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1.
J Gen Intern Med ; 36(8): 2230-2236, 2021 08.
Article in English | MEDLINE | ID: mdl-33575907

ABSTRACT

INTRODUCTION: In 2020, roughly 25% of applicants who matched into internal medicine (IM) residencies were international medical graduates (IMGs). We examine 12-year trends in distribution of IMGs among IM training programs and explore differences in program perceptions towards IMG recruitment. METHODS: Since 2007, Association of Program Directors in Internal Medicine Annual Surveys have collected data about trainees by medical school graduate type. Sixteen additional questions regarding perceptions of IMGs were included in the 2017 spring survey. RESULTS: The 2017 survey response rate was 63.3% (236/373) and ranged from 61.9 to 70.2% for the 2007-2019 Annual Surveys. During that 12-year period, 55-70% of community programs' and 22-30% of university programs' PGY1 positions were filled by IMGs. In 2017, 45% of community programs' and 15% of university programs' interview and ranking positions were allocated to IMGs. Departmental pressure (university 45.6% [95% CI 43.7-47.5]; community 28.2% [95% CI 26.6-29.7]; p = 0.007), institutional priority (university 64.0% [95% CI 62.1-66.0]; community 41% [95% CI 36.9-44.6]; p = 0.001), and reputational concerns (university 52.8% [95% CI 50.0-55.6]; community 38.5% [95% CI 36.0-40.9]; p = 0.045) were cited as factors influencing recruitment of IMGs. CONCLUSION: Our study was limited to exploring program factors in residency recruitment and did not assess applicant preferences. There is a large, longstanding difference in the recruitment of IMGs to US community-based and university residencies, beginning during the interview and ranking process. Further research in disparities in IMG recruitment is needed, including exploring pressures, preferences, and potential biases associated with the recruitment of IMGs.


Subject(s)
Foreign Medical Graduates , Internship and Residency , Education, Medical, Graduate , Humans , Internal Medicine/education , Longitudinal Studies , United States
2.
J Hosp Med ; 4(7): E11-9, 2009 Sep.
Article in English | MEDLINE | ID: mdl-19479782

ABSTRACT

BACKGROUND: One of the causes of postdischarge adverse events is poor discharge communication between hospital-based physicians, patients, and outpatient physicians. The value of hospital discharge software to improve communication and clinically relevant outcomes is unknown. OBJECTIVE: To measure effects of a discharge software application of computerized physician order entry (CPOE). DESIGN: Cluster randomized controlled trial. SETTING: Tertiary care, teaching hospital in central Illinois. PATIENTS: A total of 631 inpatients discharged to home with high risk for readmission. INTERVENTION: Seventy internal medicine hospital physicians were randomly assigned (allocation concealed) to discharge software versus usual care, handwritten discharge. MEASUREMENTS: Blinded assessment of patient readmission, emergency department visit, and postdischarge adverse event. RESULTS: A total of 590 (94%) patients provided 6-month follow-up data. Generalized estimating equations gave intervention variable coefficients with 95% confidence interval (CI). When comparing patients assigned to discharge software versus usual care, there was no difference in hospital readmission within 6 months (37.0% versus 37.8%; coefficient -0.005 [95% CI, -0.074 to 0.065]; P = 0.894), emergency department visit within 6 months (35.4% versus 40.6%; coefficient -0.052 [95% CI, -0.115 to 0.011]; P = 0.108), or adverse event within 1 month (7.3% versus 7.3%; coefficient 0.003 [95% CI; -0.037 to 0.043]; P = 0.884). CONCLUSIONS: Discharge software with CPOE did not affect readmissions, emergency department visits, or adverse events after discharge. Future studies should assess other endpoints such as patient perceptions or physician perceptions to see if discharge software has value.


Subject(s)
Continuity of Patient Care/organization & administration , Emergency Service, Hospital/statistics & numerical data , Medical Records Systems, Computerized , Patient Discharge/standards , Patient Readmission/statistics & numerical data , Software , Cluster Analysis , Female , Humans , Male , Patient Discharge/statistics & numerical data , Patient Satisfaction , Sample Size
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