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Abstract Background Worldwide growing shortages among health care staff are observed. This also holds true for medical assistants in Germany. Medical assistants mainly work in outpatient care and are the first point of contact for patients while performing clinical and administrative tasks. We sought to explore profession turnover among medical assistants, that is, in terms of the underlying decision-making process, the reasons for leaving the medical assistant profession and potential retention measures from the perspective of former medical assistants. Methods For this qualitative study, we conducted semi-structured telephone interviews with 20 former medical assistants between August and November 2023. Eligible for participation were medical assistants who (i) were of legal age, (ii) completed medical assistant vocational training and ii) were formerly employed as a medical assistant, but currently employed in another profession. The interviews were recorded, transcribed verbatim and content-analyzed. Results Former medical assistants expressed various, often interrelated reasons for leaving the profession. These were changes in priorities throughout their career (e.g., in terms of working hours and salary), a constant high workload, barriers to further training, poor career prospects, and poor interpersonal relationships particularly with supervisors, but also within the team and with patients as well as the perception of insufficient recognition by politics and society. Suggestions of former medical assistants to motivate medical assistants to stay in their profession included amongst others higher salaries, more flexible work structures, improved career prospects, and more recognition from supervisors, patients, and society. Conclusion Our study provides insights into the complex decision-making process underlying ultimate medical assistant profession turnover. In light of an already existing shortage of medical assistants, we suggest to further explore how the suggested interventions that aim at retention of working medical assistants can be implemented.

Background: Worldwide growing shortages among health care staff are observed. This also holds true for medical assistants in Germany. Medical assistants mainly work in outpatient care and are the first point of contact for patients while performing clinical and administrative tasks. We sought to explore profession turnover among medical assistants, that is, in terms of the underlying decision-making process, the reasons for leaving the medical assistant profession and potential retention measures from the perspective of former medical assistants. Methods: For this qualitative study, we conducted semi-structured telephone interviews with 20 former medical assistants between August and November 2023. Eligible for participation were medical assistants who (i) were of legal age, (ii) completed medical assistant vocational training and ii) were formerly employed as a medical assistant, but currently employed in another profession. The interviews were recorded, transcribed verbatim and content-analyzed. Results: Former medical assistants expressed various, often interrelated reasons for leaving the profession. These were changes in priorities throughout their career (e.g., in terms of working hours and salary), a constant high workload, barriers to further training, poor career prospects, and poor interpersonal relationships particularly with supervisors, but also within the team and with patients as well as the perception of insufficient recognition by politics and society. Suggestions of former medical assistants to motivate medical assistants to stay in their profession included amongst others higher salaries, more flexible work structures, improved career prospects, and more recognition from supervisors, patients, and society. Conclusion: Our study provides insights into the complex decision-making process underlying ultimate medical assistant profession turnover. In light of an already existing shortage of medical assistants, we suggest to further explore how the suggested interventions that aim at retention of working medical assistants can be implemented. [ABSTRACT FROM AUTHOR]

Background: Managing pain and distress in children experiencing procedural pain is a priority in pediatric care. Nitrous oxide (pre-mixed formulation of 50% nitrous oxide to 50% oxygen) is widely used to alleviate anxiety or pain during care procedures in various medical domains. This pharmacological intervention is safe to administer to children. Administration by non-anesthesiologist personnel is widespread throughout the world, though it is almost exclusively performed by doctors and nurses. The purpose of this study is to describe the experience of nitrous oxide (N2O) use by medical assistants and safe handling during the performance of painful procedures. Methods: A retrospective study was conducted in a pediatric outpatient unit of a regional hospital, including medical and surgical consultations. Results: Nitrous oxide was administered by medical assistants to 324 children aged 3 to 19 years. No respiratory and/or cardiovascular problems were noted. Most patients were calm and relaxed. Discussion: The use of N2O for minor procedures in children in a pediatric outpatient unit improved pain management through administration by medical assistants with a high level of satisfaction from patients, parents and health professionals. Conclusions: This study suggests that the use of nitrous oxide by trained medical assistants can be safe, feasible and effective within the specific context of this study. [ABSTRACT FROM AUTHOR]

