Medical Recommendations Research Articles

A multidisciplinary approach to improve adherence to medical recommendations in older adults at hospital discharge: The APPROACH study protocol.

Poor comprehension and medication adherence are common in older people, especially after hospitalizations, in case of changes or prescriptions of new therapeutic regimes. This randomized controlled trial aims to evaluate the effectiveness of an integrated approach in improving older individuals' adherence to medical recommendations after hospital discharge. Data from an expected sample of 360 older inpatients (and their caregivers) will be collected. Medical recommendations' understanding will be tested before and after the routine explanation received by in-charge physicians. Participants will be randomized in the control (usual care) and intervention group. The intervention consists of educational training by a multidisciplinary team (occupational therapist, dietician, and physician, in consultation with a pharmacologist) at hospital discharge and, after hospital discharge, receiving a phone recall checking for therapy adherence and having the possibility to contact the study team for potential related concerns. After 7 days, medication adherence will be assessed through structured phone interviews comparing ongoing and prescribed medications and by administering the 4-item validated Morisky, Green, Levine scale and the Medication adherence report scale (MARS-5). At 30 and 90 days from discharge, data on medication adherence, falls, rehospitalizations, and vital status will be collected through phone interviews and hospital records. Registration: NCT05719870 (clinicaltrial.gov). https://classic.clinicaltrials.gov/ct2/show/NCT05719870.

Does small talk with a medical provider affect ChatGPT's medical counsel? Performance of ChatGPT on USMLE with and without distractions.

Efforts are being made to improve the time effectiveness of healthcare providers. Artificial intelligence tools can help transcript and summarize physician-patient encounters and produce medical notes and medical recommendations. However, in addition to medical information, discussion between healthcare and patients includes small talk and other information irrelevant to medical concerns. As Large Language Models (LLMs) are predictive models building their response based on the words in the prompts, there is a risk that small talk and irrelevant information may alter the response and the suggestion given. Therefore, this study aims to investigate the impact of medical data mixed with small talk on the accuracy of medical advice provided by ChatGPT. USMLE step 3 questions were used as a model for relevant medical data. We use both multiple-choice and open-ended questions. First, we gathered small talk sentences from human participants using the Mechanical Turk platform. Second, both sets of USLME questions were arranged in a pattern where each sentence from the original questions was followed by a small talk sentence. ChatGPT 3.5 and 4 were asked to answer both sets of questions with and without the small talk sentences. Finally, a board-certified physician analyzed the answers by ChatGPT and compared them to the formal correct answer. The analysis results demonstrate that the ability of ChatGPT-3.5 to answer correctly was impaired when small talk was added to medical data (66.8% vs. 56.6%; p = 0.025). Specifically, for multiple-choice questions (72.1% vs. 68.9%; p = 0.67) and for the open questions (61.5% vs. 44.3%; p = 0.01), respectively. In contrast, small talk phrases did not impair ChatGPT-4 ability in both types of questions (83.6% and 66.2%, respectively). According to these results, ChatGPT-4 seems more accurate than the earlier 3.5 version, and it appears that small talk does not impair its capability to provide medical recommendations. Our results are an important first step in understanding the potential and limitations of utilizing ChatGPT and other LLMs for physician-patient interactions, which include casual conversations.

Public perception of psychiatry, psychology and mental health professionals: a 15-year analysis.

