Collaborating Authors


AI-Led Medical Data Labeling For Coding and Billing


The Healthcare sector is among the largest and most critical service sectors, globally. Recent events like the Covid-19 pandemic have furthered the challenge to handle medical emergencies with contemplative capacity and infrastructure. Within the healthcare domain, healthcare equipment supply and usage have come under sharp focus during the pandemic. The sector continues to grow at a fast pace and will record a 20.1% CAGR of surge; plus, it is estimated to surpass $662 billion by 2026. Countries like the US spend a major chunk of their GDP on healthcare.

Artificial Intelligence in the Intensive Care Unit - Critical Care


The past century has witnessed a massive increase in our ability to perform complex calculations. The development of the transistor in the 1950s, followed by the silicone integrated circuit, accelerated those capabilities and gave rise to what is commonly known as Moore's Law. According to this principle, the number of transistors packed into a dense integrated circuit doubles every 2 years. The corollary is that computation speed also doubles at 2-year intervals. Figure 1 is a graphical interpretation of Moore's Law, showing an exponential increase in computational power, in terms of calculations per second that can be purchased with $1000 (constant US, 2015). According to that graph, computing power has increased by a factor of 1018 from the mechanical analytical engine of the early 1900s to today's core I7 Quad chip found in personal laptop computers. The growth of computer power, based on calculations per second purchased by $1000 USD (constant 2015) during the past century. Also shown are significant developments in technology associated with increases in computer power.

Shielded from scrutiny, Epic algorithms deliver inaccurate information


Several artificial intelligence algorithms developed by Epic Systems, the nation's largest electronic health record vendor, are delivering inaccurate or irrelevant information to hospitals about the care of seriously ill patients, contrasting sharply with the company's published claims, a STAT investigation found. Employees of several major health systems said they were particularly concerned about Epic's algorithm for predicting sepsis, a life-threatening complication of infection. The algorithm, they said, routinely fails to identify the condition in advance, and triggers frequent false alarms. Some hospitals reported a benefit for patients after fine-tuning the model, but that process took at least a year. Unlock this article by subscribing to STAT and enjoy your first 30 days free!

Using AI for Digital Identification, Fraud Prevention, and Increased ROI


Technology's role in our everyday lives continues to increase exponentially. Although the shift to digital began before the pandemic, as we continue operating in a largely remote world, technology is increasingly used for daily tasks like shopping, banking, and even healthcare. When it comes to confirming identity, much is at stake for businesses and individuals, including potential time and material losses. The challenge companies are currently facing is how to confirm digital identities while maintaining a positive consumer experience. Since the start of COVID-19, 60 percent of consumers have higher expectations for their digital experience, although the added strain on providers as overall online transaction activities have also increased by 20 percent, according to a recent Experian Global Insights Report.

Challenges for Reinforcement Learning in Healthcare Artificial Intelligence

Many healthcare decisions involve navigating through a multitude of treatment options in a sequential and iterative manner to find an optimal treatment pathway with the goal of an optimal patient outcome. Such optimization problems may be amenable to reinforcement learning. A reinforcement learning agent could be trained to provide treatment recommendations for physicians, acting as a decision support tool. However, a number of difficulties arise when using RL beyond benchmark environments, such as specifying the reward function, choosing an appropriate state representation and evaluating the learned policy.

A scalable approach for developing clinical risk prediction applications in different hospitals Artificial Intelligence

Objective: Machine learning algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We provide a scalable solution to extend the process of clinical risk prediction model development of multiple diseases and their deployment in different Electronic Health Records (EHR) systems. Materials and Methods: We defined a generic process for clinical risk prediction model development. A calibration tool has been created to automate the model generation process. We applied the model calibration process at four hospitals, and generated risk prediction models for delirium, sepsis and acute kidney injury (AKI) respectively at each of these hospitals. Results: The delirium risk prediction models achieved area under the receiver-operating characteristic curve (AUROC) ranging from 0.82 to 0.95 over different stages of a hospital stay on the test datasets of the four hospitals. The sepsis models achieved AUROC ranging from 0.88 to 0.95, and the AKI models achieved AUROC ranging from 0.85 to 0.92. Discussion: The scalability discussed in this paper is based on building common data representations (syntactic interoperability) between EHRs stored in different hospitals. Semantic interoperability, a more challenging requirement that different EHRs share the same meaning of data, e.g. a same lab coding system, is not mandated with our approach. Conclusions: Our study describes a method to develop and deploy clinical risk prediction models in a scalable way. We demonstrate its feasibility by developing risk prediction models for three diseases across four hospitals.

"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment Artificial Intelligence

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system ("Brilliant Doctor") and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as "a doctor's AI assistant" to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries.

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.

Probabilistic Machine Learning for Healthcare Machine Learning

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.

Jason Ward: Technology can transform healthcare - but a culture change is also needed


Technology has proven to be a critical element of Ireland's healthcare sector in recent months. As the Covid-19 virus threat spread, healthcare professionals faced the unprecedented challenge of meeting the needs of those infected while also, where possible, maintaining distancing guidelines. There was also a need to continue to meet the healthcare needs of those with non-Covid related health challenges - often people requiring urgent or emergency care as well as those with long-term chronic illnesses. What we have seen, and applauded, over the past four months is the bravery and commitment of those working across the Irish healthcare system and, indeed, the world. These frontline workers never shied away from doing what was needed, and we thank them for that.