Patient Care System Research Articles

Time Out: The Impact of Physician Burnout on Patient Care Quality and Safety in Perioperative Medicine.

Perioperative care delivery is a patient-centered, multidisciplinary process. It relies heavily on synchronized teamwork from a well-coordinated team. Perioperative physicians-surgeons and anesthesiologists-face enormous challenges in surgical care delivery due to changing work environments, post-COVID consequences, shift work disorder, value conflict, escalating demands, regulatory complexity, and financial uncertainties. Physician burnout in this working environment has become increasingly prevalent. It is not only harmful to physicians' health and well-being, but it also affects the quality and safety of patient care. Additionally, the economic costs associated with physician burnout are untenable due to the high turnover rate, high recruitment expenses, and potential early permanent exit from medical practice. In this deteriorating environment of unbalanced physician supply/demand, recognizing, managing, and preventing physician burnout may help preserve the system's most valuable asset and contribute to higher quality and safety of patient care. Leaders in government agencies, health care systems, and organizations must work together to re-engineer the health care system for better physicians and patient care.

Advancements in diabetes Care in Poland

Background: The aging population and increasing prevalence of diabetes are placing significant strain on healthcare systems globally, including in Poland. In recent years, notable progress has been made in establishing patient care systems and improving access to treatment, with significant changes in the reimbursement of innovative therapies. However, as the impact of diabetes on the healthcare system continues to grow and advancements are introduced in patient care, there is a pressing need, to continually search for the most optimal solutions to further enhance diabetes care. Objectives: This paper summarize developments of diabetes care in Poland with a special focus on reimbursement of novel therapies introduced in recent 5 years. Methods: We conducted a review of published sources and publications on the management of diabetes care in Poland. This included an analysis of the latest advancements in coordinated care, the collaborative efforts between general practice physicians and specialists, as well as changes in reimbursement policies and other notable progressions in patient-centric care. Furthermore, we analyzed published reports from the National Fund and assessed reimbursement expenditures. Conclusion: The landscape of diabetes treatment in Poland has undergone significant changes in recent years driven by advancements in pharmacological therapies, technological innovations, and healthcare policies. While challenges remain, these changes offer opportunities for further progress in diabetes care in Poland. Continuous efforts to improve patient centered care along with embracing emerging technologies, will contribute to more effective diabetes management and improved quality of life for individuals with diabetes in the country.

A Study on Scope of Artificial Intelligence in Diagnostic Medicine

Artificial Intelligence techniques are used to improve patient care and health systems. The use of machine learning and deep learning in healthcare is indeed prevalent, and it's fascinating to see how different medical data sources can be combined to diagnose diseases accurately. The variety of diseases that can be diagnosed using Artificial Intelligence (AI) techniques is impressive, and it’s interesting to note that different medical imaging datasets are used for feature extraction and classification to make predictions. This highlights the potential of AI in healthcare to assist healthcare providers in identifying diseases and providing appropriate treatments. It is also encouraging to see that AI can enhance the patient experience in hospitals and speed up rehabilitation at home. This can help to improve patient outcomes and reduce healthcare costs. Overall, it's evident that AI has the potential to revolutionize the healthcare industry and improve patient care. As the technology continues to evolve, it will be interesting to see how it is further applied in healthcare and the impact it has on patient outcomes. In this study, the scope of AI in diagnostic medicine has been analysed and summarized.

Improving Kidney Outcomes in Patients With Nondiabetic Chronic Kidney Disease Through an Artificial Intelligence-Based Health Coaching Mobile App: Retrospective Cohort Study.

