Vestibular implants (VI) modulate the rate and amplitude of charge-balanced current pulses to encode head angular velocity or acceleration. When the battery of a VI becomes depleted, stimulation interruptions can cause vertigo. To avoid this, VIs can use alert signals such as vibration and beeping to remind the user to replace the battery. However, in distracting and noisy environments typical of activities of daily life, some patients may fail to hear or feel those alerts, so a physiological signal can be used as an alternate channel for signaling battery depletion. Pauses in the stimulation waveform can be delivered for this purpose, with the length of the pause long enough to be detected reliably by the patient but not so long as to induce dizziness or a vertigo attack. As a guide for the design of a physiologic battery depletion alert system, this study reports the ability of nine long-term, continuous VI users to detect stimulation pauses of various durations. We also show the effect of distraction on patients\' detection thresholds and response latencies for detected events.

BACKGROUND: During the SARS-CoV-2 COVID-19 pandemic, the global health system needed to review important processes involved in daily routines such as outpatient activities within the hospital, including follow-up visits of implantable cardiac electronic devices (CIEDs) carried out in office. The aim of this study is to describe our 3.5 years of real-world experience of a full remote CIED follow-up, evaluate the success rate of remote transmissions, and verify the adopted organizational model.
METHODS: From April 2020 to November 2023, all patients with an activated and well-functioning remote monitoring (RM) system and automatic algorithms, like autocapture and autosensing, underwent exclusive RM follow-up. Unscheduled in-office visits were only prompted by remote yellow or red alerts. Patients were divided into two groups, based on available technology: Manual Transmission System (MTS) and Automatic Transmission System (ATS). The ATS group, in addition to ensuring a daily transmission of any yellow or red alerts, was checked at least every 15 days to ensure a valid connection. An automatic transmission was scheduled once a year, irrespective of alerts occurred. The MTS group provided a manual transmission every 6 months.
RESULTS: One thousand nine hundred thirty-seven consecutive patients were included in the study. By the end of November 2023, a total of 1409 patients (1192 in the ATS and 217 in the MTS group) were still actively followed by our remote clinic (384 expired, 137 dismissed, 7 transferred). The overall success rate of transmissions with the adopted organizational model was 96.6% in the ATS group (connection index) and 87% in the MTS group. Conventional in-hospital follow-up visits decreased by 44%. Total clinic working time, resulting from the sum of the time spent during in-hospital and remote follow-up, after an initial increase, was progressively reduced to the actual -25%. Mortality rate for any cause was 7.5% per year in remote follow-up patients and 8.3% (p=NS) in in-office patients. In the ATS group, no device malfunctions were notified to our remote clinic, before we had already realized it through appropriate alerts.
CONCLUSIONS: The available technology makes moving to a 100% remote clinic possible, without overwhelming clinic workflow, safely. Adopting an appropriate organizational model, it is possible to maintain high transmission success rates. The automatic transmissions allow a more frequent control of patients with CIED.

METHODS: Systematic literature review and meta-analysis.
OBJECTIVE: Predicting patient risk of intraoperative neuromonitoring (IONM) alerts preoperatively can aid patient counselling and surgical planning. Sielatycki et al established an axial-MRI-based spinal cord classification system to predict risk of IONM alerts in scoliosis correction surgery. We aim to systematically review the literature on operative and radiologic factors associated with IONM alerts, including a novel spinal cord classification.
METHODS: A systematic review and meta-analysis was performed as per the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Guidelines. A literature search identifying all observational studies comparing patients with and without IONM alerts was conducted. Suitable studies were included. Patient demographics, radiological measures and operative factors were collected.
RESULTS: 11 studies were included including 3040 patients. Relative to type 3 cords, type 1 (OR = .03, CI = .01-.08, P < .00001), type 2 (OR = .08, CI = .03, P <.00001) and all non-type 3 cords (OR = .05, CI = .02-.16, P < .00001) were associated with significantly lower odds of IONM alerts. Significant radiographic measures for IONM alerts included coronal Cobb angle (MD = 10.66, CI = 5.77-15.56, P < .00001), sagittal Cobb angle (MD = 9.27, CI = 3.28-14.73, P = .0009), sagittal deformity angle ratio (SDAR) (MD = 2.76, CI = 1.57-3.96, P < .00001) and total deformity angle ratio (TDAR) (MD = 3.44, CI = 2.27-4.462, P < .00001). Clinically, estimated blood loss (MD = 274.13, CI = -240.03-788.28, P = .30), operation duration (MD = 50.79, CI = 20.58-81.00, P = .0010), number of levels fused (MD = .92, CI = .43-1.41, P = .0002) and number of vertebral levels resected (MD = .43, CI = .01-.84, P = .05) were significantly greater in IONM alert patients.
CONCLUSIONS: This study highlights the relationship of operative and radiologic factors with IONM alerts.

