Foreword
HCUP Databases
Software
Tools
Most Common Diagnoses and Procedures
Data and methods
Data
source
Study
sample
Diagnoses and
procedure categories
Calculation of
statistics
How to read the
tables
Discussion
References
Tables
Contributors
Acknowledgments
This Research Note provides information on the most frequent diagnoses and procedures for U.S. hospital inpatients in 1996. Its analysis is based on data from the Nationwide Inpatient Sample of the Healthcare Cost and Utilization Project (HCUP). For each of the most common diagnoses and procedures, information on inhospital mortality and mean and median length of stay and total charges is provided.
This information can be used to evaluate the variety of diagnoses associated with a given procedure and the variations in treatment for particular diagnoses.
By Anne Elixhauser, Ph.D., and Claudia A. Steiner, M.D., M.P.H., Agency for Health Care Policy and Research
The unprecedented volume, pace, and variation of change in the U.S. health care system requires a new information paradigm in which State, Federal, and private-sector policymakers have timely and direct access to standardized databases and the tools for using them.
Through the Healthcare Cost and Utilization Project (HCUP), a Federal-State-Industry partnership to build a standardized, multi-State health data system, the Agency for Health Care Policy and Research (AHCPR) has taken the lead in developing databases, Web-based products, software tools, and statistical reports and in making them publicly available to policymakers, health system leaders, and researchers.
This report, based on data from the latest year of the HCUP Nationwide Inpatient Sample (NIS), updates similar analyses based on 1992 data. This report and its predecessor provide answers to the most common questions about hospital care, such as:
The data presented in this research note are organized using the Clinical Classifications Software (CCS), formerly called the Clinical Classifications for Health Policy Research (CCHPR). CCS collapses about 12,000 diagnosis codes and 3,500 procedure codes from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) into a smaller number of clinically meaningful, relatively homogenous clusters. Without the CCS tool, researchers have two relatively unsatisfactory alternatives: They may work directly with the ICD-9-CM coded data; but 12,000 diagnosis codes and 3,500 procedure codes are cumbersome and often too detailed to be useful. Or they may use software that was designed to bundle or aggregate procedures for purposes of payment, such as diagnosis-related groups (DRGs). However, these "reimbursement groupers" mask clinically important details about procedures that were performed. CCS, a "clinical grouper," makes ICD-9-CM data more amenable to clinically focused statistical analyses.
Select NIS data are now available through HCUPnet, an interactive data repository which can accommodate queries on number of discharges, length of stay, charges, and inhospital mortality for specific diagnoses and procedures. Select to access more information on NIS and other HCUP databases, tools, and publications.
We invite you to tell us how you are using HCUP data and tools and to share suggestions of how these products might be improved to better meet your needs. Please E-mail hcup@ahrq.gov.
HCUP, which comprises both nationwide and State databases, contains over 100 variables, including:
State Inpatient Databases (SID). Individual data sets from 19 participating States comprise the SID; each data set contains the universe of that State's non-Federal hospital discharge abstracts. The data have been translated into a uniform format to facilitate cross-State comparisons. The SID represent more than half of all U.S. hospital discharges, and State participation is growing. The SID are particularly well suited for policy inquiries unique to a specific State, studies comparing two or more States, and small area variation analyses. Effective summer 1999, some HCUP partner States are making their inpatient databases for recent years available on CD-ROM through an AHCPR-designated central distributor.
Nationwide Inpatient Sample (NIS). The NIS is a stratified probability sample of hospitals drawn from the SID. The NIS is designed to approximate a 20-percent sample of U.S. community hospitals, including roughly 6.5 million discharges from about 900 hospitals. NIS is the largest all-payer inpatient database in the United States, and data are now available from 1988 to 1996. The NIS is useful for developing national estimates, for analyzing national trends, and for research that requires a large sample size (e.g., care patterns for rare conditions such as congenital anomalies, frequency and distribution of uncommon procedures such as organ transplantations, and hospitalization utilization for population subgroups such as children). NIS releases are available in CD-ROM form, and select data from NIS can be accessed interactively through HCUPnet.
Data files for HCUP were constructed under the technical direction of AHCPR by The MEDSTAT Group (formerly SysteMetrics, Inc.), Santa Barbara, CA, and its subcontractor, the National Association of Health Data Organizations.
AHCPR has developed two software tools from the Healthcare Cost and Utilization Project that can be used on HCUP and other administrative databases.
