# Presentation: 814825

## HCUP:  Research Applicationsand Tools 

- Claudia Steiner, M.D., M.P.H.

## HCUP Tools

- Software to assist in using the data
- SAS and SPSS load programs
- Labels (HCUP data elements, DRGs, CCS)
- Statistics
- HCUPnet
- Clinical Classifications Software
- HCUP Quality Indicators

## National Statistics

- HCUP Research Notes: (www.ahcpr.gov/data/hcup)
- Hospital Inpatient Statistics, 1996, 1995 and 1992
- Diagnosis and Procedure Combinations in Hospital Inpatient Data, 1996 and 1993
- Descriptive statistics by Insurance Status

**Notes:**
We've used HCUP data to provide national stats.

Have been publishing Research Notes for a while -- 
all available from the AHRQ Clearinghouse

## HCUPnet

- Interactive on-line access to HCUP data
- Users generate tables of outcomes by diagnoses and procedures
- Can be cross-classified by patient and hospital characteristics

**Notes:**
But we wanted to move beyond paper publication to provide web-based data and information.

HCUPnet provides interactive on-line access to HCUP data.
Users generate tables of outcomes by diagnoses and procedures, which 
Can be cross-classified by patient and hospital characteristics

## Types of Output from HCUPnet

- Rank order conditions/procedures by LOS, charges, deaths
- Examine LOS, charges, mortality, discharge status for conditions/ procedures by:
- patient characteristics (age, sex, payer, median income of ZIP Code)
- hospital characteristics (owner, teaching, location, bedsize, region)

## HCUPnet Can Answer Questions Such As:

- What percentage of hospitalizations for children are uninsured, by state?
- What are the most expensive conditions treated in U.S. hospitals?
- What is the trend in admissions for depression?
- Will I have enough cases to do my analysis?
- How do my estimates compare with HCUPnet? (validation)

## Slide 7

**Notes:**
Let's go through an example:

Here is the HCUPnet homepage.
provides an introduction . . .
> links to descriptions of the databases
> an overview of how to use HCUPnet
> a brief description of HCUP data
> detailed HELP
> press "start HCUPnet" to proceed

## Slide 8

**Notes:**
First thing you need to decide: which databases do you want to use?

Look at trends? provides info from 1993-1997 (less detailed than other parts of HCUPnet, but longitudinal)

Nationwide stats?  Use NIS

State stats?  Can choose one of 10 participating states

## Slide 9

**Notes:**
Then you can choose general stats on all stays,
or you can choose to look at diagnoses or procedures.

If you choose dx or procs, can choose to get more details on specific ones,
or you can choose to rank order dxs and procs.

You can choose individual ICD codes, 
or groupings of ICD codes called CCS
or DRGs or MDCs

Bottom of screen:
To protect confidentiality and provide speedy access to results, we've pre-aggregated all statistics.  The data are not sitting on the web -- just pre-aggregated tables.

If you need help with medical terminology -- can link to a medical dictionary

## Slide 10

**Notes:**
Let's go through an example:

Here is the HCUPnet homepage.
provides an introduction . . .
> links to descriptions of the databases
> an overview of how to use HCUPnet
> a brief description of HCUP data
> detailed HELP
> press "start HCUPnet" to proceed

## Slide 11

**Notes:**
First thing you need to decide: which databases do you want to use?

Look at trends? provides info from 1993-1997 (less detailed than other parts of HCUPnet, but longitudinal)

Nationwide stats?  Use NIS

State stats?  Can choose one of 10 participating states

## Slide 12

**Notes:**
Then you can choose general stats on all stays,
or you can choose to look at diagnoses or procedures.

If you choose dx or procs, can choose to get more details on specific ones,
or you can choose to rank order dxs and procs.

You can choose individual ICD codes, 
or groupings of ICD codes called CCS
or DRGs or MDCs

Bottom of screen:
To protect confidentiality and provide speedy access to results, we've pre-aggregated all statistics.  The data are not sitting on the web -- just pre-aggregated tables.

If you need help with medical terminology -- can link to a medical dictionary

## Slide 13

**Notes:**
If you choose individual ICD codes or CCS categories, you can look at the principal diagnosis or procedure
or you can look at all-listed diagnoses or procedures --  which means we look at all the diagnoses or all the procedures that are on the record

So let's say we're interested in all-listed diagnoses

## Slide 14

**Notes:**
And let's say we want to look at infectious dxs

## Slide 15

**Notes:**
And let's say we want information on septicemia specifically

## Slide 16

**Notes:**
And let's say we want information on septicemia by all pt characteristics

## Slide 17

**Notes:**
by teaching status of the hospital

## Slide 18

## Slide 19

**Notes:**
Then we get the results.

First you'll see a table of one-way stats

and below that would be the 2x2 tables you chose

## Slide 20

**Notes:**
At the bottom of the results are instructions for how to download the numbers

We also explain that if there are stats you'd like to see that aren't possible with HCUPnet now, let us know.

