Objective Many options are currently available for sepsis surveillance clinical decision support (CDS) from electronic medical record (EMR) vendors, third party, and homegrown models drawing on rule-based (RB) and machine learning (ML) algorithms. This study explores sepsis CDS implementation from the perspective of implementation leads by describing the motivations, tool choices, and implementation experiences of a diverse group of implementers. Materials and Methods Semi-structured interviews were conducted with and a questionnaire was administered to 21 hospital leaders overseeing CDS implementation at 15 US medical centers. Participants were recruited via convenience sampling. Responses were coded by 2 coders with consensus approach and inductively analyzed for themes. Results Use of sepsis CDS is motivated in part by quality metrics for sepsis patients. Choice of tool is driven by ease of integration, customization capability, and perceived predictive potential. Implementation processes for these CDS tools are complex, time-consuming, interdisciplinary undertakings resulting in heterogeneous choice of tools and workflow integration. To improve clinician acceptance, implementers addressed both optimization of the alerts as well as clinician understanding and buy in. More distrust and confusion was reported for ML models, as compared to RB models. Respondents described a variety of approaches to overcome implementation barriers; these approaches related to alert firing, content, integration, and buy-in. Discussion While there are shared socio-technical challenges of implementing CDS for both RB and ML models, attention to user education, support, expectation management, and dissemination of effective practices may improve feasibility and effectiveness of ML models in quality improvement efforts. Conclusion Further implementation science research is needed to determine real world efficacy of these tools. Clinician acceptance is a significant barrier to sepsis CDS implementation. Successful implementation of less clinically intuitive ML models may require additional attention to user confusion and distrust.

ObjectiveDetermine drivers of academic productivity within U.S. departments of surgery.MethodsEighty academic metrics for 3,850 faculty at the top 50 NIH-funded university- and 5 outstanding hospital-based surgical departments were collected using websites, Scopus, and NIH RePORTER.ResultsMean faculty size was 76. Overall, there were 35.3% assistant, 27.8% associate, and 36.9% full professors. Women comprised 21.8%; 4.9% were MD-PhDs and 6.1% PhDs. By faculty-rank, median publications/citations were: assistant, 14/175, associate, 39/649 and full-professor, 97/2250. General surgery divisions contributed the most publications and citations. Highest performing sub-specialties per faculty member were: research (58/1683), transplantation (51/1067), oncology (41/777), and cardiothoracic surgery (48/860). Overall, 23.5% of faculty were principal investigators for a current or former NIH grant, 9.5% for a current or former R01/U01/P01. The 10 most cited faculty (MCF) within each department contributed to 42% of all publications and 55% of all citations. MCF were most commonly general (25%), oncology (19%), or transplant surgeons (15%). Fifty-one-percent of MCF had current/former NIH funding, compared with 20% of the rest (p<0.05); funding rates for R01/U01/P01 grants was 25.1% vs. 6.8% (p<0.05). Rate of current-NIH MCF funding correlated with higher total departmental NIH rank (p < 0.05).ConclusionsDepartmental academic productivity as defined by citations and NIH funding is highly driven by sections or divisions of research, general and transplantation surgery. MCF, regardless of subspecialty, contribute disproportionally to major grants and publications. Approaches that attract, develop, and retain funded MCF may be associated with dramatic increases in total departmental citations and NIH-funding.

ObjectiveDetermine drivers of academic productivity within U.S. departments of surgery. MethodsEighty academic metrics for 3,850 faculty at the top 50 NIH-funded university-and 5 outstanding hospital-based surgical departments were collected using websites, Scopus, and NIH RePORTER. ResultsMean faculty size was 76. Overall, there were 35.3% assistant, 27.8% associate, and 36.9% full professors. Women comprised 21.8%; 4.9% were MD-PhDs and 6.1% PhDs. By faculty-rank, median publications/citations were: assistant, 14/175, associate, 39/649 and full-professor, 97/2250. General surgery divisions contributed the most publications and citations. Highest performing sub-specialties per faculty member were: research (58/1683), transplantation (51/1067), oncology (41/777), and cardiothoracic surgery (48/860). Overall, 23.5% of faculty were principal investigators for a current or former NIH grant, 9.5% for a current or former R01/U01/P01. The 10 most cited faculty (MCF) within each department contributed to 42% of all publications and 55% of all citations. MCF were most commonly general (25%), oncology (19%), or transplant surgeons (15%). Fifty-one-percent of MCF had current/former NIH funding, compared with 20% of the rest (p<0.05); funding rates for R01/U01/P01 grants was 25.1% vs. 6.8% (p<0.05). Rate of current-NIH MCF funding correlated with higher total departmental NIH rank (p < 0.05). ConclusionsDepartmental academic productivity as defined by citations and NIH funding is highly driven by sections or divisions of research, general and transplantation surgery. MCF, regardless of subspecialty, contribute disproportionally to major grants and publications. Approaches

Background Wire localization is historically the most common method for guiding excision of non-palpable breast lesions, but there are limitations to the technique. Newer technologies such as magnetic seeds may allow some of these challenges to be overcome. The aim was to compare safety and effectiveness of wire and magnetic seed localization techniques. Methods Women undergoing standard wire or magnetic seed localization for non-palpable lesions between August 2018 and August 2020 were recruited prospectively to this IDEAL stage 2a/2b platform cohort study. The primary outcome was effectiveness defined as accurate localization and removal of the index lesion. Secondary endpoints included safety, specimen weight and reoperation rate for positive margins. Results Data were accrued from 2300 patients in 35 units; 2116 having unifocal, unilateral breast lesion localization. Identification of the index lesion in magnetic-seed-guided (946 patients) and wire-guided excisions (1170 patients) was 99.8 versus 99.1 per cent (P = 0.048). There was no difference in overall complication rate. For a subset of patients having a single lumpectomy only for lesions less than 50 mm (1746 patients), there was no difference in median closest margin (2 mm versus 2 mm, P = 0.342), re-excision rate (12 versus 13 per cent, P = 0.574) and specimen weight in relation to lesion size (0.15 g/mm2  versus 0.138 g/mm2, P = 0.453). Conclusion Magnetic seed localization demonstrated similar safety and effectiveness to those of wire localization. This study has established a robust platform for the comparative evaluation of new localization devices.