Quality Indicators (QIs). This set of 33 clinical performance measures can be used with SID, NIS, and other hospital discharge abstract data as a framework for assessing quality in hospitals. The QIs assess three dimensions of care:
The QIs can be used by individual hospitals (e.g., to monitor performance over time or to compare their performance with that of other hospitals) and by States and communities (e.g., to track aggregate quality of care in hospitals or measure access to primary care).
Clinical Classifications Software (CCS). The CCS program aggregates about 12,000 diagnosis codes and 3,500 procedure codes from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) into a smaller number of clinically oriented, relatively homogenous clusters. ICD-9-CM codes are the standard codes used in all institutionally based records (e.g., hospitals, outpatient surgery centers) and in insurance claims data. CCS can be applied to all ICD-9-CM data from 1980 to date, with simple adjustments needed for data prior to 1993. CCS, a "clinical grouper," makes ICD-9-CM data more amenable to clinically focused statistical analyses than Diagnosis Related Groups (DRGs) or other "reimbursement groupers" that may mask important clinical details. CCS can help users examine ICD-9-CM data from a clinical perspective, develop clinically based profiles of resource use, and study patterns of diagnoses and procedures. CCS was formerly called the Clinical Classifications for Health Policy Research (CCHPR).
Select to download the Quality Indicators and the Clinical Classifications Software with user instructions.
This Research Note provides information drawn from a nationwide administrative database on the most frequent combinations of diagnoses and procedures for hospital inpatients, updating a previous publication based on data from 1992 (Duffy, Elixhauser, Sommers, 1996). For each of the 100 most frequently performed principal procedures, Table 1 lists the 5 principal diagnoses most commonly recorded on discharge abstracts of patients who had that procedure during the hospitalization. In addition, for each of the 100 most frequent principal diagnoses, Table 2 lists the 5 principal procedures most commonly performed. Estimated median and mean total charges and length of stay (with standard errors) are presented along with inhospital mortality rates.
This information can serve several audiences. Medical professionals can compare this information with their own practices to assess similarities and differences with the national average and to consider explanations for any differences. Health plans and insurers can compare this information with the experiences of their members and beneficiaries as a first step in determining how payment policies are affecting practice patterns. Researchers and policymakers can examine this information to learn where considerable variations occur, providing fruitful areas for further research, investigation, and potential intervention.
This study employs data from the Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) for 1996. The NIS approximates a 20-percent sample of U.S. community hospitals and collects all inpatient stays from these institutions. The HCUP NIS contains resource use information included in a typical discharge abstract. NIS data for 1996 cover 19 States (Arizona, California, Colorado, Connecticut, Florida, Illinois, Iowa, Kansas, Maryland, Massachusetts, Missouri, New Jersey, New York, Oregon, Pennsylvania, South Carolina, Tennessee, Washington, and Wisconsin) and include 906 hospitals and over 6.5 million discharges.
The NIS is designed to approximate a 20-percent sample of U.S. "community" hospitals, as defined by the American Hospital Association (AHA). The AHA defines community hospitals as all non-Federal short-term (average length of stay less than 30 days) general and specialty hospitals whose facilities are open to the general public. Specialty hospitals include obstetrics-gynecology, pediatric, short-term rehabilitation, orthopedic, and oncology hospitals, among others (American Hospital Association, 1993). Excluded are long-term hospitals, psychiatric hospitals, and alcohol/chemical dependency treatment facilities.
The sample is a stratified probability sample of hospitals in the frame, with sampling probabilities proportional to the number of U.S. community hospitals in each stratum. The hospital universe is defined using the AHA Annual Survey of Hospitals. This universe of hospitals is divided into strata using five hospital characteristics: ownership/control, bedsize, teaching status, rural/urban location, and geographic region. Hospitals from HCUP participating States (the sampling frame) are selected to represent these strata, and all discharges from sampled hospitals are included in the database. Weights indicate the number of discharges that the sample discharge represents in the universe of discharges from U.S. hospitals for that year in that stratum. The total number of discharges in the universe from that stratum is taken from the AHA Annual Survey of Hospitals.
Because administrative data on inpatient stays were not created for research purposes, there may be problems with the reliability and validity of certain data elements. Green and Wintfield (1993) summarized the literature on coding errors for hospital administrative data and described a decline in error rates during the 1970s and 1980s. Fisher, Whaley, Krushat et al. (1992) reported that the accuracy of principal diagnosis and procedure has improved since 1983, when such information became important for determining reimbursement by Medicare and other payers. Green and Wintfield (1993) reported the results of a reabstraction study using records from the California Office of Statewide Health Planning and Development. Information on age and sex was most reliable (error rates less than 1 percent), and principal diagnosis was inaccurate in 9 percent of records. Whittle, Steinberg, Anderson et al. (1991) reported that estimates of cancer incidence rates based on Medicare claims data were within 6 percent of estimates using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) data.
