We sought to assess whether HHFNC results in greater production of aerosolized particles than 6 liters per minute nasal cannula, using state-of-the-art techniques of aerosol measurement, in spontaneously breathing human volunteers in a simulated hospital room. For each volunteer, we first measured background aerosol levels in the room immediately prior to testing. We then measured aerosol levels while the healthy volunteer laid in bed - - with the head of bed at 30 degrees - - wearing the following oxygen delivery devices: (a) 6L/min nasal canula (NC) with humidification; (b) non-re-breather mask (NRB) with 15L/min gas flow, non-humidified; (c) HHFNC with 30L/min gas flow; (d) HHFNC with 60L/min gas flow. Two scanning mobility particle sizing (SMPS) systems (TSI 3080/3030, TSI 3080/3750) were used to measure aerosols 10 to 500 nanometer (nm) in size for each of the oxygen delivery devices. There was no variation in aerosol level within patients between room air, 6 L/min NC, 15 L/min NRB, 30 L/min HHFNC, and 60 L/min HHFNC, regardless of coughing.

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The resulting data set included 85% of the U.S. medical schools with Departments of Family Medicine that reported 2011 and 2012 residency match rates in family medicine. Match rates in family medicine were higher among graduates of public than private medical schools-11% versus 7%, respectively, t(92) = 4.00, p < .001. Using a linear regression model and controlling for institutional type, the results indicated 2% higher match rates among schools with smaller annual clerkship enrollments (p  = .03), 3% higher match rates among schools with clerkships lasting more than 3 to 4 weeks (p  = .003), 3% higher match rates at schools with at least 1 family medicine faculty member in a senior leadership role (p  = .04), and 8% lower match rates at private medical schools offering community medicine electives (p < .001, R(2) = .48), F(6, 64) = 9.95, p < .001. Three additional factors were less strongly related and varied by institutional type-informal mentoring, ambulatory primary care learning experiences, and institutional research focus. Insights: Educational opportunities associated with higher match rates in family medicine differ across private and public medical schools. Future research is needed to identify the qualitative aspects of educational programming that contribute to differences in match rates across institutional contexts. Results of this study should prove useful in mitigating physician shortages, particularly in primary care fields such as family medicine.

BackgroundErrors in reasoning are a common cause of diagnostic error. However, it is difficult to improve performance partly because providers receive little feedback on diagnostic performance. Examining means of providing consistent feedback and enabling continuous improvement may provide novel insights for diagnostic performance.MethodsWe developed a model for improving diagnostic performance through feedback using a six-step qualitative research process, including a review of existing models from within and outside of medicine, a survey, semistructured interviews with individuals working in and outside of medicine, the development of the new model, an interdisciplinary consensus meeting, and a refinement of the model.ResultsWe applied theory and knowledge from other fields to help us conceptualise learning and comparison and translate that knowledge into an applied diagnostic context. This helped us develop a model, the Diagnosis Learning Cycle, which illustrates the need for clinicians to be given feedback about both their confidence and reasoning in a diagnosis and to be able to seamlessly compare diagnostic hypotheses and outcomes. This information would be stored in a repository to allow accessibility. Such a process would standardise diagnostic feedback and help providers learn from their practice and improve diagnostic performance. This model adds to existing models in diagnosis by including a detailed picture of diagnostic reasoning and the elements required to improve outcomes and calibration.ConclusionA consistent, standard programme of feedback that includes representations of clinicians’ confidence and reasoning is a common element in non-medical fields that could be applied to medicine. Adapting this approach to diagnosis in healthcare is a promising next step. This information must be stored reliably and accessed consistently. The next steps include testing the Diagnosis Learning Cycle in clinical settings.