Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data

Summary

Objectives: Consumer Health Informatics (CHI) and the use of Patient-Generated Health Data (PGHD) are rapidly growing focus areas in healthcare. The objective of this paper is to briefly review the literature that has been published over the past few years and to provide a sense of where the field is going.

Methods: We searched PubMed and the ACM Digital Library for articles published between 2014 and 2016 on the topics of CHI and PGHD. The results of the search were screened for relevance and categorized into a set of common themes. We discuss the major topics covered in these articles.

Results: We retrieved 65 articles from our PubMed query and 32 articles from our ACM Digital Library query. After a review of titles, we were left with 47 articles to conduct our full article survey of the activities in CHI and PGHD. We have summarized these articles and placed them into major categories of activity. Within the domain of consumer health informatics, articles focused on mobile health and patient-generated health data comprise the majority of the articles published in recent years.

Conclusions: Current evidence indicates that technological advancements and the widespread availability of affordable consumer-grade devices are fueling research into using PGHD for better care. As we observe a growing number of (pilot) developments using various mobile health technologies to collect PGHD, major gaps still exist in how to use the data by both patients and providers. Further research is needed to understand the impact of PGHD on clinical outcomes.

Keywords

Consumer health information/methods - patient-generated health data - mHealth - user-computer interface - consumer participation in delivery of health care