Other problems inherent in hospital inpatient data include missing data, underreporting of socially stigmatized conditions such as alcoholism and drug abuse, and underreporting of minor procedures. Information is presented only on principal diagnosis and principal procedure. Analyses limited to principal diagnoses and procedures will produce an underestimate of diagnoses that tend to appear in secondary positions such as hypertension, osteoporosis, and Alzheimer's disease (May, Kelly, Mendlein et al., 1991). Diagnostic and minor therapeutic procedures, which usually appear as secondary procedures, will likewise be underrepresented when the focus is on principal procedures. Principal diagnoses and procedures are employed to avoid double-counting of stays. Furthermore, the principal diagnosis is of greater interest because it represents the diagnosis which, after evaluation, was the primary reason for admission to the hospital. Similarly, the principal procedure is of interest because it should be the primary therapeutic procedure received by the patient during the stay. Despite these definitions, other diagnoses and procedures sometimes are coded into the principal position. For example, diagnostic procedures are often coded into the principal procedure field.
The entire sample of NIS discharges for 1996 was used for this study (N = 6,542,069) from 906 hospitals. These discharges were weighted to obtain estimates that are representative of hospital inpatient discharges in the United States. The estimated total number of discharges represented in these analyses is 34,874,404. This estimate is comparable to the estimate of 34.4 million discharges (30.5 million non-newborn discharges plus 3.9 million newborns) based on the National Hospital Discharge Survey (Graves and Kozak, 1999).
The unit of analysis is the discharge, or hospital stay, rather than the patient. Because the NIS is limited to inpatient hospital data, conditions treated and procedures performed on an ambulatory basis are not represented here.
The diagnoses and procedures recorded on hospital discharge abstracts are coded using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), Fifth Edition (Public Health Service and Health Care Financing Administration, 1994). The ICD-9-CM consists of over 12,000 diagnosis codes and 3,500 procedure codes. Although it is possible to present descriptive statistics for individual ICD-9-CM codes, it is often helpful to aggregate codes into clinically meaningful categories that group similar conditions or procedures.
For this study, diagnoses and procedures were categorized using the Clinical Classification Software (CCS) which was developed to provide a convenient way to report hospital statistics by diagnosis or procedure.
The diagnosis CCS aggregates illnesses and conditions into 259 mutually exclusive categories, most of which are clinically homogeneous. Some heterogeneous categories combine several less common individual conditions. The procedure CCS contains 231 mutually exclusive categories. Many of the categories represent single procedures; however, some procedures that occur infrequently are grouped according to the body system on which they are performed, whether they are used for diagnostic or therapeutic purposes, and whether they are considered operating room or non-operating room procedures (DRGs: Diagnosis related groups definitions manual, 1996).
All ICD-9-CM coding changes in effect from January 1980 through September 1999 have been incorporated into the coding scheme. Select to access electronic versions of the classification systems.
The number of cases in the NIS was multiplied by hospital-specific discharge weights to derive national estimates of the number of discharges. Results are not presented for any diagnosis or procedure category for which the unweighted number of discharges is less than 70. Using a generalized variance technique for proportions, it was determined that a sample of at least 70 discharges is required to assure, with 95 percent confidence, that the reported proportions had a relative error of less than 30 percent (i.e., if the reported value is p, the error is <.3p).
Standard errors were calculated using SUDAAN, a software package developed by Research Triangle Institute by B.V. Shah. (SUDAAN may be purchased from the Research Triangle Institute, Research Triangle Park, NC.) SUDAAN can handle most survey designs with stratification, providing estimates of measures of central tendency and variances.
Both mean and median charges and mean and median length of stay are presented because the mean may be strongly influenced by extreme values. Charge information for procedures is not the charge for the procedure itself. Instead, it refers to the total charge for the hospitalization in which this procedure was listed as the principal procedure. All charge data are charges for the hospitalization, excluding professional (primarily physician) fees. Charges do not necessarily reflect costs nor are they synonymous with reimbursements. In this study, charge data were present for 98 percent of all discharges.
Table 1 lists in rank order the 100 most commonly performed inpatient principal procedures in the Nationwide Inpatient Sample, categorized using CCS categories. The procedure categories are numbered from P1 through P100, beginning with the most frequent. The number in brackets following the procedure name is the CCS category number.
