Using Data Science to Influence Population Health Session #NI3, February 19, 2017 Karen A. Monsen, PhD, RN, FAAN, Associate Professor University of Minnesota School of Nursing 1
Speaker Introduction Karen A. Monsen, PhD, RN, FAAN Associate Professor University of Minnesota School of Nursing Co-director, Center for Nursing Informatics Director, Omaha System Partnership DNP Specialty Coordinator, Nursing Informatics Affiliate faculty, Institute for Health Informatics 2
Conflict of Interest Karen A. Monsen, PhD, RN, FAAN Has no real or apparent conflicts of interest to report. 3
Agenda Population Health, Data Science, and the Voice of Nursing Robust nursing information Cutting edge methods Value of nursing Questions 4
Learning Objectives Outline how large, robust, well-coordinated data sets can be used to influence outcomes in populations Describe how pattern visualization and other data science methods can be used by nurses to affect outcomes Illustrate the importance and value of nurses in managing the care of patient populations 5
Population Health Value and Health IT During this session you'll hear how nurses can use and analyze large data sets to influence outcomes in patient populations. T: Treatment/clinical nursing data capture E: Electronic data use in population health P: Population management potential S: Savings r/t improved data management 6
Part 1: Population Health, Data Science, and the Voice of Nursing 7
Population Health, Data Science, and the Voice of Nursing What is Population Health? Population Health (or Total Population Health) is the health outcomes of a group of individuals, including the distribution of such outcomes within the group. It is an approach to health that aims to improve the health of an entire human population. Experts offer different perspectives about referring to patient populations; some suggest we should use the term population health management. Research using large datasets reflect patient populations; however, some may be population based if they capture an entire population of interest (e.g. all women of childbearing age in a jurisdiction). Kindig, D. (2015). Health Affairs Blog: What are we talking about when we talk about population health? Available at: http://healthaffairs.org/blog/2015/04/06/what-are-we-talking-about-when-we-talk-about-population-health/ 8
Population Health, Data Science, and the Voice of Nursing What is Data Science? Leveraging technology in research design and methods Inform hypothesis generation and testing Able to handle large datasets Requires teams of individuals with clinical domain expertise as methods and statistical skills Image from Abdelbarre Chafik available at https://www.quora.com/what-is-data-science 9
Population Health, Data Science, and the Voice of Nursing What is the Voice of Nursing? In the 1960s and 70s, the advent of computerization in healthcare sparked a quest to codify nursing knowledge for purposes of giving voice to nursing in the EHR Implementation of standardized nursing languages in EHRs remains a national priority 10
Part 2: Robust Nursing Information 11
Robust nursing information Nursing 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 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. Nursing Context Data 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 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. 12
Robust nursing information 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 LOINC (2002) x x Omaha System (1992) x x x 13 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
Robust nursing information Martin KS. (2005). The Omaha System: A key to practice, documentation, and information management(reprinted 2nd ed.). Omaha, NE: Health Connections Press. 14
Robust nursing information Big Data Laboratory 2010: Dean Delaney invited the Omaha System Partnership for Knowledge Discovery and Healthcare 2010: Dean Delaney invited the Omaha System Partnership for Quality Knowledge Discovery and Healthcare Quality within the University of Minnesota within Center the for Nursing University Informatics of Minnesota Center for Nursing Informatics Scientific teams Scientific Affiliate members teams Affiliate Data warehouse members Data collaborative 15
Robust nursing information Prototype Dashboard of SBDH Data for a Single Patient (copyright Kesler & Monsen, 2016) 16
Robust nursing information Prototype Dashboard of SBDH Data for a Patient Population (copyright Kesler & Monsen, 2016) 17
Part 3: Cutting Edge Methods 18
Cutting edge methods Using Data Visualization to Detect Client Risk Patterns Documentation patterns suggest a comprehensive, holistic nursing assessment. The presence of mental health signs and symptom tends to be associated with more problems and worse outcomes Key: Colors = problems Shading = risk Rings = Knowledge, Behavior, and Status Tabs = signs/symptoms 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
Cutting edge methods Using Data Visualization to Detect Nursing Intervention Patterns Key: Colors = problems Shading = actions (categories) Height = frequency Point on x-axis = one month Monsen, K. A., Peterson, J. J., Mathiason, M. A., Kim, E., Votova, B., & Pieczkiewicz, D. S. (2017). Discovering public health nurse specific family home visiting intervention patterns using visualization techniques. Western Journal of Nursing Research, 39, 127-146. Streamgraph development funded by a gift from Jeanne A. and Henry E. Brandt. 20
Cutting edge methods PHN Signature Styles? 21
Cutting edge methods Data Quality Issue vs. Signature? 22