X (previously known as "Twitter") serves as a platform for open discussions on mental health, providing an avenue for scrutinizing public perspectives regarding psychiatry, psychology and their associated professionals. To analyze the conversations happening on X about psychiatrists, psychologists, and their respective disciplines to understand how the public perception of these professionals and specialties has evolved over the last 15 years. We collected and analyzed all tweets posted in English or Spanish between 2007 and 2023 referring to psychiatry, psychology, neurology, mental health, psychiatrist, psychologist, or neurologist using advance topic modelling and sentiment analysis. A total of 403,767 tweets were analyzed, 155,217 (38%) were in English and 248,550 (62%) in Spanish. Tweets about mental health and mental health professionals and disciplines showed a consistent volume between 2011 and 2016, followed by a gradual increase from 2016 through 2022. The proportion of tweets discussing mental health doubled from 2016 to 2022, increasing from 20% to 67% in Spanish and from 15% to 45% in English. Several differences were observed on the volume of tweets overtime depending on the language they were written. Users associated each term with varied topics, such as seeking for help and recommendation for therapy, self-help resources, medication and side effects, suicide prevention, mental health in times of crisis, among others. The number of tweets mentioning these topics increased by 5-10% from 2016 to 2022, indicating a growing interest among the population. Emotional analysis showed most of the topics were associated with fear and anger. The increasing trend in discussions about mental health and the related professionals and disciplines over time may signify an elevated collective awareness of mental health. Gaining insights into the topics around these matters and user's corresponding emotions towards them presents an opportunity to combat the stigma surrounding mental health more effectively.

Harnessing big data for tailored health communication: A systematic review of impact and techniques

In recent years, the convergence of healthcare and big data analytics has opened new avenues for tailored health communication, enabling personalized interventions and improving healthcare outcomes. This systematic review investigates the impact and techniques of harnessing big data for tailored health communication. The review synthesizes findings from diverse studies spanning healthcare sectors, including public health campaigns, clinical interventions, and patient engagement initiatives. It examines the effectiveness of tailored communication strategies in addressing various health challenges, such as chronic diseases, infectious outbreaks, and mental health disorders. Key findings highlight the significant positive impact of personalized health communication on health behavior change, treatment adherence, and patient empowerment. Big data analytics enable the segmentation of diverse populations based on socio-demographic, behavioral, and clinical characteristics, facilitating the delivery of targeted messages tailored to individual preferences and needs. Personalization enhances engagement, fosters trust, and motivates individuals to adopt healthier lifestyles and adhere to medical recommendations. Furthermore, the review explores the diverse techniques and technologies employed in harnessing big data for tailored health communication. Machine learning algorithms, natural language processing, and predictive modeling are leveraged to analyze vast datasets, predict health outcomes, and tailor communication messages in real-time. Mobile health applications, social media platforms, and wearable devices serve as channels for delivering personalized interventions and collecting real-time health data. However, the review also identifies challenges and limitations, including privacy concerns, data security risks, and the digital divide. Ethical considerations regarding data collection, consent, and transparency are paramount in ensuring the responsible use of big data in health communication. This systematic review underscores the transformative potential of harnessing big data for tailored health communication. By leveraging advanced analytics and technology, healthcare stakeholders can deliver personalized interventions that resonate with individuals, ultimately driving positive health behavior change and improving healthcare outcomes on a population scale.

An assessment of the consumption and disposal of medications home use

In the current scenario, the market society, an economic model consolidated in many parts of the world, faces some social and environmental challenges, such as the solid waste disposal and treatment of different residues generated daily, especially in big cities. In Brazil, 4,540 municipalities provided medical waste collection services, and more than a third (36.2%) sent the MS to landfills, dumps, and septic ditches. This issue faced by municipalities is challenging as they try to promote less damaging management that impacts the environment due to urban demography and fragile socio-environmental conditions. Action as reverse logistics is a strategy that enables the return of after-sales and post-consumption waste to the business environment and the productive sector, promoting the circular economy, the recovery of waste, the life cycle of products, decrease in the use of materials, reduction of environmental impacts, compliance with current regulations and non-contamination of the environment. The assessment of unusual medications and population knowledge allows drawing an overview of the disposal of uncommon medications for home use by an electronic questionnaire addressing different questions, referring to the identification of the user's profile, various forms of consumption, storage, and disposal of medication waste, and knowledge about the local regulation. Most medications consumed by the population are antibiotics, analgesics, fever reducers, and pain relief without medical recommendations, not used to seeing the expiration date, and not reading the drug leaflet. The assessment population could only pay a little for the additional tax for final medication waste disposal. Most of the participants disposed of the unusual medications in the household garbage. Finally, the reverse logistics implementation of medication waste in Brazil will be a significant challenge for decreasing the environmental impacts and pollution because of the culture of the local population.

