WS1: Big Data Science MNRS Mission MNRS exists as a network of discovery that advances science, transforms practices and enhances careers. MNRS Vision MNRS advances science to improve health. MNRS Strategic Plan Advance science Transform practices Enhance careers

WG1 Presenters Connie White Delaney, PhD, RN, FAAN, FACMI, Professor & Dean Tom Clancy, PhD, MBA, RN, FAAN, Clinical Professor Associate Dean for Faculty Practice, Partnership, Professional Development Bonnie Westra, PhD, RN, FAAN, FACMI, Associate Professor Director, Center for Nursing Informatics Karen Monsen, PhD, RN, FAAN, Associate Professor Specialty Coordinator Doctorate of Nursing Practice in Nursing Informatics Chih Lin Chi, PhD, MBA Assistant Professor

Schedule 8:30Welcome & Introductions 9:00Big Data/ Data Science – Overview 10:00 YouTube Interlude 10:30Break 11:00Big Data Exemplars – Optum Labs, Social Media 12:00Lunch Break 1:00Big Data Research - Extended Clinical Data 2:00 Big Data Research - Omaha System & Home Health 3:00Break 3:15Big Data Research - Optum Labs Data projects 4:15Wrap up 4:30 Adjourn

WG 1 Preconference Objectives Describe regional and national context, initiatives, resources, and challenges for big data & data science. Examine big data and its core relationship to data science and nursing science. Discuss nursing data science exemplars. Examine development strategies to build big data/data science capacity with MNRS schools and scientists. Discuss potential big data/data science partnerships to enhance next steps to promote nursing science within the NINR, etc

WS1: Big Data Science WHY BIG DATA? WHY NURSING? WHY YOU?

WG1 Presenters Connie White Delaney, PhD, RN, FAAN, FACMI, Professor & Dean Tom Clancy, PhD, MBA, RN, FAAN, Clinical Professor Associate Dean for Faculty Practice, Partnership, Professional Development Bonnie Westra, PhD, RN, FAAN, FACMI, Associate Professor Director, Center for Nursing Informatics Karen Monsen, PhD, RN, FAAN, Associate Professor Specialty Coordinator Doctorate of Nursing Practice in Nursing Informatics Chih Lin Chi, PhD, MBA Assistant Professor

Disclosure I have no relevant financial interest to disclose nor am I endorsing any commercial products identified in this presentation.

Data-Enabled Science Volume Velocity Variety Veracity Value  Genome  Symptome  Exposome  Behavior … and more Analytics Visualization Cios, K., & Nguyen, D. Data mining methods and visualization. In S. J. Henly (Ed.), Routledge international handbook of advanced quantitative methods for nursing research. Oxford, UK: Routledge. (Forthcoming) SLIDE CREDIT: S Henly “…is not about analyzing small data sets that can be easily dealt with by using conventional statistics or even manually….the goal is to make sense of big data.” CWD 2016

Contexts for Big Data Science - National & Global - Learning Health System (LHS) Quadruple aim Precision medicine and person-centric care Connected communities Research: CTSA & PCORI initiatives Global connectivity

Continuous Learning, Best Care, Lower Cost Sept 2012 iom.edu/bestcare Enable Learning Health System (LHS)

2007 … Full transparency Engaged, empowered patients Digital capture of the care experience Real-time access to knowledge

LHS IOM Report (2007) Follow up Reports Leadership Commitments to Improve Value in Health Care: Finding Common Ground Evidence-Based Medicine and the Changing Nature of Health Care Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches Clinical Data as the Basic Staple of Healthcare Learning: Creating and Protecting a Public Good Engineering a Learning Healthcare System: A Look at the Future Learning What Works: Infrastructure Required for Comparative Effectiveness Research Value in Health Care: Accounting for Cost, Quality, Safety, Outcomes, and Innovation The Healthcare Imperative: Lowering Costs and Improving Outcomes Patients Charting the Course: Citizen Engagement and the Learning Health System

