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1.
Rev Alerg Mex ; 68(3): 174-179, 2021.
Artículo en Español | MEDLINE | ID: mdl-34634847

RESUMEN

OBJECTIVES: To determine the prevalence of chronic urticaria in patients diagnosed with an allergic pathology; to know the most affected sex and age group. METHODS: A descriptive, observational, and retrospective cross-sectional study. Clinical records of patients diagnosed with chronic urticaria in Unidad de Medicina Integral (Integrated Care Unit) of Tehuacan, Puebla were reviewed. They were selected by age, sex, and diagnosed allergic pathology. RESULTS: In the period of January 1st, 2015, to December 31st, 2020, 373 patients were diagnosed with chronic urticaria, with a prevalence of 10.5%. The average age of the population was 26.05 years. Women were the most prevalent, with 59.5% of the total studied population. CONCLUSIONS: The results reflect a prevalence of 10.5%, with a higher frequency in women at a ratio of 1.4: 1 with regard to males. The prevalence of chronic urticaria has increased significantly in recent years and especially in young patients (infants, preschoolers, and school-age children).

2.
BMJ Open ; 11(10): e051707, 2021 10 01.
Artículo en Inglés | MEDLINE | ID: mdl-34598988

RESUMEN

OBJECTIVES: To identify factors associated with COVID-19 test positivity and assess viral and antibody test concordance. DESIGN: Observational retrospective cohort study. SETTING: Optum de-identified electronic health records including over 700 hospitals and 7000 clinics in the USA. PARTICIPANTS: There were 891 754 patients who had a COVID-19 test identified in their electronic health record between 20 February 2020 and 10 July 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: Per cent of viral and antibody tests positive for COVID-19 ('positivity rate'); adjusted ORs for factors associated with COVID-19 viral and antibody test positivity; and per cent concordance between positive viral and subsequent antibody test results. RESULTS: Overall positivity rate was 9% (70 472 of 771 278) and 12% (11 094 of 91 741) for viral and antibody tests, respectively. Positivity rate was inversely associated with the number of individuals tested and decreased over time across regions and race/ethnicities. Antibody test concordance among patients with an initial positive viral test was 91% (71%-95% depending on time between tests). Among tests separated by at least 2 weeks, discordant results occurred in 7% of patients and 9% of immunocompromised patients. Factors associated with increased odds of viral and antibody positivity in multivariable models included: male sex, Hispanic or non-Hispanic black or Asian race/ethnicity, uninsured or Medicaid insurance and Northeast residence. We identified a negative dose effect between the number of comorbidities and viral and antibody test positivity. Paediatric patients had reduced odds (OR=0.60, 95% CI 0.57 to 0.64) of a positive viral test but increased odds (OR=1.90, 95% CI 1.62 to 2.23) of a positive antibody test compared with those aged 18-34 years old. CONCLUSIONS: This study identified sociodemographic and clinical factors associated with COVID-19 test positivity and provided real-world evidence demonstrating high antibody test concordance among viral-positive patients.


Asunto(s)
COVID-19 , Registros Electrónicos de Salud , Adolescente , Adulto , Niño , Femenino , Hispanoamericanos , Humanos , Masculino , Estudios Retrospectivos , SARS-CoV-2 , Estados Unidos , Adulto Joven
3.
Rev Alerg Mex ; 68(3): 160-164, 2021.
Artículo en Español | MEDLINE | ID: mdl-34634845