The first version of HCUPnet was very simple -- one database, one year, principal dx and procs only, no crosstabs --
 we've gone through 4 major updates in the past year and all changes have been in response user inquiries:

> added states
> added trends
> added cross-tabs
> add all-listed diagnoses
> added more detailed searching by ICD code

## Slide 21

**Notes:**
here's an example of the results of a trend query

## Slide 22

**Notes:**
here's an example of rank ordering by charges for the hospital stay

## Chartbooks

- Detailed stats on web . . . “but what does it all mean?”
- Provide easily accessible information based on HCUP data

## Slide 24

## Chartbooks (cont’d)

- Overview of hospital care
- Future chartbooks
- Procedures in U.S. hospitals
- Children’s hospitalizations
- Hospitalization of women
- Hospitals that care for patients covered by Medicaid and the uninsured

## Slide 26

## Slide 27

## Slide 28

## Clinical Classifications Software (CCS)

- Aggregates ICD-9-CM diagnosis and procedure codes
- Creates smaller number of clinically meaningful clusters
- Reporting tool
- Can be applied to any ICD-based administrative data

**Notes:**
You saw reference to the CCS in HCUPnet -- 

this is a tool developed at AHRQ, which basically aggregates ICD dx and procs into small number of clinical meaningful categories -- you can think of it as a clinical grouper.

it was developed primarily as a reporting tool -- as a way of taking ICD-based data and lumping dxs and procs into a manageable number of categories for reporting -- an alternative to reporting individual ICD codes, or truncating ICD codes at 3 digits -- an alternative to reporting DRGs

## Two Related Classification Systems

- Single level CCS
- Mutually exclusive categories
- 259 diagnosis categories
- 231 procedure categories

- Multi-level CCS
- Multi-level hierarchical system
- Aggregates single-level CCS into broader categories
- Splits CCS into more detailed categories

**Notes:**
There are 2 systems -- a single level
and a multi-level

. . .

best shown by example

## Slide 31

## Slide 32

## How can CCS be Used?

- Rank hospitalizations by type of condition -- N of cases, LOS, total charges
- Develop clinically based utilization profiles
- Explore data by grouping conditions and procedures conveniently
- Predict future resource use (Cowen et al., Med Care. 1998;36:1108-1113.

**Notes:**
In your packet you have a description of CCS and information on how to download the software from the AHRQ website

## HCUP Quality Indicators

- Use with administrative data
- Multiple dimensions of quality
- Diverse populations
- High tech and ordinary care

**Notes:**
Another tool developed by AHRQ staff is the HCUP Quality Indicators -- a set of performance measures . . .

## 33 Indicators in 3 Areas

- Potentially avoidable ***adverse hospital outcomes***
- Potentially ***inappropriate*** ***utilization*** of hospital procedures
- Potentially ***avoidable admissions ***or ***ambulatory care sensitive conditions***

## Relatively Few, Common Variables

- Diagnoses
- Procedures
- Age
- Sex
- Admission source
- Discharge status
- Procedure dates

**Notes:**
The main stipulation in  choosing these measures is that the measures be based on Relatively Few, Common Variables found on hospital discharge abstract data

## HCUP QIs: The Next Generation  

- Contract with UCSF-Stanford
- Expand / refine current QIs
- pediatric, chronic medical, ambulatory sensitive conditions
- Analytic / methods development
- population-based denominators for some QIs
- risk adjustment
- separate patient safety indicators (PSIs) module
- literature review and empirical validation

**Notes:**
Stanford is currently working under contract with AHRQ to . . 

In your packet you have a brief description of where we're going with the QIs

## Using the SID: An Example

- Use of in-hospital diagnostic and therapeutic procedures
- Comparing Hispanics and non-Hispanic whites
- Examine disparities; explore potential explanations

**Notes:**
Now we'll spend a little time going through a study that uses the SID --

We'll cover how to select a SID (documentation)
how to obtain the data
considerations in building your analytic dataset
linkages with other files

## Procedure Use

- Examined vector of procedures (PR1-PRn)
- Identified all procedures used by more than 5% of discharges:
- CT scan of head
- Diagnostic ultrasound of head
- Echocardiogram
- MRI of head
- Cerebral arteriogram
- Carotid endarterectomy

## ResultsUse of Non-Invasive Diagnostic Procedures

**Notes:**
In bivariate comparisons, we found that:

>> in both NY and CA, Hispanic patients were significantly more likely than non-Hispanic whites to receive all 4 noninvasive diagnostic tests we examined here 

(all these differences are statistically significant at P<0.05)

 (remember these are all procedures received by at least 5% of these patients).

