Download PDF
Appl Clin Inform 2019; 10(02): 254-260
DOI: 10.1055/s-0039-1683987
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Early Adopters of Patient-Generated Health Data Upload in an Electronic Patient Portal

Jessica S. Ancker
1   Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States
,
Elizabeth Mauer
1   Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States
,
Robin B. Kalish
2   Department of Obstetrics and Gynecology, Weill Cornell Medicine, New York, New York, United States
,
Joshua R. Vest
3   Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Health, Indianapolis, Indiana, United States
,
J. Travis Gossey
1   Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States
4   Department of Medicine, Weill Cornell Medicine, New York, New York, United States
5   Weill Cornell Physician Organization Information Services, Weill Cornell Medicine, New York, New York, United States
› Author Affiliations Funding This project was funded by the Agency for Healthcare Research and Quality (K01 HS021531; PI: Ancker).
Further Information

Publication History

08 October 2018

17 February 2019

Publication Date:
10 April 2019 (online)

Also available at
   Permissions and Reprints

Abstract

Background and Objective Patient-generated health data (PGHD) may help providers monitor patient status between clinical visits. Our objective was to describe our medical center's early experience with an electronic flowsheet allowing patients to upload self-monitored blood glucose to their provider's electronic health record (EHR).

Methods An academic multispecialty practice enabled the portal-linked PGHD tool in 2012. We conducted a retrospective observational study of adult ambulatory patients using this tool between 2012 and 2016, comparing clinical and demographic characteristics of data uploaders with those of a group of patients with diabetes diagnoses and patient portal accounts seen by the same health care providers.

Results Over four years, 16 providers chose to use the tool, and 53 adult patients used it to upload three or more blood glucose values within any 9-month period. Of these patients, 23 were pregnant women and 30 were nonpregnant adults with diabetes. Uploaders had more encounters and portal log-ins than comparison patients but did not differ in socioeconomic status. Among the chronic disease patients, uploaders' mean hemoglobin A1c and body mass index (BMI) both dropped significantly in the months after upload.

Conclusion Despite the potential value of PGHD in health care, the rate of adoption of a tool allowing patients to upload PGHD to their provider's EHR has been slow. Among chronic disease patients, PGHD upload was associated with improvements in blood glucose control and BMI, but it is possible that the changes were because of increased motivation or intensive changes in medical management.

Keywords

blood glucose self-monitoring - self-management - patient portals - diabetes - obstetrics–gynecology

Protection of Human and Animal Subjects

This study was approved by the Weill Cornell Institutional Review Board with a waiver of consent for use of de-identified data.


 

