Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Big Data and Practice-based Evidence: How EHR data is bringing the voice of nurisng practice into policy and research

623 views

Published on

Dr Karen A Monsen, National Nursing Informatics Conference (HiNZ 2015 Conference), Christchurch

Published in: Healthcare
  • Be the first to comment

  • Be the first to like this

Big Data and Practice-based Evidence: How EHR data is bringing the voice of nurisng practice into policy and research

  1. 1. Big Data and Practice-based Evidence: How EHR data is bringing the voice of nursing practice into policy and research NNIC 2015 Karen A. Monsen
  2. 2. Lovely to be here!
  3. 3. Steeped in EBP • Joanna Briggs – Cochrane – NZ guidelines Hendry, C. (2011). The New Zealand Institute of Community Health Care: REPORT TO THE MINISTRY OF HEALTH ON THE IMPROVING NURSING UTILISATION OF EVIDENCE TO INFORM CLINICAL PRACTICE SERVICES PROJECT. Available at: https://www.health.govt.nz/system/files/documents/publications/cdhb_report.docx http://www.dilmah.co.nz/wp-content/uploads/2014/06/dimah-ranges.jpg
  4. 4. After 20 Years of EBP • Scholars are concerned. • Sound clinical judgment is devalued • Mark Tonelli (1999) • Evidence is limited • GoodyearSmith (2012). What is evidence-based practice and how do we get there? The Journal of Primary Healthcare, 4, 2, 90-91. • Evidence is biased • Greenhalgh, T, Howick J, Maskrey N.Evidence based medicine: a movement in crisis? 2014 BMJ 348 :g3725
  5. 5. The Real World • The real world with all of its complexities and nuances is not controlled
  6. 6. Practice-based Evidence is Needed • Beyond algorithms, how do we provide the best personalized care for individuals in unique situations?
  7. 7. Gap in Commentary • Assumption that there is no data source that underlies and enables study of practice-based evidence • This assumption is false • Nurses know what to do
  8. 8. What to do?
  9. 9. Learner Objectives • Describe the role of practice-based evidence as a necessary component of nursing knowledge • Discuss possible sources of information that constitute practice-based evidence • Provide examples of Big Data research using large nursing data sets
  10. 10. Practice • What expert nurses know and do every day to ensure wellbeing and safety of patients • in the real world • for unique patients and situations
  11. 11. Practice-based Evidence • “How does adding X intervention alter the complex personalized system of patient Y before me?” Swisher, A. K. (2010). Practice-Based Evidence. Cardiopulmonary Physical Therapy Journal, 21(2), 4.
  12. 12. Practice-based Data • Data from nursing assessment and documentation that is part of routine nursing care is an important source of practice-based evidence
  13. 13. Big Data • Large datasets of structured or unstructured information that may require new approaches for analysis • Let the data speak Garcia, A., L. (2015). How big data can improve health care. American Nurse Today. Available at: http://www.americannursetoday.com/how-big-data-can-improve-health-care/
  14. 14. Big Data Research in Nursing • Traditional and new methods for big data • Using large data sets to examine important healthcare quality questions • Looking for hidden patterns in the data • Hypotheses generating vs. hypothesis testing • New voices for nursing and patients: Practice-based evidence
  15. 15. Big Data Studies in Nursing • All of the studies I’m about to share are examples of the rigorous study of data – from practicing nurses – powerful observational datasets that speak for nursing and for patients alike.
  16. 16. Outcome Variability: Nurses and Interventions • Using a logistical mixed-effects model with nursing data to evaluate outcome variability This research is partially supported by the National Science Foundation under grant # SES-0851705, and by the Omaha System Partnership. Monsen, K. A., Chatterjee, S. B., Timm, J. E., Poulsen, J. K., & McNaughton, D. B. (in review). Public health nurse, client, and intervention factors contribute to variability in health literacy outcomes for disadvantaged families. • Client (50%) • Problem (17%) • Nurse (17%) • Intervention (17%) Age was significantly positively associated with knowledge benchmark attainment
