1. From Dirta to Wisdom January 27, 2017 Tess Settergren, MHA, MA, RN-BC
Director, Nursing Informatics
Cedars-Sinai Health System

2. Objectives Learners will be able to: Describe the Data to Wisdom model
Discuss the “Nursing Knowledge: Big Data Science” national initiative
Analyze continual use benefits of coded EHR documentation elements

3. "If you cannot name it, you cannot control it, finance it, research it, teach it, or put it into public policy.“
Norma Lang, PhD, RN, FAAN, FRCN
Classification for Nursing Practice,“ International Nursing Review, 39
no. 4, (1992):

4. DIKW Pyramid

5. Data to Wisdom Continuum (Nelson, 2002)

6. Informatics Concepts: Data to Wisdom
Symbols
Raw facts that are meaningless by themselves
Discrete entities that need organization and interpretation
Can be gathered, recorded, and measured
Can be analyzed: Process of inspection, cleaning, transforming, and modeling data

7. Information:
Data that are given meaning—context, connections, relationships, based on domain knowledge and experience
Computers do not process information. They process data—only humans can put the data into context to make information
Computers can present data in an aggregated and summarized way that allows humans to analyze and give meaning to the data

8. Knowledge:
Information that has been synthesized, relationships identified and formalized, and provides a pattern that enables predictability
Outcome probabilities—what is the likelihood of an occurrence?
Benchmarking is an example of knowledge at work—it is the process of identifying best practices

9. Knowledge translated into action Examples of Wisdom:
Appropriate application of knowledge to the management and solution of human problems
Knowledge translated into action
Examples of Wisdom:
Expert Decision Support
Evidence-Based Practice
Matney, Avant, Staggers. (Oct. 30, 2015). Toward an understanding of wisdom in nursing. OJIN 21 (2).

10. DIKW Using EHR Data
BP 100/60
85 y/o; u/o doubled past 2 hours; HR 90; RR 22; temp 97°F; disoriented; pink/warm skin
VS 4 hours ago: T 101°F, HR 65, RR 14, Glasgow 15; Hx HTN; PT & DP pulses 4+ (normally trace-1+); Foley catheter day 5
Information reviewed against own knowledge and experience: Initiate RRT &/or Sepsis Protocol

11. Nursing Big Data Vision
Ability to share and compare nursing data across organizations.
Better quality outcomes will result from the standardization and integration of the information nurses enter in EHRs.
Leading to better health of individuals, families, communities, and populations.

12. National Action Plan Conveners: Connie Delaney, Bonnie Westra
Working conferences with virtual workgroups taking action between annual meetings
All focused on the same vision
Strategic inclusion of stakeholders
Practice - leaders
Industry - software vendors, content vendors
Professional organizations
National – nursing, interprofessional, informatics
Academia – faculty, researchers
*Proceedings: Available on UMN website

13. 2016-2017 Workgroups Clinical Data Analytics Care Coordination
Ensure nursing and interprofessional data inform population health analytics
Validate flowsheet information models across organizations
Integrate SNOMED CT nursing problem list into national common data models (PCORnet, CTSA, e.g.)
Care Coordination
Determine essential elements for predicting and managing patients needing care coordination, in the context of nursing big data

14. 2016-2017 Workgroups Engage & Equip Nurses in HIT Policy
Provide nurses with the education, tools, and resources to equip them to engage in policy efforts
Influence the HIT policy landscape in collaboration with other professional groups
Context of Care
Use the Nursing Management Minimum Data Set, employing ‘test kitchen’ methods to inform the quadruple aim of health

15. 2016-2017 Workgroups Education Demonstrate Nursing’s Value
Develop training for faculty to teach NI at the graduate level
Standard tools, resources, training framework
Demonstrate Nursing’s Value
Refine the model to measure nursing intensity and costs per patient across the continuum of care, including use case template to demonstrate objective measures of nursing value
Consider National Provider Identifiers

16. 2016-2017 Workgroups mHealth Data
Collect use case examples of mobile health data by nurses, including nursing- and patient-generated data to support dissemination and collaboration
Identify mHealth requirements and specifications and incorporation of data into existing data models
Social/Behavioral Determinants of Health
Develop a toolkit to support capture and use of SDOH data into electronic health records and the comprehensive longitudinal client-centered plan of care; assess clinical workflow implications