Cutting edge methods Heatmap Visualization of SBDH Index Income Mental health Abuse Substance use SBDH Item Subgroups Proportion of the sample (N=4263) married (n=914) minority (n=2435) low/no income (n=2812) able to buy only necessities (n=340) difficulty buying necessities (n=374) sadness/hopelessness/decreased selfesteem (n=669) loss of interest/involvement in activities/self-care (n=194) difficulty managing strss (n=51) attacked verbally (n=129) fearful/hypervigilant behavior (n=38) consistent negative messages (n=80) assaulted sexually (n=68) welts/bruises/burns/other injuries (n=34) abuses alcohol (n=90) smokes/uses tobacco products (n=48) 0 0.07 0.17 1 0.20 0.32 0.13 0.09 0.03 0.01 0.02 0.03 0.01 0.03 2 0.32 0.36 0.31 0.35 0.11 0.09 0.14 0.07 0.06 0.07 0.03 0.03 0.03 0.06 0.17 0.01 3 0.25 0.10 0.34 0.34 0.33 0.26 0.29 0.23 0.10 0.14 0.13 0.15 0.29 0.03 0.24 0.01 4 0.10 0.03 0.15 0.15 0.35 0.35 0.03 0.29 0.35 0.29 0.16 0.24 0.23 0.29 0.23 0.31 5 to 10 0.05 0.02 0.08 0.08 0.19 0.29 0.25 0.39 0.49 0.48 0.68 0.59 0.46 0.44 0.34 0.46 Monsen, K. A., Brandt, J. K., Brueshoff, B., Chi, C. L., Mathiason, M. A., Swenson, S. M., & Thorson, D. R. (in press). Social determinants and health disparities associated with outcomes of women of childbearing age receiving public health nurse home visiting services. JOGNN. 23
Cutting edge methods Pattern Discovery: Health Disparities Pattern Discovery: Alcohol Abuse Monsen, K. A., Brandt, J. K., Brueshoff, B., Chi, C. L., Mathiason, M. A., Swenson, S. M., & Thorson, D. R. (in press). Social determinants and health disparities associated with outcomes of women of childbearing age receiving public health nurse home visiting services. JOGNN. 24
Cutting edge methods Data-Driven Intervention Clusters 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. 25
Part 4: The Value of Nursing 26
Value of nursing Examining the Value of Nursing by Comparing Intervention Modeling Approaches for 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. 27
Value of nursing Examining the Value of Nursing for Hospitalization Outcomes Using Logistic Regression 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 Policy implications: advocate for additional care at home to avoid rehospitalization 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. 28
Value of nursing Examining the Value of Nursing 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-0851705, and by the Omaha System Partnership. Monsen, K. A., Chatterjee, S. B., Timm, J. E., Poulsen, J. K., & McNaughton, D. B. (2015). Factors explaining variability in health literacy outcomes of public health nursing clients. Public Health Nursing, 32(2), 94-100. 29
Value of nursing Examining the Value of Nursing 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 Policy implications: allocate sufficient funding for services 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. 30
Value of nursing Examining the Value of Nursing for Health Literacy Using Pattern Comparison Pre- and Post-Intervention 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.. 31
Value of nursing Examining the Value of Nursing for Women of Childbearing Age Related to SBHD All subgroups show improvement Outcomes worsen, interventions increase Monsen, K. A., Brandt, J. K., Brueshoff, B., Chi, C. L., Mathiason, M. A., Swenson, S. M., & Thorson, D. R. (in press). Social determinants and health disparities associated with outcomes of women of childbearing age receiving public health nurse home visiting services. JOGNN. 32
Value of nursing Examining the Value of Nursing for Problem Stabilization among Home Visiting Clients Survival Distribution Function 1.00 0.75 0.50 0.25 0 0 0 0 0 00 0 0 00 0 00000 00 00 0 0000 0 0 0 0 00 0 0 0 0 000 00 00 0 0 00 00 0 000 0 0 00 00 0 00 0 0 00 0 0 0 0 0 0 0 00 0 0 0 000 0 00000 00 00 000000 00 0 00 000 00 00 000 0 0 0 000 00 0 00 0 0 0 0 0 00 0 00000 0 00000000 0 000000 0 0 00000 0 0 00 0 00 0 00000 0 00 0 00 000 00 000 0 000 0 0 0 00 0 0 000000 0 0 0000 0 0 0000 0 000 0 00 00 0 00 000 0000 000 0 000 00 000 00 0000000 0 0 0 0 0 0 0 0 0 0 00 0000 0000 0 00000 0000 00 00 0 0 0 000000 0 0 00 0 0 0 00000 00 0 0 0 00 000 0 0 0 0 0 00 0 00 000 00 0000 00 00 0 0000000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 000 0 00000 0 000 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0.00 0 250 500 750 1000 1250 1500 1750 This research was supported by the National Institute of Nursing Research Time_To_Stab (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 STRATA: probname=abuse Censored probname=abuse probname=antepart Research or the National Censored Institutes probname=antepart of Health. Monsen, K. A., McNaughton, probname=caretaki D. B., Savik, K., & Farri, O. (2011). Censored Problem probname=caretaki stabilization: A metric for problem improvement probname=family in home visiting P clients. Applied Clinical Censored Informatics, probname=family 2, 437-446 P probname=income Censored probname=income probname=mental H Censored probname=mental H 33 probname=residenc Censored probname=residenc probname=substanc Censored probname=substanc
Value of nursing Examining the influence of Interprofessional Services for Adults with Complex Health Problems Correlations Knowledge, behavior, and status are positively correlated (p<.001) Signs/symptoms and interventions are positively correlated (p=.002) Patterns As interventions increase, KBS ratings increase As signs/ symptoms increase, KBS ratings decrease 34
Population Health, Data Science, and Health IT During this session we heard how nurses can use and analyze large data sets to influence outcomes in patient populations. T: Treatment/clinical nursing data capture is a critical first step E: Electronic nursing data use in population health is feasible and desirable P: Population management potential is beginning to be realized S: Savings r/t improved data management must be measured over the long term 35
Questions For further information please contact me at mons0122@umn.edu Reminder: please complete online session evaluation - thank you! 36