ABSTRACT
Patient-generated data, such as data from wearable fitness trackers and smartphone apps, are viewed as a valuable information source towards personalised healthcare. However, studies in specific clinical settings have revealed diverse barriers to their effective use. In this paper, we address the following question: are there barriers prevalent across distinct workflows in clinical settings to using patient-generated data? We conducted a two-part investigation: a literature review of studies identifying such barriers; and interviews with clinical specialists across multiple roles, including emergency care, cardiology, mental health, and general practice. We identify common barriers in a six-stage workflow model of aligning patient and clinician objectives, judging data quality, evaluating data utility, rearranging data into a clinical format, interpreting data, and deciding on a plan or action. This workflow establishes common ground for HCI practitioners and researchers to explore solutions to improving the use of patient-generated data in clinical practices.
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- 2008. "provenance, n.". In Oxford English Dictionary Online (3rd ed.). Oxford University Press. http://www.oed.com/view/Entry/153408 {Accessed 25 Jul. 2017}.Google Scholar
- Jessica S. Ancker, Holly O. Witteman, Baria Hafeez, Thierry Provencher, Mary Van de Graaf, and Esther Wei. 2015a. The invisible work of personal health information management among people with multiple chronic conditions: qualitative interview study among patients and providers. Journal of Medical Internet Research 17, 6 (2015), e137.Google ScholarCross Ref
- Jessica S. Ancker, Holly O. Witteman, Baria Hafeez, Thierry Provencher, Mary Van de Graaf, and Esther Wei. 2015b. "You Get Reminded You're a Sick Person": personal Data Tracking and Patients With Multiple Chronic Conditions. Journal of Medical Internet Research 17, 8 (2015), e202.Google ScholarCross Ref
- Geoff Appelboom, Melissa LoPresti, Jean-Yves Reginster, E. Sander Connolly, and Emmanuel P. L. Dumont. 2014. The quantified patient: a patient participatory culture. Current Medical Research and Opinion 30, 12 (2014), 2585--2587.Google ScholarCross Ref
- Earl R. Babbie. 2012. The Practice of Social Research (13th ed.). Cengage Learning, Boston.Google Scholar
- Stinne Aaløkke Ballegaard, Thomas Riisgaard Hansen, and Morten Kyng. 2008. Healthcare in Everyday Life: Designing Healthcare Services for Daily Life. In Proceedings of the 2008 Conference on Human Factors in Computing Systems (CHI '08). Association for Computing Machinery, New York, 1807--1816. Google ScholarDigital Library
- Albert Boonstra and Manda Broekhuis. 2010. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BioMed Central: Health Services Research 10, 1 (2010), 231.Google ScholarCross Ref
- Felicia M. Bowens, Patricia A. Frye, and Warren A. Jones. 2010. Health Information Technology: Integration of Clinical Workflow into Meaningful Use of Electronic Health Records. Perspectives in Health Information Management 7, Fall (2010), 1d. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2966355/Google Scholar
- Cathy Charles, Amiram Gafni, and Tim Whelan. 1997. Shared decision-making in the medical encounter: what does it mean? (Or it takes at least two to tango). Social Science&Medicine 44, 5 (1997), 681--692.Google ScholarCross Ref
- Emil Chiauzzi, Carlos Rodarte, and Pronabesh DasMahapatra. 2015. Patient-centered activity monitoring in the self-management of chronic health conditions. BioMed Central: Medicine 13, 1 (2015).Google Scholar
- Seryung Choo, Ju Young Kim, Se Young Jung, Sarah Kim, Jeong Eun Kim, Jong Soo Han, Sohye Kim, Jeong Hyun Kim, Jeehye Kim, Yongseok Kim, Dongouk Kim, and Steve Steinhubl. 2016. Development of a weight loss mobile app linked with an accelerometer for use in the clinic: usability, acceptability, and early testing of its impact on the patient-doctor relationship. Journal of Medical Internet Research: mHealth and uHealth 4, 1 (2016), e24.Google ScholarCross Ref
