Yearb Med Inform 2014; 23(01): 82-89
DOI: 10.15265/IY-2014-0014
Original Article
Georg Thieme Verlag KG Stuttgart

Big Data in Healthcare – Defining the Digital Persona through User Contexts from the Micro to the Macro

Contribution of the IMIA Organizational and Social Issues WG
C. E. Kuziemsky
1   Telfer School of Management, University of Ottawa, Ottawa, ON, Canada
,
H. Monkman
2   School of Health Information Science, University of Victoria, Victoria, BC, Canada
,
C. Petersen
3   Mayo Clinic, Rochester, MN, USA
,
J. Weber
4   Department of Computer Science, University of Victoria, Victoria, BC, Canada
,
E. M. Borycki
2   School of Health Information Science, University of Victoria, Victoria, BC, Canada
,
S. Adams
5   Tilburg Institute for Law, Technology and Society, Tilburg University, Tilburg, The Netherlands
,
S. Collins
6   Partners eCare, Partners Healthcare Systems, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA
› Author Affiliations
Further Information

Correspondence to:

Craig Kuziemsky
Telfer School of Management
University of Ottawa
Ottawa, ON, Canada
Phone: +1-613 562 5800 ext 4792   

Publication History

15 August 2014

Publication Date:
05 March 2018 (online)

 

Summary

Objectives: While big data offers enormous potential for improving healthcare delivery, many of the existing claims concerning big data in healthcare are based on anecdotal reports and theoretical vision papers, rather than scientific evidence based on empirical research. Historically, the implementation of health information technology has resulted in unintended consequences at the individual, organizational and social levels, but these unintended consequences of collecting data have remained unaddressed in the literature on big data. The objective of this paper is to provide insights into big data from the perspective of people, social and organizational considerations.

Method: We draw upon the concept of persona to define the digital persona as the intersection of data, tasks and context for different user groups. We then describe how the digital persona can serve as a framework to understanding sociotechnical considerations of big data implementation. We then discuss the digital persona in the context of micro, meso and macro user groups across the 3 Vs of big data.

Results: We provide insights into the potential benefits and challenges of applying big data approaches to healthcare as well as how to position these approaches to achieve health system objectives such as patient safety or patient-engaged care delivery. We also provide a framework for defining the digital persona at a micro, meso and macro level to help understand the user contexts of big data solutions.

Conclusion: While big data provides great potential for improving healthcare delivery, it is essential that we consider the individual, social and organizational contexts of data use when implementing big data solutions.