• References

• 1 Poushter J. Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies [Internet]. Pew Research Center; 2016. Available from: http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/
• 2 del Rosario MB, Redmond SJ, Lovell NH. Tracking the evolution of smartphone sensing for monitoring human movement. Sensors (Basel) 2015; Jul 31; 15 (08) 18901-33.
• 3 Chung AEA, Basch EM. Potential and Challenges of Patient-Generated Health Data for High-Quality Cancer Care. J Oncol Pract 2015; May; 11 (03) 195-7.
• 4 Demiris G. Consumer Health Informatics: Past, Present, and Future of a Rapidly Evolving Domain. Yearb Med Inform 2016; May 20; (Suppl. 01) S42-7.
• 5 Reeder B, Meyer E, Lazar A, Chaudhuri S, Thompson HJ, Demiris G. Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: A systematic review. Int J Med Inform 2013; Jul; 82 (07) 565-79.
• 6 Safavi K, Ratli R, Webb K, MacCracken L. Patients Want a Heavy Dose of Digital. 2016 Available from: https://www.accenture.com/_acnmedia/PDF-8/Accenture-Patients-Want-A-Heavy-Dose-of-Digital-Infographic-v2.pdf
• 7 Office of the National Coordinator for Health Information Technology. Office-based Physician Electronic Patient Engagement Capabilities, Health IT Quick-Stat #54 [Internet]. 2016 [cited 2017 Mar 27]. Available from: dashboard.health-it.gov/quickstats/pages/physicians-view-download-transmit-secure-messaging-patient-engagement.php
• 8 Wells S, Rozenblum R, Park A, Dunn M, Bates DW. Personal health records for patients with chronic disease: a major opportunity. Appl Clin Inform 2014; 05 (02) 416-29.
• 9 Flaherty D, Hoffman-Goetz L, Arocha JF. What is consumer health informatics? A systematic review of published definitions. Inform Health Soc Care 2015; Mar; 40 (02) 91-112.
• 10 Wood WA, Bennett AV, Basch E. Emerging uses of patient generated health data in clinical research. Mol Oncol 2015; May; 09 (05) 1018-24.
• 11 Petersen C. Patient-generated health data: a pathway to enhanced long-term cancer survivorship. J Am Med Inform Assoc 2016; May; 23 (03) 456-61.
• 12 Hull S. Patient-Generated Health Data Foundation for Personalized Collaborative Care. Comput Inform Nurs 2015; May; 33 (05) 177-80.
• 13 Petersen C, DeMuro P. Legal and Regulatory Considerations Associated with Use of Patient-Generated Health Data from Social Media and Mobile Health (mHealth) Devices. Appl Clin Inform 2015; Jan 14; 06 (01) 16-26.
• 14 Murthy HS, Wood WA. The Value of Patient Reported Outcomes and Other Patient-Generated Health Data in Clinical Hematology. Curr Hematol Malig Rep 2015; Sep; 10 (03) 213-24.
• 15 Lavallee DC, Chenok KE, Love RM, Petersen C, Holve E, Segal CD. et al. Incorporating Patient-Reported Outcomes Into Health Care To Engage Patients And Enhance Care. Health Aff (Millwood) 2016; Apr; 35 (04) 575-82.
• 16 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; Oct 19; 03 (02) e26.
• 17 Fisch MJ, Chung AE, Accordino MK. Using Technology to Improve Cancer Care: Social Media, Wearables, and Electronic Health Records. Am Soc Clin Oncol Educ Book 2016; 35: 200-8.
• 18 Mullaney T, Yttergren B, Stolterman E. Positional acts. In: Proceedings of the 8th International Conference on Tangible, Embedded and Embodied Interaction - TEI ‘14 [Internet]. New York, New York, USA: ACM Press; 2014: 93-6 (TEI ‘14). Available from: http://doi.acm.org/10.1145/2540930.2540943
• 19 Jung M. Consumer Health Informatics: Promoting Patient Self-care Management of Illnesses and Health. Health Care Manag 2016; Oct/ Dec; 35 (04) 312-20.
• 20 Hartzler AL, McDonald DW, Park A, Huh J, Weaver C, Pratt W. Evaluating health interest profiles extracted from patient-generated data. AMIA Annu Symp Proc 2014; Nov 14; 2014: 626-35.
• 21 Casper GR, Mcdaniel A. Introduction to Theme Issue on Technologies for Patient-defined and Patient-generated Data. Pers Ubiquitous Comput [Internet] 2015; Jan; 19 (01) 1-2 Available from: http://dx.doi.org/10.1007/s00779-014-0803-2
• 22 Liang Z, Martell MAC, Nishimura T. Mining Hidden Correlations Between Sleep and Lifestyle Factors from Quantified-self Data. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct [Internet]. New York, NY, USA: ACM; 2016: 547-52 (UbiComp ‘16). Available from: http://doi.acm.org/10.1145/2968219.2968319
