No changes in clinical presentation, treatment strategies and survival of pancreatic cancer cases during the SARS-COV-2 outbreak: A retrospective multicenter cohort study on real-world data.

The SARS-COV-2 pandemic disrupted healthcare systems. We assessed its impact on the presentation, care trajectories and outcomes of new pancreatic cancers (PCs) in the Paris area. We performed a retrospective multicenter cohort study on the data warehouse of Greater Paris University Hospitals (AP-HP). We identified all patients newly referred with a PC between January 1, 2019, and June 30, 2021, and excluded endocrine tumors. Using claims data and health records, we analyzed the timeline of care trajectories, the initial tumor stage, the treatment categories: pancreatectomy, exclusive systemic therapy or exclusive best supportive care (BSC). We calculated patients' 1-year overall survival (OS) and compared indicators in 2019 and 2020 to 2021. We included 2335 patients. Referral fell by 29% during the first lockdown. The median time from biopsy and from first MDM to treatment were 25 days (16-50) and 21 days (11-40), respectively. Between 2019 and 2020 to 2021, the rate of metastatic tumors (36% vs 33%, P = .39), the pTNM distribution of the 464 cases with upfront tumor resection (P = .80), and the proportion of treatment categories did not vary: tumor resection (32% vs 33%), exclusive systemic therapy (49% vs 49%), exclusive BSC (19% vs 19%). The 1-year OS rates in 2019 vs 2020 to 2021 were 92% vs 89% (aHR = 1.42; 95% CI, 0.82-2.48), 52% vs 56% (aHR = 0.88; 95% CI, 0.73-1.08), 13% vs 10% (aHR = 1.00; 95% CI, 0.78-1.25), in the treatment categories, respectively. Despite an initial decrease in the number of new PCs, we did not observe any stage shift. OS did not vary significantly.

Development of a natural language processing model for deriving breast cancer quality indicators : A cross-sectional, multicenter study.

OBJECTIVES: Medico-administrative data are promising to automate the calculation of Healthcare Quality and Safety Indicators. Nevertheless, not all relevant indicators can be calculated with this data alone. Our feasibility study objective is to analyze 1) the availability of data sources; 2) the availability of each indicator elementary variables, and 3) to apply natural language processing to automatically retrieve such information. METHOD: We performed a multicenter cross-sectional observational feasibility study on the clinical data warehouse of Assistance Publique - Hôpitaux de Paris (AP-HP). We studied the management of breast cancer patients treated at AP-HP between January 2019 and June 2021, and the quality indicators published by the European Society of Breast Cancer Specialist, using claims data from the Programme de Médicalisation du Système d'Information (PMSI) and pathology reports. For each indicator, we calculated the number (%) of patients for whom all necessary data sources were available, and the number (%) of patients for whom all elementary variables were available in the sources, and for whom the related HQSI was computable. To extract useful data from the free text reports, we developed and validated dedicated rule-based algorithms, whose performance metrics were assessed with recall, precision, and f1-score. RESULTS: Out of 5785 female patients diagnosed with a breast cancer (60.9 years, IQR [50.0-71.9]), 5,147 (89.0%) had procedures related to breast cancer recorded in the PMSI, and 3732 (72.5%) had at least one surgery. Out of the 34 key indicators, 9 could be calculated with the PMSI alone, and 6 others became so using the data from pathology reports. Ten elementary variables were needed to calculate the 6 indicators combining the PMSI and pathology reports. The necessary sources were available for 58.8% to 94.6% of patients, depending on the indicators. The extraction algorithms developed had an average accuracy of 76.5% (min-max [32.7%-93.3%]), an average precision of 77.7% [10.0%-97.4%] and an average sensitivity of 71.6% [2.8% to 100.0%]. Once these algorithms applied, the variables needed to calculate the indicators were extracted for 2% to 88% of patients, depending on the indicators. DISCUSSION: The availability of medical reports in the electronic health records, of the elementary variables within the reports, and the performance of the extraction algorithms limit the population for which the indicators can be calculated. CONCLUSIONS: The automated calculation of quality indicators from electronic health records is a prospect that comes up against many practical obstacles.

Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of university hospitals.

The SARS-Cov2 may have impaired care trajectories, patient overall survival (OS), tumor stage at initial presentation for new colorectal cancer (CRC) cases. This study aimed at assessing those indicators before and after the beginning of the pandemic in France. In this retrospective cohort study, we collected prospectively the clinical data of the 11.4 million of patients referred to the Greater Paris University Hospitals (AP-HP). We identified new CRC cases between 1 January 2018 and 31 December 2020, and compared indicators for 2018-2019 to 2020. pTNM tumor stage was extracted from postoperative pathology reports for localized colon cancer, and metastatic status was extracted from CT-scan baseline text reports. Between 2018 and 2020, 3602 and 1083 new colon and rectal cancers were referred to the AP-HP, respectively. The 1-year OS rates reached 94%, 93% and 76% for new CRC patients undergoing a resection of the primary tumor, in 2018-2019, in 2020 without any Sars-Cov2 infection and in 2020 with a Sars-Cov2 infection, respectively (HR 3.78, 95% CI 2.1-7.1). For patients undergoing other kind of anticancer treatment, the percentages are 64%, 66% and 27% (HR 2.1, 95% CI 1.4-3.3). Tumor stage at initial presentation, emergency level of primary tumor resection, delays between the first multidisciplinary meeting and the first anticancer treatment did not differ over time. The SARS-Cov2 pandemic has been associated with less newly diagnosed CRC patients and worse 1-year OS rates attributable to the infection itself rather than to its impact on hospital care delivery or tumor stage at initial presentation.

Fully automated opportunistic screening of vertebral fractures and osteoporosis on more than 150 000 routine computed tomography scans.

OBJECTIVE: Osteoporosis is underdiagnosed and undertreated, although severe complications of osteoporotic fractures, including vertebral fractures, are well known. This study sought to assess the feasibility and results of an opportunistic screening of vertebral fractures and osteoporosis in a large database of lumbar or abdominal CT scans. MATERIAL AND METHODS: Data were analysed from CT scans obtained in 35 hospitals from patients aged 60 years or older and stored in a Picture Archiving and Communication System in Assistance-Publique-Hôpitaux de Paris, from 2007 to 2013. Dedicated software was used to analyse the presence or absence of at least 1 vertebral fracture (VF), and the radiodensity of the lumbar vertebrae was measured Hounsfield Units (HUs). A simulated T-score was calculated. RESULTS: Data were analysed from 152 268 patients [mean age (S.D.) = 73.2 (9.07) years]. Success rates for VF assessment and HUs measurements were 82 and 87%, respectively. The prevalence of VFs was 24.5% and increased with age. Areas under the receiver operating characteristic curves for the detection of VFs were 0.61 and 0.62 for the mean HUs of the lumbar vertebrae and the L1 HUs, respectively. In patients without VFs, HUs decreased with age, similarly in males and females. The prevalence of osteoporosis (sT-score ≤ -2.5) was 23.8% and 36.5% in patients without and with VFs, respectively. CONCLUSION: It is feasible on a large scale to screen for VFs and osteoporosis during opportunistic screening in patients 60 years or older having lumbar or abdominal CT.

Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study.