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  • 31 Ortega VE, Meyers DA. Pharmacogenetics: Implications of race and ethnicity on defining genetic profiles for personalized medicine. J Allergy Clin Immunol 2014; 133 (01) 16-26.
  • 32 Schultz T. Turning Healthcare Challenges into Big Data Opportunities: A Use-Case Review Across the Pharmaceutical Development Lifecycle. Bulletin of the Association for Information Science and Technology. 2013; 39 (05) 34-40.
  • 33 McGregor C. Big Data in Neonatal Intensive Care. Computer 2013; 46 (06) 54-9.
  • 34 Cinquegrani K. Small hospital uses data for big improvements. JAHIMA. 2013; 84 (09) 58-9.
  • 35 Schilling L, Chase A, Kehrli S, Liu AY, Stiefel M, Brentari R. Kaiser Permanente’s Performance Improvement System, Part 1: From benchmarking to executing on strategic priorities. Jt Comm J Qual Patient Saf 2010; 36: 484-98.
  • 36 McLaughlin CP. Continuous quality improvement in health care: theory, implementation, and applications. Jones & Bartlett Learning; 2004
  • 37 Larson EB. Building Trust in the Power of “Big Data” Research to Serve the Public Good. JAMA 2013; 309 (23) 2443-4.
  • 38 Health & Social Care Information Centre.. Provisional monthly patient reported outcome measures (PROMs) in England. 18 March 2013 Accessed June 14, 2013 at http://www.hscic.gov.uk/media/1537/A-Guide-to-PROMs-Methodology/pdf/PROMS_Guide_v5.pdf
  • 39 Van Velsen L, Beaujean DJ, Van Gemert-Pijnen JE. Why mobile health app overload drives us crazy, and how to restore the sanity. BMC Med Inform Decis Mak 2013; Feb 11 13: 23.
  • 40 Shoaran M, Thomo A, Weber-Jahnke JH. Social Web search and analysis for social network and media. In: Alhajj R, Rokne JG. editors. Encyclopedia of Social Network Analysis and Mining. Berlin, New York: Springer; 2014
  • 41 Hanson CL, Cannon B, Burton S, Giraud-Carrier C. An Exploration of Social Circles and Prescription Drug Abuse Through Twitter. J Med Internet Res 2013; 15 (09) e189.
  • 42 Mo PKH, Coulson NS. Are online support groups always beneficial?. A qualitative exploration of the empowering and disempowering processes of participation within HIV/AIDS-related online support groups. Int J Nurs Stud 2013 in press.
  • 43 Armstrong AW, Harskamp CT, Cheeney S, Wu J, Schupp CW. Power of crowdsourcing: novel methods of data collection in psoriasis and psoriatic arthritis. J Am Acad Dermatol 2012; 67 (06) 1273-81.
  • 44 Brabham DC, Ribisl KM, Kirchner TR, Bernhardt JM. Crowdsourcing applications for public health. Am J Prev Med 2014; 46 (02) 179-87.
  • 45 Baarah A, Peyton L. Engineering a state monitoring service for real-time patient flow management. In: Proceedings of the 9th Middleware Doctoral Symposium of the 13th ACM/IFIP/ USENIX International Middleware Conference (MIDDLEWARE ‘12). New York, NY, USA: ACM; 2012. Article 8, 6 pages.
  • 46 Weber-Jahnke J, Mason-Blakley F. On the Safety of Electronic Medical Records. In: Liu Z, Wassyng A. editors. Foundations of Health Informatics Engineering and Systems. 2012 Vol. 7151 177-94.
  • 47 Denecke K, Dolog P, Smrz P. Making use of social media data in public health. In: Mille A. et al., editors. Proceedings of the 21st World Wide Web Confer-ence, WWW 2012. Lyon, France: April 16-20;. 2012. p. 243-6.
  • 48 Sofean M, Smith M. A real-time disease surveil-lance architecture using social networks. Stud Health Technol Inform 2012; 180: 823-7.
  • 49 Pedrana A, Hellard M, Gold J, Ata N, Chang S, Howard S. et al. Queer as F**k: Reaching and Engaging Gay Men in Sexual Health Promotion through Social Networking Sites. J Med Internet Res 2013; 15 (09) e25.
  • 50 Koc M, Gulyagci S. Facebook addiction among Turkish college students: the role of psychological health, demographic, and usage characteristics. Cyberpsychol Behav Soc Netw 2013; 16 (04) 279-84.
  • 51 Shoaran M, Thomo A, Weber JH. Zero-Knowledge Private Graph Summarization. In: IEEE Intl. Conference on Big Data. 2013
  • 52 National Health Service.. Equity and excellence: liberating the NHS. July 2010. Accessed June 14, 2014 at https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/213823/dh_117794.pdf.
  • 53 U.S. Food and Drug Administration. Guidance for industry: Patient-reported outcome measures: use in medical product development to support labeling claims. December 2009. Accessed June 12, 2014 at https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM193282.pdf.
  • 54 Terry K. Patient-reported functional ststus data may soon be included in EHRs. iHealthBeat. 25 November 2013. Accessed June 11, 2014 at www.ihealthbeat.org/insight/2013/patientreported-functional-status-data-may-soon-be-included-in-ehrs.
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  • 56 Keller S, Correia H. The Patient-Reported Outcome Measurement Information System (PROMIS): international update. MAPI Research Trust PRO Newsletter, No. 47. 2012. Accessed June 12, 2014 at http://www.pro-newsletter.com/images/PDF_articles/pronl47_keller.pdf
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  • 58 Basch E, Abernathy AP, Mullins CD, Reeve BB, Smith ML, Coons SJ. et al. Recommendations for incorporating patient-reported outcomes into clinical comparative effectiveness research in adult oncology. J Clin Oncol 2012; 30 (34) 4249-55.
  • 59 Shen J, Johnston M, Hays RD. Asthma outcome measures. Expert Rev Pharmacoecon Outcomes Res 2011; 11 (04) 447-53.
  • 60 Jha AK, Orav EJ, Zheng J, Epstein AM. Patients’ perception of hospital care in the United States. N Engl J Med 2008; 359: 1921-31.
  • 61 Boulding W, Glickman SW, Manary MP, Schulman KA, Staelin R. Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care 2011; 17: 41-8.
  • 62 Winograd T, Flores F. Understanding Computers and Cognition. Bosten: Addison-Wesley; 1986
  • 63 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related error. J Am Med Inform Assoc 2004; 11 (02) 104-112.
  • 64 Kuziemsky CE, Borycki E, Nøhr C, Cummings E. The nature of unintended benefits in health information systems. Stud Health Technol Inform 2012; 180: 896-900.
  • 65 Satell 2013 - Yes, Big Data Can Solve Real World Problems. Taken from http://www.forbes.comsites/gregsatell/2013/12/03/yes-big-data-can-solve-real-world-problems/ last accessed June 13, 2014
  • 66 http://www.ibmbigdatahub.com/whitepaperdata-driven-healthcare-organizations-use-big-data-analytics-big-gains last accessed June 14, 2014
  • 67 Kannampallil TG, Schauer GF, Cohen T, Patel VL. Considering complexity in healthcare systems. J Biomed Inform 2011; 44 (06) 943-7.

