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Cited by 15Mobile Health and mHealth ApplicationsTelemedicine and Telehealth ImplementationTechnology and Human Factors in Education and Health

Developing and validating high-value patient digital follow-up services: a pilot study in cardiac surgery

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Londral, A., Azevedo, S., Dias, P., Ramos, C., Santos, J., Martins, F., Silva, R., Semedo, H. +6 more·2022·BMC Health Services Research
DOI 10.1186/s12913-022-08073-4PMC PMC9123610
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Abstract

Background: The existing digital healthcare solutions demand a service development approach that assesses needs, experience, and outcomes, to develop high-value digital healthcare services. The objective of this study was to develop a digital transformation of the patients’ follow-up service after cardiac surgery, based on a remote patient monitoring service that would respond to the real context challenges. Methods: The study followed the Design Science Research methodology framework and incorporated concepts from the Lean startup method to start designing a minimal viable product (MVP) from the available resources. The service was implemented in a pilot study with 29 patients in 4 iterative develop-test-learn cycles, with the engagement of developers, researchers, clinical teams, and patients. Results: Patients reported outcomes daily for 30 days after surgery through Internet-of-Things (IoT) devices and a mobile app. The service’s evaluation considered experience, feasibility, and effectiveness. It generated high satisfaction and high adherence among users, fewer readmissions, with an average of 7 ± 4.5 clinical actions per patient, primarily due to abnormal systolic blood pressure or wound-related issues. Conclusions: We propose a 6-step methodology to design and validate a high-value digital health care service based on collaborative learning, real-time development, iterative testing, and value assessment.

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  • AI chatbots facilitate well-designed multimodal conversations that can effectively assist in patient intervention, postoperative support, and psychiatric care.

    “Previous studies have revealed that AI chatbots facilitate well-designed multimodal conversations that can effectively assist in a variety of areas such as patient intervention (8), postoperative support (9), and psychiatric care (10)”

    From Digital affordances of AI chatbots in nursing education: a systematic review of learning gains and gaps in the evidence (2026) · cites this paper as [9]

