Making Measurements Meaningful
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1 Making Measurements Meaningful Brian J. Zikmund-Fisher, PhD University of Michigan Department of Health Behavior & Health Education Department of Internal Medicine Center for Bioethics & Social Sciences in Medicine Health Informatics
2 Assumptions In Science Communication Assumption: People Lack Information (The Deficit Model) Assumed Solution: Give them more!
3 Literacy Language Barriers Numeracy Emotionality of Health Situations Volume of Relevant Information
4 More Better!
5 Evaluability
6 Good or Bad? Amend B. Welcome to Jasorassic Park, 1998, p.36.
7 Good or Bad? Amend B. Welcome to Jasorassic Park, 1998, p.36.
8 Evaluability of Risk Information
9 Imagine Robert
10 Imagine Robert Your 10-year risk of cardiovascular disease is: 11.22%
11 Am I at high risk, or not?
12 Evaluability of Laboratory Test Results
13 Can Patients Use This?
14 What Is Out of Range?
15 What Is Out of Range?
16 Am I at high risk, or not?
17 Evaluability of Exposure Information
18 Buying a Home Radon: 6 pci/l
19 Drinking Water Lead: 7 ppb
20 Am I at high risk, or not?
21 Problem #1: Numbers
22 Problem #2: Lack of Meaning
23 So now what? What can we do to help?
24 Step 1: Visual information
25 Created at iconarray.com Robert s Risk
26
27 Tables Zikmund-Fisher BJ, et al. Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results. Journal of the American Medical Informatics Association 2017;24(3):
28 Table vs. Number Line Zikmund-Fisher BJ, et al. Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results. Journal of the American Medical Informatics Association 2017;24(3):
29 Lines with More Meaning Zikmund-Fisher BJ, et al. Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results. Journal of the American Medical Informatics Association 2017;24(3):
30 Near-Normal Results vs. Extreme Results Near-Normal Extreme Zikmund-Fisher BJ, et al. Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results. Journal of the American Medical Informatics Association 2017;24(3):
31 % with No Difference in Perceived Urgency Platelets (135 vs 25 x10 9 /L) ALT (80 vs 360 U/L) Creatinine (2.2 vs 3.4 mg/dl) Table Simple Line Block Line Gradient Line Zikmund-Fisher BJ, et al. Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results. Journal of the American Medical Informatics Association 2017;24(3):
32 Step 2: Gist-full information
33 Fuzzy Trace Theory (Brainerd and Reyna, 1995) Verbatim memory vs Gist
34 Fuzzy Trace Theory (Brainerd and Reyna, 1995) Verbatim memory vs Gist What the heck is gist?
35
36 Know Your Commander s Intent Heath & Heath, Made to Stick, 2007
37 Cancer Screening Test Decisions
38 Colorectal Cancer Screening
39 Colorectal Cancer Screening at age 50
40 Colorectal Cancer Screening at age 50 vs. at age 75 (with multiple comorbidities)
41 Benefits vs. Harms Image from study materials for Promoting Veteran-Centered Colorectal Cancer Screening (I01 HX ); SD Saini, PI.
42 Gist Processing Image from study materials for Promoting Veteran-Centered Colorectal Cancer Screening (I01 HX ); SD Saini, PI.
43 Setting the Context in Visual Displays
44 Hemoglobin A1c Unpublished graphic from 1 R01 HS021681, BJ Zikmund-Fisher, Principal Investigator.
45 Same (?!?) Result Unpublished graphic from 1 R01 HS021681, BJ Zikmund-Fisher, Principal Investigator.
46 Scale Matters Unpublished graphics from 1 R01 HS021681, BJ Zikmund-Fisher, Principal Investigator.
47 Test Results for Diagnosed Patients
48 Goals for Test Results Scherer AM, Witteman HO, Solomon J, Fagerlin A, Exe NL, Zikmund-Fisher BJ. Improving understanding of test results by substituting (not adding) goal ranges. Poster presentation to the Society for Medical Decision Making, Vancouver, BC, Canada, October 23, 2016.
49 Goals for Test Results Scherer AM, Witteman HO, Solomon J, Fagerlin A, Exe NL, Zikmund-Fisher BJ. Improving understanding of test results by substituting (not adding) goal ranges. Poster presentation to the Society for Medical Decision Making, Vancouver, BC, Canada, October 23, 2016.
50 Goals for Test Results Scherer AM, Witteman HO, Solomon J, Fagerlin A, Exe NL, Zikmund-Fisher BJ. Improving understanding of test results by substituting (not adding) goal ranges. Poster presentation to the Society for Medical Decision Making, Vancouver, BC, Canada, October 23, 2016.
51 Goals for Test Results Scherer AM, Witteman HO, Solomon J, Fagerlin A, Exe NL, Zikmund-Fisher BJ. Improving understanding of test results by substituting (not adding) goal ranges. Poster presentation to the Society for Medical Decision Making, Vancouver, BC, Canada, October 23, 2016.
52 Test Results for Monitoring
53 Harms Alanine Aminotransferase (ALT): 80 IU/L Standard Range: 10-40
54 Showing the Possible Range Zikmund-Fisher BJ, Scherer AM, Witteman HO, Solomon J, Exe NL, Fagerlin A. Providing harm anchors in visual displays of test results can mitigate patient perceptions of urgency about near-normal values. Journal of Medical Internet Research, 2018.
55 Harm Anchors Zikmund-Fisher BJ, Scherer AM, Witteman HO, Solomon J, Exe NL, Fagerlin A. Providing harm anchors in visual displays of test results can mitigate patient perceptions of urgency about near-normal values. Journal of Medical Internet Research, 2018.
56 Harm Anchors Many doctors are not concerned until here Zikmund-Fisher BJ, Scherer AM, Witteman HO, Solomon J, Exe NL, Fagerlin A. Providing harm anchors in visual displays of test results can mitigate patient perceptions of urgency about near-normal values. Journal of Medical Internet Research, 2018.
57 Increased Sensitivity with Harm Anchors 6 5 Perceived Alarm Near Value Simple Extreme Value Harm Anchor Zikmund-Fisher BJ, Scherer AM, Witteman HO, Solomon J, Exe NL, Fagerlin A. Providing harm anchors in visual displays of test results can mitigate patient perceptions of urgency about near-normal values. Journal of Medical Internet Research, 2018.
58 One Last Example
59 Hemoglobin A1c Percent Months
60 Hemoglobin A1c Percent Central Message: Varies Months
61 Percent Months
62 12 10 Percent 8 Percent Months =? Months
63 12 10 Percent Months =? Percent Percent Months Months
64 12 10 Percent Varies! Months =? Percent Percent Months Months
65 12 10 Stable! Percent Varies! Months =? Percent Percent Months Months
66 12 10 Stable! Percent Varies! Months =? Percent Percent Months High! Months
67 12 10 Stable! Percent Varies! Months Scaled to data variations =? Percent Percent Months High! Months
68 Scaled to population variations Stable! Percent Varies! Months Scaled to data variations =? Percent Percent Months High! Months
69 Scaled to population variations Stable! Percent Varies! Months Scaled to data variations =? Scaled to zero Percent Percent Months Months High!
70 12 11 Good or Bad?
71 12 11 Good or Bad? Standard Range
72 Good or Bad? Target Range for Patients with Type 2 Diabetes
73 My Commander s Intent
74 We need to design for the way people ARE, not the way we wish they were - Holly O. Witteman
75 People only process or remember one thing
76 You can t change this fact!
77 BUT
78 BUT You get to choose!
79 Use context to create ONE message based on THEIR needs
80 Thank
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