Correspondence to:

Craig Kuziemsky
Telfer School of Management
University of Ottawa
Ottawa, ON, Canada
Phone: +1-613 562 5800 ext 4792   

  • References

  • 1 Pulman A. A patient centred framework for improving LTC quality of life through Web 2.0 technology. Health Informatics J 2010; Mar 16 (01) 15-23.
  • 2 Stellefson M, Chaney B, Barry AE, Chavarria E, Tennant B, Walsh-Childers K. et al. Web 2.0 chronic disease self-management for older adults: a systematic review. J Med Internet Res 2013; 15 (02) e35.
  • 3 Steinhubl SR, Muse ED, Topol EJ. CanMobile Health Technologies Transform Health Care?. JAMA 310 (22) 2395-6.
  • 4 ‘Big data is the future of healthcare’. http://www.cognizant.com/InsightsWhitepapers/Big-Data-isthe-Future-of-Healthcare.pdf last accessed June 14, 2014
  • 5 Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 2012; 13 (06) 395-405.
  • 6 http://www.ibmbigdatahub.com/whitepaperdata-driven-healthcare-organizations-use-big-data-analytics-big-gains last accessed June 14 2014
  • 7 Health IT and Patient Safety: Building Safer Systems for Better Care.. Committee on Patient Safety and Health Information Technology;. Board on Health Care Services (HCS); Institute of Medicine (IOM); 2012
  • 8 Institute of Medicine.. Crossing the quality chasm: a new health system for the twenty-first century. Washington, DC: National Academies Press; 2001
  • 9 Koppel R, Wetterneck T, Telles JL, Karsh BT. Workarounds to barcode medication administration systems: their occurrences, causes, and threats to patient safety. J Am Med Infrom Assoc 2008; 15 (04) 408-23.
  • 10 Borycki EM. Technology-Induced Errors: Where Do They Come From and What Can We Do About Them?. Stud Health Technol Inform 2013; 194: 20-6.
  • 11 Coiera E. Why system inertia makes health reform so hard. BMJ 2011; 342: d3693.
  • 12 Kohn L, Corrigan J, Donaldson M. editors. To Err Is Human: Building a Safer Health System. Washington, DC: Committee on Quality of Health Care in America, Institute of Medicine. National Academies Press; 1999
  • 13 IMIA Organizational and Social Issues Working group, http://www.imia-medinfo.org/new2node/148 Last Accessed June 14, 2014
  • 14 Lluch M. Healthcare professionals’ organisational barriers to health information technologies-A literature review. Int J Med Inform 2011; 80 (12) 849-62.
  • 15 Cresswell K, Sheikh A. Organizational issues in the implementation and adoption of health information technology innovations: an interpretative review. Int J Med Inform 2013; 82: 73-86.
  • 16 Jones SS, Rudin RS, Perry T, Shekelle PG. Health information technology: An updated systematic review with a focus on meaningful use. Ann Intern Med 2014; 160 (01) 48-55.
  • 17 Shortliffe EH, Cimino JJ. editors. Biomedical Informatics: Computer Applications in Health Care and Biomedicine, chapter 1: The Computer Meets Medicine and Biology: Emergence of a Discipline. Berlin, New York: Springer; 2006
  • 18 LeRouge C, Ma J, Sneha S, Tolle K. User profiles and personas in the design and development of consumer health technologies. In J Med Inform 2013; 82: e251-e268.
  • 19 Miaskiewicz T, Kozar KA. Personas and user-centered design: How can personas benefit product design processes?. Design Studies 2011; 32 (05) 417-30.
  • 20 Hackos JT, Redish JC. User and task analysis for interface design. New York: Wiley & Sons; 1998
  • 21 Kuziemsky C, Nohr C, Aarts J, Jaspers M, Beuscart-Zephir M-C. Context sensitive health informatics: concepts, methods, and tools. Stud Health Technol Inform 2013; 194: 1-7.
  • 22 Kushniruk A, Turner P. A framework for user involvement and context in the design and development of safe e-Health systems. Stud Health Technol Inform 2012; 180: 353-7.
  • 23 Kuziemsky CE, Bush P. Coordination considerations of health information systems. Stud Health Technol Inform 2013; 194: 133-8.