The CTS-6 can be used clinically to diagnose carpal tunnel syndrome (CTS) and has demonstrated high levels of interrater reliability when administered by nonexpert clinicians. Our purpose was to assess sensitivity (Sn), specificity (Sp), and interrater reliability of the CTS-6 when administered by medical assistants (MAs). A series of patients presenting to an academic, upper-extremity surgery clinic were screened using CTS-6 between May and June of 2023. The CTS-6 was first administered by one of seven MAs and then by one of four fellowship-trained upper-extremity surgeons. In addition to recording baseline demographics, the results of each of the six history and examination components of the CTS-6 were recorded, as was the cumulative CTS-6 score (0–26). Surgeons were blinded to the scores obtained by the MAs. Interrater reliability (Cohen's kappa) was determined between the groups with respect to the diagnosis of CTS and the individual CTS-6 items. Sensitivity/specificity was calculated for the MA-administered CTS-6, using the score obtained by the surgeon as the reference standard. A CTS-6 score >12 was considered diagnostic of CTS. Two hundred eighteen patients were included, and 26% had a diagnosis of CTS. The MA group demonstrated a Sn/Sp of 84%/91% for the diagnosis of CTS. Interrater reliability was substantial (Cohen's kappa: 0.72, 95% confidence interval: 0.62–0.83) for MAs compared with hand surgeons for the diagnosis of CTS. For individual CTS-6 components, agreement was lowest for the assessment of two-point discrimination (fair) and highest for the assessment of subjective numbness (near perfect). The CTS-6 demonstrates substantial reliability and high Sn/Sp when administrated by MAs in an upper-extremity clinic. These data may be used to inform the development of CTS screening programs and future investigations in the primary care setting. Diagnostic II. [ABSTRACT FROM AUTHOR]

In recent times, the prevalence of chatbot technology has notably increased, particularly in the realm of medical assistants. However, there is a noticeable absence of medical chatbots that cater to the Hungarian language. Consequently, Hungarian-speaking people currently lack access to an automated system capable of providing assistance with their health-related inquiries or issues. Our research aims to establish a competent medical chatbot assistant that is accessible through both a website and a mobile app. It is crucial to highlight that the project's objective extends beyond mere linguistic localization; our goal is to develop an official and effectively functioning Hungarian chatbot. The assistant's task is to answer medical questions, provide health advice, and inform users about health problems and treatments. The chatbot should be able to recognize and interpret user-provided text input and offer accurate and relevant responses using specific algorithms. In our work, we put a lot of emphasis on having steady input so that it can detect all the diseases that the patient is dealing with. Our database consisted of sentences and phrases that a user would type into a chatbot. We assigned health problems to these and then assigned the categories to the corresponding cure. Within the research, we developed a website and mobile app, so that users can easily use the assistant. The app plays a particularly important role for users because it allows them to use the assistant anytime and anywhere, taking advantage of the portability of mobile devices. At the current stage of our research, the precision and validation accuracy of the system is greater than 90%, according to the selected test methods. [ABSTRACT FROM AUTHOR]

With the soaring demand for healthcare systems, chatbots are gaining tremendous popularity and research attention. Numerous language-centric research on healthcare is conducted day by day. Despite significant advances in Arabic Natural Language Processing (NLP), challenges remain in natural language classification and generation due to the lack of suitable datasets. The primary shortcoming of these models is the lack of suitable Arabic datasets for training. To address this, authors introduce a large Arabic Healthcare Dataset (AHD) of textual data. The dataset consists of over 808k questions and answers across 90 categories, offered to the research community for Arabic computational linguistics. Authors anticipate that this rich dataset would make a great aid for a variety of NLP tasks on Arabic textual data, especially for text classification and generation purposes. Authors present the data in raw form. AHD is composed of main dataset scraped from medical website, which is Altibbi website. AHD is made public and freely available at http://data.mendeley.com/datasets/mgj29ndgrk/5.

Background: Managing pain and distress in children experiencing procedural pain is a priority in pediatric care. Nitrous oxide (pre-mixed formulation of 50% nitrous oxide to 50% oxygen) is widely used to alleviate anxiety or pain during care procedures in various medical domains. This pharmacological intervention is safe to administer to children. Administration by non-anesthesiologist personnel is widespread throughout the world, though it is almost exclusively performed by doctors and nurses. The purpose of this study is to describe the experience of nitrous oxide (N2O) use by medical assistants and safe handling during the performance of painful procedures. Methods: A retrospective study was conducted in a pediatric outpatient unit of a regional hospital, including medical and surgical consultations. Results: Nitrous oxide was administered by medical assistants to 324 children aged 3 to 19 years. No respiratory and/or cardiovascular problems were noted. Most patients were calm and relaxed. Discussion: The use of N2O for minor procedures in children in a pediatric outpatient unit improved pain management through administration by medical assistants with a high level of satisfaction from patients, parents and health professionals. Conclusions: This study suggests that the use of nitrous oxide by trained medical assistants can be safe, feasible and effective within the specific context of this study.

Every district in Malawi has at least two doctors managing the social and healthcare needs of the local population. The medical doctors at the district are involved in administrative work and have minimal time for clinical practice. As such in most district hospitals, clinical officers (COs) form the backbone of patient care provision. These are cadres that have a 3-year training in clinical medicine; they work side by side with medical assistants (MAs) and nurses. Apart from the Ministry of Health (MoH) workforce, the Department of Family Medicine (FM) of Kamuzu University of Health Sciences (KUHeS) has its main district site at Mangochi. Family physicians and residents from FM department assist in provision of mentorship and teaching to other cadres. Work-based learning requires various strategies and approaches. The experience reported here involves deliberate mentorship and support to enhance the learning of other cadres. Family medicine residents learn through the active participation in these sessions to become future consultants and leaders in primary health care.

