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Korean J Anesthesiol > Volume 77(1); 2024 > Article
Abdullah, Lim, Ke, Salim, Lan, Dong, and Feng: The SingHealth Perioperative and Anesthesia Subject Area Registry (PASAR), a large-scale perioperative data mart and registry



To enhance perioperative outcomes, a perioperative registry that integrates high-quality real-world data throughout the perioperative period is essential. Singapore General Hospital established the Perioperative and Anesthesia Subject Area Registry (PASAR) to unify data from the preoperative, intraoperative, and postoperative stages. This study presents the methodology employed to create this database.


Since 2016, data from surgical patients have been collected from the hospital electronic medical record systems, de-identified, and stored securely in compliance with privacy and data protection laws. As a representative sample, data from initiation in 2016 to December 2022 were collected.


As of December 2022, PASAR data comprise 26 tables, encompassing 153,312 patient admissions and 168,977 operation sessions. For this period, the median age of the patients was 60.0 years, sex distribution was balanced, and the majority were Chinese. Hypertension and cardiovascular comorbidities were also prevalent. Information including operation type and time, intensive care unit (ICU) length of stay, and 30-day and 1-year mortality rates were collected. Emergency surgeries resulted in longer ICU stays, but shorter operation times than elective surgeries.


The PASAR provides a comprehensive and automated approach to gathering high-quality perioperative patient data.


According to the Lancet Commission on Global Surgery, 4.2 million people worldwide die within 30 days of surgery per year. This major mortality risk exceeds that of human immunodeficiency virus, malaria, and tuberculosis combined [1,2]. Many of these deaths are potentially preventable [35]. This is in addition to other nonfatal perioperative complications, which can cause negative long-term health outcomes [6,7] and contribute to high healthcare costs [8,9]. Hence, improving perioperative outcomes is essential, as not only individual patients but also health systems as a whole benefit.
Both research and quality improvement in healthcare require an accurate understanding of patient profiles and care trajectories. To enable such efforts, a curated registry comprising high-quality real-world data for the entire perioperative period, including the preoperative, intraoperative, and postoperative periods of hospitalization, is paramount. Such a registry would provide the essential data for retrospective studies of factors influencing patient outcomes and prospective evaluations of clinical interventions. By consolidating and organizing perioperative data in an integrated database, researchers could easily access and analyze a large amount of collected data, which could lead to more accurate predictive models, an improvement in patient outcomes, and ultimately, improvements in clinical decision-making. In addition, an integrated perioperative registry would ensure access to data that is standardized and high quality, through the use of quality assessment methods. This is critical for accurate data analysis and the development of machine-learning models. By enforcing data standards and quality checks, researchers can minimize the risk of bias and errors in statistical analysis and develop machine-learning models. Furthermore, the use of a registry could address the data sharing and privacy concerns associated with the use of large institutional datasets. A secure and centralized location for data storage ensures that patient data are properly de-identified and protected and can only be accessed by authorized personnel.
Currently available high-resolution datasets include those derived from intensive care unit (ICU) stays, such as the frequently cited Medical Information Mart for Intensive Care (MIMIC) III and IV datasets and the eICU, Amsterdam UMCdb, and HiRID databases [1012]. Other datasets, such as the Multicenter Perioperative Outcomes Group (MPOG) dataset, have focused on perioperative information in the western populations [13,14]. These datasets have enabled important research such as predictive analytics using machine learning and waveform processing [15]. However, no large high-resolution databases integrating all patient-care areas throughout the perioperative period are currently available. To address this gap, we established an integrated, standardized, and curated perioperative registry database at our institution.
Singapore General Hospital (SGH) is a quaternary care and academic medical center. It is the largest hospital in Singapore, with approximately 28,000–30,000 surgeries conducted each year. A comprehensive electronic medical record (EMR) has been implemented at SGH along with a fully digital anesthesia information and monitoring system (AIMS) for the operating room. Every patient’s surgical journey generates a digital footprint that encompasses the medical doctors, nursing, and allied health consultation notes; laboratory and imaging results; physiological patient monitoring; medication or blood product prescriptions; operation time; and length of hospital stay. These data exist in the following formats: structured data (e.g., patient demographics and comorbidities), unstructured data (e.g., patient communication notes), high-resolution physiological time-series data (e.g., intraoperative heart rate and blood pressure), and imaging data (e.g., radiology images).
This study aims to describe the methodology used to set up the perioperative data mart used at SGH, the Perioperative and Anesthesia Subject Area Registry (PASAR), which combines data from the preoperative, intraoperative, and postoperative periods, allowing for a seamless investigation of a patient’s entire journey from the operating room to the ICU. An overview of the case mix and available data within this registry is also presented.  

