SCIntRuler: Guiding the integration of multiple single-cell RNA-seq datasets with a novel statistical metric.

MOTIVATION: The growing number of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets, such as augmenting sample sizes and enhancing analytical robustness. Inherent diversity and batch discrepancies within samples or across studies continue to pose significant challenges for computational analyses. Questions persist in practice, lacking definitive answers: Should we use a specific integration method or opt for simply merging the datasets during joint analysis? Among all the existing data integration methods, which one is more suitable in specific scenarios? RESULT: To fill the gap, we introduce SCIntRuler, a novel statistical metric for guiding the integration of multiple scRNA-seq datasets. SCIntRuler helps researchers make informed decisions regarding the necessity of data integration and the selection of an appropriate integration method. Our simulations and real data applications demonstrate that SCIntRuler streamlines decision-making processes and facilitates the analysis of diverse scRNA-seq datasets under varying contexts, thereby alleviating the complexities associated with the integration of heterogeneous scRNA-seq datasets. AVAILABILITY: The implementation of our method is available on CRAN as an open-source R package with a user- friendly manual available: https://cloud.r-project.org/web/packages/SCIntRuler/index.html.

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior.

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

psHarmonize: Facilitating reproducible large-scale pre-statistical data harmonization and documentation in R.

Combining pertinent data from multiple studies can increase the robustness of epidemiological investigations. Effective "pre-statistical" data harmonization is paramount to the streamlined conduct of collective, multi-study analysis. Harmonizing data and documenting decisions about the transformations of variables to a common set of categorical values and measurement scales are time consuming and can be error prone, particularly for numerous studies with large quantities of variables. The psHarmonize R package facilitates harmonization by combining multiple datasets, applying data transformation functions, and creating long and wide harmonized datasets. The user provides transformation instructions in a "harmonization sheet" that includes dataset names, variable names, and coding instructions and centrally tracks all decisions. The package performs harmonization, generates error logs as necessary, and creates summary reports of harmonized data. psHarmonize is poised to serve as a central feature of data preparation for the joint analysis of multiple studies.

A Pipeline for the Usage of the Core Data Set of the Medical Informatics Initiative for Process Mining - A Technical Case Report.

INTRODUCTION: Process Mining (PM) has emerged as a transformative tool in healthcare, facilitating the enhancement of process models and predicting potential anomalies. However, the widespread application of PM in healthcare is hindered by the lack of structured event logs and specific data privacy regulations. CONCEPT: This paper introduces a pipeline that converts routine healthcare data into PM-compatible event logs, leveraging the newly available permissions under the Health Data Utilization Act to use healthcare data. IMPLEMENTATION: Our system exploits the Core Data Sets (CDS) provided by Data Integration Centers (DICs). It involves converting routine data into Fast Healthcare Interoperable Resources (FHIR), storing it locally, and subsequently transforming it into standardized PM event logs through FHIR queries applicable on any DIC. This facilitates the extraction of detailed, actionable insights across various healthcare settings without altering existing DIC infrastructures. LESSONS LEARNED: Challenges encountered include handling the variability and quality of data, and overcoming network and computational constraints. Our pipeline demonstrates how PM can be applied even in complex systems like healthcare, by allowing for a standardized yet flexible analysis pipeline which is widely applicable.The successful application emphasize the critical role of tailored event log generation and data querying capabilities in enabling effective PM applications, thus enabling evidence-based improvements in healthcare processes.

Unlocking Real-World Health Data from Non-University Locations Through Effective Provision of Tools and Knowledge.

INTRODUCTION: The Medical Informatics Initiative (MII) in Germany has pioneered platforms such as the National Portal for Medical Research Data (FDPG) to enhance the accessibility of data from clinical routine care for research across both university and non-university healthcare settings. This study explores the efficacy of the Medical Informatics Hub in Saxony (MiHUBx) services by integrating Klinikum Chemnitz gGmbH (KC) with the FDPG, leveraging the Fast Healthcare Interoperability Resources Core Data Set of the MII to standardize and harmonize data from disparate source systems. METHODS: The employed procedures include deploying installation packages to convert data into FHIR format and utilizing the Research Data Repository for structured data storage and exchange within the clinical infrastructure of KC. RESULT: Our results demonstrate successful integration, the development of a comprehensive deployment diagram, additionally, it was demonstrated that the non-university site can report clinical data to the FDPG. DISCUSSION: The discussion reflects on the practical application of this integration, highlighting its potential scalability to even smaller healthcare facilities and to pave the way to access to more medical data for research. This exemplary demonstration of the interplay of different tools provides valuable insights into technical and operational challenges, setting a precedent for future expansions and contributing to the democratization of medical data access.