Chronic kidney disease (CKD) is a global health burden. However, the efficacy of different modes of eHealth care in facilitating self-management for patients with CKD is unclear. The aim of this study was to evaluate the effectiveness of a mobile app-based intelligent care system in improving the kidney outcomes of patients with CKD. Our study was a retrospective analysis based on the KidneyOnline intelligent system developed in China. Patients with CKD but not dependent on dialysis who registered on the KidneyOnline app between January 2017 and January 2021 were screened. Patients in the the KidneyOnline intelligent system group and those in the conventional care group were 1:1 matched according to their baseline characteristics. The intervention group received center-based follow-up combined with the KidneyOnline intelligent patient care system, which was a nurse-led, patient-oriented collaborative management system. Health-related data uploaded by the patients were integrated using deep learning optical character recognition (OCR). Artificial intelligence (AI)-generated personalized recipes, lifestyle intervention suggestions, early warnings, real-time questions and answers, and personalized follow-up plans were also provided. Patients in the conventional group could get professional suggestions from the nephrologists through regular clinical visits, but they did not have access to the service provided by AI and the health coach team. Patients were followed for at least 3 months after recruitment or until death or start of renal replacement therapy. A total of 2060 eligible patients who registered on the KidneyOnline app from 2017 to 2021 were enrolled for the analysis. Of those, 902 (43.8%) patients were assessed for survival analysis after propensity score matching, with 451(50%) patients in the KidneyOnline intelligent patient care system group and 451(50%) patients in the conventional care group. After a mean follow-up period of 15.8 (SD 9.5) months, the primary composite kidney outcome occurred in 28 (6%) participants in the KidneyOnline intelligent patient care system group and 32 (7%) in the conventional care group, with a hazard ratio of 0.391 (95% CI 0.231-0.660; P<.001). Subgroup survival analysis demonstrated that the KidneyOnline care system significantly reduced the risk of composite kidney outcome, irrespective of age, sex, baseline estimated glomerular filtration rate (eGFR), and proteinuria. In addition, the mean arterial pressure (MAP) significantly decreased from 88.9 (SD 10.5) mmHg at baseline to 85.6 (SD 7.9) mmHg at 6 months (P<.001) in the KidneyOnline intelligent patient care system group and from 89.3 (SD 11.1) mmHg to 87.5 (SD 8.2) mmHg (P=.002) in the conventional CKD care group. The utilization of the KidneyOnline intelligent care system was associated with reduced risk of unfavorable kidney outcomes in nondiabetic patients with CKD.

Hospital Management System for Enhancing Patient Care and Executive Process

Abstract: Our hospital management system in our project includes registering patients, storing information in the system and scheduling appointments with doctors. Our software has the ability to assign a unique ID to each patient and automatically store the details of each patient and staff. Users can search patient details using availability and doctor name. The hospital administration can be accessed with a username and password. It can be accessed by administrator or host. It can only add information to the database. Information is easily available. The interface is user friendly. Data is well protected for personal use and speed up data processing. There are mainly two modules. One at the Management level and the other for patient users and doctors. the program that validates the software for access. Administrative duties include managing physician information and patient information. To achieve this goal, a database has been created, one for patients and another for doctors that can be accessed by the administrator. Complaints filed by users will be answered by the authorities. The patient module includes appointments, prescription checks. Users can also pay doctor bills online.

Physicochemical and Functional Characterization of Medicinal Product Rinsulin® R

Introduction. Currently, biosimilars have found quite widespread use in the treatment of a number of chronic and life-threatening diseases. Thanks to them, there is a significant decrease of the economic pressure of biological drugs on the health care system and wide access of patients to effective and safe medicines is ensured. One of the most important stages of proving biosimilarity is the physicochemical and functional characterization of proteins. This set of studies is generally accepted, as sensitive as possible and allows us to give a conclusion about the compliance of the biosimilar with the original drug.Aim. Conducting physicochemical and functional characterization of medicinal product Rinsulin® R (GP40051) in comparison with the original drug Humulin® Regular.Materials and methods. Primary structure was analyzed by high-performance liquid chromatography with mass spectrometry detection and matrix assisted laser desorption/ionization. The identity of the higher protein structures was proved by the methods of circular dichroism, capillary isoelectric focusing, spectrometry and dynamic light scattering. The comparability of the impurity profiles of the preparations was evaluated using the methods of exclusive chromatography and reverse-phase high-performance liquid chromatography. Functional characterization included a metabolic cell test "glucose uptake" and insulin receptor–binding assay (kinetics of binding to type A and B receptors, phosphorylation of insulin receptor).Results and discussion. In the course of this research, the identity of the physicochemical and functional characteristics of GP40051 was shown. A complete overlap of primary sequence, high-order structures and impurity profiles was demonstrated between the comparison drug GP40051 and the reference drug Humulin® Regular. Functional studies have shown that GP40051 and Humulin® Regulars have the same activity.Conclusion. The results of the quality comparability study demonstrated similarity of Rinsulin® R to the reference medicinal product Humulin® Regular, providing the scientific basis for conducting a specifically designed clinical programme, and supported registration in Russian Federation.