RESUMEN

OBJECTIVE: To clinically characterize the events of anaphylaxis in a third-level pediatric hospital. METHODS: 1148 clinical records were reviewed. Eventually, the information of 35 events of anaphylaxis in 20 patients was analyzed; three of them had multiple episodes of anaphylaxis. RESULTS: The median age for the anaphylactic episodes was 11 years (Interquartile range 10 years, Q1 = 5, Q3 = 15), predominantly in adolescents between the ages of 12 and 17 years, and there was a slight predominance in women. The most frequent clinical manifestations were cutaneous (86%), followed by respiratory (83%), cardiovascular (74%), and gastrointestinal (46%) alterations. Cardiac arrest was documented in three episodes; however, no anaphylaxis-related deaths were reported. The main triggers for anaphylaxis were food (34%), medications (29%), allergen-specific immunotherapy (14%), and latex (11%). In patients with perioperative anaphylaxis, the clinical behavior was severe. Epinephrine was administered in 27 out of the 35 events (77%), but only in 11 cases it was the first-line treatment. Systemic corticosteroids were the most frequently used treatment, followed by epinephrine and antihistamines. CONCLUSIONS: The use of epinephrine, which is the mainstay of the treatment, is suboptimal; with a preferred use of second-line medications like corticosteroids. Clear protocols for the diagnosis and treatment of anaphylaxis, as well as continuous education of health personnel, are necessary.

4.
Comput Methods Programs Biomed ; 210: 106362, 2021 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-34482127

RESUMEN

BACKGROUND: Electronic medical records (EMRs) are widely used, but in many cases, they are used within a network physically separated from the Internet. Multicenter clinical studies use Internet-connected electronic data capture (EDC) systems to collect data, where data entered into the EMR are manually transcribed into the EDC system. In addition, medical images for clinical research are also collected manually. Variations in EMRs and differing data structures among vendors hamper the use of data for clinical research. METHODS: We solved this problem by developing a network infrastructure for clinical research between Osaka University Hospital and affiliated hospitals in the Osaka area and introducing a clinical data collection system (CDCS). In each hospital's EMR network, we implemented a CRF reporter that accumulated data for clinical research using a template and then sent the data to a management server in the Osaka University Hospital Data Center. To organize the patient profile data and clinical laboratory data stored in each EMR for use in clinical research, the data are retrieved from the template by an interface module developed by each vendor, according to our common data output interface specification. The data entered into the CRF reporter template for clinical research are also recorded in the EMR progress notes and sent to the data management server. This network infrastructure can also be used as a medical image collection system that automatically collects images for research from PACS at each hospital. These systems are managed under common subject numbers issued by the CDCS. RESULTS: A network infrastructure was established among 19 hospitals, and a CRF reporter was incorporated into the EMR. A medical image transfer system was introduced in 13 hospitals. Since 2013, 28 clinical studies have been conducted using this system, and data for 9,987 cases have been collected as of December 31, 2020. CONCLUSION: Incorporating a CRF reporter with medical image transfer system into the EMR has proven useful for collecting research data.


Asunto(s)
Manejo de Datos , Registros Electrónicos de Salud , Computadores , Hospitales , Humanos
5.
Comput Methods Programs Biomed ; 210: 106364, 2021 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-34500143

RESUMEN

BACKGROUND AND OBJECTIVE: This study describes the integration of a spoken dialogue system and nursing records on an Android smartphone application intending to help nurses reduce documentation time and improve the overall experience of a healthcare setting. The application also incorporates with collecting personal sensor data and activity labels for activity recognition. METHODS: We developed a joint model based on a bidirectional long-short term memory and conditional random fields (Bi-LSTM-CRF) to identify user intention and extract record details from user utterances. Then, we transformed unstructured data into record inputs on the smartphone application. RESULTS: The joint model achieved the highest F1-score at 96.79%. Moreover, we conducted an experiment to demonstrate the proposed model's capability and feasibility in recording in realistic settings. Our preliminary evaluation results indicate that when using the dialogue-based, we could increase the percentage of documentation speed to 58.13% compared to the traditional keyboard-based. CONCLUSIONS: Based on our findings, we highlight critical and promising future research directions regarding the design of the efficient spoken dialogue system and nursing records.


