Uses for aggregated EHR coded data

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Introduction

As the electronic health record (EHR) continues to become more pervasive, the utilization of clinical and administrative data for research, quality, and process improvement initiatives has also increased. Important trends in health care economics, quality, outcomes, and complications can more easily be identified with aggregated clinical data. In particular, rare or congenital disorders are difficult to study on a local basis given their relatively low frequncy. Now that computer assisted coding is also being used more frequently to improve administrative coding quality, the available aggregated data are more abundant and reliable.

The following are several examples of data sources that are readily available to the public for purchase for a (typically) nominal fee. Each data set has coded EHR elements such as ICD-9-CM diagnosis and procedure codes, age, comorbidities, complications, charge of hospital stay, and profile data related to the originating hospital.

The Agency for Healthcare Research and Quality (AHRQ)

AHRQ is an organization focused on improving quality, patient safety, outcomes, effective care, and technology assessment.

From the AHRQ website [1]: “AHRQ is the health services research arm of the U.S. Department of Health and Human Services (HHS), complementing the biomedical research mission of its sister agency, the National Institutes of Health. AHRQ is a home to research centers that specialize in major areas of health care research such as quality improvement and patient safety, outcomes and effectiveness of care, clinical practice and technology assessment, and health care organization and delivery systems.”

AHRQ HCUP Databases

The AHRQ has developed the Healthcare Cost & Utilization Project (HCUP) [2]. HCUP is family of health care databases and related software tools and products developed through a Federal-State-Industry partnership, sponsored by AHRQ. HCUP databases bring together the data collection efforts of State data organizations, hospital associations, private data organizations, and the Federal government to create a national information resource of patient-level health care data.

National Databases

AHRQ HCUP has several national databases with aggregated sampled data for use by researchers.

Nationwide Inpatient Sample (NIS)

http://www.hcup-us.ahrq.gov/nisoverview.jsp [3]

The NIS is a 20-percent stratified sample of U.S. community hospital discharges. It is the largest publicly available all-payer (Medicare, Medicaid, and private payer) database in the United States with approximately 8 million hospital stays each year. The first data file available is 1988 and the data are published yearly. Numerous important studies have been published using NIS data.

Kids’ Inpatient Database (KID)

http://www.hcup-us.ahrq.gov/kidoverview.jsp [4]

The KID is similar to the NIS database except that it focuses on inpatient data for children. The first year available is 1997 and the data are published every three years.

Nationwide Emergency Department Sample (NEDS)

http://www.hcup-us.ahrq.gov/nedsoverview.jsp [5]

The NEDS is also similar to the NIS database in that it provides a 20-percent stratified sample of U.S. hospital-based emergency department visits. The first year available is 2006 and the data are published yearly.

State Databases

AHRQ HCUP has several state databases with aggregated sampled data for use by researchers. More states have participated as years have passed.

State Inpatient Databases (SID)

http://www.hcup-us.ahrq.gov/sidoverview.jsp [6]

SID are state-based data that encompass approximately 97% of annual discharges in the United States. They are a powerful set of data. Data are available from participating states starting in 1990 and the data are published yearly.

State Ambulatory Surgery Databases (SASD)

http://www.hcup-us.ahrq.gov/sasdoverview.jsp [7]

SASD is a state-based set of databases that provide information about patients who had surgery performed on the same day in which the patients are admitted and released. Data are available from participating states starting in 1997 and the data are published yearly.

State Emergency Department Databases (SEDD)

http://www.hcup-us.ahrq.gov/seddoverview.jsp [8]

SEDD provide state-based emergency department encounter information. Only encounters that do not result in hospitalization are included, providing a unique subset of data. Data are available from participating states starting in 1999 and the data are published yearly.

Centers for Medicare & Medicaid Services (CMS) Medical Provider Analysis and Review (MEDPAR) File

CMS MEDPAR [9]

The CMS MEDPAR File contains data from claims for services provided to beneficiaries admitted to Medicare certified inpatient hospitals and skilled nursing facilities. Data are available from admission to discharge and represent a complete set (Medicare) of population health care data. Data are available annually starting in 1991.

Dartmouth Atlas of Health Care

http://www.dartmouthatlas.org/ [10]

The Dartmouth Atlas of Health Care utilizes Medicare data to analyze economic trends in health care by region using geographic information system technology. The Dartmouth Atlas of Health Care is an example of way in which researchers can use aggregated EHR coded data to help identify important trends in health care spending.

University HealthSystem Consortium (UHC)

http://www.uhc.edu [11]

UCH is a Chicago, Illinois based alliance of academic medical centers (currently 116) that was formed in 1984. Members share their EHR coded data to do things such as: measure performance, improve performance, optimize supply chain, and increase revenue. It is an extensive collaborative alliance that takes full advantage of shared data captured by the EHR.

References

  1. http://www.ahrq.gov [12]
  2. http://www.ahrq.gov/data/hcup/ [13]
  3. http://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/IdentifiableDataFiles/MedicareProviderAnalysisandReviewFile.html [14]
  4. http://www.uhc.edu [15]

Submitted by Benjamin Rosenbaum