PROMISE: A pre-trained knowledge-infused multimodal representation learning framework for medication recommendation

Electronic Health Records (EHRs) significantly enhance clinical decision-making, particularly in safe and effective medication recommendation based on complex patient data. Current methods, while encoding each medical event individually with domain-specific knowledge, inadequately harness multi-source domain knowledge and neglect the interrelations among various medical codes, the influence of historical patient visits, and the relevance of similar patient trajectories. To address these limitations, we present PROMISE, a multimodal medication recommendation framework that progressively learns patient representations from specific health states to a comprehensive view. PROMISE integrates domain knowledge into modality-specific encoders to improve local and global patient representations, facilitating enhanced medication recommendations through the interaction of patient representations from various modalities. Specifically, within the code modality, PROMISE utilizes encoding of EHR hypergraphs to learn patient representations featuring structured information. Simultaneously, in the text modality, it acquires patient representations with semantic information by encoding clinical texts obtained from tables. Our framework surpasses state-of-the-art baselines with up to 2.06% and 1.97% improvements on key metrics within the MIMIC-III and IV datasets, respectively, confirming its effectiveness and superiority.

The use of complementary and alternative medicine for functional gastrointestinal disorders among the saudi population

Background and objectiveComplementary and alternative medicine (CAM) is a common practice among patients, who experience functional gastrointestinal disorders (FGID). Among the Saudi population, less is known about CAM use for FGID. Therefore, this study aimed to determine the prevalence of CAM utilization for FGID amongst the Saudi population and determine the types of CAM used for treatment. MethodA cross-sectional study was carried out in Riyadh, Saudi Arabia during February 2023 through social media platforms using questionnaires adopted from the literature. There were three sections in the questionnaire including demographic information, questions to determine the prevalence of CAM use for FGID, the types of FGID, and the types of CAM utilization, and questions on the sources of information about CAM. Multivariable logistic regression was applied to find factors associated with CAM use. All statistical analyses were performed using SPSS version 26. ResultsA total of 828 people participated in this study. The overall prevalence of CAM use for FGID problems was 87.2 %. There were no significant differences in CAM use for FGID problems between men (87.5 %) and women (86.3 %) (P = 0.727). The most commonly used types of CAM for FGID were ginger (73.4 %), chamomile (66.6 %), mint (61.6 %), turmeric (59.0 %), anise (55.5 %), fennel (43.1 %), and Activia yogurt©️ (42.7 %). The most common FGID disorders for utilizing CAM were IBS (29.9 %), followed by constipation (29.8 %), dyspepsia (22.7 %), and bloating (17.0 %). In the multivariable regression, age, gender and employment status did not have an impact on the odds of using CAM. The subjects who had high school, university, and postgraduate education had significant odds ratios of CAM use (OR = 2.73; 95 % CI: 1.22–6.13), (OR = 4.18; 95 % CI: 2.03–8.58), and (OR = 20.85; 95 % CI: 5.51–78.80), respectively, compared to subjects who did not complete high school. Participants who had private insurance had a significant odds ratio (OR = 0.27; 95 % CI: 0.14–0.55) compared to governmental insurance. ConclusionThe use of CAM among the Saudi population is alarmingly high; however, the lack of standardized medical recommendations and treatment options may be the cause. Although there were no significant gender differences, participants with higher educational levels and private insurance coverage were more likely to use CAM for FGID. Patients suffering from FGID and limited access to medical advice and treatment options are vulnerable to being exposed to dubious and incredible information sources. Expanding access to preventative medical services, funding governmental medical websites to provide credible information, educating healthcare professionals about FGID, and conducting more research in safe and effective treatments for FGID is recommended.

