UNASSIGNED: Given the strikingly high diagnostic error rate in hospitals, and the recent development of Large Language Models (LLMs), we set out to measure the diagnostic sensitivity of two popular LLMs: GPT-4 and PaLM2. Small-scale studies to evaluate the diagnostic ability of LLMs have shown promising results, with GPT-4 demonstrating high accuracy in diagnosing test cases. However, larger evaluations on real electronic patient data are needed to provide more reliable estimates.
UNASSIGNED: To fill this gap in the literature, we used a deidentified Electronic Health Record (EHR) data set of about 300,000 patients admitted to the Beth Israel Deaconess Medical Center in Boston. This data set contained blood, imaging, microbiology and vital sign information as well as the patients\' medical diagnostic codes. Based on the available EHR data, doctors curated a set of diagnoses for each patient, which we will refer to as ground truth diagnoses. We then designed carefully-written prompts to get patient diagnostic predictions from the LLMs and compared this to the ground truth diagnoses in a random sample of 1000 patients.
UNASSIGNED: Based on the proportion of correctly predicted ground truth diagnoses, we estimated the diagnostic hit rate of GPT-4 to be 93.9%. PaLM2 achieved 84.7% on the same data set. On these 1000 randomly selected EHRs, GPT-4 correctly identified 1116 unique diagnoses.
UNASSIGNED: The results suggest that artificial intelligence (AI) has the potential when working alongside clinicians to reduce cognitive errors which lead to hundreds of thousands of misdiagnoses every year. However, human oversight of AI remains essential: LLMs cannot replace clinicians, especially when it comes to human understanding and empathy. Furthermore, a significant number of challenges in incorporating AI into health care exist, including ethical, liability and regulatory barriers.

The aim of this article is to analyze factors influencing delays and overtime during surgery. We utilized descriptive analytics and divided the factors into three levels. In level one, we analyzed each surgical metrics individually and how it may influence the Surgical Success Rate (SSR) of each operating day. In level two, we compared up to three metrics at once, and in level three, we analyzed four metrics to identify more complex patterns in data including correlations. Within each level, factors were categorized as patient, surgical team, and time specific. Retrospective data on 788 high volume arthroplasty procedures was compiled and analyzed from the 4-joint arthroplasty operating room at our institution. Results demonstrated that surgical team performance had the highest impact on SSR whereas patient metrics had the least influence on SSR. Additionally, beginning the surgical day on time has a prominent effect on the SSR. Finally, the experience of the surgeon had almost no impact on the SSR. In conclusion, we gathered a list of insights that can help influence the re-allocation of resources in daily clinical practice to offset inefficiencies in arthroplasty surgeries.

Successful days are defined as days when four cases were completed before 3:45pm, and overtime hours are defined as time spent after 3:45pm. Based on these definitions and the 460 unsuccessful days isolated from the dataset, 465 hours, 22 minutes, and 30 seconds total overtime hours were calculated. To reduce the increasing wait lists for hip and knee surgeries, we aim to verify whether it is possible to add a 5th surgery, to the typical 4 arthroplasty surgery per day schedule, without adding extra overtime hours and cost at our clinical institution. To predict 5th cases, 301 successful days were isolated and used to fit linear regression models for each individual day. After using the models\' predictions, it was determined that increasing performance to a 77% success rate can lead to approximately 35 extra cases per year, while performing optimally at a 100% success rate can translate to 56 extra cases per year at no extra cost. Overall, this shows the extent of resources wasted by overtime costs, and the potential for their use in reducing long wait times. Future work can explore optimal staffing procedures to account for these extra cases.

The article presents assessment of efficiency of implementation of the Federal project \"The struggle with cardiovascular diseases\" during COVID-19 pandemic. It is demonstrated that over three years of implementation of the Federal project significant consolidation of medical infrastructure and volume of work of regional vascular departments and centers took place. However, these achievements resulted in no improvement of quality of medical care nor in decreasing of mortality from diseases of the circulatory system. According to results of integral assessment, there is trend of pronounced decreasing in the number of subjects of the Russian Federation with average and high indicators of project efficiency when the COVID-19 pandemic that factually resulted in occurrence of emergency situation in health care and deteriorated access of population to medical care, is factor of failure in achieving main goals of the Federal project. To confirm or refute this hypothesis further research is needed.

