PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10724322

Clinical problem analysis (CPA): a systematic approach to teaching complex medical problem solving.

E J Custers·P M Stuyt·P F De Vries Robbé
View original source ↗Export metadata ↓

Abstract

The authors describe and discuss clinical problem analysis (CPA), an approach to solving complex clinical problems. They outline the five steps of the CPA model and the essential elements of each step. Next, they discuss the value of CPA's content-independent (methodical) approach and argue that teaching students to use CPA will enable them to avoid some common diagnostic reasoning errors and pitfalls. Finally, they compare CPA with two existing approaches to clinical problem solving.

Explore related subjects

Education, Medical, UndergraduateHumansProblem-Based LearningTeachingJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

E J Custers, P M Stuyt, P F De Vries Robbé. 2000. Clinical problem analysis (CPA): a systematic approach to teaching complex medical problem solving.. https://doi.org/10.1097/00001888-200003000-00024

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Conditional reliability of admissions interview ratings: extreme ratings are the most informative.

CONTEXT: Admissions interviews are unreliable and have poor predictive validity, yet are the sole measures of non-cognitive skills used by most medical school admissions departments. The low reliability may be due in part to variation in conditional reliability across the rating scale. OBJECTIVES: To describe an empirically derived estimate of conditional reliability and use it to improve the predictive validity of interview ratings. METHODS: A set of medical school interview ratings was compared to a Monte Carlo simulated set to estimate conditional reliability controlling for range restriction, response scale bias and other artefacts. This estimate was used as a weighting function to improve the predictive validity of a second set of interview ratings for predicting non-cognitive measures (USMLE Step II residuals from Step I scores). RESULTS: Compared with the simulated set, both observed sets showed more reliability at low and high rating levels than at moderate levels. Raw interview scores did not predict USMLE Step II scores after controlling for Step I performance (additional r2 = 0.001, not significant). Weighting interview ratings by estimated conditional reliability improved predictive validity (additional r2 = 0.121, P < 0.01). CONCLUSIONS: Conditional reliability is important for understanding the psychometric properties of subjective rating scales. Weighting these measures during the admissions process would improve admissions decisions.

Education, Medical, Undergraduate↗