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Nowadays, evaluating the quality of health services, especially in primary health care (PHC), is increasingly important. In a historical perspective, the Department of Health (United Kingdom) developed and proposed a range of indicators in 1998, and lately several health, social and political organizations have defined and implemented different sets of PHC quality indicators. Some systematic reviews in PHC quality indicators are reported but only in specific contexts and conditions. The aim of this study is to characterize and provide a list of indicators discussed in the literature to support managers and clinicians in decision-making processes, through an umbrella review on PHC quality indicators. The methodology was performed according to PRISMA Statement. Indicators from 33 eligible systematic reviews were categorized according to the dimensions of care, function, type of care, domains and condition contexts. Of a total of 727 indicators or groups of indicators, 74.5% (n = 542) were classified in process category and 89.5% (n = 537) with chronic type of care (n = 428; 58.8%) and effective domain (n = 423; 58.1%) with the most frequent values in categorizations by dimensions. The results of this overview of reviews are valuable and imply the need for future research and practice regarding primary health care quality indicators in the most varied conditions and contexts to generate new discussions about their use, comparison and implementation.

Google Trends (GT) data have shown promising results as a complementary tool to classical surveillance approaches. However, GT data are not necessarily provided by a representative sample of patients and may be skewed toward demographic and clinical groups that are more likely to use the internet to search for their health. In this study, we aimed to assess whether GT-based models perform differently in distinct population subgroups. To assess that, we analyzed a case study on asthma hospitalizations. We analyzed all hospitalizations with a main diagnosis of asthma occurring in 3 different countries (Portugal, Spain, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold for the same countries and time period were retrieved from GT. We estimated the correlation between GT data and the weekly occurrence of asthma hospitalizations (considering separate asthma admissions data according to patients' age, sex, ethnicity, and presence of comorbidities). In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations (for the different aforementioned subgroups) for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Overall, correlation coefficients between GT on the pseudo-influenza syndrome topic and asthma hospitalizations ranged between 0.33 (in Portugal for admissions with at least one Charlson comorbidity group) and 0.86 (for admissions in women and in White people in Brazil). In the 3 assessed countries, forecasted hospitalizations for 2015-2016 correlated more strongly with observed admissions of older versus younger individuals (Portugal: Spearman ρ=0.70 vs ρ=0.56; Spain: ρ=0.88 vs ρ=0.76; Brazil: ρ=0.83 vs ρ=0.82). In Portugal and Spain, forecasted hospitalizations had a stronger correlation with admissions occurring for women than men (Portugal: ρ=0.75 vs ρ=0.52; Spain: ρ=0.83 vs ρ=0.51). In Brazil, stronger correlations were observed for admissions of White than of Black or Brown individuals (ρ=0.92 vs ρ=0.87). In Portugal, stronger correlations were observed for admissions of individuals without any comorbidity compared with admissions of individuals with comorbidities (ρ=0.68 vs ρ=0.66). We observed that the models based on GT data may perform differently in demographic and clinical subgroups of participants, possibly reflecting differences in the composition of internet users' health-seeking behaviors.

Purpose The main goal of this study is to identify the associated factors with avoidable admissions in ED, comparing pre-COVID and COVID periods. Methods This was retrospective study that took place in a Paediatric Emergency Department of a metropolitan, university-affiliated hospital in Portugal. All visits to paediatric emergency department between 2014 and 2020 were considered. Results There was a decrease of 7.2% points in avoidable visits between pre-COVID and COVID periods. Considering both periods, this study identifies older ages, being admitted to the paediatric emergency department between 4 and 7 a.m., referral and having visited the emergency department previously within 72 h as major factors associated with a reduced likelihood for avoidable visits. On the other hand, it identifies an increased likelihood of avoidable visits in the 3 to 5 years old age group, visits that occurred during the Summer and visits that occurred between 8 and 11 p.m. When considering what changed between pre-COVID and COVID periods, while having visited the paediatric emergency department 72 h prior made it less likely for the patient to be an avoidable visit during the pandemic period, this tendency has inverted, making it more likely for return visits to be avoidable. Conclusions The relatively low decrease in avoidable visits’ ratios between pre-COVID and COVID periods, associated with the similar distribution of attendance during the day and lower odds ratio of avoidable visits during periods when primary care is available, suggests that avoidable visits are a chronical problem of the National Health system’s structure and its usage, not having a single factor nor a combination of factors as a driving force. Nevertheless, this study identified several factors associated with avoidable visits to the emergency department. Therefore, it can aid policy makers to create targeted interventions to mitigate this problem. Significance What is Known - Excess of avoidable visits have been a problem in the Emergency Department for several decades. - The fear experienced during COVID-19 pandemic, influenced the visits to the emergency departments. What is New - The change in demand introduced by the COVID-19 was used to provide insights regarding the factors associated with avoidable visits. - A triage system’s agnostic definition of avoidable visit was chosen, to better reflect the visit’s lack of necessity of hospital resources, including hospital admission.

