cwd 2019 - mahidol university€¦ · top 15 most popular social networking sites may 2018 top 15...

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4th International Conference on Prevention and

Management of Chronic Conditions: Innovations in

Nursing Practice, Education, and Research

Health and Nursing Informatics:

Evidence-based practice

Prof. Dr. Connie Delaney

Dean, School of Nursing, University of Minnesota

• Describe the science of informatics,

and specifically health and nursing.

• Discuss nursing and health data

resources.

• Illustrate data informed nursing

practice.

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Health and Nursing Informatics:

Evidence-based practice

What is informatics?

• Science of how to use data, information and knowledge to improve

human health and the delivery of health care services

• Includes information & communications technology (ICT)

• Supports improvements in the safety, quality, effectiveness and

efficiency of care

• Bioinformatics- clinical- pubic health informatics, microscopic-

macroscopic, translational informatics, consumer health.

www.amia.org cwd 2019

The Data Trilogy

Data Analytics (DA) is the science of

reporting and analyzing raw data with the purpose of drawing conclusions about that information.

Data Science is an interdisciplinary field about

processes and systems to extract knowledge orinsights from data in various forms. It is a is a continuationof data analysis fields such as statistics, machine learning, data mining, and predictive analytics.

Big Data: Electronic data sets so large and complex

that they are difficult (or impossible) to manage with traditional software and/or hardware.

Raghupathi and Raghupathi. (2014) Big data analytics in healthcare: promise and potential. Health Information Science and Systems,

2:3 http://www.hissjournal.com/content/2/1/3cwd 2019

Big Data Research

• Ability to analyze vast amounts of data about a topic rather than just use smaller sets.

• Willingness to embrace data's real-world messiness rather than privilege exactitude.

• Growing respect for correlations rather than only a continuing quest for causality.

Viktor Mayer-Schönberger and Kenneth Cukier: Big Data - A revolution that will transform how we live, work and think; John Murray Publishers, London, 2013

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Contexts for Big Data Science

- National & Global -

• Learning Health System (LHS)

• Triple/Quadruple aim

• Precision medicine and person-centric care

• Connected communities

• Research/Scholarship:– Clinical Translational Science Awards

– Patient Centered Outcomes Research Institute (PCORI)

• Global connectivity

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Big Data Drivers • Electronic Health Record

• Health Insurance Claims

• Quantified Self Movement (1

trillion sensors)

• Geo-spatial Data

• Intranet of Things (IoT)

• Social Media (1.8 billion

subscribers)

• eMobile Health (6 billion

cellphones)

• Whole Gene Sequencing (6

billion diploid pairs/genome)

Topol, E. (2015) The Patient Will See You

Now. Basic Books, New York.

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Top 15 Most Popular Social Networking Sites

May 2018Top 15 Most Popular Social Networking Sites as derived from our eBizMBA Rank which is a

continually updated average of each website's Alexa Global Traffic Rank, and U.S. Traffic Rank.

Estimated monthly visitors.

http://www.ebizmba.com/articles/social-networking-websites

1,500,000,000 + 1,499,000,000 + 400,000,000 + 275,000,000 + 250,000,000

100,000,000

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Objectives

• Describe the science of informatics,

and specifically health and nursing.

• Discuss nursing and health data

resources.

• Illustrate data informed nursing

practice.

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Health and Nursing Informatics:

Evidence-based practice

Public and Government Data Sets

•CMS – Medicare Claims Public Use Files

•CDC- National Center for Health Statistics

•AHRQ – Agency for Healthcare Research and Quality

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Health Affairs

Optum Labs: Building A Novel

Node In The Learning Health

Care SystemPaul J. Wallace,*, Nilay D. Shah, Taylor Dennen, Paul A. Bleicher

and William H. Crown

Abstract

Unprecedented change in the US health care system is being

driven by the rapid uptake of health information technology

and national investments in multi-institution research

networks comprising academic centers, health care delivery

systems, and other health system components. An example

of this changing landscape is Optum Labs, a novel network

“node” that is bringing together new partners, data, and

analytic techniques to implement research findings in

health care practice.

