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Digital health technologies (DHTs) at the intersection of health, medical informatics, and business aim to enhance patient care through personalised digital approaches. Ensuring the efficacy and reliability of these innovations demands rigorous clinical validation. A PubMed literature review (January 2006 to July 2023) identified 1250 papers, highlighting growing academic interest. A focused narrative review (January 2018 to July 2023) delved into challenges, highlighting issues such as diverse regulatory landscapes, adoption issues in complex healthcare systems, and a plethora of evaluation frameworks lacking pragmatic guidance. Existing frameworks often omit crucial criteria, neglect empirical evidence, and clinical effectiveness is rarely included as a criterion for DHT quality. The paper underscores the urgency of addressing challenges in accreditation, adoption, business models, and integration to safeguard the quality, efficacy, and safety of DHTs. A pivotal illustration of collaborative efforts to address these challenges is exemplified by the Digital Health Validation Center, dedicated to generating clinical evidence of innovative healthcare technologies and facilitating seamless technology transfer. In conclusion, it is necessary to harmonise evaluation approaches and frameworks, improve regulatory clarity, and commit to collaboration to integrate rigorous clinical validation and empirical evidence throughout the DHT life cycle.

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La medicina digital se presenta como una solución a los problemas asistenciales actuales. En este contexto nace Teckel Medical, una empresa dedicada al desarrollo de software médico basado en inteligencia artificial. Su aplicación Mediktor es el primer evaluador de síntomas avanzado del mundo, capaz de reconocer lenguaje natural para que el usuario exprese cómo se siente con sus palabras. Conduce un interrogatorio médico hasta concluir en un listado de posibles enfermedades asociadas a los síntomas referidos. La inteligencia artificial de Mediktor, junto con técnicas de gamificación, pueden utilizarse en el campo de la formación para dinamizar la enseñanza. La Universidad de Barcelona, el Hospital Clínic y Mediktor se unen para desarrollar una herramienta de formación, información y evaluación destinada a estudiantes de medicina. El objetivo es crear una herramienta de enseñanza atractiva que fidelice su uso por parte del estudiante. La revolución en formación médica ha empezado

Digital medicine can be a solution to current health care problems. This is the setting in which Teckel Medical was born - a company devoted to the development of medical software based on artificial intelligence (AI). Its Mediktor app is the world’s first advanced symptom evaluator, capable of recognizing natural language so that users can express themselves in their own words. It runs through a medical query session until it concludes with a list of possible diseases associated with the reported symptoms. Mediktor’s AI, together with gamification techniques, can be used in education in order to enhance teaching. Universitat de Barcelona-Hospital Clínic and Mediktor have joined forces to develop a training, information and evaluation tool aimed at medical students. The goal is to create an attractive teaching tool that encourages its use by the student. The revolution in medical training has begun

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PURPOSE: This study aimed to assess the prevalence and time course of asynchronies during mechanical ventilation (MV). METHODS: Prospective, noninterventional observational study of 50 patients admitted to intensive care unit (ICU) beds equipped with Better Care™ software throughout MV. The software distinguished ventilatory modes and detected ineffective inspiratory efforts during expiration (IEE), double-triggering, aborted inspirations, and short and prolonged cycling to compute the asynchrony index (AI) for each hour. We analyzed 7,027 h of MV comprising 8,731,981 breaths. RESULTS: Asynchronies were detected in all patients and in all ventilator modes. The median AI was 3.41 % [IQR 1.95-5.77]; the most common asynchrony overall and in each mode was IEE [2.38 % (IQR 1.36-3.61)]. Asynchronies were less frequent from 12 pm to 6 am [1.69 % (IQR 0.47-4.78)]. In the hours where more than 90 % of breaths were machine-triggered, the median AI decreased, but asynchronies were still present. When we compared patients with AI > 10 vs AI ≤ 10 %, we found similar reintubation and tracheostomy rates but higher ICU and hospital mortality and a trend toward longer duration of MV in patients with an AI above the cutoff. CONCLUSIONS: Asynchronies are common throughout MV, occurring in all MV modes, and more frequently during the daytime. Further studies should determine whether asynchronies are a marker for or a cause of mortality.