  • 24 Berg M, Aarts J, Van der Lei J. ICT in health care: sociotechnical approaches. Methods Inf Med 2003; 42: 297-301.
  • 25 Novak L, Brooks J, Gadd C, Anders S, Lorenzi N. Mediating the intersections of organizational routines during the introduction of a health IT system. Eur J Inf Syst 2012; 21: 552-69.
  • 26 Bloomrosen M, Starren J, Lorenzi NM, Ash JS, Patel VL, Shortliffe EH. Anticipating and addressing the unintended consequences of health IT and policy: a report from the AMIA 2009 Health Policy Meeting. J Am Med Inform Assoc 2011; 18 (01) 82-90.
  • 27 Bojadzievski T, Gabbay RA. Patient centered medical home and diabetes. Diabetes Care 2011; 34: 1047-53.
  • 28 Coiera E. Social networks, social media, and social diseases. BMJ 2013 346(f3007).
  • 29 Every B. Better for Ourselves and Better for Our Patients: Chronic Disease Management in Primary Care. Networks Healthc Q 2007; 10 (03) 70-4.
  • 30 Luzzatto L, Seneca E. G6PD deficiency: a classic example of pharmacogenetics with on-going clinical implications. Br J Haematol 2014; 164 (04) 469-80.
  • 31 Ortega VE, Meyers DA. Pharmacogenetics: Implications of race and ethnicity on defining genetic profiles for personalized medicine. J Allergy Clin Immunol 2014; 133 (01) 16-26.
  • 32 Schultz T. Turning Healthcare Challenges into Big Data Opportunities: A Use-Case Review Across the Pharmaceutical Development Lifecycle. Bulletin of the Association for Information Science and Technology. 2013; 39 (05) 34-40.
  • 33 McGregor C. Big Data in Neonatal Intensive Care. Computer 2013; 46 (06) 54-9.
  • 34 Cinquegrani K. Small hospital uses data for big improvements. JAHIMA. 2013; 84 (09) 58-9.
  • 35 Schilling L, Chase A, Kehrli S, Liu AY, Stiefel M, Brentari R. Kaiser Permanente’s Performance Improvement System, Part 1: From benchmarking to executing on strategic priorities. Jt Comm J Qual Patient Saf 2010; 36: 484-98.
  • 36 McLaughlin CP. Continuous quality improvement in health care: theory, implementation, and applications. Jones & Bartlett Learning; 2004
  • 37 Larson EB. Building Trust in the Power of “Big Data” Research to Serve the Public Good. JAMA 2013; 309 (23) 2443-4.
  • 38 Health & Social Care Information Centre.. Provisional monthly patient reported outcome measures (PROMs) in England. 18 March 2013 Accessed June 14, 2013 at http://www.hscic.gov.uk/media/1537/A-Guide-to-PROMs-Methodology/pdf/PROMS_Guide_v5.pdf
  • 39 Van Velsen L, Beaujean DJ, Van Gemert-Pijnen JE. Why mobile health app overload drives us crazy, and how to restore the sanity. BMC Med Inform Decis Mak 2013; Feb 11 13: 23.
  • 40 Shoaran M, Thomo A, Weber-Jahnke JH. Social Web search and analysis for social network and media. In: Alhajj R, Rokne JG. editors. Encyclopedia of Social Network Analysis and Mining. Berlin, New York: Springer; 2014
  • 41 Hanson CL, Cannon B, Burton S, Giraud-Carrier C. An Exploration of Social Circles and Prescription Drug Abuse Through Twitter. J Med Internet Res 2013; 15 (09) e189.
  • 42 Mo PKH, Coulson NS. Are online support groups always beneficial?. A qualitative exploration of the empowering and disempowering processes of participation within HIV/AIDS-related online support groups. Int J Nurs Stud 2013 in press.
  • 43 Armstrong AW, Harskamp CT, Cheeney S, Wu J, Schupp CW. Power of crowdsourcing: novel methods of data collection in psoriasis and psoriatic arthritis. J Am Acad Dermatol 2012; 67 (06) 1273-81.
  • 44 Brabham DC, Ribisl KM, Kirchner TR, Bernhardt JM. Crowdsourcing applications for public health. Am J Prev Med 2014; 46 (02) 179-87.
  • 45 Baarah A, Peyton L. Engineering a state monitoring service for real-time patient flow management. In: Proceedings of the 9th Middleware Doctoral Symposium of the 13th ACM/IFIP/ USENIX International Middleware Conference (MIDDLEWARE ‘12). New York, NY, USA: ACM; 2012. Article 8, 6 pages.