Partners• Mayo Clinic• AARP• AMGA • Boston Scientific • Boston University• Lehigh Valley• Pfizer Inc. • Rensselaer Polytechnic• Tufts Medical Center • UM School of Nursing• Harvard Medical School • Medica Research Institute• Merck • University of Maryland• The Brown University School of

Public Health• Johns Hopkins Bloomberg School

of Public Health• MIT Sloan School of

Management • Novartis Pharmaceuticals

Corporation• ResMed

http://content.healthaffairs.org/content/33/7/1187.full?ijkey=b8qVnVJW

pdA4s&keytype=ref&siteid=healthaff cwd 2019

UnitedHealthcare

UnitedHealth Group: A diversified managed health care company offering a spectrum of products and services to 70 million individuals through two operating businesses: UnitedHealthcare and Optum.

• UnitedHealthcare: The largest single health carrier in the United States.

• UnitedHealthcare Optum: – One of the largest health information, technology,

services and consulting companies in the world.

– Population health management, care delivery and improving the clinical and operating elements of the system.

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Data Categories

• Demographics

• Pharmacy claims

• Physician and facility claims

• Lab test results

• Socioeconomic data

• EHR data (clinical)

• Health risk appraisal

• Date of death

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Optum Labs Data Warehouse

• Approximately 150 million lives (40 million EHR)

• 3400 fields per life

• Claims and electronic health records data (~25% of data is linked)

• 20 + years of data

• Includes Medicare Advantage

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Partners Sample of Academic/Industry

Partnerships

Funding

Source

Otolaryngology Prediction model: causal factors in patients presenting with

dizziness

NIH

Nursing Prediction model: Patients experiencing adverse effects of statin

therapy

UM Internal

Prediction model: Cardiovascular disease risk prediction using

EHR/claims data

UM Internal

AHC Seed

Symptom management of liver transplant patients RO1

Prevention of urinary tract infections in young women RO1

Public Health Prediction model: Diffusion of knowledge from clinical trials to

practice.

NIH

Comparative effectiveness of extended oral anticoagulant use PCORI

Contemporary Venous Thromboembolism Treatment - NIH NIH

Neurosurgery Comparative effectiveness between surgical and non-surgical

intervention of low back pain.

NIH

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Vision for Data in a

Clinical Data Warehouse

Clinical DataNMDS

Management Data

NMMDS

Other Data Sets

Continuum of Care

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Celebrating our

foundation for

“Big Data/Data

Science”

Global standards

eMeasures

EHRs

Magnet

Resources

Workforce

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National Center Expanded Interprofessional Learning Continuum

Model

IOM. Measuring the Impact of Interprofessional Education and Collaborative Practice and Patient Outcomes

Enabling or Interfering Factors

Professional Culture

Enabling or Interfering Factors

Professional Culture Institutional Culture

Financing PolicyWorkforce Policy

Nexus Learning ContinuumEducation and Clinical Learning Environments

(Formal and Informal)

Foundational Education Graduate Education Continuing Professional Development

Interprofessional Education

Health Outcomes(Quadruple Aim)

Learning Outcomes System Outcomes

Practices for promoting, incenting, rewarding IPECP

Changes to care delivery structures + processes to

support efficient high-quality, patient-centered, IP team-based

care

Cost Effectiveness

Patient Experience

Individual Health

Population Health

Cost

Practices for protecting and enhancing provider well-

being

Reactions

Attitudes /Perceptions

Knowledge / Skills

Collaborative Behavior

Performance in Practice(individual and team)

IPE Core Data Set(Intervals)

Interprofessional Education Learning

Environment Survey

Interprofessional Clinical Learning

Environment Survey

Teamness (ACE-15)

Interprofessional Competencies

(ICCAS)

Critical Incidents

HealthOutcomes

Partnership History, Structure

Structural characteristics of

education institutions

Structural characteristics of practice settings

Nexus Program Proposal (Baseline)

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Secure, HIPAA and FERPA compliant

infrastructure and data sharing environment

focused on interprofessional practice and

education, housed at the University of Minnesota

Standard measures applicable and comparable

across environments exploring key elements of

education, practice and the Nexus

Easy access to data through dashboards and

standardized reports; additional analysis available

through advanced analytics, big data and

comparable data sets

Authorized users have the ability to manage

users access, review Nexus Program status,

and send invitations to other users to join

their Nexus Programs

Program Management

National Center IPE Information Exchange &

Core Data Set

Informatics Driven Dashboard

IPE Core Data Set

National Center Data Repository

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Network Members

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Objectives

• Describe the science of informatics,

and specifically health and nursing.

• Discuss nursing and health data

resources.

• Illustrate data informed nursing

practice.