Materials and Methods

The PASAR covers all patients who undergo surgery at SGH. It was initiated in 2016 and continues to be populated with data to the present day.
Approval for the database was granted by the SingHealth Centralised Institutional Review Board (Singhealth CIRB 2014/651/D, 2020/2915, and 2021/2547). The requirement for individual patient consent was waived as the data are collected during routine clinical care and de-identified. The PASAR operates in compliance with Singapore’s Personal Data Protection Act and the Human Biomedical Research Act as well as other ethical guidelines based on the Declaration of Helsinki, 2013 [1618]. The pipeline from routine clinical data and de-identification protocols are described below.
The main data source for the registry is the EMR (Sunrise Clinical Manager, Allscripts), AIMS (WinChart®, WinChart Health Informatics), and administrative billing (SAP ISH, SAP SE) systems at SGH. All data from internal sources are acquired during routine patient care (i.e., no specific data are collected for research). The Singapore National Registration Identification number is a national identifier unique to each individual and is the common identifier for data contained in various systems throughout Singapore.
All hospital data are stored in raw format in our enterprise data warehouse system (SingHealth-IHiS Electronic Health Intelligence System — eHINTS). This structured query language (SQL) database allows for data staging before it is uploaded to the registry. Manual data entries are unavailable. All data are stored securely within an approved access-controlled facility at SGH in a manner compliant with local privacy and data protection laws. The database is synchronized with the National Registry of Births and Deaths to ensure that all out-of-hospital deaths are captured.
All patients undergoing surgery at SGH require a preoperative anesthesia assessment. A standardized electronic clinical document has been used since 2016 to identify individuals who qualify for inclusion in the database. All patients aged ≥ 18 years who undergo surgery under anesthesia care at SGH are included.
De-identification processes are performed by a trusted third party within SGH that is not involved in research-related activities using PASAR data. This trusted third party acts in accordance with Singapore’s Personal Data Protection Act and Human Biomedical Research Act [16,17].
Our institution mandates the de-identification of 15 direct identifiers, which include case and visit numbers, death dates, postal codes, and names in structured fields and free-text data. De-identification of case and visit numbers is performed by pseudonymization using a hashing algorithm (SHA-256) that returns substitute values. Before hashing, the identifiers are salted with project-specific values at the beginning and end. This process of salting and hashing the case and visit numbers is used consistently to ensure that the study team is able to link cases across all tables using substitute values. Death dates are de-identified by generalization to months and years (i.e., the exact day is censored).
In addition to fields that explicitly contain these 15 direct identifiers, other high-risk data fields, such as unstructured text (which can contain names and other identifiers) are completely concealed.
For each individual, data are collected during the preoperative, intraoperative, and postoperative periods.
The clinical variables included in the data registry have expanded over time. At the time the PASAR was initiated in 2016, data on preoperative medical history and laboratory results as well as important clinical outcomes such as length of hospital stay, ICU admissions, and death were included [19,20]. Currently, additional data routinely captured during preoperative anesthesia assessment visits are included, such as patient demographics, comorbidities, preoperative laboratory test results, and surgery details; intraoperative AIMS data including all administered medications, procedures performed, and high-velocity vital signs time series data; and postoperative data including post-anesthesia care unit, ICU, 30-day vital signs, and fluid balance data. Furthermore, discharge summaries in both free-text entries and structured ICD-10 codes are included for all case entries. A full data dictionary is available for accredited users.
Establishing clear data ownership ensures that the data are properly collected, stored, and maintained, and that all stakeholders are aware of their roles and responsibilities regarding the data. Ownership of the PASAR lies with the first author (HRA), who is responsible for ensuring proper delegation to authorized nominees and users. Only authorized users with a legitimate need to access the data are granted access, and access is monitored and audited regularly to ensure that the data is not being accessed inappropriately. The Data Science and Artificial Intelligence Laboratory at SGH is a data management team that is responsible for establishing data quality checks and ensuring that the data are stored securely.
Data sharing is essential to facilitate research collaboration and advance perioperative research. However, data sharing must be performed in a manner that protects patient privacy and confidentiality. This includes establishing data-sharing agreements that specify how data can be used and who can access it. All data are anonymized before being released to researchers, and researchers must sign a data-use agreement that prohibits re-identification and unauthorized redistribution of the data. The authors can be contacted for details on the data-sharing agreement and protocols.  