OntoMedRec: Logically-pretrained model-agnostic ontology encoders for medication recommendation

Recommending medications with electronic health records (EHRs) is a challenging task for data-driven clinical decision support systems. Most existing models learnt representations for medical concepts based on EHRs and make recommendations with the learnt representations. However, most medications appear in EHR datasets for limited times (the frequency distribution of medications follows power law distribution), resulting in insufficient learning of their representations of the medications. Medical ontologies are the hierarchical classification systems for medical terms where similar terms will be in the same class on a certain level. In this paper, we propose OntoMedRec, the logically-pretrained and model-agnostic medical Ontology Encoders for Medication Recommendation that addresses data sparsity problem with medical ontologies. We conduct comprehensive experiments on real-world EHR datasets to evaluate the effectiveness of OntoMedRec by integrating it into various existing downstream medication recommendation models. The result shows the integration of OntoMedRec improves the performance of various models in both the entire EHR datasets and the admissions with few-shot medications. We provide the GitHub repository for the source code. (https://github.com/WaicongTam/OntoMedRec)

Analisis Metode Happy When Sick (HWS) dalam Bimbingan Rohani di Rumah Sakit

Along with the times and the complexity of the problems faced by humans, especially those who are facing illness, the problems of patients in hospitals are not only physical but also psychological, such as anxiety. Therefore, spiritual guidance is considered important to support the healing process by helping patients accept their condition with sincerity, patience and trust. An effective method is needed so that the aims and objectives of achieving healing, both physical and spiritual, can be achieved. This study aims to analyze the effectiveness of the HWS (Happy When Sick) method in Islamic spiritual guidance at Muhammadiyah Hospital using qualitative descriptive methods. Data was collected through observation and in-depth interviews, then analyzed using the interactive model of Miles, Huberman, and Saldana. The results show that the application of the HWS method has a positive impact on the psychological and spiritual condition of patients from various generations, families and medical personnel. Patients show increased calm, enthusiasm for worship, and optimism regarding recovery, as well as reduced anxiety and increased compliance with medical recommendations. This research recommends the integration of holistic and adaptive spiritual guidance methods to optimally support patients' physical and spiritual health.

Potential Benefits of a Cosmetic Ingredient Combining Thermal Spring Water and Diatom Algae Extract

The development of cosmetic ingredients improving the management of skin with irritation proclivity is an actual need. However, medical recommendations in specific cases, such as sensitive skin, include the use of as few and low-reactive ingredients as possible. In this context, we here describe the development of a new ingredient consisting of a mixture of thermal water and a diatom algae extract. First, we characterized a thermal spring water (La Solia, LS-TSW). Attending to its chemical composition, LS-TSW displayed an exclusive combination of different inorganic elements, with interesting potential properties when compared with other commercial spring waters. Then, LS-TSW hydrobiome was studied, and after finding specimens of Phaeodactylum-like sp., we proposed potentiating LS-TSW benefits with its combination with an oil extract of Phaeodactylum tricornutum (PtOE). Finally, we assessed the potential of the mixture during pro-inflammatory stimulation, in the first instance using an immune cell model, and then in an in vitro system mimicking keratinocytes under skin irritation. In the last-mentioned model, the ingredient of interest effectively attenuated the induced levels of different pro-inflammatory mediators (IL-6, IL-1, TNFα, NF-κB, and CCL1), at the level of gene expression. Thus, our results highlight the potential benefits of this combination in the context of skin irritation, opening roads for its use in new skincare regimens, and addressing an important dermatological concern.

Medication recommendations for treatment of lumbosacral radiculopathy: A systematic review of clinical practice guidelines.