Experience with the US health care system for Black and White patients with advanced prostate cancer.

The purpose of this study was to assess differences in reported information about treatment, integration into care, and respect by self-identified Black and White individuals with advanced prostate cancer in the United States. This is a prospective cohort study of 701 participants (20% identifying as Black) enrolled in the International Registry for Men with Advanced Prostate Cancer at 37 US sites from 2017 to 2022. Participants were asked six questions from the Cancer Australia National Cancer Control Indicators about their experience with care at study enrollment. Prevalence differences by self-reported race were estimated using marginal standardization of logistic-normal mixed effects models (adjusted for age at enrollment and disease state at enrollment), and 95% CIs were estimated using parametric bootstrapping. Most participants reported a high quality of care for each question. Black participants generally reported higher care quality compared with White participants. Black participants reported more frequently that they were offered a written assessment and care plan (71%) compared with White participants (58%; adjusted difference, 13 percentage points; 95% CI, 4-23). Black participants also reported more frequently being given the name of nonphysician personnel who would support them (64%) than White participants (52%; adjusted difference, 10; 95% CI, 1-20). Prevalence differences did not differ by disease state at enrollment. Black participants generally reported a higher quality of care compared with White participants. This study calls attention to the need to study potential mediating factors and interpersonal aspects of care in this population to improve survivorship.

Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study.

Unplanned patient readmissions within 30 days of discharge pose a substantial challenge in Canadian health care economics. To address this issue, risk stratification, machine learning, and linear regression paradigms have been proposed as potential predictive solutions. Ensemble machine learning methods, such as stacked ensemble models with boosted tree algorithms, have shown promise for early risk identification in specific patient groups. This study aims to implement an ensemble model with submodels for structured data, compare metrics, evaluate the impact of optimized data manipulation with principal component analysis on shorter readmissions, and quantitatively verify the causal relationship between expected length of stay (ELOS) and resource intensity weight (RIW) value for a comprehensive economic perspective. This retrospective study used Python 3.9 and streamlined libraries to analyze data obtained from the Discharge Abstract Database covering 2016 to 2021. The study used 2 sub-data sets, clinical and geographical data sets, to predict patient readmission and analyze its economic implications, respectively. A stacking classifier ensemble model was used after principal component analysis to predict patient readmission. Linear regression was performed to determine the relationship between RIW and ELOS. The ensemble model achieved precision and slightly higher recall (0.49 and 0.68), indicating a higher instance of false positives. The model was able to predict cases better than other models in the literature. Per the ensemble model, readmitted women and men aged 40 to 44 and 35 to 39 years, respectively, were more likely to use resources. The regression tables verified the causality of the model and confirmed the trend that patient readmission is much more costly than continued hospital stay without discharge for both the patient and health care system. This study validates the use of hybrid ensemble models for predicting economic cost models in health care with the goal of reducing the bureaucratic and utility costs associated with hospital readmissions. The availability of robust and efficient predictive models, as demonstrated in this study, can help hospitals focus more on patient care while maintaining low economic costs. This study predicts the relationship between ELOS and RIW, which can indirectly impact patient outcomes by reducing administrative tasks and physicians' burden, thereby reducing the cost burdens placed on patients. It is recommended that changes to the general ensemble model and linear regressions be made to analyze new numerical data for predicting hospital costs. Ultimately, the proposed work hopes to emphasize the advantages of implementing hybrid ensemble models in forecasting health care economic cost models, empowering hospitals to prioritize patient care while simultaneously decreasing administrative and bureaucratic expenses.

