1. Data, Data Everywhere and Not a Drop of Sense Standardized Data for Pain Management
Bonnie L. Westra, PhD, RN, FAAN, FACMI, Associate Professor Emerita, University of Minnesota, School of Nursing, Co-Director Center for Nursing Informatics
June 2019

2. Objectives
Describe information models and their value to standardize data for documentation and ongoing use
Identify the multiple inputs for describing pain assessment and management
Generate concepts and value sets and the relationship of these related to pain management
Compare results of exercise with a pain information model

3. Requirements for Useful Data
Common data models (aka information models)
Standardized coding of data
Standardized queries
OMOP worked to design experiments testing a variety of analytical methodologies in a range of data types to look for drug impacts that are already well-known.
Mini-Sentinel is a pilot project sponsored by the U.S. Food and Drug Administration (FDA) to create an active surveillance system - the Sentinel System - to monitor the safety of FDA-regulated medical products.
Informatics for Integrating Biology and the Bedside (i2b2) is an NIH-funded National Center for Biomedical Computing (NCBC) based at Partners HealthCare System in Boston, Mass.

5. Information Model (IM)
An organized structure to represent knowledge about a clinical condition
Data elements, definitions, their relationships
Different formats – choice lists, dates, numeric, free text
Data can be coded with standards for comparability across settings
Data are independent of implementation in EHRs
Data with standards can be mapped to EHR data
Identify semantic similarities

6. Vision – Inclusion of Nursing and Other Interprofessional Data
Clinical Data
NMDS
Management Data
NMMDS
Other Data Sets
Continuum of Care 6 6

7. UMN – Academic Health Center CDR
Flowsheets constitute 34% of all data
14,564 unique measures
2,972 groups
562 templates
1.2 billion observations
2,000 measures cover 95% of observations
Get better graph
Just counting observations, not a measure of importance

8. Value Improves communication for patient care
Reduce documentation burden limiting data to essential evidence-based care
Track change in patients’ conditions across settings and over time
Ongoing use of data for
Alerts and dashboards
Quality reporting
Public health reporting
Research

9. Information Models Information Model Name Pain CAUTI/ GU
# Flowsheet IDs Mapped to Observables
# Information Model Classes/ Observables
Pain
309 12 80
CAUTI/ GU 79 3 38
Fall Prevention
59* 4 57
Pressure Ulcers
104 6 56
VTE 67 8 16
Cardiovascular System
241 84
Gastrointestinal System 60 28
Musculoskeletal System
276 9 72
Respiratory System
272 61
Vital Signs/Anthropometrics 85 10 48

11. Chaos - Urine Color Value Set (n=53)
Actionable?
Meaning?
Evidence?

12. Standardized Urine Color
amber
blue
brown
dark red
green
orange
pale yellow
pink
red
rusty
yellow

13. Information Model Development Process
Pain
5 W’s
Identify Concepts
Answer Format
Value Sets
Compare to Pain IM
Are goal therefore was to develop a standardized process for normalizing flowsheet measures by mapping them to concepts of interest to researchers. We started by first identifying topics of interest for creating clinical data models. We then identified concepts within these topics and then map them to flowsheets. We also did a bottom-up approach then of looking within groups where we found these flowsheets to identify additional concepts that might important to researchers. Once a model was created and flowsheet measures were mapped investigator presented this to the entire group for feedback. Multiple iterations were done in order to validate the model and the mapping as well as develop consistent rules to be used across the development of additional models.

14. Scope for Exercise
Framework - Nursing process: assessments, (Diagnoses), interventions, outcomes
Electronic health record data
Flowsheets/ template formats
Structured/ semi-structured data
Primarily documented by nurses, could include others
Out of scope
Free text notes
Specific medications

15. Identifying Current State
Who documents pain
What data
Documented
Multiple uses of the data
Where
Setting (i.e. medical/ surgical, ED, rehab, etc)
Location in chart
When
Why
Conclusions about consistency

16. Discussion

17. Building a Pain IM
What essential pain data should be collected regardless of discipline or location?
List assessment, diagnosis, intervention, outcome concepts/ terms to document
List type of answers for each concept - i.e. type of pain
List values for choice lists for 2 of the concepts

18. Report Out

19. Pain IM Pain Information Model
Developed in one organization
Validated Across organizations
Shared with Encoding & Modeling Workgroup
Integrate with interprofessional pain concepts
Clarified concepts, definitions, etc
Added LOINC and SNOMED CT codes

20. Summary
Describe information models and their value to standardize data for documentation and ongoing use
Identify the multiple inputs for describing pain assessment and management.
Generate concepts and value sets and the relationship of these related to pain management
Share validated pain information model

21. References
Westra, B.L., Christie, B., Johnson, S.G., Pruinelli, L., LaFlamme, A., Sherman, S., Park, J.I., Byrne, M.D., Delaney C.W., Gao, G., Speedie, S. (2017). Modeling Flowsheet Data to Support Secondary Use. Computers Informatics Nursing, 35(9),
Westra, B.L., Johnson, S. G., Ali, S., Bavuso, K.M., Cruz, C.A., Collins, S., Furukawa, M.,. Hook, M.L., LaFlamme, A., Lytle, K., Pruinelli, L., Rajchel, T., Settergren, T., Westman, K.F., Whittenburg, L. (2018),Validation and Refinement of a Pain Information Model from EHR Flowsheet Data, Applied Clinical Informatics.
Westra BL, Christie B, Johnson SG, Pruinelli L, LaFlamme A, Park JI, Sherman SG, Byrne MD, Svenssen-Renallo P, Speedie S. (2016). Expanding Interprofessional EHR Data in i2b2. AMIA Summits on Translational Science Proceedings. 2016:
Johnson, S.G., Byrne, M.D., Christie, B., Delaney, C.W., LaFlamme, A., Park, J.I., Pruinelli, L., Sherman, S., Speedie, S., Westra, B.L. (2015). Modeling Flowsheet Data for Clinical Research. AMIA Clinical Research Informatics. AMIA Jt Summits Transl Sci Proc Mar 25;2015:77-81.