Developing and Validating a Computerized Interpretable Guideline for Asthma Management in Primary Care

Document Type : Original Article

Authors
Farahnaz Sadoughi 1
Saman Mohammadpour 2
Saba Arshi 3
Shirin Ayani 4
Kamyar Fathisalari 5
1 Professor, Health Information Management, Department of Health Information Management, School of Health Management and Information Science, Iran University of Medical Sciences, Tehran, Iran
2 MSc, Medical Informatics, Department of Health Information Management, School of Health Management and Information Science, Iran University of Medical Sciences, Tehran, Iran
3 Associate Professor, Allergy and Clinical Immunology, Department of Allergy and Clinical Immunology, School of Medicine AND Rasool-e-Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
4 PhD, Medical Informatics, Department of Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
5 MSc, Health Information Technology, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
10.22122/him.v17i4.4159
Abstract
Introduction: One of the most effective ways to improve the management of asthma in primary care is using guidelines in the patient management process; but due to lack of time, their use is very limited. One way to solve this problem is mechanized guideline in a form of decision-support system that requires computer-interpreted guideline (CIG). In this study, a CIG was developed for asthma management in primary care.Methods: This qualitative study was performed using mini-Delphi method uses a workflow-based approach to develop a CIG. The workflows and data components were extracted through Business Process Model and Notation (BPMN) language and Enterprise Architecture (EA) software. Afterwards, they were confirmed through an expert panel of five asthma specialists.Results: The developed CIG included three major workflows, four sub-workflows, and 21 data components. Main workflows included treatment, drug interaction, and follow-up time. Sub-workflows were related to treatment workflow that were developed based on the patient visit number and age.Conclusion: Because the accuracy of this workflow was confirmed by asthma specialists, it is expected that the design of this CIG will lead to development of a cost-effective software for the management of asthma and better detection of drug interactions.

Keywords

  1. Nunes C, Pereira A, Morais-Almeida M. Asthma costs and social impact. Asthma Research and Practice 2017; 3: 1.
  2. Peters SP, Ferguson G, Deniz Y, Reisner C. Uncontrolled asthma: A review of the prevalence, disease burden and options for treatment. Respir Med 2006; 100(7): 1139-51.
  3. Global Initiative for Asthma (GINA). Global Strategy for Asthma Management and Prevention 2017 [Online]. [cited 2019]; Available from: URL: https://ginasthma.org/wp-content/uploads/2019/04/wmsGINA-2017-main-report-final_V2.pdf
  4. O'Byrne PM, Pedersen S, Schatz M, Thoren A, Ekholm E, Carlsson LG, et al. The poorly explored impact of uncontrolled asthma. Chest 2013; 143(2): 511-23.
  5. Wechsler ME. Managing asthma in primary care: Putting new guideline recommendations into context. Mayo Clin Proc 2009; 84(8): 707-17.
  6. Sheehan WJ, Phipatanakul W. Difficult-to-control asthma: epidemiology and its link with environmental factors. Curr Opin Allergy Clin Immunol 2015; 15(5): 397-401.
  7. Myers TR. Guidelines for asthma management: A review and comparison of 5 current guidelines. Respir Care 2008; 53(6): 751-67.
  8. Snell NJ. Drug interactions with anti-asthma medication. Respir Med 1994; 88(2): 83-8.
  9. Wang Z, Norris SL, Bero L. The advantages and limitations of guideline adaptation frameworks. Implement Sci 2018; 13(1): 72.
  10. Dexheimer J, Gu L, Guo Y, Johnson L, Kercsmar C. Design and implementation of the asthma treat smart system in a pediatric institution. Knowledge Management and E-Learning 2015; 7(3): 353-66.
  11. Sonnenberg FA, Hagerty CG. Computer-interpretable clinical practice guidelines. Where are we and where are we going? Yearb Med Inform 2006; 145-58.
  12. Mathe J. The precise construction of patient protocols: Modeling, simulation and analysis of computer interpretable guidelines [PhD Thesis]. Nashville, TN: Vanderbilt University; 2012.
  13. Quaglini S, Stefanelli M, Cavallini A, Micieli G, Fassino C, Mossa C. Guideline-based careflow systems. Artif Intell Med 2000; 20(1): 5-22.
  14. Wang VX, Reio TG. Handbook of research on innovative techniques, trends, and analysis for optimized research methods. Hershey, PA: IGI Global; 2017.
  15. Bracha Y, Brottman G, Carlson A. Physicians, guidelines, and cognitive tasks. Eval Health Prof 2011; 34(3): 309-35.
  16. Bousquet J, Busse WW. Section 1. EPR-3 versus GINA 2008 Guidelines - Asthma Control and Step 3 Care: Highlights of the Asthma Summit 2009: Beyond the Guidelines. World Allergy Organ J 2010; 3(2): 16-22.
  17. Lorensia A, Canggih B, Wijaya R. Drug interaction analysis in hospitalized and outpatient care of asthma patient in Adi Husada Undaan Wetan Hospital, Surabaya, Indonesia. Proceedings of the 1st International Pharmacy Conference on Research and Practice: 2012 Nov 13-14; Yogyakarta, Indonesia.
  18. Mammen J, Rhee H. Adolescent asthma self-management: A concept analysis and operational definition. Pediatr Allergy Immunol Pulmonol 2012; 25(4): 180-9.
  19. Gholamzadeh M, Abtahi H, Asadi Gharabaghi M, Taleb Z, Amini S. Improving GINA adoption by designing a mobile based clinical decision support system. Eur Respir J 2018; 52(Suppl 62): OA1683.
Health Information Management
Volume 17, Issue 4 - Serial Number 4
Autumn 2020
Pages 150-158