- Michael Christ, Florian Grossmann, Daniela Winter, Roland Bingisser, and Elke Platz. 2010. Modern Triage in the Emergency Department. Deutsches -rzteblatt International 107, 50 (2010), 892--898.Google Scholar
- Chia-Fang Chung, Jonathan Cook, Elizabeth Bales, Jasmine Zia, and Sean A. Munson. 2015. More than telemonitoring: health provider use and nonuse of life-log data in irritable bowel syndrome and weight management. Journal of Medical Internet Research 17, 8 (2015), e203.Google ScholarCross Ref
- Chia-Fang Chung, Kristin Dew, Allison Cole, Jasmine Zia, James Fogarty, Julie A. Kientz, and Sean A. Munson. 2016. Boundary negotiating artifacts in personal informatics: patient-provider collaboration with patient-generated data. In Proceedings of the 2016 Conference on Computer-Supported Cooperative Work&Social Computing (CSCW '16). Association for Computing Machinery, New York, 770--786. Google ScholarDigital Library
- Deborah J. Cohen, Sara R. Keller, Gillian R. Hayes, David A. Dorr, Joan S. Ash, and Dean F. Sittig. 2016. Integrating patient-generated health data into clinical care settings or clinical decision-making: lessons learned from Project HealthDesign. Journal of Medical Internet Research: Human Factors 3, 2 (2016), e26--e26.Google ScholarCross Ref
- Jonah Comstock. 2015. Cerner taps Validic to bring patient-generated data into portal. MobiHealthNews. http://www.mobihealthnews.com/41269/ cerner-taps-validic-to-bring-patient-generated-data-into-portal {Accessed 29 Dec. 2017}.Google Scholar
- Pat Croskerry. 2002. Achieving Quality in Clinical Decision Making: Cognitive Strategies and Detection of Bias. Academic Emergency Medicine 9, 11 (2002), 1184--1204.Google ScholarCross Ref
- Mary Jo Deering, Erin Siminerio, and Scott Weinstein. 2013. Issue brief: patient-generated health data and health IT. Technical Report. Office of the National Coordinator for Health Information Technology, Washington, DC, USA.Google Scholar
- Department of Health. 2012. NHS Constitution for England. United Kingdom Department of Health, London. https://www.gov.uk/government/publications/the-nhs-constitution-for-england {Accessed 29 Nov. 2017}.Google Scholar
- Edinburgh Centre for Endocrinology and Diabetes. 2016. Diabetes Information&Common Questions. http://www.edinburghdiabetes.com/information-faqs/ {Accessed 28 Jul. 2017}.Google Scholar
- Alan S. Go, Elaine M. Hylek, Kathleen A. Phillips, YuChiao Chang, Lori E. Henault, Joe V. Selby, and Daniel E. Singer. 2001. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the anticoagulation and risk factors in atrial fibrillation (atria) study. Journal of the American Medical Association 285, 18 (2001), 2370--2375.Google ScholarCross Ref
- Matthew K. Hong, Lauren Wilcox, Daniel Machado, Thomas A. Olson, and Stephen F. Simoneaux. 2016. Care partnerships: toward technology to support teens' participation in their health care. In Proceedings of the 2016 Conference on Human Factors in Computing Systems (CHI '16). Association for Computing Machinery, New York, 5337--5349. Google ScholarDigital Library
- Nicholas Huba and Yan Zhang. 2012. Designing patient-centered personal health records: health care professionals' perspective on patient-generated data. Journal of Medical Systems 36, 6 (2012), 3893--3905. Google ScholarDigital Library
- Maia L. Jacobs, James Clawson, and Elizabeth D. Mynatt. 2014. My Journey Compass: a preliminary investigation of a mobile tool for cancer patients. In Proceedings of the 2014 Conference on Human Factors in Computing Systems (CHI '14). Association for Computing Machinery, New York, 663--672. Google ScholarDigital Library
- Christina Kelley, Bongshin Lee, and Lauren Wilcox. 2017. Self-tracking for mental wellness: understanding expert perspectives and student experiences. In Proceedings of the 2017 Conference on Human Factors in Computing Systems (CHI '17). Association for Computing Machinery, New York, 629--641. Google ScholarDigital Library
