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Appl Clin Inform 2019; 10(04): 707-718
DOI: 10.1055/s-0039-1695794
Special Topic: Visual Analytics in Healthcare
Georg Thieme Verlag KG Stuttgart · New York

Visualization of Cardiac Implantable Electronic Device Data for Older Adults Using Participatory Design

Ryan Ahmed
1   Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, Indiana, United States
,
Tammy Toscos
1   Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, Indiana, United States
2   Department of BioHealth Informatics, IUPUI School of Informatics and Computing, Indianapolis, Indiana, United States
,
Romisa Rohani Ghahari
1   Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, Indiana, United States
,
Richard J. Holden
3   Indiana University School of Medicine, Indianapolis, Indiana, United States
4   Regenstrief Institute, Inc., Indianapolis, Indiana, United States
,
Elizabeth Martin
1   Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, Indiana, United States
,
Shauna Wagner
1   Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, Indiana, United States
,
Carly Daley
1   Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, Indiana, United States
2   Department of BioHealth Informatics, IUPUI School of Informatics and Computing, Indianapolis, Indiana, United States
,
Amanda Coupe
1   Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, Indiana, United States
,
Michael Mirro
1   Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, Indiana, United States
2   Department of BioHealth Informatics, IUPUI School of Informatics and Computing, Indianapolis, Indiana, United States
3   Indiana University School of Medicine, Indianapolis, Indiana, United States
› Institutsangaben Funding This work was supported by Biotronik SE & Co. KG.
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Publikationsverlauf

15. April 2019

15. Juli 2019

Publikationsdatum:
18. September 2019 (online)

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Abstract

Patients with heart failure (HF) are commonly implanted with cardiac resynchronization therapy (CRT) devices as part of their treatment. Presently, they cannot directly access the remote monitoring (RM) data generated from these devices, representing a missed opportunity for increased knowledge and engagement in care. However, electronic health data sharing can create information overload issues for both clinicians and patients, and some older patients may not be comfortable using the technology (i.e., computers and smartphones) necessary to access this data. To mitigate these problems, patients can be directly involved in the creation of data visualization tailored to their preferences and needs, allowing them to successfully interpret and act upon their health data. We held a participatory design (PD) session with seven adult patients with HF and CRT device implants, who were presently undergoing RM, along with two informal caregivers. Working in three teams, participants used drawing supplies and design cards to design a prototype for a patient-facing dashboard with which they could engage with their device data. Information that patients rated as a high priority for the “Main Dashboard” screen included average percent pacing with alerts for abnormal pacing, other device information such as battery life and recorded events, and information about who to contact with for data-related questions. Preferences for inclusion in an “Additional Information” display included a daily pacing chart, health tips, aborted shocks, a symptom list, and a journal. These results informed the creation of an actual dashboard prototype which was later evaluated by both patients and clinicians. Additionally, important insights were gleaned regarding the involvement of older patients in PD for health technology.

Keywords

heart failure - cardiac resynchronization therapy devices - consumer health informatics - health services for the aged - data visualization - human–computer interaction

Protection of Human and Animal Subjects

This research was conducted in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects. All procedures were reviewed and approved by Parkview Health's Institutional Review Board.


 

