IntroductionThroughout the COVID-19 pandemic, many patients have sought medical advice on online medical platforms. Review data have become an essential reference point for supporting users in selecting doctors. As the research object, this study considered Haodf.com, a well-known e-consultation website in China.MethodsThis study examines the topics and sentimental change rules of user review texts from a temporal perspective. We also compared the topics and sentimental change characteristics of user review texts before and after the COVID-19 pandemic. First, 323,519 review data points about 2,122 doctors on Haodf.com were crawled using Python from 2017 to 2022. Subsequently, we employed the latent Dirichlet allocation method to cluster topics and the ROST content mining software to analyze user sentiments. Second, according to the results of the perplexity calculation, we divided text data into five topics: diagnosis and treatment attitude, medical skills and ethics, treatment effect, treatment scheme, and treatment process. Finally, we identified the most important topics and their trends over time.ResultsUsers primarily focused on diagnosis and treatment attitude, with medical skills and ethics being the second-most important topic among users. As time progressed, the attention paid by users to diagnosis and treatment attitude increased—especially during the COVID-19 outbreak in 2020, when attention to diagnosis and treatment attitude increased significantly. User attention to the topic of medical skills and ethics began to decline during the COVID-19 outbreak, while attention to treatment effect and scheme generally showed a downward trend from 2017 to 2022. User attention to the treatment process exhibited a declining tendency before the COVID-19 outbreak, but increased after. Regarding sentiment analysis, most users exhibited a high degree of satisfaction for online medical services. However, positive user sentiments showed a downward trend over time, especially after the COVID-19 outbreak.DiscussionThis study has reference value for assisting user choice regarding medical treatment, decision-making by doctors, and online medical platform design.

this paper presents a strategy for the research of insomnia based on machine learning.

This statistic displays the number of pharmacies providing medicine use reviews (MURs) in England from 2006/07 to 2021/22. In general, the number of pharmacies providing medicine use reviews has increased since the start of the provided time interval, although there is a slight stagnation since 2017/2018. The number for 2021/22 is as of March 31, since the MUR was discontinued on that date.

Real world evidence (RWE) generated from real world data (RWD) increasingly informs important decisions about the clinical effectiveness and safety of medical products and interventions. Unlike clinical trials, which can leverage the power of randomization, or non-randomized studies with prospective collection of data for a specific research purpose, most RWE studies make secondary use of longitudinal data collected as part of routine healthcare processes, including administrative claims and electronic health records. This involves numerous complex design and analytic choices. The lack of detail and structure in RWE reporting often requires substantial reviewer time for assessment of studies based on secondary healthcare data. Unambiguous reporting on key design and study implementation parameters could not only streamline and increase efficiency of review for stakeholders, but also improve confidence in the ability to judge the quality of evidence. The level of specificity must be balanced against the burdens it imposes on those reporting and those reviewing study details. In alignment with International Council of Harmonization strategic goals, this public-private collaboration developed a structured template to support RWE study planning, implementation and reporting, based on a consensus document from professional societies. The template specifies key study parameters clearly and concisely in tabular and visual formats, to fulfill several aims: 1) serve as a guiding tool for designing and conducting reproducible RWE studies, 2) set clear expectations for transparent communication of RWE methods, 3) reduce misinterpretation of prose that lacks specificity, 4) allow reviewers to orient quickly and find key information, and 5) facilitate reproducibility, validity assessment, and evidence synthesis. The template is focused on RWE on the effectiveness and safety of medical products and interventions and is compatible with multiple study designs, RWD sources, reporting guidelines, checklists and bias assessment tools. While the simplicity of a checklist is excellent for summarizing areas to report on, it leaves room for misinterpretation and ambiguity about important details of study implementation. We complement the checklist approach by developing a study implementation template where methods related items from existing checklists correspond to the main headings in structured tables where critical details are communicated. Template tables are accompanied by a detailed visual summary in the form of a design diagram. The template is intended to support research planning and preparation, then shared with the final study results to facilitate review and replication. A library of examples for different use cases were prepared to enhance usability.

A presentation of the overall cumulative number of elapsed days (including processing time for transit, medical determinations, and SSA quality review) from the date of filing through the date payment is made or the denial notice is issued for all reconsideration claims that require a medical determination. The data includes annual processing time for fiscal years 2014 on.