- Yoojung Kim, Eunyoung Heo, Hyunjeong Lee, Sookyoung Ji, Jueun Choi, Jeong-Whun Kim, Joongseek Lee, and Sooyoung Yoo. 2017. Prescribing 10,000 Steps Like Aspirin: Designing a Novel Interface for Data-Driven Medical Consultations. In Proceedings of the 2017 Conference on Human Factors in Computing Systems (CHI '17). Association for Computing Machinery, New York, 5787--5799. Google ScholarDigital Library
- Yoojung Kim, Sookyoung Ji, Hyunjeong Lee, Jeong-Whun Kim, Sooyoung Yoo, and Joongseek Lee. 2016. "My doctor is keeping an eye on me!": exploring the clinical applicability of a mobile food logger. In Proceedings of the 2016 Conference on Human Factors in Computing Systems (CHI '16). Association for Computing Machinery, New York, 5620--5631. Google ScholarDigital Library
- Paul Krebs and T. Dustin Duncan. 2015. Health App Use Among US Mobile Phone Owners: A National Survey. JMIR mHealth uHealth 3, 4 (2015), e101.Google Scholar
- Jongin Lee, Daeki Cho, Junhong Kim, Eunji Im, JinYeong Bak, Kyung ho Lee, Kwan Hong Lee, and John Kim. 2017. Itchtector: A Wearable-based Mobile System for Managing Itching Conditions. In Proceedings of the 2017 Conference on Human Factors in Computing Systems (CHI '17). Association for Computing Machinery, New York, 893--905. Google ScholarDigital Library
- Haley MacLeod, Anthony Tang, and Sheelagh Carpendale. 2013. Personal informatics in chronic illness management. In Proceedings of Graphics Interface 2013 (GI '13). Canadian Information Processing Society, Toronto, 149--156. http://dl.acm.org/citation.cfm?id=2532129.2532155 Google ScholarDigital Library
- Max van Manen. 1990. Researching Lived Experience: Human Science for an Action Sensitive Pedagogy (2nd ed.). State University of New York Press, Albany, USA.Google Scholar
- Helena M. Mentis, Anita Komlodi, Katrina Schrader, Michael Phipps, Ann Gruber-Baldini, Karen Yarbrough, and Lisa Shulman. 2017. Crafting a View of Self-Tracking Data in the Clinical Visit. In Proceedings of the 2017 Conference on Human Factors in Computing Systems (CHI '17). Association for Computing Machinery, New York, 5800--5812. Google ScholarDigital Library
- Sonali R. Mishra, Shefali Haldar, Ari H. Pollack, Logan Kendall, Andrew D. Miller, Maher Khelifi, and Wanda Pratt. 2016. "Not just a receiver": understanding patient behavior in the hospital environment. In Proceedings of the 2016 Conference on Human Factors in Computing Systems (CHI '16). Association for Computing Machinery, New York, 3103--3114. Google ScholarDigital Library
- Luc Moreau, Paul Groth, Simon Miles, Javier Vazquez-Salceda, John Ibbotson, Sheng Jiang, Steve Munroe, Omer Rana, Andreas Schreiber, Victor Tan, and Laszlo Varga. 2008. The provenance of electronic data. Communications of the Association for Computing Machinery 51, 4 (2008), 52--58. Google ScholarDigital Library
- National Health Service. 2015. Causes of Atrial fibrillation. http://www.nhs.uk/Conditions/ Atrial-fibrillation/Pages/Causes.aspx {Accessed 28 Jul. 2017}.Google Scholar
- National Health Service England. 2016. Health and care records. http://www.nhs.uk/NHSEngland/thenhs/records/ healthrecords/Pages/overview.aspx {Accessed 28 Jul. 2017}.Google Scholar
- National Information Board. 2014. Personalised health and care 2020: using data and technology to transform outcomes for patients and citizens. Department of Health, London, UK. https://www.gov.uk/government/ publications/personalised-health-and-care-2020 {Accessed 18 Jan. 2015}.Google Scholar
- Gina Neff and Dawn Nafus. 2016. The Self-Tracking. MIT Press, Cambridge, USA. Google ScholarDigital Library
- Shantanu Nundy, Chen-Yuan E. Lu, Patrick Hogan, Anjuli Mishra, and Monica E. Peek. 2014. Using patient-generated health data from mobile technologies for diabetes self-management support: provider perspectives from an academic medical center. Journal of Diabetes Science And Technology 8, 1 (2014), 74--82.Google ScholarCross Ref
- Rupa A. Patel, Predrag Klasnja, Andrea Hartzler, Kenton T. Unruh, and Wanda Pratt. 2012. Probing the benefits of real-time tracking during cancer care. In American Medical Informatics Association Annual Symposium Proceedings (AMIA '12), Vol. 2012. 1340--1349. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540467/Google Scholar
