Humans and Big Data: New Hope? Harnessing the Power of Person-Centred Data Analytics
Big data provide the hope of major health innovation and improvement. However, there is a risk of precision medicine based on predictive biometrics and service metrics overwhelming anticipatory human centered sense-making, in the fuzzy emergence of personalized (big data) medicine. This is a pressing issue, given the paucity of individual sense-making data approaches. A human-centric model is described to address the gap in personal particulars and experiences in individual health journeys. The Patient Journey Record System (PaJR) was developed to improve human-centric healthcare by harnessing the power of person-centred data analytics using complexity theory, iterative health services and information systems applications over a 10 year period. PaJR is a web-based service supporting usually bi-weekly telephone calls by care guides to individuals at risk of readmissions.
This chapter describes a case study of the timing and context of readmissions using human (biopsychosocial) particular data which is based on individual experiences and perceptions with differing patterns of instability. This Australian study, called MonashWatch, is a service pilot using the PaJR system in the Dandenong Hospital urban catchment area of the Monash Health network. State public hospital big data – the Victorian HealthLinks Chronic Care algorithm provides case finding for high risk of readmission based on disease and service metrics. Monash Watch was actively monitoring 272 of 376 intervention patients, with 195 controls over 22 months (ongoing) at the time of the study.
Three randomly selected intervention cases describe a dynamic interplay of self-reported change in health and health care, medication, drug and alcohol use, social support structure. While the three cases were at similar predicted risk initially, their cases represented different statistically different time series configurations and admission patterns. Fluctuations in admission were associated with (mal)alignment of bodily health with psychosocial and environmental influences. However human interpretation was required to make sense of the patterns as presented by the multiple levels of data.
A human-centric model and framework for health journey monitoring illustrates the potential for ‘small’ personal experience data to inform clinical care in the era of big data predominantly based on biometrics and medical industrial process. Unless the complex dynamics underpinning readmissions are understood, many efforts may be directed at disease rather than at whole patient journey systems including disease. Many new technologies will emerge to enhance health journeys. It is important that appropriate anticipatory models inform their development. in order to enhance human sense-making.
The collaboration in the development, trialling and evaluation of the PaJR project by our colleagues Narelle Hinkley and Donald Campbell at Monash University, Australia, Carl Vogel and Lucy Hederman at Trinity University, Dublin, Ireland and Kevin Smith and John-Paul Smith, University of Queensland is kindly acknowledged.
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