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