- Chris Paton, M. Margaret, L. Fernandez-Luque, and Annie Y.S. Lau. 2012. Self-tracking, social media and personal health records for patient empowered self-care. International Medical Informatics Association Yearbook (2012), 16--24. http://www.schattauer.de/t3page/1214.html-manuscript=17937&L=1Google Scholar
- Enrico Maria Piras and Francesco Miele. 2017. Clinical self-tracking and monitoring technologies: negotiations in the ICT-mediated patient-provider relationship. Health Sociology Review 26, 1 (2017), 38--53.Google ScholarCross Ref
- Catherine Plaisant, Brett Milash, Anne Rose, Seth Widoff, and Ben Shneiderman. 1996. LifeLines: Visualizing Personal Histories. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '96). ACM, New York, 221--227. Google ScholarDigital Library
- Ruth Ravichandran, Sang-Wha Sien, Shwetak N. Patel, Julie A. Kientz, and Laura R. Pina. 2017. Making Sense of Sleep Sensors: How Sleep Sensing Technologies Support and Undermine Sleep Health. In Proceedings of the 2017 Conference on Human Factors in Computing Systems (CHI '17). Association for Computing Machinery, New York, 6864--6875. Google ScholarDigital Library
- Jessica Schroeder, Jane Hoffswell, Chia-Fang Chung, James Fogarty, Sean Munson, and Jasmine Zia. 2017. Supporting patient-provider collaboration to identify individual triggers using food and symptom journals. In Proceedings of the 2017 Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17). Association for Computing Machinery, New York, 1726--1739. Google ScholarDigital Library
- Philip A. Tumulty. 1970. What Is a Clinician and What Does He Do? New England Journal of Medicine 283, 1 (1970), 20--24.Google ScholarCross Ref
- Kim M. Unertl, Kevin B. Johnson, and Nancy M. Lorenzi. 2012. Health information exchange technology on the front lines of healthcare: workflow factors and patterns of use. Journal of the American Medical Informatics Association 19, 3 (2012), 392--400.Google ScholarCross Ref
- Kim M. Unertl, Laurie L. Novak, Kevin B. Johnson, and Nancy M. Lorenzi. 2010. Traversing the many paths of workflow research: developing a conceptual framework of workflow terminology through a systematic literature review. Journal of the American Medical Informatics Association 17, 3 (2010), 265--273.Google ScholarCross Ref
- Kenton T. Unruh, Meredith Skeels, Andrea Civan-Hartzler, and Wanda Pratt. 2010. Transforming Clinic Environments into Information Workspaces for Patients. In Proceedings of the 2008 Conference on Human Factors in Computing Systems (CHI '10). Association for Computing Machinery, New York, 183--192. Google ScholarDigital Library
- Bert Vandenberghe and David Geerts. 2015. Sleep Monitoring Tools at Home and in the Hospital: Bridging Quantified Self and Clinical Sleep Research. In Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth '15). Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, ICST, Brussels, Belgium, 153--160. Google ScholarDigital Library
- Peter West, Richard Giordano, Max Van Kleek, and Nigel Shadbolt. 2016. The Quantified Patient in the Doctor's Office: Challenges&Opportunities. In Proceedings of the 2016 Conference on Human Factors in Computing Systems (CHI '16). Association for Computing Machinery, New York, 3066--3078. Google ScholarDigital Library
- Peter West, Max Van Kleek, Richard Giordano, Mark Weal, and Nigel Shadbolt. 2017. Information Quality Challenges of Patient-Generated Data in Clinical Practice. Frontiers Public Health 5 (2017), 284.Google ScholarCross Ref
- Philip A. Wolf, Robert D. Abbott, and William B. Kannel. 1991. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke 22, 8 (1991), 983--988.Google ScholarCross Ref
- H. Zhu, J. Colgan, M. Reddy, and E. K. Choe. 2016. Sharing patient-generated data in clinical practices: an interview study. In American Medical Informatics Association Annual Symposium Proceedings (AMIA '16), Vol. 2016. 1303--1312.Google Scholar
Index Terms
- Common Barriers to the Use of Patient-Generated Data Across Clinical Settings
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