• 23 Gollamudi SS, Topol EJ, Wineinger NE. A framework for smartphone-enabled, patient-generated health data analysis. PeerJ 2016; Aug 2; 04: e2284.
• 24 Tzovaras D, Valtolina S, Abdelnour-Nocera J, Votis K, Barricelli BR, Moustakas K. et al. Workshop on Mobile Healthcare for the Self-management of Chronic Diseases and the Empowerment of Patients. In: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct [Internet]. New York, NY, USA: ACM; 2016: 1069-72 (MobileHCI ‘16). Available from: http://doi.acm.org/10.1145/2957265.2965002
• 25 Liaqat D, Thukral I, Sin P, Alshaer H, Rudzicz F, de Lara E. et al. Poster: WearCOPD - Monitoring COPD Patients Remotely Using Smartwatches. In: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion [Internet]. New York, NY, USA: ACM; 2016: 139 (MobiSys ‘16 Companion). Available from: http://doi.acm.org/10.1145/2938559.2938606
• 26 Pernencar C, Romão T. Mobile Apps for IBD Self: Management Using Wearable Devices and Sensors. In: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct [Internet]. New York, NY, USA: ACM; 2016: 1089-92 (MobileHCI ‘16). Available from: http://doi.acm.org/10.1145/2957265.2965007
• 27 Meliones A, Kokkovos S. Privacy-preserving Intelligent Networked Video Surveillance for Patient Monitoring and Alarm Detection. In: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments [Internet]. New York, NY, USA: ACM; 2015: 631-638 (PETRA ‘15). Available from: http://doi.acm.org/10.1145/2769493.2769509
• 28 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; May; 23 (03) 532-7.
• 29 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; Jan; 08 (01) 74-82.
• 30 Khue LM, Ouh EL, Jarzabek S. Mood Self-assessment on Smartphones. In: Proceedings of the Conference on Wireless Health [Internet]. New York, NY, USA: ACM; 2015: 191-198 (WH ‘15). Available from: http://doi.acm.org/10.1145/2811780.2811921
• 31 Kop R, Hoogendoorn M, Klein MCA. A Personalized Support Agent for Depressed Patients: Forecasting Patient Behavior Using a Mood and Coping Model. In: Proceedings of the 2014 IEEE/ WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03 [Internet]. Washington, DC, USA: IEEE Computer Society; 2014: 302-9 (WI-IAT ‘14). Available from: http://dx.doi.org/10.1109/WI-IAT.2014.181
• 32 Venugopalan J, Cheng C-W, Wang MD. Motion-Talk: Personalized Home Rehabilitation System for Assisting Patients with Impaired Mobility. In: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics [Internet]. New York, NY, USA: ACM; 2014: 455-63 (BCB ‘14). Available from: http://doi.acm.org/10.1145/2649387.2649430
• 33 Geurts E, Haesen M, Dendale P, Luyten K, Coninx K. Back on Bike: The BoB Mobile Cycling App for Secondary Prevention in Cardiac Patients. In: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services [Internet]. New York, NY, USA: ACM; 2016: 135-46 (MobileHCI ‘16). Available from: http://doi.acm.org/10.1145/2935334.2935377
• 34 Devlin AM, McGee-Lennon M, O’Donnell CA, Bouamrane M-M, Agbakoba R, O’Connor S. et al. Delivering digital health and well-being at scale: lessons learned during the implementation of the dallas program in the United Kingdom. J Am Med Inform Assoc 2016; Jan; 23 (01) 48-59.
• 35 Jacobs ML, Clawson J, Mynatt ED. Comparing Health Information Sharing Preferences of Cancer Patients, Doctors, and Navigators. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing [Internet]. New York, NY, USA: ACM; 2015: 808-18 (CSCW ‘15). Available from: http://doi.acm.org/10.1145/2675133.2675252
• 36 Lim C, Berry ABL, Hirsch T, Hartzler AL, Wagner EH, Ludman E. et al. “It Just Seems Outside My Health”: How Patients with Chronic Conditions Perceive Communication Boundaries with Providers. In: Proceedings of the 2016 ACM Conference on Designing Interactive Systems [Internet]. New York, NY, USA: ACM; 2016: 1172-84 (DIS ‘16). Available from: http://doi.acm.org/10.1145/2901790.2901866
