Search

The target trial framework has emerged as a powerful tool for addressing causal questions in clinical practice and in public health. In the healthcare sector, where decision-making is increasingly data-driven, transactional databases, such as electronic health records (EHR) and insurance claims, present an untapped potential for answering complex causal questions. This narrative review explores the potential of the integration of the target trial framework with real-world data to enhance healthcare decision-making processes. We outline essential elements of the target trial framework, and identify pertinent challenges in data quality, privacy concerns, and methodological limitations, proposing solutions to overcome these obstacles and optimize the framework's application., Competing Interests: Declaration of conflicting interestsThe authors declare that there are no conflicts of interest.

Background: documentation of aspirin consumption is usually not complete and clear, probably because it's a cheap drug that can be easily acquired., Objective: To evaluate the quality of the record of aspirin consumption documented in the electronic medical records (EMRs) of a Private University Hospital of Argentina., Design: Qualitative and quantitative descriptive exploratory study., Results: principal findings were that 86 % of the notes mentioned aspirin as a chronic prescription. 12% mentioned its temporary suspension. Numerous EMRs mentioned the use of an "antiplatelet" drug without specifying which, and didn't specify abbreviations, consumed dose or when to start or stop aspirin., Conclusions: overall quality of aspirin related information registration in EMRs was poor. This is concerning since it's a frequently prescribed drug, not exempt from adverse events.

Through the development of a dashboard with participative methodology we present a centralized strategy to analyze and visualize EMRs data for the management of 15 public hospitals from Buenos Aires City. This approach could constitute an efficient option for public health systems from developing countries.

Despite recommendations for the routine HIV testing of all sexually active individuals, a significant percentage of HIV-positive adults are unaware of their HIV status. Therefore, a number of strategies have been implemented to expand HIV testing, which in turn makes it necessary to develop tools for identifying patients with unknown HIV status. This study presents the results of an external validation of an electronic phenotyping algorithm for identifying HIV status and its application on a retrospective cohort in order to explore temporal trends of HIV knowledge status and associated factors.

By the implementation of the EMR in the Primary Care Centers of the City of Buenos Aires, it has been claimed that there is a need to obtain systematic and accurate information about the assessment that patients have about such incorporation. A qualitative research has been carried out in order to answer this query.

The influence of social relationships on mortality is comparable with that of standard clinical factors. In order to extract this information via supervised NLP algorithms, clinical notes have to be annotated manually. At present we did not find any systematic annotation standards to extract social support. In this work, we developed a systematic text annotation standard to detect social support in couples from electronic medical records.

This paper describes how an Electronic Health Records (EHR) allowed for the implementation of a strategy to reduce maternal and child morbidity and mortality in outpatient settings of hospitals in the public healthcare system of Buenos Aires City.

On October 2018, Buenos Aires hosted the Youth Olympic Games. The organization asked the Health Information System Office to provide the health stations and clinics with an Electronic Health Record (EHR) and bring support during the duration of the games. Four hundred and fifty healthcare professionals from different disciplines were trained in the use of the EHR.