BACKGROUND: Clinical decision support (CDS) alerts and reminders aim to influence clinical decisions, yet they are often designed without considering human decision-making behaviour. While this behaviour is comprehensively described by behavioural economics (BE), the sheer volume of BE literature poses a challenge to designers when identifying behavioural effects with utility to alert and reminder designs. This study tackles this challenge by focusing on the MINDSPACE framework for behaviour change, which collates nine behavioural effects that profoundly influence human decision-making behaviour: Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitment, and Ego.
METHODS: A systematic review searching MEDLINE, Embase, PsycINFO, and CINAHL Plus to explore (i) the usage of MINDSPACE effects in alert and reminder designs and (ii) the efficacy of those alerts and reminders in influencing clinical decisions. The search queries comprised ten Boolean searches, with nine focusing on the MINDSPACE effects and one focusing on the term mindspace.
RESULTS: 50 studies were selected from 1791 peer-reviewed journal articles in English from 1970 to 2022. Except for ego, eight of nine MINDSPACE effects were utilised to design alerts and reminders, with defaults and norms utilised the most in alerts and reminders, respectively. Overall, alerts and reminders informed by MINDSPACE effects showed an average 71% success rate in influencing clinical decisions (alerts 73%, reminders 69%). Most studies utilised a single effect in their design, with higher efficacy for alerts (64%) than reminders (41%). Others utilised multiple effects, showing higher efficacy for reminders (28%) than alerts (9%).
CONCLUSIONS: This review presents sufficient evidence demonstrating the MINDSPACE framework\'s merits for designing CDS alerts and reminders with human decision-making considerations. The framework can adequately address challenges in identifying behavioural effects pertinent to the effective design of CDS alerts and reminders. The review also identified opportunities for future research into other relevant effects (e.g., framing).

BACKGROUND: Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur.
OBJECTIVE: Do ML alerts, telemedicine system (TS)-generated alerts, or biomedical monitors (BMs) have superior performance for predicting episodes of intubation or administration of vasopressors?
METHODS: An ML algorithm was trained to predict intubation and vasopressor initiation events among critically ill adults. Its performance was compared with BM alarms and TS alerts.
RESULTS: ML notifications were substantially more accurate and precise, with 50-fold lower alarm burden than TS alerts for predicting vasopressor initiation and intubation events. ML notifications of internal validation cohorts demonstrated similar performance for independent academic medical center external validation and COVID-19 cohorts. Characteristics were also measured for a control group of recent patients that validated event detection methods and compared TS alert and BM alarm performance. The TS test characteristics were substantially better, with 10-fold less alarm burden than BM alarms. The accuracy of ML alerts (0.87-0.94) was in the range of other clinically actionable tests; the accuracy of TS (0.28-0.53) and BM (0.019-0.028) alerts were not. Overall test performance (F scores) for ML notifications were more than fivefold higher than for TS alerts, which were higher than those of BM alarms.
CONCLUSIONS: ML-derived notifications for clinically actioned hemodynamic instability and respiratory failure events represent an advance because the magnitude of the differences of accuracy, precision, misclassification rate, and pre-event lead time is large enough to allow more proactive care and has markedly lower frequency and interruption of bedside physician work flows.

