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

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

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.

http://www.pcornet.org/resource-center/pcornet-common-data-model/

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

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

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

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

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

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

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

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.

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

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

Discussion

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

Report Out

Pain IM Pain Information Model Developed in one organization Validated Across organizations http://www.nursingbigdata.org/node/123 Shared with Encoding & Modeling Workgroup Integrate with interprofessional pain concepts Clarified concepts, definitions, etc Added LOINC and SNOMED CT codes

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

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), 452-458 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. 2016:260-268. 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. 2015 Mar 25;2015:77-81.