Background: Leveraging electronic health record (EHR) data for clinical or research purposes heavily depends on data fitness. However, there is a lack of standardized frameworks to evaluate EHR data suitability, leading to inconsistent quality in data use projects (DUPs). This research focuses on the Medical Informatics for Research and Care in University Medicine (MIRACUM) Data Integration Centers (DICs) and examines empirical practices on assessing and automating the fitness-for-purpose of clinical data in German DIC settings.
Objective: The study aims (1) to capture and discuss how MIRACUM DICs evaluate and enhance the fitness-for-purpose of observational health care data and examine the alignment with existing recommendations and (2) to identify the requirements for designing and implementing a computer-assisted solution to evaluate EHR data fitness within MIRACUM DICs.
Methods: A qualitative approach was followed using an open-ended survey across DICs of 10 German university hospitals affiliated with MIRACUM. Data were analyzed using thematic analysis following an inductive qualitative method.
Results: All 10 MIRACUM DICs participated, with 17 participants revealing various approaches to assessing data fitness, including the 4-eyes principle and data consistency checks such as cross-system data value comparison. Common practices included a DUP-related feedback loop on data fitness and using self-designed dashboards for monitoring. Most experts had a computer science background and a master's degree, suggesting strong technological proficiency but potentially lacking clinical or statistical expertise. Nine key requirements for a computer-assisted solution were identified, including flexibility, understandability, extendibility, and practicability. Participants used heterogeneous data repositories for evaluating data quality criteria and practical strategies to communicate with research and clinical teams.
Conclusions: The study identifies gaps between current practices in MIRACUM DICs and existing recommendations, offering insights into the complexities of assessing and reporting clinical data fitness. Additionally, a tripartite modular framework for fitness-for-purpose assessment was introduced to streamline the forthcoming implementation. It provides valuable input for developing and integrating an automated solution across multiple locations. This may include statistical comparisons to advanced machine learning algorithms for operationalizing frameworks such as the 3×3 data quality assessment framework. These findings provide foundational evidence for future design and implementation studies to enhance data quality assessments for specific DUPs in observational health care settings.
Background: Patient navigation interventions (PNIs) can provide personalized support and promote appropriate coordination or continuation of health and social care services. Online PNIs have demonstrated excellent potential for improving patient knowledge, transition readiness, self-efficacy, and use of services. However, the characteristics (ie, intervention type, mode of delivery, duration, frequency, outcomes and outcome measures, underlying theories or mechanisms of change of the intervention, and impact) of existing online PNIs to support the health and social needs of individuals with illness remain unclear.
Objective: This scoping review of the existing literature aims to identify the characteristics of existing online PNIs reported in the literature.
Methods: A scoping review based on the guidelines outlined in the Joanna Briggs Institute framework was conducted. A search for peer-reviewed literature published between 1989 and 2022 on online PNIs was conducted using MEDLINE, CINAHL, Embase, PsycInfo, and Cochrane Library databases. Two independent reviewers conducted 2 levels of screening. Data abstraction was conducted to outline key study characteristics (eg, study design, population, and intervention characteristics). The data were analyzed using descriptive statistics and qualitative content analysis.
Results: A total of 100 studies met the inclusion criteria. Our findings indicate that a variety of study designs are used to describe and evaluate online PNIs, with literature being published between 2003 and 2022 in Western countries. Of these studies, 39 (39%) studies were randomized controlled trials. In addition, we noticed an increase in reported online PNIs since 2019. The majority of studies involved White females with a diagnosis of cancer and a lack of participants aged 70 years or older was observed. Most online PNIs provide support through navigation, self-management and lifestyle changes, counseling, coaching, education, or a combination of support. Variation was noted in terms of mode of delivery, duration, and frequency. Only a small number of studies described theoretical frameworks or change mechanisms to guide intervention.
Conclusions: To our knowledge, this is the first review to comprehensively synthesize the existing literature on online PNIs, by focusing on the characteristics of interventions and studies in this area. Inconsistency in reporting the country of publication, population characteristics, duration and frequency of interventions, and a lack of the use of underlying theories and working mechanisms to inform intervention development, provide guidance for the reporting of future online PNIs.