Clinical decision support systems (CDSS) were implemented in community pharmacies over 40 years ago. However, unlike CDSS studies in other health settings, few studies have been undertaken to evaluate and improve their use in community pharmacies, where billions of prescriptions are filled every year. The aim of this scoping review is to summarize what research has been done surrounding CDSS in community pharmacies and call for rigorous research in this area.
Six databases were searched using a combination of controlled vocabulary and keywords relating to community pharmacy and CDSS. After deduplicating the initial search results, 2 independent reviewers conducted title/abstract screening and full-text review. Then, the selected studies were synthesized in terms of investigational/clinical focuses.
The selected 21 studies investigated the perception of and response to CDSS alerts (n = 7), the impact of CDSS alerts (n = 7), and drug-drug interaction (DDI) alerts (n = 8). Three causes of the failures to prevent DDIs of clinical importance have been noted: the perception of and response to a high volume of DDI alerts, a suboptimal performance of CDSS, and a dearth of sociotechnical considerations for managing workload and workflow. Additionally, 7 studies emphasized the importance of utilizing CDSS for a specific clinical focus, ie, antibiotics, diabetes, opioids, and vaccinations.
Despite the range of topics dealt in the last 30 years, this scoping review confirms that research on CDSS in community pharmacies is limited and disjointed, lacking a comprehensive approach to highlight areas for improvement and ways to optimize CDSS utilization.

The drug allergy alert system reduces the frequency of adverse drug events, although it is subjected to collateral effects, since 80-90% of alerts are not real, and a large percentage of alerts are overridden (46.2-96.2%). We reviewed how the alert system is used at University Hospital Fundación Alcorcon (HUFA).
Data were obtained from the drug allergy alert and the alert overriding notification forms (both in the period 2011-20). We also recorded drug allergy diagnoses at HUFA, drug consumption in primary care in 2016. We calculated the incidence of drug allergy alert activation, temporal trends in use, and correlations between the number of drugs in several datasets.
We collected 15,535 alerts. NSAIDs and penicillins were the drugs with the highest number of drug allergy alerts (36.55% and 26.91%, respectively). A correlation was found between the number of drug alerts and the type of drug allergy in HUFA in 2016. Only 6.83% of the alerts were removed, and, of these, 21.77% were reactivated. Approximately 100 overrides were recorded per year from 2016 (6.8% of 8,434 activated alerts during 2014-2020).
The number of drug allergy alerts recorded via the drug allergy alert system of HUFA correlates with the distribution of drug allergy diagnoses in the hospital, although many of the alerts could be false positives (as per current published evidence). We detected a very low frequency of removed alerts (6.83%), a relevant frequency of reactivations (one quarter), and a very low frequency of overrides (6.8%).

Background: Inappropriate medication in older patients with multimorbidity results in a greater risk of adverse drug events. Clinical decision support systems (CDSSs) are intended to improve medication appropriateness. One approach to improving CDSSs is to use ontologies instead of relational databases. Previously, we developed OntoPharma-an ontology-based CDSS for reducing medication prescribing errors. Objective: The primary aim was to model a domain for improving medication appropriateness in older patients (chronic patient domain). The secondary aim was to implement the version of OntoPharma containing the chronic patient domain in a hospital setting. Methods: A 4-step process was proposed. The first step was defining the domain scope. The chronic patient domain focused on improving medication appropriateness in older patients. A group of experts selected the following three use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events. The second step was domain model representation. The implementation was conducted by medical informatics specialists and clinical pharmacists using Protégé-OWL (Stanford Center for Biomedical Informatics Research). The third step was OntoPharma-driven alert module adaptation. We reused the existing framework based on SPARQL to query ontologies. The fourth step was implementing the version of OntoPharma containing the chronic patient domain in a hospital setting. Alerts generated from July to September 2022 were analyzed. Results: We proposed 6 new classes and 5 new properties, introducing the necessary changes in the ontologies previously created. An alert is shown if the Medication Regimen Complexity Index is ≥40, if the Drug Burden Index is ≥1, or if there is a trigger based on an abnormal laboratory value. A total of 364 alerts were generated for 107 patients; 154 (42.3%) alerts were accepted. Conclusions: We proposed an ontology-based approach to provide support for improving medication appropriateness in older patients with multimorbidity in a scalable, sustainable, and reusable way. The chronic patient domain was built based on our previous research, reusing the existing framework. OntoPharma has been implemented in clinical practice and generates alerts, considering the following use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events.

