Robab Abdolkhani, MSc Ann Borda, PhD Kathleen Gray, PhD Ruth De Souza, PhD
Background Data Quality Patient Generated Management Health Data DQM PGHD RPM MW Remote Patient Medical Monitoring Wearables 2
Remote Patient Monitoring & Medical Wearables Diabetes Chronic Pain Cardiac Disease https://tincture.io/healthcare-is-shifting-but-can-it-get-out-of- its-own-way-bb6771c0318e Vegesna, A., Tran, M., Angelaccio, M., & Arcona, S. (2017). Remote patient monitoring via non-invasive digital technologies: a systematic review. Telemedicine and e-Health, 23(1), 3 3-17.
Remote Patient Monitoring & Medical Wearables Market in 2016 Europe Eastern RPM:72% Mediterranean MW: 19.5% RPM: 21% MW: 4.2% South East Asia RPM: 20% MW: 15.7% America Western Pacific RPM: 50% Africa RPM: 57% MW: 59% RPM: 38% MW: 15.7% MW: 4% 4 Cisco. (2016). Regional wearable medical devices growth. Retrieved from: http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-whitepaper-c11-520862.html 1 WHO. (2016). Global diffusion of eHealth. Making universal health coverage achievable.: Global Observatory for eHealth. Retrieved from: http://apps.who.int/iris/handle/10665/252529.
Patient Generated Health Data ✓ ✘ Patients, not clinicians, are primarily responsible for capturing or recording these data Patients have some control about how to share these data with clinicians and others Shapiro, M., Johnston, D., Wald, J., & Mon, D. (2012). Patient-generated Health Data: White Paper Prepared for the Office of the National Coordinator for Health IT by RTI International. Retrieved from 5 https://www.healthit.gov/sites/default/files/rti_pghd_whitepaper_april_2012.pdf
Data Quality Management Business processes that ensure the quality of an organization’s data during collection, application (including aggregation), warehousing, and Accuracy analysis AHIMA (2012). Pocket Glossary of Health Information Management and Technology, Third Edition. Chicago, 6 IL: AHIMA Press, 2012 1
Factors affecting Data Quality Management of PGHD Wearable PGHD Flow Human Standards Settings Digital Health literacy ICT Infrastructure Battery errors 7
Australian Capital Territory ’ s Data Quality Management Framework Accuracy Coherence Accessibility Institutional Timeliness Relevancy Interpretability Environment Australian Capital Territory (ACT) Health. Data Quality Framework. (2013). Retrieved from 8 http://health.act.gov.au/sites/default/files/Policy_and_Plan/Data%20Quality%20Framework.pdf 1
Characteristics of Selected Literature No Publication Context Title Year Baig MM, GholamHosseini H, Moqeem AA, Mirza F, Lindén M. A systematic review of wearable patient monitoring Literature [1] 2017 systems – current challenges and opportunities for clinical adoption. Journal of medical systems. 2017;41(7):115. Review Habib K, Torjusen A, Leister W, editors. Security analysis of a patient monitoring system for the Internet of Things Conceptual [2] 2015 in eHealth. The Seventh International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED); Framework 2015. Larburu N, Bults R, van Sinderen M, Hermens H. Quality-of-data management for telemedicine systems. Procedia Conceptual [3] 2015 Computer Science. 2015;63:451-8. Framework Puentes J, Montagner J, Lecornu L, Lähteenmäki J. Quality analysis of sensors data for personal health records on Conceptual [4] 2013 mobile devices. In Pervasive health knowledge management: Springer; 2013. p. 103-33. Framework Vavilis S, Zannone N, Petković M, editors. Data reliability in home healthcare services. Computer-Based Medical Conceptual [5] 2013 Systems (CBMS), 2013 IEEE 26th International Symposium on; 2013: IEEE. Framework Conceptual Shin M. Secure remote health monitoring with unreliable mobile devices. Journal of biomedicine & biotechnology. [6] 2012 2012;2012:546021. Framework Rodriguez CG, Riveill M, editors. Data quality analysis for e-health monitoring applications. IADIS International Conceptual [7] 2011 Conference e-Health 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems; Framework Rome, Italy: IADIS. Sriram J, Shin M, Kotz D, Rajan A, MSastry M, Yarvis M. Challenges in data quality assurance in pervasive health [8] 2009 9 Perspective monitoring systems Future of trust in computing: Springer 2009. p. 129-42.
DQM Human PGHD Flow Wearable Dimensions Factors Factors Factors Authenticity Device application Patient Age on body authentication Calibration Data transmission Patient negligence Measurement error Accuracy error [3-6] Patient motivation Rate of data Trust in patient collection [4-6,8] [4-8] Transmission Payment for Low battery life Timeliness speed roaming data Synchronisation [3] [1,8] [1,6,7] 10
DQM Human PGHD Flow Wearable Dimensions Factors Factors Factors Data drop Hardware and Accessibility [no discussion] software attacks Data aggregation [2] [2,6] [no discussion] Consistency in data Measurement Coherence structure variations [7] [4] Conformance with Institutional Quality of evidence [no discussion] the standard [3] Environment ranges of measurements [8] 11
DQM Human PGHD Flow Wearable Dimensions Factors Factors Factors Interpretability Data definition [no discussion] [no discussion] [8] [no discussion] [no discussion] [no discussion] Relevancy 12
Discussion Lack of literature about DQM of PGHD in RPM Lack of guidelines on DQM of PGHD Lack of human factors consideration This is holding back effective clinical use of PGHD 13
Conclusion More work is needed by all PGHD stakeholders to develop practical guidelines for DQM of PGHD. Our current research is conducting case studies and focus groups for this purpose so that PGHD adoption forecast can be done in a way that produce safety and quality of care needs data quality guidelines for PGHD 14
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