Nursing Knowledge: Big Data Research Summit Clinical LOINC Report Susan Matney, MSN, PhD(c), RN-C, FAAN Informaticist 3M Health Information Systems Chair SNOMED CT Nursing Special Interest Group Chair Clinical LOINC Nursing Subcommittee Nursing Knowledge: Big Data Research for Transforming Healthcare

Conference Purpose National leaders in nursing, healthcare, and informatics came together to develop an action plan for shaping health policy and informatics initiatives that use a national nursing knowledge model. Goal was to guide consistent nursing documentation and data collection to support big data research Presentations given re: vision of the future enablers, gaps and challenges Participated in multiple, iterative, facilitated discussions

Ultimate Goal As knowledge workers, nurses must leverage clinical data from the EHR to: Optimize workflow and support clinical decision-making Tell the patient’s story Collaborate to foster knowledge translation Leverage analytics to extract actionable knowledge Use sharable, comparable data Build evidence out of nursing practice

Nursing Knowledge Knowledge Translation: The exchange, synthesis and application of knowledge within a complex system. Knowledge Transfer: A systematic approach to capture, collect and share tacit knowledge in order for it to become explicit knowledge. Knowledge Model: The capture of knowledge in an electronic reusable format for the purpose of preserving, improving, sharing, aggregating and reapplying it. Knowledge Translation: The exchange, synthesis and application of knowledge within a complex system. Knowledge Transfer: A systematic approach to capture, collect and share tacit knowledge in order for it to become explicit knowledge. Knowledge Model: The capture of knowledge in an electronic reusable format for the purpose of preserving, improving, sharing, aggregating and reapplying it.

Nursing Informatics DIKW Framework ANA, 2007

CAUSES REASON FOR Care Plan Relationships (Happy Path Health Concern [mood EVN] Goal [mood GOL] Outcome Observation [mood EVN] Intervention [mood: INT/ RQO/ etc.] [mood: EVN] SUPPORTS Observation [mood EVN] RELATES TO IS COMPONENT OF EVALUATES

Storyboard Example Joe is a 24 year-old male quadriplegic admitted to an inpatient unit from his home. During admission assessment, the nurse notes that he has no sensation from the shoulders down. He is confined to a wheelchair and requires two- person assist. His skin is occasionally moist. Joe reports that he is a “good eater” and is on a normal diet. The nurse completes the Braden Skin Scale score is 13. Further assessment by the nurse reveals skin is intact with no pressure ulcers. Hx. Subjective Findings Setting Assessment Observations

CAUSES REASON FOR Simple Skin Assessment (Happy Path) Health Concerns: Impaired mobility Risk for alteration in skin Integrity Goal: No skin breakdown Outcome Observation Interventions: Turn q 4 hours Assess skin q shift SUPPORTS Observations: Decreased Sensation Limited Mobility Braden scale = 13 RELATES TO IS COMPONENT OF EVALUATES

Interoperability using the DIKW Framework

Skin Assessment

Interoperability using the DIKW Framework

Terminology Coding 12 TypeTextTerminologyCodeFully Specified Name QuestionSkin MoistureLOINC Moisture:Type:PT:Skin:Nom:: ValueDiaphoreticSNOMED CT excessive sweating (finding) ValueMoistSNOMED CT moist skin (finding) ValueClammySNOMED CT clammy skin (finding) QuestionSkin TemperatureLOINC Temperature:Type:PT:Skin:Ord:Palp ValueConsistent With Body TemperatureSNOMED CT Skin normal temperature (finding) ValueWarmSNOMED CT warm skin (finding) ValueCoolSNOMED CT cool skin (finding) QuestionSkin TurgorLOINC Turgor:Imp:PT:Skin:Nom:: ValueGood Elasticity (normal)SNOMED CT skin turgor normal (finding) ValueDecreased Elasticity (Poor)SNOMED CT decreased skin turgor (finding) ValueTentingSNOMED CT stretched skin (finding)

Key drivers in enabling knowledge model  Knowledge exists in paper - Care plans  Knowledge exists in vended EHRs (non standardized)  Structured Nursing Knowledge beginning to emerge (e.g. Pressure Ulcer Models)  Standardized terminology  SNOMED CT, LOINC, RxNORM, CPT, ICD-10-CM  HIT standards  HL7, ONC, PHIN-VADS, VSAC

Action Plan Integrate nursing information into health and healthcare knowledge systems Optimize nursing language and healthcare information Influence policy Modify and standardize the informatics educational framework.

Specific Actions Develop a strategy/campaign for educating front line nurses, students, and faculty on informatics competencies and the value of standardized nursing data; Advocate for the adoption of SNOMED CT and LOINC as national standards for clinical data, and link them with nursing terminologies through mappings; Convene a consensus conference with leaders of the major nursing organizations and interprofessional stakeholders to educate them, hear their views, and ultimately, speak with one voice; Refresh and activate the ANA’s NIDSEC (Nursing Information & Data Set Evaluation Center) criteria to advance systems that represent and value nursing data; and Participate in standards and profile development to ensure a nursing voice.

Clinical LOINC Committee Actions Clinical LOINC Nursing Subcommittee Have a specific landing page on LOINC site. Provide access to all LOINC tutorials. Call and hold a meeting to gather nursing LOINC requirements Slides for informatics educators. Assessment Panels Organize in one place in RELMA Provide SNOMED CT enumerated values where available. Maintain LOINC mappings to nursing terminology CCC Outcomes Omaha Outcomes Structured Docs Review ANA’s NIDSEC (Nursing Information & Data Set Evaluation Center) where it pertains to clinical observations.

NURSING IS VISIBLE Vision of the Future In Health Information Systems NURSING DATA ARE AVAILABLE To Promote Evidence-Based, Quality Nursing Practice

Questions?