Development of a digital dashboard to address population-level obesity using electronic medical record data

Oliver J. Canfell; Andrew Burton-Jones; Elizabeth Eakin; Leanna Woods; Sophie Macklin; Jodie Austin; Reji Philip; Han Chang Lim; Kazi Rumana Ahmed; Christine Slade; Daniel Francis; Clair Sullivan

doi:10.1071/PU24029

RESEARCH ARTICLE (Open Access)

Development of a digital dashboard to address population-level obesity using electronic medical record data

Oliver J. Canfell ^A ^B ^* , Andrew Burton-Jones ^C , Elizabeth Eakin ^B , Leanna Woods ^D , Sophie Macklin ^D , Jodie Austin ^D , Reji Philip ^D , Han Chang Lim ^D , Kazi Rumana Ahmed ^D , Christine Slade ^D ^E , Daniel Francis ^F and Clair Sullivan ^D ^F

+ Author Affiliations

- Author Affiliations

^A Department of Nutritional Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9NH, UK.

^B School of Public Health, Faculty of Medicine, The University of Queensland, Herston, Qld, Australia.

^C UQ Business School, Faculty of Business, Economics, and Law, The University of Queensland, St Lucia, Qld, Australia.

^D Queensland Digital Health Centre, Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston, Qld, Australia.

^E Institute of Teaching and Learning Innovation, The University of Queensland, St Lucia, Qld, Australia.

^F Metro North Hospital and Health Service, Department of Health, Queensland Government, Herston, Qld, Australia.

^* Correspondence to: oliver.canfell@kcl.ac.uk, o.canfell@uq.edu.au

Public Health Research and Practice 35, PU24029 https://doi.org/10.1071/PU24029

Submitted: 13 August 2024 Accepted: 30 June 2025 Published: 3 October 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of the Sax Institute. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Objectives

(1) To safely extract electronic medical record data to co-produce a population health informatics dashboard for obesity. (2) To explore how a population health informatics dashboard for obesity might be translated into routine public health practice.

Methods

This mixed methods study was conducted in Queensland, Australia, with stakeholders (n = 27) according to the Three Horizons Framework for Digital Health Transformation. Horizon 1 established the digital infrastructure necessary for accessing routine electronic medical record data from inpatient, outpatient, emergency, and community encounters. Horizon 2 co-produced user requirements for a population health informatics dashboard and developed a proof of concept. Horizon 3 conducted usability testing to explore the theoretical feasibility of integrating the dashboard into practice.

Results

The Queensland Healthy Weight Dashboard is an interactive visualisation platform for obesity surveillance capable of using near-real-time electronic medical record data. We developed a proof of concept using a synthetic sample of 726,561 patients. Once commissioned, the dashboard will display aggregate, non-identifiable data from a total sample of >1 million patients with a measured body mass index across 71 facilities in Queensland, including 19 health services and at least 71 individual facilities that use the integrated electronic Medical Record. The dashboard can display near real-time (quarterly) data via descriptive analytics to (1) identify total raw counts and normalised values, (2) longitudinally track data, and (3) geographically heatmap obesity, overweight, and healthy weight rates, and stratify by time (2016–2022), gender, age (2–99 years), and location (geographical area, facility). Usability testing with public health practitioner end-users (n = 4) revealed above-average overall usability but mixed task-based usability. Practitioners were optimistic about integrating the dashboard into routine practice.

Conclusion

We co-produced a population health informatics tool for obesity that can display hospital electronic medical record data in near real-time. With further validation and usability improvements, this tool can be translated into public health practice to guide obesity interventions.

Keywords: clinical dashboard, digital health, electronic medical records, health care professionals, obesity, population surveillance, public health informatics, routinely collected health data.

References

1 World Health Organization. Obesity and overweight. Geneva, Switzerland: World Health Organization; 2024. Available at https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight [cited 11 September 2025].

2 Bhaskaran K, Dos-Santos-Silva I, Leon DA, et al. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK. Lancet Diabetes Endocrinol 2018; 6(12): 944-53.
| Crossref | Google Scholar | PubMed |

3 Rubino F, Cummings DE, Eckel RH, et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol 2025; 13(3): 221-62.
| Crossref | Google Scholar | PubMed |

4 World Health Organization. Global NCD Target: Halt the rise in obesity. Geneva, Switzerland: World Health Organization; 2016. Available at https://apps.who.int/iris/bitstream/handle/10665/312281/WHO-NMH-NMA-16.192-eng.pdf?sequence=1&isAllowed=y [cited 17 September 2025].

5 World Obesity Federation. Obesity: missing the 2025 targets: Executive Summary. London, UK: World Obesity Federation; 2020. Available at https://data.worldobesity.org/publications/WOF-Missing-the-2025-Global-Targets-Report-FINAL-WEB.pdf [cited 17 September 2025].

6 World Health Organization. WHO Acceleration Plan to Stop Obesity. Geneva, Switzerland: World Health Organization; 2023.

