Healthcare Agents Research Articles

Cytotoxic Effects of Biosynthesized zinc nanoparticles on Normal fibroblast cells and their antimicrobial effect on pathogenic strains

Nanoparticles, particularly zinc nanoparticles (ZnNPs), have garnered substantial interest in biomedical research for their potential applications in therapeutics and antimicrobial therapy. In this study, we investigated the cytotoxic effects of biosynthesized zinc nanoparticles on normal fibroblast cells and evaluated their antimicrobial efficacy against pathogenic strains. The biosynthesis of zinc nanoparticles was achieved through an eco-friendly and cost-effective approach utilizing biological agents. The cytotoxicity of biosynthesized zinc nanoparticles was assessed on normal fibroblast cells, serving as a model for evaluating biocompatibility. Our results indicate that biosynthesized zinc nanoparticles exhibited minimal cytotoxicity towards normal fibroblast cells, suggesting their potential safety for biomedical applications. Furthermore, the antimicrobial activity of biosynthesized zinc nanoparticles was evaluated against pathogenic strains, including antibiotic-resistant bacteria. The antimicrobial efficacy of zinc nanoparticles demonstrated promising results, highlighting their potential as alternative antimicrobial agents. Overall, this study provides valuable insights into the cytotoxic effects of biosynthesized zinc nanoparticles on normal fibroblast cells and underscores their antimicrobial potential against pathogenic strains, offering opportunities for the development of nanoparticle-based therapeutics and antimicrobial agents in medicine and healthcare.

Impact of community-based multidomain preventive health program on healthcare utilisation among elderly persons in Singapore: a propensity score matched and difference-in-differences study

ObjectivesAgeing populations present substantial challenges for healthcare systems. Community-based health worker (CHW) interventions for promoting healthy ageing and preventing/managing non-communicable diseases have gained considerable attention in recent years. This study...

Intelligent Tutoring Systems, Generative Artificial Intelligence (AI), and Healthcare Agents: A Proof of Concept and Dual-Layer Approach.

This study introduces a novel methodology for enhancing intelligent tutoring systems (ITS)through the integration of generative artificial intelligence (GenAI) and specialized AIagents. We present a proof of concept (PoC) demo that implements a dual-layer GenAI validation approach that utilizes multiple large language models to ensure the reliability and pedagogical integrity of the AI-generated content. The system features role-specific AI agents, a GenAI-powered scoring mechanism, and an AI mentor that provides periodic guidance. This approach demonstrates capabilities in dynamic scenario generation and real-time adaptability while addressing key challenges in AI-driven education, such as personalization, scalability, and domain-specific knowledge integration. Although exemplified here through a case study in healthcare root cause analysis, the methodology is designed for broad applicability across various fields. Our findings suggest that this approach has significant potential for advancing adaptive learning and personalized instruction while raising important considerations regarding ethical AI application in education. This work provides a foundation for further research into the efficacy and impact of GenAI-enhanced ITS on learning outcomes and instructional design across diverse educational domains.

A Quality Approach to Blinatumomab Delivery in Pediatric Oncology: A Children's Oncology Group Study.

Background: Blinatumomab is a promising immunotherapy agent that has been shown to improve survival outcomes in children diagnosed with relapsed B-lineage acute lymphoblastic leukemia. Expanded use of blinatumomab in the treatment of childhood cancer is expected; however, clinician perspectives regarding administering this agent in healthcare and home settings have not been explored. Method: Semistructured interviews were conducted with clinicians (N = 13) from pediatric institutions across the United States and Canada. Qualitative data were analyzed using a constant comparative analysis three-stage method. Results: Participants were primarily nurses (92%), female (77%), and had greater than 10 years of pediatric oncology experience. The selective code and overarching theme identified was "A quality approach to blinatumomab delivery in pediatric oncology." Clinicians described detailed processes that were created and implemented to promote a common goal of safe blinatumomab administration across the continuum of care. Clinicians shared how they engaged in planning activities and considered a variety of factors prior to and during blinatumomab administration. Clinicians also expressed a need to reflect and evaluate on previous patient experiences to create new or revise existing processes and workflows. Communication was also central to clinicians' work. Clinicians provided recommendations to assist others with blinatumomab administration and offered suggestions for items that could help with implementing future clinical trials containing similar agents. Discussion: Findings suggest nurses are pivotal to establishing processes which support safe administration of immunotherapies, such as blinatumomab, while also considering patient-specific needs and promoting quality of life.

