Aim To relate the time between recall visits and residual periodontal probing depths (PPDs) to periodontal stability in patients enrolled in supportive periodontal therapy (SPT). Materials and methods Retrospective data on residual PPDs from 11,842 SPT visits were evaluated in SPT patients at the Medi School of Dental Hygiene (MSDH), Bern, Switzerland, 1985–2011. A residual PPD‐based algorithm was developed to compute SPT intervals with no expected change of residual PPD. Results A total of 883 patients aged 43.9 (±13.0) years and 55.4% (n = 489) being females were identified. Linear mixed model analysis yielded highest statistically significant impact on PPD change with time between SPT visits, presence of residual PPD ≥4 mm, and bleeding on probing (p < 0.0001). Patients returning for SPT five times consecutively earlier than computed presented mean % PPDs ≥4 mm of 5.8% (±3.9) compared with patients returning later (19.2%, ±7.6) (p < 0.0001). Additionally, patients attending >50% of their SPT visits earlier versus later demonstrated increased periodontal stability after 5 years (p = 0.0002) and a reduced frequency of tooth loss (0.60, ±0.93 versus 1.45, ±2.07) after 20 years (p < 0.0001). Conclusions To reach and maintain periodontal stability during SPT, individual quantitative data from comprehensive residual PPD profiles may contribute to the improved planning of SPT intervals.

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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.

Internet of Things (IoT) is a constantly growing paradigm that promises to revolutionize healthcare applications and could be associated with several other techniques. Data prediction is another widely used paradigm, where data captured over time is analyzed in order to identify and predict problematic situations that may happen in the future. After research, no surveys that address IoT combined with data prediction in healthcare area exist in the literature. In this context, this work presents a systematic literature review on Internet of Things applied to healthcare area with a focus on data prediction, presenting twenty-three papers about this theme as results, as well as a comparative analysis between them. The main contribution for literature is a taxonomy for IoT systems with data prediction applied to healthcare. Finally, this article presents the possibilities and challenges of exploration in the study area, showing the existing gaps for future approaches.

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