CC BY-NC-ND 4.0 · Appl Clin Inform 2022; 13(04): 971-982
DOI: 10.1055/s-0042-1757292
Letter to the Editor

User Experience Design for Adoption of Asthma Clinical Decision Support Tools

Emily Gao
1   University of California Los Angeles, Los Angeles, California, United States
Ilana Radparvar
1   University of California Los Angeles, Los Angeles, California, United States
Holly Dieu
2   Department of Pediatrics, University of California Los Angeles, David Geffen School of Medicine, Los Angeles, California, United States
Mindy K. Ross
2   Department of Pediatrics, University of California Los Angeles, David Geffen School of Medicine, Los Angeles, California, United States
› Author Affiliations
Funding This study was supported by U.S. Department of Health and Human Services; National Institutes of Health; and National Heart, Lung, and Blood Institute (1K23HL148502-01A1).

Background and Significance

Asthma affects over 200 million people worldwide and uncontrolled cases typically lead to the most morbidity.[1] Guidelines can improve asthma symptom control and patient outcomes, although their use in practice is suboptimal (e.g., <40% documented key components).[2] [3] [4] To improve these rates, approaches based on clinical informatics such as guideline-adherent computerized clinical decision support (CDS) tools have been attempted.[5] [6] [7] [8] These tools can provide standardized, personalized, and comprehensive care to improve outcomes.[9] [10] [11]

Asthma CDS tools have not been readily adopted into practice, thus reducing their effectiveness due to lack of use.[9] [12] [13] [14] [15] [16] Reasons suggested for low uptake appear similar to general issues with computerized CDS[17] [18] [19] (e.g., poor workflow integration, negative end-user beliefs),[20] [21] [22] but there has not been an inventory of facilitators and barriers to use in the asthma CDS tool domain. Detailing this could improve the design process for asthma-specific computerized CDS tools by highlighting relevant aspects, centralizing knowledge about key features, and identifying the most effective implementation strategies.[23]

Protection of Human and Animal Subjects

There were no human subjects in this work.

Supplementary Material

Publication History

Received: 19 May 2022

Accepted: 09 August 2022

Article published online:
12 October 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (

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  • References

  • 1 WHO. Asthma. World Health Organization. Accessed September 6, 2022 at
  • 2 Okelo SO, Butz AM, Sharma R. et al. Interventions to modify health care provider adherence to asthma guidelines: a systematic review. Pediatrics 2013; 132 (03) 517-534
  • 3 Cloutier MM, Salo PM, Akinbami LJ. et al. Clinician agreement, self-efficacy, and adherence with the guidelines for the diagnosis and management of asthma. J Allergy Clin Immunol Pract 2018; 6 (03) 886.e4-894.e4
  • 4 Rege S, Kavati A, Ortiz B. et al. Documentation of asthma control and severity in pediatrics: analysis of national office-based visits. J Asthma 2020; 57 (02) 205-216
  • 5 Haberman S, Feldman J, Merhi ZO, Markenson G, Cohen W, Minkoff H. Effect of clinical-decision support on documentation compliance in an electronic medical record. Obstet Gynecol 2009; 114 (2, Pt 1): 311-317
  • 6 Salem HA, Caddeo G, McFarlane J. et al. A multicentre integration of a computer-led follow-up of prostate cancer is valid and safe. BJU Int 2018; 122 (03) 418-426
  • 7 Koutkias V, Bouaud J. Section Editors for the IMIA Yearbook Section on Decision Support. Contributions from the 2017 Literature on Clinical Decision Support. Yearb Med Inform 2018; 27 (01) 122-128
  • 8 Rudin RS, Fanta CH, Qureshi N. et al. A clinically integrated mHealth app and practice model for collecting patient-reported outcomes between visits for asthma patients: implementation and feasibility. Appl Clin Inform 2019; 10 (05) 783-793
  • 9 McCowan C, Neville RG, Ricketts IW, Warner FC, Hoskins G, Thomas GE. Lessons from a randomized controlled trial designed to evaluate computer decision support software to improve the management of asthma. Med Inform Internet Med 2001; 26 (03) 191-201
  • 10 Fiks AG, Mayne SL, Karavite DJ. et al. Parent-reported outcomes of a shared decision-making portal in asthma: a practice-based RCT. Pediatrics 2015; 135 (04) e965-e973
  • 11 Jacobs BR, Hart KW, Rucker DW. Reduction in clinical variance using targeted design changes in computerized provider order entry (CPOE) order sets: impact on hospitalized children with acute asthma exacerbation. Appl Clin Inform 2012; 3 (01) 52-63
  • 12 Eccles M, McColl E, Steen N. et al. Effect of computerised evidence based guidelines on management of asthma and angina in adults in primary care: cluster randomised controlled trial. BMJ 2002; 325 (7370): 941
  • 13 Fiks AG, DuRivage N, Mayne SL. et al. Adoption of a portal for the primary care management of pediatric asthma: a mixed-methods implementation study. J Med Internet Res 2016; 18 (06) e172
  • 14 Lam Shin Cheung J, Paolucci N, Price C, Sykes J, Gupta S. Canadian Respiratory Research Network. A system uptake analysis and GUIDES checklist evaluation of the electronic asthma management system: a point-of-care computerized clinical decision support system. J Am Med Inform Assoc 2020; 27 (05) 726-737
  • 15 Matui P, Wyatt JC, Pinnock H, Sheikh A, McLean S. Computer decision support systems for asthma: a systematic review. NPJ Prim Care Respir Med 2014; 24: 14005
  • 16 Greenes RA, Bates DW, Kawamoto K, Middleton B, Osheroff J, Shahar Y. Clinical decision support models and frameworks: seeking to address research issues underlying implementation successes and failures. J Biomed Inform 2018; 78: 134-143
  • 17 Shi Y, Amill-Rosario A, Rudin RS. et al. Barriers to using clinical decision support in ambulatory care: do clinics in health systems fare better?. J Am Med Inform Assoc 2021; 28 (08) 1667-1675
  • 18 Bates DW, Kuperman GJ, Wang S. et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530
  • 19 Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc 2007; 14 (02) 141-145
  • 20 van den Wijngaart LS, Geense WW, Boehmer AL. et al. Barriers and facilitators when implementing web-based disease monitoring and management as a substitution for regular outpatient care in pediatric asthma: qualitative survey study. J Med Internet Res 2018; 20 (10) e284
