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Laboratory data must be interoperable to be able to accurately compare the results of a lab test between healthcare organizations. To achieve this, terminologies like LOINC (Logical Observation Identifiers, Names and Codes) provide unique identification codes for laboratory tests. Once standardized, the numeric results of laboratory tests can be aggregated and represented in histograms. Due to the characteristics of Real World Data (RWD), outliers and abnormal values are common, but these cases should be treated as exceptions, excluding them from possible analysis. The proposed work analyses two methods capable of automating the selection of histogram limits to sanitize the generated lab test result distributions, Tukey's box-plot method and a "Distance to Density" approach, within the TriNetX Real World Data Network. The generated limits using clinical RWD are generally wider for Tukey's method and narrower for the second method, both greatly dependent on the values used for the algorithm's parameters.

Reuse of Electronic Health Records (EHRs) for specific diseases such as COVID-19 requires data to be recorded and persisted according to international standards. Since the beginning of the COVID-19 pandemic, Hospital Universitario 12 de Octubre (H12O) evolved its EHRs: it identified, modeled and standardized the concepts related to this new disease in an agile, flexible and staged way. Thus, data from more than 200,000 COVID-19 cases were extracted, transformed, and loaded into an i2b2 repository. This effort allowed H12O to share data with worldwide networks such as the TriNetX platform and the 4CE Consortium.

Mappings among terminologies to ensure homogeneous analysis among different data sources is one of the key challenges of semantic interoperability. Concretely, mappings to the International Classification of Diseases 10th Revision Procedure Classification System (ICD-10-PCS) are especially challenging due to its multiaxial structure and lack of terms used by physicians (many terminologies used in real world data (RWD) are initially intended for reimbursement, not for clinical purposes). In this work, we propose a new theoretical methodology for mapping healthcare data to the ICD-10-PCS by exploiting its multiaxial structure to reduce the search spaces within concepts and leveraging the dependencies between axes for inferring additional relevant information. We tested this methodology with a subset of the German Operation and Procedure Classification System (OPS), aiming to integrate heterogeneous data sources queried for clinical research., (Copyright © 2020. Published by Elsevier Inc.)

Clinical Decision Support Systems (CDSS) have the potential to improve patient care. We developed the Coronary Artery Disease and Diabetes Mellitus (CAD/DM) Smart Form as a documentation-based application that provides decision support for the management of chronic diseases. Results of a pilot study suggest that the CAD/DM Smart Form has the potential to improve patient care.

Context: Acinar cell carcinoma of the pancreas is a rare malignancy that may have acinar and endocrine differentiation. Clinical practice guidelines exist for pancreatic ductal adenocarcinoma. However, treatment protocols for acinar cell carcinoma of the pancreas have not been standardized., Case Report: We describe a case of a 44-year-old woman presenting with low grade fever and mid-abdominal tenderness secondary to a pancreatic mass with acinar and endocrine differentiation metastatic to the liver. The patient received multiple lines of conventional and investigational chemotherapy regimens. This included cycles of gemcitabine, irinotecan, oxaliplatin, docetaxel, capecitabine, octreotide, leucovorin, 5-fluorouracil, cisplatin, protein kinase C inhibitor, tyrosine kinase inhibitor, and a novel taxane. The patient developed Clostridium difficile colitis and septic shock resulting in death 37 months after the diagnosis of acinar cell carcinoma of the pancreas., Conclusion: This is a case of acinar cell carcinoma of the pancreas with an endocrine component, treated with multiple chemotherapeutic agents, in which the patient survived 37 months after diagnosis.

Acute Respiratory Infections (ARIs) are the number one reason for antibiotic prescribing in the United States, and much antibiotic prescribing for ARIs is inappropriate. We designed an electronic health record-integrated, documentation-based clinical decision support system for the care of patients with ARIs, the ARI Smart Form. To evaluate the ARI Smart Form and assess the feasibility of performing a larger trial, we conducted a pilot study with 10 clinicians who used the ARI Smart Form with 26 patients. Clinicians prescribed antibiotics to 6 of 6 patients with antibiotic-appropriate diagnoses and to 3 of 20 (15%) patients with antibiotic-inappropriate diagnoses. The average duration of use of the ARI Smart Form was 7.5 (SD+/-4.5) minutes. Eight of 10 respondents reported that the ARI Smart Form was either time-neutral or timesaving. The ARI Smart Form requires further evaluation but has the potential to improve workflow and reduce inappropriate antibiotic prescribing.