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Session 2: Data-Driven Policy Decisions: Uses of Minnesota Hospital Data

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On December 4, 2008, Julie Sonier participated on a panel about using administrative data to inform policy at the Using Administrative Data to Answer State Policy Questions Intensive Workshop by presenting how Minnesota used hospital data to drive quality improvement. This is the text version of the event's slide presentation. Please select the following link to access the slides: (PowerPoint® File, 330 KB).

Slides: 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22

Slide 1: 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.

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Slide 2: Overview


  • 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

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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

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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

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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

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Slide 6: Evaluating the Need for Inpatient Hospital Beds

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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

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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)

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Slide 9: Projected Minnesota Population Growth, by Age Group

This slide contains a bar graph showing the projected population growth in Minnesota until 2030.

Year Span Under 20 20 to 39 40 to 59 60+
2000-2010 < 1% 7% 18% 25%
2000-2020 5% 13% 13% 72%
2000-2030 < 9% 11% 17% 110%

Source: Minnesota State Demographic Center

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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:

Age < 5
5-14 15-24 25-34 35-44 45-54 55-64 65-74 75+
All ages
Number Hospitalizations
per 100 persons
7.75 2.19 7.19 9.32 7.60 9.29 14.24 25.43 46.49 11.27

Sources: AHRQ, National Inpatient Sample.

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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.

Region Northwest Northeast Central West Central East Minneapolis metropolis Southwest South (central) Southeast Statewide growth rate
Projected growth rate 28% 26% 26% 53% 40% 9% 19% 34% 37%

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Slide 12: Projected Occupancy Rates as Percent 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.

Region Northwest Northeast Central West Central East Minneapolis metropolis Southwest South (central) Southeast Statewide occupancy
Projected bed occupancy rate 41% 58% 35% 76% 94% 29% 46% 85% 75%

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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

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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.

Year Occupancy
1999 69.1%
2003 74.0%
2009 74.9%
2015 85.5%

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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.

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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

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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

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Slide 18: The Policy Impact of Preventable Hospitalizations

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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:

Condition Hospitalization
Rate Ratio
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

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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

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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

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Slide 22: Contact Information

Julie Sonier
Director, Health Economics Program
Minnesota Department of Health
(651) 201-3561 Disclaimer

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