of 190 eligible patients were randomly divided into training (n = 100) and validation (n = 90) cohorts. Handcrafted features and deep-learning features were extracted from T2W fat suppression images. The minimum redundancy maximum relevance algorithm and LASSO regression with 10-fold cross-validation were used for key features selection. A radiomics model that incorporated the handcrafted-signature, deep-signature, and squamous cell carcinoma antigen (SCC-Ag) levels was developed by logistic regression. The model performance was assessed and validated with respect to its calibration, discrimination, and clinical usefulness. RESULTS: Three handcrafted features and three deep-learning features were selected and used to build handcrafted- and deep-signature. The model, which incorporated the handcrafted-signature, deep-signature, and SCC-Ag, showed satisfactory calibration and discrimination in the training cohort (AUC: 0.852, 95% CI: 0.761-0.943) and the validation cohort (AUC: 0.815, 95% CI: 0.711-0.919). Decision curve analysis indicated the clinical usefulness of the radiomics model. The radiomics model yielded greater AUCs than either the radiomics signature (AUC = 0.806 and 0.779, respectively) or the SCC-Ag (AUC = 0.735 and 0.688, respectively) alone in both the training and validation cohorts. CONCLUSION: The presented radiomics model can be used for preoperative identification of LNM in patients with early-stage CSCC. Its performance outperforms that of SCC-Ag level analysis alone. ADVANCES IN KNOWLEDGE: A radiomics model incorporated radiomics signature and SCC-Ag levels demonstrated good performance in identifying LNM in patients with early-stage CSCC.