AI Is Building Highly Effective Antibodies That Humans Can’t Even Imagine

Curated on Posted on by Stefaan Verhulst

Article by Amit Katwala: “Robots, computers, and algorithms are hunting for potential new therapies in ways humans can’t—by processing huge volumes of data and building previously unimagined molecules. At an old biscuit factory in South London, giant mixers and industrial ovens have been replaced by robotic arms, incubators, and DNA sequencing machines.

James Field and his company LabGenius aren’t making sweet treats; they’re cooking up a revolutionary, AI-powered approach to engineering new medical antibodies. In nature, antibodies are the body’s response to disease and serve as the immune system’s front-line troops. They’re strands of protein that are specially shaped to stick to foreign invaders so that they can be flushed from the system. Since the 1980s, pharmaceutical companies have been making synthetic antibodies to treat diseases like cancer, and to reduce the chance of transplanted organs being rejected. But designing these antibodies is a slow process for humans—protein designers must wade through the millions of potential combinations of amino acids to find the ones that will fold together in exactly the right way, and then test them all experimentally, tweaking some variables to improve some characteristics of the treatment while hoping that doesn’t make it worse in other ways. “If you want to create a new therapeutic antibody, somewhere in this infinite space of potential molecules sits the molecule you want to find,” says Field, the founder and CEO of LabGenius…(More)”.

Whatever Happened to All Those Care Robots?

Curated on Posted on by Stefaan Verhulst

Article by Stephanie H. Murray: “So far, companion robots haven’t lived up to the hype—and might even exacerbate the problems they’re meant to solve…There are likely many reasons that the long-predicted robot takeover of elder care has yet to take off. Robots are expensive, and cash-strapped care homes don’t have money lying around to purchase a robot, let alone to pay for the training needed to actually use one effectively. And at least so far, social robots just aren’t worth the investment, Wright told me. Pepper can’t do a lot of the things people claimed he could—and he relies heavily on humans to help him do what he can. Despite some research suggesting they can boost well-being among the elderly, robots have shown little evidence that they make life easier for human caregivers. In fact, they require quite a bit of care themselves. Perhaps robots of the future will revolutionize caregiving as hoped. But the care robots we have now don’t even come close, and might even exacerbate the problems they’re meant to solve…(More)”.

Using online search activity for earlier detection of gynaecological malignancy

Curated on Posted on by Stefaan Verhulst

Paper by Jennifer F. Barcroft et al: Ovarian cancer is the most lethal and endometrial cancer the most common gynaecological cancer in the UK, yet neither have a screening program in place to facilitate early disease detection. The aim is to evaluate whether online search data can be used to differentiate between individuals with malignant and benign gynaecological diagnoses.

This is a prospective cohort study evaluating online search data in symptomatic individuals (Google user) referred from primary care (GP) with a suspected cancer to a London Hospital (UK) between December 2020 and June 2022. Informed written consent was obtained and online search data was extracted via Google takeout and anonymised. A health filter was applied to extract health-related terms for 24 months prior to GP referral. A predictive model (outcome: malignancy) was developed using (1) search queries (terms model) and (2) categorised search queries (categories model). Area under the ROC curve (AUC) was used to evaluate model performance. 844 women were approached, 652 were eligible to participate and 392 were recruited. Of those recruited, 108 did not complete enrollment, 12 withdrew and 37 were excluded as they did not track Google searches or had an empty search history, leaving a cohort of 235.s

The cohort had a median age of 53 years old (range 20–81) and a malignancy rate of 26.0%. There was a difference in online search data between those with a benign and malignant diagnosis, noted as early as 360 days in advance of GP referral, when search queries were used directly, but only 60 days in advance, when queries were divided into health categories. A model using online search data from patients (n = 153) who performed health-related search and corrected for sample size, achieved its highest sample-corrected AUC of 0.82, 60 days prior to GP referral.

Online search data appears to be different between individuals with malignant and benign gynaecological conditions, with a signal observed in advance of GP referral date. Online search data needs to be evaluated in a larger dataset to determine its value as an early disease detection tool and whether its use leads to improved clinical outcomes…(More)”.

Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic

Curated on Posted on by Stefaan Verhulst

Paper by Kaylin Bolt et al: “The COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy….

Non-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity…(More)”.