Healthcare professionals play an important role in the success of any healthcare system. In the Republic of Bulgaria, as well as in various other countries, there is a shortage of human resources in health care. This necessitates the introduction of a new health specialty "Medical Assistant", which is the successor of the "Medical Paramedic" specialty, the training of which was discontinued in 1999. The profession "Medical assistant" is in the field of "Health care", a full-time form of education lasting four years. The purpose of the training is: acquiring knowledge in various fields of medicine, building analytical thinking and professional skills. In the seventh and eighth semesters of the studies, a pre-graduate internship is held in various accredited medical institutions. Graduated students in this specialty receive the educational and qualification degree "Bachelor". The relevance and importance of the topic is determined by the fact that the provision of quality education for medical assistants is important for their professional realization, for the effective functioning of the health system in our country and the achievement of a high level of quality health services for the population offered by graduates in this professional field. The aim of the present study is to study and analyze the professional training of future medical assistants for their realization, their satisfaction with the practical skills and knowledge they have achieved, the prospects before them, and the opportunities for independent work. Materials and methods: The research includes conducting a survey among 30 students from the "Medical Assistant" specialty - first and second year in the "Health Care" department, "Medical and Clinical Diagnostic Activities" department at the University of Ruse "A. Kanchev". The proposed survey was developed specifically for the study. Almost all questions are closed-ended. Most of them require only one answer. There are a few exceptions allowing for two or more answers. Part of the scales includes an "other" option, which is open and implies a free answer other than the given ones. Conclusions: The results of the surveyed students give an accurate idea about their expectations, perspectives and opportunities for realization after completing their studies. University of Ruse "A. Kanchev" is a higher education institution with traditions and high quality of the educational process, and this is very important for the training of students and the professional growth of future specialists. A high percentage of respondents positively evaluate the application of modern methods and technical means in the learning process, and share that training in small groups enables better control by teachers and the formation of specific skills. A large part of the respondents expect to be able to realize themselves in the field of health care, to be satisfied with the medical services they provide, and to be able to make independent, effective, and accurate decisions regarding the health of patients. [ABSTRACT FROM AUTHOR]

Background: Large language models (LLMs) have the potential to enhance clinical flow and improve medical education, but they encounter challenges related to specialized knowledge in ophthalmology. Objective: This study aims to enhance ophthalmic knowledge by refining a general LLM into an ophthalmology-specialized assistant for patient inquiries and medical education. Methods: We transformed Llama2 into an ophthalmology-specialized LLM, termed EyeGPT, through the following 3 strategies: prompt engineering for role-playing, fine-tuning with publicly available data sets filtered for eye-specific terminology (83,919 samples), and retrieval-augmented generation leveraging a medical database and 14 ophthalmology textbooks. The efficacy of various EyeGPT variants was evaluated by 4 board-certified ophthalmologists through comprehensive use of 120 diverse category questions in both simple and complex question-answering scenarios. The performance of the best EyeGPT model was then compared with that of the unassisted human physician group and the EyeGPT+human group. We proposed 4 metrics for assessment: accuracy, understandability, trustworthiness, and empathy. The proportion of hallucinations was also reported. Results: The best fine-tuned model significantly outperformed the original Llama2 model at providing informed advice (mean 9.30, SD 4.42 vs mean 13.79, SD 5.70; P <.001) and mitigating hallucinations (97/120, 80.8% vs 53/120, 44.2%, P <.001). Incorporating information retrieval from reliable sources, particularly ophthalmology textbooks, further improved the model's response compared with solely the best fine-tuned model (mean 13.08, SD 5.43 vs mean 15.14, SD 4.64; P =.001) and reduced hallucinations (71/120, 59.2% vs 57/120, 47.4%, P =.02). Subgroup analysis revealed that EyeGPT showed robustness across common diseases, with consistent performance across different users and domains. Among the variants, the model integrating fine-tuning and book retrieval ranked highest, closely followed by the combination of fine-tuning and the manual database, standalone fine-tuning, and pure role-playing methods. EyeGPT demonstrated competitive capabilities in understandability and empathy when compared with human ophthalmologists. With the assistance of EyeGPT, the performance of the ophthalmologist was notably enhanced. Conclusions: We pioneered and introduced EyeGPT by refining a general domain LLM and conducted a comprehensive comparison and evaluation of different strategies to develop an ophthalmology-specific assistant. Our results highlight EyeGPT's potential to assist ophthalmologists and patients in medical settings. [ABSTRACT FROM AUTHOR]

Abstract To reduce physician burden, task shifting of clerical work from physicians to medical assistants is being promoted; however, it depends on hospitals. A questionnaire survey was conducted among 40 general physicians at Okayama University Hospital in December 2022 to investigate physicians' preferences toward task shifting. Compared to other tasks, most physicians thought that ordering examinations (14, 47%), replying to referral letters (19, 63%), and prescriptions (21, 70%) and medical record entries (22, 73%) should not be task shifted. Physicians' controversial opinions on task shifting maybe the reason behind the slow progress in task shifting.