From personal experience of working in the UAE. Note to fellow practitioners

ResumeThe article attempts to tell about what the author saw in one of the UAE hospitals. This hospital is not an ordinary one, it belongs to the leaders in the Middle East. Cooperates with John Hopkins Medicine. As part of the work, visits by lecturers and specialists are held to conduct demonstration operations and train interns.The organization of work in the operating room is described, in particular, the division of duties of nurses is carried out. Practical performance of work duties, such as storing medicines, accounting for equipment, monitoring its condition, the organization of the workplace, labeling of syringes, etc.The lack of work is greatly helped by institution, which we do not have. Foreign word Supervisor. Usually a female person comes (they are less prone to sentiments), stands, observes and records. Cleaners and nurses have supervisors. They become the best and most advanced according to the specialty.Attention is focused on the elements of the JCIA system and their importance for the organization of safe work for both the patient and the staff. Some details of work duties that are not always known to us. What is Time Out and why does it exist.A lot of attention is paid to the operation of a fully computerized hospital. There are no paper medical histories (patient files) in the hospital. They are there, they are binders with a few sheets of paper, everything else is on the server. This server is used by several hospitals and affiliated clinics.The work of the anesthesiologist in and outside the operating room, the documentation and equipment at the disposal of the anesthesiologist are described. What access and to what documentation of the patient do certain doctors have (to diaries that are written in electronic form, to the results of all possible studies, pictures, ECG, conclusions, results of computer tomography and others). A template is filled out for the statement, and this is one of the few paper documents.Describes how the patient care system works during surgery. Many technical details are given. How to warm a patient in the operating room, which aspiration systems are used, etc. The author hopes that the article will be interesting for domestic doctors and nurses, and will also find the use of some of the examples given in practical activities.

Comparing adherence to MDR-TB treatment among patients on self-administered therapy and those on directly observed therapy: non-inferiority randomized controlled trial

BackgroundAdherence is key to the treatment success of multi-drug resistant tuberculosis (MDR-TB) and prevention of community transmission. Directly observed therapy (DOT) is the recommended approach for the management of patients with MDR-TB. Uganda implements a health facility-based DOT approach where all patients diagnosed with MDR-TB report to the nearest private or public health facility for daily observation of ingesting their medicines by a health care provider. Directly observed therapy is very costly for both the patient and health care system. It follows the assumption that MDR TB patients have a history of poor adherence to TB treatment. But only 21% of MDR-TB patients notified globally and 1.4–12% notified in Uganda had been previously treated for TB. The shift to all oral treatment regimen for MDR-TB provides an opportunity for the exploration of self-administered therapy for this group of patients even with use of remotely operated adherence technology. We are conducting a non-inferiority open-label randomized controlled trial to compare adherence to MDR-TB treatment among patients on self-administered therapy (measured by Medication Events Monitoring System (MEMS) technology) with a control group on DOT.MethodsWe plan to enrol 164 newly diagnosed MDR-TB patients aged ≥ 8 years from three regional hospitals based in rural and urban Uganda. Patients with conditions that affect their dexterity and ability to operate the MEMS-operated medicine equipment will not be eligible to participate in the trial. Patients are randomized to either of the two study arms: self-administered therapy with adherence being monitored using MEMS technology (intervention arm) or health facility-based DOT (control arm) and will be followed up monthly. Adherence is measured by the number of days the medicine bottle is open to access medication as recorded by the MEMS software in the intervention arm and treatment complaint days as recorded in the TB treatment card in the control arm. The primary outcome is the comparison of adherence rates between the two study arms.DiscussionThe evaluation of self-administered therapy for patients with MDR-TB is important to inform cost-effective management strategies for these patients. The approval of all oral regimens for the treatment of MDR-TB provides an opportunity for innovations such as MEMS technology to support sustainable options for MDR-TB treatment adherence support in low-resource settings.Trial registrationPan African Clinical Trials Registry, Cochrane #PACTR202205876377808. Retrospectively registered on 13 May 2022

Internal Control Automatic Physical Distance Detection System for Patient Care against a Possible COVID-19 Infection within a Health Center

Currently, COVID-19 is still present in our environment, affecting various people regardless of age. From its appearance in the city of Wuhan (China), it caused a crisis in the health system due to its rapid transmission of contagion and the lack of medicines to combat this virus, therefore, it was declared a pandemic, due to the high mortality rate that occurred around the world. To combat this viral disease, various biosecurity measures were established such as the mandatory use of a face mask and physical distancing at least 1 meter, with physical distancing being the biosecurity measure that is not respected, worse in closed places such as medical centers where a focus of contagion could originate for various patients. By respecting physical distancing, it would avoid causing a crowd that would cause disorder in the health center, therefore, it is important to maintain distancing to avoid the spread of COVID-19. In view of this problem, in this article an automatic physical distance detection system was carried out for patient care in the face of a possible COVID-19 infection within a health center, enforcing this important biosecurity measure and preventing the spread of COVID-19. Through the development of the proposed system, 98.79% efficiency was obtained in calculating the physical distancing of patients who are inside the health center, taking only 40 seconds to analyze the images of the patients.