Why Everyone Hates The Electronic Medical Record

Curated on Posted on by Stefaan Verhulst

Article by Dharushana Muthulingam: “Patient R was in a hurry. I signed into my computer—or tried to. Recently, IT had us update to a new 14-digit password. Once in, I signed (different password) into the electronic medical record. I had already ordered routine lab tests, but R had new info. I pulled up a menu to add on an additional HIV viral load to capture early infection, which the standard antibody test might miss. R went to the lab to get his blood drawn

My last order did not print to the onsite laboratory. An observant nurse had seen the order and no tube. The patient had left without the viral load being drawn. I called the patient: could he come back? 

 Healthcare workers do not like the electronic health record (EHR), where they spend more time than with patients. Doctors hate it, as do nurse practitioners, nurses, pharmacists, and physical therapists. The National Academies of Science, Engineering and Medicine reports the EHR is a major contributor to clinician burnout. Patient experience is mixed, though the public is still concerned about privacy, errors, interoperability and access to their own records.

The EHR promised a lot: better accuracy, streamlined care, and patient-accessible records. In February 2009, the Obama administration passed the HITECH Act on this promise, investing $36 billion to scale up health information technology. No more deciphering bad handwriting for critical info. Efficiency and cost-savings could get more people into care. We imagined cancer and rare disease registries to research treatments. We wanted portable records accessible in an emergency. We wanted to rapidly identify the spread of highly contagious respiratory illnesses and other public health crises.

Why had the lofty ambition of health information, backed by enormous resources, failed so spectacularly?…(More)”.

Citizen Engagement in Evidence-informed Policy-making: A Guide to Mini-publics

Curated on Posted on by Stefaan Verhulst

Report by WHO: “This guide focuses on a specific form of citizen engagement, namely mini-publics, and their potential to be adapted to a variety of contexts. Mini-publics are forums that include a cross-section of the population selected through civic lottery to participate in evidence-informed deliberation to inform policy and action. The term refers to a diverse set of democratic innovations to engage citizens in policy-making. This guide provides an overview of how to organize mini-publics in the health sector. It is a practical companion to the 2022 Overview report, Implementing citizen engagement within evidence-informed policy-making. Both documents examine and encourage contributions that citizens can make to advance WHO’s mission to achieve universal health coverage…(More)””

How Mental Health Apps Are Handling Personal Information

Curated on Posted on by Stefaan Verhulst

Article by Erika Solis: “…Before diving into the privacy policies of mental health apps, it’s necessary to distinguish between “personal information” and “sensitive information,” which are both collected by such apps. Personal information can be defined as information that is “used to distinguish or trace an individual’s identity.” Sensitive information, however, can be any data that, if lost, misused, or illegally modified, may negatively affect an individual’s privacy rights. While health information not under HIPAA has previously been treated as general personal information, states like Washington are implementing strong legislation that will cover a wide range of health data as sensitive, and have attendant stricter guidelines.

Legislation addressing the treatment of personal information and sensitive information varies around the world. Regulations like the General Data Protection Regulation (GDPR) in the EU, for example, require all types of personal information to be treated as being of equal importance, with certain special categories, including health data having slightly elevated levels of protection. Meanwhile, U.S. federal laws are limited in addressing applicable protections of information provided to a third party, so mental health app companies based in the United States can approach personal information in all sorts of ways. For instance, Mindspa, an app with chatbots that are only intended to be used when a user is experiencing an emergency, and Elomia, a mental health app that’s meant to be used at any time, don’t make distinctions between these contexts in their privacy policies. They also don’t distinguish between the potentially different levels of sensitivity associated with ordinary and crisis use.

Wysa, on the other hand, clearly indicates how it protects personal information. Making a distinction between personal and sensitive data, its privacy policy notes that all health-based information receives additional protection. Similarly, Limbic labels everything as personal information but notes that data, including health, genetic, and biometric, fall within a “special category” that requires more explicit consent than other personal information collected to be used…(More)”.

Are Evidence-Based Medicine and Public Health Incompatible?

Curated on Posted on by Stefaan Verhulst

Essay by Michael Schulson: “It’s a familiar pandemic story: In September 2020, Angela McLean and John Edmunds found themselves sitting in the same Zoom meeting, listening to a discussion they didn’t like.

At some point during the meeting, McLean — professor of mathematical biology at the Oxford University, dame commander of the Order of the British Empire, fellow of the Royal Society of London, and then-chief scientific adviser to the United Kingdom’s Ministry of Defense — sent Edmunds a message on WhatsApp.

“Who is this fuckwitt?” she asked.

The message was evidently referring to Carl Heneghan, director of the Center for Evidence-Based Medicine at Oxford. He was on Zoom that day, along with McLean and Edmunds and two other experts, to advise the British prime minister on the Covid-19 pandemic.