Asunto(s)
Registros de Enfermería , Teléfono Inteligente , Recolección de Datos , Registros Electrónicos de Salud , Humanos
6.
Comput Methods Programs Biomed ; 210: 106395, 2021 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-34525412

RESUMEN

BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD code specific for chronic cough makes it challenging to identify such patients from electronic health records (EHRs). For clinical and research purposes, computational methods using EHR data are urgently needed to identify chronic cough cases. This research aims to investigate the data representations and deep learning algorithms for chronic cough prediction. METHODS: Utilizing real-world EHR data from a large academic healthcare system from October 2005 to September 2015, we investigated Natural Language Representation of the EHR data and systematically evaluated deep learning and traditional machine learning models to predict chronic cough patients. We built these machine learning models using structured data (medication and diagnosis) and unstructured data (clinical notes). RESULTS: The sensitivity and specificity of a transformer-based deep learning algorithm, specifically BERT with attention model, was 0.856 and 0.866, respectively, using structured data (medication and diagnosis). Sensitivity and specificity improved to 0.952 and 0.930 when we combined structured data with symptoms extracted from clinical notes. We further found that the attention mechanism of deep learning models can be used to extract important features that drive the prediction decisions. Compared with our previously published rule-based algorithm, the deep learning algorithm can identify more chronic cough patients with structured data. CONCLUSIONS: By applying deep learning models, chronic cough patients can be reliably identified for prospective or retrospective research through medication and diagnosis data, widely available in EHR and electronic claims data, thus improving the generalizability of the patient identification algorithm. Deep learning models can identify chronic cough patients with even higher sensitivity and specificity when structured and unstructured EHR data are utilized. We anticipate language-based data representation and deep learning models developed in this research could also be productively used for other disease prediction and case identification.


Asunto(s)
Aprendizaje Profundo , Adulto , Algoritmos , Tos/diagnóstico , Registros Electrónicos de Salud , Humanos , Aprendizaje Automático , Estudios Prospectivos , Estudios Retrospectivos
7.
BMC Med Inform Decis Mak ; 21(1): 267, 2021 09 17.
Artículo en Inglés | MEDLINE | ID: mdl-34535146

RESUMEN

BACKGROUND: The use of Electronic Health Records (EHR) data in clinical research is incredibly increasing, but the abundancy of data resources raises the challenge of data cleaning. It can save time if the data cleaning can be done automatically. In addition, the automated data cleaning tools for data in other domains often process all variables uniformly, meaning that they cannot serve well for clinical data, as there is variable-specific information that needs to be considered. This paper proposes an automated data cleaning method for EHR data with clinical knowledge taken into consideration. METHODS: We used EHR data collected from primary care in Flanders, Belgium during 1994-2015. We constructed a Clinical Knowledge Database to store all the variable-specific information that is necessary for data cleaning. We applied Fuzzy search to automatically detect and replace the wrongly spelled units, and performed the unit conversion following the variable-specific conversion formula. Then the numeric values were corrected and outliers were detected considering the clinical knowledge. In total, 52 clinical variables were cleaned, and the percentage of missing values (completeness) and percentage of values within the normal range (correctness) before and after the cleaning process were compared. RESULTS: All variables were 100% complete before data cleaning. 42 variables had a drop of less than 1% in the percentage of missing values and 9 variables declined by 1-10%. Only 1 variable experienced large decline in completeness (13.36%). All variables had more than 50% values within the normal range after cleaning, of which 43 variables had a percentage higher than 70%. CONCLUSIONS: We propose a general method for clinical variables, which achieves high automation and is capable to deal with large-scale data. This method largely improved the efficiency to clean the data and removed the technical barriers for non-technical people.


Asunto(s)
Registros Electrónicos de Salud , Atención Primaria de Salud , Automatización , Bélgica , Bases de Datos Factuales , Humanos
8.
BMC Med Inform Decis Mak ; 21(1): 268, 2021 09 18.
Artículo en Inglés | MEDLINE | ID: mdl-34537047