Following the procedure title, up to five diagnoses are listed, numbered d1 through d5. These are the five most frequent principal diagnoses, classified by CCS categories, for which this procedure was performed. The number in brackets following the diagnoses name is the CCS category number.
The ICD-9-CM codes associated with each CCS diagnosis and procedure category can be found at http://www.hcup-us.ahrq.gov/reports/natstats/his95/clinclas.htm#app.
For each principal procedure CCS and each procedure-diagnosis combination, the table lists the number of discharges, the percent of all discharges, and the percent of discharges with this procedure. For example, upper gastrointestinal endoscopy, biopsy (P7) was listed as principal procedure for 627,503 discharges, representing 1.8 percent of all discharges. The most common principal diagnosis is gastrointestinal hemorrhage, representing 24.3 percent of all discharges with this procedure. Finally, for each procedure and each procedure-diagnosis combination, except those based on 70 or fewer unweighted records, estimates for inhospital mortality, mean and median length of stay, and mean and median charges are presented.
Table 2 lists in rank order the 100 most frequent diagnoses in the NIS, categorized by CCS. The diagnosis categories are numbered D1 through D100, beginning with the most frequent. Table 2 provides information similar to that provided in Table 1. For each diagnosis, we list the top five procedures, the number of discharges, and the percent of discharges for the diagnosis overall and each diagnosis-procedure combination. Information on charges, length of stay and inhospital mortality are also provided, except for those based on 70 or fewer unweighted cases.
This publication helps to answer questions such as these: What are the most frequent diagnoses and procedures? What is this procedure used for? How is this diagnosis managed? Using the Clinical Classifications Software, we list the top 100 procedures in rank order, by frequency, and the top 5 diagnoses associated with each. Information on resource use and on one major outcome—inhospital mortality—is provided. Similar information is provided for the top 100 principal diagnoses and the 5 most frequent principal procedures associated with each.
Examples of some potential uses of this information are given below:
Examining the use of procedures in U.S. hospitals. Table 1 illustrates that no procedures (P1) are performed for approximately 39 percent of inpatient discharges. The most common diagnoses (liveborn, pneumonia, congestive heart failure, affective disorders and coronary atherosclerosis and other heart disease) reflect conditions that are treated medically without surgical interventions or other invasive procedures. These same conditions are among the most frequent diagnoses listed in Table 2. This table shows that no procedures are performed for about 57 percent of liveborn infants (D1), 30 percent of patients with coronary atherosclerosis (D2), 70 percent of patients with pneumonia (D3), 65 percent of patients with congestive heart failure (D4), and 77 percent of patients with affective disorders (D9).
Examining the variety of diagnoses for which a particular procedure is performed. The number of unique principal diagnoses, classified using CCS, that appear on discharge abstracts of patients with this procedure is listed on the line beneath the procedure's title. Some procedures are performed for many diagnoses. Diagnostic cardiac catheterization coronary arteriography (P6), for example, has 193 different principal diagnoses associated with it. On the other hand, other procedures appear to be specific to relatively few diagnoses. Episiotomy (P5) has only 33 associated principal diagnoses.
However, most procedures are performed for a relatively wide array of conditions. For example, in Table 1, the ninth ranked procedure (P9), hysterectomy, is performed for benign neoplasm of the uterus (accounting for about 34 percent of all hysterectomies), prolapse of female genital organs (15 percent), endometriosis (12 percent), and menstrual disorders (11 percent). The major cause of amputation of the lower extremity (P44) is diabetes mellitus, accounting for 35 percent of the reasons for amputation. Gangrene is the next most common principal diagnosis (30 percent) followed by infective arthritis and osteomyelitis (6 percent).
Scrutinizing the variations in treatment for particular diagnoses. As shown in Table 2, fracture of the neck of the femur (D24) is treated in two major ways. While most patients (about 57 percent) receive treatment of fracture directly (p1), nearly a third of patients receive a hip replacement (p2).
Comparing and contrasting the lengths of stay and charges among the top five diagnoses associated with a procedure. Cholecystectomy and common duct exploration (P12) performed for biliary tract disease (d1) is associated with a mean length of stay of 4 days and mean charges of approximately $13,000, as shown in Table 1. However, when this procedure is performed with a principal diagnosis of pancreatic disorders (d2), the mean length of stay doubles and mean charges increase to over $22,000.
Comparing resource use among alternative procedures performed for a diagnosis. Patients with a principal diagnosis of coronary atherosclerosis and other heart disease (D2) exhibit wide variations in resource use depending on the principal procedure performed. As noted earlier, about 30 percent of patients with this diagnosis have no procedure listed. Table 2 shows the mean charges for their hospitalizations are around $5,000. Patients who receive percutaneous transluminal coronary angioplasty (22 percent) have mean charges of over $19,000 while patients who receive a coronary artery bypass graft (19 percent) have mean charges over $44,000.