  • References

  • 1 Zhan C, Baine WB, Sedrakyan A, Steiner C. Cardiac device implantation in the United States from 1997 through 2004: a population-based analysis. J Gen Intern Med 2008; 23 (Suppl. 01) 13-19
    CrossrefPubMedSuche in Google Scholar
  • 2 Varma N, Piccini JP, Snell J, Fischer A, Dalal N, Mittal S. The relationship between level of adherence to automatic wireless remote monitoring and survival in pacemaker and defibrillator patients. J Am Coll Cardiol 2015; 65 (24) 2601-2610
    CrossrefPubMedSuche in Google Scholar
  • 3 Serber ER, Finch NJ, Leman RB. , et al. Disparities in preferences for receiving support and education among patients with implantable cardioverter defibrillators. Pacing Clin Electrophysiol 2009; 32 (03) 383-390
    CrossrefPubMedSuche in Google Scholar
  • 4 Daley CN, Chen EM, Roebuck AE. , et al. Providing patients with implantable cardiac device data through a personal health record: a qualitative study. Appl Clin Inform 2017; 8 (04) 1106-1116
    Thieme ConnectPubMedSuche in Google Scholar
  • 5 Daley C, Allmandinger A, Heral L. Engagement of ICD patients: direct electronic messaging of remote monitoring data via a personal health record. EP Lab Dig 2015; 15 (05) 1,6-10
    PubMedSuche in Google Scholar
  • 6 Skov MB, Johansen PG, Skov CS, Lauberg A. No news is good news: remote monitoring of implantable cardioverter-defibrillator patients. In: CHI '15 Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. New York: ACM; 2015 :827–836
    PubMedSuche in Google Scholar
  • 7 Staden A. Patients crusade for access to their medical device data. 2012 . Available at: https://www.npr.org/sections/health-shots/2012/05/28/153706099/patients-crusade-for-access-to-their-medical-device-data . Accessed April 15, 2019
    PubMedSuche in Google Scholar
  • 8 Daley C, Toscos T, Mirro M. Data integration and interoperability for patient-centered remote monitoring of cardiovascular implantable electronic devices. Bioengineering (Basel) 2019; 6 (01) 25
    CrossrefPubMedSuche in Google Scholar
  • 9 Koplan BA, Kaplan AJ, Weiner S, Jones PW, Seth M, Christman SA. Heart failure decompensation and all-cause mortality in relation to percent biventricular pacing in patients with heart failure: is a goal of 100% biventricular pacing necessary?. J Am Coll Cardiol 2009; 53 (04) 355-360
    CrossrefPubMedSuche in Google Scholar
  • 10 Longo DR, Schubert SL, Wright BA, LeMaster J, Williams CD, Clore JN. Health information seeking, receipt, and use in diabetes self-management. Ann Fam Med 2010; 8 (04) 334-340
    CrossrefPubMedSuche in Google Scholar
  • 11 Levine M, Richardson JE, Granieri E, Reid MC. Novel telemedicine technologies in geriatric chronic non-cancer pain: primary care providers' perspectives. Pain Med 2014; 15 (02) 206-213
    CrossrefPubMedSuche in Google Scholar
  • 12 Lorenz A, Oppermann R. Mobile health monitoring for the elderly: designing for diversity. Pervasive Mobile Comput 2009; 5: 478-495
    CrossrefPubMedSuche in Google Scholar
  • 13 Salvi SM, Akhtar S, Currie Z. Ageing changes in the eye. Postgrad Med J 2006; 82 (971) 581-587
    CrossrefPubMedSuche in Google Scholar
  • 14 Le T, Chi NC, Chaudhuri S, Thompson HJ, Demiris G. Understanding older adult use of data visualizations as a resource for maintaining health and wellness. J Appl Gerontol 2018; 37 (07) 922-939
    CrossrefPubMedSuche in Google Scholar
  • 15 Lindsay S, Brittain K, Jackson D, Ladha C, Ladha K, Olivier P. Empathy, participatory design and people with dementia. In: CHI '12 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM; 2012 :521–530
    PubMedSuche in Google Scholar
  • 16 Spinuzzi C. The methodology of participatory design. Tech Commun (Washington) 2005; 52 (02) 163-174
    PubMedSuche in Google Scholar
  • 17 Clemensen J, Larsen SB, Kyng M, Kirkevold M. Participatory design in health sciences: using cooperative experimental methods in developing health services and computer technology. Qual Health Res 2007; 17 (01) 122-130
    CrossrefPubMedSuche in Google Scholar
  • 18 Rogers Y, Paay J, Brereton M, Vaisutis KL, Marsden G, Vetere F. Never too old: engaging retired people inventing the future with MaKey MaKey. In: CHI '14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM; 2014 :3913–3922
    PubMedSuche in Google Scholar
  • 19 Caban JJ, Gotz D. Visual analytics in healthcare--opportunities and research challenges. J Am Med Inform Assoc 2015; 22 (02) 260-262
    CrossrefPubMedSuche in Google Scholar
  • 20 Huang D, Tory M, Aseniero BA. , et al. Personal visualization and personal visual analytics. IEEE Trans Vis Comput Graph 2015; 21 (03) 420-433
    CrossrefPubMedSuche in Google Scholar
  • 21 Mayr E, Smuc M, Risku H. Many roads lead to Rome: mapping users' problem-solving strategies. Inf Vis 2011; 10 (03) 232-247
    CrossrefPubMedSuche in Google Scholar
