Abstract
In “Effectiveness of Secondary Risk–Reducing Strategies in Patients With Unilateral Breast Cancer With Pathogenic Variants of BRCA1 and BRCA2 Subjected to Breast-Conserving Surgery: Evidence-Based Simulation Study” (JMIR Form Res 2022;6(12):e37144) the authors noted one error. In the originally published article Figure 6 appeared as a duplicate of Figure 5. Figure 6 has been corrected as follows: (Figure Presented) The correction version appeared in the online version of the paper on the JMIR Publications website on January 18, 2023. Because this was made after submission to full-text repositories, the corrected article has also been resubmitted to those repositories. © Jelena Maksimenko, Pedro Pereira Rodrigues, Miki Nakazawa-Miklaševica, David Pinto, Edvins Miklaševics, Genadijs Trofimovics, Janis Gardovskis, Fatima Cardoso, Maria João Cardoso.

Abstract
Background Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. Objective This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. Method We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011-2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. Results We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. Discussion We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Conclusion Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.

Abstract
Knowledge discovery techniques try to extract patterns and concepts from raw data, and clustering certainly is one of the most popular processes in this research field. However, nowadays data is being produced in streaming fashion and distributed locations, turning most classical methods obsolete. This thesis addresses two different clustering problems in ubiquitous and streaming scenarios, presenting evidence of the advantages produced by applying distributed and streaming machine learning algorithms, and proposing new ones to solve the addressed problems.

Abstract
Implantable electronic cardiovascular devices (IECD) include a broad spectrum of devices that have the ability to maintain rhythm, provide cardiac resynchronization therapy, and/or prevent sudden cardiac death. The incidence of bradyarrhythmias and other cardiac problems led to a broader use of IECD, which turned traditional follow-up into an extremely heavy burden for healthcare systems to support. Our aim was to assess the impact of remote monitoring on the follow-up of patients with IECD. We performed a review through PubMed using a specific query. The paper selection process included a three-step approach in which title, abstract, and cross-references were analyzed. Studies were then selected using previously defined inclusion criteria and analyzed according to the country of origin of the study, year, and journal of publication; type of study; and main issues covered. Twenty articles were included in this review. Eighty percent of the selected papers addressed clinical issues, from which 94% referred clinical events identification, clinical stability, time savings, or physician satisfaction as advantages, whereas 38% referred disadvantages that included both legal and technical issues. Forty-five percent of the papers referred patient issues, from which 89% presented advantages, focusing on patient acceptance/satisfaction, and patient time-savings. The main downsides were technical issues but patient privacy was also addressed. All the papers dealing with economic issues (20%) referred both advantages and disadvantages equally. Remote monitoring is presently a safe technology, widely accepted by patients and physicians, for its convenience, reassurance, and diagnostic potential. This review summarizes the principles of remote IECD monitoring presenting the current state-of-the-art. Patient safety and device interaction, applicability of current technology, and limitations of remote IECD monitoring are also addressed. The use of remote monitor should consider the selection of patients, the type of disease, and centers' availability to receive, interpret and respond to device alerts. Before remote IECD monitoring can be routinely used, technical, procedure, and ethical/legal issues should be addressed.

Abstract
Although IT governance practices (like ITIL, which recommends on the use of Audit Trails (AT) for proper service level management) are being introduced in many Hospitals to cope with increasing levels of information quality and safety requirements, the standard maturity levels of hospital IT departments is still not enough to reach the level of frequent use of AT. This paper aims to address the issues related to the existence and quality of AT in Hospital Information Systems (HIS). A total of 5 hospitals participated in the study (4 CIO interviews and 7 AT from 4 hospitals). Very few AT are known to exist in these hospitals (about 1 per hospital in an estimate of 21 existing IS). The existing AT has poor quality (garbled, incomplete and misformatted information). CIOs should be much more concerned with the existence, quality and maintenance of AT. Recommendations include server clock synchronization, using advanced AT visualization tools, using also real-time auditing tools, and periodically audit the access to all data of a random patient.

Abstract
Although IT governance practices (like ITIL, which recommends on the use of audit logs for proper service level management) are being introduced in many Hospitals to cope with increasing levels of information quality and safety requirements, the standard maturity levels of hospital IT departments is still not enough to reach the level of frequent use of audit logs. This paper aims to address the issues related to the existence of AT in patient records, describe the Hospitals scenario and to produce recommendations. Representatives from four hospitals were interviewed regarding the use of AT in their Hospital IS. Very few AT are known to exist in these hospitals (average of 1 per hospital in an estimate of 21 existing IS). CIOs should to be much more concerned with the existence and maintenance of AT. Recommendations include server clock synchronization and using advanced log visualization tools.