Knowledge Discovery Data Analytics Methods: Potential use in Nurse Credentialing Research

Transcription

1 Knowledge Discovery Data Analytics Methods: Potential use in Nurse Credentialing Research Karen A. Monsen, PhD, RN, FAAN September 3, 2014

2 Or: Developments in Research Methodologies, Health Metrics, and Data Infrastructure to better evaluate the impact of nursing credentialing

3 The Challenge: Identify critical knowledge gaps and methodological limitations in the field Whether credentialing within nursing actually improves or signals better quality depends a great deal on researchers ability to produce convincing evidence that there is an effect on health care outcomes. McHugh, M. D. et al. (2014). Challenges and opportunities in nurse credentialing research design. Discussion paper. National Academies Press. Knowledge discovery data analytics methods have potential to produce convincing evidence. Mayer-Schonberger, V. & Cukier, K. (2013) Big Data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, Boston.

4 More, Messy, Good Enough (p. 12, Mayer-Schonberger & Cukier, 2013) Big Data changes the scientific paradigm More data means less sampling error Messy means we can try to understand and account for the biases inherent within observational data Good enough means we can let go of our fixation on causation Description Pattern Discovery Hypothesis generation Letting the data give voice to nurses and patients Hey, T., Tansley, S., & Tolle, K. (2009). The fourth paradigm: Data intensive scientific discovery. Redmond, WA: Microsoft Research. Mayer-Schonberger, V. & Cukier, K. (2013) Big Data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, Boston.

5 Resources Nursing Data Nursing Minimum Data Set s_71413.pdf a minimum set of elements of information with uniform definitions and categories concerning the specific dimensions of nursing, which meets the information needs of multiple data users in the health care system. Client characteristics & outcomes Nursing assessments & interventions Nursing Context Data Nursing Management Minimum Data Set core essential data needed to support the administrative and management information needs for the provision of nursing care. The standardized format allows for comparable nursing data collection within and across organizations. Nurse and health system characteristics Nurse and health system credentials Werley HH. Nursing minimum data: abstract tool for standardized comparable, essential data. Am J Public Health. 1991;81(4): doi: /AJPH Huber D, Delaney C. The American Organization of Nurse Executives (AONE) research column. the Nursing Management Minimum Data Set. Appl Nurs Res. 1997;10:

6 Recognized Nursing Terminologies American Nurses Association Terminology NANDA (1992) NIC (1992) NOC (1997) Nursing Specific Items from the NMDS Nursing Problem Nursing Intervention Nursing Outcome Nursing Intensity x Nursing Specific Terminologies The above three terminologies must be used together to obtain information about the nursing problem (diagnosis), intervention and outcome. The below terminologies all have terms for the nursing problem, intervention, and outcome. x x CCC (HHCC) (1992) x x x PNDS (1997) x x x ICNP (2000) x x x Interdisciplinary Terminologies SNOMED-CT (1999) x x x SNOMED-CT Nursing Subset x LOINC (2002) x Omaha System (1992) x x x Sewell, J. P. & Thede, L. Q. (2012). Nursing and Informatics: Opportunities and Challenges. Nursing Documentation in the Age of the EHR. Available at:

7 Martin KS. (2005). The Omaha System: A key to practice, documentation, and information management(reprinted 2nd ed.). Omaha, NE: Health Connections Press.

8 Big Data Laboratory 2010: Dean Delaney invited the Omaha 2010: Dean Delaney invited the Omaha System Partnership for System Knowledge Partnership Discovery and Healthcare for Knowledge Quality within the University of Minnesota Center for Nursing Informatics Discovery and Healthcare Quality within the Scientific teams University Affiliate members of Minnesota Center for Nursing Data warehouse Informatics Scientific teams Affiliate members Data warehouse

9 Using a Logistical Mixed-effects Model with Nursing Data How do nurses and interventions contribute to variability in patient and population health? Nurse (17%) Client (50%) Problem (17%) Intervention (17%) Client age was significantly positively associated with knowledge benchmark attainment in all models This research is partially supported by the National Science Foundation under grant # SES , and by the Omaha System Partnership. Monsen, K. A., Chatterjee, S. B., Timm, J. E., Poulsen, J. K., & McNaughton, D. B. (2014). Factors explaining variability in health literacy outcomes of public health nursing clients. PHN. Epub ahead of print: doi: /phn

10 Using Data Visualization to Detect Client Risk Patterns Monsen, K. A. et al., 2014 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.

11 Using Generalized Estimating Equations for Cohort Comparison 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 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), doi: /j x

12 Using Graphing Methods with Multilevel Kway Partitioning to Form Non-Overlapping Intervention Clusters This research was supported by a Midwest Nursing Research Society New Investigator Seed Grant. Monsen, K. A., Banerjee, A., & Das, P. (2010). Discovering client and intervention patterns in home visiting data. Western Journal of Nursing Research, 32(8), doi: /

13 Using Kaplan-Meier Curves to Depict 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,

14 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. et al., 2014 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.

15 Using Inductive and Deductive Approaches to Create Overlapping Intervention Groups Relationships between four intervention grouping/clustering methods for wound care. COMPREHENSIVE WOUND CARE Surveillance Monitoring Treatments & procedures BASIC WOUND CARE Teaching, guidance, & counseling Providing Therapy Informing Case management 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), doi: /nur.20354

16 Using Receiver Operating Curves to Understand Model Fit Comparison of Intervention Modeling Approaches and Hospitalization Outcomes for Frail and Non-frail Elderly Home Care Patients 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), doi: /nur NIHMS274649

17 Using Logistic Regression to Associate Home Care Interventions and Hospitalization Outcomes 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), doi: /nur NIHMS274649

18 Using Pattern Comparison Pre- and Post- Intervention to Demonstrate Intervention Effectiveness Knowledge scores across problems over time Pre-intervention, patterns by race/ethnicity Post-intervention, patterns by problem 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..

19 Further Research Monsen, K. A. et al., 2014 Credentialing What is the credentialing effect relative to patient assessment, intervention tailoring, and patient outcomes? What is the value of credentialing? Examine associations between intervention patterns and client outcomes Is there differential effectiveness of intervention patterns for similar client profiles? What is optimal intervention tailoring? What is the nurse effect? Evaluate patterns across agencies and programs Do patterns persist across agencies? What aspects of nursing interventions are similar across programs and populations? How does individualized care relate to evidence-based practice? Monsen, K. A., Chatterjee, S. B., Timm, J. E., Poulsen, J. K., & McNaughton, D. B. (2014). Factors explaining variability in health literacy outcomes of public health nursing clients. PHN. Epub ahead of print: doi: /phn.12139

20 Recommendations Recommendations Expand the development of data analytics Expand the development of data analytics methods to incorporate nursing and interprofessional datasets and encompass all standardized methods terminologies to incorporate and structured nursing data. and Test new methods on existing datasets. interprofessional datasets and encompass all standardized terminologies and structured data. Continue to develop and test new methods on existing datasets.