The economic trend and the health care landscape are rapidly evolving across Asia. Effective real-world data (RWD) for regulatory and clinical decision-making is a crucial milestone associated with this evolution. This necessitates a critical evaluation of RWD generation within distinct nations for the use of various RWD warehouses in the generation of real-world evidence (RWE). In this article, we outline the RWD generation trends for 2 contrasting nation archetypes: "Solo Scholars"-nations with relatively self-sufficient RWD research systems-and "Global Collaborators"-countries largely reliant on international infrastructures for RWD generation. The key trends and patterns in RWD generation, country-specific insights into the predominant databases used in each country to produce RWE, and insights into the broader landscape of RWD database use across these countries are discussed. Conclusively, the data point out the heterogeneous nature of RWD generation practices across 10 different Asian nations and advocate for strategic enhancements in data harmonization. The evidence highlights the imperative for improved database integration and the establishment of standardized protocols and infrastructure for leveraging electronic medical records (EMR) in streamlining RWD acquisition. The clinical data analysis and reporting system of Hong Kong is an excellent example of a successful EMR system that showcases the capacity of integrated robust EMR platforms to consolidate and produce diverse RWE. This, in turn, can potentially reduce the necessity for reliance on numerous condition-specific local and global registries or limited and largely unavailable medical insurance or claims databases in most Asian nations. Linking health technology assessment processes with open data initiatives such as the Observational Medical Outcomes Partnership Common Data Model and the Observational Health Data Sciences and Informatics could enable the leveraging of global data resources to inform local decision-making. Advancing such initiatives is crucial for reinforcing health care frameworks in resource-limited settings and advancing toward cohesive, evidence-driven health care policy and improved patient outcomes in the region.
Background: In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming medical fields, with the use of clinical decision support systems (CDSSs) as a notable application. Laboratory tests are vital for accurate diagnoses, but their increasing reliance presents challenges. The need for effective strategies for managing laboratory test interpretation is evident from the millions of monthly searches on test results' significance. As the potential role of CDSSs in laboratory diagnostics gains significance, however, more research is needed to explore this area.
Objective: The primary objective of our study was to assess the accuracy and safety of LabTest Checker (LTC), a CDSS designed to support medical diagnoses by analyzing both laboratory test results and patients' medical histories.
Methods: This cohort study embraced a prospective data collection approach. A total of 101 patients aged ≥18 years, in stable condition, and requiring comprehensive diagnosis were enrolled. A panel of blood laboratory tests was conducted for each participant. Participants used LTC for test result interpretation. The accuracy and safety of the tool were assessed by comparing AI-generated suggestions to experienced doctor (consultant) recommendations, which are considered the gold standard.
Results: The system achieved a 74.3% accuracy and 100% sensitivity for emergency safety and 92.3% sensitivity for urgent cases. It potentially reduced unnecessary medical visits by 41.6% (42/101) and achieved an 82.9% accuracy in identifying underlying pathologies.
Conclusions: This study underscores the transformative potential of AI-based CDSSs in laboratory diagnostics, contributing to enhanced patient care, efficient health care systems, and improved medical outcomes. LTC's performance evaluation highlights the advancements in AI's role in laboratory medicine.
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.
Background: Recently, the growing demand for pediatric sedation services outside the operating room has imposed a heavy burden on pediatric centers in China. There is an urgent need to develop a novel system for improved sedation services.
Objective: This study aimed to develop and implement a computerized system, the Pediatric Sedation Assessment and Management System (PSAMS), to streamline pediatric sedation services at a major children's hospital in Southwest China.
Methods: PSAMS was designed to reflect the actual workflow of pediatric sedation. It consists of 3 main components: server-hosted software; client applications on tablets and computers; and specialized devices like gun-type scanners, desktop label printers, and pulse oximeters. With the participation of a multidisciplinary team, PSAMS was developed and refined during its application in the sedation process. This study analyzed data from the first 2 years after the system's deployment.
Unlabelled: From January 2020 to December 2021, a total of 127,325 sedations were performed on 85,281 patients using the PSAMS database. Besides basic variables imported from Hospital Information Systems (HIS), the PSAMS database currently contains 33 additional variables that capture comprehensive information from presedation assessment to postprocedural recovery. The recorded data from PSAMS indicates a one-time sedation success rate of 97.1% (50,752/52,282) in 2020 and 97.5% (73,184/75,043) in 2021. The observed adverse events rate was 3.5% (95% CI 3.4%-3.7%) in 2020 and 2.8% (95% CI 2.7%-2.9%) in 2021.
Conclusions: PSAMS streamlined the entire sedation workflow, reduced the burden of data collection, and laid a foundation for future cooperation of multiple pediatric health care centers.
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