J Korean Diabetes.  2020 Sep;21(3):149-155. 10.4093/jkd.2020.21.3.149.

Out-of-Hospital Data: Patient Generated Health Data

• Lee J
• Kim HS
• Kim J

• KMID: 2507046
• DOI: http://doi.org/10.4093/jkd.2020.21.3.149

Abstract

Patient-generated health data (PGHD) are health-related data generated, recorded, and collected by patients or caregivers. Its main advantage is that patients can actively participate in their own health care, since the data-generating agents are patients and caregivers, not hospitals. Due to the development and popularization of information and communications technology and digital devices, the number of studies using PGHD for better health care is increasing. When PGHD was used in the outpatient setting, healthcare providers were better able to understand each patients’ condition using more accurate data, and to monitor patient health status between visits. In particular, to manage chronic diseases such as diabetes, it is essential to monitor daily blood sugar and change nutrient intake in the context of medication, overall diet, and exercise. However, problems associated with data quality, data extraction, and insufficient evidence and research to guide use of this kind of data in clinical setting are yet to be solved. Further, the gap between patient and healthcare providers’ perceptions of PGHD persists. We suggest that PGHD, electronic medical record data in hospitals, and claims and genome data could be combined to good effect. This combination can help patients and healthcare providers make better decisions with respect to patient health and to maintain patient engagement. In addition, the collection of PGHD through sophisticated sensors, and data analysis through advanced portals could combine medical big data with daily big data. Eventually, a personalized healthcare automation system through PGHD-based algorithms could provide healthcare artificial intelligence services.

Keyword

Artificial intelligence; Big data; Consumer health informatics; Diabetes mellitus; Mobile health; Patient generated health data

Reference

1. Tresp V, Overhage JM, Bundschus M, Rabizadeh S, Fasching PA, Yu S. Going digital: a survey on digitalization and large-scale data analytics in healthcare. Proc IEEE. 2016; 104:2180–206.

2. Lee CH, Yoon HJ. Medical big data: promise and challenges. Kidney Res Clin Pract. 2017; 36:3–11.

3. Weber GM, Mandl KD, Kohane IS. Finding the missing link for big biomedical data. JAMA. 2014; 311:2479–80.

4. Kim HS, Kim DJ. Dementia research using healthcare big data. Dement Neurocogn Disord. 2019; 18:73–6.

5. What are patient-generated health data? Available from: https://www.healthit.gov/topic/otherhot-topics/what-are-patient-generated-health-data (updated 2018 Jan 19).

6. Codella J, Partovian C, Chang HY, Chen CH. Data quality challenges for person-generated health and wellness data. IBM J Res Dev. 2018; 62(1):1–8.

7. Lai AM, Hsueh PYS, Choi YK, Austin RR. Present and future trends in consumer health informatics and patientgenerated health data. Yearb Med Inform. 2017; 26:152–9.

8. Kang JH. Factors affecting diabetic eye disease and kidney disease screening in diabetic patients. J Korea Acad Ind Cooper Soc. 2020; 21:226–35.

9. Elenko E, Underwood L, Zohar D. Defining digital medicine. Nat Biotechnol. 2015; 33:456–61.

10. Dimitrov DV. Medical internet of things and big data in healthcare. Healthc Inform Res. 2016; 22:156–63.

11. Shan R, Sarkar S, Martin SS. Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia. 2019; 62:877–87.

12. Wurmser Y. Wearables 2019 advanced wearables pick up pace as fitness trackers slow. Available from: https://www.emarketer.com/content/wearables-2019 (updated 2019 Jan 3).

13. Shapiro M, Johnston D, Wald J, Mon D. Patient-generated health data. Research Triangle Park, NC: RTI International;2012. p. 1–24.

14. Wood WA, Bennett AV, Basch E. Emerging uses of patient generated health data in clinical research. Mol Oncol. 2015; 9:1018–24.

15. Chung AE, Basch EM. Potential and challenges of patient-generated health data for high-quality cancer care. J Oncol Pract. 2015; 11:195–7.

16. Raja JM, Elsakr C, Roman S, Cave B, Pour-Ghaz I, Nanda A, et al. Apple watch, wearables, and heart rhythm: where do we stand? Ann Transl Med. 2019; 7:417.