Challenges in Retrieving Patterns from Generic Data Structures in Clinical Systems - A Technical Case Report.

INTRODUCTION: The secondary use of data in clinical environments offers significant opportunities to enhance medical research and practices. However, extracting data from generic data structures, particularly the Entity-Attribute-Value (EAV) model, remains challenging. This study addresses these challenges by developing a methodological approach to convert EAV-based data into a format more suitable for analysis. BACKGROUND: The EAV model is widely used in clinical information systems due to its adaptability, but it often complicates data retrieval for research purposes due to its vertical data structure and dynamic schema. OBJECTIVE: The objective of this study is to develop a methodological approach to address the handling of these generic data structures, Methods: We introduce a five-step methodological approach: 1) understanding the specific clinical processes to determine data collection points and involved roles; 2) analysing the data source to understand the data structure and metadata; 3) reversing a use-case-specific data structure to map the front-end data input to its storage format; 4) analysing the content to identify medical information and establish connections; and 5) managing schema changes to maintain data integrity. RESULTS: Applying this method to the hospital information system has shown that EAV-based data can be converted into a structured format, suitable for research. This conversion reduced data sparsity and improved the manageability of schema changes without affecting other classes of data. CONCLUSION: The developed approach provides a systematic method for handling complex data relationships and maintaining data integrity in clinical systems using EAV models. This approach facilitates the secondary use of clinical data, enhancing its utility for medical research and practice.

Metadata Driven Integration of Clinical Data for Secondary Use in FHIR - A Pilot Study at the UKSH.

INTRODUCTION: The reuse of clinical data from clinical routine is a topic of research within the field of medical informatics under the term secondary use. In order to ensure the correct use and interpretation of data, there is a need for context information of data collection and a general understanding of the data. The use of metadata as an effective method of defining and maintaining context is well-established, particularly in the field of clinical trials. The objectives of this paper is to examine a method for integrating routine clinical data using metadata. METHODS: To this end, clinical forms extracted from a hospital information system will be converted into the FHIR format. A particular focus is placed on the consistent use of a metadata repository (MDR). RESULTS: A metadata-based approach using an MDR system was developed to simplify data integration and mapping of structured forms into FHIR resources, while offering many advantages in terms of flexibility and data quality. This facilitated the management and configuration of logic and definitions in one place, enabling the reusability and secondary use of data. DISCUSSION: This work allows the transfer of data elements without loss of detail and simplifies integration with target formats. The approach is adaptable for other ETL processes and eliminates the need for formatting concerns in the target profile.

Advancing Pharmacy Practice: The Role of Intelligence-Driven Pharmacy Practice and the Emergence of Pharmacointelligence.

The field of healthcare is experiencing a significant transformation driven by technological advancements, scientific breakthroughs, and a focus on personalized patient care. At the forefront of this evolution is artificial intelligence-driven pharmacy practice (IDPP), which integrates data science and technology to enhance pharmacists' capabilities. This prospective article introduces the concept of "pharmacointelligence", a paradigm shift that synergizes artificial intelligence (AI), data integration, clinical decision support systems (CDSS), and pharmacy informatics to optimize medication-related processes. Through a comprehensive literature review and analysis, this research highlights the potential of pharmacointelligence to revolutionize pharmacy practice by addressing the complexity of pharmaceutical data, changing healthcare demands, and technological advancements. This article identifies the critical need for integrating these technologies to enhance medication management, improve patient outcomes, and streamline pharmacy operations. It also underscores the importance of regulatory and ethical considerations in implementing pharmacointelligence, ensuring patient privacy, data security, and equitable healthcare delivery.

