data-driven policy decisions: uses of minnesota hospital data julie sonier director, health...
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Data-Driven Policy Decisions:Uses of Minnesota Hospital Data
Julie Sonier
Director, Health Economics Program
Minnesota Department of Health
December 4, 2008
Overview
Context– Importance of data to the policy process– Data collection and use in Minnesota
Specific examples of how data has informed policy debates and decisions– Evaluating the need for new inpatient hospital
capacity– Analyzing costs associated with preventable
hospitalizations
The Importance of Data to the Policy Process
An old saw: “The plural of anecdote is not data”
Legislators and policymakers are there to: legislate and make policy– Do so in the presence or absence of data to inform their
decisions– Will use data to inform their decisions but in absence of
data, still need to make decisions– Data and information availability doesn’t always guarantee
they’ll be used to inform the decision…but lack of data guarantees that they won’t
– So, the “plural of anecdote” can sometimes be legislation and law, in the absence of data
The (at least) Four Uses of Data in a Policy Context
Four (not mutually exclusive) areas of influence:– Framing the issue– Informing policymakers (and the
public) and the debate– Making the case– Developing the solution– And probably more
Collection and Use of Data in Minnesota
Comprehensive health reforms in the early 1990s invested in data collection, research, and analysis to inform policy
MDH collects administrative and survey data from:– Health plans, hospitals, physician clinics, employers,
households, government agencies Data are used to:
– Monitor health care market trends (access, cost, and quality)
– Produce special studies/reports High expectations from Legislature about data to
inform policy decisions
Evaluating the Need for Inpatient Hospital Beds
Regulatory Environment for Hospital Construction in Minnesota
Moratorium on hospital construction or expansion of licensed beds - in place since 1984– Exceptions require specific authorization from
Legislature 2004 law established a “public interest review”
process to evaluate requests for exceptions– MDH recommends whether a proposal is “in the
public interest”; Legislature remains the ultimate decision-maker on whether to grant an exception
Examples from the 2 main reviews conducted since the public interest review law was passed
Factors Affecting Future Need for Hospital Capacity in Minnesota
Population growth– MN population expected to grow by 1
million people (20%) between 2000 and 2020
Changing demographics (aging)Changes in use rates of health care
services (caused by factors other than aging population)
Projected Minnesota Population Growth,by Age Group
0% 20% 40% 60% 80% 100% 120%
2000-2030
2000-2020
2000-2010
60+
40 to 59
20 to 39
Under 20
Source: Minnesota State Demographic Center
How Does Use of Health Care Services Vary by Age? Hospital Example
0
5
10
15
20
25
30
35
40
45
50
<5yrs 5-14 15-24 25-34 35-44 45-54 55-64 65-74 75yrs+ Allages
# h
osp
italiz
atio
ns
per 100 p
opula
tion
Sources: AHRQ, National Inpatient Sample.
Hospitalization Rates by Age
0 50 100 150 20025Miles ±
28%26%
53%26%
40%9%
19% 34%
Projected Growth in Inpatient Hospital Days by Region, 2000 to 2020
Statewide Growth Rate= 37%
0 50 100 150 20025Miles ±
41%58%
76%35%
94%29%
46% 85%
Projected Occupancy Rates as % of 2003 Available Beds, by Region, 2020
Statewide Occupancy Rate= 75%
2005: Requests to build a new community hospital in a fast-growing suburb of Minneapolis (Maple Grove)
Would be the first major facility constructed since moratorium in 1984
Use of aggregate and claims-level hospital data was critical in the analysis and findings
Examination of local level occupancy rates and projections of use of services based on:– Population projections, by age and geography– Current patient flows (discharge data)– Projections of changed patient flows in the construction of
a new facility
Occupancy Rates at Existing Hospitals Serving the Maple Grove Community
69.1%74.0%
79.4%85.5%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1999 2003 2009 2015
2015 Weekly Projected Occupancy Rates for Hospitals Serving Residents of the Maple Grove Area
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
85.5%, annual average# of weeks above annual avg: 29Maximum weekly occupancy: 91.9%
Occupancy rates calculated based on 2003 available beds.
Policy Outcome
MDH determined the hospital proposal to be in the public interest
Legislature passed an exception to the construction moratorium, allowing the new facility to be built
Construction currently under way – hospital opening in 2009
2008: Request to build an inpatient psychiatric facility in an eastern suburb of the Twin Cities
Determination here was whether the beds were needed to provide timely access to services
Again, discharge data, this time on inpatient psychiatric services, was critical to the analysis
Data analysis led to determination that a new inpatient psychiatric facility of the size proposed was not in the public interest– Legislature did not grant the exception
The Policy Impact of Preventable Hospitalizations
Framing the Issue: Ratio of Potentially Preventable Hospitalization Rates for the US Compared with Minnesota
1.8 1.7
2.32.0
1.6 1.61.3
2.01.6 1.6
1.3 1.20.9
1.21.0
1.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Diabe
tes S
hort
Trm C
ompli
catio
n
Diabe
tes L
ong T
erm
Com
plica
tion
Diabe
tes U
ncontro
lled
Lower
Extr
emity
Amput
ation
Hyper
tens
ion
Conges
tive
Heart
Failur
e
Angina
(w/o
a p
roce
dure
)
Pediat
ric A
sthm
a
COPD
Adult A
sthm
a
Bacte
rial P
neum
onia
Perfo
rate
d App
endix
Pediat
ric G
astroe
nterit
is
Low B
irth
Weig
ht
Dehydra
tion
Urinar
y Inf
ectio
n
Informing the Debate: Preventable Hospitalizations
10% of all hospitalizations in Minnesota were estimated to be potentially preventable
Cost associated with these hospitalizations estimated at $440 million (payments, not charges)
Data used in health reform debates; spurred discussion about payment reform
Policy Outcome Comprehensive health reform law that focused on:
– Payment reforms to align incentives for quality– Payment for care coordination, especially to
prevent complications of chronic disease
Summary
Legislators and policymakers will make decisions with or without data– Data should and does help guide that debate
Hospital data has been essential to smart policy decision making in Minnesota
Moving forward, data will become increasingly important as the issues facing lawmakers become increasingly complex
Contact Information
Julie Sonier
Director, Health Economics Program
Minnesota Department of Health
(651) 201-3561
julie.sonier@state.mn.us
www.health.state.mn.us/healtheconomics
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