The purpose of this systematic review was to ascertain guideline-recommended pharmaceutical approaches to lumbosacral radicular symptoms, assess the quality of the clinical practice guidelines (CPGs) with the Appraisal of Guidelines for Research and Evaluation II (AGREE II) tool, and qualitatively synthesize the guideline recommendations. Literature searches were performed in PubMed, Cochrane Database of Systematic Reviews, Index to Chiropractic Literature, Allied and Complementary Medicine Database (AMED), Cumulative Index for Nursing and Allied Health Literature (CINAHL), and Physiotherapy Evidence Database (PEDro). We included guidelines published between January 1, 2017, and January 9, 2022, written in the English language, related to radiculopathy, sciatica, and/or low back pain with leg pain, and that provided recommendations on oral medication. The review was performed in accordance with Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) and the protocol was pre-registered with the International Prospective Register of Systematic Reviews (PROSPERO). Eligibility screening, full-text review, extraction of information pertaining to pharmacological management, and synthesis of results were performed independently by two authors and a third investigator was recruited to arbitrate any disagreements. The AGREE II tool was administered by four authors to appraise CPG quality. After screening 413 citations and assessing 37 full-text articles, 11 CPGs met the inclusion criteria. They represented seven countries (Belgium, Canada, England, France, Japan, Korea, and United States) and three continents (Asia, Europe, and North America), as well as the Global Spine Care Initiative aimed at a worldwide presence. The mean overall AGREE II score was 87.1% (standard deviation [SD] 12.6%), generally reflecting high-quality CPGs. The highest domain mean score was for Clarity of Presentation (96.7%, SD 4.4%), and the lowest was Applicability (75.6%, SD 22.8%). Five classes of medications were recommended by at least one CPG: anticonvulsants, antidepressants, oral corticosteroids, nonsteroidal anti-inflammatory drugs (NSAIDs), and opioids. The most common medication class recommended by the CPGs for lumbar radiculopathy was antidepressants. No CPGs recommended prescribing acetaminophen, benzodiazepines, muscle relaxants, or antibiotics. There was very little agreement between the CPGs, and all the medication classes had at least one CPG recommended against its use. Three guidelines reviewed did not recommend any medications due to lack of supporting literature, and instead recommended nonpharmacologic therapy.

AKA-SafeMed: A safe medication recommendation based on attention mechanism and knowledge augmentation

Medication recommendation (MR) focuses on generating a medication combination without adverse drug-drug interactions (DDIs) based on electronic health records (EHRs) in making prescriptions. However, how to capture the temporality in historical visits and concurrence in clinical events are two challenges that determine the outcomes of an MR model. To achieve effective and safe MR with imbalanced EHR data, we propose a medication recommendation model based on the attention mechanism and knowledge augmentation strategy (AKA-SafeMed). Specifically, in the patient representation module, bidirectional long short-term memory (BiLSTM) models are deployed to encode the patient's sequential visits, and a self-attention mechanism is employed to learn the weights of diagnosis and procedure vectors from a patient's historical visits; in the medication generation module, the EHR and DDI graphs are leveraged to fully explore the relationships between pairwise drugs as a knowledge augmentation strategy. Finally, extensive experiments are conducted to evaluate the performance of AKA-SafeMed on public EHR datasets. The experimental results demonstrate that the AKA-SafeMed model achieves superior performance in MR tasks compared with the state-of-the-art baseline models.

New S2k guideline "Surgical techniques for gender incongruence"

Various techniques for the surgical treatment of gender incongruence (GI) have been available for years. The spectrum is broad and covers various specialties. In recent years, there has been an increase in the number of clinics offering body-modifying procedures. This has led to aconsiderable variation in methods in some cases. Although the topic has recently found its way more and more into teaching and the relevant specialist literature, there are still no evidence-based recommendations on the various techniques. Acompendium of established surgical techniques was to be compiled, containing recommendations on indication, performance and aftercare as well as complication management and subjecting them to aconsensus-based evaluation. In accordance with "Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften" (AWMF) regulations, the guideline was developed at level S2k, i.e., it is consensus based. The guideline group was founded in February 2019 in aformal constituent meeting. Atotal of 14medical societies and 2interest groups were recruited for the guideline work. In the end, two consensus meetings were held, which was due to the large number of recommendations and background texts to be adopted. The guideline focuses on the choice and applicability of surgical techniques as well as the wishes of those seeking treatment and protection of fertility. There is awide variety of methods and treatment goals for each individual. Taking into account medical standards, recommendations, and contraindications, an optimal result that minimizes individual suffering can be achieved together with the person seeking treatment. The content of the guideline represents aunique compendium of surgical methods, recommendations for the selection of procedures, and common indications in the field of body-modifying surgery for gender incongruence.

Gaps in kidney stone disease management: From clinical theory to patient reality.