Biobased Polybutyrolactam Nanofiber with Excellent Biodegradability and Cell Growth for Sustainable Healthcare Textiles.

The white pollution caused by unsustainable materials is a significant challenge around the globe. Here, a novel and fully biobased polybutyrolactam (PBY) nanofiber membrane was fabricated via the electrospinning method. As-spun PBY nanofiber membranes have good thermal stability, high porosity of up to 71.94%, and excellent wetting behavior. The biodegradability in soil, UV aging irradiation, and seawater was investigated. The PBY nanofiber membrane is almost completely degraded in the soil within 80 days, showing excellent degradability. More interestingly, γ-aminobutyric acid, as a healthcare agent with intrinsic hypotensive, tranquilizing, diuretic, and antidiabetic efficacy, can be detected in the degradation intermediates. In addition, the PBY nanofiber membrane also exhibits antibacterial ability against Escherichia coli. As a fully biomass-derived material, the PBY membrane has excellent biodegradable performance in various environments as well as negligible cytotoxicity and commendable cell proliferation. Our PBY nanofiber membrane shows great potential as biodegradable packaging and in vitro healthcare materials.

Older adults as users of conversational agents in health and social care: Designers' and developers' motivations and images of ageing

Older adults as users of conversational agents in health and social care: Designers' and developers' motivations and images of ageing

Towards Reliable Healthcare LLM Agents: A Case Study for Pilgrims during Hajj

There is a pressing need for healthcare conversational agents with domain-specific expertise to ensure the provision of accurate and reliable information tailored to specific medical contexts. Moreover, there is a notable gap in research ensuring the credibility and trustworthiness of the information provided by these healthcare agents, particularly in critical scenarios such as medical emergencies. Pilgrims come from diverse cultural and linguistic backgrounds, often facing difficulties in accessing medical advice and information. Establishing an AI-powered multilingual chatbot can bridge this gap by providing readily available medical guidance and support, contributing to the well-being and safety of pilgrims. In this paper, we present a comprehensive methodology aimed at enhancing the reliability and efficacy of healthcare conversational agents, with a specific focus on addressing the needs of Hajj pilgrims. Our approach leverages domain-specific fine-tuning techniques on a large language model, alongside synthetic data augmentation strategies, to optimize performance in delivering contextually relevant healthcare information by introducing the HajjHealthQA dataset. Additionally, we employ a retrieval-augmented generation (RAG) module as a crucial component to validate uncertain generated responses, which improves model performance by 5%. Moreover, we train a secondary AI agent on a well-known health fact-checking dataset and use it to validate medical information in the generated responses. Our approach significantly elevates the chatbot’s accuracy, demonstrating its adaptability to a wide range of pilgrim queries. We evaluate the chatbot’s performance using quantitative and qualitative metrics, highlighting its proficiency in generating accurate responses and achieve competitive results compared to state-of-the-art models, in addition to mitigating the risk of misinformation and providing users with trustworthy health information.

Implementation of standardized electronic documentation of goals of care discussions to improve cancer care.

