This retrospective population-based cohort study was approved by the Institutional Review Board (IRB) (IRB approval number: X-2303–819-902). The sharing of data for this initiative was authorized by the Big Data Center of the National Health Insurance Service (NHIS) (NHIS-2023–1-526). Informed consent was not necessary for data analyses due to the retrospective nature of this study and the utilization of anonymized data.

This research utilized information from the NHIS, South Korea’s sole public insurance system. All disease diagnoses and prescription information for any medication, procedure, or both must be entered into the NHIS database by law. Registration enables individuals to qualify for government-sponsored health insurance programs. Classifications from the 10th Revision of the International Classification of Diseases (ICD-10) were used to extract all diagnoses. The NHIS, a healthcare system operated by the South Korean government, mandates the registration of foreign residents who have been in the country for more than 6 months. Moreover, comprehensive data regarding the death dates and socioeconomic status of each individual can be found in the NHIS database (Lee et al. 2017).

We included adult patients who were admitted to the hospital and underwent orthopedic surgery between January 1, 2019, and December 31, 2021, in South Korea. The orthopedic procedures are detailed in Table S1. Orthopedic procedures were classified into four groups: total knee arthroplasty (TKA), total hip arthroplasty (THA), fracture surgery, and other arthroplasties. Only initial orthopedic surgery was included in the study if it was performed more than twice during the study period. By applying these inclusion criteria, we aimed to ensure that the patients included in our study had similar characteristics, thereby promoting homogeneity. Among the included patients, those who were admitted to the hospital that used the RRS were assigned to the RRS group, whereas those who were admitted to the hospital that did not operate the RRS were assigned to the non-RRS group.

South Korea’s Ministry of Health and Welfare has been paying insurance payments to hospitals that use the RRS since 2019 (Lee and Hong 2019). When a hospital establishes a separate rapid response team and offers monitoring or an RRS to patients in general wards, the “RRS operating charge” is calculated once each day of hospitalization. The RRS must be supported by experts in internal medicine, neurology, surgery, neurosurgery, thoracic surgery, anesthesiology, pain medicine, and emergency medicine. RRS nurses must have at least 3 years of clinical experience in a regular hospital emergency department or intensive care unit. A video laryngoscope, portable mechanical ventilator, portable ultrasonography device, and point-of-care testing device are required for the RRS operation. Type 1 RRS must be operational 24 h a day, 365 days a year; Type 2 must be operational for at least 5 days per week, 16 h per day; and Type 3 for at least 5 days per week, 8 h per day.

This study had two endpoints: in-hospital mortality and postoperative complications. In-hospital mortality was defined as death after orthopedic surgery during hospitalization. Postoperative complications were defined as the occurrence of the following diseases during hospitalization after orthopedic surgeries: cerebral infarction or hemorrhage (ICD-10 codes I60 to I64), acute coronary events (I21, I22, and I252), heart failure (I50), pulmonary embolism (I26), acute and subacute hepatic failure (K720), acute renal failure (N17), sepsis (A40 and A41), wound infection (T793 and T814), pneumonia (J12 to J18 and J69), and urinary tract infection (N390, T835, and N30). Categorization criteria for postoperative complications were based on previous research (Makito et al. 2020).

Demographic data, such as age and sex, were obtained. Employment status, household income level, and residence were collected as covariates to indicate the patients' socioeconomic status. Five categories of household income levels were developed, including the four quartile ratio groups and the medical aid program group. Individuals who were poor and unable to pay insurance premiums were classified as participants in the government medical aid programs. The capital and other important communities were classified as urban areas, while the remaining regions were classified as rural areas.

The Charlson Comorbidity Index and underlying disability were used to reflect the comorbidity status of the patients. Charlson Comorbidity Index scores at hospital admission were calculated using the ICD-10 codes (Table S2) registered in the NHIS database.

Furthermore, it is mandatory to register all disabilities in the NHIS database to determine eligibility for a diverse range of benefits provided by social welfare programs in South Korea. Every disability must be formally diagnosed by a medical professional, based on the challenges encountered during the execution of routine activities. In Table S3, the classification of disabilities is detailed. The severity of the condition determined which patient was assigned to one of six severity classifications (first: most severe; sixth: least severe). Grades one through three were deemed “severe,” whereas grades four through six were deemed “mild to moderate.”

To reflect hospital capacity, hospital level (A, B, C, and D), postoperative intensive care unit (ICU), duration of stay in the ward, type of anesthesia (general or regional), and year of surgery were collected as covariates. Duration of stay in the ward (days) was collected because the RRS targeted hospitalized patients in the ward (not the ICU).

Clinicopathological characteristics, represented as categorical variables, are expressed as means and standard deviations, and categorical variables are expressed as numbers and percentages. To compare the clinicopathological characteristics of the RRS group with those of the non-RRS group, the t-test was used for continuous variables and the chi-square test was used for categorical variables.

A hierarchical approach was used to determine the hospital levels, which were included as covariates. Hierarchical cluster analysis was performed using hospital-related variables, such as hospital type (tertiary general, general, and other types of hospital), total number of general and specialist doctors, nurses, pharmacists, hospital beds, operating room, and adult ICU beds using agglomerative clustering. Four hospital levels were determined based on the hierarchical clustering analysis results. Table S4 provides information about the characteristics of the hospitals.

After adjusting for covariates, we used multivariable logistic regression to determine whether the RRS group had an increased risk of in-hospital mortality or postoperative complications compared with the non-RRS group. The adjustment model incorporated all covariates, and the outcomes were displayed as odds ratios (ORs) with 95% confidence intervals (CIs). In addition, we performed multivariable logistic regression analyses to examine whether the RRS group had postoperative complications. Sensitivity and subgroup analyses were conducted based on the type of RRS and surgery to determine whether these factors influenced the results. All statistical analyses were conducted using R software (version 4.0.3, R Utilities). The threshold for significance was set at P < 0.05.