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Health and Nursing Informatics:

Evidence-based practice

Comparison of Observational Studies to Secondary Analysis of Big Data

Observational Studies

• Few data sources

• Limited set of variables (10’s –1000’s)– Demographics

– Clinical

– Insurance claims

– Census

• Small number of hypothesis

• Long, expensive data collection, analysis and evaluation cycle

Secondary Data Analysis

• Multi-source, data mash-up

• Many variables (> million)– EHR

– Imaging

– Social media

– Genomic

• Large number of hypothesis

• Short data collection, analysis and evaluation cycle

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Knowledge Discovery: Data Mining

• The computational process of discovering patterns in large, complex datasets.

• Goal of KDD: Extract information and transform it into an understandable structure (knowledge).

• Exploratory studies

• Pattern recognition

• Data visualization

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Nursing Data, Visualization

Techniques, & Home Health Care

• Karen A. Monsen, PhD, RN, FAAN

Professor and Director, Center for

Nursing Informatics & Scientific Team

• Omaha System Partnership for

Knowledge Discovery and Healthcare

Quality Data Repository

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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).

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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

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Using Data Visualization to Detect

Client Risk PatternsMonsen, 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.cwd 2019

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

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Purpose

To develop a predictive model for hospital-acquired

CAUTIs using multiple data sources

Specific Aims

Aim 1: Create a quality, de-identified dataset combining

multiple data sources for machine learning tasks

Aim 2: Develop and evaluate predictive models to find

the best predictive model for hospital-acquired CAUTI

Park, J. (2016). Developing a Predictive Model for Hospital-Acquired Catheter-

Associated Urinary Tract Infections Using Electronic Health Records and Nurse

Staffing Data. Dissertation. University of Minnesota

Acute Care – Cather Associated Urinary Tract Infection

(CAUTI) J Park

Factors Associated with CAUTI

Decision Tree model Linear Regression model

• Female

• Longer length of Stay

• Presence of rationale for

continued use of catheter

• Less total nursing hours per

patient day

• Lower percent of direct care

RNs with specialty nursing

certification

• Higher percent of direct care

RNs with BSN, MSN, or PhD

• Age ( ≥56)

• Longer length of stay

• Presence of rationale for

continued use of catheter

• Charlson comorbidity index

score ≥ 3

• Glucose lab result > 200 mg/dl

• Higher percent of direct care

RNs with associate’s degree in

nursing

• Higher percent of direct care

RNs with BSN, MSN, or PhD

Park, J. (2016). Developing a Predictive Model for Hospital-Acquired Catheter-Associated Urinary Tract

Infections Using Electronic Health Records and Nurse Staffing Data. Dissertation. University of Minnesota

Acute Care – Catheter Associated Urinary Tract

Infection (CAUTI)

A Data Mining Approach to Determine

Sepsis Guideline Impact on Inpatient

Mortality and Complications

Michael Steinbach, PhD; Bonnie L. Westra, PhD, RN, FAAN, FACMI; György J. Simon, PhD

Lisiane Pruinelli, MSN, RN, PhD-C; Pranjul Yadav, PhD-C;

Andrew Hangsleben; Jakob Johnson; Sanjoy Dey, PhD;

Maribet McCarty, PhD, RN; Vipin Kumar, PhD; Connie W. Delaney, PhD, RN, FAAN, FACMI

Support for this study is provided by NSF grant IIS-1344135 , National Center for Research Resources

of the NIH 1UL1RR033183.

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AimThe overall aim is to evaluate and extend evidence-based guidelines for patients with health disparities for the prevention and management of sepsis complications

1. Map EHRs data to SSC guideline recommendations

2. Estimate the compliance with the SSC guideline recommendations; and

3. Estimate the effect of the SSC individual recommendations on the prevention of in-hospital mortality and sepsis-related complications

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SSC guideline - Interventions

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Compliance with SSC GuidelinesRules Description Patient Count / %

Y N % Compl N/A

1. Was Blood Culture done? (BCulture) 126 51 71 0

2. Was Antibiotic given after Blood Culture? (Antibiotic) 99 27 79 51

3. Was Lactate checked? (Lactate) 127 50 72 0

4. Was Fluid Resuscitation done if Lactate > 4? (LactateFluid) 36 0 100 141

5. Was Blood Glucose checked? (BGlucose) 132 45 75 06. Was Insulin given if two Blood Glucose measures were > 180?

(GlucoseInsulin)

38 8 83 131

7. Was MAP checked? (MAP) 177 0 100 0

8. Was Fluid Resuscitation give if MAP < 65? (MAPFluids) 160 6 96 11

9. Was Vasopressor given if MAP < 65 after Fluid

Resuscitation? (Vasopressor)26 140 16 11

10. Was CVP checked? (CVP) 121 56 68 011. Was Fluid Resuscitation done if CVP < 2? (CVPFluids) 15 162 9 0

12. Was Albumin given if CVP < 2 after Fluid Resuscitation? (Albumin) 4 11 27 162

13. Was a Diuretic given if CVP above 12? (Diuretic) 10 71 12 96

14. Was there Respiratory Distress*? (RespDistress) 167 10 94 015. Was a ventilator given if there was Respiratory Distress?