Background and Objective: Recent progression towards precision medicine has encouraged the use of electronic health records (EHRs) as a source for large amounts of data, which is required for studying the effect of treatments or risk factors in more specific subpopulations. Phenotyping algorithms allow to automatically classify patients according to their particular electronic phenotype thus facilitating the setup of retrospective cohorts. Our objective is to compare the performance of different classification strategies (only using standardized problems, rule-based algorithms, statistical learning algorithms (six learners) and stacked generalization (five versions)), for the categorization of patients according to their diabetic status (diabetics, not diabetics and inconclusive; Diabetes of any type) using information extracted from EHRs., Methods: Patient information was extracted from the EHR at Hospital Italiano de Buenos Aires, Buenos Aires, Argentina. For the derivation and validation datasets, two probabilistic samples of patients from different years (2005: n = 1663; 2015: n = 800) were extracted. The only inclusion criterion was age (≥40 & <80 years). Four researchers manually reviewed all records and classified patients according to their diabetic status (diabetic: diabetes registered as a health problem or fulfilling the ADA criteria; non-diabetic: not fulfilling the ADA criteria and having at least one fasting glycemia below 126 mg/dL; inconclusive: no data regarding their diabetic status or only one abnormal value). The best performing algorithms within each strategy were tested on the validation set., Results: The standardized codes algorithm achieved a Kappa coefficient value of 0.59 (95% CI 0.49, 0.59) in the validation set. The Boolean logic algorithm reached 0.82 (95% CI 0.76, 0.88). A slightly higher value was achieved by the Feedforward Neural Network (0.9, 95% CI 0.85, 0.94). The best performing learner was the stacked generalization meta-learner that reached a Kappa coefficient value of 0.95 (95% CI 0.91, 0.98)., Conclusions: The stacked generalization strategy and the feedforward neural network showed the best classification metrics in the validation set. The implementation of these algorithms enables the exploitation of the data of thousands of patients accurately., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: The implementation of electronic medical records (EMR) is becoming increasingly common. Error and data loss reduction, patient-care efficiency increase, decision-making assistance and facilitation of event surveillance, are some of the many processes that EMRs help improve. In addition, they show a lot of promise in terms of data collection to facilitate observational epidemiological studies and their use for this purpose has increased significantly over the recent years. Even though the quantity and availability of the data are clearly improved thanks to EMRs, still, the problem of the quality of the data remains. This is especially important when attempting to determine if an event has actually occurred or not. We sought to assess the sensitivity, specificity, and agreement level of a codes-based algorithm for the detection of clinically relevant cardiovascular (CaVD) and cerebrovascular (CeVD) disease cases, using data from EMRs., Methods: Three family physicians from the research group selected clinically relevant CaVD and CeVD terms from the international classification of primary care, Second Edition (ICPC-2), the ICD 10 version 2015 and SNOMED-CT 2015 Edition. These terms included both signs, symptoms, diagnoses and procedures associated with CaVD and CeVD. Terms not related to symptoms, signs, diagnoses or procedures of CaVD or CeVD and also those describing incidental findings without clinical relevance were excluded. The algorithm yielded a positive result if the patient had at least one of the selected terms in their medical records, as long as it was not recorded as an error. Else, if no terms were found, the patient was classified as negative. This algorithm was applied to a randomly selected sample of the active patients within the hospital's HMO by 1/1/2005 that were 40-79 years old, had at least one year of seniority in the HMO and at least one clinical encounter. Thus, patients were classified into four groups: (1) Negative patients (2) Patients with CaVD but without CeVD; (3) Patients with CeVD but without disease CaVD; (4) Patients with both diseases. To facilitate the validation process, a stratified sample was taken so that each of the groups represented approximately 25% of the sample. Manual chart review was used as the gold standard for assessing the algorithm's performance. One-third of the patients were assigned randomly to each reviewer (Cohen's kappa 0.91). Both coded and un-coded (free text) sections of the EMR were reviewed. This was done from the first present clinical note in the patients chart to the last one registered prior to 1/1/2005., Results: The performance of the algorithm was compared against manual chart review. It yielded high sensitivity (0.99, 95% CI 0.938-0.9971) and acceptable specificity (0.86, 95% CI 0.818-0.895) for detecting cases of CaVD and CeVD combined. A qualitative analysis of the false positives and false negatives was performed., Conclusions: We developed a simple algorithm, using only standardized and non-standardized coded terms within an EMR that can properly detect clinically relevant events and symptoms of CaVD and CeVD. We believe that combining it with an analysis of the free text using an NLP approach would yield even better results.

During the implementation of an electronic health record (EHR) system in the public system of the city of Buenos Aires, Argentina, different patient indexing strategies were devised and implemented. We sought to assess the association between these strategies and effective indexing (proportion of patients who are indexed and have a consultation registered in the EHR). Strategies were grouped into three modalities (High, Intermediate, and Low intensity). We estimated hazard ratios (HR) comparing the High and Low intensity to the Intermediate strategies. The crude analyses showed a significant difference between the curves (p &lt; 0.0001). In the multivariate analysis, the HRs of High and Low intensity interventions showed on average, values above 1 from 0 to 90 days compared to the Intermediate intensity strategy (High: 2.08 (1.65, 2.52); Low: 2.59 (2.29, 2.9)). From that point on, the HRs of both strategies were not different from 1.

Background: In recent years, there has been growing concern about the overuse of colonoscopy (CC). Our objective was to evaluate the incidence rate and cumulative probability of having a potentially inadequate CC (PI-CC, e.g. a CC that was performed earlier that recommended) and the association between the report of a hyperplastic polyp in the baseline CC report and the probability of having a PI-CC.Methods: A retrospective cohort of adults 50y/o or older with a complete baseline CC between January 1st and December 31st 2005, without reported lesions or with hyperplastic polyps, based on secondary data extracted from the electronic medical record of the Hospital Italiano of Buenos Aires. The outcome consisted of time until a PI-CC, defined as the time measured between basal colonoscopy and a colonoscopy performed earlier than the inter-screening interval recommended by the USPSTF and the USMSTF.Results: 389 patients were included. The cumulative probability of receiving a PI-CC over 10 years was 0.29 (95% CI 0.241, 0.342). The incidence rate resulted in 30.91 PI-CC per 1000 person-years (95% CI 25.14, 38). The crude analysis of the association between the outcome and the presence of hyperplastic polyps in the baseline CC, showed a statistically significant difference between both groups (log rank, p 0.036). The multivariate analysis yielded a hazard ratio of 1.67 (95% CI 1.02-2.73).Conclusion: We observed that 3 in every 10 patients treated in our health system received a PI-CC during the first ten consecutive years after a normal complete CC. Furthermore, this could be in part attributed to the presence of a hyperplastic polyp in the baseline CC. [ABSTRACT FROM AUTHOR]

In order to evaluate adherence to colorectal cancer (CRC) screening among members of the Health Plan of the Hospital Italiano de Buenos Aires in Argentina, we conducted a retrospective cohort study using secondary data from the electronic medical record. We included all members over 50 years of age during the period 2008-2022. We assessed the number and type of screening tests performed and the proportion of members covered for screening. We analyzed 112,112 participants, with a median age of 58.6 years and a follow-up time of 8.6 years. Colonoscopy was the most commonly used test. The maximum coverage reached was 47.1% in December 2022. In conclusion, adherence to CRC screening was suboptimal, as was the method used. This information can be used for the design of a multicomponent intervention. [ABSTRACT FROM AUTHOR]