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DOI: 10.1055/a-2174-7820
Visualization of Patient-Generated Health Data: A Scoping Review of Dashboard Designs
Funding This work is based on research conducted by NORC at the University of Chicago under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, Maryland, United States (contract no.: 75Q80120D00018 for the Clinical Decision Support Innovation Center [CDSiC]).Abstract
Background Patient-centered clinical decision support (PC CDS) aims to assist with tailoring decisions to an individual patient's needs. Patient-generated health data (PGHD), including physiologic measurements captured frequently by automated devices, provide important information for PC CDS. The volume and availability of such PGHD is increasing, but how PGHD should be presented to clinicians to best aid decision-making is unclear.
Objectives Identify best practices in visualizations of physiologic PGHD, for designing a software application as a PC CDS tool.
Methods We performed a scoping review of studies of PGHD dashboards that involved clinician users in design or evaluations. We included only studies that used physiologic PGHD from single patients for usage in decision-making.
Results We screened 468 titles and abstracts, 63 full-text papers, and identified 15 articles to include in our review. Some research primarily sought user input on PGHD presentation; other studies garnered feedback only as a side effort for other objectives (e.g., integration with electronic health records [EHRs]). Development efforts were often in the domains of chronic diseases and collected a mix of physiologic parameters (e.g., blood pressure and heart rate) and activity data. Users' preferences were for data to be presented with statistical summaries and clinical interpretations, alongside other non-PGHD data. Recurrent themes indicated that users desire longitudinal data display, aggregation of multiple data types on the same screen, actionability, and customization. Speed, simplicity, and availability of data for other purposes (e.g., documentation) were key to dashboard adoption. Evaluations were favorable for visualizations using common graphing or table formats, although best practices for implementation have not yet been established.
Conclusion Although the literature identified common themes on data display, measures, and usability, more research is needed as PGHD usage grows. Ensuring that care is tailored to individual needs will be important in future development of clinical decision support.
Keywords
clinical decision support - patient-centered care - shared decision-making - patient-generated health data - other clinical informatics applications - patient engagementProtection of Human and Animal Subjects
No human or animal subjects were included in this project.
Authors' Contributions
All authors made substantial contributions to the conception, design, and execution of this research. All authors participated in drafting the manuscript or revising it critically for important intellectual content and gave final approval of the version published.
Publication History
Received: 05 June 2023
Accepted: 11 September 2023
Accepted Manuscript online:
13 September 2023
Article published online:
22 November 2023
© 2023. Thieme. All rights reserved.
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References
- 1 Gerteis M. Ed. Through the Patient's Eyes: Understanding and Promoting Patient-Centered-Care. 1st ed.. Jossey-BassTM An Imprint of Wiley; 1993
- 2 Dullabh P, Sandberg SF, Heaney-Huls K. et al. Challenges and opportunities for advancing patient-centered clinical decision support: findings from a horizon scan. J Am Med Inform Assoc 2022; 29 (07) 1233-1243
- 3 What are patient-generated health data? | HealthIT.gov. Accessed June 24, 2022 at: https://www.healthit.gov/topic/otherhot-topics/what-are-patient-generated-health-data
- 4 Loncar-Turukalo T, Zdravevski E, Machado da Silva J, Chouvarda I, Trajkovik V. Literature on wearable technology for connected health: scoping review of research trends, advances, and barriers. J Med Internet Res 2019; 21 (09) e14017
- 5 Boston D, Cohen D, Stone J. et al. Integrating patient-generated health data into electronic health records in ambulatory care settings: a practical guide. Prepared for: Agency of Healthcare Research and Quality Contract No. 75Q80120D00019 AHRQ Publication No. AHRQ 22-0013; 2021
- 6 Cohen DJ, Keller SR, Hayes GR, Dorr DA, Ash JS, Sittig DF. Integrating patient-generated health data into clinical care settings or clinical decision-making: lessons learned from project HealthDesign. JMIR Human Factors 2016; 3 (02) e26
- 7 Dullabh P, Heaney-Huls K, Lobach DF. et al. The technical landscape for patient-centered CDS: progress, gaps, and challenges. J Am Med Inform Assoc 2022; 29 (06) 1101-1105
- 8 Ye J. The impact of electronic health record-integrated patient-generated health data on clinician burnout. J Am Med Inform Assoc 2021; 28 (05) 1051-1056
- 9 Unger T, Borghi C, Charchar F. et al. 2020 International Society of Hypertension global hypertension practice guidelines. Hypertension 2020; 75 (06) 1334-1357
- 10 Lavallee DC, Lee JR, Austin E. et al. mHealth and patient generated health data: stakeholder perspectives on opportunities and barriers for transforming healthcare. mHealth 2020; 6: 8
- 11 Pham MT, Rajić A, Greig JD, Sargeant JM, Papadopoulos A, McEwen SA. A scoping review of scoping reviews: advancing the approach and enhancing the consistency. Res Synth Methods 2014; 5 (04) 371-385
- 12 Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol 2005; 8: 19-32
- 13 Garritty C, Gartlehner G, Nussbaumer-Streit B. et al; Interim Guidance from the Cochrane Rapid Reviews Methods Group. Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews. J Clin Epidemiol 2021; 130: 13-22