  • 46 Weber-Jahnke J, Mason-Blakley F. On the Safety of Electronic Medical Records. In: Liu Z, Wassyng A. editors. Foundations of Health Informatics Engineering and Systems. 2012 Vol. 7151 177-94.
  • 47 Denecke K, Dolog P, Smrz P. Making use of social media data in public health. In: Mille A. et al., editors. Proceedings of the 21st World Wide Web Confer-ence, WWW 2012. Lyon, France: April 16-20;. 2012. p. 243-6.
  • 48 Sofean M, Smith M. A real-time disease surveil-lance architecture using social networks. Stud Health Technol Inform 2012; 180: 823-7.
  • 49 Pedrana A, Hellard M, Gold J, Ata N, Chang S, Howard S. et al. Queer as F**k: Reaching and Engaging Gay Men in Sexual Health Promotion through Social Networking Sites. J Med Internet Res 2013; 15 (09) e25.
  • 50 Koc M, Gulyagci S. Facebook addiction among Turkish college students: the role of psychological health, demographic, and usage characteristics. Cyberpsychol Behav Soc Netw 2013; 16 (04) 279-84.
  • 51 Shoaran M, Thomo A, Weber JH. Zero-Knowledge Private Graph Summarization. In: IEEE Intl. Conference on Big Data. 2013
  • 52 National Health Service.. Equity and excellence: liberating the NHS. July 2010. Accessed June 14, 2014 at https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/213823/dh_117794.pdf.
  • 53 U.S. Food and Drug Administration. Guidance for industry: Patient-reported outcome measures: use in medical product development to support labeling claims. December 2009. Accessed June 12, 2014 at https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM193282.pdf.
  • 54 Terry K. Patient-reported functional ststus data may soon be included in EHRs. iHealthBeat. 25 November 2013. Accessed June 11, 2014 at www.ihealthbeat.org/insight/2013/patientreported-functional-status-data-may-soon-be-included-in-ehrs.
  • 55 Patient-Centered Outcomes Research Institute.. How we’re funded. Accessed June 11, 2014 at http://www.pcori.org/about-us/how-were-funded.
  • 56 Keller S, Correia H. The Patient-Reported Outcome Measurement Information System (PROMIS): international update. MAPI Research Trust PRO Newsletter, No. 47. 2012. Accessed June 12, 2014 at http://www.pro-newsletter.com/images/PDF_articles/pronl47_keller.pdf
  • 57 PatientsLikeMe.. PatientsLikeMe selects first pilot users for open research exchange. Press release. 13 August 2013 Accessed June 130, 2014 at http://news.patientslikeme.com/press-release/patientslikeme-selects-first-pilot-users-open-re-search-exchange
  • 58 Basch E, Abernathy AP, Mullins CD, Reeve BB, Smith ML, Coons SJ. et al. Recommendations for incorporating patient-reported outcomes into clinical comparative effectiveness research in adult oncology. J Clin Oncol 2012; 30 (34) 4249-55.
  • 59 Shen J, Johnston M, Hays RD. Asthma outcome measures. Expert Rev Pharmacoecon Outcomes Res 2011; 11 (04) 447-53.
  • 60 Jha AK, Orav EJ, Zheng J, Epstein AM. Patients’ perception of hospital care in the United States. N Engl J Med 2008; 359: 1921-31.
  • 61 Boulding W, Glickman SW, Manary MP, Schulman KA, Staelin R. Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care 2011; 17: 41-8.
  • 62 Winograd T, Flores F. Understanding Computers and Cognition. Bosten: Addison-Wesley; 1986
  • 63 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related error. J Am Med Inform Assoc 2004; 11 (02) 104-112.
  • 64 Kuziemsky CE, Borycki E, Nøhr C, Cummings E. The nature of unintended benefits in health information systems. Stud Health Technol Inform 2012; 180: 896-900.
  • 65 Satell 2013 - Yes, Big Data Can Solve Real World Problems. Taken from http://www.forbes.comsites/gregsatell/2013/12/03/yes-big-data-can-solve-real-world-problems/ last accessed June 13, 2014
  • 66 http://www.ibmbigdatahub.com/whitepaperdata-driven-healthcare-organizations-use-big-data-analytics-big-gains last accessed June 14, 2014
  • 67 Kannampallil TG, Schauer GF, Cohen T, Patel VL. Considering complexity in healthcare systems. J Biomed Inform 2011; 44 (06) 943-7.