With nephrolithiasis projected to affect 25% of the global population in the next three decades, there is an urgent call for innovative management strategies to prevent and reduce stone recurrence. This study aims to explore the evolving management needs in nephrolithiasis from both patient and healthcare provider perspectives. An expert-collaborative online survey comprising 10 targeted questions on kidney stone management was developed and disseminated. This survey was designed to gather comprehensive insights from patients, physicians and dietician and other person in the field of nephrolithiasis. Analysis of responses from 120 participants, including 45 nephrologists, 38 dieticians, 11 urologists, and 14 kidney stones patients followed in our hospital, revealed critical insights. A significant 97.5% emphasized the necessity of optimizing daily water intake, and 94.1% recognized the need for practical dietary modifications. Additionally, 88.3% of respondents found timely hydration reminders beneficial. Notably, monitoring urine color and pH was valued by 85% and 84.3% of the participants, respectively. A striking disparity emerged in the perception of fatigue and wellness monitoring, with 65% of patients prioritizing fatigue monitoring, a view less shared by healthcare professionals. Similarly, 71% of patients deemed wellness monitoring essential, highlighting a gap in understanding between patients and their caregivers. This study underscores the critical need for more tailored guidance on hydration strategies and the promise of remote urine parameters monitoring in nephrolithiasis management. The findings strongly advocate for a patient-centered approach, aligning medical recommendations with patient lifestyles and experiences, to enhance the effectiveness of nephrolithiasis management.

Medical education and artificial intelligence: Question answering for medical questions based on intelligent interaction

SummaryComputer assisted medical diagnosis technology is widely used in the field of medical assistance to assist doctors in making diagnostic decisions. But as the number of patients increases, the diagnostic pressure on doctors gradually increases, and more efficient computer‐aided medical diagnosis technology is needed to improve the accuracy of doctors' diagnosis. Today's computer‐based medically assisted diagnostic technologies suffer from the inability to fully simulate physical attributes and environmental factors, the large computational resources required for high‐precision models, the need for professional training for user operation, and the limited intuition for innovative design. For improving diagnostic efficiency, this study designs a medical Question answering intelligent interaction system in view of artificial intelligence algorithms. The system is constructed with an active interactive intelligent Q&A model consisting of a medical reasoning module and a medical examination recommendation module. Then, it uses local Bayesian network algorithm as the foundation to establish an intelligent strategy optimization network. And it puts forward the answer selection model of medical Question answering in view of hierarchical interaction for natural language processing tasks in the medical context. The performance test results show that when the diagnostic end threshold of the medical reasoning module is 0.8, the shortest diagnostic path is 3.33. When the diagnostic threshold is 0.85, the maximum length of the diagnostic path is 4.66, and the maximum difference between the diagnostic paths is 1.33, which is basically not affected by the diagnostic end threshold. The local Bayesian network algorithm can reduce the impact of noise features and extract more valuable information. The accuracy of the multilevel interactive answer selection model on the Stanford Natural Language Inference dataset without using external resources reached 89.2%. The ablation test results show that the overall accuracy of the model is 89.64%. The visualization results of the attention weight distribution test between interaction layers show that under different levels of interaction, the attention distribution will undergo significant changes.

Acupuncture and tuina knowledge graph with prompt learning.

Acupuncture and tuina, acknowledged as ancient and highly efficacious therapeutic modalities within the domain of Traditional Chinese Medicine (TCM), have provided pragmatic treatment pathways for numerous patients. To address the problems of ambiguity in the concept of Traditional Chinese Medicine (TCM) acupuncture and tuina treatment protocols, the lack of accurate quantitative assessment of treatment protocols, and the diversity of TCM systems, we have established a map-filling technique for modern literature to achieve personalized medical recommendations. (1) Extensive acupuncture and tuina data were collected, analyzed, and processed to establish a concise TCM domain knowledge base. (2)A template-free Chinese text NER joint training method (TemplateFC) was proposed, which enhances the EntLM model with BiLSTM and CRF layers. Appropriate rules were set for ERE. (3) A comprehensive knowledge graph comprising 10,346 entities and 40,919 relationships was constructed based on modern literature. A robust TCM KG with a wide range of entities and relationships was created. The template-free joint training approach significantly improved NER accuracy, especially in Chinese text, addressing issues related to entity identification and tokenization differences. The KG provided valuable insights into acupuncture and tuina, facilitating efficient information retrieval and personalized treatment recommendations. The integration of KGs in TCM research is essential for advancing diagnostics and interventions. Challenges in NER and ERE were effectively tackled using hybrid approaches and innovative techniques. The comprehensive TCM KG our built contributes to bridging the gap in TCM knowledge and serves as a valuable resource for specialists and non-specialists alike.