For example 49% of Hispanics received a CT scan of the head, while 26% of non-Hispanic whites received this test.

## ResultsUse of Invasive Procedures

**Notes:**
On the other hand, Hispanics were significantly less likely than non-Hispanic whites to receive cerebral arteriograms and carotid endarterectomies.

## Results - Logistic Regression (NY)Odds ratios for Hispanic vs. Non-Hispanic whites

**Notes:**
The next 2 slides show the results of our logistic regression analysis in which we sequentially added patient and hospital-level variables.
>> These are odds ratios for Hispanic vs. non-Hispanic white.  
>> When only ethnicity (Hispanic) is in the model, we see the same results as just shown on the graphs -- Hispanics receive significantly more non-invasive diagnostic tests and fewer invasive procedures.
>> When we add patient level variables -- the odds ratios go down a little for the noninvasive procedures and up a little for the invasive procedures, but remain significant.
>> When we add hospital variables, we see very little change.
>> However, when we add to the model our variable for the hospital's experience with Hispanic patients, the difference between Hispanics and non-Hispanic whites is eliminated or greated reduced.
>> For example, Hispanics were more than 2x as likely to receive MRI of the head -- this went down a little to 1.7 when we controlled for patient characteristics.
>> It went down a little further to 1.3 when we controlled for hospital characteristics, but when we controlled for the % of Hispanics in the hospital, overall, the odds ratio became 1.0 -- indicating no difference between Hispanics and non-Hispanic whites, when we control for the % of Hispanics in the hospital.
>> Only CT scan remained significantly higher for Hispanics in NY -- but still greatly reduced from earlier models.

## Summary of Findings

- Hispanics had higher use of noninvasive tests and lower use of invasive procedures.
- Controlling for hospitals’ experience with Hispanic patients eliminated or greatly reduced disparities in procedure use.
- Disparities are lower in CA than in NY

**Notes:**
So, to summarize, 3 main findings emerge from this study:

(1) For patients with cerebrovascular disease, Hispanics had significantly higher use of noninvasive diagnostic testing and significantly lower use of invasive procedures

(2) Controlling for the hospital's experience with Hispanic patients eliminated or greatly reduced disparities in procedure use

(3) Disparities in use of procedures were lower in CA than in NY -- and the impact of the Hispanic concentration variable was not as great in CA as in NY

## Use the AS for:

- Compare inpatient and outpatient surgery
- Small area variation research
- Study surgeries that only occur outpatient
- Compare state-specific trends in use of outpatient setting

## Using the AS: An Example

- Use of outpatient setting for major surgery
- current public policy concern
- Examine use of outpatient setting for procedures grouped by body system
- compare across states

**Notes:**
Now we'll spend a little time going through a study that uses the SID --

We'll cover how to select a SID (documentation)
how to obtain the data
considerations in building your analytic dataset
linkages with other files

## Ranking of Rates for Major OR Procedure Groupings,

- 			Outpatient, 1997

## NIS: An Example

- Charges for Childhood Asthma by Hospital Characteristics. *Pediatrics, Dec 1998 *
- John Meurer, Medical College of Wisconsin
- Uses NIS 1994 data, linked to Hospital Weights file
- Applies All Patient Refined-Diagnosis Related Group (APDRG) severity classification

## Meurer et al. Results

- Study finds asthma severity did not differ significantly by hospital location or teaching status
- Nonprofit hospitals treated higher proportion of children with extreme severity asthma
- Urban teaching hospitals treated more children from low-income neighborhoods

## NIS: An Example

- Patients Undergoing Major Surgery in Hospitals with Limited Nursing Staffs Suffer more Complications *Image, Nov 1998                   *
- Christine Kovner, New York University
- Uses NIS 1993 data, linked to AHA Survey
- Applies Quality Indicator software to the NIS

## Kovner Results

- Study finds that the fewer FTE registered nurses per inpatient day 
- higher incidence of
- urinary tract infection
- pneumonia
- thrombosis
- pulmonary compromise

## Slide 51

## Conclusion

- HCUP provides multiple resources
- Databases for study
- Research reports
- Tools for analysis
- Statistics to support policy decisions, provide national comparisons

## For More Information - How to Reach us

- E-mail:
- 		hcup@ahrq.gov
- Phone:
- 		301-594-3075
- Website:
- 		http://www.ahrq.gov/data/hcup
- HCUPnet:
- 		 http://www.ahrq.gov/data/hcup/hcupnet.htm