^{1,2}is known about the demographics of pediatric surgery and the types of procedures performed in pediatric

*versus*nonpediatric hospitals. For example, pediatric hospitals may not perform much of the pediatric surgery in a state. In California, more than 85% of hospitals providing inpatient surgical care for infants performed less than one operation per week on infants.

^{1}In a rural state such as Iowa, pediatric hospitals are located far from many patients. Children may have routine surgery at closer facilities that perform few procedures each year, or they may undergo physiologically complex procedures at nonpediatric

^{3}facilities.

^{4}Our goals were twofold. First, we evaluated how to use statewide discharge abstracts to quantify where infants and young children have surgery in a state. Second, we investigated the volume, diversity, and physiologic complexity of operative procedures to learn how a pediatric hospital can differentiate itself from nonpediatric facilities and from other pediatric hospitals.

#### Methods

##### Databases

*International Classification of Diseases, 9th Revision, Clinical Modification*(ICD-9-CM). Some of the outpatient procedures were coded using Current Procedural Terminology codes. These were converted to the corresponding ICD-9-CM as specified by the Iowa Hospital Association's Statewide Outpatient Database's Outpatient Procedure Dictionary. Population sizes for Iowa and its counties were from the year 2000 census.

^{3}This was determined from the pediatric residency section of the American Medical Association's Fellowship and Residency Electronic Interactive Database (accessed on September 1, 2002).

##### Operative Procedures

*i.e.*, procedures that are frequently associated with operating room charges).

^{5}For example, diagnostic procedures (ICD-9-CM 87.0 and greater) were excluded. We added the requirement that an incision be made for a procedure to be operative.

^{5}For example, cardiopulmonary bypass (39.61) was excluded. We did not require that there be surgical closure for a procedure to be operative. For example, myringotomy with insertion of tube was included (20.01). With these definitions, there were 5,671 operative procedures performed during 462 inpatient admissions and 3,143 outpatient visits.

##### Comparison of Operative Procedures among Facilities

##### Physiologic Complexity of Procedures

^{6–8}We considered a procedure to be “physiologically complex” if it had more than seven ASA RVG basic units.

^{6,7}For example, repair of syndactyly (three units), repair of inguinal hernia (four units), adenoidectomy (five units), and pyloromyotomy (seven units) were not considered to be physiologically complex.

^{5–8}Repair of myelomeningocele (eight units), creation of ventriculoperitoneal shunt (10 units), craniectomy for craniosynostosis (11 units), posterior segmental instrumentation (13 units), Blalock-Taussig shunt (15 units), and complex pediatric cardiac surgery repairs (20 units) were considered to be physiologically complex.

^{6–8}

^{8}As described in detail previously,

^{6}we obtained the ASA RVG basic units from the ICD-9-CM procedure codes by modifying a Current Procedural Terminology to ICD-9-CM Crosswalk (ADP Context, Inc., Westmont, IL, 1999 edition). Although for each ICD-9-CM there were from one to 122 different Current Procedural Terminology codes, for almost all ICD-9-CM all of the relevant Current Procedural Terminology codes either had seven or fewer ASA RVG basic units or eight or more basic units.

^{6}Exceptions were ICD-9-CM for which the relevant number of basic units depended on the anatomic location. For example, the basic units for “biopsy of lymphatic structure” (40.11) vary depending on whether these are internal mammary nodes, superficial nodes, and so forth. Then, we relied on the anatomic location determined from the patient's other diagnoses and procedures.

##### Number of Different Types of Procedures and Internal Herfindahl Index

^{9}For example, outpatient surgery facilities are often classified based on whether they are single

*versus*multiple specialty.

^{10}Quantifying diversity using the number of different types of procedures is simple conceptually. However, it is a biased estimator with poor precision because it addresses only whether a type of procedure is performed, not how often.

^{9}That is, it equals the probability that if two procedures are selected at random, both will be of the same type of procedure. Whereas the number of different types of procedures only considers whether a type of procedure is performed at a facility, the internal Herfindahl index uses data on how often each type of procedure is performed.