The generic structure potential (GSP) of STI doctor-patient interactions in southwest Nigeria

This study describes the generic structure potential (GSP) of sexually transmitted infection (STI) consultations in Nigeria. It is a further effort from previous studies not only in its consideration of the phase structure of consultations in a specific medical context, but also in its exploration of both the broad and narrow generic structure potential of the consultations. Fifty audio recordings of mixed-visit doctor-patient interactions in hospitals in three states in southwest Nigeria constitute the data. The data were analysed using Halliday and Hasan’s (1985) Generic Structure Potential theory (GSP). The analysis revealed four broad stages in the structural organisation of STI medical interactions, namely Opening (O), Diagnostic Interactions (DI), Treatment Recommendations (TR), and Closing (C). The obligatory elements in the broad catalogue are the diagnostic interactions and treatment recommendations, while the narrow GSP of the phases revealed the doctor’s problem elicitation as the obligatory element in the opening phase, the patient’s problem presentation activity was solely obligatory in DI, and the doctor’s treatment procedure, the only mandatory activity of the TP phase. The study concludes that the diagnostic interaction and treatment recommendation phases define medical consultations in STI (and HIV) encounters in Nigeria.

Classification system for primary care provider eConsults about medications for older adults with frailty

BackgroundProviding primary care for people with frailty can be challenging due to an increased risk of adverse outcomes and use of potentially inappropriate medications which may exacerbate characteristics of frailty. eConsult is a service where primary care providers can receive timely specialist advice for their patients through a secure web-based application. We aimed to develop a classification system to characterize medication-focused eConsult questions for older adults with frailty and assess its usability.MethodsA classification system was developed and refined over three cycles of improvement through a cross-sectional study of 35 cases categorized as medication-focused from cases submitted in 2019 for patients aged 65 or older with frailty through the Champlain BASE eConsult service (Ontario, Canada). The final classification system was then applied to each case.ResultsThe classification system contains 5 sections: (1) case descriptives; (2) intent and type of question; (3) medication recommendations and additional information in the response; (4) medication classification; and (5) potentially inappropriate medications. Among the 35 medication-focused cases, the most common specialties consulted were endocrinology (9 cases, 26%) and cardiology (5 cases, 14%). Medication histories were available for 29 cases (83%). Many patients were prescribed potentially inappropriate medications based on explicit tools (AGS Beers Criteria®, STOPPFall, Anticholinergic Cognitive Burden Scale, ThinkCascades) yet few consults inquired about these medications.ConclusionA classification system to describe medication-related eConsult cases for patients experiencing frailty was developed and applied to 35 eConsult cases. It can be applied to more cases to identify professional development opportunities and enhancements for eConsult services.

Insulin Analogs and the Mode of Insulin Delivery: Recent Advances and Challenges.

Diabetes is a medical condition associated with impaired glucose regulation caused either due to insufficient insulin production or resistance to insulin (Type 2 diabetes, gestational diabetes) or the absence of insulin through the selective killing of beta cells in the pancreas (Type 1 diabetes). Irregular insulin production leads to various health complications. To prevent such complications, patients must adhere to medical recommendations before availing of any advanced insulin therapy(ies), considered productive for the treatment. Natural insulin, although highly effective in controlling blood glucose levels, patients are often at risk of developing hypoglycemia and many other complications. This has led to the development of insulin analogs, the modified variants of natural insulin having a minimal risk of causing hypoglycemia. Besides the development of analogs, the mode of insulin delivery is also considered critical in achieving better glycemic control in diabetic patients. Until recently, various exogenous insulin delivery methods were practiced, but effective glycemic control without any associated risk and ease of delivery remains a subject of paramount concern. It countered attenuation or delayed onset of diabetes-associated complications without a permanent cure, raising an unmet demand for insulin formulations and delivery methods that offer stability, biocompatibility, reproducibility, precision dosing, non-immunogenicity, and safety. The current practice utilizes non-physiological delivery methods with less invasive administration routes, offering glycemic stability and therapeutic effectiveness. This review focuses on the recent advances made and future perspectives envisioned about newer insulin therapies and delivery methods that tend to improve the management of diabetes by inculcating ideas to reduce the disease's severity and improve the quality of life.

