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Homeopathy 2023; 112(02): 097-106
DOI: 10.1055/s-0042-1748841
Original Research Article

Attitudes To and Uptake of Repertory Software in Homeopathy Clinical Practice—Results of an International Survey

Alastair C. Gray 
1   Independent Researcher, HOHM Foundation, Office of Research, Philadelphia, Pennsylvania, United States
,
Parker Pracjek
1   Independent Researcher, HOHM Foundation, Office of Research, Philadelphia, Pennsylvania, United States
,
Denise Straiges
1   Independent Researcher, HOHM Foundation, Office of Research, Philadelphia, Pennsylvania, United States
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Abstract

Background Despite the substantial size of the maturing complementary medicine (CM) industry, the technologies used by practitioners have received little research attention. In the clinical delivery of homeopathy services, repertory software can be employed to cross-reference client symptoms with numerous databases, making the process of seeking a clinical intervention quicker and more accurate. The purpose of the study is to learn about the quantitative patterns of usage, uptake and attitudes to repertory software amongst professional homeopaths.

Methods An online cross-sectional survey of 15 questions was completed by practicing professional homeopaths between August 2016 and May 2017, using non-probability snowball sampling. Questions gathered demographic information, reflections and attitudes on the use of electronic repertories in clinical homeopathy practice.

Results In total, 59% of respondents reported using software regularly in practice and 71% found that it adds clear value in their work. Sixty-eight percent of respondents learned about repertory software during homeopathy training, and 47% were introduced to software when they began clinical practice. Lack of sufficient training is a very important barrier to the use of repertory software, indicating that more robust and accessible software training is needed for practitioners. Many respondents agreed with a statement that repertory software represents good value for money and yet 46% agreed that it is cost prohibitive for most practitioners, signaling a challenge for software companies. Few respondents reported regularly using more than three of the most common repertory features.

Conclusion This preliminary study presents some potentially significant uptake, usage and attitude markers that stand to shed light on the practice of homeopathy and the place of emerging technologies such as repertory software. Ultimately, more research is needed to help identify and address the challenges, risks and tensions around integration of practice-enhancing technologies in CM educational and clinical settings to best serve the diverse and changing needs of practitioners.

Keywords

complementary medicine - clinical practice technologies - repertory software - homeopathy - clinical decision support system