  • 22 Woods SS, Evans NC, Frisbee KL. Integrating patient voices into health information for self-care and patient-clinician partnerships: Veterans Affairs design recommendations for patient-generated data applications. J Am Med Inform Assoc 2016; 23 (03) 491-495
    CrossrefPubMedSuche in Google Scholar
  • 23 Wang TD, Wongsuphasawat K, Plaisant C, Shneiderman B. Extracting insights from electronic health records: case studies, a visual analytics process model, and design recommendations. J Med Syst 2011; 35 (05) 1135-1152
    CrossrefPubMedSuche in Google Scholar
  • 24 Terp M, Laursen BS, Jørgensen R, Mainz J, Bjørnes CD. A room for design: Through participatory design young adults with schizophrenia become strong collaborators. Int J Ment Health Nurs 2016; 25 (06) 496-506
    CrossrefPubMedSuche in Google Scholar
  • 25 Ghods A, Caffrey K, Lin B. , et al. Iterative design of visual analytics for a clinician-in-the-loop smart home. J Biomed Health Inform 2018 ; PP(99):1
    PubMed
  • 26 Rohani Ghahari R, Holden RJ, Flanagan M. , et al. Using cardiac implantable electronic device data to facilitate health decision making: a design study. Int J Ind Ergon 2018; 64: 143-154
    CrossrefPubMedSuche in Google Scholar
  • 27 Duke CC, Lynch WD, Smith B, Winstanley J. Validity of a new patient engagement measure: the Altarum Consumer Engagement (ACE) Measure™. Patient 2015; 8 (06) 559-568
    CrossrefPubMedSuche in Google Scholar
  • 28 Weiss BD, Mays MZ, Martz W. , et al. Quick assessment of literacy in primary care: the Newest Vital Sign. Ann Fam Med 2005; 3 (06) 514-522
    CrossrefPubMedSuche in Google Scholar
  • 29 Hwang AS, Truong KN, Mihailidis A. Using participatory design to determine the needs of informal caregivers for smart home user interfaces. In 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops; IEEE, May 21, 2012:41–48
    PubMedSuche in Google Scholar
  • 30 Caine KE, Zimmerman CY, Schall-Zimmerman Z. , et al. DigiSwitch: design and evaluation of a device of older adults to preserve privacy while monitoring health at home. In: IHI'10–Proceedings of the 1 st ACM International Health Informatics Symposium. New York: ACM; 2010 :153–162
    PubMedSuche in Google Scholar
  • 31 Delbecq AL, Van de Ven AH, Gustafson DH. Group techniques for program planning: A guide to nominal group and Delphi processes. Scott Foresman; 1975: 44-69
    Suche in Google Scholar
  • 32 Kendal SE, Milnes L, Welsby H, Pryjmachuk S. ; Co-Researchers' Group. Prioritizing young people's emotional health support needs via participatory research. J Psychiatr Ment Health Nurs 2017; 24 (05) 263-271
    CrossrefPubMedSuche in Google Scholar
  • 33 Rayment J, Lanlehin R, McCourt C, Husain SM. Involving seldom-heard groups in a PPI process to inform the design of a proposed trial on the use of probiotics to prevent preterm birth: a case study. Res Involv Engagem 2017; 3 (01) 11
    CrossrefPubMedSuche in Google Scholar
  • 34 Le T, Reeder B, Yoo D, Aziz R, Thompson HJ, Demiris G. An evaluation of wellness assessment visualizations for older adults. Telemed J E Health 2015; 21 (01) 9-15
    CrossrefPubMedSuche in Google Scholar
  • 35 Brants W, Sharif B, Serebrenik A. Assessing the meaning of emojis for emotional awareness–a pilot study. Paper presented at: 2nd International Workshop on Emoji Understanding and Applications in Social Media Co-located with The Web Conference 2019. May 13–17, 2019 ; San Francisco
    PubMedSuche in Google Scholar
  • 36 Heo S, Lennie TA, Okoli C, Moser DK. Quality of life in patients with heart failure: ask the patients. Heart Lung 2009; 38 (02) 100-108
    CrossrefPubMedSuche in Google Scholar
  • 37 Mirro M, Daley C, Wagner S, Rohani Ghahari R, Drouin M, Toscos T. Delivering remote monitoring data to patients with implantable cardioverter-defibrillators: does medium matter?. Pacing Clin Electrophysiol 2018; 41 (11) 1526-1535
    CrossrefPubMedSuche in Google Scholar
  • 38 Hartzler AL, Izard JP, Dalkin BL, Mikles SP, Gore JL. Design and feasibility of integrating personalized PRO dashboards into prostate cancer care. J Am Med Inform Assoc 2016; 23 (01) 38-47
    CrossrefPubMedSuche in Google Scholar
  • 39 Dekker-van Weering MGH, Vollenbroek-Hutten MM. Development of a telemedicine service that enables functional training for stroke patients in the home environment. In Proceedings of the 3rd 2015 Workshop on ICTs for Improving Patients Rehabilitation Research Techniques. ACM; October 2015 :109–112
    PubMedSuche in Google Scholar
  • 40 Couture B, Lilley E, Chang F. , et al. Applying user-centered design methods to the development of an mHealth application for use in the hospital setting by patients and care partners. Appl Clin Inform 2018; 9 (02) 302-312
    Thieme ConnectPubMedSuche in Google Scholar
  • 41 Reddy A, Lester CA, Stone JA, Holden RJ, Phelan CH, Chui MA. Applying participatory design to a pharmacy system intervention. Res Social Adm Pharm 2018; pii: S1551-7411(17):30866-5
    PubMedSuche in Google Scholar