Introduction: Malaysia is one of the hardest-hit countries by COVID-19 in Asia. The rapidly rising number of cases had sparked fear among healthcare providers. This study aimed to assess the determinants of fear towards COVID-19 among healthcare providers in primary care settings. Methods: This online-based cross-sectional study was conducted among 1280 healthcare providers aged ≥18 years from 30 primary care clinics in the state of Selangor, Malaysia. The Fear of COVID-19 Scale was used to assess the level of fear, and the results were analysed using multiple linear regression. Results: The mean age of the respondents was 36 years, and the mean working experience was 11 years. The majority of the respondents were women (82.4%) and Malays (82.3%). The factors that were significantly correlated with higher levels of fear were underlying chronic disease (ß=1.12, P=0.002, 95% confidence interval [CI]=0.08, 3.15), concern about mortality from COVID-19 (ß=3.3, P<0.001, 95% CI=0.19, 7.22), higher risk of exposure (ß=0.8, P<0.001, 95% CI=0.14, 5.91), concern for self at work (ß=2.8, P=0.002, 95% CI=0.08, 3.10) and work as a nurse (ß=3.6, P<0.001, 95% CI=0.30, 7.52), medical laboratory worker (ß=3.0, P<0.001, 95% CI=0.12, 4.27) and healthcare assistant (ß=3.9, P<0.001, 95% CI=0.17, 5.73). The level of fear was inversely correlated with a higher work-related stress management score (ß=−0.9, P<0.001, 95% CI=−0.14, −5.07) and a higher sleep quality score (ß=−1.8, P<0.001, 95% CI=−0.28, −10.41). Conclusion: Family physicians should be vigilant and identify healthcare providers at risk of developing COVID-19-related fear to initiate early mental health intervention. [ABSTRACT FROM AUTHOR]

Primary care practices face significant challenges as they pursue the Quadruple Aim. Redistributing care across the interprofessional primary care team by expanding the role of the medical assistant (MA) is a potential strategy to address these challenges. Two sequential, linked processes to expand the role of the MA, called Enhanced Rooming and Visit Assistance, were implemented in four family medicine residency clinics in Minnesota. In Enhanced Rooming, MAs addressed preventive services, obtained a preliminary visit agenda, and completed a warm hand-off to the provider. In Visit Assistance, MAs stayed in the room the entire visit to assist with the visit workflow. Enhanced Rooming and Visit Assistance processes were successfully implemented and sustained for over one year. MAs and providers were satisfied with both processes, and patients accepted the expanded MA roles. Mammogram ordering rates increased from 10% to 25% (p < 0.0001). After Visit Summary (AVS) print rates increased by 12% (p < 0.0001). Visit Turn-Around-Time (TAT) decreased 3.1 minutes per visit (p = 0.0001). Expanding the MA role in a primary care interprofessional team is feasible and a potentially useful tool to address the Quadruple Aim. [ABSTRACT FROM AUTHOR]

Abstract Objective We sought to examine the association of psychosocial working conditions with concerns to have made important medical errors and to identify possible intermediate factors in this relationship. Methods We used data from 408 medical assistants (MAs) in Germany who participated in a 4-year prospective cohort study (follow-up period: 03–05/2021). Psychosocial working conditions were assessed at baseline by the effort-reward imbalance questionnaire and by a MA-specific questionnaire with seven subscales. MAs reported at follow-up whether they are concerned to have made an important medical error throughout the last 3 months, 12 months or since baseline (yes/no). These variables were merged into a single variable (any affirmative response vs. none) for primary analyses. Potential intermediate factors measured at baseline included work engagement (i.e., vigor and dedication, assessed by the UWES), work satisfaction (COPSOQ), depression (PHQ-2), anxiety (GAD-2) and self-rated health. We ran Poisson regression models with a log-link function to estimate relative risks (RRs) and 95% confidence intervals (CIs). Doing so, we employed the psychosocial working condition scales as continuous variables (i.e. z-scores) in the primary analyses. Potential intermediate factors were added separately to the regression models. Results Poor collaboration was the only working condition, which was significantly predictive of the concern of having made an important medical error (RR = 1.26, 95%CI = 1.00–1.57, p = 0.049). Partial intermediate factors in this association were vigor, depression and anxiety. Conclusion We found weak and mostly statistically non-significant associations. The only exception was poor collaboration whose association with concerns to have made an important medical error was partially explained by vigor and poor mental health.

