Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost–benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively.

Smart cities can improve the quality of life of citizens by optimizing the utilization of resources. In an IoT-connected environment, people’s health can be constantly monitored, which can help identify medical problems before they become serious. However, overcrowded hospitals can lead to long waiting times for patients to receive treatment. The literature presents alternatives to address this problem by adjusting care capacity to demand. However, there is still a need for a solution that can adjust human resources in multiple healthcare settings, which is the reality of cities. This work introduces HealCity, a smart-city-focused model that can monitor patients’ use of healthcare settings and adapt the allocation of health professionals to meet their needs. HealCity uses vital signs (IoT) data in prediction techniques to anticipate when the demand for a given environment will exceed its capacity and suggests actions to allocate health professionals accordingly. Additionally, we introduce the concept of multilevel proactive human resources elasticity in smart cities, thus managing human resources at different levels of a smart city. An algorithm is also devised to automatically manage and identify the appropriate hospital for a possible future patient. Furthermore, some IoT deployment considerations are presented based on a hardware implementation for the proposed model. HealCity was evaluated with four hospital settings and obtained promising results: Compared to hospitals with rigid professional allocations, it reduced waiting time for care by up to 87.62%.

The manufacturers of IoT-based wearable devices present different ways to deliver the captured vital signs. Healthcare third-party systems need to adapt to the different approaches, rising complexity and acts against any idea of seamless integration. In addition to the interoperability problem, we observed that the state-of-the-art does not present alternatives that capture and analyze vital signs in such a way that they can be used to diagnose the user’s health status automatically. This article presents the HealthTranslator model, which provides interoperability by integrating multiple wearable devices and delivering their data through an HL7 FHIR healthcare standard format. More precisely, our contribution combines different user-oriented periodicity strategies to collect vital signs with data enrichment over an FHIR file, effortlessly delivering valuable insights regarding the user’s health status. We developed a microservices-based prototype that collects data on different APIs from different companies/wearables. The resources were encouraging, where data from Xiaomi, Apple, Garmin, and Samsung devices were read and delivered in an FHIR format. Thinking at the intelligent cities level, the provided interoperability is essential to generate the new vision of healthcare, where users and patients will be 24/7 monitored from their own homes.

A pandemia de COVID-19 agravou a superlotação hospitalar. Em cidades inteligentes, a saúde das pessoas pode ser monitorada em tempo real para detectar situações que exijam suporte médico. Neste contexto, propomos o modelo CityHealth, baseado no conceito de elasticidade em Cloud Computing, onde é possível adicionar, migrar ou remover recursos de acordo com a necessidade. Ele monitora o uso dos ambientes de saúde e adapta a capacidade de atendimento de acordo com a demanda. Através de uma abordagem preditiva, o CityHealth antecipa a sobrecarga de ambientes de saúde e propõe ações para movimentação dos recursos humanos e otimizar o atendimento. Utilizando dados de uma policlínica como base para emulação de uma cidade inteligente com dois hospitais resultou em redução de até 95,6% no tempo de espera.

Healthcare services and IoT, as highlighted by Hu et al. [9], generate enormous volumes of time series data. Using caching in serverless functions can significantly reduce latency and improve performance when storing frequently accessed data in memory. Although several approaches offer improvements, such as the use of in-memory caching, data prediction, and distributed systems, none of them fully addresses the need for a robust and efficient system for time series in healthcare, leaving a gap in necessary data availability and optimization. The TriCache model proposes a three-tier caching system to optimize storage and access to time series data in healthcare serverless functions, using a combination of memory in the serverless function, in-memory cache, and disk storage, in addition to predictive intelligence. The main contribution of the model is the significant reduction in latency and the improvement in the hit rate by efficiently predicting and allocating data across different cache layers. Experiments demonstrated a notable reduction in response time, with a 110 millisecond decrease in the 99th percentile. Additionally, the model performed significantly, achieving a 93% hit rate, compared to the 78% observed in the traditional model.