
Data-Driven Policy Decisions: Uses of Minnesota Hospital Data
Julie Sonier
Director, Health Economics Program
Minnesota Department of Health
December 4, 2008
The Minnesota Department of Health logo is located on the lower left corner throughout the slide deck.
Slide 2
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
Slide 3
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
Slide 4
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
Slide 5
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
Slide 6
Evaluating the Need for Inpatient Hospital Beds
Slide 7
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
Slide 8
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)
Slide 9
Projected Minnesota Population Growth, by Age Group
This slide contains a bar graph showing the projected population growth in Minnesota until 2030.
From 2000-2010, population will increase by less than 1 percent for those under 20 years old, by 7 percent for those between 20 and 39 years old, by 18 percent for those between 40 and 59 years old, and by 25 percent for those over 60 years old.
From 2000-2020, population will increase by 5 percent for those under 20 years old, by 13 percent for those between 20 and 39 years old, by 13 percent for those between 40 and 59 years old, and by 72 percent for those over 60 years old.
From 2000-2030, population will increase by less than 9 percent for those under 20, by 11 percent for those between 20 and 39 years old, by 17 percent for those between 40 and 59 years old, and by 110 percent for those over 60 years old.
Source: Minnesota State Demographic Center
Slide 10
How Does Use of Health Care Services Vary by Age? Hospital Example
This slide contains a bar graph entitled "Hospitalization Rates by Age."
The number of hospitalizations per 100 persons in a specific age group is:
Less than 5 years old: 7.75
5-14 years old: 2.19
15-24 years old: 7.19
25-34 years old: 9.32
35-44 years old: 7.60
45-54 years old: 9.29
55-64 years old: 14.24
65-74 years old: 25.43
Over 75 years old: 46.49
All ages: 11.27
Sources: AHRQ, National Inpatient Sample.
Slide 11
Projected Growth in Inpatient Hospital Days by Region, 2000 to 2020
This slide contains a regional map of Minnesota indicating the projected growth of inpatient hospital days from 2000 to 2020.
Northwest: 28 percent
Northeast: 26 percent
Central West: 26 percent
Central East: 53 percent
Minneapolis metropolis: 40 percent
Southwest: 9 percent
South (central): 19 percent
Southeast: 34 percent
Statewide growth rate: 37 percent
Slide 12
Projected Occupancy Rates as % of 2003 Available Beds, by Region, 2020
This slide contains a regional map of Minnesota indicating projected bed occupancy rates in 2020 as a percentage of available beds in 2003.
Northwest: 41 percent
Northeast: 58 percent
Central West: 35 percent
Central East: 76 percent
Minneapolis metropolis: 94 percent
Southwest: 29 percent
South (central): 46 percent
Southeast: 85 percent
Statewide occupancy rate: 75 percent
Slide 13
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
Slide 14
Occupancy Rates at Existing Hospitals Serving the Maple Grove Community
This slide contains a bar graph showing the occupancy rates at existing hospitals serving the Maple Grove Community from 1999 until 2015.
1999: 69.1 percent
2003: 74.0 percent
2009: 79.4 percent
2015: 85.5 percent
Slide 15
2015 Weekly Projected Occupancy Rates for Hospitals Serving Residents of the Maple Grove Area
This slide contains a bar graph indicating the projected weekly occupancy rates for hospitals serving residents of the Maple Grove area. The annual average is 85.5 percent, the maximum weekly occupancy is 91.9 percent, and the number of weeks above the annual average is 29. The occupancy rates were calculated based on 2003 available beds.
Slide 16
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
Slide 17
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
Slide 18
The Policy Impact of Preventable Hospitalizations
Slide 19
Framing the Issue: Ratio of Potentially Preventable Hospitalization Rates for the US Compared with Minnesota
This slide contains a bar graph indicating the ratio of potentially preventable hospitalization rates for the US compared with Minnesota for the following conditions:
Diabetes with Short Term Complication: 1.8
Diabetes with Long Term Complication: 1.7
Diabetes Uncontrolled: 2.3
Lower Extremity Amputation: 2.0
Hypertension: 1.6
Congestive Heart Failure: 1.6
Angina (w/o a procedure): 1.3
Pediatric Asthma: 2.0
COPD: 1.6
Adult Asthma: 1.6
Bacterial Pneumonia: 1.3
Perforated Appendix: 1.2
Pediatric Gastroenteritis: 0.9
Low Birth Weight: 1.2
Dehydration: 1.0
Urinary Infection: 1.5
Slide 20
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
Slide 21
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
Slide 22
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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