11127 Background: Early documentation of physician-led goals of care (GOC) discussions is important to providing comprehensive and individualized quality care to cancer patients as it can provide all members of the patient care team with a context for the type of care each patient wishes to receive prior to and at end of life. The electronic medical record (EMR) should provide easy access to these conversations to seamlessly integrate outpatient, emergency room and inpatient care of cancer patients. Methods: The institution-wide initiative was introduced via presentations and emails between 8/2021 and 11/2021 to all Moores Cancer Center faculty at UC San Diego Health. A SmartPhrase was created in our EMR Epic as the intervention, including critical components of a GOC discussion: people present during discussion, goal of cancer-directed treatment, anticipated cancer trajectory shared with the patient, and patient’s health care agent. The SmartPhrase was radiolabeled to be easily searchable for any clinician accessing the patient’s chart. Physician documentation was tracked for the purposes of achieving a group goal with a target of greater than 50% documentation of GOC discussions within 30 days of the first day of the first cycle of chemotherapy. Prior to physician education, 4% of patients had a GOC note using a searchable SmartPhrase. In 12/2021, we began sending individual emails to physicians to remind them of eligible patients. Results: By 1/2022, after education, 50% of physicians ordering chemotherapy had used the GOC SmartPhrase and 29% of patients initiating chemotherapy had a GOC note using the SmartPhrase. After initiation of monthly reminder emails, documentation increased to 51% by 10/2022. An increased use of Advanced Care Planning billing codes was also noted. Conclusions: Implementation of an initiative to standardize electronic documentation of GOC conversations improved early documentation of these vital conversations. Radiolabeling of the SmartPhrase made it easily accessible to all members of a patient’s care team.

Urban people’s preferences for life-sustaining treatment or artificial nutrition and hydration in advance decisions

BackgroundThe Patient Right to Autonomy Act (PRAA), implemented in Taiwan in 2019, enables the creation of advance decisions (AD) through advance care planning (ACP). This legal framework allows for the withholding and withdrawal of life-sustaining treatment (LST) or artificial nutrition and hydration (ANH) in situations like irreversible coma, vegetative state, severe dementia, or unbearable pain. This study aims to investigate preferences for LST or ANH across various clinical conditions, variations in participant preferences, and factors influencing these preferences among urban residents.MethodsEmploying a survey of legally structured AD documents and convenience sampling for data collection, individuals were enlisted from Taipei City Hospital, serving as the primary trial and demonstration facility for ACP in Taiwan since the commencement of the PRAA in its inaugural year. The study examined ADs and ACP consultation records, documenting gender, age, welfare entitlement, disease conditions, family caregiving experience, location of ACP consultation, participation of second-degree relatives, and the intention to participate in ACP.ResultsData from 2337 participants were extracted from electronic records. There was high consistency in the willingness to refuse LST and ANH, with significant differences noted between terminal diseases and extremely severe dementia. Additionally, ANH was widely accepted as a time-limited treatment, and there was a prevalent trend of authorizing a health care agent (HCA) to make decisions on behalf of participants. Gender differences were observed, with females more inclined to decline LST and ANH, while males tended towards accepting full or time-limited treatment. Age also played a role, with younger participants more open to treatment and authorizing HCA, and older participants more prone to refusal.ConclusionDiverse preferences in LST and ANH were shaped by the public’s current understanding of different clinical states, gender, age, and cultural factors. Our study reveals nuanced end-of-life preferences, evolving ADs, and socio-demographic influences. Further research could explore evolving preferences over time and healthcare professionals’ perspectives on LST and ANH decisions for neurological patients..

Chemical composition and antimicrobial activity of an apolar extract from Lactuca serriola L. leaves

Lactuca serriola L. is an annual herbaceous plant species belonging to the Asteraceae family, called Prickly lettuce, Wild lettuce, and Jagged lettuce. L. serriola grows on grassy and rocky slopes in many regions in Europe, Asia, Australia, Africa, and North, Central and South America. The plant is used as a natural pharmaceutical agent in primary health care due to its analgesic, anti-inflammatory, antibacterial, and antioxidant properties, which are attributed to the compounds it contains. However, this is the first study demonstrating the antimicrobial activity of L. serriola essential oil. In this study, L. serriola essential oil was obtained by hydrodistillation of plant leaves and analyzed by Gas Chromatography-Mass Spectrometry (GS-MS); subsequently, antimicrobial properties of the essential oil were evaluated using agar dilution and broth microdilution methods. As a result of the GS-MS analysis, hexadecanoic acid, oleic acid, myristic acid, hexahydrofarnesyl acetone, β-ionone, and n-tetradecyl butanoate were found as dominant components in the plant essential oil with the rate of 6.61%, 4.84%, 2.05, 1.55%, 1.3%, and 1.09% (v/v), respectively. The MIC value of L. serriola essential oil against Enterobacter aerogenes,Staphylococcus aureus, Pseudomonas aeruginosa, Candida albicans, and Candida tropicalis was found to be 0.94 μLmL−1, and MIC values were determined as 1.87 μLmL−1 and 0.47 μLmL−1 against Klebsiella pneumonia and Candidaparapsilosis, respectively. These results have opened a new horizon regarding the usability of L. serriola essential oil as a pharmacological therapeutic antibacterial and antifungal agent.