RESUMEN

BACKGROUND: The glycated hemoglobin (A1c) test is not recommended for sickle cell disease (SCD) patients. We examine ordering patterns of diabetes-related tests for SCD patients to explore misutilization of tests among this underserved population. METHODS: We used de-identified electronic health record (EHR) data in the Cerner Health Facts™ (HF) data warehouse to evaluate the frequency of A1c and fructosamine tests during 2010 to 2016, for 37,151 SCD patients from 393 healthcare facilities across the United States. After excluding facilities with no A1c data, we defined three groups of facilities based on the prevalence of SCD patients with A1c test(s): adherent facilities (no SCD patients with A1c test(s)), minor non-adherent facilities, major non-adherent facilities. RESULTS: We determined that 11% of SCD patients (3927 patients) treated at 393 facilities in the US received orders for at least one A1c test. Of the 3927 SCD patients with an A1c test, only 89 patients (2.3%) received an order for a fructosamine test. At the minor non-adherent facilities, 5% of the SCD patients received an A1c test while 58% of the SCD patients at the least adherent facilities had at least one A1c test. Overall, the percent of A1c tests ordered for SCD patients between 2010 and 2016 remained similar. CONCLUSIONS: Inappropriate A1c test orders among a sickle cell population is a significant quality gap. Interventions to advance adoption of professional recommendations that advocate for alternate tests, such as fructosamine, can guide clinicians in test selection to reduce this quality gap are discussed. The informatics strategy used in this work can inform other largescale analyses of lab test utilization using de-identified EHR data.


Asunto(s)
Anemia de Células Falciformes , Diabetes Mellitus , Anemia de Células Falciformes/diagnóstico , Registros Electrónicos de Salud , Fructosamina , Hemoglobina A Glucada , Humanos , Estados Unidos
9.
Stud Health Technol Inform ; 283: 194-201, 2021 Sep 21.
Artículo en Inglés | MEDLINE | ID: mdl-34545836

RESUMEN

Patient portals provide patients access to their electronic health record and other functions as secure messaging. For over a decade, more and more patient portals are developed for various settings. The aim of this scoping review of reviews is to systematically search the literature for existing reviews to provide an overview of patient portals' objectives, acceptance and effects on outcome. We followed the PRISMA Statement and its extension for scoping reviews, and searched for articles published in 2011-2021. The 19 included articles were considerably heterogeneous concentrating on health outcome or patient portal facilitators and barriers.


Asunto(s)
Portales del Paciente , Registros Electrónicos de Salud , Humanos , Evaluación de Resultado en la Atención de Salud
10.
BMC Res Notes ; 14(1): 377, 2021 Sep 26.
Artículo en Inglés | MEDLINE | ID: mdl-34565465

RESUMEN

OBJECTIVE: The study of care trajectories of psychiatric patients across hospitals was previously not possible in Belgium as each hospital stores its data autonomously, and government-related registrations do not contain a unique identifier or are incomplete. A new longitudinal database called iPSYcare (Improved Psychiatric Care and Research) was therefore constructed in 2021, and links the electronic medical records of patients in psychiatric units of eight hospitals in the Antwerp Province, Belgium. The database provides a wide range of information on patients, care trajectories and delivered care in the region. In a first phase, the database will only contain information about adult patients who were admitted to a hospital or treated by an outreach team and who gave explicit consent. In the future, the database may be expanded to other regions and additional data on outpatient care may be added. RESULTS: IPSYcare is a close collaboration between the University of Antwerp and hospitals in the province of Antwerp. This paper describes the development of the database, how privacy and ethical issues will be handled, and how the governance of the database will be organized.


Asunto(s)
Registros Electrónicos de Salud , Hospitales , Adulto , Bases de Datos Factuales , Hospitalización , Humanos , Privacidad
11.
Mayo Clin Proc ; 96(9): 2332-2341, 2021 09.
Artículo en Inglés | MEDLINE | ID: mdl-34481597

RESUMEN

OBJECTIVES: To assess the impact of the COVID-19 pandemic on clinical research and the use of electronic approaches to mitigate this impact. METHODS: We compared the utilization of electronic consenting, remote visits, and remote monitoring by study monitors in all research studies conducted at Mayo Clinic sites (Arizona, Florida, and Minnesota) before and during the COVID-19 pandemic (ie, between May 1, 2019 and December 31, 2020). Participants are consented through a participant-tracking system linked to the electronic health record. RESULTS: Between May 2019, and December 2020, there were 130,800 new consents across every modality (electronic and paper) to participate in a non-trial (107,176 [82%]) or a clinical trial (23,624 [18%]). New consents declined from 5741 in February 2020 to 913 in April 2020 but increased to 11,864 in November 2020. The mean (standard deviation [SD]) proportion of electronic consent increased from 22 (2%) before to 45 (20%) during the pandemic (P=.001). Mean (SD) remote electronic consenting increased from 0.3 (0.5%) to 29 (21%) (P<.001). The mean (SD) number of patients with virtual visits increased from 3.5 (2.4%) to 172 (135%) (P=.003) per month between pre-COVID (July 2019 to February 2020) and post-COVID (March to December 2020) periods. Virtual visits used telemedicine (68%) or video (32%). Requests for remote monitor access to complete visits increased from 44 (17%) per month between May 2019 and February 2020 to 111 (74%) per month between March and December 2020 (P=.10). CONCLUSION: After a sharp early decline, the enrollment of new participants and ongoing study visits recovered during the COVID-19 pandemic. This recovery was accompanied by the increased use of electronic tools.


