Applied and Computational Mathematics

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Mobile Online Computer-Adaptive Tests (CAT) for Gathering Patient Feedback in Pediatric Consultations

Received: 19 December 2016    Accepted: 09 January 2017    Published: 06 February 2017
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Abstract

Background: Few studies have used online patient feedback from smartphones for computer adaptive testing (CAT). Objective: We developed a mobile online CAT survey procedure and evaluated whether it was more precise and efficient than traditional non-adaptive testing (NAT) when gathering patient feedback about their perceptions of interaction with a physician after a consultation. Method: Two hundred proxy participants (parents or guardians) were recruited to respond to twenty 5-point questions (the P4C_20 scale) about perceptions of doctor-patient and doctor-family interaction in clinical pediatric consultations. Through the parameters calibrated using a Rasch partial credit model (PCM) and a Rasch rating scale model (RSM), two paired comparisons of empirical and simulation data were administered to calculate and compare the efficiency and precision of CAT and NAT in terms of shorter item length and fewer counts of difference number ratio (< 5%) using independent t tests. An online CAT was designed using two modes of PCM and RSM for use in clinical settings. Results: The graphical online CAT for smartphones used by the parents or guardians of pediatric hospital patients was more efficient and no less precise than NAT. Conclusions: CAT-based administration of the P4C_20 substantially reduced respondent burden without compromising measurement precision.

DOI 10.11648/j.acm.s.2017060401.16
Published in Applied and Computational Mathematics (Volume 6, Issue 4-1, July 2017)

This article belongs to the Special Issue Some Novel Algorithms for Global Optimization and Relevant Subjects

Page(s) 64-71
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Computer Adaptive Testing, Non-daptive Testing, Partial Credit Model, Rasch Analysis, Rating Scale Model

