Nursing Informatics Foundation for Quality Improvement and Big Data Research ACENDIO, Dublin, Ireland March 23, 2013

Transcription

1 Nursing Informatics Foundation for Quality Improvement and Big Data Research ACENDIO, Dublin, Ireland March 23, 2013 Bonnie L. Westra, PhD, RN, FAAN, FACMI, Associate Professor, Coordinator, DNP Nursing Informatics Specialty Director, Center for Nursing Informatics University of Minnesota School of Nursing

2 Objectives Relate big data research to nursing informatics Describe key principles to create reusable data Provide research exemplars when terminologies are implemented and used effectively Identify resources for global collaboration to create nursing data for big data research

3 Big Data New Science Needed Every day, we create 2.5 quintillion bytes of data so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. Quintillion (US : a number equal to 1 followed by 18 zeros) 1,000,000,000,000,000,000

4 Ocean Science Ocean cover 70% of the earth Complex biome Influences global health How do we understand it Multiple types of data input Sensors Video Internet Connecting countries computers Longitudinal data

5 Other Science Earth-like planet found, Kepler, light years away 300 million light years away from Earth, each black hole has a mass equivalent to 10 billion suns.

6 Discoveries

7 Enabling Technologies

8 Harnessing the EHR for Research in areas of escience such as [data capture], Databases, Workflow management, Visualization Computing technologies.

9 4 th Paradigm of Science Hey H, Tansley S Tolle K. (Eds.) (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Corporation, Seattle, WA. Horn SD, Gassaway J. Practice-based evidence study design for comparative effectiveness research. Med Care 2007;45: S50-7.

10 Science Paradigms The Time Keeper Mitchel Albom Electronic Health Records cript.pdf

11 Essential Elements of 21 st Centry Health Care Learning System Build Knowledgeable Teams Reinvent Workflow Integrate Innovations Remove Outdated Practices Reduce Variation (Tailor/ Personalize) Improve Safety/Quality while Reducing Costs Manage the Base of Knowledge 2011 National Academies: Building a Better Delivery System National Academy Press, 2005

12 Nursing Informatics Sciences Nursing Information Computer Cognitive Ethics ANA, 2007

13 Requirements for Reusable Data What data represents nursing? How is it linked with other data elements? How is evidence based practice represented with standardized data? How is the data stored so it can be extracted in a relational way? What is the consistency and therefore the quality of the data? (Canadian Inst for Hlth Inf, 2009) What kind of research can you do with nursing data?

14 Vision for Nursing Data in a Clinical Data Warehouse Clinical Data NMDS inmds Other Data Sets Management Data NMMDS Continuum of Care 14

15 Nursing Minimum Data set (NMDS) Werley, HH & Divine, E., & Zorn, C. (1988). Nursing Minimum Data Set Data Collection Manual. University of Wisconsin, Milwaukee, WI

16 Nursing Management Minimum Data Set (NMMDS) Huber D, Schumacher L, Delaney C. Nursing management minimum data set (NMMDS). J Nurs Adm. 1997;27(4):42-48.

17 NIDSEC SM Nursing Information & Data Set Evaluation Center To evaluate information systems that support the documentation of nursing practice 2012

18 NIDSEC SM Standards Nomenclature Clinical Content Data Repository General Systems Characteristics

19 NIDSEC SM Standards Nomenclature Terminology is appropriate for the domain of nursing ANA recognized nomenclatures/ teminologies Local terms are mapped; can add new terms Structured terminology is available to document the nursing process Nursing assessment diagnoses interventions outcomes Used for documentation Unique identifier for nurse provider 2012

20 NIDSEC SM Standards Clinical Content Associations exist on the screen and in the data base assessment, diagnoses, expected outcomes, interventions, actual outcomes Complete, appropriate, & accurate choices within pathways Ability to record all actions for care plan Planned care and delivered care stored Need to be able to link these in the data base Many systems standardize care planning but not actual care 2012

21 NIDSEC SM Standards Data Repository Patient specific data stored permanently in standard database format, coded NMDS stored permanently Associations reflecting clinical decision making retained in repository Data retrieval by standard & ad hoc reports Electronic data interchange Confidentiality & security of data Stored data linked to nurse provider 2012

22 Lessons Learned from Using the NMDS Nursing Care Elements for Data Warehouses Creating a Dataset to Correlate Care with Outcomes Beverly J. Collins, PhD C, RN Bonnie L. Westra, PhD, RN, FAAN, FACMI Deepika Sharma, BHMS, MS; Cynthia Coviak, PhD, RN; Cathy Olson, PhD C; Linda Fletcher, MS; Karen Monsen, PhD, RN, FAAN Heather Swanberg, BA, RN