Abstract Background In Germany, general practices are usually contacted first by patients with health complaints, including symptoms characteristic of SARS-CoV-2. Within general practices, medical assistants (MAs) are the first contact person for patients and perform various tasks in close physical patient contact. Working conditions of MAs have been characterized as challenging, e.g., due to low salaries, a high workload, time pressure and frequent interruptions. The potential changes of working conditions and job-related challenges experienced by MAs due to the SARS-CoV-2 pandemic have not been fully explored. We aimed to address this knowledge gap among MAs working in general practices in Germany. Methods Semi-structured telephone interviews were conducted between March and April 2021 with 24 MAs. Medical assistants of legal age, who worked in general practices in Germany, and who were continuously employed and without change of employer in 2020 were eligible for participation. Interview recordings were transcribed verbatim and content-analyzed using MAXQDA, using deductive and inductive coding. Results The SARS-CoV-2 pandemic posed great challenges for MAs, including a dramatic increase in workload, changes in occupational tasks, increased hygiene measures, rearrangements of work organization, childcare issues, and structural and personnel challenges within their practice. Participants described both improved but also worsened collaboration with their employers and colleagues due to the pandemic. Many MAs complained about issues regarding SARS-CoV-2-related billing processes and an increase in unpleasant patient behavior, including disregard of practice rules or frequent verbal insults. Many also did not feel adequately appreciated by politics, media, or society for their efforts during the pandemic. Positive changes were perceived to be the expansion of digital communication channels and a growing social cohesiveness of practice teams. Conclusions Our study suggests that the SARS-CoV-2 pandemic posed great challenges for MAs. The pandemic seems to have worsened MAs’ working conditions, which had been described as challenging already prior to the pandemic. In order to improve job satisfaction and to prevent loss of healthcare personnel, measures must be taken to improve working conditions of MAs in general practices.

In Japan, there is a growing initiative to construct centralized databases and platforms that can aggregate and manage a vast range of medical, health, and caregiving data for research and analysis. Recent advancements in artificial intelligence (AI), particularly in general-purpose models like the Segment Anything model and Chat GPT, promise significant progress towards utilizing such data-rich platforms effectively for healthcare. Traditionally, AI has displayed superior performance by learning specific images or languages, but now it is advancing towards creating models capable of learning universal traits from images and languages by training on extensive datasets. The challenge lies in the fact that these general-purpose models are trained on data that does not sufficiently incorporate medical information, making their direct application to healthcare difficult. However, the introduction of data platforms can potentially solve this problem. This would lead to the development of universally applicable models to process medical images and AI assistants that can support both doctors and patients. These medical AI assistants can function as a "sub-doctor" with extensive knowledge, assisting in comprehensive analysis of symptoms, early detection of rare diseases, and more. They can also serve as an intermediary between the doctor and the patient, helping to simplify consultations and enhance patient understanding of medical conditions and treatments. By bridging this gap, the AI assistant can help to reduce doctors' workload, improve the quality of healthcare, and facilitate early detection and prevention in the elderly population. [ABSTRACT FROM AUTHOR]

To reduce physician burden, task shifting of clerical work from physicians to medical assistants is being promoted; however, it depends on hospitals. A questionnaire survey was conducted among 40 general physicians at Okayama University Hospital in December 2022 to investigate physicians' preferences toward task shifting. Compared to other tasks, most physicians thought that ordering examinations (14, 47%), replying to referral letters (19, 63%), and prescriptions (21, 70%) and medical record entries (22, 73%) should not be task shifted. Physicians' controversial opinions on task shifting maybe the reason behind the slow progress in task shifting. [ABSTRACT FROM AUTHOR]

Automated medical assistant systems with less or no human interventions are very much important during pandemic situations to prevent the spread of highly contagious diseases like COVID. During critical situations the number of patients requiring immediate medical assistants rises exponentially in a shorter period of time and providing assistants to all at the same time will be a practically difficult. This warrants for an automated system to perform preliminary examinations of the patients without human intervention. In rural areas availability of doctors will be less and diagnosing the disease at the earlier stage turns out to be difficult. The proposed Digital Medical Assistant System (DMAS) is to enable people to observe their health parameters via kiosks in public locations like pharmacy stores, malls, bus/railway stops, on highways, and other places. This prototype prevents the serious risks of patients before they reach the hospital. People will receive a prescription for preliminary medication through text message to their registered mobile number, which is a significant benefit of this system. Further it will advise the patient to see a doctor for further treatment based on the severity of symptoms. Cloudbased database stores the patient's medical history for future access. Comparative analysis with the other voice based medical assistant and direct visit to hospitals, this system performs better and only 11.9% of patients are visiting hospitals for further treatment. Since the diagnosis is made early and the recommendation for subsequent treatment is given immediately, fatality rate is lowered significantly. [ABSTRACT FROM AUTHOR]

