Measuring the Effects of Nurse
Staffing on Patient Outcomes:
The
e Military
a y Nursing
u s g Outcomes
Ou co es
Database (MilNOD
(MilNOD)) Project
Mary McCarthy,
McCarthy PhD,
PhD RN
Lori Loan, PhD, RNC
Patricia A. Patrician, PhD, RN, FAAN
Funding Acknowledgement & Disclaimer

This project was funded by the TriService Nursing Research
Program, Uniformed Services University of the Health
Sciences
S i
(G
(Grantt #N03#N03-P07).
P07)

However the information or content and conclusions do not
However,
necessarily represent the official position or policy of, nor
should any official endorsement be inferred by, the TriService
Nursing Research Program
Program, Uniformed Services University of
the Health Sciences, the Department of Defense, or the U.S.
Government.
MilNOD Team




Principal Investigators
 Patricia A. Patrician,, RN,, PhD,, FAAN
 Lori A. Loan, PhD, RNC
 Laura R. Brosch, RN, PhD
 Mary McCarthy, RN, PhD
Associate Investigators: Army, Navy, Air Force
Consultants
– Nancy Donaldson, RN, PhD, FAAN, UCSF, CalNOC
– Diane
Di
Brown,
B
RN
RN, PhD
PhD, K
Kaiser
i
Permanente,
P
CalNOC
C lNOC
– Bonnie Jennings, RN, DNSc,
DNSc, FAAN
N me o s RAs
Numerous
RAs, Database Manage
Manager, Database Programmer,
P og amme
Report Writer, Statisticians
R
Research
hQ
Questions
i
1. Does staffing affect adverse events at the
shift level?
2. Over time, has there been a change in
adverse events in MilNOD facilities?
Background
Institute of Medicine (IOM)
Report (1996):
•Little empirical evidence
that nurse staffing
affects p
patient outcomes
IOM Quality Series (20002004):
•Illuminated the critical
role of nurses in patient
safety
Background
American Nurses
Association (1995).
Report Card for
Acute Care Settings
Collaborative Alliance for
Nursing Outcomes
(CALNOC)
Began to identify
id
if patient
i
outcomes that may be linked to
nursing care, “nursing-sensitive
indicators” of care quality.
• 250 hospitals
currently
yp
participate
p
• Standardized data
definitions and
ll ti
methods
th d
collection
6
Background

A growing national trend towards
standardized measurement of nurse staffing
and patient outcome indicators

Limited standardized nurse staffing and
nurse--sensitive patient outcome reporting
nurse
mechanisms
h i
iin use iin D
Department
t
t off
Defense ((DoD
DoD)) hospitals

Difficult to compare staffing adequacy or to
compare outcomes among DoD hospitals
MilNOD Purposes

Collect data to support evidence based
clinical and administrative decisions.
decisions

Create a valid and reliable database
consisting
i ti off nurse staffing
t ffi and
d patient
ti t safety
f t
indicators.

Provide a basis for internal and external
comparisons.
p

Analyze relationships between staffing and
outcomes.
outcomes
MilNOD Indicators

Nursing Structural Indicators
– RN,, LPN,, NA
– Active Duty, GS Civilian,
Reservist, Contract




N i care hours
Nursing
h
Nursing skill mix
Nursing staff education &
experience
Explanatory Variables
– Patient
P ti t acuity
it
– Patient turnover

Contextual Features
– Work environment attributes

Nurse Outcomes
– Job satisfaction
– Needlestick injuries

Patient Outcomes
–
–
–
–
Pressure ulcer prevalence
Restraint use prevalence
F ll and
Falls
d falls
f ll with
ith injury
i j
Nursing medication
administration errors
– Satisfaction with




Care in general
Nursing care
Pain management
Education
National Quality Forum
National Voluntary Consensus Standards for NursingNursing-Sensitive Care
CATEGORY
Patientcentered
outcome
measures
MEASURE
1. Death among surg. inpts with treatable serious complications-failure to rescue
2. Pressure ulcer prevalence
3. Falls prevalence**
4 Falls
4.
F ll with
ith iinjury
j
5. Restraint prevalence (vest and limb only)
6. Urinary catheter-associated UTI ICU patients**
pts)**
)
7. Central line catheter-assoc. blood stream infection rate ((ICU & NICU p
8. Ventilator-associated pneumonia for ICU and NICU patients**
Nursingcentered
intervention
measure
9. Smoking cessation counseling for acute myocardial infarction**
10. Smoking cessation counseling for heart failure**
11. Smoking cessation counseling for pneumonia**
Systemcentered
measures
12. Skill mix (RN, LVN/LPN, unlicensed assistive personnel [UAP], and contract)
13. Nursing care hours per patient day (RN, LPN, and UAP)
14. Practice Environment Scale—Nursing Work Index
15. Voluntary turnover
** Al
Also an NQF
NQF-endorsed
d
d voluntary
l
consensus standard
d d ffor h
hospital
i l care.
Available through the Military Nursing Outcomes Database (MilNOD)
10
Data Acquisition & Dissemination
Nurses enter
daily staffing &
census data
into the unitunit
level MilNOD
database.
Every month trained
on-site staff collect
fall, medication error
& needlestick injury
data from incident
reports.
MilNOD team checks and
improves data quality.
Once each year
patient & staff
surveys are
conducted by the
MilNOD team.
On-site trained staff
collected pressure
ulcer & restraint use
prevalence data on a
semiannual basis.
MilNOD team prepares reports
& investigates best practices.
Reports are disseminated to hospitals.
MilNOD team
t
works
k with
ith h
hospitals
it l
to understand & use reports.
Hospital
H
it l lleaders
d
use d
data
t tto id
identify
tif
problems & evaluate solutions.
Best practices, evidence
evidence-based
based practice policies,
and effective solutions are shared across hospitals.
Data Analysis

