Background There is an increasing need to integrate patient-generated health data (PGHD) into health information systems (HISs). The use of health information standards based on the dual model allows the achievement of semantic interoperability among systems. Although there is evidence in the use of the Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) framework for standardized communication between mobile apps and electronic health records (EHRs), the use of European Norm/International Organization for Standardization (EN/ISO) 13606 has not been explored yet, despite some advantages over FHIR in terms of modeling and formalization of clinical knowledge, as well as flexibility in the creation of new concepts. Objective This study aims to design and implement a methodology based on the dual-model paradigm to communicate clinical information between a patient mobile app (Xemio Research) and an institutional ontology-based clinical repository (OntoCR) without loss of meaning. Methods This paper is framed within Artificial intelligence Supporting CAncer Patients across Europe (ASCAPE), a project that aims to use artificial intelligence (AI)/machine learning (ML) mechanisms to support cancer patients’ health status and quality of life (QoL). First, the variables “side effect” and “daily steps” were defined and represented with EN/ISO 13606 archetypes. Next, ontologies that model archetyped concepts and map them to the standard were created and uploaded to OntoCR, where they were ready to receive instantiated patient data. Xemio Research used a conversion module in the ASCAPE Local Edge to transform data entered into the app to create EN/ISO 13606 extracts, which were sent to an Application Programming Interface (API) in OntoCR that maps each element in the normalized XML files to its corresponding location in the ontology. This way, instantiated data of patients are stored in the clinical repository. Results Between December 22, 2020, and April 4, 2022, 1100 extracts of 47 patients were successfully communicated (234/1100, 21.3%, extracts of side effects and 866/1100, 78.7%, extracts of daily activity). Furthermore, the creation of EN/ISO 13606–standardized archetypes allows the reuse of clinical information regarding daily activity and side effects, while with the creation of ontologies, we extended the knowledge representation of our clinical repository. Conclusions Health information interoperability is one of the requirements for continuity of health care. The dual model allows the separation of knowledge and information in HISs. EN/ISO 13606 was chosen for this project because of the operational mechanisms it offers for data exchange, as well as its flexibility for modeling knowledge and creating new concepts. To the best of our knowledge, this is the first experience reported in the literature of effective communication of EN/ISO 13606 EHR extracts between a patient mobile app and an institutional clinical repository using a scalable standard-agnostic methodology that can be applied to other projects, data sources, and institutions.
Since Argentina’s government declared a national emergency to combat the COVID-19 pandemic with a lockdown status, it has produced consequences on the healthcare system. We aimed to quantify the effect on the Emergency Department (ED) visits at Hospital Italiano de Buenos Aires. Our electronic health data showed that ED in-person visits declined 46% during the COVID-19 pandemic, from an overall of 176,370 visits during 2019 to 95,421 visits during 2020. Simultaneously, there was a telehealth visits boom when mandatory quarantine began (March 20, 2020): from a median of 12 daily in February 2020 to a median of 338 daily in April 2020; reaching a maximum daily peak of 1,132 on March 26 2020. For a while, teleconsultations replaced ED visits. Then, when face-to-face visits began to increase, teleconsultations began to decrease slowly, as the phenomenon reversed.
Background To discover new knowledge from data, they must be correct and in a consistent format. OntoCR, a clinical repository developed at Hospital Clínic de Barcelona, uses ontologies to represent clinical knowledge and map locally defined variables to health information standards and common data models. Objective The aim of the study is to design and implement a scalable methodology based on the dual-model paradigm and the use of ontologies to consolidate clinical data from different organizations in a standardized repository for research purposes without loss of meaning. Methods First, the relevant clinical variables are defined, and the corresponding European Norm/International Organization for Standardization (EN/ISO) 13606 archetypes are created. Data sources are then identified, and an extract, transform, and load process is carried out. Once the final data set is obtained, the data are transformed to create EN/ISO 13606–normalized electronic health record (EHR) extracts. Afterward, ontologies that represent archetyped concepts and map them to EN/ISO 13606 and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standards are created and uploaded to OntoCR. Data stored in the extracts are inserted into its corresponding place in the ontology, thus obtaining instantiated patient data in the ontology-based repository. Finally, data can be extracted via SPARQL queries as OMOP CDM–compliant tables. Results Using this methodology, EN/ISO 13606–standardized archetypes that allow for the reuse of clinical information were created, and the knowledge representation of our clinical repository by modeling and mapping ontologies was extended. Furthermore, EN/ISO 13606–compliant EHR extracts of patients (6803), episodes (13,938), diagnosis (190,878), administered medication (222,225), cumulative drug dose (222,225), prescribed medication (351,247), movements between units (47,817), clinical observations (6,736,745), laboratory observations (3,392,873), limitation of life-sustaining treatment (1,298), and procedures (19,861) were created. Since the creation of the application that inserts data from extracts into the ontologies is not yet finished, the queries were tested and the methodology was validated by importing data from a random subset of patients into the ontologies using a locally developed Protégé plugin (“OntoLoad”). In total, 10 OMOP CDM–compliant tables (“Condition_occurrence,” 864 records; “Death,” 110; “Device_exposure,” 56; “Drug_exposure,” 5609; “Measurement,” 2091; “Observation,” 195; “Observation_period,” 897; “Person,” 922; “Visit_detail,” 772; and “Visit_occurrence,” 971) were successfully created and populated. Conclusions This study proposes a methodology for standardizing clinical data, thus allowing its reuse without any changes in the meaning of the modeled concepts. Although this paper focuses on health research, our methodology suggests that the data be initially standardized per EN/ISO 13606 to obtain EHR extracts with a high level of granularity that can be used for any purpose. Ontologies constitute a valuable approach for knowledge representation and standardization of health information in a standard-agnostic manner. With the proposed methodology, institutions can go from local raw data to standardized, semantically interoperable EN/ISO 13606 and OMOP repositories.
