healthcare technology

Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized.

What is this study all about?

This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications.

The authors sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis.

The method adopted here was primarily literature review and analysis: The authors systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review.

The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences.

Conclusions: While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications.

This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. The findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations.

Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications.

read the unedited paper at https://mhealth.jmir.org/2024/1/e59587/

Electronics giant Samsung discussed innovations in robotics and wearable devices as key technologies for delivering personalised care. 

In the HIMSS24 APAC keynote, "Smart Hospital Revolution: Redefining Patient Care with Technology," Samsung Medical Centre CIO, Dr Won-Chul Cha, and Dr Hon Pak, SVP and head of Digital Health at Samsung Electronics, discussed the importance of technological integration in healthcare. 

Dr Cha likened a smart hospital digital system to a human organism, hosting circuities and neurological networks that integrate.     

Integrated technologies, for example, can facilitate risk identification, pattern detection, and personalised care, he said. 

Integrated devices can also digitally bridge at-home users with timely medical interventions.      

read the original unedited article at https://www.healthcareitnews.com/news/asia/opening-digital-front-door-healthcare

Rapidly evolving artificial intelligence (AI) is capable of structuralizing social, political, and economic determinants of health into the invisible algorithms that shape every facet of modern life.

AI could be a powerful public health tool, enabling beneficial objectives like precision public health and medicine.

Developing an AI governance framework that can maximize the benefits and minimize the risks of AI is a significant challenge. The benefits of public health engagement in AI governance could be extensive

In this paper, the authors describe how several public health concepts could enhance AI governance.  Public health theories, perspectives, and innovations could substantially enrich and improve AI governance, creating a more equitable and socially beneficial path for AI development.

read more at https://publichealth.jmir.org/2024/1/e58358/

The past several decades have seen an alarming spike in communicable disease outbreaks worldwide. Given a confluence of host, virologic, environmental, and human factors, experts agree that the next pandemic could already be on the horizon.

In a globalized world, changes in how people use land and interact with their ecosystems—such as rapid deforestation and agricultural expansion—have resulted in humans and animals coming into more frequent and intense contact with one another, increasing opportunities for what is known as "zoonotic disease spillover."

In the past few years alone, numerous disease outbreaks have had suspected or confirmed zoonotic origin, including mpox (formerly known as monkeypox), Ebola virus disease, dengue fever, and COVID-19.

Experts also recognize the need to prepare for another possible Disease X, a term used to describe a currently unknown pathogen with pandemic potential.

To direct resources toward the most high-consequence pathogens, it is paramount that leaders have an accurate concept of pandemic risk—for individual viruses as well as viral families. Several institutions are developing disease rankings at national and global levels, including the Priority Zoonotic Diseases Lists facilitated by the U.S. Centers for Disease Control and Prevention and the Research and Development (R&D) Blueprint created by the World Health Organization. 

The original SpillOver risk ranking framework (SpillOver 1.0), an open-source webtool launched by researchers at the University of California, Davis One Health Institute, estimated the relative spillover potential of wildlife-origin viruses to humans based on a series of host, viral, and environmental risk factors determined via expert opinion and scientific evidence. 

Its next iteration, SpillOvers 2.0, has rebranded to better describe the diversity and frequency of virus spillovers to people. The new platform uses a One Health approach, which recognizes the interdependence of human, animal, and environmental health. It will expand to include domestic animal and vector-borne viruses and assess pandemic risk rather than just spillover risk for wildlife viruses.

South Korea introduced a new health care IT system named the Narcotics Information Management System (NIMS) with the objective of managing all aspects of opioid use, including manufacturing, distribution, sales, disposal, etc.

The authors conducted an analysis using national claims data from 45,582 patients diagnosed with musculoskeletal and connective tissue disorders between 2016 and 2020.  The approach included using an interrupted time-series analysis and constructing segmented regression models. Within these models, the authors considered the primary intervention to be the implementation of NIMS, while we treated the COVID-19 outbreak as the secondary event.

To comprehensively assess inappropriate opioid use, we examined 4 key indicators, as established in previous studies:

(1) the proportion of patients on high-dose opioid treatment,

(2) the proportion of patients receiving opioid prescriptions from multiple providers,

(3) the overlap rate of opioid prescriptions per patient, and

(4) the naloxone use rate among opioid users.

This study suggests that, in its current form, the NIMS may not have brought significant improvements to the identified indicators of opioid overuse and misuse. Additionally, the COVID-19 outbreak exhibited no significant influence on opioid use patterns.

