The digital transformation of healthcare is contributing to Leroy Hood's vision of P4 medicine . Using the car analogy, in which car maintenance has been transformed from dealing with symptoms to ide...
Quantifying your body: A how‐to guide from a systems biology perspective
The digital transformation of healthcare is contributing to Leroy Hood's vision of P4 medicine . Using the car analogy, in which car maintenance has been transformed from dealing with symptoms to ide...
Your Quantified Body, Your Quantified Self | WNYC
What happens when we start thinking of ourselves as walking, breathing, calorie-consuming piles of data? We asked hundreds of people to weigh in (figuratively speaking).
The Medium is the Message: How Non-Clinical Information Shapes Clinical Decisions in LLMs | Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
There has been a growing interest in the HCI community to study Health, with particular focus in understanding healthcare practices and designing technologies to support and to enhance these practices. A majority of current health studies in HCI have ...
Why Doctors Hate Their Computers
Digitization promises to make medical care easier and more efficient. But are screens coming between doctors and patients?
Opinion | Educate Your Immune System (Published 2016)
Our bodies are confused by this 21st-century world.
Evaluation framework for systems models
Abstract As decisions in drug development increasingly rely on predictions from mechanistic systems models, assessing the predictive capability of such models is becoming more important. Several frameworks for the development of quantitative systems pharmacology (QSP) models have been proposed. In this paper, we add to this body of work with a framework that focuses on the appropriate use of qualitative and quantitative model evaluation methods. We provide details and references for those wishing to apply these methods, which include sensitivity and identifiability analyses, as well as concepts such as validation and uncertainty quantification. Many of these methods have been used successfully in other fields, but are not as common in QSP modeling. We illustrate how to apply these methods to evaluate QSP models, and propose methods to use in two case studies. We also share examples of misleading results when inappropriate analyses are used.
Quantified self
Quantified self is both the cultural phenomenon of self-tracking with technology and a community of users and makers of self-tracking tools who share an interest in "self-knowledge through numbers". Quantified self practices overlap with the practice of lifelogging and other trends that incorporate technology and data acquisition into daily life, often with the goal of improving physical, mental, and emotional performance. The widespread adoption in recent years of wearable fitness and sleep trackers such as the Fitbit or the Apple Watch, combined with the increased presence of Internet of things in healthcare and in exercise equipment, have made self-tracking accessible to a large segment of the population.
The more we rely on tech, the more we deskill our bodies | Aeon Essays
We are, moment by moment, doing less sensing, navigating, remembering, walking, choosing, and attending
The more we rely on tech, the more we deskill our bodies | Aeon Essays
We are, moment by moment, doing less sensing, navigating, remembering, walking, choosing, and attending
Meta’s New AI Asked for My Raw Health Data—and Gave Me Terrible Advice
Meta’s Muse Spark model offers to analyze users’ health data, including lab results. Beyond the obvious privacy risks, it’s not a capable stand-in for a real doctor.
Preparing Physicians for the Clinical Algorithm Era
The U.S. government recently took steps to ensure that clinical decision support algorithms are safe for clinical use. The next and larger step will be teaching physicians how to use the algorithms ...
Preparing Physicians for the Clinical Algorithm Era
The U.S. government recently took steps to ensure that clinical decision support algorithms are safe for clinical use. The next and larger step will be teaching physicians how to use the algorithms ...
We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change… | Samuel G. Rodriques | 491 comments
We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change that. Kosmos is now the first agent that can compress months of drug development into weeks, from the earliest stages of scientific discovery through to FDA approval. Incyte will be the first company to deploy it across their pipeline. Work that used to take a team of scientists months now happens in weeks. Patients can't wait, and neither can we. | 491 comments on LinkedIn
Fact Sheet 3: ME/CFS: Information for Medical Professionals
Fact Sheet 3: ME/CFS: Information for Medical Professionals Published December 2025 Link to pdf: ME/CFS: Information for Medical Professionals.pdf Discussion thread: Fact sheet #3: Information...
Implementing an online pharmaceutical service using design science research
The rising prevalence of chronic diseases is pressing health systems to introduce reforms. Primary healthcare and multidisciplinary models have been suggested as approaches to deal with this challenge, with new roles for nurses and pharmacists being advocated. More recently, implementing healthcare based on information systems and technologies (e.g. eHealth) has been proposed as a way to improve health services. However, implementing online pharmaceutical services, including their adoption by pharmacists and patients, is still an open research question. In this paper we present ePharmacare, a new online pharmaceutical service implemented using Design Science Research.
A model qualification method for mechanistic physiological QSP models to support model‐informed drug development
Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision‐making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug‐specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.