Supplementary Material



Publication History

Received: 21 October 2021

Accepted: 11 February 2022

Article published online:
22 September 2022

© 2022. Faculty of Homeopathy. This article is published by Thieme.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Adams J, Barbery G, Lui C-W. Complementary and alternative medicine use for headache and migraine: a critical review of the literature. Headache 2013; 53: 459-473
    CrossrefPubMedGoogle Scholar
  • 2 Adams J, Andrews G, Barnes J, Broom A, Magin P. Traditional, Complementary and Integrative Medicine: An International Reader. London: Palgrave; 2012
    CrossrefGoogle Scholar
  • 3 Wardle J. Regulation of Complementary Medicines: A Brief Report on the Regulation and Potential Role of Complementary Medicines in Australia. Brisbane: Naturopathy Foundation; 2008
    Google Scholar
  • 4 Gray AC, Steel A, Adams J. A critical integrative review of complementary medicine education research: key issues and empirical gaps. BMC Complement Altern Med 2019; 19: 73
    CrossrefPubMedGoogle Scholar
  • 5 Gray AC, Steel A, Adams J. Complementary medicine students' perceptions, perspectives and experiences of learning technologies. A survey conducted in the US and Australia. Eur J Integr Med 2021; 42: 101304
    CrossrefPubMedGoogle Scholar
  • 6 Harris PE, Cooper KL, Relton C, Thomas KJ. Prevalence of complementary and alternative medicine (CAM) use by the general population: a systematic review and update. Int J Clin Pract 2012; 66: 924-939
    CrossrefPubMedGoogle Scholar
  • 7 Xue CCL, Zhang AL, Lin V, Da Costa C, Story DF. Complementary and alternative medicine use in Australia: a national population-based survey. J Altern Complement Med 2007; 13: 643-650
    CrossrefPubMedGoogle Scholar
  • 8 Reid R, Steel A, Wardle J, Trubody A, Adams J. Complementary medicine use by the Australian population: a critical mixed studies systematic review of utilisation, perceptions and factors associated with use. BMC Complement Altern Med 2016; 16: 176
    Google Scholar
  • 9 Steel A, McIntyre E, Harnett J. et al. Complementary medicine use in the Australian population: results of a nationally-representative cross-sectional survey. Sci Rep 2018; 8: 17325
    Google Scholar
  • 10 Harnett JE, McIntyre E, Steel A, Foley H, Sibbritt D, Adams J. Use of complementary medicine products: a nationally representative cross-sectional survey of 2019 Australian adults. BMJ Open 2019; 9: e024198
    CrossrefPubMedGoogle Scholar
  • 11 Wardle J, Adams J, Magalhães RJ, Sibbritt D. Distribution of complementary and alternative medicine (CAM) providers in rural New South Wales, Australia: a step towards explaining high CAM use in rural health?. Aust J Rural Health 2011; 19: 197-204
    CrossrefPubMedGoogle Scholar
  • 12 Jonas WB, Eisenberg D, Hufford D, Crawford C. The evolution of complementary and alternative medicine (CAM) in the USA over the last 20 years. Forsch Komplement Med 2013; 20: 65-72
    PubMedGoogle Scholar
  • 13 Adams J, Tovey P, Easthope G. Mainstreaming Complementary and Alternative Medicine: Studies in Social Context. eBook ed. London: Routledge; 2017
    Google Scholar
  • 14 Wardle J, Steel A, Adams J. A review of tensions and risks in naturopathic education and training in Australia: a need for regulation. J Altern Complement Med 2012; 18: 363-370
    CrossrefPubMedGoogle Scholar
  • 15 Gray AC, Diezel H, Steel A. The use of learning technologies in complementary medicine education: results of a student technology survey. Adv Int Med 2019; 6: 174
    CrossrefPubMedGoogle Scholar
  • 16 Gray AC, Steel A, Adams J. Attitudes to and uptake of learning technologies in complementary medicine education: results of an international faculty survey. J Altern Complement Med 2020; 26: 335-345
    CrossrefPubMedGoogle Scholar
  • 17 Gray AC, Steel A, Adams J. An examination of technologies in complementary medicine education and practice: the perceptions and experiences of naturopathy students, faculty and educational leaders. Complement Ther Med 2021; 63: 102793
    CrossrefPubMedGoogle Scholar
  • 18 Gray AC, Steel A, Adams J. Learning technologies and health technologies in complementary medicine clinical work and education: Examination of the perspectives of academics and students in Australia and the United States. Advs Int Med 2022; 9: 22-29
    PubMedGoogle Scholar
  • 19 World Health Organization. WHO Global Report on Traditional and Complementary Medicine 2019. Geneva: World Health Organization; 2019
    Google Scholar
  • 20 Levy DC. Clinical Reasoning and Decision-Making in Homeopathy: An Interpretative Phenomenological Analysis. University of Sydney; 2017
    Google Scholar
  • 21 Yasgur J. Homoeopathic Dictionary. Delhi: B. Jain Publishers; 2007
    Google Scholar
  • 22 Gray A. Learning technologies in homeopathic medicine education: drilling deeper into the dynamics and changing behaviours of the student body in complementary and homeopathic medicine. Homeopathy 2016; 105: 30-31
