The Effects of Accumulated Experience on Radiofrequency Ablation of Accessory Pathways.

Tai, Ching‐Tai; Chen, Shih‐Ann; Chiang, Chern‐En; Chiou, Chuen‐Wang; Kuo, Benjamin Ing Tiau; Wu, Tsu‐Juey; Cheng, Chen‐Chuen; Lee, Shih‐Huang; Ueng, Kwo‐Chang; Wen, Zu Chi; Chang, Mau‐Song

doi:10.1536/ihj.36.729

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…A linear relationship between experience and outcome was most commonly described. [20][21][22][23][24][25][26][27][28][29][30][31] A variety of other curves were used to describe the learning relationship: logarithmic, [32][33][34][35] 43 In addition to the 24 papers using least squares regression, Monte Carlo simulation was used in another paper to estimate the shape of the learning curve. 44 The correlation between experience and outcome was tested by Spearman's correlation coefficient, 45,46 chi-squared test for trend, 47,48 or a Komolgorov-Smirnoff test.…”

Section: Univariate Trendmentioning

confidence: 99%

Statistical assessment of the learning curves of health technologies

Ramsay

Grant

Wallace

et al. 2001

Health Technol Assess

310

269

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Section: Univariate Trendmentioning

confidence: 99%

Statistical assessment of the learning curves of health technologies

Ramsay

Grant

Wallace

et al. 2001

Health Technol Assess

310

269

View full text Add to dashboard Cite

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Comparing learning curves of two established “single-shot” devices for ablation of atrial fibrillation

Leitz

Mönnig

Güner

et al. 2018

J Interv Card Electrophysiol

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ASSESSMENT OF THE LEARNING CURVE IN HEALTH TECHNOLOGIES A Systematic Review

Ramsay

Grant

Wallace

et al. 2000

J. of Inter. Tech. of Health Care

183

135

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Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve." Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning.

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The Effects of Accumulated Experience on Radiofrequency Ablation of Accessory Pathways.

Cited by 4 publications

References 10 publications

Statistical assessment of the learning curves of health technologies

Statistical assessment of the learning curves of health technologies

Comparing learning curves of two established “single-shot” devices for ablation of atrial fibrillation

ASSESSMENT OF THE LEARNING CURVE IN HEALTH TECHNOLOGIES A Systematic Review

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