Informatics: Make sense of health data

Elliott, Julian; Grimshaw, Jeremy; Altman, Russ B.; Bero, Lisa; Goodman, Steven N.; Henry, David; Macleod, Malcolm; Tovey, David; Tugwell, Peter; White, Howard; Sim, Ida

doi:10.1038/527031a

Cited by 23 publications

(18 citation statements)

References 5 publications

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“…And the analysis of Big Data needs to be informed by a technical understanding of causal relationships. Correlation is not causation not matter how big the data Elliot et al (2015). But these are manageable risks which are outweighed by the benefits.…”

Section: Introductionmentioning

confidence: 99%

The twenty-first century experimenting society: the four waves of the evidence revolution

White

2019

Palgrave Commun

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This paper presents a personal perspective-drawing especially on the author's experience in international development-of the evidence revolution, which has unfolded in fours waves over the last 30 years: (1) the results agenda as part of New Public Management in the 1990s, (2) the rise of impact evaluations, notably randomized controlled trials (RCTs) since the early 2000s, (3) increased production of systematic reviews over the last ten years, and (4) moves to institutionalize the use of evidence through the emergence of knowledge brokering agencies, most notably the What Works movement in the United States and the United Kingdom. A fifth wave may come from the potential from AI, machine learning and Big Data. Each successive wave has built on the last, and together they comprise the supply side of the evidence architecture. To support the use of evidence demand side activities such as Evidence Needs Assessments and Use of Evidence Awards are proposed.

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Section: Introductionmentioning

confidence: 99%

The twenty-first century experimenting society: the four waves of the evidence revolution

White

2019

Palgrave Commun

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“…To unravel this bigger picture we should use an integral, cross-disciplinary approach (6, 7). Although the idea of such an approach is not entirely new, most challenges to transform it into truly integral scientific pan-disease projects are still to be met.…”

Section: Introductionmentioning

confidence: 99%

Embracing Complexity beyond Systems Medicine: A New Approach to Chronic Immune Disorders

Velde

Bezema²,

Kampen

et al. 2016

Front. Immunol.

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In order to combat chronic immune disorders (CIDs), it is an absolute necessity to understand the bigger picture, one that goes beyond insights at a one-disease, molecular, cellular, and static level. To unravel this bigger picture we advocate an integral, cross-disciplinary approach capable of embracing the complexity of the field. This paper discusses the current knowledge on common pathways in CIDs including general psychosocial and lifestyle factors associated with immune functioning. We demonstrate the lack of more in-depth psychosocial and lifestyle factors in current research cohorts and most importantly the need for an all-encompassing analysis of these factors. The second part of the paper discusses the challenges of understanding immune system dynamics and effectively integrating all key perspectives on immune functioning, including the patient’s perspective itself. This paper suggests the use of techniques from complex systems science in describing and simulating healthy or deviating behavior of the immune system in its biopsychosocial surroundings. The patient’s perspective data are suggested to be generated by using specific narrative techniques. We conclude that to gain more insight into the behavior of the whole system and to acquire new ways of combatting CIDs, we need to construct and apply new techniques in the field of computational and complexity science, to an even wider variety of dynamic data than used in today’s systems medicine.

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“…For this reason, confronting uncertainty has been the first and foremost driver for the introduction of computational Decision Support Systems (DSS) in medicine and their increasingly wide adoption in clinical settings [67]. We could just speculate on why medicine has turned to technology to "make sense of health data" 18 (to cite a position paper on Nature published a couple of years ago [25]). Quite subtly, Katz [49] has argued that the traditional mechanisms that physicians use to adopt to cope with uncertainty (e.g., terminological standards, standard care protocols, guidelines based on statistical studies) could have had a role in slowly pushing them towards disregarding or even opposing uncertainty.…”

Section: Discussionmentioning

confidence: 99%

A Giant with Feet of Clay: On the Validity of the Data that Feed Machine Learning in Medicine

Cabitza

Ciucci

Rasoini

2018

Lecture Notes in Information Systems and Organisation

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This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of ML models, thus undermining the clinical significance of their output. Recognizing this can motivate both medical doctors, in taking more responsibility in the development and use of these decision aids, and the researchers, in pursuing different ways to assess the value of these systems. In so doing, both designers and users could take this intrinsic characteristic of medicine more seriously and consider alternative approaches that do not "sweep uncertainty under the rug" within an objectivist fiction, which everyone can come up by believing as true.

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Informatics: Make sense of health data

Cited by 23 publications

References 5 publications

The twenty-first century experimenting society: the four waves of the evidence revolution

The twenty-first century experimenting society: the four waves of the evidence revolution

Embracing Complexity beyond Systems Medicine: A New Approach to Chronic Immune Disorders

A Giant with Feet of Clay: On the Validity of the Data that Feed Machine Learning in Medicine

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