- 14 Barnett-Page E, Thomas J. Methods for the synthesis of qualitative research: a critical review. BMC Med Res Methodol 2009; 9 (01) 59
- 15 Karni L, Jusufi I, Nyholm D, Klein GO, Memedi M. Toward improved treatment and empowerment of individuals with Parkinson disease: design and evaluation of an internet of things system. JMIR Form Res 2022; 6 (06) e31485
- 16 Cassarino N, Bergstrom B, Johannes C, Gualtieri L. Monitoring older adult blood pressure trends at home as a proxy for brain health. Online J Public Health Inform 2021; 13 (03) e16
- 17 Alpert JM, Kota NSP, Ranka S. et al. A simulated graphical interface for integrating patient-generated health data from smartwatches with electronic health records: usability study. JMIR Human Factors 2020; 7 (04) e19769
- 18 Koopman RJ, Canfield SM, Belden JL. et al. Home blood pressure data visualization for the management of hypertension: designing for patient and physician information needs. BMC Med Inform Decis Mak 2020; 20 (01) 195
- 19 Backonja U, Haynes SC, Kim KK. Data visualizations to support health practitioners' provision of personalized care for patients with cancer and multiple chronic conditions: user-centered design study. JMIR Human Factors 2018; 5 (04) e11826
- 20 Gardner CL, Liu F, Fontelo P, Flanagan MC, Hoang A, Burke HB. Assessing the usability by clinicians of VISION: a hierarchical display of patient-collected physiological information to clinicians. BMC Med Inform Decis Mak 2017; 17 (01) 41
- 21 Paterson M, McAulay A, McKinstry B. Integrating third-party telehealth records with the general practice electronic medical record system: a solution. J Innov Health Inform 2017; 24 (04) 915
- 22 Keogh A, Johnston W, Ashton M. et al. “It's not as simple as just looking at one chart”: a qualitative study exploring clinician's opinions on various visualisation strategies to represent longitudinal actigraphy data. Digit Biomark 2020; 4 (Suppl. 01) 87-99
- 23 Salvi E, Bosoni P, Tibollo V. et al. Patient-generated health data integration and advanced analytics for diabetes management: the AID-GM platform. Sensors (Basel) 2019; 20 (01) E128
- 24 Kumar RB, Goren ND, Stark DE, Wall DP, Longhurst CA. Automated integration of continuous glucose monitor data in the electronic health record using consumer technology. J Am Med Inform Assoc 2016; 23 (03) 532-537
- 25 Jung SY, Kim JW, Hwang H. et al. Development of comprehensive personal health records integrating patient-generated health data directly from Samsung S-Health and Apple health apps: retrospective cross-sectional observational study. JMIR Mhealth Uhealth 2019; 7 (05) e12691
- 26 Weissmann J, Mueller A, Messinger D, Parkin CG, Amann-Zalan I. Improving the quality of outpatient diabetes care using an information management system: results from the observational VISION study. J Diabetes Sci Technol 2015; 10 (01) 76-84
- 27 Cohen DJ, Wyte-Lake T, Canfield SM. et al. Impact of home blood pressure data visualization on hypertension medical decision making in primary care. Ann Fam Med 2022; 20 (04) 305-311
- 28 Pevnick JM, Elad Y, Masson LM, Riggs RV, Duncan RG. Patient-initiated data: our experience with enabling patients to initiate incorporation of heart rate data into the electronic health record. Appl Clin Inform 2020; 11 (04) 671-679
- 29 Hsu WC, Lau KHK, Huang R. et al. Utilization of a cloud-based diabetes management program for insulin initiation and titration enables collaborative decision making between healthcare providers and patients. Diabetes Technol Ther 2016; 18 (02) 59-67
- 30 Tiase VL, Hull W, McFarland MM. et al. Patient-generated health data and electronic health record integration: a scoping review. JAMIA Open 2020; 3 (04) 619-627
- 31 Zhang R, Burgess ER, Reddy MC. et al. Provider perspectives on the integration of patient-reported outcomes in an electronic health record. JAMIA Open 2019; 2 (01) 73-80
- 32 Rotenstein LS, Agarwal A, O'Neil K. et al. Implementing patient-reported outcome surveys as part of routine care: lessons from an academic radiation oncology department. J Am Med Inform Assoc 2017; 24 (05) 964-968
- 33 Shneiderman B. The eyes have it: a task by data type taxonomy for information visualizations. In: VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages. IEEE Computer Society Press; 1996: 336 . Accessed July 29, 2022 at: https://www.cs.umd.edu/~ben/papers/Shneiderman1996eyes
- 34 Brooke J. SUS: a quick and dirty usability scale. Usability Eval Ind. 1995: 189 . Accessed July 29, 2022 at: https://www.researchgate.net/publication/228593520_SUS_A_quick_and_dirt
- 35 Affairs AS for P. System Usability Scale (SUS). September 6, 2013. Accessed July 7, 2022 at: https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html
- 36 Langer J, Zeiller M. Evaluation of the user experience of interactive infographics in online newspapers. Forum Media Technol 2017; x: 97-106
- 37 Crossen S, Romero C, Reggiardo A, Michel J, Glaser N. Feasibility and impact of remote glucose monitoring among patients with newly diagnosed type 1 diabetes: single-center pilot study. JMIR Diabetes 2022; 7 (01) e33639
- 38 Jeevanandan N, Nøhr C. Patient-generated health data in the clinic. Stud Health Technol Inform 2020; 270: 766-770
- 39 Reading MJ, Merrill JA. Converging and diverging needs between patients and providers who are collecting and using patient-generated health data: an integrative review. J Am Med Inform Assoc 2018; 25 (06) 759-771
- 40 Blackford AL, Wu AW, Snyder C. Interpreting and acting on PRO results in clinical practice: lessons learned from the patient viewpoint system and beyond. Med Care 2019; 57 (Suppl 5 1): S46-S51
- 41 Melstrom LG, Rodin AS, Rossi LA, Fu Jr P, Fong Y, Sun V. Patient generated health data and electronic health record integration in oncologic surgery: a call for artificial intelligence and machine learning. J Surg Oncol 2021; 123 (01) 52-60