References
[1] Eack SM, Singer JB, Greeno CG. Screening for anxiety and depression in community mental health: the Beck Anxiety and Depression Inventories. Community Ment Health J 2008;44 (6): 465-474.
[2] Ramirez Basco M, Bostic JQ, Davies D, et al. Methods to improve diagnostic accuracy in a community mental health setting. Am J Psychiatry 2000;157 (10): 1599-1605.
[3] Shear MK, Greeno C, Kang J, et al. Diagnosis of nonpsychotic patients in community clinics. Am J Psychiatr 2000;157: 581-587.
[4] De Beurs DP, de Vries AL, de Groot MH, de Keijser J, Kerkhof AJ. Applying computer adaptive testing to optimize online assessment of suicidal behavior: a simulation study. J Med Internet Res 2014;16 (9): e207.
[5] Lai WP, Chien TW, Lin HJ, Su SB, Chang CH. A screening tool for dengue fever in children. Pediatr Infect Dis J 2013;32 (4): 320-324.
[6] Chien TW, Wang WC, Huang SY, Lai WP, Chou JC. A web-based computerized adaptive testing (CAT) to assess patient perception of Hospitalization. J Med Internet Res 2011;13 (3): e61.
[7] Ma SC, Chien TW, Wang HH, Li YC, Yui MS. Applying computerized adaptive testing to the negative acts questionnaire-revised: Rasch analysis of workplace bullying. J Med Internet Res 2014;16 (2): e50.
[8] Rasch G. Probabilistic Models for Some Intelligence and Attainment Tests. Chicago: University of Chicago Press, 1960.
[9] Wang WC. Recent Developments in Rasch Measurement. Hong Kong: The Hong Kong Institute of Education Press; 2010.
[10] Andrich D. A rating scale formulation for ordered response categories. Psychometrika 1978;43: 561-573.
[11] Masters GN. A Rasch model for partial credit scoring. Psychometrika 1982;47: 149-174.
[12] Raîche G, Blais JG, Riopel MA. SAS solution to simulate a Rasch computerized adaptive test. Rasch Meas Trans 2006;20 (2): 1061.
[13] Linacre, JM. Computer-adaptive tests (CAT), standard errors and stopping rules. Rasch Meas Trans 2006;20 (2): 1062.
[14] Chien TW, Wang WC, Lin SB, Lin CY, Guo HR, Su SB. KIDMAP, a web based system for gathering patients’ feedback on their doctors. BMC Med Res Methodol 2009;9: 38.
[15] Crossley J, Davies H. Doctors’ consultations with children and their parents: a model of competencies, outcomes and confounding influences. Med Educ 2005;39 (8): 807-819.
[16] Davies A, Ware J. Involving consumers in quality of care assessment. Health Aff (Millwood) 1988;7 (1): 33-48.
[17] Levine A. Medical professionalism in the new millennium: a physician charter. 2002;136: 243-226.
[18] Epstein R, Hundert E. Defining and assessing professional competence. JAMA 2002;287: 226-235.
[19] Maudsley R, Wilson D, Neufield V, Hennen B, DeVillaer M, Wakefield J. Educating future physicians for Ontario: phase II. Acad Med 2000;75: 113-126.
[20] Delbanco T. Enriching the doctor-patient relationship by inviting the patient's perspective. Ann Intern Med 1992;116: 414-418.
[21] Hall W, Violato C, Lewkonia R, et al. Assessment of physician performance in Alberta: the Physician Achievement Review. CMAJ 1999;161 (1): 52-57.
[22] Hearnshaw H, Baker R, Cooper A, Eccles M, Soper J. The costs and benefits of asking patients their opinions about general practice. Fam Pract 1996;13 (1): 52-58.
[23] Violato C, Lockyer J, Fidler H. Multisource feedback: a method of assessing surgical practice. BMJ 2003;326: 546-548.
[24] Teutsch C. Patient-doctor communication. Med Clin North Am 2003;87 (5): 1115-1145.
[25] Crossley J, Eiser C, Davies HA. Children and their parents assessing the doctor-patient interaction: a rating system for doctors’ communication skills. Med Educ 2005;39 (8): 820-828.
[26] Beckett MK, Elliott MN, Richardson A, Mangione SR. Outpatient satisfaction: the role of nominal versus perceived communication. Health Serv Res 2009;44 (5 Pt 1): 1735-1749.
[27] Linacre JM. WINSTEPS [computer program]. Chicago, IL: http://www.Winsteps.com, 2014.
[28] Smith EV. Detecting and evaluation the impact of multidimensionality using item fit statistics and principal component analysis of residuals. J Appl Meas 2002;3: 205-231.
[29] Linacre JM. User's Guide to Winsteps. Chicago: Mesa Press; 2014.
[30] Tennant A, Pallant JF. Unidimensionality matters! (A tale of two Smiths?). Rasch Meas Trans 2006;20 (1): 1048-1051.
[31] Horn JL. A rationale and test for the number of factors in factor analysis. Psychometrika 1965; 30 (2): 179-185.
[32] Birnbaum A. Some latent ability models and their use in inferring an examinee's ability. In Lord FM, Novick MR (eds.), Statistical Theories of Mental Test Scores. Reading, MA: Addison-Wesley; 1968.
[33] Embretson S, Reise S, Reise SP. Item Response Theory for Psychologists. Mahwah, NJ: Erlbaum; 2000.
[34] Hsueh IP, Chen JH, Wang CH, Hou WH, Hsieh CL. Development of a computerized adaptive test for assessing activities of daily living in outpatients with stroke. Phys Ther 2013;93 (5): 681-693.
[35] Linacre JM. How to simulate Rasch data. Rasch Meas Trans 2007;21 (3): 1125.
[36] Chien TW. Cronbach’s alpha with the dimension coefficient to jointly assess a scale’s quality. Rasch Meas Trans 2012;26 (3): 1379.
[37] Chien TW, Wu HM, Wang WC, Castillo RV, Chou W. Reduction in patient burdens with graphical computerized adaptive testing on the ADL scale: tool development and simulation. Health Qual Life Outcomes 2009: 39.
[38] Wainer HW, Dorans NJ, Flaugher R, et al. Computerized adaptive testing: a primer. Hillsdale, NJ: Erlbaum; 1990.
[39] Eastaugh SR. Cost containment for the public health. J Health Care Finance 2006;32: 20-27.
[40] Fliege H, Becker J, Walter OB, Bjorner JB, Klapp BF, Rose M. Development of a computer-adaptive test for depression (D-CAT). Qual Life Res 2005;14 (10): 2277-2291.
[41] Chien TW, Wang WC, Huang SY, Lai WP, Chou JC. A web-based computerized adaptive testing (CAT) to assess patient perception of hospitalization. J Med Internet Res 2011;13 (3): e61.
[42] Ma SC, Chien TW, Wang HH, Li YC, Yui MS. Applying computerized adaptive testing to the negative acts questionnaire-revised: Rasch analysis of workplace bullying. J Med Internet Res 2014;16 (2): e50.
[43] Teutsch C. Patient-doctor communication. Med Clin North Am 2003;87 (5): 1115-1145.
[44] Crossley J, Eiser C, Davies HA. Children and their parents assessing the doctor-patient interaction: a rating system for doctors’ communication skills. Med Educ 2005;39 (8): 820-828.
[45] Beckett MK, Elliott MN, Richardson A, Mangione SR. Outpatient satisfaction: the role of nominal versus perceived communication. Health Serv Res 2009;44 (5 Pt 1): 1735-1749.
[46] Linacre JM. Optimizing rating scale category effectiveness. J Appl Meas 2002;3 (1): 85-106.
[47] Mitchell SJ, Godoy L, Shabazz K, Horn IB. Internet and mobile technology use among urban African American parents: survey study of a clinical population. J Med Internet Res 2014;16 (1): e9.
[48] Linacre JM. RUMM2020 item-trait chi-square and Winsteps DIF size. Rasch Meas Trans 2007;21 (1): 1096.
Author Information
  • Research Department, Chi-Mei Medical Center, Tainan, Taiwan; Department of Hospital and Health Care Administration, Chia-Nan University of Pharmacy and Science, Tainan, Taiwan

  • Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan

  • Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan

Cite This Article
  • APA Style

    Tsair-Wei Chien, Wen-Pin Lai, Ju-Hao Hsieh. (2017). Mobile Online Computer-Adaptive Tests (CAT) for Gathering Patient Feedback in Pediatric Consultations. Applied and Computational Mathematics, 6(4-1), 64-71. https://doi.org/10.11648/j.acm.s.2017060401.16

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    ACS Style

    Tsair-Wei Chien; Wen-Pin Lai; Ju-Hao Hsieh. Mobile Online Computer-Adaptive Tests (CAT) for Gathering Patient Feedback in Pediatric Consultations. Appl. Comput. Math. 2017, 6(4-1), 64-71. doi: 10.11648/j.acm.s.2017060401.16

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    AMA Style

    Tsair-Wei Chien, Wen-Pin Lai, Ju-Hao Hsieh. Mobile Online Computer-Adaptive Tests (CAT) for Gathering Patient Feedback in Pediatric Consultations. Appl Comput Math. 2017;6(4-1):64-71. doi: 10.11648/j.acm.s.2017060401.16

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  • @article{10.11648/j.acm.s.2017060401.16,
      author = {Tsair-Wei Chien and Wen-Pin Lai and Ju-Hao Hsieh},
      title = {Mobile Online Computer-Adaptive Tests (CAT) for Gathering Patient Feedback in Pediatric Consultations},
      journal = {Applied and Computational Mathematics},
      volume = {6},
      number = {4-1},
      pages = {64-71},
      doi = {10.11648/j.acm.s.2017060401.16},
      url = {https://doi.org/10.11648/j.acm.s.2017060401.16},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.acm.s.2017060401.16},
      abstract = {Background: Few studies have used online patient feedback from smartphones for computer adaptive testing (CAT). Objective: We developed a mobile online CAT survey procedure and evaluated whether it was more precise and efficient than traditional non-adaptive testing (NAT) when gathering patient feedback about their perceptions of interaction with a physician after a consultation. Method: Two hundred proxy participants (parents or guardians) were recruited to respond to twenty 5-point questions (the P4C_20 scale) about perceptions of doctor-patient and doctor-family interaction in clinical pediatric consultations. Through the parameters calibrated using a Rasch partial credit model (PCM) and a Rasch rating scale model (RSM), two paired comparisons of empirical and simulation data were administered to calculate and compare the efficiency and precision of CAT and NAT in terms of shorter item length and fewer counts of difference number ratio (t tests. An online CAT was designed using two modes of PCM and RSM for use in clinical settings. Results: The graphical online CAT for smartphones used by the parents or guardians of pediatric hospital patients was more efficient and no less precise than NAT. Conclusions: CAT-based administration of the P4C_20 substantially reduced respondent burden without compromising measurement precision.},
     year = {2017}
    }
    

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    T1  - Mobile Online Computer-Adaptive Tests (CAT) for Gathering Patient Feedback in Pediatric Consultations
    AU  - Tsair-Wei Chien
    AU  - Wen-Pin Lai
    AU  - Ju-Hao Hsieh
    Y1  - 2017/02/06
    PY  - 2017
    N1  - https://doi.org/10.11648/j.acm.s.2017060401.16
    DO  - 10.11648/j.acm.s.2017060401.16
    T2  - Applied and Computational Mathematics
    JF  - Applied and Computational Mathematics
    JO  - Applied and Computational Mathematics
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    EP  - 71
    PB  - Science Publishing Group
    SN  - 2328-5613
    UR  - https://doi.org/10.11648/j.acm.s.2017060401.16
    AB  - Background: Few studies have used online patient feedback from smartphones for computer adaptive testing (CAT). Objective: We developed a mobile online CAT survey procedure and evaluated whether it was more precise and efficient than traditional non-adaptive testing (NAT) when gathering patient feedback about their perceptions of interaction with a physician after a consultation. Method: Two hundred proxy participants (parents or guardians) were recruited to respond to twenty 5-point questions (the P4C_20 scale) about perceptions of doctor-patient and doctor-family interaction in clinical pediatric consultations. Through the parameters calibrated using a Rasch partial credit model (PCM) and a Rasch rating scale model (RSM), two paired comparisons of empirical and simulation data were administered to calculate and compare the efficiency and precision of CAT and NAT in terms of shorter item length and fewer counts of difference number ratio (t tests. An online CAT was designed using two modes of PCM and RSM for use in clinical settings. Results: The graphical online CAT for smartphones used by the parents or guardians of pediatric hospital patients was more efficient and no less precise than NAT. Conclusions: CAT-based administration of the P4C_20 substantially reduced respondent burden without compromising measurement precision.
    VL  - 6
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    ER  - 

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