Vision A system that is designed to generate and apply the best evidence for the collaborative health care choices of each patient and provider; to drive the process of new discovery as a natural outgrowth of patient care; and to ensure innovation, quality, safety, and value in health care. (Charter of the Institute of Medicine Roundtable on Value & Science-Driven Health Care)

Quadruple Aim Triple Aim better care better health lower costs Quadruple Aim that emphasizes the original three goals plus the goal of improving caregivers' experiences Quadruple Aim that emphasizes the original three goals plus the goal of improving caregivers' experiences

Quadruple Aim Triple Aim better care better health lower costs Quadruple Aim that emphasizes the original three goals plus the goal of improving caregivers' experiences Quadruple Aim that emphasizes the original three goals plus the goal of improving caregivers' experiences + Improved Provider Satisfaction

Vision for health CWD 2016

center-interprofessional-practice-and-education

Precision Medicine/Health president-obama-s-precision-medicine-initiative

Precision medicine an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.

Are we prepared to link/expand nursing’s data?

Epigenome What makes the epigenome change? Lifestyle Environmental factors (such as smoking, diet and infectious disease) Ability of the epigenome to adjust to the pressures of life appears to be required for normal human health.

Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care

CTSA is a national Clinical and Translational Science Award (CTSA) consortium created to accelerate laboratory discoveries into treatments for patients. NIH Re-engineering clinical research. CWD 2016

Apr 25, 2015

USA Patient-Centered Outcomes Research Institute (PCORI) Research Priorities - PCORI has approved 279 awards totaling more than $464.4 million to fund patient-centered comparative clinical effectiveness research projects to date (12/2013). PCORnet – 11 clinical data research networks (CDRNs) – 18 patient powered research networks (PPRNs) Example: UMN in PCORI (C Delaney, Site PI) – Greater Plains Collaborative Network – 10 Sites Kansas - University of Kansas Medical Center (PI); Missouri, Iowa, Wisconsin (3), Minnesota, Nebraska, Texas (2) – ~$7M/1.5 years; Building Research Network CWD 2016

Map of clinical data research networks (CDRN) and patient-powered research networks (PPRN) across the USA Rachael L Fleurence et al. J Am Med Inform Assoc 2014;21: Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

Synergy – Collaboration Digital Infrastructure

Top 15 Most Popular Social Networking Sites | March 2016 Top 15 Most Popular Social Networking Sites as derived from our eBizMBA Rank which is a continually updated average of each website's Alexa Global Traffic Rank, and U.S. Traffic Rank from both Compete and Quantcast."*#*" Denotes an estimate for sites with limited data. 1, ,000, ,000, ,000, ,000,000

6977 Airports Spanning Globe (OpenFlights Airports Database 01/2012)

13 Animal-to-Human Diseases Kill 2.2 Million People Each Year

Informatics in Perspective Basic Research Applied Research And Practice Biomedical Informatics Methods, Techniques, and Theories Bioinformatics Clinical Informatics Imaging Informatics Public Health Informatics

Biomedical Informatics in Perspective Basic Research Applied Research And Practice Biomedical Informatics Methods, Techniques, and Theories Imaging Informatics Clinical Informatics Bioinformatics Public Health Informatics Molecular and Cellular Processes Tissues and Organs Individuals (Patients) Populations And Society Continuum with “Fuzzy” Boundaries Clinical Translational Science

Big Data Sources/Resources (examples) Federal Agencies CMS Claims Data Academic Repositories Commercial/Industry Optum Labs Social Media Electronic Health Records Wearable Technologies National Institutes of Health Big Data to Knowledge (BD2K) ( 11 Centers of Excellence for Big Data Computing and two Centers that are collaborative projects with the NIH Common Fund LINCS program, the LINCS- BD2K Perturbation Data Coordination and Integration Center, and the Broad Institute LINCS Center for Transcriptomics. Data Index: A data discovery index (DDI) prototype ( NSF created the Directorate for Computer & Information Science & Engineering (CISE) ( sp accessed 12/23/2015) sp accessed 12/23/2015