  • References

  • 1 Say P, Stein DM, Ancker JS, Hsieh CK, Pollak JP, Estrin D. Smartphone data in rheumatoid arthritis - what do rheumatologists want?. AMIA Annu Symp Proc 2015; 2015: 1130-1139
    PubMedSearch in Google Scholar
  • 2 American Association of Diabetes Educators. The American Association of Diabetes Educators position statement: self-monitoring of blood glucose using glucose meters in the management of type 2 diabetes. 2010 copyright, Chicago, IL: American Association of Diabetes Educators. Available at: https://www.diabeteseducator.org/docs/default-source/practice/practice-documents/practice-papers/smbg-using-glucose-meters-in-management-of-t2d.pdf?sfvrsn=0 . Accessed March 18, 2019
    PubMed
  • 3 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
    CrossrefPubMedSearch in Google Scholar
  • 4 Greenhalgh T. Patient and public involvement in chronic illness: beyond the expert patient. BMJ 2009; 338 (7695): b49
    CrossrefPubMedSearch in Google Scholar
  • 5 Hibbard JH, Greene J. What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs. Health Aff (Millwood) 2013; 32 (02) 207-214
    CrossrefPubMedSearch in Google Scholar
  • 6 Hibbard JH, Mahoney ER, Stock R, Tusler M. Do increases in patient activation result in improved self-management behaviors?. Health Serv Res 2007; 42 (04) 1443-1463
    CrossrefPubMedSearch in Google Scholar
  • 7 Fox S, Duggan M. Pew Research Center: Internet & Technology. Mobile Health 2012. 2013. . Available at: http://www.pewinternet.org/2012/11/08/mobile-health-2012/ . Accessed March 13, 2019
    Search in Google Scholar
  • 8 Ancker JS, Barrón Y, Rockoff ML. , et al. Use of an electronic patient portal among disadvantaged populations. J Gen Intern Med 2011; 26 (10) 1117-1123
    CrossrefPubMedSearch in Google Scholar
  • 9 Roblin DW, Houston II TK, Allison JJ, Joski PJ, Becker ER. Disparities in use of a personal health record in a managed care organization. J Am Med Inform Assoc 2009; 16 (05) 683-689
    CrossrefPubMedSearch in Google Scholar
  • 10 Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc 2018; 25 (08) 1080-1088
    CrossrefPubMedSearch in Google Scholar
  • 11 Ancker JS, Witteman HO, Hafeez B, Provencher T, Van de Graaf M, Wei E. “You get reminded you're a sick person”: personal data tracking and patients with multiple chronic conditions. J Med Internet Res 2015; 17 (08) e202
    CrossrefPubMedSearch in Google Scholar
  • 12 Nundy S, Lu C-YE, Hogan P, Mishra A, Peek ME. Using patient-generated health data from mobile technologies for diabetes self-management support: provider perspectives from an academic medical center. J Diabetes Sci Technol 2014; 8 (01) 74-82
    CrossrefPubMedSearch in Google Scholar
  • 13 West P, Kleek MV, Giordano R, Weal MJ, Shadbolt N. Common Barriers to the Use of Patient-Generated Data across Clinical Settings. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, Quebec, Canada, 2018
    PubMedSearch in Google Scholar
  • 14 Lv N, Xiao L, Simmons ML, Rosas LG, Chan A, Entwistle M. Personalized hypertension management using patient-generated health data integrated with electronic health records (EMPOWER-H): six-month pre-post study. J Med Internet Res 2017; 19 (09) e311
    CrossrefPubMedSearch in Google Scholar
  • 15 Office of the National Coordinator for Health Information Technology. A practical approach to the use of patient-generated health data (PGHD). Available at: https://www.healthit.gov/sites/default/files/onc_pghd_pilot_demonstrations_infographic.pdf . Accessed March 13, 2019
    PubMed
  • 16 Weiner JP, Starfield BH, Steinwachs DM, Mumford LM. Development and application of a population-oriented measure of ambulatory care case-mix. Med Care 1991; 29 (05) 452-472
    CrossrefPubMedSearch in Google Scholar
  • 17 Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B. A social vulnerability index for disaster management. J Homel Secur Emerg Manag 2011; 8 (Suppl. 01) 1-24
    PubMedSearch in Google Scholar
  • 18 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 (Suppl. 02) 183-196
    CrossrefPubMedSearch in Google Scholar
  • 19 Sarkar U, Karter AJ, Liu JY. , et al. Social disparities in internet patient portal use in diabetes: evidence that the digital divide extends beyond access. J Am Med Inform Assoc 2011; 18 (03) 318-321
    CrossrefPubMedSearch in Google Scholar
  • 20 Rainie L. Digital Divides 2016. Washington, DC: Pew Research Center; 2016
    Search in Google Scholar
  • 21 Zickuhr K. Who's Not Online and Why. Washington, DC: Pew Research Center; 2013
    Search in Google Scholar
  • 22 Prey JE, Polubriaginof F, Grossman LV. , et al. Engaging hospital patients in the medication reconciliation process using tablet computers. J Am Med Inform Assoc 2018; 25 (11) 1460-1469
    CrossrefPubMedSearch in Google Scholar
  • 23 Reading M, Baik D, Beauchemin M, Hickey KT, Merrill JA. factors influencing sustained engagement with ECG self-monitoring: perspectives from patients and health care providers. Appl Clin Inform 2018; 9 (04) 772-781
    Thieme ConnectPubMedSearch in Google Scholar
  • 24 Peel E, Parry O, Douglas M, Lawton J. Blood glucose self-monitoring in non-insulin-treated type 2 diabetes: a qualitative study of patients' perspectives. Br J Gen Pract 2004; 54 (500) 183-188
    PubMedSearch in Google Scholar
  • 25 Epstein DA, Caraway M, Johnston C, Ping A, Fogarty J, Munson SA. Beyond abandonment to next steps: understanding and designing for life after personal informatics tool use. Proc SIGCHI Conf Hum Factor Comput Syst 2016; 2016: 1109-1113
    PubMedSearch in Google Scholar
  • 26 Clawson J, Pater JA, Miller AD, Mynatt E, Mamykina L. No longer wearing: investigating the abandonment of personal health-tracking technologies on craigslist. Paper presented at the UbiComp '15 Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, September 7–11, 2015, Osaka, Japan
    PubMed