Transcript of Building Capacity for Supply-Side Modeling, Simulation, and Research: An Example Using HCUP Data to...
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- Building Capacity for Supply-Side Modeling, Simulation, and
Research: An Example Using HCUP Data to Improve Timeliness of
Estimates September 21, 2011 Claudia Steiner, M.D, M.P.H.
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- What is HCUP? HCUP is HCUP is Longitudinal Multi-Year and
All-Payer, Inpatient, Emergency Department, and Ambulatory Surgery
Databases based on Hospital Billing Data. 2
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- Demographic Data DiagnosesProceduresCharges 3 The Foundation of
HCUP Data is Hospital Billing Data
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- The HCUP Partnership 4 State Federal Industry
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- 5 Partnership: HCUP Database Participation By State
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- Three state-level databases Three state-level databases State
Ambulatory Surgery Databases (SASD) State Inpatient Databases(SID)
State Emergency Department Databases (SEDD) 6 HCUP Has Six Types of
Databases
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- Three nationwide databases Three nationwide databases HCUP Has
Six Types of Databases Nationwide Inpatient Sample(NIS) Kids
Inpatient Database(KID) Nationwide Emergency Department Sample
(NEDS) 7
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- 8 Included in HCUP Inpatient care State Inpatient Databases
(SID) Nationwide Inpatient Sample (NIS) Kids Inpatient Database
(KID) Emergency Department State Emergency Department Databases
(SEDD) Nationwide Emergency Department Sample (NEDS) Ambulatory
Surgery State Ambulatory Surgery Databases (SASD) Not Included in
HCUP Physician office visits Pharmacy Labs/Radiology What Types of
Care Are and Are Not Captured by HCUP?
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- 9 Source: American Hospital Association (AHA), 2008 14% (N=805)
Typically not included in HCUP data Included in HCUP data 86%
(N=5,010) HCUP data is mostly from community hospitals Where Do We
Get HCUP Data?
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- What Are Community Hospitals? 10 IncludedExcluded
Multi-specialty general hospitalsLong-term care OB-GYNPsychiatric
ENT Alcoholism/Chemical dependency OrthopedicRehabilitation
PediatricDoD / VA / IHS Public Academic medical centers American
Hospital Association Definition: Non-Federal, short-term, general,
and other specialty hospitals, excluding hospital units of other
institutions (e.g., prisons)
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- Accelerating HCUP Data and Information Need for timely
projections on trends Provide analysts and policy makers timely
information that can be used to evaluate the impact of quality
improvement efforts HCUP Nationwide Inpatient Sample (NIS)
typically lags the current calendar year by 17 months 2009 NIS
available in June 2011 Demonstrate feasibility of producing
gap-year national estimates Demonstrate feasibility of collecting
and processing quarterly data 11
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- Which HCUP Partners Collect Quarterly Data? A total of 40 of 44
States (91%) reported that they collect data at more frequent
intervals than annually: A total of 40 of 44 States (91%) reported
that they collect data at more frequent intervals than annually: 23
States collect quarterly data (AR, CT, FL, GA, HI, IA, IL, IN, KY,
MA, ME, MD, MI, MN, MO, MT, NC, NE, NM, NY, OH, OR, PA, RI, TN, TX,
UT, VA, VT, WI & WY) 4 States collect monthly data (NJ, SC, WA
& WV) 3 States collect both quarterly and monthly data (CO, NH
& NV) 2 State collects semi-annual data (AZ, CA) Four of the 44
States do not collect data more frequently than annually: Kansas,
Louisiana, Oklahoma, and South Dakota. Four of the 44 States do not
collect data more frequently than annually: Kansas, Louisiana,
Oklahoma, and South Dakota. 7
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- HCUP Data for Timely National Projections Factors that
contribute to success of the initiative: Longitudinal nature of the
HCUP databases 1988 forward Breadth of data across 44 states 295
million inpatient discharges from the 2001 to 2009 Capacity of
states to produce early quarterly data Modeling expertise at AHRQ
and contract staff Availability of SAS Econometric Time Series
Software Leveraging of report technology developed under the NHQR
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- Selected HAIs and Outcomes Eight HAIs selected; six reported
separately for adults and pediatrics The HAIs reported in this
study may have originated from either inpatient or outpatient
health care services HAIs are identified by a principal or
secondary diagnosis on an inpatient stay Indication that the
diagnosis was present on admission (POA) could not be considered
because POA is not available in historical SID Approach provides
nationwide, population- based prevalence instead of the
hospital-based performance or accountability measures 14
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- Five Outcomes of Interest Projections focus on hospital
utilization and outcomes: Number of inpatient discharges Rate per
1,000 discharges Average total charge (includes hospital services,
no professional fees, not inflation-adjusted) Average length of
stay In-hospital mortality rate 15
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- Postoperative Sepsis (Adult) Population at risk: Elective,
non-maternal, adult, surgical discharges with a length of stay
>= four days, excluding discharges with any diagnosis of
immunocompromised state, discharges with any diagnosis of cancer,
and discharges with a principal diagnosis of infection 16
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- Postoperative Sepsis (Pediatric) Population at risk:
Non-neonatal, pediatric, surgical discharges with a length of stay
>= four days, excluding discharges with a principal diagnosis of
infection or a DRG indicating surgery for likely infection 17
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- Clostridium Difficile Infections (Adult) Population at risk:
Non-maternal, adult discharges 18
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- Clostridium Difficile Infections (Pediatric) Population at
risk: Pediatric discharges 19
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- HCUP Data for Timely National Projections HCUP projections in
newest report are based on: 295 million inpatient discharges from
the 2001 to 2009 HCUP SID Early 2010 data from 14 selected HCUP
States that submitted data by July 2011 Ten cardiovascular /
cerebrovascular conditions and procedures selected Each stratified
by adult age (18-44, 45-64, 65+) and gender 20
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- Five Outcomes of Interest Projections focus on hospital
utilization and outcomes: Number of inpatient discharges Average
total cost (includes hospital services, no professional fees, not
inflation-adjusted) Average length of stay In-hospital mortality
rate 21
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- Acute Myocardial Infarction (Adult Age Group) 22
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- Acute Myocardial Infarction (Gender) 23
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- HCUP Data Mining Purpose: Use early 2010 State Inpatient Data
to identify diagnoses and procedures for which observed outcomes in
2010 digressed substantially from those outcomes predicted for 2010
using historical data from 2001 - 2009. Purpose: Use early 2010
State Inpatient Data to identify diagnoses and procedures for which
observed outcomes in 2010 digressed substantially from those
outcomes predicted for 2010 using historical data from 2001 - 2009.
Method: Analyze normalized residuals to identify the 2010 residuals
that were statistical outliers compared with residuals observed
during the 2001 - 2009 baseline period. These outlier residuals
indicate potentially radical changes to the established trend for
the outcome under consideration. Method: Analyze normalized
residuals to identify the 2010 residuals that were statistical
outliers compared with residuals observed during the 2001 - 2009
baseline period. These outlier residuals indicate potentially
radical changes to the established trend for the outcome under
consideration. 24
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- Procedure Categories with Substantial Deviations Between Actual
vs. Expected 25
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- Questions? 26
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- 27 Healthcare Cost and Utilization Project (HCUP)