Shift level, nested data:
– 115, 000 shifts in 57 units from 13 hospitals
(7 large and 6 small)

Dichotomous outcome
– Shift with any adverse event = 1
– Adverse events = falls, medication errors,
needlestick
dl i k injuries
i j i

Separate analysis for critical care, medmedsurg,, stepsurg
step-down units
Results: Shift Level Covariates
by Unit Type
Mean values
N (#shifts)
Med-Surg
Step-Down
Critical care
57,913
18,039
35,570
51
58
77
% LPN
22
24
14
%U
Unlicensed
li
d
28
19
9
44
36
41
% Civilian
34
39
47
% Contract
19
22
8
% Reservist
3
3
5
Total nursing care hours per
patient per shift (NCHPPS)
4.3
5.4
9.4
Patient-to-RN ratio
4.8
3.3
1.5
Skill Mix: % RN
Provider Category:
% Military
Hierarchical Logistic Regression Modeling
R
Results:
lt All Adverse
Ad
Events
E
t
Predictors
Shift
Evening shift
Night shift
RN Skill mix (10%
Medical-Surgical
OR (95% CS)
Step-Down
OR (95% CS)
Critical Care
OR (95% CS)
1.01 (0.89-1.15)
(0.89 1.15)
0.75 (0.65-0.85)
1.07 (1.00-1.16)
0.83 (0.65-1.04)
(0.65 1.04)
0.76 (0.57-0.97)
1.07 (0.98-1.19)
0.93 (0.71-1.20)
(0.71 1.20)
0.51 (0.37-0.68)
1.10 (0.95-1.30)
1.04
1
04 (0
(0.97-1.13)
97 1 13)
1.42 (1.10-1.79)
1.04 (0.97-1.13)
1.08 (1.03-1.14)
1.06
1
06 (0
(0.96-1.19)
96 1 19)
1.29 (0.99-1.61)
1.04 (0.93-1.18)
1.01 (0.97-1.07)
1.06
1
06 (0
(0.95-1.18)
95 1 18)
1.45 (1.00-2.03)
1.05 (0.93-1.18)
1.05 (1.01-1.09)
1.13 ((1.04-1.23))
1.03 ((0.84-1.25))
1.01 ((0.80-1.25))
1.13 (1.08-1.18)
1.27 (1.13-1.44)
1.09 (1.01-1.18)
d
decrease)
)
Provider category
(10% decrease)
% Military
% Civilian
% Contract
Total NCHPPS (1 hour
decrease)
Average
g Acuity
y ((1 SD
increase)
Census ( by 3)
R
Results
lt Summary
S
•
•
•
Adverse events were lower on night shifts
Higher occurrences of adverse events on a shift
were associated with lower RN skill mix
mix, lower
proportion of civilians, decreased total nursing
care hours
hours, higher acuity
acuity, and higher census
Civilian proportion may be a proxy for
experience: mean difference between military
and civilian experience levels was -9.39
(t = -17.88, p <.001)
Di
Discussion/Implications
i /I
li ti
•Poorly
staffed shifts are likely to have bad
outcomes
t
Every shift must be staffed appropriately with
attention to not only numbers of staff (nursing
presence) but also skill mix (expertise) and
presence),
experience level
•
Acuity, although an imperfect measure of
workload, is an important consideration for
staffing, especially on medicalmedical-surgical units
•
Question
Q
i
#2

Over time, has there been a change in
adverse events in MilNOD facilities?
17
Patient Outcomes Significantly Improved
Patient Fall Rates  by 69% (p = 0.028)
.33
Bed
Days
33 per 100 B
dD
.10 per 100 Bed Days
(n=47 Units)
(n=45 Units)
(n=36 Units)
(n=34 Units)
(n=30 Units)
(n=25 Units)
(n=25 Units)
(n=24 Units)
(n=23 Units)
(n-23 Units)
18
Patient Outcomes Significantly Improved
Nurse Administered Medication Error Rates  by 50% (p = 0.019)
.57 per 100 Bed Days
.26 per 100 Bed Days
(n=47 Units)
(n=45 Units)
(n=36 Units)
(n=34 Units)
(n=30 Units)
(n=25 Units)
(n=25 Units)
(n=24 Units)
(n=23 Units)
(n-23 Units)
19
Li it ti
Limitations

Use of adverse event reports
– UnderUnder-reporting of medication errors?
– Falls with injury are typically reported because of
additional
dditi
l care requirements
i
t



Did not control for falls risk in this analysis
Did not control for medication doses
dispensed
Cannot assume cause and effect
C
Conclusions
l i


The MilNOD is an efficient, replicable, and
sustainable
t i bl measurementt strategy
t t
for
f inpatient
i
ti t
care units.
Evidence--based management requires data for
Evidence
decision making:
– Local data to determine baseline
– National data for comparisons
– Evidence from literature to make changes and
establish benchmarks
– Local continual monitoring of progress towards goals
Questions?
Q
ti
?
22