  • 21 Denton E, Hore-Lacy F, Radhakrishna N. et al. Severe Asthma Global Evaluation (SAGE): an electronic platform for severe asthma. J Allergy Clin Immunol Pract 2019; 7 (05) 1440-1449
  • 22 Kercsmar CM, Sorkness CA, Calatroni A. et al; National Institute of Allergy and Infectious Diseases–sponsored Inner-City Asthma Consortium. A computerized decision support tool to implement asthma guidelines for children and adolescents. J Allergy Clin Immunol 2019; 143 (05) 1760-1768
  • 23 van Leeuwen D, Mittelman M, Fabian L, Lomotan EA. Nothing for me or about me, without me: codesign of clinical decision support. Appl Clin Inform 2022; 13 (03) 641-646
  • 24 Tricco AC, Lillie E, Zarin W. et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018; 169 (07) 467-473
  • 25 Health Clinical Decision Support. U.S. Department of Health and Human Services. Accessed September 6, 2022 at:
  • 26 Tai SS, Nazareth I, Donegan C, Haines A. Evaluation of general practice computer templates. Lessons from a pilot randomised controlled trial. Methods Inf Med 1999; 38 (03) 177-181
  • 27 Eccles M, Grimshaw J, Steen N. et al. The design and analysis of a randomized controlled trial to evaluate computerized decision support in primary care: the COGENT study. Fam Pract 2000; 17 (02) 180-186
  • 28 Bell LM, Grundmeier R, Localio R. et al. Electronic health record-based decision support to improve asthma care: a cluster-randomized trial. Pediatrics 2010; 125 (04) e770-e777
  • 29 Davis AM, Cannon M, Ables AZ, Bendyk H. Using the electronic medical record to improve asthma severity documentation and treatment among family medicine residents. Fam Med 2010; 42 (05) 334-337
  • 30 Hoeksema LJ, Bazzy-Asaad A, Lomotan EA. et al. Accuracy of a computerized clinical decision-support system for asthma assessment and management. J Am Med Inform Assoc 2011; 18 (03) 243-250
  • 31 Shapiro A, Gracy D, Quinones W, Applebaum J, Sarmiento A. Putting guidelines into practice: improving documentation of pediatric asthma management using a decision-making tool. Arch Pediatr Adolesc Med 2011; 165 (05) 412-418
  • 32 Buenestado D, Elorz J, Pérez-Yarza EG. et al. Evaluating acceptance and user experience of a guideline-based clinical decision support system execution platform. J Med Syst 2013; 37 (02) 9910
  • 33 Kuhn L, Reeves K, Taylor Y. et al. Planning for action: the impact of an asthma action plan decision support tool integrated into an electronic health record (EHR) at a large health care system. J Am Board Fam Med 2015; 28 (03) 382-393
  • 34 Tamblyn R, Ernst P, Winslade N. et al. Evaluating the impact of an integrated computer-based decision support with person-centered analytics for the management of asthma in primary care: a randomized controlled trial. J Am Med Inform Assoc 2015; 22 (04) 773-783
  • 35 Lee J, Gogo A, Tancredi D, Fernandez Y Garcia E, Shaikh U. Improving asthma care in a pediatric resident clinic. BMJ Qual Improv Rep 2016; 5 (01) u214746.w6381
  • 36 Matiz LA, Robbins-Milne L, Krause MC, Peretz PJ, Rausch JC. Evaluating the impact of information technology tools to support the asthma medical home. Clin Pediatr (Phila) 2016; 55 (02) 165-170
  • 37 Gupta S, Price C, Agarwal G. et al. The Electronic Asthma Management System (eAMS) improves primary care asthma management. Eur Respir J 2019; 53 (04) 1802241
  • 38 Adams WG, Fuhlbrigge AL, Miller CW. et al. TLC-Asthma: an integrated information system for patient-centered monitoring, case management, and point-of-care decision support. AMIA Annu Symp Proc 2003; 2003: 1-5
  • 39 Mammen JR, Java JJ, Halterman J. et al. Development and preliminary results of an electronic medical record (EMR)-integrated smartphone telemedicine program to deliver asthma care remotely. J Telemed Telecare 2021; 27 (04) 217-230