  17. 17. Implications • Research • We need to incorporate the ‘nurse’ as an important part of the research model • Policy • To ensure optimal outcomes, we need the best nurses • Best fit with assigned patients • Support expertise • Ensure wellbeing
  18. 18. Mothers with Mental Health Problems Monsen, K. A. et al., 2014 Method: Data Visualization Each image (sunburst) was created in d3 from public health nursing assessment data for a single patient. Data were generated by use of the Omaha System signs and symptoms and Problem Rating Scale for Outcomes Key: •Colors = problems •Shading = risk •Rings = Knowledge, Behavior, and Status •Tabs = signs/symptoms Documentation patterns suggest a comprehensive, holistic nursing assessment. Kim et al. found that the presence of mental health signs and symptom tends to be associated with more diagnostic problems and worse patient condition Kim, E., Monsen, K. A., Pieczkiewicz, D. S. (2013). Visualization of Omaha System data enables data-driven analysis of outcomes. American Medical Informatics Association Annual Meeting, Washington D. C. Funded by a gift from Jeanne A. and Henry E. Brandt.
  19. 19. Implications • Research • Visualization methods can help identify individuals with similar patterns in complex multidimensional data • Policy • It is critical to identify and serve the individuals who most need our help
  20. 20. • Method: Generalized Estimating Equations for cohort comparison • Results: Mothers with intellectual disabilities have twice as many problems as mothers without intellectual disabilities • Receive more public health nursing service • Twice as many encounters and interventions • Show improvement in all areas • Do not reach the desired health literacy benchmark in Caretaking/parenting Mothers with Intellectual Disabilities Monsen, K. A., Sanders, A. N., Yu, F., Radosevich, D. M, & Geppert, J. S. (2011). Family home visiting outcomes for mothers with and without intellectual disabilities. Journal of Intellectual Disabilities Research, 55(5), 484-499. doi:10.1111/j.1365-2788.2011.01402.x
  21. 21. Implications • Research • Large datasets will enable research into situations that are relatively rare and otherwise difficult to study • Policy • Extra time and effort (and therefore funding) is needed to produce the positive outcomes we desire for mothers with intellectual disabilities
  22. 22. Studies of Intervention/Outcome Patterns • Problem-specific intervention patterns • Individual-specific intervention patterns • Population-specific intervention patterns • Home care patients • Mothers with low health literacy
  23. 23. Using Kaplan-Meier Curves to Detect Problem Stabilization This research was supported by the National Institute of Nursing Research (Grant #P20 NR008992; Center for Health Trajectory Research). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Nursing Research or the National Institutes of Health. Monsen, K. A., McNaughton, D. B., Savik, K., & Farri, O. (2011). Problem stabilization: A metric for problem improvement in home visiting clients. Applied Clinical Informatics, 2, 437-446 http://dx.doi.org/10.4338/ACI-2011-06-RA-0038
  24. 24. Using Data Visualization to Detect Nursing Intervention Patterns Each image (streamgraph) was created in d3 from longitudinal public health nursing intervention data for a single patient. Data were generated by use of the Omaha System in clinical documentation Key: •Colors = problems •Shading = actions (categories) •Height = frequency •Point on x-axis = one month From 403 images, 29 distinct patterns were identified and validated by clinical experts Documentation patterns suggest both a unique nurse style and consistent patient-specific intervention tailoring Monsen, K.A., Hattori, K., Kim, E., Pieczkiewicz, D. (In review). Using visualization methods to discover nurse-specific patterns in nursing intervention data. Streamgraph development funded by a gift from Jeanne A. and Henry E. Brandt. Monsen, K. A. et al., 2014