Patient and researcher stakeholder preferences for use of electronic health record data: a qualitative study to guide the design and development of a platform to honor patient preferences.

This qualitative study aimed to understand patient and researcher perspectives regarding consent and data-sharing preferences for research and a patient-centered system to manage consent and data-sharing preferences. We conducted focus groups with patient and researcher participants recruited from three academic health centers via snowball sampling. Discussions focused on perspectives on the use of electronic health record (EHR) data for research. Themes were identified through consensus coding, starting from an exploratory framework. We held two focus groups with patients (n = 12 patients) and two with researchers (n = 8 researchers). We identified two patient themes (1-2), one theme common to patients and researchers (3), and two researcher themes (4-5). Themes included (1) motivations for sharing EHR data, (2) perspectives on the importance of data-sharing transparency, (3) individual control of personal EHR data sharing, (4) how EHR data benefits research, and (5) challenges researchers face using EHR data. Patients expressed a tension between the benefits of their data being used in studies to benefit themselves/others and avoiding risk by limiting data access. Patients resolved this tension by acknowledging they would often share their data but wanted greater transparency on its use. Researchers expressed concern about incorporating bias into datasets if patients opted out. A research consent and data-sharing platform must consider two competing goals: empowering patients to have more control over their data and maintaining the integrity of secondary data sources. Health systems and researchers should increase trust-building efforts with patients to engender trust in data access and use.

Identify and classify interprofessional primary care performance indicators: a scoping review protocol

IntroductionMeasuring the performance of interprofessional primary care is needed to examine whether this model of care is achieving its desired outcomes on patient care and health system effectiveness as well...

Postpartum Readmission for Hypertension: The Patient Experience [ID: 1377602

INTRODUCTION: Hypertensive disorders of pregnancy (HDP) are the most common cause of postpartum readmission. Prior research has led to the creation of clinical guidelines for postpartum management; however, the patient experience is often missing from this work. The objective of this project is to understand the perspective of patients readmitted for postpartum hypertension. METHODS: Institutional review board committee approval was obtained for the study. This was a qualitative study with data generated through semistructured interviews. Patients readmitted with postpartum HDP at an urban academic medical center were approached and consented for a single interview. The same researcher conducted all interviews and patient recruitment continued until thematic saturation was reached (n=9). Two coders coded all interviews using NVivo software with both deductive and inductive coding processes. Discrepancies were discussed and resolved with consensus. Researchers identified themes through grounded theory and were reflexive in their thematic generation. RESULTS: Five themes were generated through the grounded theory analysis: Every pregnancy is different, symptoms of preeclampsia are easily dismissed or often not taken seriously, miscommunication regarding medical changes can hinder patient care, readmission logistics are not baby-friendly, and patient well-being improved when conversations acknowledged the struggles of readmission. CONCLUSION: A qualitative research process revealed patient-identified gaps in care that may have led to readmission for HDP. The specific recommendations that emerge from these themes include: addressing barriers to blood pressure management prior to discharge, improving postpartum discharge follow-up, providing newborn care coordination, and improving counseling on the risk of postpartum preeclampsia during discharge. Incorporating these patient perspectives in hospital discharge policy can be helpful in creating patient-centered systems of care and may help reduce rates of readmission.

A Case Report on Complex Polytrauma with Multiple Complications

Polytrauma involving injury to multiple organ systems poses tremendous challenges in emergency management and long term recovery. Averting disastrous complications while managing life-threatening conditions frequently requires painful judgements that weigh delivering prompt definitive treatment of any specific problem against cautious, step-by-step management of the entire frail system of patient care. This case report aims to convey the challenges inherent to polytrauma through injury to multiple organ systems simultaneously. Prevention of the worst fate remains the sole definition of success, attained not through perfection but persistence. Though complex, diligent management of threats is possible through a continuum of care. Monitoring entire systems, not individual tissues or limbs alone, determines the difference between loss and preserved ability. This complex case of traumatic leg fractures in a patient with uncontrolled hypertension and bradycardia highlights the challenges of surgical management in such cases. Despite hours of cardiovascular resuscitation, pharmacologic control of blood pressure and pacemaker placement, delayed healing of the fractures and development of post-traumatic stress disorder with severe anxiety and insomnia occurred. This case emphasizes the importance of vigilance, coordination of specialists, willingness to delay procedures when stability cannot be achieved pharmacologically and diligence in post-operative monitoring for complications. The patient continues rehabilitation and close follow-up aims to maximize function and manage health issues to prevent future catastrophic events.