  • 40 Mammen JR, Schoonmaker JD, Java J. et al. Going mobile with primary care: smartphone-telemedicine for asthma management in young urban adults (TEAMS). J Asthma 2022; 59 (01) 132-144
  • 41 Fiks AG, Mayne S, Karavite DJ, DeBartolo E, Grundmeier RW. A shared e-decision support portal for pediatric asthma. J Ambul Care Manage 2014; 37 (02) 120-126
  • 42 Martens JD, van der Aa A, Panis B, van der Weijden T, Winkens RA, Severens JL. Design and evaluation of a computer reminder system to improve prescribing behaviour of GPs. Stud Health Technol Inform 2006; 124: 617-623
  • 43 Martens JD, van der Weijden T, Severens JL. et al. The effect of computer reminders on GPs' prescribing behaviour: a cluster-randomised trial. Int J Med Inform 2007; 76 (Suppl. 03) S403-S416
  • 44 Gupta S, Wan FT, Hall SE, Straus SE. An asthma action plan created by physician, educator and patient online collaboration with usability and visual design optimization. Respiration 2012; 84 (05) 406-415
  • 45 Twiggs JE, Fifield J, Jackson E, Cushman R, Apter A. Treating asthma by the guidelines: developing a medication management information system for use in primary care. Dis Manag 2004; 7 (03) 244-260
  • 46 Tierney WM, Overhage JM, Murray MD. et al. Can computer-generated evidence-based care suggestions enhance evidence-based management of asthma and chronic obstructive pulmonary disease? A randomized, controlled trial. Health Serv Res 2005; 40 (02) 477-497
  • 47 Shegog R, Bartholomew LK, Sockrider MM. et al. Computer-based decision support for pediatric asthma management: description and feasibility of the Stop Asthma Clinical System. Health Informatics J 2006; 12 (04) 259-273
  • 48 Lomotan EA, Hoeksema LJ, Edmonds DE, Ramírez-Garnica G, Shiffman RN, Horwitz LI. Evaluating the use of a computerized clinical decision support system for asthma by pediatric pulmonologists. Int J Med Inform 2012; 81 (03) 157-165
  • 49 Penkalski MR, Kenneally M. Provider adherence to evidence-based asthma guidelines in a community health center. J Dr Nurs Pract 2016; 9 (01) 128-138
  • 50 Ash JS, Chase D, Wiesen JF, Murphy EV, Marovich S. Studying readiness for clinical decision support for worker health using the rapid assessment process and mixed methods interviews. AMIA Annu Symp Proc 2017; 2016: 285-294
  • 51 Shiffman RN, Freudigman Md, Brandt CA, Liaw Y, Navedo DD. A guideline implementation system using handheld computers for office management of asthma: effects on adherence and patient outcomes. Pediatrics 2000; 105 (4, Pt 1): 767-773
  • 52 Kuilboer MM, van Wijk MA, Mosseveld M. et al. Computed critiquing integrated into daily clinical practice affects physicians' behavior–a randomized clinical trial with AsthmaCritic. Methods Inf Med 2006; 45 (04) 447-454
  • 53 van den Wijngaart LS, Roukema J, Boehmer ALM. et al. A virtual asthma clinic for children: fewer routine outpatient visits, same asthma control. Eur Respir J 2017; 50 (04) 1700471
  • 54 Wasylewicz ATM, Scheepers-Hoeks A. Clinical decision support systems. In: Kubben P, Dumontier M, Dekker A. eds. Fundamentals of Clinical Data Science. Cham: Springer; 2019: 153-169
  • 55 Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA 2018; 320 (21) 2199-2200
  • 56 Field TS, Rochon P, Lee M. et al. Costs associated with developing and implementing a computerized clinical decision support system for medication dosing for patients with renal insufficiency in the long-term care setting. J Am Med Inform Assoc 2008; 15 (04) 466-472
  • 57 Jacob V, Thota AB, Chattopadhyay SK. et al. Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: a community guide systematic review. J Am Med Inform Assoc 2017; 24 (03) 669-676
  • 58 Pope-Ruark R. Agile Faculty: Practical Strategies for Managing Research, Service, and Teaching. Chicago, IL: University of Chicago Press; 2017
