DRIVERLESS HEALTH DISRUPTION: the rise of the iDoctor's relationship with the health consumer in the fourth Industrial Revolution AUTHOR Grant Newton
PEER REVIEWER: Jodi Wishnia
EXECUTIVE SUMMARY This paper highlights the phenomenon of patient-generated digital health data (PGHD) from wearable tracker devices (WATs), fuelling the rise of digital self-quantified care. This may disconnect clinical first-line support, potentially placing private practice and patients’ health at risk (Dimitrov, 2016). It exposes the problem that clinicians could be disintermediated by consumers’ exponential adoption of ambient health intelligent (AmHI) WATs. The character of the ‘iPatient’ is emerging who, through day-to-day interaction with smart devices, is being drawn into the world of ‘driverless’, artificial intelligence (AI)-driven healthcare. The hypothesis is that the axiomatic prevalence of PGHD from AmHI WATs can be leveraged by clinicians. They must digitally position their expertise as a critical and value-added component to the self and the artificial assessment of vital signs and physical data that are freely available. Conceptually, this paper presents new thinking that looks at clinicians and their consumers joining forces to employ AmHI PGHD to their mutual benefit. It highlights the need for further research to determine ways in which clinicians can harness the power of WATs, PGHD and AI and integrate clinician-reviewed PGHD to develop more robust consumer-centric models.
INTRODUCTION South African clinicians have not adjusted to the fourth industrial revolution, described by Schwab (2017) as the ‘exciting times of fundamental technological change’. If they don't, the possibility is that private practice will perish (Shetty, 2020). Big data agents, using consumer-generated data and AI, are empowering health consumer autonomy (Horgan et al, 2019). Clinicians must consider the possibility of AI replacing them entirely (Krittanawong, 2018). Clinicians are not currently equipped to react and redesign practice workflows to embrace the rapid shift to AI and big data management. This leaves the iDoctor, a ‘machine theoretically capable of replacing the judgement of primary care clinicians’ (Karches, 2018), to collect health-consumer data and present a competitive diagnosis that may disintermediate primary and chronic care health services (Zhang et al, 2017). Machines are already capable of imitating intelligent human behaviour and can extract and analyse clinical and scientific data in a fraction of the time it would take a clinician (Nagy and Sisk, 2020). The importance of this paper lies in its raising the question: what will the need for human resources for health be when machines can successfully partner with the health consumer (Arnold and Wilson, 2017) for equal or sometimes even better health outcomes?
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