Financial Benefits of Renal Dose-Adjusted Dipeptidyl Peptidase-4 Inhibitors for Patients with Type 2 Diabetes and Chronic Kidney Disease.

Background: Dipeptidyl peptidase-4 (DPP4) inhibitors are frequently prescribed for patients with type 2 diabetes; however, their cost can pose a significant barrier for those with impaired kidney function. This study aimed to estimate the economic benefits of substituting non-renal dose-adjusted (NRDA) DPP4 inhibitors with renal dose-adjusted (RDA) DPP4 inhibitors in patients with both impaired kidney function and type 2 diabetes. Methods: This retrospective cohort study was conducted from January 1, 2012 to December 31, 2018, using data obtained from common data models of five medical centers in Korea. Model 1 applied the prescription pattern of participants with preserved kidney function to those with impaired kidney function. In contrast, model 2 replaced all NRDA DPP4 inhibitors with RDA DPP4 inhibitors, adjusting the doses of RDA DPP4 inhibitors based on individual kidney function. The primary outcome was the cost difference between the two models. Results: In total, 67,964,996 prescription records were analyzed. NRDA DPP4 inhibitors were more frequently prescribed to patients with impaired kidney function than in those with preserved kidney function (25.7%, 51.3%, 64.3%, and 71.6% in patients with estimated glomerular filtration rates [eGFRs] of ≥60, <60, <45, and <30 mL/min/1.73 m2, respectively). When model 1 was applied, the cost savings per year were 7.6% for eGFR <60 mL/min/1.73 m2 and 30.4% for eGFR <30 mL/min/1.73 m2. According to model 2, 15.4% to 51.2% per year could be saved depending on kidney impairment severity. Conclusion: Adjusting the doses of RDA DPP4 inhibitors based on individual kidney function could alleviate the economic burden associated with medical expenses.

How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.

BACKGROUND: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. METHODS: In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. RESULTS: We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. DISCUSSION: This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. CONCLUSION: The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

Key Research Areas for Building and Deploying a Common Data Model for an Intensive Medicine Data Space in Europe and Beyond.

Key Research Areas (KRAs) were identified to establish a semantic interoperability framework for intensive medicine data in Europe. These include assessing common data model value, ensuring smooth data interoperability, supporting data standardization for efficient dataset use, and defining anonymization requirements to balance data protection and innovation.

How to Accurately Detect Renal Replacement Therapy Weaning in Intensive Care: Data Quality and Standardization Considerations for the OMOP Common Data Model.

This study assesses the effectiveness of the Observational Medical Outcomes Partnership common data model (OMOP CDM) in standardising Continuous Renal Replacement Therapy (CRRT) data from intensive care units (ICU) of two French university hospitals. Our objective was to extract and standardise data from various sources, enabling the development of predictive models for CRRT weaning that are agnostic to the data's origin. Data for 1,696 ICU stays from the two data sources were extracted, transformed, and loaded into the OMOP format after semantic alignment of 46 CRRT standard concepts. Although the OMOP CDM demonstrated potential in harmonising CRRT data, we encountered challenges related to data variability and the lack of standard concepts. Despite these challenges, our study supports the promise of the OMOP CDM for ICU data standardization, suggesting that further refinement and adaptation could significantly improve clinical decision making and patient outcomes in critical care settings.

Review of Key Elements in Developing a Common Data Model for Rare Diseases: Identifying Common Success Factors.

This paper explores key success factors for the development and implementation of a Common Data Model (CDM) for Rare Diseases (RDs) focusing on the European context. Several challenges hinder RD care and research in diagnosis, treatment, and research, including data fragmentation, lack of standardisation, and Interoperability (IOP) issues within healthcare information systems. We identify key issues and recommendations for an RD-CDM, drawing on international guidelines and existing infrastructure, to address organisational, consensus, interoperability, usage, and secondary use challenges. Based on these, we analyse the importance of balancing the scope and IOP of a CDM to cater to the unique requirements of RDs while ensuring effective data exchange and usage across systems. In conclusion, a well-designed RD-CDM can bridge gaps in RD care and research, enhance patient care and facilitate international collaborations.

Development and Evaluation of a Data Quality Management System for the Korea Biobank Network.