• 37 Chung C-F, Dew K, Cole AM, Zia J, Fogarty JA, Kientz JA. et al. Boundary Negotiating Artifacts in Personal Informatics: Patient-Provider Collaboration with Patient-Generated Data. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW ‘16 [Internet]. New York, New York, USA: ACM Press; 2016: 768-84 (CSCW ‘16). Available from: http://doi.acm.org/10.1145/2818048.2819926
• 38 Briggs P, Hardy C, Harris PR, Sillence E. Patient-led Perspectives on Ehealth: How Might Hyperpersonal Data Inform Design?. In: Proceedings of HCI Korea [Internet]. South Korea: Hanbit Media, Inc; 2014: 115-21 (HCIK ‘15). Available from: http://dl.acm.org/citation.cfm?id=2729485.2729504
• 39 Maniam A, Dhillon JS, Baghaei N. Determinants of Patients’ Intention to Adopt Diabetes Self-Management Applications. In: Proceedings of the 15th New Zealand Conference on Human-Computer Interaction - CHINZ 2015 [Internet]. New York, New York, USA: ACM Press; 2015: 43-50 (CHINZ 2015). Available from: http://doi.acm.org/10.1145/2808047.2808059
• 40 PricewaterhouseCoopers. Personal health management - the rise of the empowered consumer [Internet]. Consumer Health Experience Radar. 2015 Available from: www.pwc.com
• 41 Gownder JP, McQuivey JL, Reitsma R, Gillett FE, Husson T, Ask JA. et al. Five Key Truths About Wearables That Every Leader Should Know: Wearables Are Poised To Change The Marketing Landscape. 2015
• 42 Office of the National Coordinator for Health Information Technology. Patient-Generated Health Information Technical Expert Panel FINAL REPORT. 2013 (December).
• 43 Deering M. Issue Brief: Patient-Generated Health Data and Health IT. Washington, DC: US … [Internet]; 2013 Available from: http://wanghaisheng.github.io/images/pghd_brief_final122013.pdf
• 44 Mattfeldt-Beman MK, Corrigan SA, Stevens VJ, Sugars CP, Dalcin AT, Givi MJ. et al. Participants’ evaluation of a weight-loss program. J Am Diet Assoc 1999; 99 (01) 66-71.
• 45 Mossavar-Rahmani Y, Henry H, Rodabough R, Bragg C, Brewer A, Freed T. et al. Additional self-monitoring tools in the dietary modification component of the women’s health initiative. J Am Diet Assoc 2004; Jan; 104 (01) 76-85.
• 46 Hsueh PYS, Zhu X, Deng V, Ramarishnan S, Ball M. Dynamic and accretive composition of patient engagement instruments for personalized plan generation. Stud Health Technol Inform 2014; 201: 447-51.
• 47 Borrelli B, Ritterband LM. Special Issue on eHealth and mHealth : Challenges and Future Directions for Assessment, Treatment, and Dissemination. Heal Psychol 2015; 34: 1205-8.
• 48 HL7 FHIR (Fast Healthcare Interoperability Resources) Specification [Internet]. Available from: https://www.hl7.org/fhir/
• 49 Frank L, Basch E, Selby JV. The PCORI Perspective on Patient-Centered Outcomes Research. JAMA 2014; Oct 15; 312 (15) 1513-4.
• 50 Bloomfield RA, Polo-Wood F, Mandel JC, Mandl KD. Opening the Duke electronic health record to apps: Implementing SMART on FHIR. Int J Med Inform 2017; Mar; 99: 1-10.
• 51 Wagholikar KB, Mandel JC, Klann JG, Wattanasin N, Mendis M, Chute CG. et al. SMART-on-FHIR implemented over i2b2. J Am Med Inform Assoc 2017; Mar 1; 24 (02) 398-402.
• 52 Krebs P, Duncan DT. Health App Use Among US Mobile Phone Owners: A National Survey. JMIR Mhealth Uhealth 2015; Nov 4; 03 (04) e101.
• 53 Rock Health. Digital Health Consumer Adoption: 2015 [Internet]. Available from: https://rockhealth.com/reports/digital-health-consumer-adoption-2015/
• 54 Mamykina L, Smaldone AM, Bakken SR. Adopting the sensemaking perspective for chronic disease self-management. J Biomed Inform 2015; 56: 406-17.
• 55 McGillicuddy JW, Gregoski MJ, Weiland AK, Rock R a, Brunner-Jackson BM, Patel SK. et al. Mobile Health Medication Adherence and Blood Pressure Control in Renal Transplant Recipients: A Proof-of-Concept Randomized Controlled Trial. JMIR Res Protoc 2013; Sep 4; 02 (02) e32.
• 56 Jakicic JM, Davis KK, Rogers RJ, King WC, Marcus MD, Helsel D. et al. Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss. JAMA 2016; Sep 20; 316 (11) 1161-71.
• 57 Keselman A, Smith CA, Divita G, Kim H, Browne AC, Leroy G. et al. Consumer Health Concepts That Do Not Map to the UMLS: Where Do They Fit?. J Am Med Informatics Assoc 2008; 15 (04) 496-505.