Collaborating Authors

Does Amazon have answers for the future of the NHS?

The Guardian

Enthusiasts predicted the plan would relieve the pressure on hard-pressed GPs. Critics saw it as a sign of creeping privatisation and a data-protection disaster in waiting. Reactions to news last month that Amazon's voice-controlled digital assistant Alexa was to begin using NHS website information to answer health queries were many and varied. US-based healthcare tech analysts say the deal is just the latest of a series of recent moves that together reveal an audacious, long-term strategy on the part of Amazon. From its entry into the lucrative prescription drugs market and development of AI tools to analyse patient records, to Alexa apps that manage diabetes and data-driven experiments on how to cut medical bills, the $900bn global giant's determination to make the digital disruption of healthcare a central part of its future business model is becoming increasingly clear.

Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach Artificial Intelligence

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This serves two potential functions: 1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and 2) the basis for clinical artificial intelligence - an AI that can think like a doctor. This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans. This framework was evaluated using real patient data from an electronic health record. Such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare (Cost per Unit Change: $189 vs. $497) while obtaining a 30-35% increase in patient outcomes. Tweaking certain model parameters further enhances this advantage, obtaining roughly 50% more improvement for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.

Microsoft Cloud for Healthcare: Unlocking the power of health data for better care


As healthcare providers have faced unprecedented workloads (individually and institutionally) around the world, the pandemic response continues to cause seismic shifts in how, where, and when care is provided. Longer-term, it has revealed the need for fundamental shifts across the care continuum. As a physician, I have seen first-hand the challenges of not having the right data, at the right time, in the right format to make informed shared decisions with my patients. These challenges amplify the urgency for trusted partners and solutions to help solve emergent health challenges. Today we're taking a big step forward to address these challenges with the general availability of Microsoft Cloud for Healthcare.

How Healthcare Organizations Are Embracing Digital Transformation


Healthcare organizations are now opening up to digitization to improve their efficiency, effectiveness, profitability, and competitiveness. Healthcare organizations have gone digital by adopting Electronic Medical Records (EMRs) and other digital applications that have helped hospital staff do their work easily and more efficiently, simplify inventory management, track schedules, manage patient safety, identify bottlenecks, and improve overall administration. Digital transformation also takes patient care to a new higher level, improves the quality of life and helps increase the life expectancy of people. Be it advancements in genomics or smart health monitors to telemedicine, digital transformation is making its presence in every aspect of healthcare. Analysts from a leading American multinational finance company predict that by adopting digital technologies, the healthcare sector could save about $300 billion, especially in the area of chronic diseases management just by eliminating wasteful expenses.

Innovation, Artificial Intelligence, and the Bedside Nurse - Daily Nurse


Nurses have always played a critical role at the bedside while bearing witness to numerous changes in technology. In the past 50 years alone, the advancements seem unfathomable to nurses of the not-so-distant past such as "test-tube" babies, medical lasers, the artificial heart, genome mapping, CT and MRI imaging, angioplasty, dialysis, endoscopic procedures, bionic prosthetics, the internet and health information technology (IT), the electronic health record (EHR), and robotic surgeries. However, as health care races toward telemedicine and artificial intelligence, nurses must strategically position themselves to stay relevant. In a recent article in Nursing Management, the author stated: "Artificial Intelligence (AI) is a branch of computer science dealing with the simulation of intelligent behavior in computers. Combining the experience, knowledge, and human touch of clinicians with the power of AI will improve the quality of patient care and lower its cost."