Publications

BACKGROUND

In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. Studies comparing treatments for the management of asymptomatic carotid stenosis are challenging for at least two reasons: 1) administrative billing codes (i.e., Current Procedural Terminology (CPT) codes) that identify carotid images do not denote which neurovascular arteries are affected and 2) the majority of the image reports are negative for carotid stenosis. Studies that rely on manual chart abstraction can be labor-intensive, expensive, and time-consuming. Natural Language Processing (NLP) can expedite the process of manual chart abstraction by automatically filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings; thus, potentially reducing effort, costs, and time.

METHODS

In this pilot study, we conducted an information content analysis of carotid stenosis mentions in terms of their report location (Sections), report formats (structures) and linguistic descriptions (expressions) from Veteran Health Administration free-text reports. We assessed an NLP algorithm, pyConText's, ability to discern reports with significant carotid stenosis findings from reports with no/insignificant carotid stenosis findings given these three document composition factors for two report types: radiology (RAD) and text integration utility (TIU) notes.

RESULTS

We observed that most carotid mentions are recorded in prose using categorical expressions, within the Findings and Impression sections for RAD reports and within neither of these designated sections for TIU notes. For RAD reports, pyConText performed with high sensitivity (88 %), specificity (84 %), and negative predictive value (95 %) and reasonable positive predictive value (70 %). For TIU notes, pyConText performed with high specificity (87 %) and negative predictive value (92 %), reasonable sensitivity (73 %), and moderate positive predictive value (58 %). pyConText performed with the highest sensitivity processing the full report rather than the Findings or Impressions independently.

CONCLUSION

We conclude that pyConText can reduce chart review efforts by filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings from the Veteran Health Administration electronic health record, and hence has utility for expediting a comparative effectiveness study of treatment strategies for stroke prevention.

As the world's aging population grows, the surgical population is increasingly made up of older adults. Due to changes in physiologic function and increasing comorbidity burden, older adults are at increased risk of morbidity, mortality, and functional decline after surgery. In addition, decision to undergo surgery for the older adult may be based on the postoperative functional outcome rather than survival. Although few studies have evaluated an older adult's function as a postoperative outcome, surgeons are becoming increasingly aware of the importance of maintaining or regaining function in an older patient. Interventions to improve postoperative functional outcomes are being developed and show promising results. This review discusses existing literature on postoperative functional outcomes in older adults and recently developed interventions.

PROBLEM

The Association of Program Directors in Internal Medicine, the Accreditation Council for Graduate Medical Education, the Alliance for Academic Internal Medicine, and the Carnegie Foundation report on medical education recommend creating individualized learning pathways during medical training so that learners can experience broader professional roles beyond patient care. Little data exist to support the success of these specialized pathways in graduate medical education.

INTERVENTION

We present the 10-year experience of the Primary Care Medicine Education (PRIME) track, a clinical-outcomes research pathway for internal medicine residents at the University of California San Francisco (UCSF). We hypothesized that participation in an individualized learning track, PRIME, would lead to a greater likelihood of publishing research from residency and accessing adequate career mentorship and would be influential on subsequent alumni careers.

CONTEXT

We performed a cross-sectional survey of internal medicine residency alumni from UCSF who graduated in 2001 through 2010. We compared responses of PRIME and non-PRIME categorical alumni. We used Pearson's chi-square and Student's t test to compare PRIME and non-PRIME alumni on categorical and continuous variables.

OUTCOME

Sixty-six percent (211/319) of alumni responded to the survey. A higher percentage of PRIME alumni published residency research projects compared to non-PRIME alumni (64% vs. 40%; p = .002). The number of PRIME alumni identifying research as their primary career role was not significantly different from non-PRIME internal medicine residency graduates (35% of PRIME vs. 29% non-PRIME). Process measures that could explain these findings include adequate access to mentors (M 4.4 for PRIME vs. 3.6 for non-PRIME alumni, p < .001, on a 5-point Likert scale) and agreeing that mentoring relationships affected career choice (M 4.2 for PRIME vs. 3.7 for categorical alumni, p = .001). Finally, 63% of PRIME alumni agreed that their research experience during residency influenced their subsequent career choice versus 46% of non-PRIME alumni (p = .023).

LESSONS LEARNED

Our results support the concept that providing residents with an individualized learning pathway focusing on clinical outcomes research during residency enables them to successfully publish manuscripts and access mentorship, and may influence subsequent career choice. Implementation of individualized residency program tracks that nurture academic interests along with clinical skills can support career development within medicine residency programs.