Background: Communication among health care professionals is essential for the delivery of safe clinical care. Secure messaging has rapidly emerged as a new mode of asynchronous communication. Despite its popularity, relatively little is known about how secure messaging is used and how such use contributes to communication burden. Objective: This study aims to characterize the use of an electronic health record–integrated secure messaging platform across 14 hospitals and 263 outpatient clinics within a large health care system. Methods: We collected metadata on the use of the Epic Systems Secure Chat platform for 6 months (July 2022 to January 2023). Information was retrieved on message volume, response times, message characteristics, messages sent and received by users, user roles, and work settings (inpatient vs outpatient). Results: A total of 32,881 users sent 9,639,149 messages during the study. Median daily message volume was 53,951 during the first 2 weeks of the study and 69,526 during the last 2 weeks, resulting in an overall increase of 29% (P =.03). Nurses were the most frequent users of secure messaging (3,884,270/9,639,149, 40% messages), followed by physicians (2,387,634/9,639,149, 25% messages), and medical assistants (1,135,577/9,639,149, 12% messages). Daily message frequency varied across users; inpatient advanced practice providers and social workers interacted with the highest number of messages per day (median 19). Conversations were predominantly between 2 users (1,258,036/1,547,879, 81% conversations), with a median of 2 conversational turns and a median response time of 2.4 minutes. The largest proportion of inpatient messages was from nurses to physicians (972,243/4,749,186, 20% messages) and physicians to nurses (606,576/4,749,186, 13% messages), while the largest proportion of outpatient messages was from physicians to nurses (344,048/2,192,488, 16% messages) and medical assistants to other medical assistants (236,694/2,192,488, 11% messages). Conclusions: Secure messaging was widely used by a diverse range of health care professionals, with ongoing growth throughout the study and many users interacting with more than 20 messages per day. The short message response times and high messaging volume observed highlight the interruptive nature of secure messaging, raising questions about its potentially harmful effects on clinician workflow, cognition, and errors. [ABSTRACT FROM AUTHOR]

Objective: We sought to examine the association of psychosocial working conditions with concerns to have made important medical errors and to identify possible intermediate factors in this relationship. Methods: We used data from 408 medical assistants (MAs) in Germany who participated in a 4-year prospective cohort study (follow-up period: 03–05/2021). Psychosocial working conditions were assessed at baseline by the effort-reward imbalance questionnaire and by a MA-specific questionnaire with seven subscales. MAs reported at follow-up whether they are concerned to have made an important medical error throughout the last 3 months, 12 months or since baseline (yes/no). These variables were merged into a single variable (any affirmative response vs. none) for primary analyses. Potential intermediate factors measured at baseline included work engagement (i.e., vigor and dedication, assessed by the UWES), work satisfaction (COPSOQ), depression (PHQ-2), anxiety (GAD-2) and self-rated health. We ran Poisson regression models with a log-link function to estimate relative risks (RRs) and 95% confidence intervals (CIs). Doing so, we employed the psychosocial working condition scales as continuous variables (i.e. z-scores) in the primary analyses. Potential intermediate factors were added separately to the regression models. Results: Poor collaboration was the only working condition, which was significantly predictive of the concern of having made an important medical error (RR = 1.26, 95%CI = 1.00–1.57, p = 0.049). Partial intermediate factors in this association were vigor, depression and anxiety. Conclusion: We found weak and mostly statistically non-significant associations. The only exception was poor collaboration whose association with concerns to have made an important medical error was partially explained by vigor and poor mental health. [ABSTRACT FROM AUTHOR]

Background: In Germany, general practices are usually contacted first by patients with health complaints, including symptoms characteristic of SARS-CoV-2. Within general practices, medical assistants (MAs) are the first contact person for patients and perform various tasks in close physical patient contact. Working conditions of MAs have been characterized as challenging, e.g., due to low salaries, a high workload, time pressure and frequent interruptions. The potential changes of working conditions and job-related challenges experienced by MAs due to the SARS-CoV-2 pandemic have not been fully explored. We aimed to address this knowledge gap among MAs working in general practices in Germany. Methods: Semi-structured telephone interviews were conducted between March and April 2021 with 24 MAs. Medical assistants of legal age, who worked in general practices in Germany, and who were continuously employed and without change of employer in 2020 were eligible for participation. Interview recordings were transcribed verbatim and content-analyzed using MAXQDA, using deductive and inductive coding. Results: The SARS-CoV-2 pandemic posed great challenges for MAs, including a dramatic increase in workload, changes in occupational tasks, increased hygiene measures, rearrangements of work organization, childcare issues, and structural and personnel challenges within their practice. Participants described both improved but also worsened collaboration with their employers and colleagues due to the pandemic. Many MAs complained about issues regarding SARS-CoV-2-related billing processes and an increase in unpleasant patient behavior, including disregard of practice rules or frequent verbal insults. Many also did not feel adequately appreciated by politics, media, or society for their efforts during the pandemic. Positive changes were perceived to be the expansion of digital communication channels and a growing social cohesiveness of practice teams. Conclusions: Our study suggests that the SARS-CoV-2 pandemic posed great challenges for MAs. The pandemic seems to have worsened MAs' working conditions, which had been described as challenging already prior to the pandemic. In order to improve job satisfaction and to prevent loss of healthcare personnel, measures must be taken to improve working conditions of MAs in general practices. [ABSTRACT FROM AUTHOR]