Big Data-Planetary Health approach for evaluating the Brazilian Dengue Control Program.

This study aims to integrate the concepts of planetary health and big data into the Donabedian model to evaluate the Brazilian dengue control program in the state of São Paulo. Data science methods were used to integrate and analyze dengue-related data, adding context to the structure and outcome components of the Donabedian model. This data, considering the period from 2010 to 2019, was collected from sources such as Department of Informatics of the Unified Health System (DATASUS), the Brazilian Institute of Geography and Statistics (IBGE), WorldClim, and MapBiomas. These data were integrated into a Data Warehouse. K-means algorithm was used to identify groups with similar contexts. Then, statistical analyses and spatial visualizations of the groups were performed, considering socioeconomic and demographic variables, soil, health structure, and dengue cases. Using climate variables, the K-means algorithm identified four groups of municipalities with similar characteristics. The comparison of their indicators revealed certain patterns in the municipalities with the worst performance in terms of dengue case outcomes. Although presenting better economic conditions, these municipalities held a lower average number of community healthcare agents and basic health units per inhabitant. Thus, economic conditions did not reflect better health structure among the three studied indicators. Another characteristic of these municipalities is urbanization. The worst performing municipalities presented a higher rate of urban population and human activity related to urbanization. This methodology identified important deficiencies in the implementation of the dengue control program in the state of São Paulo. The integration of several databases and the use of Data Science methods allowed the evaluation of the program on a large scale, considering the context in which activities are conducted. These data can be used by the public administration to plan actions and invest according to the deficiencies of each location.

Safety Assessment of Hydrogen Peroxide as Used in Cosmetics.

The Expert Panel for Cosmetic Ingredient Safety (Panel) assessed the safety of Hydrogen Peroxide for use in cosmetics. This ingredient is reported to function in cosmetics as an antimicrobial agent, cosmetic biocide, oral health care agent, and oxidizing agent. The Panel reviewed the data relevant to the safety of this ingredient and concluded that Hydrogen Peroxide is safe in cosmetics in the present practices of use and concentration described in this safety assessment.

Generative AI – The Revolutionizing Virtual Agents in Health Care

The world of health insurance and Medicare has traditionally been perceived as complex and difficult to navigate. Fortunately, the application of Generative AI to virtual agents has begun to transform the industry. Large language and image, AI models, also known as generative AI or foundation models, have opened up new prospects for organizations and people involved in content creation. Once trained, a generative model can be "fine-tuned" for a certain content domain with far less data.

Using Quantitative Polymerase Chain Reaction (qPCR) to Identify a Myriad of Carbapenemase Genes in Fresh Cow Dung in Bangladesh.