As the reliance on clinical epidemiological information from human specimens grows, so does the need for effective clinical information management systems, particularly for biobanks. Our study focuses on enhancing the Korea Biobank Network's (KBN) system with data quality verification features. By comparing the quality of data collected before and after these enhancements, we observed a notable improvement in data accuracy, with the error rate decreasing from 0.1198% to 0.0492%. This advancement underscores the importance of robust data quality management in supporting high-quality clinical research and sets a precedent for the development of clinical information management systems.

Converting Entity-Attribute-Value Data Sources to OMOP's CDM: Lessons Learned.

Clinical data repositories often use entity-attribute-value (EAV) data models. To be valuable for secondary use, these health data can be transformed to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The present paper describes the lessons learned from such an endeavour based on the concept of registering transformation functions on source data elements. We further provide future work directions for follow-up projects.

Development of a Big Data Platform for the Collection and Utilization of Korea Biobank Network Clinical Information.

Biobanks serve as vital repositories for human biospecimens and clinical data, promoting biomedical and clinical research. The integration of electronic health records particularly enhances research opportunities in the era of genomics and personalized medicine, improving understanding of tumor development and disease progression. Based on the Korea Biobank Network Common Data Model, it is possible to expand data collection across various diseases. We have developed an innovative big data platform designed to efficiently collect large-scale clinical information within the KBN. By implementing the system structure, data quality management processes, and basic statistical preprocessing functionalities, we have collected data from 136,473 individuals from 2021 to 2023, demonstrating the platform's continuous and efficient data collection capabilities. Integration with hospital systems and robust quality management ensure the acquisition of high-quality data.

Multi-Source Data ETL (Extract, Transform, Load) for a Genetic Epilepsy Diagnosis and Treatment Dashboard.

The growing number of genes identified in relation to epilepsy represents a major breakthrough in diagnosis and treatment, but experts face the challenge of efficiently accessing and consolidating the vast amount of genetic data available. Therefore, we present the process of transforming data from different sources and formats into an Entity-Attribute-Value (EAV) model database. Combined with the use of standard coding systems, this approach will provide a scalable and adaptable database to present the data in a comprehensive way to experts via a dashboard.

Transforming Primary Care Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study.

Background: Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research. Objective: This study aimed to transform primary care data into the OMOP CDM format. Methods: We extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard. Results: Data from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data. Conclusions: Primary care data from a French health care facility have been implemented into the OMOP CDM format. Data concerning demographics, units, measurements, and primary care consultation steps were already available in OHDSI vocabularies. Laboratory test results and drug prescription data were mapped to available vocabularies and structured in the final model. A dashboard application provided health care professionals with feedback on their practice.

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study.

BACKGROUND: Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. OBJECTIVE: To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. METHODS: The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. RESULTS: The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. CONCLUSIONS: The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

Harmonizing Norwegian registries onto OMOP common data model: Mapping challenges and opportunities for pregnancy and COVID-19 research.

OBJECTIVE: Norwegian health registries covering entire population are used for administration, research, and emergency preparedness. We harmonized these data onto the Observational Medical Outcomes Partnership common data model (OMOP CDM) and enrich real-world data in OMOP format with COVID-19 related data. METHODS: Data from six registries (2018-2021) covering birth registrations, selected primary and secondary care events, vaccinations, and communicable disease notifications were mapped onto the OMOP CDM v5.3. An Extract-Transform-Load (ETL) pipeline was developed on simulated data using data characterization documents and scanning tools. We ran dashboard quality checks, cohort generations, investigated differences between source and mapped data, and refined the ETL accordingly. RESULTS: We mapped 1.5 billion rows of data of 5,673,845 individuals. Among these, there were 804,277 pregnancies, 483,585 mothers together with 792,477 children, and 472,948 fathers. We identified 382,516 positive tests for COVID-19 in 380,794 patients. These figures are consistent with results from source data. In addition to 11 million source codes mapped automatically, we mapped 237 non-standard codes to standard concepts and introduced 38 custom concepts to accommodate pregnancy-related terminologies that were not supported by OMOP CDM vocabularies. A total of 3,700/3,705 (99.8%) checks passed. The 5 failed checks could be explained by the nature of the data and only represent a small number of records. DISCUSSION AND CONCLUSION: Norwegian registry data were successfully harmonized onto OMOP CDM with high level of concordance and provides valuable source for federated COVID-19 related research. Our mapping experience is highly valuable for data partners with Nordic health registries.