Background: Primary care is undergoing a transformation to become increasingly team-based and multidisciplinary. The medical assistant (MA) is considered a core occupation in the primary care workforce, yet existing studies suggest problematic rates and costs of MA turnover. Purpose: We investigated what MAs perceive their occupation to be like and what they value in it to understand how to promote sustainable employability, a concept that is concerned with an employee's ability to function and remain in their job in the long term. Approach: We used a case of a large, integrated health system in the United States that practices team-based care and has an MA career development program. We conducted semistructured interviews with 16 MAs in this system and performed an inductive analysis of themes. Results: Our analysis revealed four themes on what MAs value at work: (a) using clinical competence, (b) being a multiskilled resource for clinic operations, (c) building meaningful relationships with patients and coworkers, and (d) being recognized for occupational contributions. MAs perceived scope-of-practice regulations as limiting their use of clinical competence. They also perceived task similarity with nurses in the primary care setting and expressed a relative lack of performance recognition. Conclusion: Some of the practice changes that enable primary care transformation may hinder MAs' ability to attain their work values. Extant views on sustainable employability assume a high bar for intrinsic values but are limited when applied to low-wage health care workers in team-based environments. Practice Implications: Efforts to effectively employ and retain MAs should consider proactive communications on scope-of-practice regulations, work redesign to emphasize clinical competence, and the establishment of greater recognition and respect among MAs and nurses. [ABSTRACT FROM AUTHOR]

Medical assistants (MAs) are among the fastest-growing occupations in the United States, yet health care employers report high turnover rates and difficulty filling MA positions. Employers are increasingly using apprenticeship to meet emerging workforce needs. This qualitative study examined the perspectives of 14 employers using registered MA apprenticeships in 8 states. The findings revealed motivations for using apprenticeship, perceived benefits to the organization, challenges with implementation, and reflections on successful implementation. We detail how MA apprenticeship is successfully meeting recruitment and training needs in a variety of health care organizations, especially where program support resources are available. [ABSTRACT FROM AUTHOR]

Introduction: The patient-centered medical home model stresses the importance of team-based care as a foundation to improving care, costs, and patient experience. Medical assistants (MAs) are being used as key care team members even as traditional educational programs may not equip them for this new way of working. Methods: This paper describes an on-the-job, predominantly virtual training program aimed at building care teams by redefining the role of the MA and fostering team-based functioning. Participating MAs, clinic managers, and clinicians in 11 primary care clinics completed 18-item pre- and post-training surveys to assess confidence in MA skills and performance. In-depth 1-hour qualitative interviews were conducted with selected participating MAs, managers, and clinicians to assess MA self-efficacy and to understand their use of new skills. Results: MAs, clinicians, and managers agreed that MAs improved skills in planned care and population management, self-management support and health coaching, and interdisciplinary communication and collaboration. MAs reported a positive training experience, that they shared their knowledge with other MAs in their clinic, and that their job satisfaction increased. Discussion: A predominantly virtual 12-week program built the skills and confidence of MAs in proactive population management, health coaching, and collaboration and communication. This program shows the promise of a virtual approach to training that identifies, trains, and recognizes high-potential MAs. [ABSTRACT FROM AUTHOR]

Background Back pain is common among people working in the sitting position. Quality of the sitting position depends among others on the standard of work station. Dental units are built on the basis of contemporary ergonomic workplace concepts, while medical assistant workstations are typically computer office workstations. The purpose of the study was pain estimation in association with the identification of the spine arrangements during sitting work with better and worse ergonomic adjustment. Material and Methods The study involved 80 women working in the sitting position suffering from pain – 40 dentists working on dental units with a higher ergonomic potential and 40 medical assistants working on stations with lower ergonomic potential. The study included interviews, pain assessment and working spine arrangements analysis using the ultrasonographic measurement system. Results There were exceeds of work parameters in particular segments of the spine observed in all examined persons. The relationship between pain intensity and work parameters as frontal bending amplitude of lumbar spine segment, transversal amplitude of cervical spine segment and transversal amplitude of lumbar spine segment were observed in the group of dentists. There was observed the relationship between pain severity and the values of sagittal bending amplitude of cervical spine segment and thoracic spine segment in the group of medical assistants. Other determinants of pain also have been identified. Conclusions Exceeded values of work parameters were observed amongst subjects working both at the higher and the lower ergonomic potential work stations. The working postures of people working in the sitting position are similar only to their external characteristics. Pain depends on working position. The pain intensity is determined by other factors: length of professional experience, duration of the pain, the number of working hours per week, body mass index (BMI). Med Pr 2018;69(5):509–522