    The Methods (Pilot study and evaluation; page unavailable) describe a chatbot application that exchanged messages and wound photographs during cardiac-surgery follow-up. The Results (Implementation; page unavailable) describe predefined, scheduled literacy messages, and patient feedback indicates that the service offered postoperative support. The copy does not establish that the chatbot used AI or conducted the well-designed multimodal conversations claimed. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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  1. [1]Peirce SC Hardisty AR Preece AD Elwyn G Designing and implementing telemonitoring for early detection of deterioration in chronic disease: defining the requirements Health Informatics J 2011 17 173 190 10.1177/1460458211409717 21937461 · DOI
  2. [2]Drummond M Griffin A Tarricone R Economic evaluation for devices and drugs—same or different? Value Health 2009 12 402 404 10.1111/j.1524-4733.2008.00476_1.x 19138306 · DOI
  3. [3]Guo C Challenges for the evaluation of digital health solutions—a call for innovative evidence generation approaches Npj Digit Med 2020 3 110 10.1038/s41746-020-00314-2 32904379 PMC7453198 · DOI
  4. [4]Murray E Evaluating digital health interventions Am J Prev Med 2016 51 843 851 10.1016/j.amepre.2016.06.008 27745684 PMC5324832 · DOI · Paper page
  5. [6]Mulgund P Sharman R Rifkin D Marrazzo S Design, development, and evaluation of a telemedicine platform for patients with sleep apnea (Ognomy): design science research approach JMIR Form Res 2021 5 e26059 10.2196/26059 34279237 PMC8329758 · DOI
  6. [7]Lapão LV Implementation of digital monitoring services during the COVID-19 pandemic for patients with chronic diseases: design science approach J Med Internet Res 2021 23 e24181 10.2196/24181 34313591 PMC8396539 · DOI
  7. [8]Driggin E Cardiovascular considerations for patients, health care workers, and health systems during the COVID-19 pandemic J Am Coll Cardiol 2020 75 2352 2371 10.1016/j.jacc.2020.03.031 32201335 PMC7198856 · DOI
  8. [9]Crawford TC Complications after cardiac operations: all are not created equal Ann Thorac Surg 2017 103 32 40 10.1016/j.athoracsur.2016.10.022 27884410 · DOI
  9. [10]Efthymiou CA O’Regan DJ Postdischarge complications: what exactly happens when the patient goes home? Interact Cardiovasc Thorac Surg 2011 12 130 134 10.1510/icvts.2010.249474 21123196 · DOI
  10. [11]Khoury H Readmission after surgical aortic valve replacement in the United States Ann Thorac Surg 2020 110 849 855 10.1016/j.athoracsur.2019.11.058 31981500 · DOI
  11. [12]McElroy I Use of digital health kits to reduce readmission after cardiac surgery J Surg Res 2016 204 1 7 10.1016/j.jss.2016.04.028 27451860 · DOI
  12. [13]Park DK Telecare system for cardiac surgery patients: implementation and effectiveness Healthc Inform Res 2011 17 93 10.4258/hir.2011.17.2.93 21886870 PMC3155172 · DOI
  13. [14]Pekmezaris R Home Telemonitoring in heart failure: a systematic review and meta-analysis Health Aff (Millwood) 2018 37 1983 1989 10.1377/hlthaff.2018.05087 30633680 · DOI
  14. [17]Mori M, et al. Characterizing patient-centered postoperative recovery after adult cardiac surgery: a systematic review. J Am Heart Assoc. 2019;8(21):e013546. PMC6898802 31617435
  15. [18]Silva-Cardoso J, et al. SmartBEAT: a smartphone-based heart failure telemonitoring solution. In: Henriques J, Neves N, de Carvalho P, editors. XV Mediterranean conference on medical and biological engineering and computing – MEDICON 2019, vol. 76: Springer International Publishing; 2020. p. 1369–76.
  16. [19]Clemensen J Rothmann MJ Smith AC Caffery LJ Danbjorg DB Participatory design methods in telemedicine research J Telemed Telecare 2017 23 780 785 10.1177/1357633X16686747 28027678 · DOI
  17. [21]Eisenmann T, Ries E, Dillard S. Hypothesis-driven entrepreneurship: the lean startup. Harv Bus Sch Backgr Note. 2011; Case No. 812-095. Available at SSRN: https://ssrn.com/abstract=2037237.
  18. [23]Bhattacharyya O Redesigning care: adapting new improvement methods to achieve person-centred care BMJ Qual Saf 2019 28 242 248 10.1136/bmjqs-2018-008208 30242019 PMC6860732 · DOI
  19. [24]Pufahl L, Zerbato F, Weber B. Weber I. BPMN in healthcare: Challenges and best practices. Information Systems. 2022;107:102013.
  20. [26]Stilgoe J Owen R Macnaghten P Developing a framework for responsible innovation Res Policy 2013 42 1568 1580 10.1016/j.respol.2013.05.008 · DOI · Paper page
  21. [27]Iakovleva T. Responsible innovation in digital health. In: Responsible Innovation in Digital Health: Edward Elgar Publishing; 2019. p. 1–8. 10.4337/9781788975063.00008. · DOI
  22. [29]Cota MP Thomaschewski J Schrepp M Gonçalves R Efficient measurement of the user experience. A Portuguese Version Procedia Comput Sci 2014 27 491 498 10.1016/j.procs.2014.02.053 · DOI
  23. [30]Hamilton DF Assessing treatment outcomes using a single question: the net promoter score Bone Jt J 2014 96-B 622 628 10.1302/0301-620X.96B5.32434 24788496 · DOI
  24. [31]Woolson RF. Wilcoxon signed-rank test. In: D’Agostino RB, Sullivan L, Massaro J, editors. Wiley encyclopedia of clinical trials: John Wiley & Sons, Inc.; 2008. p. eoct979. 10.1002/9780471462422.eoct979. · DOI
  25. [32]Reichheld FF The one number you need to grow Harv Bus Rev 2003 81 46 54, 124 14712543
  26. [33]Sauro J, Lewis JR. Did we meet or exceed our goal? In: Quantifying the user experience: Elsevier; 2012. p. 41–62. 10.1016/B978-0-12-384968-7.00004-7. · DOI
  27. [34]Coelho PNMP Miranda LMRPC Barros PMP Fragata JIG Quality of life after elective cardiac surgery in elderly patients Interact Cardiovasc Thorac Surg 2019 28 199 205 10.1093/icvts/ivy235 30085061 · DOI
  28. [35]Cooper RG Perspective: the stage-gate ® idea-to-launch process—update, What’s new, and NexGen systems J Prod Innov Manag 2008 25 213 232 10.1111/j.1540-5885.2008.00296.x · DOI
  29. [36]Riley WT, Glasgow RE, Etheredge L, Abernethy AP. Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise. Clin Transl Med. 2013; 2(1):10. PMC3658895 23663660
  30. [37]Pham Q Wiljer D Cafazzo JA Beyond the randomized controlled trial: a review of alternatives in mHealth clinical trial methods JMIR MHealth UHealth 2016 4 e107 10.2196/mhealth.5720 27613084 PMC5035379 · DOI · Paper page
  31. [42]Cresswell KM Bates DW Ten Sheikh A Key considerations for the successful implementation and adoption of large-scale health information technology J Am Med Inform Assoc 2013 20 e9 e13 10.1136/amiajnl-2013-001684 23599226 PMC3715363 · DOI
  32. [43]Dugstad J Eide T Nilsen ER Eide H Towards successful digital transformation through co-creation: a longitudinal study of a four-year implementation of digital monitoring technology in residential care for persons with dementia BMC Health Serv Res 2019 19 366 10.1186/s12913-019-4191-1 31182093 PMC6558683 · DOI
  33. [44]Morton E Barnes SJ Michalak EE Participatory digital health research: a new paradigm for mHealth tool development Gen Hosp Psychiatry 2020 66 67 69 10.1016/j.genhosppsych.2020.07.005 32702489 PMC7354771 · DOI
  34. [45]Hanley J Pinnock H Paterson M McKinstry B Implementing telemonitoring in primary care: learning from a large qualitative dataset gathered during a series of studies BMC Fam Pract 2018 19 118 10.1186/s12875-018-0814-6 30021535 PMC6052602 · DOI
  35. [46]Ware P Evaluating the implementation of a mobile phone–based telemonitoring program: longitudinal study guided by the consolidated framework for implementation research JMIR MHealth UHealth 2018 6 e10768 10.2196/10768 30064970 PMC6092591 · DOI · Paper page
  36. [47]Lee D A model for designing healthcare service based on the patient experience Int J Healthc Manag 2019 12 180 188 10.1080/20479700.2017.1359956 · DOI
  37. [48]Shaw J Beyond “implementation”: digital health innovation and service design Npj Digit Med 2018 1 48 10.1038/s41746-018-0059-8 31304327 PMC6550242 · DOI
  38. [49]Maeder A Poultney N Morgan G Lippiatt R Patient compliance in home-based self-care telehealth projects J Telemed Telecare 2015 21 439 442 10.1177/1357633X15612382 26556057 · DOI
  39. [50]Lettice F, Rogers H, Yaghmaei E, Pawar KS. Responsible research and innovation revisited: aligning product development processes with the corporate responsibility agenda. In: Brem A, Viardot E, editors. Revolution of innovation management: Palgrave Macmillan UK; 2017. p. 247–69. 10.1057/978-1-349-95123-9_9. · DOI
  40. [51]Di Stefano G Gambardella A Verona G Technology push and demand pull perspectives in innovation studies: current findings and future research directions Res Policy 2012 41 1283 1295 10.1016/j.respol.2012.03.021 · DOI