^{2}+ (0.15)

^{2}+ (0.10)

^{2}.

*e.g.*, myringotomy with insertion of tube). Then, the internal Herfindahl index would equal 1.0, where 1.0 = (1.00).

^{2}The index equals its maximum value of one when a facility “specializes” in only one type of procedure.

^{2}The minimum value of the internal Herfindahl index equals one divided by the number of different types of procedures performed at the facility.

##### Similarity of the Surgical Practices at the Two Pediatric Hospitals

^{11}This similarity index is a correlation coefficient between the percentages of all procedures at each facility that are accounted for by each type of procedure. Specifically, suppose that a procedure is selected at random from all of the procedures performed at each of the two facilities. The numerator gives the probability that both procedures will be of the same type of procedure (

*i.e.*, analogous to the internal Herfindahl index). The denominator normalizes the sum of the probabilities to between 0 and 1. The similarity index equals zero when there is no overlap in the types of procedures performed between two facilities. The index equals one when the relative frequency with which each type of procedure is performed is the same between facilities.

##### Statistical Methods

^{12}Equations for the standard error of the internal Herfindahl index are given in the Appendix. Equations and algorithms to calculate the standard error of the similarity index are given in the Appendix. In the Appendix, we also describe the algorithm that we used for comparing similarity indices between two or more pairs of hospitals.

#### Results

##### Volume

*i.e.*, an average of < 1 procedure per week) accounted for 7.3% of the 5,671 procedures performed statewide (95% CI, 6.6% to 8.0%).

##### Diversity of Procedures (Comprehensiveness)

##### Physiologically Complex Procedures

##### Age of Patients

*versus*31% of the children (n = 2,976) treated at the other 91 facilities (

*P*< 10

^{−4}). The percentages are shown in figure 3 for facilities caring for at least 25 children during the 6-month period studied.

##### Differentiating between Hospitals

*P*< 0.001) (fig. 4).

#### Discussion

*e.g.*, “our patients are younger,” or “our procedures are more complicated”). We studied how each pediatric hospital can use discharge abstract data to investigate how it differs from nonpediatric hospitals. In Iowa, each pediatric hospital can show that it provides a more diverse, comprehensive, and physiologically complex selection of procedures in younger patients than 90 of 93 facilities performing pediatric surgery (figs. 1 through 3, tables 2 and 3). For example, the larger pediatric hospital can show that it performs 64% of all physiologically complex pediatric surgery statewide, and is the highest-volume facility for 67% of the different types of procedures.

*i.e.*, that the hospitals are not one collective group, interchangeable in all but location). Administrators and physicians may perceive value in showing that their hospital is not providing a commodity, “pediatric surgery,” but rather is serving a unique role in its state healthcare system. Using the methodology that we developed, we showed that the surgical practices of the pediatric hospitals were highly distinguishable (fig. 4).

*e.g.*, appropriately sized endotracheal tubes). They also have frequent practical experience in performing pediatric inductions and placements of intravenous catheters. However, very few facilities provide anesthesia care for physiologically complex procedures (fig. 1, table 3). Many of the highest volume facilities perform no physiologically complex procedures. Therefore, analysis of discharge abstract databases provides an opportunity for pediatric hospitals to assist organizations in appreciating that, for purposes of disaster planning, both total pediatric surgical volume and whether a facility performs pediatric surgery are misleading.

^{4}: “There should be a…policy designating…the types of pediatric…procedures…, and indicating the minimum number of cases required in each category for the facility to maintain its clinical competence in their performance.” The Agency for Healthcare Research & Quality's fact sheet to help prevent medical errors in children recommends that parents “choose a hospital at which many children have the procedure…your child needs.”

^{13}However, for purposes of differentiating the services of a pediatric hospital from those of nearby nonpediatric facilities, there may be an advantage to focusing not on such volume issues but instead on the diversity of the types of procedures performed. Although a pediatric hospital is likely to be among the highest-volume facilities for pediatric surgery, it may not be the single highest-volume facility (fig. 1).