Introduction The emergence of antimicrobial resistance (AMR) is driven by the selection pressure of frequent uses of antimicrobial agents in healthcare, the food chain, agriculture, fishery, and the food animal industry, which poses a serious health risk for transmission-linked humans and the surrounding environment. Livestock, particularly cattle, play an essential role in the food sector in Bangladesh. The food-animal chains can be the potential routes of exposure to AMR-microorganisms for every domain of one health. Antimicrobial resistance genes (ARGs) can impart a reservoir of AMR within the food supply chain, even without pathogenic microorganisms. This study investigated the history of infection for the last six-month period of antimicrobials utilized in cattle farms and the distribution of selected carbapenemase resistance genes, namely, bla-KPC, bla-IMP, bla-VIM, bla-NDM-1, bla-SIM, bla-GIM, bla-SPM, andbla-SME, in cattle feces in Bangladesh. Methods A cross-sectional study was designed to analyze ARGs in fresh cow dung samples collected from commercial farms and individual houses in four Bangladesh districts, namely, Dhaka, Gazipur, Manikganj, and Tangail. Types of cattle breeds, their existing diseases, recent antimicrobial uses, and vaccine uses were recorded. DNA was extracted from each cow dung sample using commercial kits (Qiagen GmbH, Germany). Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to assess the eight carbapenem resistance genes in the extracted DNA.The eight carbapenem resistance genes in the extracted DNA were assessed byRT-qPCR using the qTOWER3 thermal cycler (Analytik Jena GmbH, Konrad-Zuse-Straße 1, 07745 Jena, Germany). Results Group A carbapenemase, bla-KPC, was detected in 66.7% of the samples. However, no bla-SME was identified in all of the test samples. Group B metallo carbapenemase, bla-IMP, bla-NDM-1, bla-VIM, bla-SIM, bla-GIM, andbla-SPM, were in 66.7% (80/120), 49.2% (59/120), 48.3% (58/120), 68.3% (82/120), 58.3% (70/120), and 12.5% (15/120), respectively. Only 8.3% of the tested samples contained no MBL gene; 10% carried a single-type carbapenemase gene; and the remaining 81.7% carried two or more carbapenemase genes concurrently. Co-carriage of four or more genes was found in over 59% of samples. As many as seven genes were found together in 6.7% of samples. ARG detection in commercial cattle samples and household feces is not statistically significant. Conclusions Substantial carbapenem-resistance ARGs were detected in commercially farmed cow dung and household cattle samples. Frequent use of antibiotics for cattle for treatment and prophylactic purposes may influence the high acquisition of ARGs. Bangladeshi cattle farms are reservoirs and routes of AMR, posing a significant threat to the country's public health.

A Survey of State Correctional Health Care Providers on Advance Care Planning: Opportunity for Collaboration With Corrections.

Prison populations are rapidly aging. Persons in prison age quicker and suffer more chronic illness and disability than their nonincarcerated peers, posing challenges to caring for prisoners who are chronically ill and dying. The goal of our study was to describe state prisons' practices and policies addressing persons in prison with advanced chronic and life limiting illness through a national web-based survey of state-level prison health care professionals. In particular, we focused on advanced care planning, use of health care directives, decision-making about goals of care, including life sustaining treatments, The response rate was 22% for a sample size 152 completed surveys. The average age of respondent was 52years; majority were female and Caucasian, and had worked in corrections more than 8years. Nearly half were registered nurses. Most reported their prison did not have a dedicated end-of-life care program and only 11% offered a peer-care program. However, two-thirds indicated their facility provided the opportunity to designate a health care agent with physicians most likely responsible for facilitating completion of a health care directive. It is evident the care of persons aging and dying in prison is complex and requires further investigation addressing staff and prison population education, ethics guidelines for care, compassionate release, and advance care planning. This study suggests that hospice and palliative care professionals could collaborate with corrections professionals and national organizations to provide innovative education and support to enhance the humane care of this vulnerable population.

Conversational Agents in Palliative Care: Potential Benefits, Risks, and Next Steps.

Conversational agents (sometimes called chatbots) are technology-based systems that use artificial intelligence to simulate human-to-human conversations. Research on conversational agents in health care is nascent but growing, with recent reviews highlighting the need for more robust evaluations in diverse settings and populations. In this article, we consider how conversational agents might function in palliative care-not by replacing clinicians, but by interacting with patients around select uncomplicated needs while facilitating more targeted and appropriate referrals to specialty palliative care services. We describe potential roles for conversational agents aligned with the core domains of quality palliative care and identify risks that must be considered and addressed in the development and use of these systems for people with serious illness. With careful consideration of risks and benefits, conversational agents represent promising tools that should be explored as one component of a multipronged approach for improving patient and family outcomes in serious illness.