  25. 25. COMPREHENSIVE WOUND CARE BASIC WOUND CARE Treatments & procedures Case management Surveillance Monitoring Teaching, guidance, & counseling Informing Providing Therapy Using Inductive and Deductive Approaches to Create Overlapping Intervention Groups Relationships between four intervention grouping/clustering methods for wound care. Monsen, K. A., Westra, B. L., Yu, F., Ramadoss, V. K., & Kerr, M. J. (2009). Data management for intervention effectiveness research: Comparing deductive and inductive approaches. Research in Nursing and Health, 32(6), 647-656. doi:10.1002/nur.20354
  26. 26. Home Care Interventions and Hospitalization Outcomes • Method: Logistic regression • Results: Too little care may result in hospitalization when patients have more intensive needs • Frail elders are more likely to be hospitalized if they have low frequencies of four skilled nursing intervention clusters Monsen, K. A., Westra, B. L., Oancea, S. C., Yu, F., & Kerr, M. J. (2011). Linking home care interventions and hospitalization outcomes for frail and non-frail elderly patients. Research in Nursing and Health, 34(2), 160-168. doi:10.1002/nur.20426. NIHMS274649
  27. 27. Knowledge scores across problems over time •Pre-intervention, patterns by race/ethnicity •Post-intervention, patterns by problem Health Literacy Outcomes Benchmark = 3 Monsen, K. A., Areba, E. M., Radosevich, D. M., Brandt, J. K., Lytton, A. B., Kerr, M. J., Johnson, K. E., Farri, O, & Martin, K. S. (2012). Evaluating effects of public health nurse home visiting on health literacy for immigrants and refugees using standardized nursing terminology data. Proceedings of NI2012: 11th International Congress on Nursing Informatics, 614..
  28. 28. Implications • Research • Nurses address multiple problems in different ways over time • Future research should take into account and evaluate factors of timing, specific problem, and individual needs • Policy • Encourage personalized interventions tailored to meet individual needs
  29. 29. Data Mining for Translation to Practice (Chih-Lin Chi et al., 2015)
  30. 30. Problem: A small percentage of clients consume a high percentage of service resources (80-20 rule) 20% patients use 70% of intervention resource
  31. 31. Research Question 1: Predict Intervention Usage• Regardless of outcome, who will need more interventions? For 75% threshold Maximal accuracy ~ 74% Maximal AUC ~ 77% Prediction measured using receiver operating curves and area under the curve (AUC). For 50% threshold Maximal accuracy ~ 60% Maximal AUC ~ 75%
  32. 32. Research Question 2: Predict Responsiveness to Interventions • Within the population, which individuals will be responsive to more interventions for this problem, compared to those who are less responsive? More responsive Less responsive
  33. 33. Research Question 3: Predict Personalized Nursing Intervention • How to personalize care planning based on an individual’s characteristics and what intervention patterns can be used to help personalization? • Intervention patterns typically used in Oral health Teaching, guidance, and counseling Treatments and procedures Case management Surveillance Number of clients A 0.00% 0.00% 0.00% 100.00% 24 B 0.00% 10.00% 0.00% 90.00% 2 C 0.00% 20.00% 0.00% 80.00% 285 D 30.00% 0.00% 30.00% 40.00% 1 E 30.00% 10.00% 10.00% 50.00% 1 F 40.00% 0.00% 10.00% 50.00% 210 G 50.00% 0.00% 10.00% 40.00% 234 H 60.00% 0.00% 10.00% 30.00% 1
  34. 34. Research Question 4: Predict Relative Improvement for Personalized Nursing Intervention • Relative improvement is 51% (compared to maximum possible improvement for all clients) • Choosing the right pattern can improve care (efficiency and effectiveness) 51%
  35. 35. Next steps • Nursing Big Data has been shown to enable the identification of personalized algorithms to improve nursing care quality and efficiency • Practice-based dissemination and implementation research proposals in development and review
  36. 36. Implications • Research • It is becoming feasible to amass large quantities of data and create a pipeline for research into personalized care • Policy • It is critical to support data sharing agreements and collaborations that support use of clinical data for research
  37. 37. Nurses: Let the data speak! • Thank you! • Questions? • mons0122@umn.edu

×