Marianne Weiss
Olga Yakusheva
Kathleen
Bobay
K thl
B b
Marquette University

Quality
◦ Focal concerns in healthcare reform
 Improve hospital discharge
 Reduce ED use and readmission

Cost
◦ Nursing labor costs

Little is known about the return on investment in
nursing labor
◦ Higher RN staffing has been linked to improved quality
of patient outcomes:
 ⇩length of stay (Needleman et al
al., 2002);
 ⇩risk of readmission (Heggestad, 2002);
 ⇧patient satisfaction and ⇩medication errors (Williamson &
Atwood (2006)
◦ No studies ever examined cost-benefit of investing in
nursing labor

Limitations of earlier studies:
◦ Most used system-level data
◦ Most estimated reduced form models:
 Staffing → Patient Outcome
◦ All performed cross-sectional analysis



Examine both quality and cost-benefit
implications of investing in nurse labor
Use unit-level
unit level data within one healthcare
system
Estimate a structural model:
 Staffing → Process of Patient Care → Patient Outcome


Exploit within
within-unit
unit over-time
over time variance in
nurse staffing
Use administrative cost data


Aim 1: To investigate direct and indirect
relationships from nurse staffing to discharge
teaching to feeling ready to go home
teaching,
home, and to
ED visits & Readmission
Aim 2: To conduct cost-benefit analysis of
investment in nurse staffing to improve
patient outcomes
Process
Structure
Nurse
St ffi
Staffing
Discharge
Preparation
Outcomes
Readiness
f
for
Discharge
ED Visits
and
Readmission

Prospective,
longitudinal, observational design
Prospective longitudinal

Hierarchical model
◦
◦
◦
◦

Hospital
Unit
Patient
(Nurse)
Within-unit
Within
unit random selection of patient
sample

Sample:

Data sources:
◦ 1892 adult medical-surgical patients
◦ 16 nursing units in 4 Midwest hospitals
◦ From 01/08 to 07/08
◦ Hospital Information Systems
◦ Electronic / paper medical records
◦ Patient Questionnaires administered before hospital
discharge
 Quality of Discharge Teaching Scale
 Readiness for Hospital Discharge Scale

Structure:
◦ Nurse staffing





RN hours
per patient day
h
d
Non RN hours per patient day
RN overtime hours per patient day
Non RN overtime hours per patient day
Process:
◦ Discharge preparation
 Quality
Q li off Discharge
Di h
Teaching
T
hi
Scale
S l (Weiss
(W i et al.,
l 2007)
 Delivery Subscale
 Content Received Subscale

Outcomes:
◦ At Discharge
 Readiness for Hospital Discharge Scale (Weiss&Piacetine, 2006)
Post discharge
◦ Post-discharge
 Occurrence of an ED visit or readmission within 30 days that
was unplanned and for a reason related to the primary
diagnosis or a comorbidity of the index hospitalization
Unit-level variables

Structure =>Nurse staffing
(hours per patient day):
◦
◦
◦
◦

RN: 5
RN
5.0
0 (0
(0.75)
75)
Non-RN: 3.8 (0.7)
RN overtime: 0.2 (0.1)
Non-RN
Non
RN overtime: 0.1 (0.1)
Patient-level variables

◦ D
Delivery
li
(0
(0-10):
10) 7
7.0
0 (2
(2.1)
1)
◦ Content received (0-10): 5.0 (2.7)

Outcomes:

Controls:
Controls:
◦
◦
◦
◦
Vacancy rate:
V
t 10.3
10 3 (8
(8.1)
1)
# beds: 35.1 (7.9)
Mon. adm.: 175.9 (41.0)
Mon. dis.: 197.3 (45.3)
Process =>Quality of Discharge
Teaching:
◦ Readiness (0-10):
(0 10): 8
8.0
0 (1
(1.4)
4)
◦ ED visits: 4.8%
◦ Readmissions: 11.6%
◦
◦
◦
◦
◦
◦
Age: 58.2 (16.8)
Male: 45%
White: 80
80.4%;
4%; Black: 13
13.4%
4%
Surgical: 41.9%
Prior Hosp.: 42.1%
Severity
y of illness:
 1- 24.1%; 2-41%; 3-29%; 4-5.9%
◦ Length of stay: 4.2 (3.8)

The estimation model was a system of four simultaneous
recursive equations with robust standard errors (estimated
using SUEST [seemingly unrelated estimation] command in
Stata 11.0))

Hierarchical panel analysis

Unit and patient level control variables

Unit and hospital level fixed effects

Linear time trend

Clustering by unit
Process
Structure
Nurse
St ffi
Staffing
 RN hours per
patient day
 overtime RN
hour per
patient day
Discharge
Preparation
+.27* quality
l
Outcomes
Readiness
f
for
Discharge
+.35**
+
35**
readine
ss
ED Visits
-0.04*
and
Readm.
Readmission
+0.02*
ED visits
Hospital ‘Costs’
per hospitalized
patient
i
g
RN staffing
.75 HPPD=
$145.74
Loss of revenue
from readmission/ED
$45.06
Payer Savings
per hospitalized patient
Readmission /
ED costs
$524.66
Net Savings =
Net $333.86
$

When units are staffed with more RNs:

When RNs work more overtime hours:

◦ patients receive better quality discharge
preparation and feel more ready to leave hospital;
◦ Patients have a lower likelihood of being readmitted
after leaving hospital
◦ Patients are more likely to have an ED visit after
leaving hospital
Investing in better RN staffing (more nurses,
less overtime):
◦ Could improve quality of care and patient outcomes
◦ Could reduce healthcare costs
◦ Benefit insurance companies but has a negative
impact on hospital’s
hospital s bottom line
Manage
within
fluctuation
to
M
i hi unit
i staffing
ffi
fl
i
avoid understaffing.
Establish discharge teaching evaluation and
discharge readiness assessment (by the
patient and nurse) as standard nursing
practices.
Realign payment model to benefit hospitals
and payers through investment in optimal
nurse staffing that promotes reduction in
readmission and emergency visit costs.

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Marianne Weiss
Email:
E il marianne.weiss@marquette.edu
i
i @
d
Phone: 414 – 288 - 3855
Olga Yakusheva
Email: olga.yakusheva@marquette.edu
Phone: 414 – 288 - 3409
Kathleen Bobay
Email: kathleen.bobay@marquette.edu
Phone: 414 – 288 - 3851