7 Public Health England. PHE Strategy 2020-25. London, England: Department of Health and Social Care; 2019.

8 Commonwealth of Australia. The National Obesity Strategy 2022-2032. Health Ministers Meeting; 2022.

9 Canfell OJ, Davidson K, Woods L, et al. Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases. Front Public Health 2022; 10: 854525.
| Crossref | Google Scholar | PubMed |

10 Canfell OJ, Davidson K, Sullivan C, et al. Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia. BMC Public Health 2022; 22(1): 584.
| Crossref | Google Scholar | PubMed |

11 Canfell OJ, Kodiyattu Z, Eakin E, et al. Real-world data for precision public health of noncommunicable diseases: a scoping review. BMC Public Health 2022; 22(1): 2166.
| Crossref | Google Scholar | PubMed |

12 Roberts MC, Fohner AE, Landry L, et al. Advancing precision public health using human genomics: examples from the field and future research opportunities. Genome Med 2021; 13(1): 97.
| Crossref | Google Scholar | PubMed |

13 Cocoros NM, Kirby C, Zambarano B, et al. RiskScape: A Data Visualization and Aggregation Platform for Public Health Surveillance Using Routine Electronic Health Record Data. Am J Public Health 2021; 111(2): 269-76.
| Crossref | Google Scholar | PubMed |

14 Shaban-Nejad A, Lavigne M, Okhmatovskaia A, et al. PopHR: a knowledge‐based platform to support integration, analysis, and visualization of population health data. Ann N Y Acad Sci 2017; 1387(1): 44-53.
| Crossref | Google Scholar | PubMed |

15 Baker JL, Bjerregaard LG. Advancing precision public health for obesity in children. Rev Endocr Metab Disord 2023; 24(5): 1003-10.
| Crossref | Google Scholar | PubMed |

16 Canfell OJ, Littlewood R, Burton-Jones A, et al. Digital health and precision prevention: shifting from disease-centred care to consumer-centred health. Aust Health Rev 2022; 46(3): 279-83.
| Crossref | Google Scholar | PubMed |

17 Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care 2007; 19(6): 349-57.
| Crossref | Google Scholar | PubMed |

18 Australian Bureau of Statistics. National Health Survey methodology. Australian Government; 2023.

19 Miro. The Innovation Workspace [Software]. Miro; 2025. Available at https://miro.com/ [cited 17 September 2025].

20 Checkbox. Checkbox Survey Software. Checkbox Technology Inc; 2025. Available at https://www.checkbox.com/ [cited 17 September 2025].

21 Brooke J. SUS: a quick and dirty usability scale. CRC Press; 1995.

22 Hyzy M, Bond R, Mulvenna M, et al. System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis. JMIR Mhealth Uhealth 2022; 10(8): e37290.
| Crossref | Google Scholar | PubMed |

23 Wu DTY, Vennemeyer S, Brown K, et al. Usability Testing of an Interactive Dashboard for Surgical Quality Improvement in a Large Congenital Heart Center. Appl Clin Inform 2019; 10(5): 859-69.
| Crossref | Google Scholar | PubMed |

24 Ssegawa JK, Muzinda M. Feasibility Assessment Framework (FAF): A Systematic and Objective Approach for Assessing the Viability of a Project. Procedia Comput Sci 2021; 181: 377-85.
| Crossref | Google Scholar |

25 Jisc. Building digital capabilities framework: the six elements defined. Bristol, UK: Jisc data analytics; 2022.

26 Gale NK, Heath G, Cameron E, et al. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 2013; 13(1): 117.
| Crossref | Google Scholar | PubMed |

27 Lumivero. NVivo: Leading Qualitative Data Analysis Software. Denver, CO: Lumivero; 2025. Available at https://lumivero.com/products/nvivo/ [cited 17 September 2025].

28 Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud 2009; 4(3): 114-23.
| Google Scholar |

29 Georgsson M, Staggers N. Quantifying usability: an evaluation of a diabetes mHealth system on effectiveness, efficiency, and satisfaction metrics with associated user characteristics. J Am Med Inform Assoc 2016; 23(1): 5-11.
| Crossref | Google Scholar | PubMed |

30 Lewis JR, Sauro J. Item benchmarks for the system usability scale. J Usability Stud 2018; 13(3): 158-67.
| Google Scholar |

31 Centers for Disease Control and Prevention. CDC growth charts. USA: National Center for Health Statistics; 2000. Available at http://www.cdc.gov/growthcharts/ [cited 11 September 2025].

32 Schulze A, Brand F, Geppert J, et al. Digital dashboards visualizing public health data: a systematic review. Front Public Health 2023; 11: 999958.
| Crossref | Google Scholar | PubMed |

33 Allen CG, Turbitt E, Smit AK, et al. Precision Public Health Initiatives in Cancer: Proceedings from the Transdisciplinary Conference for Future Leaders in Precision Public Health. BMC Proc 2022; 16(4): 4.
| Crossref | Google Scholar | PubMed |

34 Allen CG, Olstad DL, Kahkoska AR, et al. Extending an Antiracism Lens to the Implementation of Precision Public Health Interventions. Am J Public Health 2023; 113(11): 1210-8.
| Crossref | Google Scholar | PubMed |

35 Tufford AR, Diou C, Lucassen DA, et al. Toward Systems Models for Obesity Prevention: A Big Role for Big Data. Curr Dev Nutr 2022; 6(9): nzac123.
| Crossref | Google Scholar | PubMed |

36 Hallinan CM, Ward R, Hart GK, et al. Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM. BMJ Health Care Inform 2024; 31(1): e100953.
| Crossref | Google Scholar | PubMed |

37 Woods L, Eden R, Canfell OJ, et al. Show me the money: how do we justify spending health care dollars on digital health? Med J Aust 2023; 218(2): 53-7.
| Crossref | Google Scholar | PubMed |