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BACKGROUND: The purpose of this study is to establish a relationship between tolerance of enteral nutrition (EN) and intra-abdominal pressure (IAP) in critical patients, establish an objective measure for monitoring tolerance, and determine a threshold value for IAP. MATERIALS AND METHODS: Prospective and observational study at the critical care unit. Seventy-two patients were recruited with an expected stay of more than 72 hours and scheduled to receive EN. We recorded IAP and clinical and laboratory variables to describe predictive ones for tolerance of EN at the start of nutrition. RESULTS: The largest group was polytrauma patients (41.7%). Of the patients, 40.3% had undergone surgery prior to inclusion in the study. Most patients (87.5%) were fed via nasogastric tube. Physiological POSSUM (Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity) on admission was 26.4 ± 7.6, and surgical POSSUM was 22.4 ± 8.0. The mean Acute Physiology and Chronic Health Evaluation II (APACHE II) score was 13.6 ± 6.0. Mortality was 31.9%. In all, 70.8% tolerated EN. The univariate analysis revealed a statistically significant relation between tolerance of EN and surgical POSSUM, APACHE II, and baseline IAP. The multivariate analysis showed a relationship between APACHE II score, baseline IAP, and the tolerance of EN. So, on the basis of these 2 variables, logistic regression analysis can predict whether a patient will tolerate the diet with an overall precision of 80.3%. CONCLUSIONS: In critically ill patients, there is a relation between IAP values and the tolerance of EN. The baseline IAP with the APACHE II score can predict the tolerance of EN.

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UNLABELLED: BACKGROUND PATIENT: ventilator dyssynchrony is common and may influence patients' outcomes. Detection of such dyssynchronies relies on careful observation of patients and airway flow and pressure measurements. Given the shortage of specialists, critical care nurses could be trained to identify dyssynchronies. OBJECTIVE: To evaluate the accuracy of specifically trained critical care nurses in detecting ineffective inspiratory efforts during expiration. METHODS: We compared 2 nurses' evaluations of measurements from 1007 breaths in 8 patients with the evaluations of experienced critical care physicians. Sensitivity, specificity, positive predictive value, negative predictive value, and the Cohen κ for interobserver agreement were calculated. RESULTS: For the first nurse, sensitivity was 92.5%, specificity was 98.3%, positive predictive value was 95.4%, negative predictive value was 97.1%, and κ was 0.92 (95% CI, 0.89-0.94). For the second nurse, sensitivity was 98.5%, specificity was 84.7%, positive predictive value was 70.7%, negative predictive value was 99.3%, and κ was 0.74 (95% CI, 0.70-0.78). CONCLUSION: Specifically trained nurses can reliably detect ineffective inspiratory efforts during expiration.

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PURPOSE: Ineffective respiratory efforts during expiration (IEE) are a problem during mechanical ventilation (MV). The goal of this study is to validate mathematical algorithms that automatically detect IEE in a computerized (Better Care®) system that obtains and processes data from intensive care unit (ICU) ventilators in real time. METHODS: The Better Care® system, integrated with ICU health information systems, synchronizes and processes data from bedside technology. Algorithms were developed to analyze airflow waveforms during expiration to determine IEE. Data from 2,608,800 breaths from eight patients were recorded. From these breaths 1,024 were randomly selected. Five experts independently analyzed the selected breaths and classified them as IEE or not IEE. Better Care® evaluated the same 1,024 breaths and assigned a score to each one. The IEE score cutoff point was determined based on the experts' analysis. The IEE algorithm was subsequently validated using the electrical activity of the diaphragm (EAdi) signal to analyze 9,600 breaths in eight additional patients. RESULTS: Optimal sensitivity and specificity were achieved by setting the cutoff point for IEE by Better Care® at 42%. A score >42% was classified as an IEE with 91.5% sensitivity, 91.7% specificity, 80.3% positive predictive value (PPV), 96.7% negative predictive value (NPV), and 79.7% Kappa index [confidence interval (CI) (95%) = (75.6%; 83.8%)]. Compared with the EAdi, the IEE algorithm had 65.2% sensitivity, 99.3% specificity, 90.8% PPV, 96.5% NPV, and 73.9% Kappa index [CI (95%) = (71.3%; 76.3%)]. CONCLUSIONS: In this pilot, Better Care® classified breaths as IEE in close agreement with experts and the EAdi signal.

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Critical care medicine is the specialty that cares for patients with acute life-threatening illnesses where intensivists look after all aspects of patient care. Nevertheless, shortage of physicians and nurses, the relationship between high costs and economic restrictions, and the fact that critical care knowledge is only available at big hospitals puts the system on the edge. In this scenario, telemedicine might provide solutions to improve availability of critical care knowledge where the patient is located, improve relationship between attendants in different institutions and education material for future specialist training. Current information technologies and networking capabilities should be exploited to improve intensivist coverage, advanced alarm systems and to have large critical care databases of critical care signals.