Critical information quality dimensions of conversational agents for healthcare

Introduction. The new generation of information technology changes the ways of information seeking. Conversational agents start to be applied in public to support information seeking and decision making and provide a variety of services to users such as healthcare education and consultation. The information quality of conversational agents for healthcare determines the quality of these services, while identifying critical dimensions used to assess the agents’ information quality that helps better strategise priorities for ensuring information quality has received limited attention in the literature. Method. This study conducted a questionnaire survey to investigate the critical dimensions of information quality of healthcare conversational agents. After excluding two responses from participants who declined to fill in the questionnaire, this study retained 231 responses for data analysis, out of the total 233 participants who initially responded to the survey. Analysis. The research describes the demographic information of the participants, the behavioural characteristics of using healthcare conversational agents, and the critical dimensions of information quality of the agents perceived by the participants in the survey, employing descriptive statistics. Furthermore, ANOVA was employed to compare the variances in the perceived importance of information quality dimensions between participants who had used a healthcare conversational agent and those who had not. Results. Understandability and trustworthiness were the two top concerns for the information quality of the agents from the participants’ perspective in this study. Conclusions. Results of the study show that the experience of using or not using the agents affected the participants’ perceived importance of the agent’ information quality dimensions.

Potential and pitfalls of conversational agents in health care.

Potential and pitfalls of conversational agents in health care.

Conversational Agents in Health Care: Expert Interviews to Inform the Definition, Classification, and Conceptual Framework.

Conversational agents (CAs), or chatbots, are computer programs that simulate conversations with humans. The use of CAs in health care settings is recent and rapidly increasing, which often translates to poor reporting of the CA development and evaluation processes and unreliable research findings. We developed and published a conceptual framework, designing, developing, evaluating, and implementing a smartphone-delivered, rule-based conversational agent (DISCOVER), consisting of 3 iterative stages of CA design, development, and evaluation and implementation, complemented by 2 cross-cutting themes (user-centered design and data privacy and security). This study aims to perform in-depth, semistructured interviews with multidisciplinary experts in health care CAs to share their views on the definition and classification of health care CAs and evaluate and validate the DISCOVER conceptual framework. We conducted one-on-one semistructured interviews via Zoom (Zoom Video Communications) with 12 multidisciplinary CA experts using an interview guide based on our framework. The interviews were audio recorded, transcribed by the research team, and analyzed using thematic analysis. Following participants' input, we defined CAs as digital interfaces that use natural language to engage in a synchronous dialogue using ≥1 communication modality, such as text, voice, images, or video. CAs were classified by 13 categories: response generation method, input and output modalities, CA purpose, deployment platform, CA development modality, appearance, length of interaction, type of CA-user interaction, dialogue initiation, communication style, CA personality, human support, and type of health care intervention. Experts considered that the conceptual framework could be adapted for artificial intelligence-based CAs. However, despite recent advances in artificial intelligence, including large language models, the technology is not able to ensure safety and reliability in health care settings. Finally, aligned with participants' feedback, we present an updated iteration of the conceptual framework for health care conversational agents (CHAT) with key considerations for CA design, development, and evaluation and implementation, complemented by 3 cross-cutting themes: ethics, user involvement, and data privacy and security. We present an expanded, validated CHAT and aim at guiding researchers from a variety of backgrounds and with different levels of expertise in the design, development, and evaluation and implementation of rule-based CAs in health care settings.

Development and Validation of a Measure of Self-Efficacy for Advance Care Planning.

Medical care received at end-of-life is often not aligned with individuals' values and care preferences. Much can get in the way of an individual communicating and documenting their preferences to care providers and close others, even if it is a goal to do so. The objective of this work was to develop a measure of Advance Care Planning Self-Efficacy (ACP SE) focused on three important behaviors: completing a living will, documenting a healthcare agent, and discussing quality versus quantity of life issues. Measure development was framed by the Transtheoretical Model (TTM) of behavior change. Following the completion of focus groups and formative qualitative work, an independent sample (N = 310, adults aged 50+) was randomly split into two halves for exploratory and confirmatory factor analyses (EFA/CFA). Multivariate analyses examined relationships between ACP SE and other TTM constructs. Results of the CFA demonstrated excellent internal consistency (α = .95) and good model fit (CFI = .89, RMSEA = .13). Self-efficacy was framed by the TTM as situation-specific confidence. Through the measure development process, it was found that confidence to do ACP was impacted by various situational and attitudinal variables. ACP Self-Efficacy was found to differ significantly by Stage of Change. This study produced a reliable and valid measure of situation-specific confidence for ACP grounded within the TTM that could enhance future interventions aimed at increasing participation in ACP.