Innovations In the Indian Healthcare Industry to Brave the Vuca Times

Volatility, Uncertainty, Complexity, Ambiguity—in a VUCA world the moment of surprise is everywhere, making things extremely unpredictable and planning and strategizing far more challenging. The healthcare industry is witnessing seismic shifts as medicine becomes more personalised, health data becomes valuable, and data sharing becomes essential, demanding the healthcare sector to re-strategise. This research aims to understand the various innovations and change-management techniques based on technology that have been adopted by healthcare industries in today's VUCA world while exploring the innovations needed to drive the Indian healthcare sector in the years to come. Thus, it examines and highlight the implications of the rising emphasis being placed on innovations in the healthcare industry - helping the sector to re-strategise and face the changes in today’s VUCA world with greater clarity. This paper is also designed to call attention to and discuss contemporary perspectives taken by top healthcare organisations in India before and during the COVID-19 pandemic. This research is based on secondary data, including intensive analysis of research papers, media articles and grey literature reports, along with examining the recent innovations undertaken by the top healthcare companies and hospitals in India. The findings suggest that moving away from a supply-driven healthcare system toward a patient-centred system is necessary, and therefore organisations in India have to embrace innovation, agility, and adaptability in unison to brave the VUCA times.

Identification of Factors Associated With 30-Day Readmissions After Posterior Lumbar Fusion Using Machine Learning and Traditional Models: A National Longitudinal Database Study.

Retrospective cohort study. To identify factors associated with readmissions after PLF using machine learning and logistic regression (LR) models. Readmissions following posterior lumbar fusion (PLF) place significant health and financial burden on the patient and overall healthcare system. The Optum Clinformatics® Data Mart database was used to identify patients who underwent posterior lumbar laminectomy, fusion, and instrumentation between 2004 and 2017. Four machine learning models and a multivariable LR model were used to assess factors most closely associated with 30-day readmission. These models were also evaluated in terms of ability to predict unplanned 30-day readmissions. The top performing model (Gradient Boosting Machine; GBM) was then compared to the validated LACE index in terms of potential cost savings associated with implementation of the model. A total of 18,981 patients were included, of which 3,080 (16.2%) were readmitted within 30 days of initial admission. Discharge status, prior admission, and geographic division were most influential for the LR model, while discharge status, length of stay, and prior admissions had greatest relevance for the GBM model. GBM outperformed LR in predicting unplanned 30-day readmission (mean AUC 0.865 vs. 0.850, P<0.0001). Use of GBM also achieved a projected 80% decrease in readmission-associated costs relative to those achieved by the LACE index model. Factors associated with readmission vary in terms of predictive influence based on standard logistic regression and machine learning models used, highlighting the complementary roles these models have in identifying relevant factors for prediction of 30-day readmissions. For posterior lumbar fusion procedures, Gradient Boosting Machine yielded greatest predictive ability and associated cost savings for readmission. 3.

Antimicrobial Prescription Pattern in ENT Outpatient Department in a Tertiary Care Teaching Hospital, Telangana, India

Objectives Otorhinolaryngological infections are very common in adults and in children also. These infections are caused by various microorganisms and to treat these infections, antimicrobial agents are used frequently. As irrational use of antimicrobials leads to the development of antibiotic resistance and it is a big threat to the society, the present study was planned to get awareness among the practitioners regarding the antimicrobial prescription pattern. To study the prescription pattern of antimicrobial agents in outpatient department of otorhinolaryngology in a tertiary care teaching hospital. Material and Methods An observational study was planned over a period of 8 months from July 2019 to February 2020 in ENT outpatient department (OPD) at Mamata General Hospital, Khammam in Telangana. In total, 562 prescriptions were collected. Patient's details, probable diagnosis, prescribed medication details such as generic name, dose, and dosage forms were taken. The collected data was analyzed and the conclusions were drawn using descriptive analysis. Results Total number of prescriptions were 562. The majority of the prescriptions belonged to the 21 to 40 years of age group. Males were included more than females (males 320 and females 242). Chronic suppurative otitis media (CSOM) (16.2%) was the commonest infection. Most commonly prescribed antimicrobial was amoxicillin and clavulanic acid combination (26.17%), followed by cefpodoxime with clavulanic acid (21.9%) and ciprofloxacin (19.06%). In 371 (66.01%) prescriptions, only one antimicrobial was prescribed, whereas in 36 (6.40%) prescriptions no antibiotic was given. The average number of drugs per prescription was 3.18 and the average number of antimicrobials per prescription was 1.25. Tablet form (52.9%) was the commonest dosage form and most of the drugs were prescribed by generic name (74.06%). Conclusion The present study though prescription pattern of antibiotics and adherence to the hospital formulary by the physicians in the hospital is encouraging, still there is a chance in reducing the number of drugs prescribed per patient. The findings of this study will be useful to prescribe antimicrobials rationally so that the patient and health care system on the whole will be benefited.

