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1.  Associations between subspecialty fellowship interest and knowledge of internal medicine: A hypothesis-generating study of internal medicine residents 
Little is known about whether and how medical knowledge relates to interest in subspecialty fellowship training. The purpose of this study was to examine the relationships between residents' interest in subspecialty fellowship training and their knowledge of internal medicine (IM).
A questionnaire was emailed to 48 categorical postgraduate-year (PGY) two and three residents at a New York university-affiliated IM residency program in 2007 using the Survey Monkey online survey instrument. Overall and content area-specific percentile scores from the IM in-training examination (IM-ITE) for the same year was used to determine objective knowledge.
Forty-five of 48 residents (response rate was 93.8%) completed the survey. Twenty-two (49%) were PG2 residents and 23(51%) were PGY3 residents. Sixty percent of respondents were male. Six (13%) residents were graduates of U.S. medical schools. Eight (18%) reported formal clinical training prior to starting internal medicine residency in the U.S. Of this latter group, 6 (75%) had training in IM and 6 (75) % reported a training length of 3 years or less. Thirty-seven of 45 (82%) residents had a subspecialty fellowship interest. Residents with a fellowship interest had a greater mean overall objective knowledge percentile score (56.44 vs. 31.67; p = 0.04) as well as greater mean percentile scores in all content areas of IM. The adjusted mean difference was statistically significant (p < 0.02) across three content areas.
More than half of surveyed residents indicated interest in pursuing a subspecialty fellowship. Fellowship interest appears positively associated with general medical knowledge in this study population. Further work is needed to explore motivation and study patterns among internal medicine residents.
PMCID: PMC3038163  PMID: 21281500
2.  Using Claims Data to Examine Mortality Trends Following Hospitalization for Heart Attack in Medicare 
Health Services Research  2003;38(5):1253-1262.
To see if changes in the demographics and illness burden of Medicare patients hospitalized for acute myocardial infarction (AMI) from 1995 through 1999 can explain an observed rise (from 32 percent to 34 percent) in one-year mortality over that period.
Data Sources
Utilization data from the Centers for Medicare and Medicaid Services (CMS) fee-for-service claims (MedPAR, Outpatient, and Carrier Standard Analytic Files); patient demographics and date of death from CMS Denominator and Vital Status files. For over 1.5 million AMI discharges in 1995–1999 we retain diagnoses from one year prior, and during, the case-defining admission.
Study Design
We fit logistic regression models to predict one-year mortality for the 1995 cases and apply them to 1996–1999 files. The CORE model uses age, sex, and original reason for Medicare entitlement to predict mortality. Three other models use the CORE variables plus morbidity indicators from well-known morbidity classification methods (Charlson, DCG, and AHRQ's CCS). Regressions were used as is—without pruning to eliminate clinical or statistical anomalies. Each model references the same diagnoses—those recorded during the pre- and index admission periods. We compare each model's ability to predict mortality and use each to calculate risk-adjusted mortality in 1996–1999.
Principal Findings
The comprehensive morbidity classifications (DCG and CCS) led to more accurate predictions than the Charlson, which dominated the CORE model (validated C-statistics: 0.81, 0.82, 0.74, and 0.66, respectively). Using the CORE model for risk adjustment reduced, but did not eliminate, the mortality increase. In contrast, adjustment using any of the morbidity models produced essentially flat graphs.
Prediction models based on claims-derived demographics and morbidity profiles can be extremely accurate. While one-year post-AMI mortality in Medicare may not be worsening, outcomes appear not to have continued to improve as they had in the prior decade. Rich morbidity information is available in claims data, especially when longitudinally tracked across multiple settings of care, and is important in setting performance targets and evaluating trends.
PMCID: PMC1360945  PMID: 14596389
Risk adjustment; Charlson; DCG; CCS; AMI; event-centered database

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