  • 59 Sauer G. Applying usability and user experience within academic contexts: why progress remains slow. Tech Commun Q 2018; 27 (04) 362-371
  • 60 Ku B, Lupton E. Health Design Thinking: Creating Products and Services for Better Health. 2nd ed. Cambridge, MA: MIT Press; 2022
  • 61 Brunner J, Chuang E, Goldzweig C, Cain CL, Sugar C, Yano EM. User-centered design to improve clinical decision support in primary care. Int J Med Inform 2017; 104: 56-64
  • 62 Ogrinc G, Davies L, Goodman D, Batalden P, Davidoff F, Stevens D. SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): revised publication guidelines from a detailed consensus process. BMJ Qual Saf 2016; 25 (12) 986-992
  • 63 Cooper RG. The drivers of success in new-product development. Ind Mark Manage 2019; 76: 36-47
  • 64 Rudin RS, Fanta CH, Predmore Z. et al. Core components for a clinically integrated mhealth app for asthma symptom monitoring. Appl Clin Inform 2017; 8 (04) 1031-1043
  • 65 Van de Velde S, Kunnamo I, Roshanov P. et al; GUIDES expert panel. The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support. Implement Sci 2018; 13 (01) 86
  • 66 Osheroff JA. Healthcare Information and Management Systems Society. Improving Outcomes with Clinical Decision Support: An Implementer's guide. 2nd ed. Chicago, IL: HIMSS; 2012
  • 67 Budde R, Kuhlenkamp K, Mathiassen L. et al. Approaches to Prototyping Proceedings of the Working Conference on Prototyping, October 25–28, 1983, Namur, Belgium. Berlin, Heidelberg: Springer Berlin/Heidelberg; 1984
  • 68 Dearle A. Software deployment, past, present and future. Future of Software Engineering 2007; 269-284
  • 69 Hartson HR, Pyla PS. The UX Book: Process and Guidelines for Ensuring a Quality User Experience. Vol. 1. Waltham, MA: Morgan Kaufmann; 2012
  • 70 Thomas J, McDonagh D. Empathic design: Research strategies. Australas Med J 2013; 6 (01) 1-6
  • 71 Augustin L, Kokoschko B, Wiesner M. et al. Toward a comprehensive definition of the non-user. In: Proceedings of the Design Society: DESIGN Conference. 2020;1:1853–1862
  • 72 Rogers EM. Diffusion of Innovations. 5th ed. New York, NY: Free Press; 2003
  • 73 Silverman D. Qualitative Research: Issues of Theory, Method, and Practice. 3rd ed. Los Angeles, CA: Sage; 2011
  • 74 Michie S, Johnston M, Abraham C, Lawton R, Parker D, Walker A. “Psychological Theory” Group. Making psychological theory useful for implementing evidence based practice: a consensus approach. Qual Saf Health Care 2005; 14 (01) 26-33
  • 75 Leonard-Barton D, Rayport JF. Spark innovation through empathic design. Harvard Bus Rev 1997; 75 (06) 102-113
  • 76 Ulwick AW. Jobs to Be Done: Theory to Practice. Houston, TX: Idea Bite Press; 2016
  • 77 Interaction Design Foundation. Five stages in the design thinking process. Accessed September 6, 2022 at:
  • 78 Kelly CJ, Young AJ. Promoting innovation in healthcare. Future Healthc J 2017; 4 (02) 121-125
  • 79 Nelson SD, Del Fiol G, Hanseler H, Crouch BI, Cummins MR. Software prototyping: a case report of refining user requirements for a health information exchange dashboard. Appl Clin Inform 2016; 7 (01) 22-32
  • 80 Williams L. Agile software development methodologies and practices. Advances in Computers 2010; 80 (Jan 1): 1-44
  • 81 Liikkanen LHKH, Svan L, Hiltunen M. Lean UX: the Next Generation of User-Centered Agile Development? 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational. Helsinki, Finland: Association for Computing Machiner; 2014
  • 82 Provost LP, Murray SK. The Health Care Data Guide: Learning from Data for Improvement. 1st ed. San Francisco, CA: Jossey-Bass; 2011
  • 83 Langley GJ. The Improvement Guide: A Practical Approach to Enhancing Organizational Performance. 1st ed. San Francisco: Jossey-Bass Publishers; 1996
  • 84 Gerald JL, Ronald DM, Kevin MN. et al. The Improvement Guide: A Practical Approach to Enhancing Organizational Performance. 2nd ed. San Francisco, CA: Jossey Bass Inc.; 2009