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.
- 1.Oxford University P. Merriam-Webster Online Dictionary. The New Oxford dictionary of English; 1998.Google Scholar
- 3.Norbury A, Seymour B. Response heterogeneity: challenges for personalised medicine and big data approaches in psychiatry and chronic pain. F1000 Res. 2018;7:55.Google Scholar
- 5.Smith A. The big data revolution: from drug development to better health outcomes? Am J Manag Care. 2014;20(8 Spec No.):E4.Google Scholar
- 6.Tilly C. The old new social history and the new old social history. Ann Arbor: Center for Research on Social Organization. Harvard; 2007.Google Scholar
- 9.Lakoff G, Johnson M. Metaphors we live by. Chicago: The University of Chicago Press; 1980.Google Scholar
- 10.Rosen R. Anticipatory systems: philosophical, mathematical, and methodological foundations. 1st ed. Oxford: Pergamon Press; 1985.Google Scholar
- 17.McWhinney IR. An acquaintance with particulars …. Fam Med. 1989;21(4):296–8.Google Scholar
- 25.Too much medicine. https://www.bmj.com/too-much-medicine.
- 26.Duerden M, Avery T, Payne R. Polypharmacy and medicines optimisation. Making it safe and sound. London: The King’s Fund; 2013.Google Scholar
- 28.WHO. Ottawa charter for health promotion. First International Conference on Health Promotion. Ottawa, 21 November 1986. 1986: WHO/HPR/HEP/95.1. Available from, http://www.who.int/hpr/NPH/docs/ottawa_charter_hp.pdf.
- 29.A Primer on Precision Medicine: US National Library of Medicine; 2018. Available from, https://ghr.nlm.nih.gov/primer/precisionmedicine/precisionvspersonalized.
- 30.What is pharmacogenomics? US National Library of Medicine. 2018. Available from, https://ghr.nlm.nih.gov/primer/genomicresearch/pharmacogenomics.
- 36.Lutomski JE, Baars MAE, Boter H, Buurman BM, den Elzen WPJ, Jansen APD, et al. [Frailty, disability and multi-morbidity: the relationship with quality of life and healthcare costs in elderly people]. Ned. Tijdschr. Geneeskd. 2014;158:A7297.Google Scholar
- 40.MonashWatch: Keeping people healthy at home: Monash Health; 2016. Available from, http://monashhealth.org/page/monashwatch.
- 42.Martin C, Sturmberg JP, Stockman K, Campbell D, Hederman L, Vogel C, et al. Supporting complex dynamic health journeys using conversation to avert hospital readmissions from the community: an ecological perspective incorporating interoception. In: Sturmberg J, editor. Putting systems and complexity science into practice. Cham: Springer; 2018.Google Scholar
- 43.Ferrier D, Diver F, Corin S, McNair P, Cheng C. HealthLinks: incentivising better value chronic care in Victoria. Int J Integr Care. 2017;17(3):A129. Available from, https://www.ijic.org/articles/abstract/10.5334/ijic.3241/.
- 44.Staiger TO, Kritek PA, Blakeney EL, Zierler BK, O’Brien K, Ehrmantraut R. A conceptual framework for applying the anticipatory theory of complex systems to improve safety and quality in healthcare. In: Nadin M, editor. Anticipation and medicine. Cham: Springer; 2016. p. 31–40.Google Scholar
- 45.Goldwater D, Dharmarajan K, McEwen BS, Krumholz HM. Is posthospital syndrome a result of hospitalization-induced allostatic overload? J Hosp Med. 2018;13(5). https://doi.org/10.12788/jhm.2986.
- 46.Martin C, Hinckley N, Stockman K, Campbell D. Post-hospital syndrome (PHS) and potentially preventable hospitalizations (PPH) in adults. MonashWatch adult cohort patient telehealth journeys. JMIR Preprints 2018. Available from, http://preprints.jmir.org/preprint/11952.
- 48.Rosen R. An interview with Robert Rosen. In: Rosen J, editor. (This is a corrected version [July 14, 2000], thanks to the help of Esther Wieringa). Available from, http://www.people.vcu.edu/~mikuleck/rsntpe.html1997.
- 50.Sturmberg JP. Knowledge translation in healthcare - towards understanding its true complexities; comment on “using complexity and network concepts to inform healthcare knowledge translation”. Int J Health Policy Manag. 2018;7(5):455–8. Available from, http://ijhpm.com/article_3415.html.