Coded data are the basis of information systems in all countries that rely on Diagnosis Related Groups in order to reimburse/finance hospitals, including both administrative and clinical data. To identify the problems and barriers that affect the quality of the coded data is paramount to improve data quality as well as to enhance its usability and outcomes. This study aims to explore problems and possible solutions associated with the clinical coding process. Problems were identified according to the perspective of ten medical coders, as the result of four focus groups sessions. This convenience sample was sourced from four public hospitals in Portugal. Questions relating to problems with the coding process were developed from the literature and authors’ expertise. Focus groups sessions were taped, transcribed and analyzed to elicit themes. Variability in the documents used for coding, illegibility of hand writing when coding on paper, increase of errors due to an extra actor in the coding process when transcribed from paper, difficulties in the diagnoses’ coding, coding delay and unavailability of resources and tools designed to help coders, were some of the problems identified. Some problems were identified and solutions such as the standardization of the documents used for coding an episode, the adoption of the electronic coding, the development of tools to help coding and audits, and the recognition of the importance of coding by the management were described as relevant factors for the improvement of the quality of data.

Background Dementia is a leading factor in the institutionalization of older adults. Informal caregivers’ desire to institutionalize (DI) their care recipient with dementia (PwD) is a primary predictor of institutionalization. This study aims to develop a prediction model for caregivers’ DI by mining data from an eHealth platform in a high-prevalence dementia country. Methods Cross-sectional data were collected from caregivers registering on isupport-portugal.pt. One hundred and four caregivers completed the Desire to Institutionalize Scale (DIS) and were grouped into DI (DIS score ≥ 1) and no DI (DIS score = 0). Participants completed a comprehensive set of sociodemographic, clinical, and psychosocial measures, pertaining to the caregiver and the PwD, which were accounted as model predictors. The selected model was a classification tree, enabling the visualization of rules for predictions. Results Caregivers, mostly female (82.5%), offspring of the PwD (70.2), employed (65.4%), and highly educated (M 15 years of schooling), provided intensive care (Mdn 24 h. week) over a median course of 2.8 years. Two-thirds (66.3%) endorsed at least one item on the DIS (DI group). The model, with caregivers’ perceived stress as the root of the classification tree (split at 28.5 points on the Zarit Burden Interview) and including the ages of caregivers and PwD (split at 46 and 88 years, respectively), as well as cohabitation, employed five rules to predict DI. Caregivers scoring 28.5 and above on burden and caring for PwD under 88 are more prone to DI than those caring for older PwD (rules 1–2), suggesting the influence of expectations on caregiving duration. The model demonstrated high accuracy (0.83, 95%CI 0.75, 0.89), sensitivity (0.88, 95%CI 0.81, 0.95), and good specificity (0.71, 95%CI 0.56, 0.86). Conclusions This study distilled a comprehensive range of modifiable and non-modifiable variables into a simplified, interpretable, and accurate model, particularly useful at identifying caregivers with actual DI. Considering the nature of variables within the prediction rules, this model holds promise for application to other existing datasets and as a proxy for actual institutionalization. Predicting the institutional placement of PwD is crucial for intervening on modifiable factors as caregiver burden, and for care planning and financing.