Cited by

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Authors
Londral, A., Azevedo, S., Dias, P., Ramos, C., Santos, J., Martins, F., Silva, R., Semedo, H., Vital, C., Gualdino, A., Falcão, J., Lapão, L. V., Coelho, P., & Fragata, J. G.
Affiliations
  • Value for Health CoLAB (PT)
  • University of Lisbon (PT)
  • Unidade Local de Saúde de São José (PT)
Corresponding author
Londral, A.
ORCID
Venue
BMC Health Services Research
Publisher
Springer Science and Business Media LLC
Published
2022-05-21
Volume
22
Issue
1
Pages
680-680
ISSN
1472-6963 (ISSN-L)
DOI
10.1186/s12913-022-08073-4
PMC
PMC9123610
OpenAlex
W4281264507
Cited by
15
References
9
Citations by year
2026: 2 · 2025: 3 · 2024: 4 · 2023: 5 · 2022: 1
FWCI
3.73
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Health Services Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Fundação para a Ciência e a Tecnologia (Lisboa-05-3559-FSE-3, DSAIPA/AI/0094/2020)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
Keywords
digital health services, cardiac surgery, postoperative follow-up, remote patient monitoring, high-value care, service design, lean startup, minimum viable product, IoT devices, mobile application, patient engagement, hospital readmission, design science research
MeSH terms
Delivery of Health Care, Follow-Up Studies, Cardiac Surgical Procedures, Humans, Learning, Pilot Projects

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