23 Defining a Population Level II Babies (Skilled Care Nursery [SCN]) 10 hospitals care for level II babies We believed they used the level II care plan consistently They were accustomed to using NIC/NOC/NANDA and were willing to convert to ICNP They shared a discipline specific flowsheet for documentation Their leadership was eager to participate

24 Linking Standardized Language to EHR Content Care Plan ICNP Flow Sheet Medication Education

25 Data Storage

26 Lessons Learned Planned Care Actual Care Provided

27 Measuring Outcomes Problem All problems are resolved at discharge Goal outcome field NMDS is resolution of the problem met, not met etc. Outcome metrics Only used in SCN of largest hospital Defined as change from first documentation to last

28 Integrating Flowsheet Data into CDR University of Minnesota Clinical Translational Science Award (CTSA) Adding extended clinical data (nursing/ flowsheets) to CDR No standardized data may use standardized assessment tools Unsure of consistency of documentation

29 EHR CDR

30 Steps in the Process Transfer data to UMN s CDR 6 M records inpatient and outpatient ~1 M patients Data quality chart audit, data audit Normalize to SNOMED CT/ LOINC Pilot research projects

31 EXAMPLES OF RESEARCH START SIMPLE

32 Comparison of Data Sets Westra BL, Solomon D, Ashley DM. (2006). Use of the Omaha System data to validate Medicare required outcomes in home care. Journal of Healthcare Information Management, 20(3), 88-94

33 Integration Across Agencies/ Vendors Westra, B.L., Oancea, C., Savik, K., & Marek, K.D. (2010) The Feasibility of Integrating the Omaha System Data across Homecare Agencies and Vendors, CIN: Computers, Informatics, and Nursing, 28(3):

34 12,600 K Monsen 2009

35 EXAMPLES OF RESEARCH METHODS

36 Comparison of Methods for Grouping Interventions Given the large number of interventions, what is the best method of grouping these? Use of Omaha System Intervention Categories AHRQ Clinical Classification System (Expert Consensus) Data mining for co occuring clusters of interventions

37 Clinical Expert Consensus Providing Respiration & Circulation Therapy Injury Prevention Treatment Medication Treatment Pain Treatment Wound Care Treatment Bowel and Bladder Treatment Other Treatment Teaching Respiration & Circulation Disease Process Medications Disease Treatment Emotional & Cognitive Issues Other Coordinating Community Resources Supplies & Equipment Other Monitoring Respiration and Circulation Medications Pain Skin Emotional & Cognitive Status Other

38 Knowledge Discovery Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, pp Fayyad.pdf. P

39 Grouping Interventions Data Mining Clustering Complexity of Data KDD Clustering Interventions Monsen, K., Westra, B.L., Yu, F., Ramadoss, V.K. & Kerr, M.J. (2009). Data Management for Intervention Effectiveness Research: Comparing Deductive and Inductive Approaches, Research in Nursing and Health, 32(6)

40 Practitioner Signature Styles? Monsen, K.A. (2012, Publication in process)

41 EXAMPLES OF RESEARCH METHODS APPLIED TO CLINICAL PROBLEMS

42 Rules for Medication Management Build CDS 2,065 episodes of care, 15 home care agencies, 2 software vendors Westra, B.L., Dey, S., Fang, G., Steinbach, M., Kumar, V., Savik, K., Oancea, C., & Dierich, M. (2011). Interpretable Predictive Models for Knowledge Discovery from Homecare Electronic Health Records. Journal of Healthcare Engineering, 2 (1), 55-74

43 Data Mining Decision Trees Improvement in Medication Management Westra, B.L., et al. (2011). Data Mining Techniques for Knowledge Discovery from Electronic Health Records. Journal of Healthcare Engineering, 2 (1), 55-74

44 Data Mining - Discriminate Pattern Analysis

45 RESOURCES FOR COLLABORATION

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49 Summary Relate big data research to nursing informatics Describe key principles to create reusable data Provide research exemplars when terminologies are implemented and used effectively Identify resources for global collaboration to create nursing data for big data research

50 Thank you, for further information: Bonnie Westra, PhD, RN, FAAN, FACMI Associate Professor & Director, Center for Nursing Informatics University of Minnesota, School of Nursing W westr006@umn.edu