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Data, Data Everywhere and Not a Drop of Sense Standardized Data for Pain Management

This presentation discusses the use of information models to standardize data for pain assessment and management. It explores the benefits of standardized coding, queries, and common data models. The speaker also shares insights on the development process of a pain information model and its integration with interprofessional pain concepts.

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Data, Data Everywhere and Not a Drop of Sense Standardized Data for Pain Management

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  1. Data, Data Everywhere and Not a Drop of SenseStandardized 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

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

  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 Continuum of Care 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

  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

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

  11. Standardized Urine Color

  12. Information Model Development Process

  13. 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

  14. 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

  15. Discussion

  16. 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

  17. Report Out

  18. 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

  19. 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

  20. 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 TranslSci Proc. 2015 Mar 25;2015:77-81.

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