Background Crowding has been a longstanding issue in emergency departments. To address this, a fast-track system for avoidable patients is being implemented in the Paediatric Emergency Department where our study is conducted. Our goal is to develop an optimized Decision Support System that helps in directing patients to this fast track. We evaluated various Machine Learning models, focusing on a balance between complexity, predictive performance, and interpretability. Methods This is a retrospective study considering all visits to a university-affiliated metropolitan hospital’s PED between 2014 and 2019. Using information available at the time of triage, we trained several models to predict whether a visit is avoidable and should be directed to a fast-track area. Results A total of 507,708 visits to the PED were used in the training and testing of the models. Regarding the outcome, 41.6% of the visits were considered avoidable. Except for the classification made by triage rules, i.e. considering levels 1,2, and 3 as non-avoidable and 4 and 5 as avoidable, all models had similar results in model’s evaluation metrics, e.g. Area Under the Curve ranging from 74% to 80%. Conclusions Regarding predictive performance, the pruned decision tree had evaluation metrics results that were comparable to the other ML models. Furthermore, it offers a low complexity and easy to implement solution. When considering interpretability, a paramount requisite in healthcare since it relates to the trustworthiness and transparency of the system, the pruned decision tree excels. Overall, this paper contributes to the growing body of research on the use of machine learning in healthcare. It highlights practical benefits for patients and healthcare systems of the use ML-based DSS in emergency medicine. Moreover, the obtained results can potentially help to design patients’ flow management strategies in PED settings, which has been sought as a solution for addressing the long-standing problem of overcrowding.

Background: In contrast to air pollution and pollen exposure, data on the occurrence of the common cold are difficult to incorporate in models predicting asthma hospitalizations. Objective: This study aims to assess whether web-based searches on common cold would correlate with and help to predict asthma hospitalizations. Methods: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 5 different countries (Portugal, Spain, Finland, Norway, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold were retrieved from Google Trends (GT) using the pseudo-influenza syndrome topic and local language search terms for common cold for the same countries and periods. We applied time series analysis methods to estimate the correlation between GT and hospitalization data. In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Results: In time series analyses, GT data on common cold displayed strong correlations with asthma hospitalizations occurring in Portugal (correlation coefficients ranging from 0.63 to 0.73), Spain (ρ=0.82-0.84), and Brazil (ρ=0.77-0.83) and moderate correlations with those occurring in Norway (ρ=0.32-0.35) and Finland (ρ=0.44-0.47). Similar patterns were observed in the correlation between forecasted and observed asthma hospitalizations from June 2015 to June 2016, with the number of forecasted hospitalizations differing on average between 12% (Spain) and 33% (Norway) from observed hospitalizations. Conclusions: Common cold–related web-based searches display moderate-to-strong correlations with asthma hospitalizations and may be useful in forecasting them.

Background Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies. Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques. Methods This study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology. This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa – EPE), from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer. Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48) and neural networks (multilayer perceptron). The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia. Results The application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic. Conclusions The findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.

Following up the previously published systematic review on the same topic and realizing that new studies and evidence had emerged on the matter, we conducted an update on the same research terms. With the objective of updating the information relating environmental risk factors on neurodegenerative diseases and the geographic approaches used to address them, we searched PubMed, Web of Science and Scopus for all scientific studies considering the following three domains: neurodegenerative disease, environmental atmospheric factor and geographical analysis, using the same keywords as in the previously published systematic review. From February 2020 to February 2023, 35 papers were included versus 34 in the previous period, with dementia (including Alzheimer’s disease) being the most focused disease (60.0%) in this update, opposed to multiple sclerosis on the last review (55.9%). Also, environmental pollutants such as PM 2.5 and NO 2 have gained prominence, being represented in 65.7% and 42.9% of the new studies, opposed to 9.8% and 12.2% in the previous review, compared to environmental factors such as sun exposure (5.7% in the update vs 15.9% in the original). The mostly used geographic approach remained the patient’s residence (82.9% in the update vs 21.2% in the original and 62.3% in total), and remote sensing was used in 45.7% of the new studies versus 19.7% in the original review, with 42.0% of studies using it globally, being the second most common approach, usually to compute the environmental variable. This review has been registered in PROSPERO with the number CRD42020196188.

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals’ perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.