Asunto(s)
Atención Ambulatoria/tendencias , COVID-19/epidemiología , Registros Electrónicos de Salud/tendencias , SARS-CoV-2 , Telemedicina/tendencias , Humanos , Pandemias , Estudios Retrospectivos , Estados Unidos/epidemiología
12.
Math Biosci Eng ; 18(5): 5347-5363, 2021 06 16.
Artículo en Inglés | MEDLINE | ID: mdl-34517491

RESUMEN

With the development of online medical service platform, patients can find more medical information resources and obtain better medical treatment. However, it is difficult for patients to discover the most suitable doctors from the complex information resources. Therefore, the analysis and mining of Electronic Health Record(EHR) is very important for patients' timely and accurate treatment. Discovering the most suitable doctor is actually predicting the exact performance of the doctor for a specific disease. We believe that "a curative/bad treatment is likely to be caused by a good/bad doctor, and a good/bad doctor has a higher/lower evaluation by the patient(s)". In this paper, we propose a novel approach named SeekDoc, which is to seek the most effective doctor for a specific disease. Specifically, we build a doctor-disease heterogeneous information network and collect patients reviews and rating records for doctors. Then, we embed the comprehensive comment data for doctors and the constructed heterogeneous information network. Next, we use the autoencoder mechanism to learn the embedded features, which is an effective learning algorithm for constructing the latent feature representation in an unsupervised manner. After this learning, the latent features are input into the extreme gradient boosting (XGBoost) algorithm to improve their detection capabilities. Finally, extensive experiments show that our method can effectively and efficiently predict the doctor's experience score for specific diseases and has good performance compared with other algorithms.


Asunto(s)
Registros Electrónicos de Salud , Médicos , Algoritmos , Humanos , Proyectos de Investigación
13.
PLoS One ; 16(9): e0256891, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34478463

RESUMEN

BACKGROUND: Research on COVID-19 during pregnancy has mainly focused on women hospitalized for COVID-19 or other reasons during their pregnancy. Little is known about COVID-19 in the general population of pregnant women. OBJECTIVE: To describe the prevalence of COVID-19, symptoms, consequent healthcare use, and possible sources of COVID-19 exposure among a population-based sample of pregnant women residing in Northern California. METHODS: We analyzed data from 19,458 members of Kaiser Permanente Northern California who were pregnant between January 2020 and April 2021 and responded to an online survey about COVID-19 testing, diagnosis, symptoms, and their experiences during the COVID-19 pandemic. Medical diagnosis of COVID-19 during pregnancy was defined separately by self-report and by documentation in electronic health records (EHR). We examined relationships of COVID-19 with sociodemographic factors, underlying comorbidities, and survey measures of COVID-19-like symptoms, consequent healthcare utilization, and possible COVID-19 exposures. RESULTS: Among 19,458 respondents, the crude prevalence of COVID-19 was 2.5% (n = 494) according to self-report and 1.4% (n = 276) according to EHR. After adjustment, the prevalence of self-reported COVID-19 was higher among women aged <25 years compared with women aged ≥35 years (prevalence ratio [PR], 1.75, 95% CI: 1.23, 2.49) and among Hispanic women compared with White women (PR, 1.91, 95% CI: 1.53, 2.37). Prevalence of self-reported COVID-19 was higher among women affected by personal or partner job loss during the pandemic (PR, 1.23, 95% CI: 1.02, 1.47) and among women living in areas of high vs. low neighborhood deprivation (PR, 1.74, 95% CI: 1.33, 2.27). We did not observe differences in self-reported COVID-19 between women with and without underlying comorbidities. Results were similar for EHR-documented COVID-19. Loss of smell or taste was a unique and common symptom reported among women with COVID-19 (42.3% in self-reported; 54.0% in EHR-documented). Among women with symptomatic COVID-19, approximately 2% were hospitalized, 71% had a telehealth visit, and 75% quarantined at home. Over a third of women with COVID-19 reported no known exposure to someone with COVID-19. CONCLUSIONS: Observed COVID-19 prevalence differences by sociodemographic and socioeconomic factors underscore social and health inequities among reproductive-aged women. Women with COVID-19 reported unique symptoms and low frequency of hospitalization. Many were not aware of an exposure to someone with COVID-19.