A prior meta-analysis showed that antidepressant use in major depressive disorder was associated with reduced plasma levels of several pro-inflammatory mediators, which have been associated with severe COVID-19. Recent studies also suggest that several antidepressants may inhibit acid sphingomyelinase activity, which may prevent the infection of epithelial cells with SARS-CoV-2, and that the SSRI fluoxetine may exert in-vitro antiviral effects on SARS-CoV-2. We examined the potential usefulness of antidepressant use in patients hospitalized for COVID-19 in an observational multicenter retrospective cohort study conducted at AP-HP Greater Paris University hospitals. Of 7230 adults hospitalized for COVID-19, 345 patients (4.8%) received an antidepressant within 48 h of hospital admission. The primary endpoint was a composite of intubation or death. We compared this endpoint between patients who received antidepressants and those who did not in time-to-event analyses adjusted for patient characteristics, clinical and biological markers of disease severity, and other psychotropic medications. The primary analysis was a multivariable Cox model with inverse probability weighting. This analysis showed a significant association between antidepressant use and reduced risk of intubation or death (HR, 0.56; 95% CI, 0.43-0.73, p < 0.001). This association remained significant in multiple sensitivity analyses. Exploratory analyses suggest that this association was also significant for SSRI and non-SSRI antidepressants, and for fluoxetine, paroxetine, escitalopram, venlafaxine, and mirtazapine (all p < 0.05). These results suggest that antidepressant use could be associated with lower risk of death or intubation in patients hospitalized for COVID-19. Double-blind controlled randomized clinical trials of antidepressant medications for COVID-19 are needed.

Dexamethasone use and mortality in hospitalized patients with coronavirus disease 2019: A multicentre retrospective observational study.

AIMS: To examine the association between dexamethasone use and mortality among patients hospitalized for COVID-19. METHODS: We examined the association between dexamethasone use and mortality at AP-HP Greater Paris University hospitals. Study baseline was defined as the date of hospital admission. The primary endpoint was time to death. We compared this endpoint between patients who received dexamethasone and those who did not in time-to-event analyses adjusted for patient characteristics (such as age, sex and comorbidity) and clinical and biological markers of clinical severity of COVID-19, and stratified by the need for respiratory support, i.e. mechanical ventilation or oxygen. The primary analysis was a multivariable Cox regression model. RESULTS: Of 12 217 adult patients hospitalized with a positive COVID-19 reverse transcriptase-polymerase chain reaction test, 171 (1.4%) received dexamethasone orally or by intravenous perfusion during the visit. Among patients who required respiratory support, the end-point occurred in 10/63 (15.9%) patients who received dexamethasone and 298/1129 (26.4%) patients who did not. In this group, there was a significant association between dexamethasone use and reduced mortality in the primary analysis (hazard ratio, 0.46; 95% confidence interval 0.22-0.96, P = .039). Among patients who did not require respiratory support, there was no significant association between dexamethasone use and the endpoint. CONCLUSIONS: In this multicentre observational study, dexamethasone use administered either orally or by intravenous injection at a cumulative dose between 60 mg and 150 mg was associated with reduced mortality among patients with COVID-19 requiring respiratory support.

Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned.

BACKGROUND: The development of Electronic Health Records (EHRs) in hospitals offers the ability to reuse data from patient care activities for clinical research. EHR4CR is a European public-private partnership aiming to develop a computerized platform that enables the re-use of data collected from EHRs over its network. However, the reproducibility of queries may depend on attributes of the local data. Our objective was 1/ to describe the different steps that were achieved in order to use the EHR4CR platform and 2/ to identify the specific issues that could impact the final performance of the platform. METHODS: We selected three institutional studies covering various medical domains. The studies included a total of 67 inclusion and exclusion criteria and ran in two University Hospitals. We described the steps required to use the EHR4CR platform for a feasibility study. We also defined metrics to assess each of the steps (including criteria complexity, normalization quality, and data completeness of EHRs). RESULTS: We identified 114 distinct medical concepts from a total of 67 eligibility criteria Among the 114 concepts: 23 (20.2%) corresponded to non-structured data (i.e. for which transformation is needed before analysis), 92 (81%) could be mapped to terminologies used in EHR4CR, and 86 (75%) could be mapped to local terminologies. We identified 51 computable criteria following the normalization process. The normalization was considered by experts to be satisfactory or higher for 64.2% (43/67) of the computable criteria. All of the computable criteria could be expressed using the EHR4CR platform. CONCLUSIONS: We identified a set of issues that could affect the future results of the platform: (a) the normalization of free-text criteria, (b) the translation into computer-friendly criteria and (c) issues related to the execution of the query to clinical data warehouses. We developed and evaluated metrics to better describe the platforms and their result. These metrics could be used for future reports of Clinical Trial Recruitment Support Systems assessment studies, and provide experts and readers with tools to insure the quality of constructed dataset.

