Robab Abdolkhani, MSc Ann Borda, PhD Kathleen Gray, PhD Ruth De - - PowerPoint PPT Presentation

Robab Abdolkhani, MSc Ann Borda, PhD Kathleen Gray, PhD Ruth De Souza, PhD

DQM RPM PGHD MW Data Quality Management Remote Patient Monitoring Patient Generated Health Data Medical Wearables

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Background

Cardiac Disease

Chronic Pain Diabetes

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), 3-17. https://tincture.io/healthcare-is-shifting-but-can-it-get-out-of- its-own-way-bb6771c0318e

Remote Patient Monitoring & Medical Wearables

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Africa RPM: 38% MW: 4% America RPM: 50% MW: 59% Europe RPM:72% MW: 19.5% Western Pacific RPM: 57% MW: 15.7% South East Asia RPM: 20% MW: 15.7% Eastern Mediterranean RPM: 21% MW: 4.2%

• 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
• 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.

Remote Patient Monitoring & Medical Wearables Market in 2016

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 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 https://www.healthit.gov/sites/default/files/rti_pghd_whitepaper_april_2012.pdf

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Patient Generated Health Data

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Business processes that ensure the quality of an organization’s data during collection, application (including aggregation), warehousing, and analysis

Accuracy

Data Quality Management

AHIMA (2012). Pocket Glossary of Health Information Management and Technology, Third Edition. Chicago, IL: AHIMA Press, 2012

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Settings Battery Digital Health literacy errors Standards ICT Infrastructure

Factors affecting Data Quality Management of PGHD

PGHD Flow Wearable Human

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Accuracy

Australian Capital Territory (ACT) Health. Data Quality Framework. (2013). Retrieved from http://health.act.gov.au/sites/default/files/Policy_and_Plan/Data%20Quality%20Framework.pdf

Timeliness

Interpretability Institutional Environment

Coherence Accessibility Relevancy

Australian Capital Territory’s Data Quality Management Framework

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No Publication Year Context Title

[1] 2017

Literature Review

Baig MM, GholamHosseini H, Moqeem AA, Mirza F, Lindén M. A systematic review of wearable patient monitoring systems–current challenges and opportunities for clinical adoption. Journal of medical systems. 2017;41(7):115.

[2] 2015

Conceptual Framework

Habib K, Torjusen A, Leister W, editors. Security analysis of a patient monitoring system for the Internet of Things in eHealth. The Seventh International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED); 2015.

[3] 2015

Conceptual Framework

Larburu N, Bults R, van Sinderen M, Hermens H. Quality-of-data management for telemedicine systems. Procedia Computer Science. 2015;63:451-8.

[4] 2013

Conceptual Framework

Puentes J, Montagner J, Lecornu L, Lähteenmäki J. Quality analysis of sensors data for personal health records on mobile devices. In Pervasive health knowledge management: Springer; 2013. p. 103-33.

[5] 2013

Conceptual Framework

Vavilis S, Zannone N, Petković M, editors. Data reliability in home healthcare services. Computer-Based Medical Systems (CBMS), 2013 IEEE 26th International Symposium on; 2013: IEEE.

[6] 2012

Conceptual Framework

Shin M. Secure remote health monitoring with unreliable mobile devices. Journal of biomedicine & biotechnology. 2012;2012:546021.

[7] 2011

Conceptual Framework

Rodriguez CG, Riveill M, editors. Data quality analysis for e-health monitoring applications. IADIS International Conference e-Health 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems; Rome, Italy: IADIS.

[8] 2009

Perspective

Sriram J, Shin M, Kotz D, Rajan A, MSastry M, Yarvis M. Challenges in data quality assurance in pervasive health monitoring systems Future of trust in computing: Springer 2009. p. 129-42.

Characteristics of Selected Literature

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Accuracy Timeliness Human Wearable

Factors

PGHD Flow

Factors Factors

DQM Dimensions

Device application

• n body

Patient negligence Patient motivation Trust in patient [4-6,8] Payment for roaming data [3] Authenticity Age Calibration Measurement error Rate of data collection [4-8] Low battery life Synchronisation [1,6,7] Patient authentication Data transmission error [3-6] Transmission speed [1,8] 10

Human Wearable

Factors

PGHD Flow

Factors Factors

DQM Dimensions

Accessibility Coherence Institutional Environment

Data drop Data aggregation [2,6] Measurement variations [4] Conformance with the standard ranges of measurements [8] Hardware and software attacks [2] Consistency in data structure [7] Quality of evidence [3]

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Human Wearable

Factors

PGHD Flow

Factors Factors

DQM Dimensions

Interpretability Relevancy

[no discussion] [no discussion] Data definition [8] [no discussion] [no discussion] [no discussion] 12

Lack of guidelines on DQM of PGHD Lack of human factors consideration

This is holding back effective clinical use of PGHD

Lack of literature about DQM of PGHD in RPM

Discussion

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 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

Conclusion

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