As of December 2022, the PASAR consists of a relational database containing 26 tables linked by the patient’s case number (a unique identifier for every admission), operation session identifier, and operation identifier (as multiple operations can be performed in a single session). The database is segmented into three schemata based on the perioperative period the data were collected: pre_op (preoperative), intra_op (intraoperative), and post_op (postoperative). The pre_op, intra_op, and post_op schemata consist of 5, 7, and 14 tables, respectively. Detailed entity relationship diagrams of these three schemata and variable descriptions are available in Supplementary Fig. 1 and Supplementary Table 1. A total of 153,312 patients were admitted, among which 168,977 operation sessions containing 183,687 operations were conducted. Fig. 1 shows these data broken down by year.
The profiles of the patients included in the study are listed in Table 1. Discrete variables are reported as counts and percentages and continuous variables are reported as median (Q1, Q3). These measures provide a concise overview of patient characteristics, allowing for other studies or populations to be easily compared. This patient cohort consisted of 153,312 patients, with a median age of 60.0 years (45.0, 69.0) and a balanced sex distribution (females: 50.2%). The majority of patients were Chinese (72.4%), followed by Indian (10.6%) and Malay (9.6%). Almost half of the patients had hypertension (44.9%) and a significant number of cardiovascular comorbidities were reported, such as ischemic heart disease (12.7%), kidney dysfunction (5.8%), diabetes mellitus on insulin (4.8%), and congestive heart failure (3.3%). Operation characteristics are provided in Table 2. Summary statistics were calculated on a per-operation basis. Counts and percentages are presented for discrete variables, while median (Q1, Q3) are presented for continuous variables. Most patients had American Society of Anesthesiologists physical status scores of 2 (50.0%) and 3 (35.1%), while 14.6% of the patients underwent emergency surgeries.
Table 3 provides information on surgical outcomes. Summary statistics were calculated on a per-operation basis. Counts and percentages are presented for discrete variables, while median (Q1, Q3) are presented for continuous variables. Clinically relevant outcomes included the operation time, ICU length of stay, and 30 days and 1-year mortality. The median ICU length of stay after emergency surgery was 3 days longer than that after elective operations (5.1 vs 1.9 days); however, the median operation time was shorter (0.9 vs 1.2 h).