##### Limitations

^{2}Quantifying the diversity of the types of procedures performed at a facility using these databases is sound, because it is procedures

*per se*that are relevant to that goal. These databases are weaker for making volume-based arguments, because the number of procedures is strongly correlated to but less relevant than the number of cases.

*e.g.*, based on hospital charges or diagnosis-related groups), but include several procedures. Thus, we suspect that it is best to use these databases to focus on issues related to the diversity of the types of procedures performed at facilities.

#### Conclusions

##### Appendix

##### Calculation of Point Estimate and Standard Error of Similarity Index

Equation A1 Image Tools |
Equation A2 Image Tools |
Equation A3 Image Tools |

Equation A4 Image Tools |
Equation A5 Image Tools |
Equation UA6 Image Tools |

Equation UA7 Image Tools |
Equation UA8 Image Tools |
Equation A9 Image Tools |

Equation UA10 Image Tools |
Equation UA11 Image Tools |
Equation UA12 Image Tools |

Equation UA13 Image Tools |

_{jk}represent the proportion of procedures performed at the j

^{th}facility that are of the k

^{th}type of procedure, k = 1, 2, …, S, where S refers to the total number of different types of procedures. Then, the similarities of the relative frequencies with which different types of procedures are performed at two facilities

^{11}: Let n

_{j}refer to the number of procedures performed at the j

^{th}facility during the observation period. Let X

_{ij}specify the type of the i

^{th}procedure at the j

^{th}facility, i = 1, 2, …, n

_{j}. Let P

_{k}refer to the k

^{th}type of procedure, k = 1, 2, …, S. Finally, let IS( ) equal 1 if the value in the expression is true, and 0 otherwise. Then, the observed proportion of procedures at the j

^{th}facility that are of the k

^{th}type is: The nonparametric maximum likelihood estimator for θ is obtained by substituting the observed proportions p̂;

_{jk}in equation 2 for the true proportions p

_{jk}in equation 1: ˆθ is asymptotically normally distributed

^{11,14}We used Cramér's delta method

^{15}in the following manner

^{11,14}to obtain the standard error for ˆθ. Define: and MATH Equations 4 and 5 are the nonparametric maximum likelihood estimators for the internal Herfindahl index, shown in figure 2. Expanding the denominator of equation 3:MATH The square of the standard error of ˆθ equals:MATH Where MATH MATH MATH and MATH Equation 9 was used to calculate the standard error of the internal Herfindahl index.

Equation UA14 Image Tools |
Equation UA15 Image Tools |
Equation UA16 Image Tools |

Equation UA17 Image Tools |
Equation UA18 Image Tools |
Equation UA19 Image Tools |

Equation UA20 Image Tools |
Equation UA21 Image Tools |
Equation UA22 Image Tools |

Equation UA23 Image Tools |

_{11}, X

_{12}, …, and MATH, with notation as given in equation 2. A value MATH was selected at random from the original n

_{1}observations. A second value MATH was selected at random from the original n

_{1}observations. The process was continued until there were n

_{1}new bootstrap samples of the original observations from the first facility:MATH. These values were substituted into equation 2 to obtain MATH. A new bootstrap sample was made of the original observations from the second facility:MATH. These values were likewise substituted into equation 2 to obtain MATH. The bootstrapped observed proportions, MATH, were then substituted into equation 3 to obtain MATH.

^{15}

*i.e.*, which had a low volume for purposes of this type of analysis). We also repeated this analysis by comparing, for each bootstrap sample, the smaller and larger pediatric hospital to the mean of the N–2 similarities between the smaller pediatric hospital and each of the other facilities. The differences in similarities and the standard errors of the differences were essentially the same (−0.06 ± 0.08 pooled

*vs.*−0.04 ± 0.08 using the mean of N–2 similarities to create each bootstrap difference). Cited Here...