Healthgrades Obstetrics & Gynecology Ratings 2022 Methodology

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Contents

Healthgrades Obstetrics & Gynecology Ratings 2022 Methodology

Obstetrics & Gynecology Ratings

Data Source

Evaluating Performance in Labor and Delivery and Gynecologic Surgeries

Multivariate Logistic Regression-Based Ratings

Developing Healthgrades Ratings

Statistical Models

Limitations of the Data Analysis

Obstetrics & Gynecology Excellence Ratings

To help consumers evaluate and compare hospital performance in Labor and Delivery (C-Section Delivery, Vaginal Delivery) and Gynecologic Surgeries (Hysterectomy and Gynecologic Procedures), Healthgrades analyzed patient outcome data for all patients (all-payer data) provided by 16 individual states for years 2018 through 2020.

  • The Labor and Delivery service line refers to the care of a patient during labor and delivery.
  • The Gynecologic Surgeries service line refers to surgery on the reproductive system and includes surgeries for benign conditions, cancer, infertility, and various other conditions. Ratings were based on Healthgrades risk-adjustment methodology, and the Healthgrades ratings are available at www.healthgrades.com.

Data Source

For the Labor and Delivery and Gynecologic Surgeries hospital ratings, all-payer state data were used for those states that make data available. These data were chosen because they represent virtually all discharges (across nearly all patient ages) for the associated states. However, patient volumes may differ due to data masking by state agencies to protect patient privacy. The data represent three years of discharges (2018 through 2020).

The following 16 states evaluated were:

  • Colorado
  • Florida
  • Maryland
  • Nevada*
  • Oregon
  • Pennsylvania
  • Virginia*
  • Washington*
  • Illinois
  • New Jersey
  • Rhode Island*
  • West Virginia
  • Iowa
  • New York*
  • Texas
  • Wisconsin

      *See the Healthgrades Obstetrics & Gynecology Ratings 2022 Methodology for more information on all-payer states citations and disclaimers

Evaluating Performance in Labor and Delivery and Gynecologic Surgeries

Fair and valid comparisons between hospital providers can be made only to the extent that the risk-adjustment methodology considers important differences in patient demographic and clinical characteristics. The purpose of risk adjustment is to obtain fair statistical comparisons among disparate populations or groups. Significant differences in demographic and clinical risk factors are found among patients treated in different hospitals. Risk adjustment of the data is needed to make accurate and valid comparisons of clinical outcomes at different hospitals.

The risk-adjustment methodology used by Healthgrades defines risk factors as those clinical and demographic variables that influence patient outcomes in significant and systematic ways. Risk factors may include age, gender, specific procedure performed, and comorbid conditions, such as hypertension, chronic renal failure, heart failure, and diabetes. The methodology is disease-specific and outcome-specific. This means that individual risk models are constructed and tailored for each clinical condition or procedure using multivariate logistic regression.

For multivariate logistic regression-based ratings (see below), Healthgrades conducted a series of data quality checks to preserve the integrity of the ratings. Based on the results of these checks, we excluded a limited number of cases because they were inappropriate for inclusion due to miscoding or missing data or other reasons as listed below.

Labor and Delivery Exclusions

For Labor and Delivery (C-Section Delivery and Vaginal Delivery), hospital performance was evaluated in two areas:

  • Patients undergoing single live-born vaginal deliveries
  • Patients undergoing single or twin live-born C-section deliveries

The following patient records were excluded:

  • Patients who left the hospital against medical advice, were transferred to another acute care facility, or whose discharge status was unknown
  • Patients who were still in the hospital when the claim was filed
  • Patients with gender listed as male or unknown recorded in their hospital claims data
  • Patients under the age of 15 or over the age of 55

Gynecologic Surgeries Exclusions

For Gynecologic Surgeries (Hysterectomy and Gynecologic Procedures), hospital performance was evaluated in two areas:

  • Patients undergoing a hysterectomy
  • Patients undergoing all other gynecologic procedures

The following patient records were excluded:

  • Patients who left the hospital against medical advice or who were transferred to another acute care hospital
  • Patients who were still in the hospital when the claim was filed
  • Patients with male gender recorded in their hospitals claims data
  • Patients under the age of 18

Multivariate Logistic Regression-Based Ratings

The initial analysis utilized all-payer data from 16 states for years 2018 through 2020. Patients were identified by their ICD-10 principal procedure for gynecologic surgeries and Diagnosis Related Groups (DRGs) for labor and delivery. A full list of ICD-10 codes can be found in Healthgrades ICD-10 Mapping Tool at https://icd10mappingtool.healthgrades.com/.

