Learning clinical reasoning: how virtual patient case format and prior knowledge interact

被引:24
|
作者
Kiesewetter, Jan [1 ]
Sailer, Michael [2 ]
Jung, Valentina M. [1 ]
Schoenberger, Regina [1 ]
Bauer, Elisabeth [2 ]
Zottmann, Jan M. [1 ]
Hege, Inga [3 ]
Zimmermann, Hanna [4 ]
Fischer, Frank [2 ]
Fischer, Martin R. [1 ]
机构
[1] Ludwig Maximilians Univ Munchen, Univ Hosp, Inst Med Educ, Munich, Germany
[2] Ludwig Maximilians Univ Munchen, Educ & Educ Psychol, Munich, Germany
[3] Univ Augsburg, Sch Med, Augsburg, Germany
[4] Ludwig Maximilians Univ Munchen, Univ Hosp, Dept Radiol, Munich, Germany
关键词
Instructional materials; methods; Clinical reasoning; Virtual patients; Case formats; COGNITIVE LOAD THEORY; EXAMPLES; IMPROVE;
D O I
10.1186/s12909-020-1987-y
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Introduction Clinical reasoning has been fostered with varying case formats including the use of virtual patients. Existing literature points to different conclusions regarding which format is most beneficial for learners with diverse levels of prior knowledge. We designed our study to better understand which case format affects clinical reasoning outcomes and cognitive load, dependent on medical students' prior knowledge. Methods Overall, 142 medical students (3 rd to 6 th year) were randomly assigned to either a whole case or serial cue case format. Participants worked on eight virtual patients in their respective case format. Outcomes included diagnostic accuracy, knowledge, and cognitive load. Results We found no effect of case format on strategic knowledge scores pre- vs post-test (whole case learning gain = 3, 95% CI. -.01 to .01, serial cue learning gain = 3, 95% CI. -.06 to .00 p = .50). In both case formats, students with high baseline knowledge (determined by median split on the pre-test in conceptual knowledge) benefitted from learning with virtual patients (learning gain in strategic knowledge = 5, 95% CI .03 to .09, p = .01) while students with low prior knowledge did not (learning gain = 0, 95%CI -.02 to .02). We found no difference in diagnostic accuracy between experimental conditions (difference = .44, 95% CI -.96 to .08, p = .22), but diagnostic accuracy was higher for students with high prior knowledge compared to those with low prior knowledge (difference = .8, 95% CI 0.31 to 1.35, p < .01). Students with low prior knowledge experienced higher extraneous cognitive load than students with high prior knowledge (multiple measurements, p < .01). Conclusions The whole case and serial cue case formats alone did not affect students' knowledge gain or diagnostic accuracy. Students with lower knowledge experienced increased cognitive load and appear to have learned less from their interaction with virtual patients. Cognitive load should be taken into account when attempting to help students learn clinical reasoning with virtual patients, especially for students with lower knowledge.
引用
下载
收藏
页数:10
相关论文
共 50 条
  • [21] Research on generation of emergency virtual case based on ontology and knowledge reasoning
    Cai, Fanglin
    Chen, Xi
    Xue, Long
    Li, Wei
    Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2015, 43 : 93 - 96
  • [22] Virtual patient activity patterns for clinical learning
    Ellaway, Rachel
    Topps, David
    Lee, Sonya
    Armson, Heather
    CLINICAL TEACHER, 2015, 12 (04): : 267 - 271
  • [23] IMPACT OF THE VIRTUAL PATIENT INTRODUCTION ON THE CLINICAL REASONING PROCESS IN DENTAL EDUCATION
    Zary, Nabil
    Johnson, Gunilla
    Fors, Uno G. H.
    BIO-ALGORITHMS AND MED-SYSTEMS, 2012, 8 (02) : 173 - 184
  • [24] Virtual patient simulation: Promotion of clinical reasoning abilities of medical students
    Aghili, Rokhsareh
    Khamseh, Mohammad E.
    Taghavinia, Mansoureh
    Malek, Mojtaba
    Emami, Zahra
    Baradaran, Hamid R.
    Mafinejad, Mahboobeh Khabaz
    KNOWLEDGE MANAGEMENT & E-LEARNING-AN INTERNATIONAL JOURNAL, 2012, 4 (04) : 518 - 527
  • [25] VIRTUAL PATIENT SIMULATION TO TEACH CLINICAL REASONING AND REDUCE DIAGNOSTIC ERROR
    Pincavage, Amber
    Dekhtyar, Michael
    Chudgar, Saumil
    Fedoriw, Kelly Bossenbroek
    Park, Yoon S.
    Fowler, Michael
    Johnson, Khadeja
    Kalinyak, Judy
    Knoche, Craig
    Mingioni, Nina
    Sanfilippo, Fred
    Sozio, Stephen M.
    Wood, Sarah
    Stern, Scott
    JOURNAL OF GENERAL INTERNAL MEDICINE, 2020, 35 (SUPPL 1) : S777 - S777
  • [26] Assessing clinical reasoning skills following a virtual patient dizziness curriculum
    Kotwal, Susrutha
    Singh, Amteshwar
    Tackett, Sean
    Bery, Anand K.
    Omron, Rodney
    Gold, Daniel
    Newman-Toker, David E.
    Wright, Scott M.
    DIAGNOSIS, 2024, 11 (01) : 73 - 81
  • [27] Syllabic rhythm and prior linguistic knowledge interact with individual differences to modulate phonological statistical learning
    Varela, Ireri Gomez
    Orpella, Joan
    Poeppel, David
    Ripolles, Pablo
    Assaneo, M. . Florencia
    COGNITION, 2024, 245
  • [28] Automatic Knowledge Learning Using Case-Based Reasoning
    de Souza, Viviane Dal Molin
    Borges, Andre Pinz
    Vecino Sato, Denise Maria
    Avila, Braulio Coelho
    Scalabrin, Edson Emilio
    2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 4579 - 4585
  • [29] Learning adaptation knowledge to improve case-based reasoning
    Craw, Susan
    Wiratunga, Nirmalie
    Rowe, Ray C.
    ARTIFICIAL INTELLIGENCE, 2006, 170 (16-17) : 1175 - 1192
  • [30] Engineering and learning of adaptation knowledge in case-based reasoning
    Cordier, Amelie
    Fuchs, Beatrice
    Mille, Alain
    MANAGING KNOWLEDGE IN A WORLD OF NETWORKS, PROCEEDINGS, 2006, 4248 : 303 - 317