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Using Data Science to Influence Population HealthSession #NI3, February 19, 2017

Karen A. Monsen, PhD, RN, FAAN, Associate Professor

University of Minnesota School of Nursing

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Speaker Introduction

Karen A. Monsen, PhD, RN, FAANAssociate 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

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Conflict of Interest

Karen A. Monsen, PhD, RN, FAAN

Has no real or apparent conflicts of interest to report.

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Agenda

• Population Health, Data Science, and the Voice of Nursing

• Robust nursing information

• Cutting edge methods

• Value of nursing

• Questions

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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

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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

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Part 1: Population Health, Data Science, and the Voice of Nursing

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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/

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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

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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

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Part 2: Robust Nursing Information

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Nursing Data Nursing Context DataNursing Minimum Data Sethttp://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 Sethttp://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.

Robust nursing information

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Recognized Nursing TerminologiesAmerican 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

Robust nursing information

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Martin KS. (2005). The Omaha System: A key to practice, documentation, and information

management(Reprinted 2nd ed.). Omaha, NE: Health Connections Press.

Robust nursing information

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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 collaborative

Robust nursing information

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Prototype Dashboard of SBDH Data for a Single Patient (copyright Kesler & Monsen, 2016)

Robust nursing information

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Robust nursing information

Prototype Dashboard of SBDH Data for a Patient Population (copyright Kesler & Monsen, 2016)

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Part 3: Cutting Edge Methods

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Using Data Visualization to Detect Client Risk Patterns

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.

• 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

Cutting edge methods

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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.

Cutting edge methods

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PHN Signature Styles?

Cutting edge methods

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Data Quality Issue vs. Signature?

Cutting edge methods

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Heatmap Visualization of SBDH IndexSB

DH

Item

Su

bgr

ou

ps

Pro

po

rtio

n o

f th

e sa

mp

le (

N=

42

63

)

mar

ried

(n

=9

14

)

min

ori

ty (

n=

24

35

)

low

/no

inco

me

(n=

28

12

)

able

to

bu

y o

nly

nec

essi

ties

(n

=3

40

)

dif

ficu

lty

bu

yin

g n

eces

siti

es (

n=

37

4)

sad

nes

s/h

op

eles

snes

s/d

ecre

ased

sel

f-

este

em (

n=

66

9)

loss

of

inte

rest

/in

volv

emen

t in

acti

viti

es/s

elf-

car

e (n

=1

94

)

dif

ficu

lty

man

agin

g st

rss

(n=

51

)

atta

cked

ver

bal

ly (

n=

12

9)

fear

ful/

hyp

ervi

gila

nt

beh

avio

r (n

=3

8)

con

sist

ent

neg

ativ

e m

essa

ges

(n=

80

)

assa

ult

ed s

exu

ally

(n

=6

8)

wel

ts/b

ruis

es/b

urn

s/o

ther

inju

ries

(n

=3

4)

abu

ses

alco

ho

l (n

=9

0)

smo

kes/

use

s to

bac

co p

rod

uct

s (n

=4

8)

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

Substance useIncome Mental health Abuse

Cutting edge methods

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.

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Pattern Discovery: Health Disparities

Pattern Discovery: Alcohol Abuse

Cutting edge methods

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.

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Data-DrivenInterventionClustersRelationships 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.

Cutting edge methods

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Part 4: The Value of Nursing

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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.

Value of nursing

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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 re-hospitalization

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.

Value of nursing

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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?

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.

• Nurse (17%)

• Client (50%)

• Problem (17%)

• Intervention (17%)

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

Value of nursing

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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.

Value of nursing

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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..

Value of nursing

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Examining the Value of Nursing for Women of Childbearing Age Related to SBHD

Value of nursing

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.

All subgroups show improvement

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Examining the Value of Nursing for Problem Stabilization among Home Visiting Clients

Surv

ival D

istr

ibution F

unction

0.00

0.25

0.50

0.75

1.00

Time_To_Stab

0 250 500 750 1000 1250 1500 1750

0

00 00000

0000000

00 00 00

00000 00000 0 0 0

000

0 0 0 00 0 0 0 0 0

000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 0000 000000000000000000000000 000 0000 00000 000 00 0000 00 0 0 000 0 0 0 0 00 0 0

0

000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 00000000000000000000000000000000000000000000000000000000000000000000000000 0 0 000

000 000000000 0 0 00 00 0 00 0 0 00 0 0 0 00 0

000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000

0000000000000

00000000 00000 000

0000

0000000000 0

0000000 00

00 0 00 0 0

0 0 0 0 0 0

0

00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000

0000 000000000000000000000

0 00000000 00000

000 0 0 0 00 0 0

0 0 0

0

0

00000000

0 0 0000000 0

000 0 00 000000 00 00 0 000 000000

0 0000 00

0 0 0

0 0 0

0

00000000000000000000000000000

000000000000000000000000000000000000000 0000000000000000000000 000000 0000000000000000 000000

00000 000 0000000000000 000 000000 0 00

00 0 0 0 00

0 0 0

00

000

0000000000000000000000000000000000000000000

0000 00000000000000000000000000000000000000000000000000000 0 000000000 0 0000000

00 00000000 00 00000

00 0

0 0 0 0 0

STRATA: probname=Abuse Censored probname=Abuse probname=Antepart

Censored probname=Antepart probname=Caretaki Censored probname=Caretaki

probname=Family P Censored probname=Family P probname=Income

Censored probname=Income probname=Mental H Censored probname=Mental H

probname=Residenc Censored probname=Residenc probname=Substanc

Censored probname=Substanc

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

Value of nursing

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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

Value of nursing

Examining the influence of Interprofessional Services for Adults with Complex Health Problems

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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

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Questions

• For further information please contact me at mons0122@umn.edu

• Reminder: please complete online session evaluation - thank you!