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Monitoring the vital status of an aging population especially those with chronic diseases can potentially reduce the multiple emergency room visits and hospitalizations if patients and care providers are provided with information that might help them make informed decisions on appropriate cause of actions. This process can be enabled by utilizing temporal abstraction and deriving temporal patterns in order to understand the underlying temporal relationships on vital status in data collected from patients participating in telehealth programs in combination with other data sets in order to get a complete patient flow. Such discovery can highlight when an elderly patient is at risk of an adverse event and this is information that can be utilized for provision of appropriate care for the patient. This paper demonstrates application of a method for deriving temporal patterns from patient's physiological data thereby quantifying the many states a patient can transition to before, during and after an adverse event. With this approach, it is possible to quantify patients with vital scores based on their physiology.

© 2017 IEEE. The costs of lengthy hospital admissions (HA) and multiple emergency room visits (ER Visits) from patients with conditions such as heart failure (HF) and chronic obstructive pulmonary disease (COPD) can place a significant burden on healthcare systems. Understanding the various factors contributing to hospitalization and ER visits could aid cost-effective management in the delivery of services leading to potential improvement on quality of life for patients. This can be facilitated by collecting data using remoting patient monitoring (RPM) services and using analytics to discover important factors about patients. This paper presents our research that utilizes predictive modeling to determine key factors that are significant determinant to hospitalization and multiple ER Visits. The results shows that gender, past medical history and vital status are key factors to hospital admissions and ER Visits. Additionally, when a factor to indicate the period before, during and after an ER Visits was included, the resulting model shows a very high likelihood ratio and improved p values on all vital status. Our results shows that more research is needed to fully understand the temporal patterns among variables during hospitalization or ER visit.

In large hospitals, nurses' intervention within certain timeframes can be a decisive factor in patients' recovery and might lead to serious and irreversible repercussions if not adequate. In this paper, a smart digital medical bed called MedBed is devised to solve the most challenging and relevant problems in an efficient manner from time, space and economical perspectives. The ultimate objective is to provide patients a certain independency thus, allowing them to take some vital actions when nurses are late or unavailable. The various features that the system offers are relevant and range from regular/plain to highly decisive in terms of impact on the patient's vital status. The bed recognizes voice commands from the patient and is perceived to communicate as Internet of Think (IoT) via a customized and user-friendly smartphone application. A database is provided to keep track of various related activities such as medication and other crucial reminders, as well. Finally, the utmost feature of this model is that it can be implemented in medical and healthcare centers with minimal infrastructure requirements or modifications.

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Clinical teams in acute inpatient settings can greatly benefit from automated charting technologies that continuously monitor patient vital status. NewYork-Presbyterian has designed and developed a real-time patient monitoring system that integrates vital signs sensors, networking, and electronic health records, to allow for automatic charting of patient status. We evaluate the representativeness (a combination of agreement, safety and timing) of a core vital sign across nursing intensity care protocols for preliminary feasibility assessment. Our findings suggest an automated way of summarizing heart rate provides representation of true heart rate status and can facilitate alternatives approaches to burdensome manual nurse charting of physiological parameters.

The development of wireless patient monitoring system has been quite intensive in the past decade. Patients at a critical status can greatly benefit from technologies that continuously monitor their vital status. We have designed and developed a real-time patient monitoring system including biological sensors, integrated networking, electronic patient records, and web technology to allow remote monitoring of patient status. This system shall facilitate communication between medical professionals at local hospitals, and specialists available for consultation from distant clinics and hospitals to take the right decision with the patient status. Integrated Health Monitoring System (IHM) is a complete solution of acquiring, processing, monitoring and storing the vital signs of the patients starting from home until reaching the hospital. The IHM consists of five major components: intensive care unit patient monitor (BioMon), wireless patient monitor (WiMon), a mobile application (Healthpad), Central Monitoring System (CMS), and a cloud-based server.

H@H (Health at Home) is a web based monitoring system for de-hospitalization of patients suffering from chronic diseases, ensuring continuous assistance by monitoring, interaction and home therapy. Patients can greatly benefit from continuous in home monitoring of vital status via a patient monitoring system that integrates vital sign sensors, video communication with physicians and medical gases supply. The system is able to detect emergencies and activate a remote team for on site intervention, furthermore patients can require medical consultations at any times.

A large database (Myocardial Infarction Data Acquisition System, or MIDAS) including all 49,250 myocardial infarctions that occurred in the state of New Jersey in 1986 and 1987 with follow-up as long as five years was used in the development and testing of the neural networks. Since the information included in the database was not sufficient to allow the exact prediction of vital status in all patients with 100% accuracy, a neural network able to categorize patients according to the probability of dying within a given period of time rather than predicting categorically whether a given patient will be dead or alive at a given time in the future was developed. An algorithm to accommodate cases where identical input vectors were associated with different outputs (vital status) and a method of linear output adjustment to describe the degree of confidence of each prediction were also developed. The neural network was able to learn and was successful in predicting vital status at six months. >