Peggy Jenkins Amy Garcia Lynn Choromanski

Diana Farm-Franks John M. Welton

Academic/Practice/Industry Collaboration to Develop Nursing Value Research Data Warehouse

Governance EXECUTIVE SUMMARY

Big data for nursing research is complex and requires collabora- tion from multiple organizations. The Nursing Value Workgroup (NVW), formed from the Big Data Science initiative in 2014, plans to measure nursing value and develop new real-time met- rics to monitor costs, quality, and effectiveness of nursing care through understanding the indi- vidual contribution of a nurse to a patient. The vision of the NVW is to cre- ate a warehouse that hosts data from multiple disparate data sources within multiple organiza- tions for use in research to dis- cover, explore, and test ques- tions related to nursing value. Business and managerial acu- men is necessary to take on governance and board develop- ment work. The collective open minds of several diverse partners includ- ing academia, practice, and industry allowed for robust dis- cussion, freethinking, and led to development of an innovative governance structure for a nurs- ing value research data ware- house.

B IG DATA USED FOR NURSING research can provide greater precision and granularity for measuring care quality,

patient outcomes, and cost of nurs- ing care. However, big data for nurs- ing research is complex and requires collaboration from multiple organi- zations. A national action plan to make nursing data sharable, compa- rable, timely, and more relevant to improving health (Delaney, Pruinelli, Alexander, & Westra, 2016) was developed at the 2013 Nursing Knowledge: Big Data Science Initiative hosted by the University of Minnesota. The Nursing Value Workgroup (NVW)

formed from the Big Data Science initiative in 2014 plans to measure nursing value and develop new real- time metrics to monitor costs, quali- ty, and effectiveness of nursing care through understanding the individ- ual contribution of a nurse to a patient (Welton & Harper, 2015). The vision of the NVW is to create a warehouse that hosts data from mul- tiple disparate data sources within multiple organizations for use in research to discover, explore, and test questions related to nursing value.

The NVW defines value of nursing as quality patient out- comes divided by cost of nursing

PEGGY JENKINS, PhD, RN, is Assistant Professor/Specialty Director iLEAD, University of Colorado College of Nursing, Aurora, CO.

AMY GARCIA, DNP, MSN, RN, CENP, is Healthcare Executive, Cerner Corporation, Kansas City, MO.

DIANA FARM-FRANKS, DNP, RN-BC, L-HTT-HP, CPHIMS, CPPS, CCRN-K, CPHQ, is Senior Clinical Analyst, Fairview Health Services, Minneapolis, MN.

LYNN CHOROMANSKI, PhD, RN-BC, is Informatics Specialist, HCMC, Minneapolis, MN.

JOHN M. WELTON, PhD, RN, FAAN, is Professor and Senior Scientist, Health Systems Research, University of Colorado College of Nursing, Anschutz Health Sciences Campus, Aurora, CO; and a Nursing Economic$ Editorial Board Member.

FUNDING: This article was supported by grants from the Agency for Healthcare Research and Quality (AHRQ) 1R03 HS025495-01 and the University of Colorado School of Medicine Data to Value (D2V) project.

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Table 1. Definitions for Data Governance

Term Definition Data Collation Assembling data obtained from disparate data sources such as electronic medical records,

administrative, billing, plan enrollment, pharmacy/laboratory, patient behaviors/preferences, human resources, scheduling/payroll systems, education platforms, competency records

Data Dictionary A database about the database defining structure/individual data points. Mandatory for the control/maintenance of large databases.

Data Stewardship Focuses on providing the appropriate access to users, helping users understand the data and taking ownership of data quality (Saba & McCormick, 2015). Includes negotiating data use agreements, determining institutional review boards, coordinating executive leadership oversight, auditing, compliance, legal departments.

Data Privacy Keeping confidential patient and organizational data. Includes reviewing/approving policies/standards that reflect ethical, regulatory, and statutory compliance with data privacy requirements.

Data Security Protecting data from unauthorized access and data corruption. Includes overseeing establishment of policies/procedures for secure transmission, storage of data via networks, appropriate data encryption, firewalls, password protection (Ballard et al., 2014).

Data Use Purposes for which data are requested, accessed, and analyzed Data Queries Process and tools used to pull requested data from a large database Data Retention Period of time data are maintained; Dictated by legal, organizational continuity, research

standards Data Audits Evaluate information system/date integrity, identify unauthorized system access, ensure

appropriate collection/representation of data. User Training Safeguard quality researchers will obtain training on data warehouse.

care (Pappas, 2013). Nurse staffing software available today provides data that can potentially measure nursing cost more precisely by recording variables such as indi- vidual nurse work time, salary, benefits, and patient acuity. Electronic health record (EHR) data captures the interaction between nurse and patient, which can be translated into time spent per patient. These data can be linked directly to each patient through the nurse-patient assign- ment. The linkage of nurses and patients is accomplished through unique identifiers; therefore, other data can also be linked such as patient demographics, diagnosis data (e.g., diagnosis-related group), and other hospital-level or clini- cally relevant information. In addi- tion, data unique to each nurse can be collected such as nurse demo- graphics and experience level. The

combination of unique patient and nurse data allows analysis that until now has been nearly impos- sible (Welton, 2016). One author completed a pilot study using data to explore patient and nurse char- acteristics contributing to nursing cost per patient using unique patient and nurse data (Jenkins & Welton, 2014). Extracting, organiz- ing, warehousing, and analyzing such data can be a complex under- taking involving many different players and organizations. To ensure validity, reliability, and standardization of these data for research, a data warehouse gover- nance structure is imperative.

Methods A collaborative inter-organiza-

tional data warehouse requires explicit governance so all parties understand and agree to roles, requirements, processes, and legal

implications for use of data for research. A task force of the NVW focused on creating a governance structure for the data warehouse. The consortium model, governing operational board structure, gover- nance charter, and lessons learned of a nursing value research data warehouse (NVRDW) are present- ed.

Task Force Members The original NVRDW gover-

nance task force was a subcommit- tee of the NVW comprised of nine members including representatives from hospitals, academia, and industry. Additional members from primary care and industry lent expertise for thorough planning. Representatives were from diverse settings, both national and interna- tional. The task force included informatics experts such as clinical analyst, chief nurse executive strate-

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Figure 1. Nursing Value Research Data Warehouse Governance Structure

Consortium Executive Board

(MOU) Strategic oversight academic

Board of Directors (Bylaws)

Volunteers to develop strategy for development, maintenance,

use of data

Operational Committee

Volunteers to solve specific issues

Operational Committee

Volunteers to solve specific issues

Host Organization Physical location of warehouse,

responsible for data maintenance, security and privacy; funded by

grants and data use fees