УЧЕТ ИНДЕКСА КОМОРБИДНОСТИ ПРИ ПРОВЕДЕНИИ ПРОТИВООПУХОЛЕВОЙ ЛЕКАРСТВЕННОЙ ТЕРАПИИ У ПАЦИЕНТОВ ПОЖИЛОГО И СТАРЧЕСКОГО ВОЗРАСТА С КОЛОРЕКТАЛЬНЫМ РАКОМ (ОБЗОР ЛИТЕРАТУРЫ)

The problem of treating elderly patients is one of the most pressing in modern clinical medicine. Colorectal cancer is one of the main causes of mortality from malignant neoplasms in Russia and other developed countries. At the same time, the incidence of tumors of the colon and rectum steadily increases with age. Before starting treatment for elderly patients, it is necessary to carefully weigh the potential risks and the possibility of achieving positive results of antitumor therapy. Objective: To summarize the literature on the use of a comprehensive geriatric assessment, including the Cancer and Aging Research Group (CARG) toxicity assessment, to predict the risk of adverse events during anticancer drug treatment in elderly patients diagnosed with colorectal cancer. Materials and methods: The search for scientific sources was carried out in PubMed / Medline / UpToDate using the following keywords: “malignant tumors”, “metastatic colorectal cancer”, “senile age”, “antitumor drug therapy”. The time horizon covered 2016–2023. Using a time filter, the most relevant and innovative research papers on the issues of chemotherapy treatment of elderly and senile patients were selected. Results: Most current evidence demonstrates the importance of performing a comprehensive geriatric assessment (CGA), Cancer and Aging Research Group (CARG) toxicity assessment in elderly and geriatric patients. Information obtained from geriatric assessment should be used to predict and manage treatment toxicity. And also for assessing and predicting overall survival. Activities based on CGA can reduce the toxicity of chemotherapy, improve the quality of life of patients, and increase compliance with medical recommendations. Undoubtedly, a comprehensive geriatric assessment (CGA) can help achieve the necessary balance between the potential effectiveness and adverse events of chemotherapy. The choice of treatment tactics for an elderly and senile cancer patient should be based on the patient’s functional status (PS), general somatic status according to the ECOG scale, and the risk of toxicity according to the CARG scale. In the presence of colorectal cancer, patients with good functional status who need antitumor systemic effects can be prescribed polychemotherapy with FOLFOX, XELOX, FOLFIRI doublets with the addition of targeted drugs: bevacizumab or cetuximab (depending on the results of gene profiling of the tumor). Patients who are not candidates for polychemotherapy in standard doses, who have a general somatic status on the ECOG scale of 1-2 and an average risk of developing toxicity of grade 3 on the CARG scale, can undergo PCT with FOLFOX, XELOX, FOLFIRI doublets with a reduction in the starting dose by 20%, or monochemotherapy with antimetabolites: capecitabine, 5-fluorouracil; topoisomerase I inhibitor irinotecan. Patients with poor functional status, general somatic status on the ECOG scale 3-4, and a high risk of developing toxicity on the CARG scale are not candidates for systemic combination chemotherapy. Particular attention should be paid to maintenance therapy in this subgroup of patients. However, if the decrease in PS level is directly related to malignancy and PS ≥2, the possibility of monochemotherapy by infusion of a combination of 5-fluorouracil and leucovorin FU/LV can be evaluated.