Knowledge Discovery Data Analytics Methods: Potential use in Nurse

Credentialing Research

Karen A. Monsen, PhD, RN, FAAN September 3, 2014

mons0122@umn.edu

Or: Developments in Research Methodologies, Health Metrics,

and Data Infrastructure to better evaluate the impact of nursing credentialing

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.”

• 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.

McHugh, M. D. et al. (2014). Challenges and opportunities in nurse credentialing research design. Discussion paper. National Academies Press.

“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.

Resources Nursing Data Nursing Context Data Nursing Minimum Data Set

http://www.nursing.umn.edu/prod/groups/nurs/@pub/@nurs/documents/asset/nurs_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 Management Minimum Data Set http://www.nursing.umn.edu/icnp/usa-nmmds/

• 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):421–6. doi: 10.2105/AJPH.81.4.421.

Huber D, Delaney C. The American Organization of Nurse Executives (AONE) research column. the Nursing Management Minimum Data Set. Appl Nurs Res. 1997;10:164-165.

Recognized Nursing Terminologies American Nurses Association

Terminology Nursing Specific Items from the NMDS Nursing Problem Nursing Intervention Nursing Outcome Nursing Intensity

Nursing Specific Terminologies NANDA (1992) x NIC (1992) x NOC (1997) x 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.

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:

http://dlthede.net/informatics/Chap16Documentation/anarecterm.html http://dlthede.net/informatics/Chap16Documentation/chap16.html

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

Connections Press.

Big Data Laboratory

• 2010: Dean Delaney invited the Omaha System Partnership for Knowledge Discovery and Healthcare Quality within the University of Minnesota Center for Nursing Informatics

• Scientific teams • Affiliate members • Data warehouse

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

Using a Logistical Mixed-effects Model with Nursing Data

• How do nurses and interventions contribute to variability in patient and population health?

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. (2014). Factors explaining variability in health literacy outcomes of public

health nursing clients. PHN. Epub ahead of print: doi: 10.1111/phn.12139.

• Nurse (17%) • Client (50%) • Problem (17%) • Intervention (17%)

• Client age was significantly positively associated with knowledge benchmark attainment in all models

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.

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), 484-499. doi:10.1111/j.1365-2788.2011.01402.x

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), 1031-1054. doi:10.1177/0193945910370970

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, 437-446 http://dx.doi.org/10.4338/ACI-2011-06-RA-0038

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

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

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), 160-168. doi:10.1002/nur.20426. NIHMS274649

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), 160-168. doi:10.1002/nur.20426. NIHMS274649

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..

Further Research

• 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. et al., 2014

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: 10.1111/phn.12139

Recommendations

• Expand the development of data analytics methods to incorporate nursing and interprofessional datasets and encompass all standardized terminologies and structured data.

• Test new methods on existing datasets.

Recommendations

• Expand the development of data analytics methods to incorporate nursing and interprofessional datasets and encompass all standardized terminologies and structured data.

• Continue to develop and test new methods on existing datasets.