Introduction. Deoxyribonucleic acid (DNA) is not a random sequence of four nucleotides combinations: comprehensive reviews [1, 2] persuasively shows long- and short-range correlations in DNA, periodic properties and correlations structure of sequences. Information theory methods, like Entropy, imply quantifying the amount of information contained in sequences. the relationship between entropy and patient survival is widespread in some branches of medicine and medical researches: cardiology, neurology, surgery, trauma. Therefore, it appears there is a necessity for implementing advantages of information theory methods for exploration of relationship between mortality of some category of patients and entropy of their DNA sequences. Aim of the research. The goal of this paper is to provide a reliable formula for calculating entropy accurately for short DNA sequences and to show how to use existing entropy analysis to examine the mortality of leukemia patients. Materials and Methods. We used University of Barcelona (UB) leukemia patient’s data base (DB) with 117 anonymized records that consists: Date of patient’s diagnosis, Date of patient’s death, Leukemia diagnoses, Patient’s DNA sequence. Average time for patient death after diagnoses: 99 ± 77 months. The formal characteristics of DNA sequences in UB leukemia patient’s DB are: average number of bases N = 496 ± 69; min (N) = 297 bases; max(N) = 745 bases. The generalized form of the Robust Entropy Estimator (EnRE) for short DNA sequences was proposed and key EnRE futures was showed. The Survival Analysis has been done using statistical package IBM SPSS 27 by Kaplan-Meier survival analysis and Cox Regressions survival modelling. Results. The accuracy of the proposed EnRE for calculating entropy was proved for various lengths of time series and various types of random distributions. It was shown, that in all cases for N = 500, relative error in calculating the precise value of entropy does not exceed 1 %, while the magnitude of correlation is no worse than 0.995. In order to yield the minimum EnRE standard deviation and coefficient of variation, an initial DNA sequence's alphabet code was converted into an integer code of bases using an optimization rule for only one minimal numerical decoding around zero. Entropy EnRE were calculated for leukemia patients for two samples: 2 groups divided by median EnRE = 1.47 and 2 groups of patients were formed according to their belonging to 1st (EnRE ≤ 1.448) and 4th (EnRE ≥ 1.490) quartiles. The result of Kaplan-Meier survival analysis and Cox Regressions survival modelling are statistically significant: p < 0,05 for median groups and p < 0,005 for patient’s groups formed of 1st and 4th quartiles. The death hazard for a patient with EnRE below median is 1.556 times that of a patient with EnRE over median and that the death hazard for a patient of 1st entropy quartile (lowest EnRE) is 2.143 times that of a patient of 4th entropy quartile (highest EnRE). Conclusions. The transition from widen (median) to smaller (quartile) patients’ groups with more EnRE differentiation confirmed the unique significance of the entropy of DNA sequences for leukemia patient’s mortality. This significance is proved statistically by increasing hazard and decreasing of average time of death after diagnoses for leukemia patients with lower entropy of DNA sequences.
The purpose of this study is to provide an accurate formula for calculating entropy for short DNA sequences and to demonstrate how to use it to examine leukemia patient surviving. We used IDIBAPS leukemia patient’s data base with 117 anonymized records. The generalized form of the Robust Entropy Estimator (EnRE) for short DNA sequences was proposed and key EnRE futures was showed. The Survival Analysis has been done using statistical package IBM SPSS. Entropy EnRE were calculated for leukemia patients for two samples: A. 2 groups divided by median EnRE and B. 2 groups of patients were formed according to their belonging to 1st and 4th quartiles of EnRE. The result of survival analysis are statistically significant: A. p &lt; 0.05; B. p &lt; 0.005. The death hazard for a patient with EnRE below median is 1.556 times that of a patient with EnRE over median and that the death hazard for a patient of 1st quartile (lowest EnRE) is 2.143 times that of a patient of 4th quartile (highest EnRE). The transition from median to quartile patients’ groups with more EnRE differentiation confirmed the unique significance of the entropy of DNA sequences for leukemia patients surviving.
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