Asunto(s)
COVID-19/diagnóstico , COVID-19/epidemiología , Pandemias , Adolescente , Adulto , COVID-19/patología , COVID-19/virología , Prueba de COVID-19 , California/epidemiología , Registros Electrónicos de Salud , Grupo de Ascendencia Continental Europea , Femenino , Hispanoamericanos , Humanos , Embarazo , Mujeres Embarazadas , SARS-CoV-2/aislamiento & purificación , SARS-CoV-2/patogenicidad , Autoinforme , Factores Socioeconómicos , Encuestas y Cuestionarios , Adulto Joven
14.
Stud Health Technol Inform ; 283: 86-94, 2021 Sep 21.
Artículo en Inglés | MEDLINE | ID: mdl-34545823

RESUMEN

High throughput sequencing technologies have facilitated an outburst in biological knowledge over the past decades and thus enables improvements in personalized medicine. In order to support (international) medical research with the combination of genomic and clinical patient data, a standardization and harmonization of these data sources is highly desirable. To support this increasing importance of genomic data, we have created semantic mapping from raw genomic data to both FHIR (Fast Healthcare Interoperability Resources) and OMOP (Observational Medical Outcomes Partnership) CDM (Common Data Model) and analyzed the data coverage of both models. For this, we calculated the mapping score for different data categories and the relative data coverage in both FHIR and OMOP CDM. Our results show, that the patients genomic data can be mapped to OMOP CDM directly from VCF (Variant Call Format) file with a coverage of slightly over 50%. However, using FHIR as intermediate representation does not lead to further information loss as the already stored data in FHIR can be further transformed into OMOP CDM format with almost 100% success. Our findings are in favor of extending OMOP CDM with patient genomic data using ETL to enable the researchers to apply different analysis methods including machine learning algorithms on genomic data.


Asunto(s)
Registros Electrónicos de Salud , Genómica , Algoritmos , Humanos , Aprendizaje Automático , Medicina de Precisión
15.
Stud Health Technol Inform ; 283: 104-110, 2021 Sep 21.
Artículo en Inglés | MEDLINE | ID: mdl-34545825

RESUMEN

Harmonized and interoperable data management is a core requirement for federated infrastructures in clinical research. Institutions participating in such infrastructures often have to invest large degrees of time and resources in implementing necessary data integration processes to convert their local data to the required target structure. If the data is already available in an alternative shared data structure, the transformation from source to the desired target structure can be implemented once and then be distributed to all participants to reduce effort and harmonize results. The HL7® FHIR® standard is used as a basis for the shared data model of several medical consortia like DKTK and GBA. It is based on so-called resources which can be represented in XML. Oncological data in German university hospitals is commonly available in the ADT/GEKID format. From this common basis we conceptualized and implemented a transformation which accepts ADT/GEKID XML files and returns FHIR resources. We identified several problems with using the general ADT/GEKID structure in federated research infrastructures, as well as some possible pitfalls relating to the FHIR need for resource ids and focus on semantic coding which differs from the approach in the ADT/GEKID standard. To facilitate participation in federated infrastructures, we propose the ADT2FHIR transformation tool for partners with oncological data in the ADT/GEKID format.


