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Dawn Dowding, PhD, RN, FAAN

Does Level of Numeracy and Graph Literacy Impact Comprehension of Quality Targets? Findings from a Survey of Home Care Nurses Information Needs and Information Seeking Behavior S15. Dawn Dowding, PhD, RN, FAAN Columbia University School of Nursing and The Visiting Nurse Service of New York

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Dawn Dowding, PhD, RN, FAAN

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  1. Does Level of Numeracy and Graph Literacy Impact Comprehension of Quality Targets? Findings from a Survey of Home Care Nurses Information Needs and Information Seeking Behavior S15 Dawn Dowding, PhD, RN, FAAN Columbia University School of Nursing and The Visiting Nurse Service of New York Twitter: #AMIA2017

  2. Disclosure • I and my co-authors have no relevant relationships with commercial interests to disclose • Co-Authors • David Russell, PhD, Appalachian State University • Karyn Jonas MSN, New York University • Nicole Onorato, BA, Visiting Nurse Service of New York • Yolanda Barrón, MS, Visiting Nurse Service of New York • Jacqueline A. Merrill, PhD, Columbia University School of Nursing • Robert J. Rosati, PhD, VNA Health Group New Jersey AMIA 2017 | amia.org

  3. Learning Objectives • After participating in this session the learner should be better able to: • Analyze how numeracy and graph literacy are related to the comprehension of quality target data among a sample of home care nurses AMIA 2017 | amia.org

  4. Background • Dashboards • Use data visualization to provide feedback to clinicians on quality metrics • Often include performance relative to quality target data or benchmarks • Home Care Settings • Quality target data may include hospital readmission or wound infections “Creative commons popHealth Practice-Level CQM Dashboard” by Rob McCready is licensed under CC BY 3.0 AMIA 2017 | amia.org

  5. Background • Comprehension of visualized information may be affected by • Numeracy • Ability to understand basic probability and math concepts1 • Graph literacy • Ability to comprehend information displayed as graphs2 • Impact • For general population • High graph literacy – comprehension better with graphs • Low graph literacy – comprehension better with numbers1,2 • For physicians and nurses • Interaction between graph literacy and types of data display3-5 Grayson Wheatley for LEARN NC, CC 2.5 AMIA 2017 | amia.org

  6. Method • Online Survey • RN’s currently employed at a Certified Home Health Agency • Visited patients at least once a week • Email invitation • Completed demographic questionnaire, numeracy scale, graph literacy scale • Randomized into 4 groups – viewed different graph formats • Questions on comprehension of quality target data (all groups) AMIA 2017 | amia.org

  7. Study Measures: Numeracy • Expanded numeracy scale6 • 11 items • probability • conversion of percentages into proportions • risk magnitude • High reliability • Cronbach α 0.7- 0.75 • Median cut off = 8 • <8 = low numeracy • ≥ 8 = high numeracy (Lipkus, IM et al. 2001) AMIA 2017 | amia.org

  8. Study Measures: Graph Literacy • Graph literacy scale7 • 13 items • Measures: • Ability to read the data • Ability to read between the data (find relationships) • Ability to read beyond the data (e.g. predict a future trend) • High internal consistency • α = 0.74-0.79 • Median cut off = 9 • <9 = low graph literacy • ≥ 9 = high graph literacy Galesic, M & Garcia-Retamero, R. 2011 AMIA 2017 | amia.org

  9. Study Measures: Comprehension of Quality Targets • Questions asked nurses to interpret quality data presented as table and graph AMIA 2017 | amia.org

  10. Analysis • Descriptive statistics – demographics • Chi Square/t-test – compared 2 home health agencies • Linear regression models to estimate bivariate relationships of quality target data comprehension with: • Graph literacy • Numeracy • Combinations of graph literacy and numeracy AMIA 2017 | amia.org