Evaluating changes over time. Compared with data from 1992 (Duffy, Elixhauser, and Sommers, 1996), Table 1 shows that the use of certain procedures has declined over time. Inguinal and femoral hernia repair decreased in rank from the 48th most common procedure in 1992 to 93rd in 1996. Endoscopy and endoscopic biopsy of the urinary tract decreased in rank from 43rd to 74th during this time, and transurethral resection of the prostate dropped from 22nd to 39th. On the other hand, the use of some procedures increased over time. Spinal fusion increased from 91st to 48th most common procedure. Prophylactic vaccinations and inoculations (primarily hepatitis B vaccine for infants) did not even appear in the top 100 procedures in 1992 but ranked 19th in 1996.
As with use of procedures, changes in the rank order of principal diagnoses can also be evaluated over time. As shown in Table 2, hyperplasia of the prostate was the 40th most common diagnosis in 1992 but fell to 78th in 1996. Cancer of the prostate, which was 60th in 1992, ranked 81st 4 years later. Other conditions increased dramatically during this time period. Substance-related mental disorders rose from the 75th most common principal diagnosis in 1992 to 40th in 1996. Aspiration pneumonitis increased from 86th to 54th and septicemia increased from the 29th to 18th.
Appropriate precautions should be taken in using administrative data for research purposes, as described in this report. Also, it should be noted that this analysis does not control for important determinants in the use of medical resources, such as severity of illness. Despite these limitations, the information in these tables can be used as a national comparison or as a starting point to explore a number of issues.
American Hospital Association hospital statistics. 1993-94 edition. Chicago, IL: American Hospital Association; 1993.
DRGs: Diagnosis related groups definitions manual, version 14.0. Wallingford, CT: 3M Health Information Systems, 1996.
Duffy SQ, Elixhauser A, Sommers JP. Diagnosis and procedure combinations in hospital inpatient data. Healthcare Cost and Utilization Project, HCUP-3 Research Note 3. Rockville, MD: Agency for Health Care Policy and Research; 1996. AHCPR Pub. No. 96-0047.
Fisher ES, Whaley FS, Krushat WM, et al. The accuracy of Medicare's hospital claims data: Progress has been made, but problems remain. American Journal of Public Health 1992; 82: 243-248.
Graves EJ, Kozak LJ. National Hospital Discharge Survey: Annual Summary, 1996. Vital and Health Statistics 13(140). National Center for Health Statistics; 1999.
Green J, Wintfield N. How accurate are hospital discharge data for evaluating effectiveness of care? Medical Care 1993; 31(8): 719-731.
May DS, Kelly JJ, Mendlein JM, Garbe PL. Surveillance of major causes of hospitalization among the elderly, 1988. Morbidity and Mortality Weekly Reports 1991; 40(SS-1): 7-17.
Public Health Service and Health Care Financing Administration. International classification of diseases, 9th revision, clinical modification. Vols. 1, 2, and 3; fifth edition. Washington, DC: Public Health Service; 1994. HHS Publication No. (PHS) 94-1260.
Whittle J, Steinberg E, Anderson G, Herbert R. Accuracy of Medicare claims data for estimation of cancer incidence and resection rates among elderly Americans. Medical Care 1991; 29(12):1226-1236.
AHCPR thanks the following HCUP partner organizations for their data contributions:
AHCPR supplements the HCUP databases with data on hospitals and local communities from various sources. The American Hospital Association has provided data from its Annual Survey of Hospitals and various special surveys since 1970. County-level statistics are obtained from the Area Resource File, compiled by the Bureau of Health Professions within the Health Resources and Services Administration. Bureau of the Census statistics at the ZIP Code level, provided by CACI Marketing Systems, are also used.
Social and Scientific Systems, Inc., Bethesda, MD, provides computer programming support for AHCPR researchers. We would like to thank Suzanne Worth and Teresita Monasterio at Social and Scientific Systems for their work on this project.
AHCPR Pub. No. 99-0046
Current as of October 1999
Send Questions & Comments to: hcup@ahrq.gov
Internet Citation:
Elixhauser A, Steiner CA. Most Common Diagnoses and Procedures in U.S. Community Hospitals, 1996. Summary, HCUP Research Note. Agency for Health Care Policy and Research, Rockville, MD. http://www.hcup-us.ahrq.gov/reports/natstats/commdx/commdx.htm
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