The Cancer Clinical Library Database (CCLD) from the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) Project.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea's cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development.

Common data models provide a standardized way to represent data used in federated learning tasks. The aim of this review was to explore the development and use of common data models to harmonize electronic health record data in health research. The data search yielded 724 records, of which 19 were included for this study. None of the research focused on nursing specific topics. All studies either utilized the Observational Medical Outcomes Partnership (OMOP) common data model, or developed a model partly based on the OMOP. A roadmap to guide research for the development of common data models for federated learning are warranted.

allofus: an R package to facilitate use of the All of Us Researcher Workbench.

OBJECTIVES: Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program data, and we demonstrate this functionality with an example of creating a cohort of All of Us participants by synthesizing electronic health record and survey data with time dependencies. TARGET AUDIENCE: All of Us Research Program data are widely available to health researchers. The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the package. SCOPE: We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research.

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.

BACKGROUND: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. OBJECTIVE: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. METHODS: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. RESULTS: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. CONCLUSIONS: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.

Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study.

Background: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare. Objective: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN. Methods: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA. Results: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase. Conclusions: Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.

Data Model for the Comprehensive Management of Biobanks and Its Contribution to Personalized Medicine.

Biobanks are infrastructures essential for research involving multi-disciplinary teams and an increasing number of stakeholders. In the field of personalized medicine, biobanks play a key role through the provision of well-characterized and annotated samples protecting at the same time the right of donors. The Andalusian Public Health System Biobank (SSPA Biobank) has implemented a global information management system made up of different modules that allow for the recording, traceability and monitoring of all the information associated with the biobank operations. The data model, designed in a standardized and normalized way according to international initiatives on data harmonization, integrates the information necessary to guarantee the quality of results from research, benefiting researchers, clinicians and donors.

Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

BACKGROUND AND AIMS: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors. DESIGN: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data. SETTING AND CASES: Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively. MEASUREMENTS: Predict continuous prescription of buprenorphine-naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC-AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time-to-event analysis on a single site to estimate the duration of buprenorphine-naloxone treatment continuity evaluated by the C-index and a comparative evaluation against predictions made by three human clinical experts. FINDINGS: Attrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC-AUCs up to 75.8 (95% confidence interval [CI] = 73.6-78.0). Addiction medicine specialists' predictions show a ROC-AUC of 67.8 (95% CI = 50.4-85.2). Time-to-event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C-index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence. CONCLUSIONS: US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts.

Enhancing healthcare process analysis through object-centric process mining: Transforming OMOP common data models into object-centric event logs.

OBJECTIVES: This study aims to enhance the analysis of healthcare processes by introducing Object-Centric Process Mining (OCPM). By offering a holistic perspective that accounts for the interactions among various objects, OCPM transcends the constraints of conventional patient-centric process mining approaches, ensuring a more detailed and inclusive understanding of healthcare dynamics. METHODS: We develop a novel method to transform the Observational Medical Outcomes Partnership Common Data Models (OMOP CDM) into Object-Centric Event Logs (OCELs). First, an OMOP CDM4PM is created from the standard OMOP CDM, focusing on data relevant to generating OCEL and addressing healthcare data's heterogeneity and standardization challenges. Second, this subset is transformed into OCEL based on specified healthcare criteria, including identifying various object types, clinical activities, and their relationships. The methodology is tested on the MIMIC-IV database to evaluate its effectiveness and utility. RESULTS: Our proposed method effectively produces OCELs when applied to the MIMIC-IV dataset, allowing for the implementation of OCPM in the healthcare industry. We rigorously evaluate the comprehensiveness and level of abstraction to validate our approach's effectiveness. Additionally, we create diverse object-centric process models intricately designed to navigate the complexities inherent in healthcare processes. CONCLUSION: Our approach introduces a novel perspective by integrating multiple viewpoints simultaneously. To the best of our knowledge, this is the inaugural application of OCPM within the healthcare sector, marking a significant advancement in the field.