Background: Inappropriate medication in older patients with multimorbidity results in a greater risk of adverse drug events. Clinical decision support systems (CDSSs) are intended to improve medication appropriateness. One approach to improving CDSSs is to use ontologies instead of relational databases. Previously, we developed OntoPharma-an ontology-based CDSS for reducing medication prescribing errors. Objective: The primary aim was to model a domain for improving medication appropriateness in older patients (chronic patient domain). The secondary aim was to implement the version of OntoPharma containing the chronic patient domain in a hospital setting. Methods: A 4-step process was proposed. The first step was defining the domain scope. The chronic patient domain focused on improving medication appropriateness in older patients. A group of experts selected the following three use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events. The second step was domain model representation. The implementation was conducted by medical informatics specialists and clinical pharmacists using Protégé-OWL (Stanford Center for Biomedical Informatics Research). The third step was OntoPharma-driven alert module adaptation. We reused the existing framework based on SPARQL to query ontologies. The fourth step was implementing the version of OntoPharma containing the chronic patient domain in a hospital setting. Alerts generated from July to September 2022 were analyzed. Results: We proposed 6 new classes and 5 new properties, introducing the necessary changes in the ontologies previously created. An alert is shown if the Medication Regimen Complexity Index is ≥40, if the Drug Burden Index is ≥1, or if there is a trigger based on an abnormal laboratory value. A total of 364 alerts were generated for 107 patients; 154 (42.3%) alerts were accepted. Conclusions: We proposed an ontology-based approach to provide support for improving medication appropriateness in older patients with multimorbidity in a scalable, sustainable, and reusable way. The chronic patient domain was built based on our previous research, reusing the existing framework. OntoPharma has been implemented in clinical practice and generates alerts, considering the following use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events.

BACKGROUND: Nocturnal enuresis (NE) is a frequent diagnosis in pediatric and adolescent populations with an estimated prevalence of around 15% at the age of 6 years. NE can have a substantial impact on multiple health domains. Bedwetting alarms, which typically consist of a sensor and moisture-activated alarm, are a common treatment.
OBJECTIVE: This study aimed to determine areas of satisfaction versus dissatisfaction reported by the parents and caregivers of children using current bedwetting alarms.
METHODS: Using the search term \"bedwetting alarms\" on the Amazon marketplace, products with >300 reviews were included. For each product, the 5 reviews ranked the \"most helpful\" for each star category were selected for analysis. Meaning extraction method was applied to identify major themes and subthemes. A percent skew was calculated by summing the total number of mentions of each subtheme,+1 for a positive mention, 0 for a neutral mention, and -1 for a negative mention, and dividing this total by the number of reviews in which that particular subtheme was observed. Subanalyses were performed for age and gender.
RESULTS: Of 136 products identified, 10 were evaluated based on the selection criteria. The main themes identified across products were long-term concerns, marketing, alarm systems, and device mechanics and features. The subthemes identified as future targets for innovation included alarm accuracy, volume variability, durability, user-friendliness, and adaptability to girls. In general, durability, alarm accuracy, and comfort were the most negatively skewed subthemes (with a negative skew of -23.6%, -20.0%, and -12.4% respectively), which are indicative of potential areas for improvement. Effectiveness was the only substantially positively skewed subtheme (16.8%). Alarm sound and device features were positively skewed for older children, whereas ease of use had a negative skew for younger children. Girls and their caretakers reported negative experiences with devices that featured cords, arm bands, and sensor pads.
CONCLUSIONS: This analysis provides an innovation roadmap for future device design to improve patient and caregiver satisfaction and compliance with bedwetting alarms. Our results highlight the need for additional options in alarm sound features, as children of different ages have divergent preferences in this domain. Additionally, girls and their parents and caretakers provided more negative overall reviews regarding the range of current device features compared to boys, indicating a potential focus area for future development. The percent skew showed that subthemes were often more negatively skewed toward girls, with the ease of use being -10.7% skewed for boys versus -20.5% for girls, and comfort being -7.1% skewed for boys versus -29.4% for girls. Put together, this review highlights multiple device features that are targets for innovation to ensure translational efficacy regardless of age, gender, or specific family needs.