Maria Wolters discusses the challenges of using data-driven methods for medical diagnosis and screening. While technology promises objective diagnosis, evidence-based medicine shows diagnostic tests have uncertainties. Case studies demonstrate how true positives can disrupt peoples' lives and identities, while false positives cause distress. Any data-driven health solution must consider a person's context, priorities and coping strategies, and have strong evidence that the benefits outweigh the harms. Diagnosis and screening also require supporting people through complex emotional impacts. Overall, health technologies must first do no harm, respect the individual, and be grounded in scientific evidence.

1. Data Power Ottawa, 2017
Give Me Your Data, And
I Will Diagnose You!
Maria Wolters
School of Informatics, University of
Edinburgh
Alan Turing Institute for Data
Science, London
Twitter: @mariawolters
mariawolters.net

2. Overview
❖ Background
❖ How Evidence-Based Medicine can help
❖ Case Study 1: The Cost of the True Positive
❖ Case Study 2: The Cost of the False Positive

3. Background
❖ Focus of my work: eHealth
❖ Involved in many tracking / monitoring projects
❖ for better self-management
❖ for detecting incidence (is a disease there?) and
exacerbations / relapses (another depressive
episode?)

4. But what am I doing
when I work on these projects?

5. The Tech Ideal (A Satire)
Subjective data is very messy. So, it would be great if we
could base diagnosis on „objectively observable“ metrics
only. And we can also deliver the information, no need for
doctors!
And Government funds that stuff.

6. Challenging the Ideal
❖ From the point of view of Evidence-Based Medicine
(Sackett et al, 1996)
❖ From the point of view of sociology of health and
illness, especially Corbin and Strauss’ (1985) notions of
biographical work, and Charmaz’ (1995) symbolic
interactionist work
❖ in addition to all the other points one could challenge …

7. Tests for diagnosis are always messy
Test detects Has disease
Does not have
disease
disease true positive (TP) false positive (FN)
no disease false negative (FP) true negative (TN)

8. Useful concepts
❖ Sensitivity:
true positives / people with disease
❖ Specificity:
true negative / people without disease
❖ Positive Predictive Value:
true positives / all positives detected by test
All mistakes come with a cost / an acceptability.
Evidence-based medicine helps assess and synthesise the body of
data & studies that yield these numbers

9. Case Study 1: The True Positive
Congratulations!
You have dementia. / Your depression is back.
What does this news do to people, and how do they live
with it?

10. Dementia
❖ When, how, to whom, and whether to break the news is
a difficult judgement call
❖ People react in a variety of ways, so individualised
approach is key
❖ Post-diagnostic support is essential
❖ Any eHealth solution needs to respect these complexities
(Carpenter and Dave, 2004; Aminzadeh et al, 2007;
Mattsson et al., 2010)

11. Relapse / Exacerbation
❖ People adjust how they manage, so that they can live
with an illness, not live the illness (Kralik et al., 2004;
Whittemore & Dixon, 2008; Audulv et al., 2012)
❖ Everyday work vs illness work vs biographical work
(Corbin & Strauss 1985)
❖ Living with a chronic illness requires adjusting identity
goals (Charmaz 1995)

12. Considerations for Design
❖ What purpose does it serve to tell people they’re getting
worse?
❖ How is the news timed?
❖ How can we integrate with IT solutions & strategies for
managing life?

13. Case Study 2: The False Positive
❖ Congratulations, your mammogram indicates we need
to do a biopsy!

14. The Ideal of Screening
❖ Screening programmes often produce many false
positives (1 TP : 9 FP, mammography) to ensure cases
are caught
❖ Ideally, the benefits of early detection outweigh any
harm

15. The Trouble with Screening
❖ Strong evidence for benefits of screening is hard to
establish (Salmi et al., 2016)
❖ False positives may lead people to seek help later when
disease does strike, and be less likely to keep up
screening (Renzi et al., 2015)
❖ Considerable distress caused by positive screen itself;
hard to distinguish from diagnosis for patient (Wardle &
Pope, 1992)

16. Questions For Data-Driven Diagnosis
❖ Who is being diagnosed? What are their identity goals?
What are their coping strategies?
❖ What is the evidence that this information is beneficial for
the patient?
❖ How do we communicate the (sometimes considerable)
uncertainty associated with all screening and many
diagnoses?
❖ What support is in place for them and their families /
carers?

17. Summary
❖ First, do no harm.
❖ Second, consider the person
❖ Third, consider the evidence
(And we haven’t even followed the power/money yet!)
Questions?
Contact details: maria.wolters@ed.ac.uk, mariawolters.net,
@mariawolters