17. 2016-2017 Workgroups Transform Nursing Documentation
Move from discipline-specific documentation silos toward a more useful representation of the patient story; continuum patient-centered documentation; decrease the documentation burden.
Create a repository to share workgroup products
Encode & Model Nursing Data
Implement coded nursing assessments using Logical Observation Identifiers Names and Codes (LOINC®) and SNOMED Clinical Terms® for electronic health records & develop governance for information model creation and curation

18. Transform Nursing Documentation
Explored the current challenges and opportunities for nursing documentation in EHR
Examined best practice exemplars of nursing documentation supported by clinical decision support producing positive outcomes
Formed subgroup to explore feasibility of creating a repository/ web resource for nursing documentation best practices, evidence based content and decision support exemplars to support knowledge sharing
Defined and explored Precision Nursing: Highly reliable, evidence based and personalized nursing practice that supports quality outcomes.
Ann O’Brien, Kaiser-Permanente

19. Transform Nursing Documentation
Data silos create disconnects in care due to lack of views into each discipline’s contribution and insights.
Nursing documentation is primarily data entry without linkages to real time knowledge
It is challenging to access the right information at the right time within the nurse’s workflow and personalize care to each patient.
Clinical decision support relies on accurate and timely documentation but nurses may not always have the tools or perceive the value.
Ann O’Brien, Kaiser-Permanente

20. Moving from Data to Knowledge
Goal: “By 2020, 90% of clinical decisions will be supported by accurate, timely, and up-to-date information and will reflect the best evidence available.” (IOM 2015)
Creating a road map:
Simplify and speed documentation. (AMIA EHR 2020 Taskforce May JAMIA)
Ensure nursing data is accurate, complete and timely.
Promote biomedical device integration for decreasing duplicate documentation & non-value added tasks.
Create, standardize and share best practices, nurse-initiated protocols and clinical decision support tools.
Maximize clinical decision support to link data entry to evidence based, personalized care.
Develop real time dashboards and reminders to support nursing workflow and data visualization.
The goal set forth by the IOM Roundtable on Value and Science Driven Care
Ann O’Brien, Kaiser-Permanente

21. Precision Nursing**
Patient centered, highly reliable, evidence based and personalized nursing practice across the continuum of care that supports:
Quality outcomes
Safety
Decreased costs
Efficiency
Nurse satisfaction
**Nursing Knowledge Big Data Science
University of Minnesota Sponsor
Transforming Nursing Documentation Workgroup 2016.
Transform Nursing Doc: Streamline, Simplify, Standardize, Smart
Ann O’Brien, Kaiser-Permanente

22. Valuing Nursing & Nursing Data
Pam Cipriano, President of the ANA
2016 is the year of Patient Safety
1 out of every 100 people in the US is a nurse
Nurses are the primary coordinators of care
Reinvent the plan of care so it is patient-centered, interoperable, longitudinal and meaningful.
Rediscover the patient’s story
Move away from episodic care and data

23. Florence Nightingale, 1863
“ In attempting to arrive at the truth, I have applied everywhere for information, but in scarcely an instance have I been able to obtain hospital records fit for any purpose of comparison. If they could be obtained they would enable us to decide many other questions besides the one alluded to. They would show the subscribers how their money was being spent, what good was really being done with it, or whether the money was not doing mischief rather than good.”
Nightingale, F. (1863). Notes on Hospitals. London; Longman, Green, Longman, Roberts, & Green, p. 176

24. Standardized Data Benefits
Measure the contributions of nursing, within the interprofessional team context
Support ‘Value of Nursing’ measurement
Enable eCQMs
Enhance care coordination across the continuum (nursing data in CCD)
Enable cross-institutional nursing-sensitive outcomes studies and quality improvement initiatives

25. Semantic Interoperability
The Office of the National Coordinator for HIT uses IEEE definition:
“…the ability of systems to exchange and use electronic health information from other systems without special effort on the part of the user.”