Significance of the PASAR

The PASAR can be used to obtain high-fidelity data on a patient’s perioperative journey at scale via automated retrieval from EMR sources and clinical databases. This ensures complete coverage of all relevant cases without requiring human data entry, thus reducing human labor, ensuring data accuracy, and minimizing the risk of privacy and security breaches. We believe that the link between high-frequency intraoperative data and long-term postoperative outcomes will further facilitate quality improvements and research in perioperative medicine. Moreover, a large-scale perioperative registry provides a centralized location for perioperative data, ensures data quality and standardization, and addresses the privacy concerns associated with the use of large institutional datasets. The availability of such data repositories can drive innovation in perioperative medicine and improve patient outcomes. To the best of our knowledge, this is the first large database in Asia to achieve this.
Our cases follow national gender and ethnic distributions [21]. Patients in our cohort were older adults, with a median age of 60 years. This is similar to the MPOG database, where older adults aged > 65 years comprised a significant proportion of patients. However, patient distribution may be atypical or skewed owing to the reduction in elective surgeries performed due to COVID pandemic measures. Only semi-urgent elective procedures, such as malignancy operations, were permitted [22] in our institution from February 2020 [23], and normal operations resumed slowly over the course of 2021–2022. This may have affected the summary statistics for the median age, length of stay, and mortality rates. However, with elective operations resuming in the post-pandemic period, the case distributions are expected to normalize.
We recognize that an EMR-based approach to data collection has limitations [24] and may not fully capture every aspect of a patient’s perioperative status. For example, events recorded in the EMR (e.g., administration of a drug) may deviate slightly from the actual administration time, as documentation may be retrospective in emergencies. Important qualitative observations such as patient discomfort are also challenging to capture. Records external to electronic data sources, such as laboratory results and prescriptions from different healthcare hospitals, cannot be directly accessed. Although free-text records may provide useful information, they are censored in the current database because of the lack of standardized de-identification protocols for free text. These factors can cause unmeasured data loss and bias within the PASAR, similar to other EMR-based registries. Data users must be aware of these limitations.
We envision the PASAR as the cornerstone for Singaporean researchers interested in leading and participating in international perioperative studies. Although this registry is currently not available as an open-access resource for international researchers owing to prevailing local regulatory limitations, collaborative access with legal and infrastructural safeguards is possible. Further steps to improve data usability would include implementing mappings to a widely accepted data model such as the common data model (CDM) promulgated by the Observational Medical Outcomes Partnership (OMOP) [25]. The OMOP CDM is an open community data standard designed to standardize the structure and content of observational data to enable efficient analyses and produce reliable evidence. The use of such a model would allow for pooled analyses using the PASAR and other databases [26]. At present, no perioperative databases have been fully mapped using the OMOP CDM. We envision our PASAR dataset to act as a leading case study to form a perioperative research consortium for our region.

Limitations and future work

The handling of large relational databases can be challenging for average clinical users. Even after taking steps to manage the “4 Vs of big data” (volume, velocity, variety, veracity), EHR data often overwhelms clinical providers with the sheer size and complexity of the database structure. Relational databases can be challenging to navigate, particularly for users unfamiliar with the underlying data model, making complex queries or data analysis tasks difficult to perform. At present, information technology tools such as SQL, which require further technical training, are used to query databases. The lack of user-friendly and convenient access to structured data for clinical providers is frequently a rate-limiting step in clinical research and quality improvements. We hope to implement low-code or no-code interfaces in the future to facilitate clinician interaction with the data.
In conclusion, the PASAR provides a comprehensive and automated approach to collecting high-fidelity data on a patient’s perioperative journey. By linking high-frequency intraoperative data with long-term postoperative outcomes, this database has the potential to facilitate quality improvements and research in perioperative medicine.



This work was supported by the National Research Foundation Singapore under the AI Singapore Programme (Award Number: AISG-100E-2020-055) and the RIE2025 Industry Alignment Fund (I2101E0002 – Cisco-NUS Accelerated Digital Economy Corporate Laboratory).

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Data Availability

The data that support the findings of this study are available from Singapore General Hospital but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Singapore General Hospital.