  11. Results • 1052 invitations sent • 322 (31%) respondents accessed survey • 195 completed responses; • 129 Agency #1 • 66 Agency #2 • Reliability • Numeracy: α = 0.68 • Graph Literacy: α = 0.76 • Correlation : r = 0.56, p<0.001 AMIA 2017 | amia.org

  12. Results • Sample Characteristics Female (89.7%) Mean age: 49 years (SD 11.0) Ethnically diverse: white (50%); African American/Black (24%); Asian (14%) Educated: 64% had Bachelors or Post graduate degree Mean years of experience: 19.7 years (SD 11.4) Mean Numeracy score: 8.4 (SD 2.0) Mean Graph Literacy score: 9.7 (2.4) Mean Target Comprehension Score: 3.4 (0.8)

  13. Results • Numeracy • High 75% • n =147 nurses • Low 25% • n = 48 nurses • Graph Literacy • High 75% • n = 146 nurses • Low 25% • n= 49 nurses • Interaction • Low Numeracy Low Graph Literacy n = 24 (12%) • High Numeracy Low Graph Literacy n = 24 (12%) • Low Numeracy High Graph Literacy n = 25 (13%) • High Numeracy High Graph Literacy n = 122 (63%) AMIA 2017 | amia.org

  14. Results High Numeracy and/or High Graph Literacy positive significant association with quality target data comprehension *p<0.05; **p<0.01; ***p<0.001 Greater comprehension associated with higher graph literacy Individuals with both low graph literacy low numeracy have lower comprehension AMIA 2017 | amia.org

  15. Conclusion • Variation in graph literacy and numeracy across a sample of home care nurses • Comprehension of quality data associated with both numeracy and graph literacy • Design feedback (such as quality dashboards) to enhance comprehension – to take into account individual variation in numeracy and graph literacy • Future Research: • Numeracy and graph literacy of other clinician populations • Influence of numeracy and graph literacy on practice • Design dashboards, other feedback to accommodate individual differences AMIA 2017 | amia.org

  16. Acknowledgements • This project was supported by grant number R21HS023855 from the Agency for Healthcare Research and Quality • The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality AMIA 2017 | amia.org

  17. References • GaissmaierW, Wegwarth O, Skopec D, Muller AS, Broschinski S, Politi MC. Numbers can be worth a thousand pictures: individual differences in understanding graphical and numerical representations of health-related information. Health Psychol. 2012;31(3):286-96. • OkanY, Garcia-Retamero R, Cokely ET, Maldonado A. Individual Differences in Graph Literacy: Overcoming Denominator Neglect in Risk Comprehension. Journal of Behavioral Decision Making. 2012;25(4):390-401. • EltingL, Martin C, Cantor S, Rubenstein E. Influence of data display formats on physician investigators' decisions to stop clinical trials: prospective trial with repeated measures. British Medical Journal. 1999;318:1527-31. • FebrettiA, Sousa VEC, Lopez KD, Yao Y, Johnson A, Keenan GM, et al. One Size Doesn't Fit All: The Efficiency of Graphical, Numerical and Textual Clinical Decision Support for Nurses. 2014. • Lopez KDPMPH, Wilkie DJPRNF, Yao YP, Sousa VPMSNRN, Febretti AMS, Stifter JPMSRN, et al. Nurses' Numeracy and Graphical Literacy: Informing Studies of Clinical Decision Support Interfaces. Journal of Nursing Care Quality April/June. 2016;31(2):124-30. • Lipkus IM, Samsa G, Rimer BK. General Performance on a Numeracy Scale among Highly Educated Samples. Medical Decision Making. 2001;21(1):37-44. • Galesic M, Garcia-Retamero R. Graph literacy: a cross-cultural comparison. Med Decis Making. 2011;31(3):444-57. AMIA 2017 | amia.org

  18. Thank you! Email me at: dd2724@cumc.columbia.edu

  19. AMIA is the professional home for more than 5,400 informatics professionals, representing frontline clinicians, researchers, public health experts and educators who bring meaning to data, manage information and generate new knowledge across the research and healthcare enterprise. AMIA 2017 | amia.org

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