26. ברכות
selamlar
hilsener
talofa
ohé
cześć
Здраво
تحيات
敬礼。
xαιρετισμούς
สวัสดี
saudações
Привет
moi

27. Pain
Discomfort
Burning
Aching
Stabbing
Uncomfortable
Hurting
Stinging
Throbbing
Sore
Soreness
Agony
Smarting
Twinge
Tender
Tenderness
Distress
Ache
Irritation
Painful
Raw
Hurts

28. Encoding Workgroup Objectives:
Create a framework for organizing assessment data in Logical Observations, Identifiers, Names & Codes (LOINC)
Map observations (the ‘questions’) to LOINC
Map values (the ‘answers’) to SNOMED Clinical Terms (SNOMED CT)
Determine the LOINC coverage for frequently documented assessment observations & submit new concepts as needed
Determine SNOMED CT coverage for the frequently documented assessment values & submit new concepts as needed

29. Why LOINC & SNOMED CT?

30. Why LOINC & SNOMED CT?

31. Healthit.gov

32. ONC Interoperability Standard

33. Westra, B.

34. Approach
Compared top 100 physiologic assessment measures from six institutions
submitted 61 – 128 observations
68 unique concepts used by 2 or more sources
Mapped to LOINC (70%)
Grouped observations according to physiological system (for Panels and Framework)
Created assessment panels by system
Evaluated observations in panels to ensure a nursing assessment minimum data set
Identified observation values

35. Cedars-Sinai Flowsheet Metadata
By the Numbers:
Settings Represented = Inpatient, Hospital Outpatient, Ambulatory
Specific Years of Data = 7 (Nov 2009-Nov 2016)
Measures (all data points) = 1,973,267,733
Unique Templates = 804
Unique Groups = 2639
Unique Flowsheet Measures = 14,181

36. Flowsheet Row Examples
Unique ID
Name
Value Set
IP Skin Assessment (WDL)
WDL;X;x;
303450
Skin Condition
Clammy; Cool; Diaphoretic; Dry; Elastic turgor; Flaky; Hot; Laceration; Other (Comment); Peeling; Petechiae; Poor turgor; Pressure Ulcer; Rash; Surgical Incision; Swollen; Warm; Wound
303440
Skin Color
Bronze; Cyanotic; Dusky; Ecchymosis; Flushed; Jaundice; Mottled; Other (Comment); Pale;
Acrocyanosis; Bronze; Circumoral cyanosis; Dusky; Ecchymosis; Flushed; Jaundice; Mottled; Other (Comment); Pale; Pink; Plethoric;
2056
Appropriate, even tone; Ashen; Cyanotic; Dusky; Flushed; Jaundiced; Mottled; Other (Comment); Pale; Pink; Translucent;

37. WDL Documentation Each institution has WDL parameters defined.
Skin WDL example:
Skin Color = normal for ethnicity
Skin Temperature = Warm
Skin Moisture = Dry
“Impression” codes are used to document a nursing judgment.
WDL is one value for the impression observation.
If there is concerns, risks, etc. this would be documented as text for this code
When WDL is selected, the “Defined Limits” name/value pairs must store on the EHR back-end

38. Results*
15 Standardized Nursing med/surg physiologic assessment panels (86% new LOINC)
106 Observations (50% new LOINC)
348 Values (20% new SNOMED CT)
New LOINC codes approved, including “Nursing Impression” for WDL
New SNOMED CT codes approved
*WJNR article: “Standardizing physiologic nursing assessment data to enable big data analytics” (Matney, Settergren, Carrington, Richesson, Sheide, Westra)
More work to do, but this is a good start and the coding will be available publically for Nursing to use in their own EHRs—Epic has provided strong support for this work.

39. LOINC Panels (search.loinc.org)

41. Initiatives
Continue nursing/interprofessional assessment coding to LOINC & SNOMED CT, and expand beyond assessment data based on use cases (Pain, e.g.)
Publish value sets on UMLS (VSAC)
Implement coded data elements into EHRs
Model creation/curation, in alignment with CIMI; create FHIR profiles
Incorporate evidence/clinical knowledge
VOLUNTEERS WELCOME (NEEDED)!!

42. Thank you! Tess.Settergren@cshs.org

43. Selected References
nursing-informatics/events/2016-nursing- knowledge-big-data-science-conference (or search.loinc.org) art.html d/nursing_problemlist_subset.html advisory/draft mind/health/health-sciences/the-joy-stats-the- lady-data-visualisation