Using electronic health records for clinical research: the case of the EHR4CR project.

OBJECTIVES: To describe the IMI EHR4CR project which is designing and developing, and aims to demonstrate, a scalable, widely acceptable and efficient approach to interoperability between EHR systems and clinical research systems. METHODS: The IMI EHR4CR project is combining and extending several previously isolated state-of-the-art technical components through a new approach to develop a platform for reusing EHR data to support medical research. This will be achieved through multiple but unified initiatives across different major disease areas (e.g. cardiovascular, cancer) and clinical research use cases (protocol feasibility, patient identification and recruitment, clinical trial execution and serious adverse event reporting), with various local and national stakeholders across several countries and therefore under various legal frameworks. RESULTS: An initial instance of the platform has been built, providing communication, security and terminology services to the eleven participating hospitals and ten pharmaceutical companies located in seven European countries. Proof-of-concept demonstrators have been built and evaluated for the protocol feasibility and patient recruitment scenarios. The specifications of the clinical trial execution and the adverse event reporting scenarios have been documented and reviewed. CONCLUSIONS: Through a combination of a consortium that brings collectively many years of experience from previous relevant EU projects and of the global conduct of clinical trials, of an approach to ethics that engages many important stakeholders across Europe to ensure acceptability, of a robust iterative design methodology for the platform services that is anchored on requirements of an underlying Service Oriented Architecture that has been designed to be scalable and adaptable, EHR4CR could be well placed to deliver a sound, useful and well accepted pan-European solution for the reuse of hospital EHR data to support clinical research studies.

Why Are Data Missing in Clinical Data Warehouses? A Simulation Study of How Data Are Processed (and Can Be Lost).

In recent years, the development of clinical data warehouses (CDW) has put Electronic Health Records (EHR) data in the spotlight. More and more innovative technologies for healthcare are based on these EHR data. However, quality assessments on EHR data are fundamental to gain confidence in the performances of new technologies. The infrastructure developed to access EHR data - CDW - can affect EHR data quality but its impact is difficult to measure. We conducted a simulation on the Assistance Publique - Hôpitaux de Paris (AP-HP) infrastructure to assess how a study on breast cancer care pathways could be affected by the complexity of the data flows between the AP-HP Hospital Information System, the CDW, and the analysis platform. A model of the data flows was developed. We retraced the flows of specific data elements for a simulated cohort of 1,000 patients. We estimated that 756 [743;770] and 423 [367;483] patients had all the data elements necessary to reconstruct the care pathway in the analysis platform in the "best case" scenarios (losses affect the same patients) and in a random distribution scenario (losses affect patients at random), respectively.

How to Improve Cancer Patients ENrollment in Clinical Trials From rEal-Life Databases Using the Observational Medical Outcomes Partnership Oncology Extension: Results of the PENELOPE Initiative in Urologic Cancers.

PURPOSE: To compare the computability of Observational Medical Outcomes Partnership (OMOP)-based queries related to prescreening of patients using two versions of the OMOP common data model (CDM; v5.3 and v5.4) and to assess the performance of the Greater Paris University Hospital (APHP) prescreening tool. MATERIALS AND METHODS: We identified the prescreening information items being relevant for prescreening of patients with cancer. We randomly selected 15 academic and industry-sponsored urology phase I-IV clinical trials (CTs) launched at APHP between 2016 and 2021. The computability of the related prescreening criteria (PC) was defined by their translation rate in OMOP-compliant queries and by their execution rate on the APHP clinical data warehouse (CDW) containing data of 205,977 patients with cancer. The overall performance of the prescreening tool was assessed by the rate of true- and false-positive cases of three randomly selected CTs. RESULTS: We defined a list of 15 minimal information items being relevant for patients' prescreening. We identified 83 PC of the 534 eligibility criteria from the 15 CTs. We translated 33 and 62 PC in queries on the basis of OMOP CDM v5.3 and v5.4, respectively (translation rates of 40% and 75%, respectively). Of the 33 PC translated in the v5.3 of the OMOP CDM, 19 could be executed on the APHP CDW (execution rate of 58%). Of 83 PC, the computability rate on the APHP CDW reached 23%. On the basis of three CTs, we identified 17, 32, and 63 patients as being potentially eligible for inclusion in those CTs, resulting in positive predictive values of 53%, 41%, and 21%, respectively. CONCLUSION: We showed that PC could be formalized according to the OMOP CDM and that the oncology extension increased their translation rate through better representation of cancer natural history.

