# Presentation: 394045

## The Healthcare Cost and Utilization Project:Edits, Quality and Redesign

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

## HCUP:  Purpose

- multi-state,   administrative, population-based, databases;
- all-payer
- uniform format
- used to conduct health services research
- tools to aid researchers
- derivative databases

**To design and disseminate:**

**Notes:**
I would like to begin by reviewing the major purpose and vision of the HCUP project. 

## HCUP

**Nationwide Inpatient ****Sample (NIS)**

**Uniform**

**Diverse**

**Products and Tools**

**H e a l t h c a r e   C o s t   a n d   U t i l i z a t i o n   P r o j e c t ,   A g e n c y   f o r   H e a l t h c a r e   R e s e a r c h  a n d   Q u a l i t y**

**State Outpatient Databases (SOD)**** **

**HCUP**

**CA**

**CO**

**CT**

**FL**

**GA**

**HI**

**IA**

**IL**

**KS**

**MD**

**MA**

**MO**

**NJ**

**NY**

**OR**

**PA**

**SC**

**TN**

**UT**

**WA**

**WI**

**Ambulatory**
**Surgery**

**Emergency **
**Department**

**Notes:**
This graphic is our best and most inclusive representation of the totality of HCUP.  The state data collection  agency, which can be a private hospital association or public data entity, collects discharge abstract data from all of the community hospital in the state.   These data sets are formatted in many ways.  We  then purchase the state data, once it has been processed and cleared by the state, following the close of the calendar year. The states have their own edit checks.  A great deal of effort is put into developing the uniform format, and then processing all of the state data into this standard format, along with edit checks to secure the validity and  reliability of the data.  Once this process is complete, many data products are produced. 

## HCUP:  Key Features

- Redesign efforts
- Uniform format
- Data editing and quality checks
- Future challenges

**Notes:**
Over the next twenty minutes, I will discuss several key features to the producing the final HCUP data products.  This includes our redesign efforts, our efforts  to provide a uniform format,  the data editing and quality checks, and finally a few key challenges we have identified for ourselves and our state partners.

## Statewide Data SystemsParticipating in HCUP

**1988****	****	****    8****	****	****   CA, CO, FL, IA, MA, NJ, WA**

**1989-1992****	****    11****	****	****+ AZ, PA, WI**

**1993-1994****	****    17****	****	****+ CT, KS, MD, NY, OR, SC**

**1995-1996****	****    19****	****	****+ MO, TN**

**1997****	****	****    22****	****	****+ HI, UT, GA**

**1999****	****	****    26****	****	****+ ME, MI, MN, VA**

_**Data year**_

_**# of States**_

_**States**_

**Notes:**
As you see, we have recruited and added states to almost every data year of the project.  This has improved our geographic representation. We have also managed to recruit several of the largest states in the nation, which has increased the percentage of all discharges our databases captures. 

## HCUP Databases

- State Inpatient Databases (SID)
- comprehensive hospital discharge data from states
- State Outpatient Databases (SOD)
- ambulatory surgery data (SASD) from states
- ED data (pilot phase)
- Nationwide Inpatient Sample (NIS)
- sample of community hospitals from SID
- approximates 20% sample of community hospitals in the US

**Notes:**
There are three major components to the HCUP databases at this time.  The first are the SID data, which are the backbone of the entire HCUP project.  We also are collecting and formatting outpatient data, currently represented by AS data, and a pilot phase of ED data.  Finally, our main derivative database at this time is the NIS, which is built on a 20% sample of community hospitals pulled from the participating states, and aims to mimic a representative 20% sample of all US community hospitals,  This NIS allows for national and regional estimates of diagnoses and procedures.

## Types of HCUP Files

- Discharge-level
- Core
- uniformly available across states
- State-specific
- variables unique to certain states
- Sensitive (data development)
- not released
- Hospital-level
- Hospital identifiers and characteristics from    AHA file

## Core SID Data Elements

- Patient demographics (age, sex)
- Admission source and type
- Total charges
- Disposition (discharge status, death in hospital)
- Length of stay
- Expected payer
- Diagnoses and procedures (ICD-9-CM, CCS, DRG)

**Notes:**
Remember that these are discharge level files.  There is a single record for each discharge.  The data are sorted by hospital.
In general, core data elements share the following characteristics:
>  Data elements available from all or nearly all sources
>  Lend themselves to uniform coding across the sources
>  Needed for the most common research applications

## State-specific SID Data Elements

- Procedure date (in days from admission)
- Severity of illness
- Scheduled vs. unscheduled admission
- Readmission
- Diagnosis at time of onset
- Birthweight

**Notes:**
In general state-specific data elements share the following characteristics:
>  are available from a limited number of data sources (OR)
>  do not lend themselves to uniform coding

States vary both in what they collect and in what they determine is appropriate for  release to whom and for what purpose. 