The absence of real-time monitoring feature within the NIMS could be a key contributing factor. Further exploration and enhancements are needed to maximize the NIMS’ impact on curbing inappropriate opioid use.

read the original at https://publichealth.jmir.org/2024/1/e47130/

The recent SARS-CoV-2 pandemic underscored the effectiveness and rapid deployment of digital public health interventions, notably the digital proximity tracing apps, leveraging Bluetooth capabilities to trace and notify users about potential infection exposures.

Digital proximity tracings showcased the promise of digital public health.

As the world pivots from pandemic responses, it becomes imperative to address noncommunicable diseases (NCDs) that account for a vast majority of health care expenses and premature disability-adjusted life years lost.

The narrative of digital transformation in the realm of NCD public health is distinct from infectious diseases.

The power of artificial intelligence (AI) in this digital transformation is noteworthy.

• AI can automate repetitive tasks, facilitating health care providers to prioritize personal interactions, especially those that cannot be digitalized like emotional support.
• Moreover, AI presents tools for individuals to be proactive in their health management. However, the human touch remains irreplaceable;
• AI serves as a companion guiding through the health care landscape.

Digital evolution, while revolutionary, poses its own set of challenges.

Issues of equity and access are at the forefront. Vulnerable populations, whether due to economic constraints, geographical barriers, or digital illiteracy, face the threat of being marginalized further.

This transformation mandates an inclusive strategy, focusing on not amplifying existing health disparities but eliminating them.

Population-level digital interventions in NCD prevention demand societal agreement.

Policies, like smoking bans or sugar taxes, though effective, might affect those not directly benefiting. Hence, all involved parties, from policy makers to the public, should have a balanced perspective on the advantages, risks, and expenses of these digital shifts.

For a successful digital shift in public health, especially concerning NCDs, AI’s potential to enhance efficiency, effectiveness, user experience, and equity—the “quadruple aim”—is undeniable.

However, it is vital that AI-driven initiatives in public health domains remain purposeful, offering improvements without compromising other objectives.

The broader success of digital public health hinges on transparent benchmarks and criteria, ensuring maximum benefits without sidelining minorities or vulnerable groups.

Especially in population-centric decisions, like resource allocation, AI’s ability to avoid bias is paramount.

Therefore, the continuous involvement of stakeholders, including patients and minority groups, remains pivotal in the progression of AI-integrated digital public health.

read the original paper at https://publichealth.jmir.org/2024/1/e49575/

Enabling bi-directional APIs is one way to offer speed, efficiency and security while preserving the most necessary components of human intervention. These closed-loop data retrieval processes will shape the future of ROI in healthcare for higher quality and faster intake and fulfillment.

Bi-directional APIs give healthcare providers maximum data visibility and control. Here’s how they’re shaping the future of release of information.

In recent years, a fire has been lit under healthcare’s use of application programming interfaces (APIs). Actually, it has been a FHIR [Fast Healthcare Interoperability Resources] as regulations encourage electronic medical record (EMR) vendors to continue to build its standards into their systems, expanding functionality and improving usability. But that does not mean the road to digitization and interoperability has been seamless, particularly as it relates to release of information (ROI).

more at https://www.healthcareittoday.com/2021/07/22/healthcare-apis-a-two-way-street/

Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge.

Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients’ behavior.

Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk.

This review aims to present the most-used visualization techniques to estimate CVD risk.

Results:

The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies.

Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing–related results.

The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%).

The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%).

On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk.

Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health’s effectiveness in improving CVD outcomes is limited.

read the unedited research paper at https://publichealth.jmir.org/2024/1/e60128/

WellSpan Health is partnering with Hippocratic AI to launch the first patient-facing clinical GenAI healthcare agent.

– The conversational AI-powered service will directly interact with patients, enhancing healthcare access, equity, and outcomes.

The GenAI agent engages patients in comprehensive and empathetic conversations about their colorectal health, providing detailed transcripts to WellSpan clinicians for review. In some cases, the agent can transfer patients to a human clinician or facilitate follow-up calls.

Future GenAI agents are being developed to support critical workflows like chronic care management, post-discharge follow-up for specific conditions, wellness surveys, health risk assessments, and pre-operative instructions.

read the original unedited article at https://hitconsultant.net/2024/09/26/wellspan-health-launches-first-patient-facing-genai-healthcare-agent/

A new way of diagnosing lung cancer with a blood draw is 10 times faster and 14 times more sensitive than earlier methods, researchers report.