TransFAIR study: a European multicentre experimental comparison of EHR2EDC technology to the usual manual method for eCRF data collection.

PURPOSE: Regulatory authorities including the Food and Drug Administration and the European Medicines Agency are encouraging to conduct clinical trials using routinely collected data. The aim of the TransFAIR experimental comparison was to evaluate, within real-life conditions, the ability of the Electronic Health Records to Electronic Data Capture (EHR2EDC) module to accurately transfer from EHRs to EDC systems patients' data of clinical studies in various therapeutic areas. METHODS: A prospective study including six clinical trials from three different sponsors running in three hospitals across Europe has been conducted. The same data from the six studies were collected using both traditional manual data entry and the EHR2EDC module. The outcome variable was the percentage of data accurately transferred using the EHR2EDC technology. This percentage was calculated considering all collected data and the data in four domains: demographics (DM), vital signs (VS), laboratories (LB) and concomitant medications (CM). RESULTS: Overall, 6143 data points (39.6% of the data in the scope of the TransFAIR study and 16.9% when considering all data) were accurately transferred using the platform. LB data represented 65.4% of the data transferred; VS data, 30.8%; DM data, 0.7% and CM data, 3.1%. CONCLUSIONS: The objective of accurately transferring at least 15% of the manually entered trial datapoints using the EHR2EDC module was achieved. Collaboration and codesign by hospitals, industry, technology company, supported by the Institute of Innovation through Health Data was a success factor in accomplishing these results. Further work should focus on the harmonisation of data standards and improved interoperability to extend the scope of transferable EHR data.

Impact of the COVID-19 pandemic on clinical presentation, treatments, and outcomes of new breast cancer patients: A retrospective multicenter cohort study.

BACKGROUND: The SARS CoV-2 pandemic disrupted healthcare systems. We compared the cancer stage for new breast cancers (BCs) before and during the pandemic. METHODS: We performed a retrospective multicenter cohort study on the data warehouse of Greater Paris University Hospitals (AP-HP). We identified all female patients newly referred with a BC in 2019 and 2020. We assessed the timeline of their care trajectories, initial tumor stage, and treatment received: BC resection, exclusive systemic therapy, exclusive radiation therapy, or exclusive best supportive care (BSC). We calculated patients' 1-year overall survival (OS) and compared indicators in 2019 and 2020. RESULTS: In 2019 and 2020, 2055 and 1988, new BC patients underwent cancer treatment, and during the two lockdowns, the BC diagnoses varied by -18% and by +23% compared to 2019. De novo metastatic tumors (15% and 15%, p = 0.95), pTNM and ypTNM distributions of 1332 cases with upfront resection and of 296 cases with neoadjuvant therapy did not differ (p = 0.37, p = 0.3). The median times from first multidisciplinary meeting and from diagnosis to treatment of 19 days (interquartile 11-39 days) and 35 days (interquartile 22-65 days) did not differ. Access to plastic surgery (15% and 17%, p = 0.08) and to treatment categories did not vary: tumor resection (73% and 72%), exclusive systemic therapy (13% and 14%), exclusive radiation therapy (9% and 9%), exclusive BSC (5% and 5%) (p = 0.8). Among resected patients, the neoadjuvant therapy rate was lower in 2019 (16%) versus 2020 (20%) (p = 0.02). One-year OS rates were 99.3% versus 98.9% (HR = 0.96; 95% CI, 0.77-1.2), 72.6% versus 76.6% (HR = 1.28; 95% CI, 0.95-1.72), 96.6% versus 97.8% (HR = 1.09; 95% CI, 0.61-1.94), and 15.5% versus 15.1% (HR = 0.99; 95% CI, 0.72-1.37), in the treatment groups. CONCLUSIONS: Despite a decrease in the number of new BCs, there was no tumor stage shift, and OS did not vary.