Risk factors of involuntary referral by police to ER psychiatric services for patients with a severe mental illness: A GEE analysis.

This study aimed to identify risk factors for involuntary referral by police to emergency room (ER) psychiatric services for community-based patients with a mental illness via a generalized estimating equation (GEE) analysis. The analysis was based on data from the Management Information System of Psychiatric Care (MISPC) system for patients with a severe mental illness in Taipei, Taiwan and registered referral records of the police. Data on 6378 patients aged ≥20years were used in this study, including 164 patients who were involuntarily referred to the ER by the police and 6214 patients who were not during the period of January 1, 2018 to December 31, 2020. GEEs were utilized to explore possible risk factors of repeated involuntary referral to ER psychiatric services for patients with a severe mental illness. The logistic regressions indicated that patients defined as "severe" according to the Mental Health Act of Taiwan (crude odds ratio (OR): 3.840, 95% confidence interval (CI): 2.407-6.126), with a disability (crude OR: 3.567, 95% CI: 1.339-9.501), with two or more family members with a psychiatric disorder (crude OR: 1.598, 95% CI: 1.002-2.548), with a history of a suicide attempt (crude OR: 25.582, 95% CI: 17.608-37.167), and with a history of domestic violence (crude OR: 16.141, 95% CI: 11.539-22.579) were positively associated with involuntary referral to ER psychiatric services. However, age (crude OR: 0.971, 95% CI: 0.960-0.983) and the MISPC score (crude OR: 0.834, 95% CI: 0.800-0.869) were inversely associated with involuntary referral to ER psychiatric services. After adjusting for demographics and potential confounders, we found that patients defined as "severe" (Exp (β): 3.236), with a disability (Exp (β): 3.715), with a history of a suicide attempt (Exp (β): 8.706), and with a history of domestic violence (Exp (β): 8.826), as well as age (Exp (β): 0.986) and the MISPC score (Exp (β): 0.902) remained significantly associated with repeated involuntary referral to ER psychiatric services. In conclusion, community-based mentally ill patients with a history of a suicide attempt, with a history of domestic violence, with a severe illness, and with a profound level of disability were highly associated with involuntary referral to ER psychiatric services. We suggest that community mental health case managers identify significant factors associated with involuntary referral to ER psychiatric services to accordingly arrange case management plans.

Trends of Academic Faculty Identifying as Hispanic at US Medical Schools, 1990-2021.

According to recent census data, Hispanic and Latino populations comprise the largest minority group in the United States. Despite ongoing efforts for improved diversity, equity, and inclusion, Hispanics remain underrepresented in medicine (UIM). In addition to well-established benefits to patient care and health systems, physician diversity and increased representation in academic faculty positively impact the recruitment of trainees from UIM backgrounds. Disproportionate representation (as compared to increases of certain underrepresented groups in the US population) has direct implications for recruitment of UIM trainees to residency programs. To examine the number of full-time US medical school faculty physicians who self-identify as Hispanic in light of the increasing Hispanic population in the United States. We analyzed data from the Association of American Medical Colleges from 1990 to 2021, looking at those academic faculty who were classified as Hispanic, Latino, of Spanish Origin, or of Multiple Race-Hispanic. We used descriptive statistics and visualizations to illustrate the level of representation of Hispanic faculty by sex, rank, and clinical specialty over time. Overall, the proportion of faculty studied who identified as Hispanic increased from 3.1% (1990) to 6.01% (2021). Moreover, while the proportion of female Hispanic academic faculty increased, there remains a lag between females versus males. Our analysis shows that the number of full-time US medical school faculty who self-identify as Hispanic has not increased, though the population of Hispanics in the United States has increased.