The microchip the researchers developed captures exosomes—tiny packages released by cells—from blood plasma to identify signs of lung cancer.

Once thought to be trash ejected from cells for cleanup, researchers discovered in the past decade that exosomes are tiny parcels containing proteins or DNA and RNA fragments that are valuable for communication between cells.

Although healthy cell exosomes move important signals throughout the body, cancer cell exosomes can help tumors spread by preparing tissues to accept tumor cells before they arrive.

more at https://www.futurity.org/blood-draw-lung-cancer-diagnosis-3252152/

As we progress deeper into the digital age, the robust development and application of advanced artificial intelligence (AI) technology, specifically generative language models like ChatGPT (OpenAI), have potential implications in all sectors including medicine.

ChatGPT, a generative language model launched by OpenAI in November 2022 has essentially has essentially revolutionized the IT world.. What makes ChatGPT a promising tool is the vast amounts of data used in its training and its ability to generate human-like conversations covering diverse topics.

The unprecedented capacity of ChatGPT to generate human-like responses, refined through Reinforcement Learning with Human Feedback, could significantly reshape the pedagogical methodologies within medical education.

The use of AI in medicine, including the use of generative language models, is often accompanied by challenges and contentions. Some common challenges include privacy, data security, algorithmic transparency and explainability, errors and liability, as well as regulatory issues associated with AI medicine.

Lately, the use of generative language models in scientific writing has also stirred up controversies in the academic and publishing communities. Some journals have declined ChatGPT as a coauthor, whereas others have happily accepted manuscripts authored by ChatGPT

Through a comprehensive review and the authors’ personal experiences, this viewpoint article elucidates the pros, cons, and ethical considerations of using ChatGPT within clinical medicine and notably, its implications for medical education.

This exploration is crucial in a transformative era where AI could potentially augment human capability in the process of knowledge creation and dissemination, potentially revolutionizing medical education and clinical practice.

The importance of maintaining academic integrity and professional standards is highlighted. The relevance of establishing clear guidelines for the responsible and ethical use of AI technologies in clinical medicine and medical education is also emphasized.

read the whole article at https://mededu.jmir.org/2023/1/e47274

The world of healthcare is ever-evolving. We are learning and growing every day, which means we are also coming up with new ideas, technologies, and practices every day.

what emerging technologies in Health IT are currently shaping the landscape,

and how do they contribute to improved patient outcomes and healthcare delivery?

This Healthcare IT today's article has a collection of opinions from people from the ever growing digital health community.

includes thoughts like

- proactive patient communication and automated text updates for patient approved people

- using adaptive AI for patient communication

- using AI in billing to bring about a fairness to costs

- using ambient documentation to improve patient provider connections in the presence of an EHR

- use of digital therapeutics

amongst others

read the article at

https://www.healthcareittoday.com/2024/02/12/emerging-technologies-shaping-the-health-it-landscape-and-how-they-contribute-to-improving-patient-outcomes-and-healthcare-delivery/

Scientists propose a modeling framework that could predict how antibiotic resistance will evolve in response to different drug combinations.

A new framework may suggest which drug combinations would speed up, slow down, or even reverse antibiotic resistance.

The research could help doctors optimize the choice, timing, dose, and sequence of antibiotics used to treat common infections in order to help halt the growing threat of antibiotic resistance to modern medicine.

“Drug combinations are a particularly promising approach for slowing resistance, but the evolutionary impacts of combination therapy remain difficult to predict, especially in a clinical setting,” says Erida Gjini, a researcher at the University of Lisbon, Portugal, and first author of the paper in eLife.

read more at https://www.futurity.org/antibiotic-resistance-drug-combinations-2605182-2/

Silicone wristbands are just as good as traditional testing methods at detecting chemicals in the air that can be harmful during pregnancy.

Inexpensive, convenient silicone wristbands can measure exposure to a class of chemicals that can be harmful during pregnancy, researchers report.

The researchers found that the wristbands, when used as passive samplers, have the ability to bind smaller molecular weight semi-volatile polycyclic aromatic hydrocarbons (PAHs) in a similar pattern as active sampling. PAHs are a class of chemicals that occur naturally in coal, crude oil, and gasoline and are produced when coal, oil, gas, wood, garbage, and tobacco are burned.

The use of wristbands is appealing because it is inexpensive and easy to wear,”

access the study at https://doi.org/10.1038/s41370-021-00348-y

read the original unedited article at https://www.futurity.org/silicone-wristbands-polycyclic-aromatic-hydrocarbons-chemicals-pregnancy-2602202/