[Whole slide imaging technology: from digitization to online applications]. / Technologie des lames virtuelles - De la numérisation à la mise en ligne.

As e-health becomes essential to modern care, whole slide images (virtual slides) are now an important clinical, teaching and research tool in pathology. Virtual microscopy consists of digitizing a glass slide by acquiring hundreds of tiles of regions of interest at different zoom levels and assembling them into a structured file. This gigapixel image can then be remotely viewed over a terminal, exactly the way pathologists use a microscope. In this article, we will first describe the key elements of this technology, from the acquisition, using a scanner or a motorized microscope, to the broadcasting of virtual slides through a local or distant viewer over an intranet or Internet connection. As virtual slides are now commonly used in virtual classrooms, clinical data and research databases, we will highlight the main issues regarding its uses in modern pathology. Emphasis will be made on quality assurance policies, standardization and scaling.

SNOMED CT in pathology.

Pathology information systems have been using SNOMED II for many years, and in most cases, they are in a migration process to SNOMED CT. COST Action IC0604 (EURO-TELEPATH) has considered terminology normalization one of its strategic objectives. This paper reviews the use of SNOMED CT in healthcare, with a special focus in pathology. Nowadays, SNOMED CT is mainly used for concept search and coding of clinical data. Some ontological errors found in SNOMED CT are described. The Integrating the Healthcare Enterprise (IHE) initiative has fostered the use of SNOMED CT, also in Pathology, as recommended in the Supplement Anatomic Pathology Structured Reports of the IHE Anatomic Pathology Technical Framework. Rule governing concept post-coordination is also described. Some recent initiatives are trying to define a SNOMED CT subset for Pathology. The Spanish Society of Pathology has defined a subset for specimens and procedures in Pathology. Regarding diagnosis coding, the morphological abnormality sub-hierarchy of SNOMED CT need to be significantly extended and improved to become useful for pathologists. A consensus is needed to encode pathology reports with the adequate hierarchies and concepts. This will make the implementation of pathology structured reports more feasible.

The Electronic Healthcare Record for Clinical Research (EHR4CR) information model and terminology.

A major barrier to repurposing routinely collected data for clinical research is the heterogeneity of healthcare information systems. Electronic Healthcare Record for Clinical Research (EHR4CR) is a European platform designed to improve the efficiency of conducting clinical trials. In this paper, we propose an initial architecture of the EHR4CR Semantic Interoperability Framework. We used a model-driven engineering approach to build a reference HL7-based multidimensional model bound to a set of reference clinical terminologies acting as a global as view model. We then conducted an evaluation of its expressiveness for patient eligibility. The EHR4CR information model consists in one fact table dedicated to clinical statement and 4 dimensions. The EHR4CR terminology integrates reference terminologies used in patient care (e.g LOINC, ICD-10, SNOMED CT, etc). We used the Object Constraint Language (OCL) to represent patterns of eligibility criteria as constraints on the EHR4CR model to be further transformed in SQL statements executed on different clinical data warehouses.

State of the art in pathology business process analysis, modeling, design and optimization.

For analyzing current workflows and processes, for improving them, for quality management and quality assurance, for integrating hardware and software components, but also for education, training and communication between different domains' experts, modeling business process in a pathology department is inevitable. The authors highlight three main processes in pathology: general diagnostic, cytology diagnostic, and autopsy. In this chapter, those processes are formally modeled and described in detail. Finally, specialized processes such as immunohistochemistry and frozen section have been considered.

Business process modeling in healthcare.

