David C. Mohr, PhD
Center for Organization, Leadership,
&Management Research; VA Boston
Healthcare System
AcademyHealth Annual Research
Meeting, June 27-29, 2010
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1 2
David
D
id Mohr,
M h PhD1,
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Gary Young, JD, PhD1,2
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Mark Meterko,
Meterko PhD1,2
Bert White, DMin1,2
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Justin Benzer,
Benzer PhD
Marjorie Nealon Seibert, MBA1
Kelly
y Stolzmann,, MA1
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Katerine Osatuke, PhD
Scott Moore, PhD3
1. VA Boston
1
B t Healthcare
H lth
System
2. Boston University School
of Public Health
3. VA National Center for
O
Organization
Development
D l
Work supported
s pported b
by
Department of Veterans
Affairs, Health Services
Research & Development
grant IIR 05-221
2
• A
Approximately
i t l 5 to
t 20 percentt off Americans
A
i
contract
t t influenza,
i fl
or the flu, each year (CDC, 2009)
• Influenza has been attributed to over 220,000
220 000 hospitalizations
each year (Thompson et al 2004) resulting in substantial
hospital
p
costs in providing
p
g care ((NFID,, 2010))
• Meta-analysis showed lower risks of pneumonia, hospitalization
& death (Gross et al, 1995)
• Annual vaccinations has been promoted as the most effective
way to prevent influenza & complications (CDC, 1999)
• HEDIS performance measure (50-64, 65+)
• Cost effectiveness shown for these age groups
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• Despite the likely benefits, vaccination not always done
• Rate of 48.6% for commercial health plans & 68.6% for Medicare
• B
Barriers:
i
iinclude
l d access, patient-demand,
i
d
d severe time
i restrictions
i i
for providers & practice-related factors
• Because vaccinations occur during a few months of the year,
year a
higher resource demand is placed on practices
• Meta-analysis found organizational changes (e.g.,
(e g clinical
procedures, jobs, or infrastructure) as the strongest type of
intervention strategy for increased vaccination rates
• We examined one potential explanatory organizational factor
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• Organizational slack - extra organizational resources
(e.g., staff, space, equipment) available to meet a
given level of demand
• Allows organizations a “cushion”; to adjust resources &
strategies to accommodate internal & external pressures &
disruptions
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• Divergent views of slack (“fat to be trimmed” vs. “muscle”)
• More slack can allow expansion of services, partnerships, working
conditions innovation
conditions,
innovation, buffer against disruptive changes
• Too much slack can reflect poor internal control systems, inefficiencies in
resource uses, reduced managerial discipline
• Thus, Bourgeois (1981) suggested a curvilinear or inverse-U
shaped relationship exists for slack & general organizational
performance
f
6
• Setting
• Primary care (PC) divisions in the Veterans Health Administration (VA)
• Flu
Fl season starting
t ti iin llate
t 2006 (six-months)
(i
th )
• Sample
• Patient-level
Patient level (n≈35,000)
(n≈35 000)
• Patients aged 50+
• Chart review for vaccinations
• Made at least 1 visit to PC during flu season
• Primary care division-level (n≈600)
• All divisions with at least 10 linked patient records
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• Examined panel size workload ratio measure
• Based on VA staffing policy model that assigns patients to providers in PC
• MDs ≈ 1,200 patient panel size; NP or PA ≈ 900 patient panel size
• Workload computed as number of active patients divided by
maximum panel size capacity of provider
• Aggregated to division-level
• Re-coded so that a value of 0 is balanced
• Positive value indicates slack (i.e., division has extra resources)
A. Active
patients
Providers (adj)
B. Maximum
Capacity
Slack
1-(A/B)
Clinic 1
5,200
4
4,800
-.08
Clinic 2
11,000
10
12,000
.08
Clinic 3
18,000
20
18,000
0
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Outcome
• Influenza vaccination receipt (no or yes)
C
Covariates
i t
• Patient-level: age (50-64 or 65+), gender, marital status,
means test, mental health utilization, & number of primary care
visits in influenza period
• Division-level: urban/rural, COTH academic affiliation, census
region, community- or hospital-based, & total staff FTE (10s)
Analysis
• Multilevel
M ltil l logistic
l i ti regression
i model
d l for
f vaccination
i ti receipt
i t (0/1)
• Patients nested within clinic (at least 10 patients)
• SAS PROC GLIMMIX
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• 77.3% of patients received vaccination
• Average division slack was 7.3% (SD=1.1)
• Patients who in older age group (OR=1.06), married
(OR 1 49) had
(OR=1.49),
h d no co-payment (OR=1.15)
(OR 1 15) & more primary
care visits were more likely to receive vaccination
• Patients
P ti t seen iin clinics
li i att a tteaching
hi affiliated
ffili t d hhospital
it l
(OR=1.08), located in Northeast or Midwest (ref. South) region,
& smaller staff size (OR=.99)
(OR .99) more likely to receive vaccination
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• Linear slack term was not significant (OR=1.28), but quadratic
term was significant (OR=.12)
• Tested
T
d if teaching
hi affiliation
ffili i & totall staff
ff size
i moderated
d
d this
hi
relationship, but they were not significant in the model
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Odds
Odd
estimate
OR range
Odds
Odd
estimate
OR range
Urban
1.05
.97-1.13
1.05
.97-1.13
COTH
1.07*
1.00-1.13
1.08*
1.02-1.15
Community-based clinic
.93
.86-1.00
.94
.87.-1.02
Total staff size (10s)
.99*
99*
.97-1.00
97 1 00
.99*
99*
.97-1.00
97 1 00
Slack
.94
.70-1.20
1.28
.91-1.80
.12**
.03-.46
Slack squared
12
0 80
0.80
Vaccinatiion rate
0.75
0 70
0.70
0.65
0 60
0.60
0.55
0 50
0.50
0.45
0 40
0.40
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
Organizational slack
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• Findings support curvilinear relationship between organizational
slack (i.e. workload ratio) & influenza vaccination rates
• Patients
P i
seen at PC divisions
di i i
with
ihb
balanced
l
d or moderate
d
slack
l k
were more likely to receive vaccination
• At 0% slack
slack, rate was 74%
• At 5% & 10% slack, rate was 76%
• At 15% slack, rate was 74%
• Patients seen in clinics without slack less likely to be vaccinated
• At -5% slack, rate was 71%
• At -10% slack, rate was 64%
• At -15% slack, rate was 56%
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• While many patient, environmental, & clinical practice factors
can influence vaccination, we found one organizational variable
that is sensitive to workload to be explanatory
• In additional analysis, we found curvilinear relationship between
slack & other measures: patient overall quality of care rating,
rating
continuity of care, & appointment wait times
• Further examination to aid understanding
g of workload effects
have potential for significance as well (e.g., financial measures,
process of care, staff satisfaction, & burnout)
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• Panel size monitoring is receiving more attention as part of
medical home movement in both VA & non-VA
• PPatient
ti t per provider
id ratio
ti llarger iin non-VA
VA settings
tti
as population
l ti differs,
diff
but could be adjusted accordingly in other settings
• Findings
g suggest
gg an appropriately
pp p
y attuned workload policy
p y
• Consistent pattern of findings may hold in non-Federal &
specialty care settings
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