Presentation at the June, 2012 Taking the Longview Nurse Workforce Conference Indianapolis, IN Patricia Moulton, PhD; Pamela Wiebusch, BA; Duane Napier, MSN, RN; Jeannie Cimiotti, DNSc, RN; Cynthia Bienemy, PhD, RN and Sandra Anne LeVasseur, PhD, RN

IOM Future of Nursing Cites the need for improved data collection and an enhanced information infrastructure as requirements for effective workforce planning and policy making. Data “on the numbers and types of health professionals currently employed, where they are employed and in what roles” is imperative to the establishment of accurate workforce projection models

Our Story 1995: Colleagues in Caring Minimum Supply Dataset June, 2008: National Forum of State Nursing Workforce Centers Meeting in Denver, CO Identified the problem as a priority Established a subcommittee to assess current data collection practices Set a goal of producing minimum datasets, the set of core data elements needed for our work November, 2008: The project design was finalized through a contract between the Center to Champion Nursing in America and the Florida Center for Nursing (acting on behalf of the Forum) Three additional states invited to participate via CCNA State Education Capacity Teams A total of 31 states participated in one or more phases of the project

State data assessment (July-December 08) MDS Survey of States (Jan 09) Drafting Workgroups (Feb-March) Data Summit (late March 09) Public Comment Period (May-June 09) Ratification (September 09) Implementation (ongoing) Final data sets are available at:

Where are we Now? Survey of Forum members regarding implementation of all three data sets Summer/Fall 2011 Follow-up validation with each center 30 states completed the survey

2011 Education Data Set Status Variable # of States Collecting Variable 1: Accreditation19 Variable 2: Seats for new students21 Variable 3: Qualified applicants19 Variable 4: Admitted applicants23 Variable 5: New enrollees21 Variable 6: Graduates26 Variable 7: NCLEX Pass Rates20 Variable 8: Total enrollment22 Variable 9: Student gender20 Variable 10: Student race/ethnicity16 Variable 11: Student age15 Variable 12: Full- and part-time faculty counts25 Variable 13: Full- and part-time faculty vacancies19 Variable 14: Highest degree of faculty20 Variable 15: Faculty gender16 Variable 16: Faculty race/ethnicity13 Variable 17: Faculty age18

How does that compare to 2008?

Barriers to education data collection include: firewalls between nursing education programs and the entity collecting data restricting the use of online surveys large schools use an administrative assistant to collect/report data which is not always accurate issues with differences in common terminology data collection doesn’t include proprietary institutions nursing programs answer multiple surveys in the field and not wanting to respond to another one slow response rates from Deans/Chairs cost of data collection online programs don’t report in and out of state students.

2011 Supply Data Set Status Variables # of States Collecting (n) Variable 1: Gender22 Variable 2: Race/Ethnicity22 Variable 3: Year of birth24 Variable 4: Entry level education20 Variable 5: Highest level of education21 Variable 6: License type26 Variable 7: Year of Initial licensure17 Variable 8: Country of initial licensure14 Variable 9: License Status18 Variable 10: APN License/Certification23 Variable 11: Employment status18 Variable 12: Reason for being unemployed8 Variable 13: Number of positions employed in14 Variable 14: Hours worked per week18 Variable 15: Employer’s address17 Variable 16: Employment setting24 Variable 17: Employment position19 Variable 18: Employment specialty17

How does that compare to 2008?

Barriers to implementing the supply data set include: low response rates as the survey is not mandatory server outage issues for online survey that caused a large loss of data issues with surveying and counting nurses in compact states cost of analyzing survey responses.

2011 Demand Data Set Status Variables # of States Collecting Variable 1: Full-time equivalent positions currently occupied 10 Variable 2: FTE Vacancies currently being recruited on/put on hold/frozen 11 Variable 3: Average full-time workers employed 9 Variable 4: Average part-time workers employed 7 Variable 5: Per diem workers5 Variable 6: Contract, Agency, and Traveling FTEs 7 Variable 7: FTE Separations7 Variable 8: FTEs organization intends to employ 5

How does that compare to 2008?

Demand data collection barriers include: low response rates it is time consuming and costly use of FTE versus head counts use of Occupational Employment Statistics (BLS) data which doesn’t allow breakdown of data into FTE full time or part time.

Overall Summary of Findings and Policy Implications The original intention of the Forum’s national minimum data set was to provide an avenue for the collection of a national minimum data set and to enable states to compare their data with other states and with national numbers. Forum and other interested organizations working on putting together National Minimum datasets have many steps to reach this goal. These include: · Determining where the data will be housed and what entity/entities will assemble and maintain the data. · Getting buy-in at the state level for both collecting the minimum dataset and sharing it with the warehousing entity. · Mentoring states with fewer human and fiscal resources in beginning or changing data collection routines. · Determining how the data will be used and under what conditions it will be shared with researchers across the country. · Determining a stable funding source for collection, housing and analysis of data sets.