The importance of the process point of view is not restricted to a specific enterprise sector. In the field of health, as a result of the nature of the service offered, health institutions' processes are also the basis for decision making which is focused on achieving their objective of providing quality medical assistance. In this chapter the application of business process modelling - using the Business Process Modelling Notation (BPMN) standard is described. Main challenges of business process modelling in healthcare are the definition of healthcare processes, the multi-disciplinary nature of healthcare, the flexibility and variability of the activities involved in health care processes, the need of interoperability between multiple information systems, and the continuous updating of scientific knowledge in healthcare.

Standards and specifications in pathology: image management, report management and terminology.

For making medical decisions, healthcare professionals require that all necessary information is both correct and easily available. Collaborative Digital Anatomic Pathology refers to the use of information technology that supports the creation and sharing or exchange of information, including data and images, during the complex workflow performed in an Anatomic Pathology department from specimen reception to report transmission and exploitation. Collaborative Digital Anatomic Pathology is supported by standardization efforts toward knowledge representation for sharable and computable clinical information. The goal of the international integrating the Healthcare Enterprise (IHE) initiative is precisely specifying how medical informatics standards should be implemented to meet specific health care needs and making systems integration more efficient and less expensive. The IHE Anatomic Pathology initiative was launched to implement the best use of medical informatics standards in order to produce, share and exchange machine-readable structured reports and their evidences (including whole slide images) within hospitals and across healthcare facilities. DICOM supplements 122 and 145 provide flexible object information definitions dedicated respectively to specimen description and WSI acquisition, storage and display. The profiles "Anatomic Pathology Reporting for Public Health" (ARPH) and "Anatomic Pathology Structured Report" (APSR) provide standard templates and transactions for sharing or exchanging structured reports in which textual observations - encoded using PathLex, an international controlled vocabulary currently being mapped to SNOMED CT concepts - may be bound to digital images or regions of interest in images. Current implementations of IHE Anatomic Pathology profiles in North America, France and Spain demonstrate the applicability of recent advances in standards for Collaborative Digital Anatomic Pathology. The use of machine-readable format of Anatomic Pathology information supports the development of computer-based decision support as well as secondary use of Anatomic Pathology information for research or public health.

Clinical Research Informatics.

OBJECTIVES: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021. METHOD: Using PubMed, we did a bibliographic search using a combination of MeSH descriptors and free-text terms on CRI, followed by a double-blind review in order to select a list of candidate best papers to be peer-reviewed by external reviewers. After peer-review ranking, three section editors met for a consensus meeting and the editorial team was organized to finally conclude on the selected three best papers. RESULTS: Among the 1,096 papers (published in 2021) returned by the search and in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes an operational and scalable framework for generating EHR datasets based on a detailed clinical model with an application in the domain of the COVID-19 pandemics. The authors of the second best paper present a secure and scalable platform for the preprocessing of biomedical data for deep data-driven health management applied for the detection of pre-symptomatic COVID-19 cases and for biological characterization of insulin-resistance heterogeneity. The third best paper provides a contribution to the integration of care and research activities with the REDCap Clinical Data and Interoperability sServices (CDIS) module improving the accuracy and efficiency of data collection. CONCLUSIONS: The COVID-19 pandemic is still significantly stimulating research efforts in the CRI field to improve the process deeply and widely for conducting real-world studies as well as for optimizing clinical trials, the duration and cost of which are constantly increasing. The current health crisis highlights the need for healthcare institutions to continue the development and deployment of Big Data spaces, to strengthen their expertise in data science and to implement efficient data quality evaluation and improvement programs.

Reliability of Drug-Drug Interaction Measurement on Real-Word Data: The ReMIAMes Project.

The ReMIAMes project proposes a methodological framework to provide a reliable and reproducible measurement of the frequency of drug-drug interactions (DDI) when performed on real-world data. This framework relies on (i) a fine-grained and contextualized definition of DDIs, (ii) a shared minimum information model to select the appropriate data for the correct interpretation of potential DDIs, (iii) an ontology-based inference module able to handle missing data to classify prescription lines with potential DDIs, (iv) a report generator giving the value of the measurement and explanations when potential false positive are detected due to a lack of available data. All the tools developed are intended to be publicly shared under open license.