Asunto(s)
Manejo de Datos , Registros Electrónicos de Salud , Estándar HL7 , Humanos , Oncología Médica , Semántica
16.
Stud Health Technol Inform ; 283: 119-126, 2021 Sep 21.
Artículo en Inglés | MEDLINE | ID: mdl-34545827

RESUMEN

With the steady increase in the connectivity of the healthcare system, new requirements and challenges are emerging. In addition to the seamless exchange of data between service providers on a national level, the local legacy data must also meet the new requirements. For this purpose, the applications used must be tested securely and sufficiently. However, the availability of suitable and realistic test data is not always given. Therefore, this study deals with the creation of test data based on real electronic health record data provided by the Medical Information Mart for Intensive Care (MIMIC-IV) database. In addition to converting the data to the current FHIR R4, conversion to the core data sets of the German Medical Informatics Initiative was also presented and made available. The test data was generated to simulate a legacy data transfer. Moreover, four different FHIR servers were tested for performance. This study is the first step toward comparable test scenarios around shared datasets and promotes comparability among providers on a national level.


Asunto(s)
Informática Médica , Atención a la Salud , Registros Electrónicos de Salud
17.
Stud Health Technol Inform ; 283: 127-135, 2021 Sep 21.
Artículo en Inglés | MEDLINE | ID: mdl-34545828

RESUMEN

To ensure semantic interoperability within healthcare systems, using common, curated terminological systems to identify relevant concepts is of fundamental importance. The HL7 FHIR standard specifies means of modelling terminological systems and appropriate ways of accessing and querying these artefacts within a terminology server. Hence, initiatives towards healthcare interoperability like IHE specify not only software interfaces, but also common codes in the form of value sets and code systems. The way in which these coding tables are provided is not necessarily compatible to the current version of the HL7 FHIR specification and therefore cannot be used with current HL7 FHIR-based terminology servers. This work demonstrates a conversion of terminological resources specified by the Integrating the Healthcare Initiative in the ART-DECOR platform, partly available in HL7 FHIR, to ensure that they can be used within a HL7 FHIR-based terminological server. The approach itself can be used for other terminological resources specified within ART-DECOR but can also be used as the basis for other code-driven conversions of proprietary coding schemes.


Asunto(s)
Registros Electrónicos de Salud , Programas Informáticos , Atención a la Salud , Estándar HL7
18.
J Am Board Fam Med ; 34(5): 907-913, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34535516

RESUMEN

INTRODUCTION: Reports of post-acute sequelae of COVID-19 continue to emerge, but it remains unclear how the severity of a patient's COVID-19 infection affects risk for future hospitalizations for non-COVID-19 problems. METHODS: An analysis of electronic health records (EHR) was performed for a cohort of 10,646 patients who were followed for 6 months post-COVID-19 episode at 1 health system. COVID-19-positive patients were classified as severe if they were hospitalized within the first 30 days of their initial positive test. Assessment of hospitalizations overall and conditions that could be seen as complications of COVID-19 (cardiovascular, respiratory, and clotting diagnoses) was assessed. The 6-month risk of a new hospitalization was assessed in both unadjusted and adjusted Cox regressions. RESULTS: Of the 10,646 patients included in this cohort,114 had severe COVID-19, 211 had mild/moderate COVID-19, and 10,321 were COVID-19 negative. After adjustment for potential confounding variables, there was no significantly increased risk in future hospitalization for any condition for patients who were COVID-19 positive versus those who were COVID-19 negative (HR, 1.31; 95% CI, 0.98-1.74). In adjusted analyses, individuals with severe COVID-19 had an increased risk of hospitalization for potential complications compared with both mild/moderate COVID-19 (HR, 2.20; 95% CI, 1.13-4.28) and COVID-19 negative patients (HR, 2.24; 95% CI, 1.52-3.30). DISCUSSION: Patients with a severe COVID-19 episode were at greater risk for future hospitalizations. This study reinforces the importance of preventing infection in patients at higher risk for severe COVID-19 cases.


Asunto(s)
COVID-19 , Estudios de Cohortes , Registros Electrónicos de Salud , Hospitalización , Humanos , SARS-CoV-2
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