Examining the effect of prescription drug monitoring program integration and mandatory use policies on the distribution of methadone and buprenorphine for opioid use disorder, United States, 2009-2021.

BACKGROUND: Prescription drug monitoring programs (PDMPs) have been shown to reduce opioid prescribing for pain, but it is not well understood whether PDMPs influence utilization of medications for opioid use disorder. PDMP integration and mandatory use policies are two approaches implemented by states to increase use of PDMPs by prescribers. This study examined the effect of these approaches on distribution of methadone and buprenorphine from 2009 to 2021 for 50 states and DC. METHODS: The effect of PDMP integration and mandatory use policies on four outcomes (distribution of buprenorphine to opioid treatment programs, distribution of buprenorphine to pharmacies, distribution of methadone to opioid treatment programs, and the total combined distribution of methadone and buprenorphine) was estimated using a Callaway and Sant'Anna difference-in-differences model, controlling for co-occurring opioid-related state policies. RESULTS: Distribution of buprenorphine to pharmacies decreased 8 % (95 % CI -14 %, -1 %) following implementation of mandatory use policies. Distribution of methadone to opioid treatment programs increased 17 % (95 % CI 4 %, 34 %) and the total combined distribution of methadone and buprenorphine increased 6 % (95 % CI -0 %, 14 %) following the joint implementation of both approaches. CONCLUSION: Distribution of methadone and buprenorphine has increased since 2009, but less than a quarter of people with opioid use disorder currently receive these medications. We observed a small net benefit of PDMP integration and mandatory use policies on distribution of methadone and buprenorphine. Policymakers should continue to assess the impact of PDMPs on access to medications for opioid use disorder and consider additional approaches to increase access to treatment.

Early Multimodal Data Integration for Data-Driven Medical Research - A Scoping Review.

INTRODUCTION: Data-driven medical research (DDMR) needs multimodal data (MMD) to sufficiently capture the complexity of clinical cases. Methods for early multimodal data integration (MMDI), i.e. integration of the data before performing a data analysis, vary from basic concatenation to applying Deep Learning, each with distinct characteristics and challenges. Besides early MMDI, there exists late MMDI which performs modality-specific data analyses and then combines the analysis results. METHODS: We conducted a scoping review, following PRISMA guidelines, to find and analyze 21 reviews on methods for early MMDI between 2019 and 2024. RESULTS: Our analysis categorized these methods into four groups and summarized group-specific characteristics that are relevant for choosing the optimal method combination for MMDI pipelines in DDMR projects. Moreover, we found that early MMDI is often performed by executing several methods subsequently in a pipeline. This early MMDI pipeline is usually subject to manual optimization. DISCUSSION: Our focus was on structural integration in DDMR. The choice of MMDI method depends on the research setting, complexity, and the researcher team's expertise. Future research could focus on comparing early and late MMDI approaches as well as automating the optimization of MMDI pipelines to integrate vast amounts of real-world medical data effectively, facilitating holistic DDMR.

Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.

Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.

Patient Input into the Electronic Health Record: Co-Designing Solutions with Patients and Healthcare Professionals.

Patient-generated health data (PGHD) is the person's health-related data collected outside the clinical environment. Integrating this data into the electronic health record (EHR) supports better patient-provider communication and shared decision-making, empowering patients to actively manage their health conditions. In this study, we investigated the essential features needed for patients and healthcare providers to effectively integrate PGHD functionality into the EHR system. Through our collaborative design approach involving healthcare professionals (HCPs) and patients, we developed a prototype and suggestion, using Estonia as a model, which is the ideal approach for collecting and integrating PGHD into the EHR.

Integrating Automation, Interactive Visualization, and Unsupervised Learning for Enhanced Diabetes Management.

Effective management of diabetes necessitates efficient data handling, insightful analytics, and personalized interventions. In this study, we present a comprehensive system that automates the extraction, transformation, and loading of continuous glucose monitoring data. Data is integrated into an interactive dashboard with dual access levels: one for healthcare management professionals and another for patients for clinical management. The dashboard provides real-time updates and customizable visualization options, empowering users with actionable insights into their glucose levels. Furthermore, a clustering model to categorize patients into distinct groups based on their glucose profiles was developed. Through this model, three clusters representing different patterns of glucose control are identified. Healthcare professionals can utilize these insights to tailor treatment strategies, allocate resources effectively, and identify high-risk patients.

