Brigham and Women's Hospital: "Moving the Needle" Takes People, Processes, and Leadership

Describes three strategies implemented simultaneously to enhance patient satisfaction: creating strong leadership commitment; improving care processes; and training a customer-focused staff. Discusses patient surveys and performance scorecards

Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare:State-of-the-Art and Future Prospects

In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare

A multicentre integration of a computer-led follow-up of prostate cancer is valid and safe

Background Prostate cancer (CaP) has a rising number of patients requiring routine follow up. In this study, we aimed to test a computer led follow up service for prostate cancer in two UK hospitals. The testing aimed to validate the computer Expert system in making clinical decisions according to the individual patient’s clinical need. The valid model should accurately identify patients with disease recurrence or treatment failure based on their blood test and clinical picture. Methods A clinical decision support system (CDSS) was developed from European (EAU) and national (NICE) guidelines along with knowledge acquired from Urologists. This model was then applied in two UK hospitals to review patients post CaP treatment. These patients’ data (n= 200) were then reviewed by two independent Urology consultants (blinded from the CDSS and other consultant’s rating) and the agreement was calculated by kappa statistics for validation. The second objective aimed to verify the system by estimating the system reliability. Results The two individual urology consultants identified 12 % & 15% of the patients to have potential disease progression and recommended their referral to the Urology care. The kappa coefficient for the agreement between the CDSS and the 2 consultants was 0.81 (p < 0.001) and 0.84 (p < 0.001). The agreement among both specialist was also high with k = 0.83 (p < 0.001). The system reliability was estimated on all cases and this demonstrated 100% repeatability of the decisions. Conclusion The computer led follow up is a valid model for providing safe follow up for prostate cancer

Testing of the voice communication in smart home care

This article is aimed to describe the method of testing the implementation of voice control over operating and technical functions of Smart Home Come. Custom control over operating and technical functions was implemented into a model of Smart Home that was equipped with KNX technology. A sociological survey focused on the needs of seniors has been carried out to justify the implementation of voice control into Smart Home Care. In the real environment of Smart Home Care, there are usually unwanted signals and additive noise that negatively affect the voice communication with the control system. This article describes the addition of a sophisticated system for filtering the additive background noise out of the voice communication with the control system. The additive noise significantly lowers the success of recognizing voice commands to control operating and technical functions of an intelligent building. Within the scope of the proposed application, a complex system based on fuzzy-neuron networks, specifically the ANFIS (Adaptive Neuro-Fuzzy Interference System) for adaptive suppression of unwanted background noises was created. The functionality of the designed system was evaluated both by subjective and by objective criteria (SSNR, DTW). Experimental results suggest that the studied system has the potential to refine the voice control of technical and operating functions of Smart Home Care even in a very noisy environment.Web of Science5art. no. 1

Innovative Business Model for Smart Healthcare Insurance

Information revolution and technology growth have made a considerable contribution to restraining the cost expansion and empowering the customer. They disrupted most business models in different industries. The customer-centric business model has pervaded the different sectors. Smart healthcare has made an enormous shift in patient life and raised their expectations of healthcare services quality. Healthcare insurance is an essential business in the healthcare sector; patients expect a new business model to meet their needs and enhance their wellness. This research develops a holistic smart healthcare architecture based on the recent development of information and communications technology. Then develops a disruptive healthcare insurance business model that adapts to this architecture and classifies the patient according to their technology needs. Finally, and implementing a prototype of a system that matches and suits the healthcare recipient condition to the proper healthcare insurance policy by applying Web Ontology Language (OWL) and rule-based reasoning model using SWRL using Protég