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1.5. Medical information provision through patient decision aids
In order to make well-informed medical decisions, the patient and the healthcare
provider need access to medical information about the options to which a patient is
eligible, including risk information.
$ 8
A classic example of how the boosting framework may help to make better
decisions and
involves boosts that help to better understand medical risk information.
Generally, patients as well as healthcare providers have difficulty understanding
conditional probabilities [e.g., 21]. For instance, consider the conditional probabilities
for breast cancer. Let’s assume the base-rate (prevalence) to get breast cancer is one out
of 100 women. The accuracy of a mammogram, an X-ray test to indicate whether a
person has breast cancer, is about 80-90%. More specifically, the probability of the
mammogram resulting in a positive test result when breast cancer is present (sensitivity)
is 80%. The probability of the test result of the mammogram being negative when the
disease is absent (specificity) is 90%. Now, a woman is tested positively on the X-ray
test, what is the chance this woman has breast cancer? In other words, what is the positive
predictive value of the mammogram, what is the probability that a patient has the disease
when the test result is positive? Both healthcare providers and patients typically
overestimate this chance and judge it to be around 75%, whereas the actual chance is
much lower: It is only 7-8%. This is because people tend to neglect the base-rate.
Gigerenzer and colleagues indicated that we could make these risks much more
understandable for healthcare providers and patients by using natural frequencies rather
than conditional probabilities. Risk information about breast cancer would then be
explained in the following way: Out of 1000 women, 10 women will have breast cancer
and 990 will not. Out of those 10 women who do have breast cancer, 9 will receive a
positive result on the X-ray test and 1 will not (false negative). Out of the 990 women
who do not have breast cancer, 99 will receive a positive result (false positive) and 891
women will receive a negative result. This way, it is more transparent to see the role of
base-rates: many women without cancer are in fact tested positively. This approach to
presenting risk information can be considered a boost, because by presenting risk
information in terms of frequency information, the understanding of the information
increases and therefore potentially the quality of decisions based on this information
increases as well. In a similar way, illustrations, animations and videos can serve as
boosts. Illustrations, in particular those supporting a text, are widely used to facilitate
learning of information by improving comprehension and recall [22]. Adding videos to
online texts, particularly personalized videos using a conversational narration style, also
improves memory for medical information [22], and animations can even bridge the
information processing gap between audiences with low and high health literacy [23].
M.deVriesetal. /FosteringSharedDecisionMakingwithHealth Informatics Interventions 115
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book Applied Interdisciplinary Theory in Health Informatics - Knowledge Base for Practitioners"
Applied Interdisciplinary Theory in Health Informatics
Knowledge Base for Practitioners
- Title
- Applied Interdisciplinary Theory in Health Informatics
- Subtitle
- Knowledge Base for Practitioners
- Authors
- Philip Scott
- Nicolette de Keizer
- Andrew Georgiou
- Publisher
- IOS Press BV
- Location
- Amsterdam
- Date
- 2019
- Language
- English
- License
- CC BY-NC 4.0
- ISBN
- 978-1-61499-991-1
- Size
- 16.0 x 24.0 cm
- Pages
- 242
- Category
- Informatik