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Abstract
A systemic model for making sense of health data is presented, in which networked foresight complements intelligent data analytics. Data here serves the goal of a future systems medicine approach by explaining the past and the current, while foresight can serve by explaining the future. Anecdotal evidence from a case study is presented, in which the complex decisions faced by the traditional stakeholder of results—the policymaker—are replaced by the often mundane problems faced by an individual trying to make sense of sensor input and output when self-tracking wellness. The conclusion is that the employment of our systemic model for successful sensemaking integrates not only data with networked foresight, but also unpacks such problems and the user practices associated with their solutions.
Highlights
We argue that if the reality of clinical work is to successfully meet with new technology and methods for prevention and care, even the most intelligent analytics one could conceive of will not suffice
The conclusion is that the employment of our systemic model for successful sensemaking integrates data with networked foresight, and unpacks such problems and the user practices associated with their solutions
Realising the idea of aggregating local clouds of personal data for wellness comes with a list of challenges [2]: privacy, technical difficulties related to the combination of disparate data sets or data collected at different temporal scales, and the need for novel analytical methods that can extract valuable knowledge from massive datasets
Summary
We argue that if the reality of clinical work is to successfully meet with new technology and methods for prevention and care, even the most intelligent analytics one could conceive of will not suffice. The interplay with a community of practice requires a process of sensemaking: finding out how to best inform a stakeholder based on data and the conclusions drawn from it, in a particular context. Non-medical health data resulting, e.g., from fitness sensor data or from syndromic surveillance are sometimes put to use in medical contexts [26]. We make informed guesses about the future of intelligent analytics applied to such data. Artificial intelligence methods will be our main focus, and the methods useful for health data analytics in particular. The uncertainties involved are immense and, as a result, we anchor our extrapolations in observations of theory as well as practice, analysing challenges as well as opportunities
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