Digital Health Tools Used to Collect Patient-Generated Health Data

Introduction

The universality of digital devices and applications have has a significant transformative impact on several aspects of human action and interactions. These digital innovations enable individuals to keep track of their daily activities, health, fitness and well-being. The upsurge of these innovations has resulted in an increased public interest in the activity of self-tracking. This is because they facilitate self-tracking - by allowing this recording to be undertaken in a granular manner, and with little effort and cost (Neff and Nafus, 2016); thereby enabling individuals to monitor, manage and maintain their health and well-being (Castle-Clarke and Imison, 2016). The feasibility of the use of these digital applications and devices for health purposes following their support of self-tracking has established powerful rationale for the collection of patient-generated data.

Following the proliferation and pervasive use digital health technologies by the public and in all healthcare levels, numerous of these digital innovations have sought to expand in order to integrate patient-generated data, patient portals, patient reported health outcomes and social health determinants, as a way of promoting patient interaction and engagement (Adler-Milstein et al., 2014; Irizarry, Vito and Curran, 2015; Rexhepi et al., 2018). As such, interaction design has emerged as a vital aspect in relation to digital health innovations and how patients engage and interact with them, and collect or create patient-generated data.

The aim of this study, therefore, is to investigate and contribute to the understanding and conceptualization of interaction design with regard to patient-generated data, and to establish the validity and reliability of the digital tools through which the patient-generated data is collected. In line with its aim, this study seeks to answer the following research questions:

Are patients involved in the interaction design of the digital patient-generated data tools?

What are the benefits of actively engaging/involving patients in their care through patient-generated data collection tools and electronic health portals?

What is the validity and reliability of digital patient-generated data tools?

Literature Review

Patient-generated data has been broadly defined as constituting any health-related data that patients or their appointed abettor/intermediary create or collect with the aim of addressing their health issues/concerns (Cohen et al., 2016; Nittas et al., 2018; Reading and Merrill, 2018). Wood, Bennett and Basch (2015) identify patient-generated data as including dietary consumption, physical activity, medication dosage, mood and sleep patterns, all of which can be collected in various ways.

Unlike previous digital innovations that limited patients’ active role in their care due to challenges with respect to their functionality, accessibility and interoperability (Perlin, 2016), many of today’s digital devices and applications are designed such that they enhance patient interaction and active engagement in their care and health records. This brings about multiple benefits such as patient satisfaction, improved health outcomes, and effective monitoring and management, especially of chronic illnesses that involve complex coordination and frequent patient and care giver engagement (Kruse et al., 2015).

Digital health technologies are important as they facilitate the collection of patient-generated data which enable many forms of health information to be frequently, remotely and longitudinally tracked, and to be shared wirelessly between patients and their healthcare providers (Zhu, Colgan and Reddy, 2017). This data could also serve to complement information collected during medical consultations, thereby contributing to the promotion of personalized and patient-centred care (Park et al., 2018). Additionally, patient-generated data potentially promotes the undertaking of more appropriate interventions and in a timely manner (Claborn et al., 2018). This is due to its provision of improved and real-time insights into health anomalies or fluctuations compared to a defined baseline.

Research Methodology

To complete this study, the researcher will undertake a systematic literature review. This approach will be adopted due to its facilitation of a wider focus and synthesis of existing evidence into this topic, and it will be conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols (Moher et al., 2015). The researcher will first conduct a literature search from CINAHL, Embase, Medline, PsycINFO, Science Direct, Web of Science and PubMed databases to identify candidate articles to be reviewed.

The identified articles’ titles and abstracts will then be screened for eligibility, and select qualifying articles based on the inclusion and exclusion criteria. Only full-text articles written in English and which are peer reviewed, and which focused on patient-generated data collected through digital but non-clinical tools and how this data was used by clinicians will be included Those written in any other language besides English, which are not peer-reviewed, do not address patient-generated data that is personal health related or with regard to its collection, sharing, utilization or context or do not focus on prevention or health promotion will be excluded. The included articles will then be subjected to empirical appraisal and thematic analysis to evaluate their empirical quality.

References

Adler-Milstein, J., Sarma, N., Woskie, L.R. and Jha, A.K., 2014. A comparison of how four countries use health IT to support care for people with chronic conditions. Health Affairs, 33(9), pp.1559-1566.

Castle-Clarke, S. and Imison, C., 2016. The digital patient: transforming primary care. London: Nuffield Trust.

Claborn, K.R., Meier, E., Miller, M.B., Leavens, E.L., Brett, E.I. and Leffingwell, T., 2018. Improving adoption and acceptability of digital health interventions for HIV disease management: a qualitative study. Translational behavioral medicine, 8(2), pp.268-279.

Cohen, D.J., Keller, S.R., Hayes, G.R., Dorr, D.A., Ash, J.S. and Sittig, D.F., 2016. Integrating patient-generated health data into clinical care settings or clinical decision-making: lessons learned from project healthdesign. JMIR human factors, 3(2), p.e5919.

Irizarry, T., Dabbs, A.D. and Curran, C.R., 2015. Patient portals and patient engagement: a state of the science review. Journal of medical Internet research, 17(6), p.e148.

Kruse, C.S., Argueta, D.A., Lopez, L. and Nair, A., 2015. Patient and provider attitudes toward the use of patient portals for the management of chronic disease: a systematic review. Journal of medical Internet research, 17(2), p.e3703.

Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., ... & Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic reviews, 4(1), 1-9.

Neff, G. and Nafus, D., 2016. Self-tracking. MIT Press.

Nittas, V., Mütsch, M., Ehrler, F. and Puhan, M.A., 2018. Electronic patient-generated health data to facilitate prevention and health promotion: a scoping review protocol. BMJ open, 8(8), p.e021245.

Park, Y.R., Lee, Y., Kim, J.Y., Kim, J., Kim, H.R., Kim, Y.H., Kim, W.S. and Lee, J.H., 2018. Managing patient-generated health data through mobile personal health records: analysis of usage data. JMIR mHealth and uHealth, 6(4), p.e9620.

Perlin, J.B., 2016. Health information technology interoperability and use for better care and evidence. Jama, 316(16), pp.1667-1668.

Reading, M.J. and Merrill, J.A., 2018. Converging and diverging needs between patients and providers who are collecting and using patient-generated health data: an integrative review. Journal of the American Medical Informatics Association, 25(6), pp.759-771.

Rexhepi, H., Åhlfeldt, R.M., Cajander, Å. and Huvila, I., 2018. Cancer patients’ attitudes and experiences of online access to their electronic medical records: A qualitative study. Health informatics journal, 24(2), pp.115-124.

Wood, W.A., Bennett, A.V. and Basch, E., 2015. Emerging uses of patient generated health data in clinical research. Molecular oncology, 9(5), pp.1018-1024.

Zhu, H., Colgan, J., Reddy, M. and Choe, E.K., 2016. Sharing patient-generated data in clinical practices: an interview study. In AMIA Annual Symposium Proceedings (Vol. 2016, p. 1303). American Medical Informatics Association.

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