(Ventilator)

92 75 55 10

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Results - Complications

Cardiovascula

r

Respiratory Kidney Cerebrovascu

lar

Death

BCulture (-0.11, 0.15) (-0.16, 0.12) (-0.15, 0.11) (-0.09, 0.20) (-0.14, 0.09)

Antibiotic (-0.16, 0.10) (-0.23, 0.13) (-0.08, 0.26) (-0.09, 0.28) (-0.21, 0.10)

Lactose (-0.05, 0.19) (-0.20, 0.07) (-0.08, 0.18) (-0.04, 0.21) (-0.12, 0.10)

BGlucose (-0.02, 0.25) (-0.02, 0.28) (-0.16, 0.14) (-0.06, 0.18) (-0.19, 0.09)

Vasopressor (-0.11, 0.27) (0.04, 0.35) (-0.20, 0.17) (-0.32, -0.07) (-0.10, 0.21)

CVP (-0.03, 0.16) (-0.06, 0.17) (-0.10, 0.14) (-0.08, 0.16) (-0.08, 0.13)

RespDistress (-0.25, 0.36) (-0.36, 0.37) (-0.14, 0.40) (-0.30, 0.37) (-0.25, 0.14)

Ventilator (0.04, 0.19) (0.08, 0.32) (-0.11, 0.09) (-0.08, 0.11) (0.03, 0.20)

CI (0.04,

0.35)

CI (0.04,

0.19)

CI (0.08,

0.32)CI (-0.32, -

0.07)

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Conclusions

• EHR data can be used to estimate compliance with

individual guideline recommendations

• EHR can be used to estimate the effect of the guideline

adherence on sepsis-related complications

• Some guideline recommendations are protective for

patients for certain outcomes

• Other variables may be needed to control for variation in

severity of illness or variation in practice

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38

z.umn.edu/bigdata

References & Resources

• NIH Big Data to Knowledge (BD2K) Workshops: https://datascience.nih.gov/bd2k/events/bd2kworkshops

• NINR Advancing Nursing Research through Data Science http://www.ninr.nih.gov/training/online-developing-nurse-scientists#.VtdHJvkrLIU

• University of Minnesota School of Nursing. Nursing Knowledge: Big Data Conference 2016: http://www.nursing.umn.edu/icnp/center-projects/big-data/2016-nursing-knowledge-big-data-science-conference/index.htm

• American Medical Informatics Association: https://www.amia.org/

• Health Information and Management Systems Society (HIMSS): http://www.himss.org/aboutHIMSS/

• Coursera: Six courses on data science: https://www.coursera.org/

• Health Catalyst Knowledge Center: https://www.healthcatalyst.com/knowledge-center/

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• D3 Data Driven Documents. (2016). https://d3js.org/

• Hey, T., Tansley, S., & Tolle, K. (2009). The fourth paradigm: Data intensive scientific discovery. Redmond, WA:

Microsoft Research.

• 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

• 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.

• Mayer-Schonberger, V. & Cukier, K. (2013) Big Data: A revolution that will transform how we live, work, and think.

Houghton Mifflin Harcourt, Boston.

• 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

• 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.

• Monsen, K.A., Peterson, J. J. , Mathiason, M. A., Kim, E., Lee, S., Chi, C. L., Pieczkiewicz, D. S. (2015). Data

visualization techniques to showcase nursing care quality. Computers, Informatics, Nursing, 33(10), 417-426. doi:

10/1097/CIN.000000000000190

• Tableau (2016). http://www.tableau.com/

• 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.

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References & Resources

Connie White Delaney, PhD, RN, FAAN, FACMI, FNAP

• Professor & Dean | University of Minnesota School of Nursing

• delaney@umn.edu | 612.624.5959

• @conniewdelaney | @UMNNursing | LinkedIn: conniewhitedelaney

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