Nurse Staffing Patient Outcomes Study Workgroup
Advice on Staffing Data - Overview
October 2013 –December 2013 [DRAFT]
Background
In October 2013, the Minnesota Department of Health (MDH) convened a workgroup to consult with MDH
on a legislatively required study of the correlation between nurse staffing and patient outcomes.
MDH sought advice in these main areas:
 Study methodology (including whether the study is conducted across patient groups or across
institutions, whether and how to control for external factors such as acuity, etc.);
 Metrics of patient outcomes to be considered in the study;
 Data necessary and reasonably available for analysis; and

Level of data granularity (such as shift, unit, or daily averages) and licensure levels.
The workgroup met four times from October 2013 to December 2013, providing MDH with expertise and
perspective that will help MDH shape the study. This document provides a high-level summary of the
workgroup’s discussions and advice on nurse staffing for the study. The summary provided below does not
represent consensus among all workgroup members—consensus was not the goal of the group’s work. In
fact, MDH has benefited from the diversity of perspective and wide range of advice provided by the group.
MDH thanks the workgroup members for their work so far and for their shared commitment to quality and
patient outcomes.
Nurse Staffing: Data sources and considerations
The legislation requiring the MDH study presents this research question: “What is the relationship between
nurse staffing and patient outcomes?” Though this formulation suggests that connection between data on
staffing and data on patient outcomes could be relatively straightforward, MDH and the workgroup agreed
that there are many potential questions about the basic data elements and many potentially confounding
variables.
Sources of nurse staffing data
The workgroup offered several ideas of sources for nurse staffing data:
 Payroll systems
 EMR systems (which may offer proxy measures of staffing)
 Minnesota Hospital Association staffing plan data
 Bargaining units
Considerations regarding nurse staffing data
The workgroup discussed these considerations:
 Averages or overall staffing ratios can mask wide variation in staffing practices
 The distinction between RNs and other staff is important
 The whole care team is relevant to patient care
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Shift variation is relevant (both in terms of length of shifts and workload patterns on shifts)
The type of work a nurse does on the floor can be different on any given day
Other important factors that contribute to patient outcomes
The workgroup identified many factors—in addition to staffing patterns—that impact patient outcomes:
Variations among patients
 Demographics
 Differences in patients and families as people
 Patient distribution, flow, turnover, and readmissions vary widely
 Patient acuity (and these differences may not be adequately captured by electronic systems)
Variations among hospitals (and units within hospitals)
 Organizational culture
 Geography (urban, rural, suburban)
 Physical space and layout
 Available technologies (both patient care and administrative)
 Organizational structure and hierarchy
 Administrative practices, including use of float nurses
 Available supports and specialties (such as wound care consults, therapy teams, transport teams)
 Use of rapid response teams instead of nurses stationed on the floor
 Work environment (including risk awareness, patient safety awareness, nurse autonomy, and
teamwork)
 Approach to staffing and scheduling varies—the method may be in an experienced scheduler’s
head or run through an acuity system
 There is variation among individual nurses (including the amount of experience, education, and
expertise)
Key challenges
In their first months of work, the workgroup reflected on several important issues for the study:
 It will be challenging to connect staffing data to outcome data in a meaningful way. For example,
staffing data would be by unit, but reported outcomes data would be by hospitals.
 For both staffing and outcome data, there are inconsistencies in level of detail and location of data
in hospitals.
 The Minnesota Hospital Association data will not have a level of granularity that would allow for
detailed comparisons of shifts.
 Alignment of timing of outcomes data (nurse sensitive indicators) and staffing data will be a
challenge.
 Common benchmark data points may not account for all relevant activities. For example, a nurse’s
work on after-care plans may not be captured by data.
 There is a gap between the ideal and the feasible.
Document prepared by: Beth Bibus & Kris Van Amber, Management Analysis & Development (MAD),
Minnesota Management & Budget
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