    Article in Thieme ConnectPubMedGoogle Scholar
  • 23 Steel A, Adams J. Approaches to clinical decision-making: a qualitative study of naturopaths. Complement Ther Clin Pract 2011; 17: 81-84
    CrossrefPubMedGoogle Scholar
  • 24 Steel A, Adams J. The interface between tradition and science: naturopaths' perspectives of modern practice. J Altern Complement Med 2011; 17: 967-972
    CrossrefPubMedGoogle Scholar
  • 25 Steel A, Peng W, Gray A, Adams J. The role and influence of traditional and scientific knowledge in naturopathic education: a qualitative study. J Altern Complement Med 2019; 25: 196-201
    CrossrefPubMedGoogle Scholar
  • 26 Dimitriadis G. Hahnemann's Pharmacography. Hahnemann Institute Sydney 2019; 1-20
    PubMedGoogle Scholar
  • 27 Dimitriadis G. The First Repertory: Bönninghausen's Model for our Profession. Am J Homeopath Med 2006; 99
    PubMedGoogle Scholar
  • 28 Dillman DA. Mail and Internet Surveys: The Tailored Design Method—2007 Update with New Internet, Visual, and Mixed-Mode Guide. Hoboken: John Wiley & Sons; 2011
    Google Scholar
  • 29 Kosinski M. Will Facebook Replace Traditional Research Methods? Social Media Offers Researchers a Window into the Human Experience. Insights by Stanford Business; 2015
    Google Scholar
  • 30 Curtin R, Presser S, Singer E. The effects of response rate changes on the index of consumer sentiment. Public Opin Q 2000; 64: 413-428
    CrossrefPubMedGoogle Scholar
  • 31 Massey DS, Tourangeau R. Where do we go from here? Nonresponse and social measurement. Ann Am Acad Pol Soc Sci 2013; 645: 222-236
    CrossrefPubMedGoogle Scholar
  • 32 Peytchev A. Consequences of survey nonresponse. Ann Am Acad Pol Soc Sci 2013; 645: 88-111
    CrossrefPubMedGoogle Scholar
  • 33 iDCA Digital Competence Assessment. Accessed October 02, 2019 at: http://www.digitalcompetence.org/
    PubMedGoogle Scholar
  • 34 Põldoja H, Väljataga T, Laanpere M, Tammets K. Web-based self- and peer-assessment of teachers' digital competencies. World Wide Web (Bussum) 2014; 17: 255-269
    CrossrefPubMedGoogle Scholar
  • 35 Arnone MP, Small RV, Reynolds R. Supporting inquiry by identifying gaps in student confidence: development of a measure of perceived competence. School Lib Worldwide 2010; 16: 47-60
    PubMedGoogle Scholar
  • 36 Tondeur J, Aesaert K, Pynoo B, van Braak J, Fraeyman N, Erstad O. Developing a validated instrument to measure preservice teachers' ICT competencies: meeting the demands of the 21st century. Br J Educ Technol 2017; 48: 462-472
    CrossrefPubMedGoogle Scholar
  • 37 Diogo C, António M. A model for discussing the quality of technology-enhanced learning in blended learning programmes. Int J Mobile Blended Learn 2017; 9: 1-20
    CrossrefPubMedGoogle Scholar
  • 38 Walker R, Voce J, Jenkins M. Charting the development of technology-enhanced learning developments across the UK higher education sector: a longitudinal perspective (2001–2012). Interact Learn Environ 2016; 24: 438-455
    CrossrefPubMedGoogle Scholar
  • 39 Devaraj S, Sharma SK, Fausto DJ, Viernes S, Kharrazi H. Barriers and facilitators to clinical decision support systems adoption: a systematic review. J Bus Admin Res 2014; 3: 36
    PubMedGoogle Scholar
  • 40 Garg AX, Adhikari NK, McDonald H. et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293: 1223-1238
    CrossrefPubMedGoogle Scholar
  • 41 Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 2020; 3: 17
    CrossrefPubMedGoogle Scholar
  • 42 Byambasuren O, Beller E, Hoffmann T, Glasziou P. Barriers to and facilitators of the prescription of mHealth Apps in Australian general practice: qualitative study. JMIR Mhealth Uhealth 2020; 8: e17447
    CrossrefPubMedGoogle Scholar
  • 43 Hor CP, O'Donnell JM, Murphy AW, O'Brien T, Kropmans TJ. General practitioners' attitudes and preparedness towards clinical decision support in e-Prescribing (CDS-eP) adoption in the West of Ireland: a cross sectional study. BMC Med Inform Decis Mak 2010; 10: 2
    CrossrefPubMedGoogle Scholar
  • 44 Peek N, Goud R, De Keizer N, van Engen-Verheul M, Kemps H, Hasman A. CARDSS: Development and evaluation of a guideline based decision support system for cardiac rehabilitation. Paper presented at: Conference on Artificial Intelligence in Medicine in Europe;; 2011
    PubMedGoogle Scholar
  • 45 Lai F, Macmillan J, Daudelin DH, Kent DM. The potential of training to increase acceptance and use of computerized decision support systems for medical diagnosis. Hum Factors 2006; 48: 95-108
    CrossrefPubMedGoogle Scholar
  • 46 Cobos A, Vilaseca J, Asenjo C. et al. Cost effectiveness of a clinical decision support system based on the recommendations of the European Society of Cardiology and other societies for the management of hypercholesterolemia. Dis Manag Health Outcomes 2005; 13: 421-432
    CrossrefPubMedGoogle Scholar