CC BY-NC-ND 4.0 · ACI open 2022; 06(02): e123-e128
DOI: 10.1055/s-0042-1749191
Case Report

Design and Development of Halyos: A Patient-Facing Visual EHR Interface for Longitudinal Risk Awareness

Samson Mataraso*
1   Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States
2   Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California, United States
3   Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States
,
Vimig Socrates*
1   Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States
4   Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, United States
5   Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
,
Fritz Lekschas
6   Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts, United States
,
Nils Gehlenborg
1   Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States
› Author Affiliations
Funding This work was enabled by NIH grants U54HG007963 and R00HG007583.

Abstract

Objectives We have developed Halyos, a visual electronic health record (EHR) web application that complements existing patient portals. Halyos is designed to integrate with existing EHR systems to help patients interpret their health data.

Methods The Halyos application utilizes the Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) platform to create an interoperable interface that provides interactive visualizations of clinically validated risk scores and longitudinal data derived from a patient's clinical measurements.

Results These visualizations allow patients to investigate the relationships between clinical measurements and risk over time.

Discussion By enabling patients to set hypothetical future values for these clinical measurements, patients can see how changes in their health will impact their risks.

Conclusion Using Halyos, patients are provided with the opportunity to actively improve their health based on increased understanding of longitudinal information available in EHRs and to begin a dialogue with their providers.

Protection of Human and Animal Subjects

The human-centered design workshop is exempt under 45 CFR 46.101(b) Categories of Exempt Human Subjects Research Exemption 2 with no identifying information recorded.


* These authors contributed equally to this work.




Publication History

Received: 26 January 2021

Accepted: 03 February 2022

Article published online:
22 December 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. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Sarkar U, Karter AJ, Liu JY. et al. The literacy divide: health literacy and the use of an internet-based patient portal in an integrated health system-results from the diabetes study of northern California (DISTANCE). J Health Commun 2010; 15 (02, Suppl 02): 183-196
  • 2 Office of the National Coordinator for Health Information Technology. Hospital progress to meaningful use by size, type, and urban/rural location. Published online August 2017. Available at: https://dashboard.healthit.gov/quickstats/pages/FIG-Hospital-Progress-to-Meaningful-Use-by-size-practice-setting-area-type.php
  • 3 Choi NG, Dinitto DM. The digital divide among low-income homebound older adults: Internet use patterns, eHealth literacy, and attitudes toward computer/Internet use. J Med Internet Res 2013; 15 (05) e93
  • 4 Goldzweig CL, Orshansky G, Paige NM. et al. Electronic patient portals: evidence on health outcomes, satisfaction, efficiency, and attitudes: a systematic review. Ann Intern Med 2013; 159 (10) 677-687
  • 5 Baker DW. The meaning and the measure of health literacy. J Gen Intern Med 2006; 21 (08) 878-883
  • 6 Tieu L, Schillinger D, Sarkar U. et al. Online patient websites for electronic health record access among vulnerable populations: portals to nowhere?. J Am Med Inform Assoc 2017; 24 (e1): e47-e54
  • 7 Tieu L, Sarkar U, Schillinger D. et al. Barriers and facilitators to online portal use among patients and caregivers in a safety net health care system: a qualitative study. J Med Internet Res 2015; 17 (12) e275
  • 8 Sox CM, Gribbons WM, Loring BA, Mandl KD, Batista R, Porter SC. Patient-centered design of an information management module for a personally controlled health record. J Med Internet Res 2010; 12 (03) e36
  • 9 Britto MT, Jimison HB, Munafo JK, Wissman J, Rogers ML, Hersh W. Usability testing finds problems for novice users of pediatric portals. J Am Med Inform Assoc 2009; 16 (05) 660-669
  • 10 Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 2007; 297 (06) 611-619
  • 11 Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 2008; 118 (22) 2243-2251
  • 12 Krause J, Perer A, Ng K. Interacting with predictions: visual inspection of black-box machine learning models. Paper presented at: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems; May 2016: 5686-5697
  • 13 Ant Ozok A, Wu H, Garrido M, Pronovost PJ, Gurses AP. Usability and perceived usefulness of Personal Health Records for preventive health care: a case study focusing on patients' and primary care providers' perspectives. Appl Ergon 2014; 45 (03) 613-628
  • 14 Welschen LMC, Bot SDM, Kostense PJ. et al. Effects of cardiovascular disease risk communication for patients with type 2 diabetes on risk perception in a randomized controlled trial: the @RISK study. Diabetes Care 2012; 35 (12) 2485-2492
  • 15 Harle CA, Downs JS, Padman R. Effectiveness of personalized and interactive health risk calculators: a randomized trial. Med Decis Making 2012; 32 (04) 594-605
  • 16 Ancker JS, Senathirajah Y, Kukafka R, Starren JB. Design features of graphs in health risk communication: a systematic review. J Am Med Inform Assoc 2006; 13 (06) 608-618
  • 17 Zikmund-Fisher BJ. Risk reduction to motivate specific action using an icon array display. Visualizing Health. Published January 27, 2014. Available at: http://www.vizhealth.org/
  • 18 Turchioe MR, Myers A, Isaac S. et al. A systematic review of patient-facing visualizations of personal health data. Appl Clin Inform 2019; 10 (04) 751-770
  • 19 Irizarry T, DeVito Dabbs A, Curran CR. Patient portals and patient engagement: a state of the science review. J Med Internet Res 2015; 17 (06) e148
  • 20 Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016; 23 (05) 899-908
  • 21 Scalia P, Ahmad F, Schubbe D. et al. Integrating option grid patient decision aids in the epic electronic health record: case study at 5 health systems. J Med Internet Res 2021; 23 (05) e22766
  • 22 Karhade AV, Schwab JH, Del Fiol G, Kawamoto K. SMART on FHIR in spine: integrating clinical prediction models into electronic health records for precision medicine at the point of care. Spine J 2021; 21 (10) 1649-1651
  • 23 Galesic M, Garcia-Retamero R, Gigerenzer G. Using icon arrays to communicate medical risks: overcoming low numeracy. Health Psychol 2009; 28 (02) 210-216
  • 24 Krist AH, Woolf SH, Bello GA. et al. Engaging primary care patients to use a patient-centered personal health record. Ann Fam Med 2014; 12 (05) 418-426
  • 25 Chunara R, Aman S, Smolinski M, Brownstein JS. Flu Near You: an online self-reported influenza surveillance system in the USA. Online J Public Health Inform 2013; 5 (01)
  • 26 Kerzner E, Goodwin S, Dykes J, Jones S, Meyer M. A framework for creative visualization-opportunities workshops. IEEE Trans Vis Comput Graph 2018; 25 (01) 748-758
  • 27 Haggstrom DA, Saleem JJ, Russ AL, Jones J, Russell SA, Chumbler NR. Lessons learned from usability testing of the VA's personal health record. J Am Med Inform Assoc 2011; 18 (01, Suppl 01): i13-i17
  • 28 Angulo P, Hui JM, Marchesini G. et al. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology 2007; 45 (04) 846-854
  • 29 Tangri N, Stevens LA, Griffith J. et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011; 305 (15) 1553-1559
  • 30 Shorr AF, Sun X, Johannes RS, Yaitanes A, Tabak YP. Validation of a novel risk score for severity of illness in acute exacerbations of COPD. Chest 2011; 140 (05) 1177-1183
  • 31 Lindström J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 2003; 26 (03) 725-731