Mental healthcare is one of the prominent parts of the healthcare industry with alarming concerns related to patients depression, stress leading to self-harm and threat to fellow patients and medical staff. To provide a therapeutic environment for both patients and staff, aggressive or agitated patients need to be monitored remotely and track their vital signs and physical activities continuously. Remote patient monitoring (RPM) using non-invasive technology could enable contactless monitoring of acutely ill patients in a mental health facility. Enabling the RPM system with AI unlocks a predictive environment in which future vital signs of the patients can be forecasted. This paper discusses an AI-enabled RPM system framework with a non-invasive digital technology RFID using its in-built NCS mechanism to retrieve vital signs and physical actions of patients. Based on the retrieved time series data, future vital signs of patients for the upcoming 3 hours and classify their physical actions into 10 labelled physical activities. This framework assists to avoid any unforeseen clinical disasters and take precautionary measures with medical intervention at right time. A case study of a middle-aged PTSD patient treated with the AI-enabled RPM system is demonstrated in this study.

An understanding of how Social Overhead Capital (SOC) is associated with the land value of the local community is important for effective urban planning. However, even within a district, there are multiple sections used for different purposes; the term for this is spatial heterogeneity. The spatial heterogeneity issue has to be considered when attempting to comprehend land prices. If there is spatial heterogeneity within a district, land prices can be managed by adopting the spatial clustering method. In this study, spatial attributes including SOC, socio-demographic features, and spatial information in a specific district are analyzed with Finite Mixture Modeling (FMM) in order to find (a) the optimal number of clusters and (b) the association among SOCs, socio-demographic features, and land prices. FMM is a tool used to find clusters and the attributes' coefficients simultaneously. Using the FMM method, the results show that four clusters exist in one district and the four clusters have different associations among SOCs, demographic features, and land prices. Policymakers and managerial administration need to look for information to make policy about land prices. The current study finds the consideration of closeness to SOC to be a significant factor on land prices and suggests the potential policy direction related to SOC.

We study the problem of allocating limited supply of medical resources in developing countries, in particular, Sierra Leone. We address this problem by combining machine learning (to predict demand) with optimization (to optimize allocations). A key challenge is the need to align the loss function used to train the machine learning model with the decision loss associated with the downstream optimization problem. Traditional solutions have limited flexibility in the model architecture and scale poorly to large datasets. We propose a decision-aware learning algorithm that uses a novel Taylor expansion of the optimal decision loss to derive the machine learning loss. Importantly, our approach only requires a simple re-weighting of the training data, ensuring it is both flexible and scalable, e.g., we incorporate it into a random forest trained using a multitask learning framework. We apply our framework to optimize the distribution of essential medicines in collaboration with policymakers in Sierra Leone; highly uncertain demand and limited budgets currently result in excessive unmet demand. Out-of-sample results demonstrate that our end-to-end approach can significantly reduce unmet demand across 1040 health facilities throughout Sierra Leone.

This research paper proposes a COVID-19 monitoring and response system to identify the surge in the volume of patients at hospitals and shortage of critical equipment like ventilators in South-east Asian countries, to understand the burden on health facilities. This can help authorities in these regions with resource planning measures to redirect resources to the regions identified by the model. Due to the lack of publicly available data on the influx of patients in hospitals, or the shortage of equipment, ICU units or hospital beds that regions in these countries might be facing, we leverage Twitter data for gleaning this information. The approach has yielded accurate results for states in India, and we are working on validating the model for the remaining countries so that it can serve as a reliable tool for authorities to monitor the burden on hospitals.

Gagan is a leader with 20 years of experience in Healthcare across Providers, Insurers, Pharma, Med Devices, and Digital Health. He thrives in growth focused Healthcare businesses that sit at intersection of healthcare and technology trends reshaping the industry. His is the Co-Founder and CEO of Exacta.Health, a digital health analytics company focused on enabling Providers, ACOs and Payers to predict and manage high risk patients with high quality, cost-effective care. Exacta is building a proprietary, patent protected intelligent health stack that unifies health data across multiple health facilities and patient generated sources, and uses AI, machine learning, cloud edge computing paradigms to provide predictive analytics and critical insights into patient, population level health. This helps healthcare entities provide right interventions at right setting of care to balance cost, quality and profitability.

Artificial intelligence (AI) has potential to drive game-changing improvements for underserved communities in global health. In response, The Rockefeller Foundation and USAID partnered with the Bill and Melinda Gates Foundation to develop AI in Global Health: Defining a Collective Path Forward. Research began with a broad scan of instances where artificial intelligence is being used, tested, or considered in healthcare, resulting in a catalogue of over 240 examples. This grouping involves tools that leverage AI to monitor and assess population health, and select and target public health interventions based on AI-enabled predictive analytics. It includes AI-driven data processing methods that map the spread and burden of disease while AI predictive analytics are then used to project future disease spread of existing and possible outbreaks.

The Dubai Health Authority, DHA, has begun to sterilise its hospitals and health centres by using smart robots, in line with precautionary measures to enhance the safety of all staff members and patients across the DHA health facilities. The sterilisation process coincides with the return of all diagnostic and therapeutic services for patients. Using smart technology makes the sterilisation process thorough, efficient and less time-consuming. Kholoud Abdullah Al Ali, Project Manager and leader of the DHA's Dubai Future Accelerators team, said that the Authority has begun using eight intelligent robots to perform UV sterilisation scans for all rooms and corridors in its health facilities. Al Ali said the move is part of the DHA's ongoing efforts to adopt the latest technologies and smart systems in its operations and procedures, in line with DHA's strategic plan to keep pace with global developments in the field of Artificial Intelligence. Al Ali explained the multiple advantages of the UV robot, which can move automatically without the need for human intervention and ensure greater and better coverage of high-contact areas.

There have been many cases of misdiagnosis in Kenya. The Kenya Medical Practitioners and Dentists Board blames machines for these cases. However, this is misleading since machines do not interpret the results. This is the role of medical personnel. Ironically, machines powered by Artificial Intelligence (AI) are learning fast and becoming so clever that it will be easier for medics to make more accurate decisions.

For island nations and countries without the infrastructure for reliable transportation, drones can do more than take photos or collect data: they can transport supplies to save lives. The Pacific island country of Vanuatu, for instance, has teamed up with UNICEF and two drone companies to deliver vaccines to rural areas. Vanuatu is composed of 83 islands spread over an area that covers 1,600 kilometers ( 1,000 miles). To deliver vaccines to its more rural communities, health workers often have to walk for hours -- sometimes, it can even take them days by cars and/or boats. Drones could ensure that local health facilities have quick access to lifesaving supplies when needed.

Last month in Rwanda, a young woman started bleeding after giving birth by C-section. Try as they might, her doctors couldn't stop it. They'd already transfused the two units of matching blood that they had on-hand. They could have called the national blood bank in the capital of Kigali to request more, but ordering it, and sending it the 25 miles over mountainous roads to the hospital would take up to four hours. The woman didn't have that kind of time.