Open AccessRheumatology algorithms for primary care physicians
Author(s) -
Ambrose Richard F.,
Kendall Lee G.,
Alarcón Graciela S.,
Brown Samuel,
Lipstate James M.,
Wirtschafter David D.,
Jackson James R.,
Glass Steven,
Rossi Kevin,
Margolis Carmi Z.
Publication year - 1990
Publication title -
arthritis & rheumatism
Language(s) - English
Resource type - Journals
eISSN - 1529-0131
pISSN - 0004-3591
DOI - 10.1002/art.1790030204
Subject(s) - rheumatology , primary care , medicine , inter rater reliability , reliability (semiconductor) , continuing medical education , algorithm , significant difference , physical therapy , continuing education , medical physics , machine learning , family medicine , computer science , medical education , mathematics , statistics , power (physics) , rating scale , physics , quantum mechanics
Primary care physicians were trained on three rheumatology topics to assess the effectiveness of an educational strategy for continuing medical education. Algorithm training was shown to be at least as effective as that based on standard prose monographs. Both training groups improved their knowledge of patient management skills but there were no statistically significant differences between groups in the amount learned. When algorithms were used to design text materials, the designed texts required less study time than did the annotated clinical algorithms alone. That difference was significant for the shoulder pain materials (P < 0.05) but not for the osteoporosis materials. The ratio of knowledge gained to study time was significantly higher for the algorithm group on the low back pain topic (P < 0.05) but not for the other topics. Taped interview problems tests were studied as a method for assessing patient management skills related to problem‐specific indicator conditions and were found to produce interrater reliability greater than 0.80 on five of the six tests.