17. Li B, Dong Q, Downen RS, Tran N, Jackson JH, Pillai D, et al. A wearable IoT aldehyde sensor for pediatric asthma research and management. Sens Actuators B Chem. 2019; 287:584–94.

18. McConnell MV, Turakhia MP, Harrington RA, King AC, Ashley EA. Mobile health advances in physical activity, fitness, and atrial fibrillation: moving hearts. J Am Coll Cardiol. 2018; 71:2691–701.

19. Lv N, Xiao L, Simmons ML, Rosas LG, Chan A, Entwistle M. Personalized hypertension management using patientgenerated health data integrated with electronic health records (EMPOWER-H): six-month pre-post study. J Med Internet Res. 2017; 19:e311.

20. WellDoc Inc. (2020). Bluestar (5.5.1) [Mobile application software]. Retrieved from https://apps.apple.com/us/app/bluestar-diabetes/id700329056.

21. Huraypositive Corp. (2017). >Switch (1.3.0) [Mobile application software]. Retrieved from https://play.google.com/store/apps/details?id=net.huray.lipidcho&hl=ko.

22. 510(k) premarket notification: WellDoc BlueStar. Available from: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K190013 (updated 2020 Aug 10).

23. Lee DY, Park J, Choi D, Ahn HY, Park SW, Park CY. The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders: a randomized, controlled, open-label study. Sci Rep. 2018; 8:3642.

24. 510(k) premarket notification: Insulia Diabetes Management Companion. Available from: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K170669 (updated 2020 Aug 10).

25. Athinarayanan SJ, Adams RN, Hallberg SJ, McKenzie AL, Bhanpuri NH, Campbell WW, et al. Long-term effects of a novel continuous remote care intervention including nutritional ketosis for the management of type 2 diabetes: a 2-year non-randomized clinical trial. Front Endocrinol (Lausanne). 2019; 10:348.

26. Su W, Chen F, Dall TM, Iacobucci W, Perreault L. Return on investment for digital behavioral counseling in patients with prediabetes and cardiovascular disease. Prev Chronic Dis. 2016; 13:E13.

27. Alwashmi MF, Mugford G, Abu-Ashour W, Nuccio M. A digital diabetes prevention program (Transform) for adults with prediabetes: secondary analysis. JMIR Diabetes. 2019; 4:e13904.

28. Kim JM, Yun JH, Kim BJ. Applications of precision medicine to overcome diabetes. Public Health Wkly Rep. 2017; 10:826–9.

29. Kim MY. Chat-bot service for self-management of diabetic patients. The Institute of Electronics and Information Engineers. 2018; 1711–4.

30. Eversense continuous glucose monitoring system. Available from: https://www.fda.gov/medical-devices/recently-approved-devices/eversense-continuous-glucose-monitoring-system-p160048s006 (updated 2019 Jun 6).

31. Bailey TS, Chang A, Christiansen M. Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm. J Diabetes Sci Technol. 2015; 9:209–14.

32. Laffel L. Improved accuracy of continuous glucose monitoring systems in pediatric patients with diabetes mellitus: results from two studies. Diabetes Technol Ther. 2016; 18(Suppl 2):S223–33.

33. Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019; 42:1593–603.

34. Tiase VL, Hull W, McFarland MM, Sward KA, Del Fiol G, Staes C, et al. Patient-generated health data and electronic health record integration: protocol for a scoping review. BMJ Open. 2019; 9:e033073.

35. Abdolkhani R, Borda A, Gray K. Quality management of patient generated health data in remote patient monitoring using medical wearables- a systematic review. Stud Health Technol Inform. 2018; 252:1–7.

36. 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 Hum Factors. 2016; 3:e26.

37. Saravana kumar NM, Eswari T, Sampath P, Lavanya S. Predictive methodology for diabetic data analysis in big data. Procedia Comput Sci. 2015; 50:203–8.

38. Purswani JM, Dicker AP, Champ CE, Cantor M, Ohri N. Big data from small devices: the future of smartphones in oncology. Semin Radiat Oncol. 2019; 29:338–47.

39. Cornet VP, Holden RJ. Systematic review of smartphone-based passive sensing for health and wellbeing. J Biomed Inform. 2018; 77:120–32.

40. Akter S, Wamba SF. Big data analytics in E-commerce: a systematic review and agenda for future research. Electron Mark. 2016; 26:173–94.