Their disagreement — recently made public as part of a British government inquiry into the Covid-19 response — is one small chapter in a long-running clash between two schools of thought within the world of health care.

McLean and Edmunds are experts in infectious disease modeling; they build elaborate simulations of pandemics, which they use to predict how infections will spread and how best to slow them down. Often, during the Covid-19 pandemic, such models were used alongside other forms of evidence to urge more restrictions to slow the spread of the disease. Heneghan, meanwhile, is a prominent figure in the world of evidence-based medicine, or EBM. The movement aims to help doctors draw on the best available evidence when making decisions and advising patients. Over the past 30 years, EBM has transformed the practice of medicine worldwide.

Whether it can transform the practice of public health — which focuses not on individuals, but on keeping the broader community healthy — is a thornier question…(More)”.

Community views on the secondary use of general practice data: Findings from a mixed-methods study

Curated on Posted on by Stefaan Verhulst

Paper by Annette J. Braunack-Mayer et al: “General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance, evaluation, health service planning and research.Using general practice data is particularly important in countries where general practitioners (GPs) are the first and principal source of health care for most people.

Although there is broad public support for the secondary use of health data, there are good reasons to question whether this support extends to general practice settings. GP–patient relationships may be very personal and longstanding and the general practice health record can capture a large amount of information about patients. There is also the potential for multiple angles on patients’ lives: GPs often care for, or at least record information about, more than one generation of a family. These factors combine to amplify patients’ and GPs’ concerns about sharing patient data….

Adams et al. have developed a model of social licence, specifically in the context of sharing administrative data for health research, based on an analysis of the social licence literature and founded on two principal elements: trust and legitimacy.In this model, trust is founded on research enterprises being perceived as reliable and responsive, including in relation to privacy and security of information, and having regard to the community’s interests and well-being.

Transparency and accountability measures may be used to demonstrate trustworthiness and, as a consequence, to generate trust. Transparency involves a level of openness about the way data are handled and used as well as about the nature and outcomes of the research. Adams et al. note that lack of transparency can undermine trust. They also note that the quality of public engagement is important and that simply providing information is not sufficient. While this is one element of transparency, other elements such as accountability and collaboration are also part of the trusting, reflexive relationship necessary to establish and support social licence.

The second principal element, legitimacy, is founded on research enterprises conforming to the legal, cultural and social norms of society and, again, acting in the best interests of the community. In diverse communities with a range of views and interests, it is necessary to develop a broad consensus on what amounts to the common good through deliberative and collaborative processes.

Social licence cannot be assumed. It must be built through public discussion and engagement to avoid undermining the relationship of trust with health care providers and confidence in the confidentiality of health information…(More)”

How Health Data Integrity Can Earn Trust and Advance Health

Curated on Posted on by Stefaan Verhulst

Article by Jochen Lennerz, Nick Schneider and Karl Lauterbach: “Efforts to share health data across borders snag on legal and regulatory barriers. Before detangling the fine print, let’s agree on overarching principles.

Imagine a scenario in which Mary, an individual with a rare disease, has agreed to share her medical records for a research project aimed at finding better treatments for genetic disorders. Mary’s consent is grounded in trust that her data will be handled with the utmost care, protected from unauthorized access, and used according to her wishes. 

It may sound simple, but meeting these standards comes with myriad complications. Whose job is it to weigh the risk that Mary might be reidentified, even if her information is de-identified and stored securely? How should that assessment be done? How can data from Mary’s records be aggregated with patients from health systems in other countries, each with their own requirements for data protection and formats for record keeping? How can Mary’s wishes be respected, both in terms of what research is conducted and in returning relevant results to her?

From electronic medical records to genomic sequencing, health care providers and researchers now have an unprecedented wealth of information that could help tailor treatments to individual needs, revolutionize understanding of disease, and enhance the overall quality of health care. Data protection, privacy safeguards, and cybersecurity are all paramount for safeguarding sensitive medical information, but much of the potential that lies in this abundance of data is being lost because well-intentioned regulations have not been set up to allow for data sharing and collaboration. This stymies efforts to study rare diseases, map disease patterns, improve public health surveillance, and advance evidence-based policymaking (for instance, by comparing effectiveness of interventions across regions and demographics). Projects that could excel with enough data get bogged down in bureaucracy and uncertainty. For example, Germany now has strict data protection laws—with heavy punishment for violations—that should allow de-identified health insurance claims to be used for research within secure processing environments, but the legality of such use has been challenged…(More)”.