Report of the AMIA EHR 2020 Task Force on the Status & Future Direction of EHRs JAMIA, May 28,

AMIA EHR 2020 Task Force Recommendations SIMPLIFY & SPEED DOCUMENTION 1.Decrease data entry burden (all team members) 2.Separate data entry from data reporting 3.Enable systematics learning & research at POC REFOCUS REGULATION 4. Regulation focus on a) Clarifying & simplifying certification & MU regulations, b) improving data exchange & interoperability, c) reducing need for re-entering data, & d) prioritizing pt outcomes. Vendors & providers streamline workflows! 5. Change in reimbursement regulations should support novel changes & innovation in EHR systems

INCREASE TRANSPARENCY & STREAMLINE CERTIFICATION 6. In order to improve usability & safety, to foster innovation, & empower providers & EHR purchasers, how vendor satisfied certification criterion should be flexible & transparent. 7. In order to improve usability & safety & to foster innovation, health care organizations, providers, & vendors should be fully transparent about unintended consequences & new safety risks introduced by health IT, as well as best practices for mitigating risks FOSTER INNOVATION 8. EHR vendors should use public standards-based application programming interfaces (APIs) & data standards that enable EHRs to be more open to innovators, researchers, & patients. AMIA EHR 2020 Task Force Recommendations

EHR of 2020 MUST SUPPORT PERSON-CENTERED DELIVERY 9. Promote integration of EHRs into full social context of care, including all areas of care 10. Improve the designs of interfaces so that they support & build upon how people think (e.g. cognitive-support design) Individual actions: input into RFPs, purchasing decisions, proposed regulations & legislation, research URGENCY – PROBLEMS ARE SOLUBLE (SOLVABLE) AMIA EHR 2020 Task Force Recommendations

Celebrating our nursing foundation for “Big Data/Data Science” Global standards eMeasures EHRs Magnet, etc Resources Workforce

Nursing Minimum Data set (NMDS) National Standard – SNOMEDCt, LOINC Werley, HH & Divine, E., & Zorn, C. (1988). Nursing Minimum Data Set Data Collection Manual. University of Wisconsin, Milwaukee, WI CWD 2016

Huber D, Schumacher L, Delaney C. Nursing management minimum data set (NMMDS). J Nurse Adm. 1997;27(4): CWD 2016

45 z.umn.edu/bigdata

Vision Better health outcomes from the standardization and integration of the information nurses gather in electronic health records EHR increasingly the source of insights and evidence Used to prevent, diagnose, treat and evaluate health conditions. Other IS - Rich contextual data about patients (including environmental, geographical, behavioral, imaging data, and more) Lead to breakthroughs for the health of individuals, families, communities and populations.

HHS will push nurses to use new codes By Joseph Conn | March 1, HIMSS Conference LAS VEGAS—HHS' Office of the National Coordinator for Health Information Technology is pushing nurses across the nation to use common language in electronic health records. The office's chief nursing officer, Rebecca Freeman, is promoting the use of the LOINC (Logical Observation Identifiers Names and Codes) and Snomed (Systematized Nomenclature of Medicine) codes for nursing documentation. The aim is to end a cacophony of more than a dozen different terminologies in use today, she said.Joseph ConnOffice of the National Coordinator for Health Information Technologyelectronic health recordsLogical Observation Identifiers Names and CodesSystematized Nomenclature of Medicine

You Are Invited to Get Involved Nursing Knowledge: 2016 Big Data Science Conference June 1-3, 2016 Minneapolis, Minnesota Registration open! Early bird discount through April 1,

4 th Paradigm of Science: eScience 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. Scientific breakthroughs will be: powered by advanced computing capabilities researchers manipulate/explore massive datasets Speed at which any given scientific discipline advances will depend on: researchers’ collaboration partnership with technologists in areas of eScience o databases o workflow management o visualization o cloud computing

Questions – Follow up Connie Delaney, PhD, RN, FAAN, FACMI