Abstract Background Deaths in developing countries often occur outside health facilities, making it extremely difficult to gather reliable cause of death (COD) information. Automated COD assignment using a verbal autopsy instrument (VAI) has been proposed as a reliable and cost-effective alternative to traditional physician-certified verbal autopsy, but its performance is still being evaluated. The purpose of this study was to compare the similarity of diagnosis by Medical Assistants (MA) in the Matlab Health and Demographic Surveillance System (HDSS) with the SmartVA Analyze 1.2 (Tariff 2.0) diagnosis. Methods This study took place between January 2011 and April 2014 in Matlab, Bangladesh. MA with 3 years of medical training assigned COD to Matlab residents by reviewing the information collected using the Population Health Metrics Research Consortium (PHMRC) long-form VAI. Smart VA Analyze 1.2 automatically assigned COD using the same questionnaire. COD agreement and cause-specific mortality fractions (CSMFs) were compared for MA and Tariff. Results Of the 4969 verbal autopsy cases reviewed, 4328 were adults, 296 were children, and 345 were neonates. Cohen’s kappa was 0.38 (0.36, 0.40) for adults, 0.43 (0.38, 0.49) for children, and 0.27 (0.22, 0.33) for neonates. For adults, the top two COD for MA were stroke (29.6%) and ischemic heart diseases (IHD) (14.2%) and for Tariff these were stroke (32.0%) and IHD (14.0%). For children, the top two COD for MA were drowning (33.5%) and pneumonia (13.2%) and for Tariff these were also drowning (36.8%) and pneumonia (12.4%). For neonates, the top two COD for MA were birth asphyxia (41.2%) and meningitis/sepsis (22.3%) and for Tariff these were birth asphyxia (37.0%) and preterm delivery (30.9%). Conclusion The CSMFs for Tariff and MA showed very close agreement across all age categories but some differences were observed for neonate preterm delivery and meningitis/sepsis. Given the known advantages of automated methods over physician certified verbal autopsy, the SmartVA software, incorporating the shortened VAI questionnaire and Tariff 2.0, could serve as a cost-effective alternative to Matlab MA to routinely collect and analyze verbal autopsy data in a HDSS to generate essential population level COD data for planning.

Medical assistants (MAs) are a rapidly growing and increasingly important workforce. High MA turnover, however, is common and employers report applicants frequently do not meet their needs. We collected survey responses from a representative sample of 3,355 of Washington's MAs with certified status (MA-Cs) to understand their demographic, education, and employment backgrounds; job satisfaction; and career plans. Descriptive analyses showed 93.0% were female with a $19.91 mean hourly wage, and while generally satisfied, 56.2% indicated they would seek training or employment in another health care occupation within 5 years, with higher percentages among MA-Cs who felt overwhelmed by their workload and/or not satisfied with promotion opportunities. Regression analyses showed Hispanic, Black, and Asian MA-Cs were more likely than White MA-Cs to express interest in other health care careers. Strategies that strengthen MA career pathways and retain qualified workers should reward both employers and MAs and contribute to a stable and diverse workforce. [ABSTRACT FROM AUTHOR]

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The authors discuss the role of drummer boys during the U.S. Civil War. They mention the number of boys who enlisted in the Union army as drummers, the state of medical care to soldiers during the war, and the role of drummer boys as medical assistants carrying stretchers, bringing equipment and supplies, and on occasion assisting in surgery.

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Copyright of HeilberufeSCIENCE is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Stroke is the number two killer after heart disease since it is responsible for almost 10% of all deaths worldwide. The main problem with a stroke is a significant delay in treatment that happened mainly due to inappropriate detection of stroke symptoms or inability of a person to perform further necessary actions, and might cause death, permanent disabilities, as well as more expensive treatment and rehabilitation. Nowadays assessment of a stroke is done by human, following widely adopted FAST approach of stroke assessment. Since a human factor become one of the causes of treatment delay, offered solution will try to minimize this factor. Artificial Intelligence, Cognitive Computing, Machine Learning and Data Mining, NLP and other technologies make possible to elaborate a smart solution that enable automated stroke symptoms detection on earlier stages without self-assessment or assistance of another person, solution that in time provides notification to corresponding caregivers (family members, responsible medical worker, etc.) and even able to directly call emergency, explaining the cases and providing all necessary evidences to support further decision making. Thus, the paper presents feasibility study of IBM Watson cognitive computing services and tools to address the issue of automated stroke symptoms detection to elaborate smart supportive tool in the pocket of people under high risk of a stroke attack. [ABSTRACT FROM AUTHOR]