## State-specific SID Data Elements

- Detailed charges
- Physician identifiers
- Patient’s county/ ZIP code
- Patient identifiers (encrypted)
- medical record number; person number
- Race/ethnicity

## HCUP Redesign 

- New contract competed and awarded recently
- Evaluated and re-structured:
- file structure
- edits checks
- variable coding
- Enhance flexibility and ease-of-use
- Improve speed of processing
- Continue to check internal consistency
- Maintain uniform coding, while availing the project of state innovation

## Attributes of Data Elements

- Numeric format
- for variables that should be expressed numerically (e.g., LOS, AGE)
- for categorical variables (SEX, RACE)
- Character format (alphanumeric coding)
- for variables that require alpha characters be preserved (e.g., ICD-9-CM codes, encrypted variables)

## HCUP Coding

- Diagnosis/procedure codes 
- Identifiers 
- Sex, race, payer 
- Age, LOS, procedure day 
- DRGs, CCS 

- Retained in original form
- Encrypted into synthetic values
- Recoded into uniform coding
- Calculated
- Assigned using external algorithms

**Notes:**
We handle each type of data element differently, depending on the type of data element
The CCS is a clinical grouper developed at AHRQ, and will be described by Anne Elixhauser later in the day
While I do not have the time to go over every variable’s coding (nor do I think you would like me to!), I will give several examples of how we a re balancing uniform coding with maintaining the state’s innovation.  Also, we are always happy to share our coding.  We have all current coding available on the HCUP website, and the new coding Is documented as well.

## HCUP Coding

- Sex  
- Race 

- Five different formats from the states					*FEMALE*  0 = M, 1 = F
- 17 states code; 11 code race and  ethnicity reliably				*RACE*  W, B, H, Asian, Native, Other, Missing, Invalid			*RACE_N*  non-uniform HI	*RACE_X*  as received from source					*HISPANIC_X*  0 , 1  (7 states)

**Notes:**
Several of the current and previous HCUP staff published a manuscript in HA in 1997, which described the variability in coding and placed this in the context of HIPPA

## HCUP Coding

- Payer 

- Tremendous variation / innovation	*PAY1*  Medicare, Medicaid, Private, Self-pay, No charge, Other, Missing, Invalid				*PAY1_X*  as received from source					*PAYER1X*  for plans		*HMOPPO1*  additional indicator to distinguish Medicare/Medicaid/Private HMO or PPO distinctions

**Notes:**
The states are rapidly innovating around the Payer variable.  So much so that we had to re-think our coding strategy as well.  I will also tell you that the data consortium, is trying to develop a typology that can be used with the HIPAA standard.

## Missing Values

- Missing
- the value is unknown or unavailable
- “.” or “-9”
- Invalid
- out-of-range, undocumented values
- “.A” or “-8”
- Data unavailable from source
- not provided by source
- “.B” or “-7”

**Notes:**
Missing - simply not available for that case, but normally is coded 

Invalid - negative value for age, or age higher than 124

Unavailable - data source does not provide the variable

## Missing Values (cont’d)

- Inconsistent
- fails internal logical consistency check
- “.C” or “-6”

**Notes:**
e.g., hysterectomy for males

## Quality Control 

- Reviewed and compared edit checks from several states(CA, CO, NY) and Medicare
- Criteria:
- make data usable
- confirm data are valid
- internal consistency
- never impute
- tabulate edit failures

## Quality Control (cont’d)

- Provides information on expected values for key variables, e.g. AGE
- Minimum: 0
- Maximum: 124
- Mean: 40
- If mean <40, look for high percentage of births
- Distribution should be trimodal:
- age 0 (10-13% of cases)
- ages 14-43 (childbearing years)
- ages 49-72

## Quality Control (cont’d)

- Edit Checks: List of all edit checks performed on HCUP data, for example:
- Diagnoses and procedures inconsistent with sex
- Neonatal or maternal diagnoses and procedures inconsistent with age
- Long LOS (>365, LOS = .C ; retain LOS_X)
- Day of procedure not during hospital stay
- Charges per day excessively high or low
- IP: < $25, >$1,000,000 ; AS: <$25, >$50,000

## Quality Control (cont’d)

- Provides all diagnosis and procedure screens:
- Maternal
- Neonatal
- Long term care
- Male diagnoses and procedures
- Female diagnoses and procedures

## Quality Control (cont’d)

- DXVn and PRVn
- validity flags for all diagnoses and procedures
- checked against age and sex on record
- 0 = valid and consistent
- 1 = invalid as of discharge date (± 3 months)
- DXn and PRn
- validity flags for all diagnoses and procedures
- checked against age and sex on record
- incn = inconsistent for age or sex
- invl = invalid as of discharge date (± 6 months)

## Current Data Linkages

- AHA identifier with name/address of hospital 
- Hospital/Patient FIPS county code 
- Hospital/Patient ZIP code 

- AHA  Annual Survey
- Medicare public use files, including cost reports (as of 1997)
- Area Resource File  (ARF) - Bureau of Health Professions, HRSA
- ZIP code files

**Notes:**
The data linkages possible through the link variables provided on the data files increase the value and applications and depth of the HCUP data in large ways.  I am providing you the links we make available, and several examples of what kinds of data can then be linked to.  However, there are many other examples as well.

## Future Challenges

- New validity checks
- Outpatient data sources
- CPT in addition to ICD-9-CM
- Data Quality
- variation in E codes
- variation in diagnosis coding (e.g. AMI as elective)
- external validation (e.g. medical records; other electronic data sources)

**Notes:**
Mention NCHS for the NASS

## Future Challenges

- Data Quality (cont’d)
- variation in coding of secondary diagnoses
- accounting for observation stays / swing beds
- New methodology / coding
- cost-charge ratios, accounting for payer
- implementation of ICD-10-CM
- HIPAA?
- HCUP will move toward HIPAA standard
- ? States
- ? Incorporate state innovation into HIPAA standard

**Notes:**
To the extent possible, given inherent lags in state data accrual and processing and release policies and procedures;  our contractor processing adds only approximately 2 months to the final HCUP product.