For these populations, potential risk factors and the outcome measure (complications) were then defined.

1.        Potential risk factors were defined as all clinically relevant comorbid conditions and procedures. In addition, patient demographic factors, such as age, gender, and source of admission were also considered. Some diagnosis codes were merged together (e.g., primary and secondary pulmonary hypertension) to minimize the impact of coding variations.

2.        Complications were identified using previous peer-reviewed research and thorough input from clinical and coding experts. While complications sometime occur during a patient's hospital stay, Healthgrades pinpoints complications that should not occur with a typical patient. Many of these complications are preventable and usually cause a prolonged hospital stay, additional and costly medical treatments, harm, and sometimes even death.

Due to variation in coding requirements, practices, and quality in the Labor and Delivery service line, complications were defined solely by the ICD-10 code. Present on admission was not considered in these definitions, as research determined it to be an unreliable indicator for this service line.

In some cases, an ICD-10 code can be either a risk or a complication. In these cases, if Present on Admission information is not available, a code is differentiated by the presence or absence of a postoperative complication code. For example, in the case where a patient record contains “I48.0 Atrial Fibrillation,” that code is considered a risk if it occurs by itself and a complication if there is a corresponding “Cardiac Complications NEC” ICD-10 code also present in the patient record. Outcomes were binary, with documented complications either present or not. Mortality is considered a complication.

Developing Healthgrades Ratings

Developing the Healthgrades ratings involved four steps.

1.        The predicted value (predicted complications) was obtained using a logistic regression model discussed in the next section.

2.        The predicted value was compared with the actual or observed number of complications. Only hospitals with at least 30 cases across three years of data and at least five cases in the most current year were included.

3.        A test was conducted to determine whether the difference between the predicted and actual values was statistically significant. This test was performed to make sure that differences were very unlikely to be caused by chance alone.

4.        Hospital performance was stratified into one of three performance categories as listed below:

        ★★★★★        Better Than Expected – Actual performance was better than predicted and the difference was statistically significant.

        ★★★        As Expected – Actual performance was not significantly different from what was predicted.

        ★        Worse Than Expected – Actual performance was worse than predicted and the difference was statistically significant.

Statistical Models

Using the list of potential risk factors described above, we used logistic regression to determine to what extent each one was correlated with the quality measure (complications). A risk factor stayed in the model if it had an odds ratio greater than one (except clinically relevant procedures, cohort defining principal diagnoses, and some protective factors, as documented in the medical literature, were allowed to have an odds ratio less than one) and were also required to be statistically significant (p <0.05).

Complications were not counted as risk factors, as they were considered a result of care received during the admission. Risk factors are those diagnoses that are the most highly correlated with the outcomes studied (complications). The most highly correlated risk factors are not necessarily those with the highest volume.

The statistical model was checked for validity and finalized. This model was then used to estimate the probability of a complication for each patient in the cohort. Patients were then aggregated for each hospital to obtain the predicted number of complications for each hospital. Statistical significance tests were performed to identify, by hospital, whether the actual and predicted rates were significantly different.

Limitations of the Data Analysis

While these analyses may be valuable in identifying hospitals that perform better than others, one should not use this information alone to determine the quality of care provided at each hospital. The analyses are limited by the following factors:

  • Cases may have been coded incorrectly or incompletely by the hospital.
  • Healthgrades conditions and procedures models can only account for risk factors that are coded into the billing data. Therefore, if a particular risk factor was not coded into the billing data (such as a patient's socioeconomic status and health behavior) then it was not accounted for.
  • Although Healthgrades has taken steps to carefully compile these data, no techniques are infallible; therefore, some information may be missing, outdated or incorrect.

Please note that a high ranking for a particular hospital is not a recommendation or endorsement by Healthgrades Operating Company, Inc. of a particular hospital; it means that the data associated with a particular hospital has met the foregoing qualifications. Only individual patients can decide whether a particular hospital is suited for their unique needs.

Also note that if more than one hospital reported under a single provider identifier, Healthgrades analyzed patient outcome data for those hospitals as a single unit. Throughout this document, therefore, “hospital” refers to one hospital or a group of hospitals reporting under a single provider identifier.

For the full appendices, see the Healthgrades Obstetrics & Gynecology Ratings 2022 Methodology PDF.

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