Leveraging Clinical Data Treasures: Integration of an AI Platform into Clinical IT.

In recent years, there has been a rapid growth in the use of AI in the clinical domain. In order to keep pace with this development, a framework should be created in which clinical AI models can be easily trained, managed and applied. In our study, we propose a clinical AI platform that supports the development cycle and application of clinical AI models. We consider not only the development of an isolated clinical AI platform, but also its integration into clinical IT. This includes the consideration of so-called medical data integration centers. We evaluate our approach with the aid of a clinical AI use case to demonstrate the functionality of our clinical AI platform.

Challenges and Lessons Learned in Mapping HL7 v2 Data to openEHR: Insights from UKSH Medical Data Integration Center.

This paper explores the challenges and lessons learned during the mapping of HL7 v2 messages structured using custom schema to openEHR for the Medical Data Integration Center (MeDIC) of the University Hospital, Schleswig-Holstein (UKSH). Missing timestamps in observations, missing units of measurement, inconsistencies in decimal separators and unexpected datatypes were identified as critical inconsistencies in this process. These anomalies highlight the difficulty of automating the transformation of HL7 v2 data to any standard, particularly openEHR, using off-the-shelf tools. Addressing these anomalies is crucial for enhancing data interoperability, supporting evidence-based research, and optimizing clinical decision-making. Implementing proper data quality measures and governance will unlock the potential of integrated clinical data, empowering clinicians and researchers and fostering a robust healthcare ecosystem.

Integration of Oncological Data into openEHR: A Path Towards Improved Cancer Care and Research.

The integration of tumor-related diagnosis and therapy data is a key factor for cancer-related collaborative projects and research projects on-site. The Medical Data Integration Center (MeDIC) of the University Hospital Schleswig-Holstein, resulting from the Medical Informatics Initiative and Network University Medicine in Germany, has agreed on an openEHR-based data management based on a centralized repository with harmonized annotated data. Consequently, the oncological data should be integrated into the MeDIC to interconnect the information and thus gain added value. A uniform national data set for tumor-related reports is already defined for the cancer registries. Therefore, this work aims to transform the national oncological basis data set for tumor documentation (oBDS) so that it can be stored and utilized properly in the openEHR repository of the MeDIC. In a previous work openEHR templates representing the oncological basis data set were modeled. These templates were used to implement a processing pipeline including a metadata repository, which defines the mappings between the elements, a FHIR terminology service for annotation and validation, resulting in a tool to automatically build openEHR compositions from oBDS data. The prototype proved the feasibility of the referred mapping, integration into the MeDIC is straightforward and the architecture introduced is adaptable to future needs by design.

Advancing Healthcare Research Through Live-Data Integration: Implementing IEEE 11073 and FHIR in Clinical Environment.

The efficient direct integration of real-time medical device data is a promising approach to improve patient care enabling a direct and eminent intervention. This study presents a comprehensive approach for integrating real-time medical device data into clinical environments using the HL7® FHIR® standards and IEEE 11073 Service-Oriented Device Connectivity (SDC). The study proposes a conceptual framework and an opensource proof-of-concept implementation for real-time data integration within the Medical Data Integration Center (MeDIC) at UKSH. Key components include a selective recording mechanism to mitigate storage issues and ensure accurate data capture. Our robust network architecture utilizes Kafka brokers for seamless data transfer in isolated networks. The study demonstrates the selective capturing of real-time data within a clinical setting to enable medical device data for a down-stream processing and analysis.

Real-Time Visualization and Analysis Architecture for Data Integration Processes at Cologne University Hospital's Medical Data Integration Center.

This paper presents an implementation of an architecture based on open-source solutions using ELK Stack - Elasticsearch, Logstash, and Kibana - for real-time data analysis and visualizations in the Medical Data Integration Center, University Hospital Cologne, Germany. The architecture addresses challenges in handling diverse data sources, ensuring standardized access, and facilitating seamless analysis in real-time, ultimately enhancing the precision, speed, and quality of monitoring processes within the medical informatics domain.