UNASSIGNED: Coronary artery disease (CAD) costs healthcare billions of dollars annually and is the leading cause of death despite available noninvasive diagnostic tools.
UNASSIGNED: This study aims to examine the usefulness of machine learning in predicting hemodynamically significant CAD using routine demographics, clinical factors, and laboratory data.
UNASSIGNED: Consecutive patients undergoing cardiac catheterization between March 17, 2015, and July 15, 2016, at UNC Chapel Hill were screened for comorbidities and CAD risk factors. In this pilot, single-center, prospective cohort study, patients were screened and selected for moderate CAD risk (n = 185). Invasive coronary angiography and CAD prediction with machine learning were independently performed. Results were blinded from operators and patients. Outcomes were followed up for up to 90 days for major adverse cardiovascular and renal events (MACREs). Greater than 70% stenosis or a fractional flow reserve less than or equal to 0.8 represented hemodynamically significant coronary disease. A random forest model using demographic, comorbidities, risk factors, and lab data was trained to predict CAD severity. The Random Forest Model predictive accuracy was assessed by area under the receiver operating characteristic curve with comparison to the final diagnoses made from coronary angiography.
UNASSIGNED: Hemodynamically significant CAD was predicted by 18-point clinical data input with a sensitivity of 81% ± 7.8%, and specificity of 61% ± 14.4% by the established model. The best machine learning model predicted a 90-day MACRE with specificity of 44.61% ± 14.39%, and sensitivity of 57.13% ± 18.70%.
UNASSIGNED: Machine learning models based on routine demographics, clinical factors, and lab data can be used to predict hemodynamically significant CAD with accuracy that approximates current noninvasive functional modalities.

Approaches to assessing the role of health care in reducing mortality in Russia from the standpoint of controlling manageable causes are discussed. Based on the concept of avoidable mortality, trends in regional variability of mortality, the nosological and gender characteristics for the years 2000-2019 have been analyzed. The patterns revealed indicate the following: a significant contribution of medicine and health care to the decrease in the premature reduction in the life expectancy of the population, the expediency of developing a regional classification of the list of avoidable causes of mortality, and the decisive role of prevention and the improvement of the lifestyle of the population of young and middle ages in the past two decades against the background of a slow increase in the capacity of clinical medicine in the diagnostics and treatment of diseases.

Patient portals are becoming increasingly popular worldwide even though their impact on individual health and health system efficiency is still unclear.
The aim of this systematic review was to summarize evidence on the impact of patient portals on health outcomes and health care efficiency, and to examine user characteristics, attitudes, and satisfaction.
We searched the PubMed and Web of Science databases for articles published from January 1, 2013, to October 31, 2019. Eligible studies were primary studies reporting on the impact of patient portal adoption in relation to health outcomes, health care efficiency, and patient attitudes and satisfaction. We excluded studies where portals were not accessible for patients and pilot studies, with the exception of articles evaluating patient attitudes.
Overall, 3456 records were screened, and 47 articles were included. Among them, 11 studies addressed health outcomes reporting positive results, such as better monitoring of health status, improved patient-doctor interaction, and improved quality of care. Fifteen studies evaluated the impact of digital patient portals on the utilization of health services with mixed results. Patient characteristics were described in 32 studies, and it was reported that the utilization rate usually increases with age and female gender. Finally, 30 studies described attitudes and defined the main barriers (concerns about privacy and data security, and lack of time) and facilitators (access to clinical data and laboratory results) to the use of a portal.
Evidence regarding health outcomes is generally favorable, and patient portals have the potential to enhance the doctor-patient relationship, improve health status awareness, and increase adherence to therapy. It is still unclear whether the use of patient portals improves health service utilization and efficiency.

The article presents theoretical propositions concerning placement and dissemination of information about medical organization and practical recommendations of the advertising campaign of medical services. The analysis of the advertising activities\' influence on the patient choice of the medical organization out of top annual revenue medical institutions in Russia was carried out. In economically developed countries, the support of possibility of choosing particular medical organization and physician in economic theory and in practice of organization of health care system is targeted to improving medical care quality and health care efficiency. The process of expanding possibilities of choice is officially declared as one of the priorities of development of Russian health care. However, the influence of consumer choice of patient of medical organization while addressing for medical care is still to be an object of research study in Russia.

In China, health care resources for expectant mothers and children are still not utilized to full efficiency, with health requirements still not being met. The purpose of this study is to critically examine the efficiency of gynecology and obstetrics hospital (OB/GYN) units in Shanxi province of China, with the overarching objective of exploring methods for improving their efficiency. We employ the three-stage data envelopment analysis (DEA) model to measure the efficiency of 134 OB/GYN units in Shanxi. The results show that the technical efficiency and scale efficiency scores of the sample units were low (0.48 and 0.54, respectively). The efficiency of the OB/GYN units varies by region, city, and county and by type of unit. We conclude that the main reason for the low efficiency of OB/GYN units in Shanxi province lies in the unreasonable scale. The government should, therefore, allocate health resources more reasonably, improving the efficiency of different regions, cities, and counties, as well as different types of OB/GYN units.

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