Author Contributions

Hairil Rizal Abdullah (Conceptualization; Data curation; Supervision; Writing – original draft)

Daniel Yan Zheng Lim (Investigation; Methodology)

Yuhe Ke (Investigation; Writing – original draft)

Nur Nasyitah Mohamed Salim (Investigation)

Xiang Lan (Methodology; Software)

Yizhi Dong (Formal analysis; Writing – original draft)

Mengling Feng (Conceptualization; Data curation; Formal analysis; Writing – review & editing)

Supplementary Materials

Supplementary Fig. 1.
Entity relationship diagram (ERD) of the three schematas: pre_op, intra_op, and post_op. This ERD was generated using the web application draw.io (https://www.drawio.com). Except for table intra_op.nur_vitables which only has a single Anon_Case_No identifier, every table is linked together using three identifiers: Anon_Case_No, session_id, and operation_id. The pre_op.char table stores the fundamental characteristics of each patient, operation session, and operation, collected before the operation. This table forms the core of the database.
Supplementary Table 1.
Description of the variables used for each schema (pre_op, intra_op, and post_op).

Fig. 1.
Number of patients, operation sessions, and operations per year.
Table 1.
Patient Characteristics
Variable Patient cohort (n = 153,312)
Age 60.0 (45.0, 69.0)
 Female 72,759 (50.2)
 Chinese 104,902 (72.4)
 Indian 15,442 (10.6)
 Malay 13,917 (9.6)
 Other races 10,663 (7.4)
Weight (kg) 64.7 (55.8, 75.0)
 Ex-smoker 10,945 (8.0)
 No 96,981 (70.5)
 Not asked 15,473 (11.2)
 Yes 14,242 (10.3)
Creatinine > 2 mg/dl 8,467 (5.8)
Diabetes mellitus on insulin 6,786 (4.8)
History of congestive heart failure 4,634 (3.3)
History of cerebrovascular accident 5,319 (3.8)
History of ischemic heart disease 18,002 (12.7)
History of hypertension 57,213 (44.9)

Values are presented as median (Q1, Q3) or number (%). Creatinine > 2 mg/dl indicate patients with kidney dysfunction.

Table 2.
Operation Characteristics
Variable Operation sessions (n = 168,977)
ASA class
 Ⅰ 12,398 (11.1)
 Ⅱ 55,864 (50.0)
 Ⅲ 39,205 (35.0)
 Ⅳ 4,250 (3.8)
 Ⅴ 110 (0.1)
 Ⅵ 7 (0.0)
 Elective 144,358 (85.4)
 Emergency 24,619 (14.6)
 Transplant 119 (0.1)
 ENT 5,690 (4.6)
 General surgery 30,774 (24.8)
 Gynecology 7,311 (5.9)
 Neurosurgery 2,475 (2.0)
 Orthopedics 33,656 (27.2)
 Plastics 3,565 (2.9)
 Urology 11,660 (9.4)
 Vascular 5,292 (4.3)
 Burns 1,237 (1.0)
 Cardiothoracic surgery 7,786 (6.3)
 Neonatology 47 (0.0)
 Obstetrics 2,521 (2.0)
 Others 11,771 (9.5)

Values are presented as number (%). ASA: American Society of Anaesthesiologists physical status, ENT: ear, nose and throat.

Table 3.
Surgical Outcomes
Parameter Urgency
Overall Elective Emergency
Number of patients 168,977 144,358 24,619
Operation time (h) 1.2 (0.6, 2.1) 1.2 (0.6, 2.2) 0.9 (0.5, 1.7)
ICU stay > 24 h 9719 (78.3) 7,087 (74.9) 2,632 (89.3)
ICU stay (day) 2.2 (1.0, 6.8) 1.9 (1.0, 4.8) 5.1 (2.0, 13.4)
30-day mortality 2,268 (1.3) 1,382 (1.0) 886 (3.6)
1-year mortality 8,008 (4.7) 5,815 (4.0) 2,193 (8.9)

Values are presented as number, median (Q1, Q3) or number (%). ICU: intensive care unit.


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