In 2020, the Washington State Legislature enacted Engrossed Substitute Senate Bill (ESSB) 6404 (Chapter 316, Laws of 2020, codified at RCW 48.43.0161), which requires that health carriers with at least one percent of the market share in Washington State annually report certain aggregated and de-identified data related to prior authorization to the Office of the Insurance Commissioner (OIC). Prior authorization is a utilization review tool used by carriers to review the medical necessity of requested health care services for specific health plan enrollees. Carriers choose the services that are subject to prior authorization review. The reported data includes prior authorization information for the following categories of health services: • Inpatient medical/surgical • Outpatient medical/surgical • Inpatient mental health and substance use disorder • Outpatient mental health and substance use disorder • Diabetes supplies and equipment • Durable medical equipment The carriers must report the following information for the prior plan year (PY) for their individual and group health plans for each category of services: • The 10 codes with the highest number of prior authorization requests and the percent of approved requests. • The 10 codes with the highest percentage of approved prior authorization requests and the total number of requests. • The 10 codes with the highest percentage of prior authorization requests that were initially denied and then approved on appeal and the total number of such requests. Carriers also must include the average response time in hours for prior authorization requests and the number of requests for each covered service in the lists above for: • Expedited decisions. • Standard decisions. • Extenuating-circumstances decisions. Engrossed Second Substitute House Bill 1357 added additional prescription drug prior authorization reporting requirements for health carriers beginning in reporting year 2024. Carriers were provided the opportunity to submit voluntary prescription drug prior authorization data for the 2023 reporting period. Prescription drug reporting was required for the 2024 reporting period.

The General Practice Workforce series of Official Statistics presents a snapshot of the primary care general practice workforce. A snapshot statistic relates to the situation at a specific date, which for these workforce statistics is the last calendar day in the reporting period. Until July 2021, the snapshots were produced each quarter and were a record as of 31 March, 30 June, 30 September, and 31 December. However, we now collect and publish data on the general practice workforce on a monthly basis and the snapshot therefore relates to the last calendar day of each month, including weekends and public holidays. This monthly snapshot reflects the general practice workforce at 28 February 2022. In the publication relating to the general practice workforce at 31 December 2021, we introduced a significant methodological change and revised the entire time series back to September 2015. This means that figures in this release differ from and supersede those published prior to the 31 December 2021 release. Please see the Methodology Review and Changes chapter of this release for more information. These statistics present full-time equivalent (FTE) and headcount figures by four staff groups, (GPs, Nurses, Direct Patient Care (DPC) and administrative staff), with breakdowns of individual job roles within these high-level groups. For the purposes of NHS workforce statistics, we define full-time working to be 37.5 hours per week. Full-time equivalent is a standardised measure of the workload of an employed person. Using FTE, we can convert part-time and additional working hours into an equivalent number of full-time staff. For example, an individual working 37.5 hours would be classed as 1.0 FTE while a colleague working 30 hours would be 0.8 FTE. The term “headcount” relates to distinct individuals, and as the same person may hold more than one role, care should be taken when interpreting headcount figures. Please refer to the Using this Publication section for information and guidance about the contents of this publication and how it can and cannot be used. England-level time series figures for all job roles are available in the Excel bulletin tables back to September 2015 when this series of Official Statistics began. The Excel file also includes CCG-level FTE and headcount breakdowns for the current reporting period. This publication series also includes CSVs for selected reporting periods back to September 2015, containing practice-level summaries and CCG-level counts of individuals. Please refer to the Publication content, analysis, and release schedule in the Using this publication section for more details of what’s available. In addition to the snapshot of the main general practice workforce, Annexes B and C in the Excel Bulletin tables include figures relating to the number of ad-hoc locum GPs working in general practice and information about their working hours. These figures used to be included in the main totals, but data relating to the ad-hoc locum workforce is collected differently and these figures do not constitute a snapshot. As a result, because they are not directly comparable to the snapshot, we now report these figures separately rather than including them in the overall totals. We are planning to introduce a quarterly publication to complement this monthly publication, which will bring together staff working in general practice, including ad-hoc locums, and those working in Primary Care Networks and potentially GPs working in other settings such as A&E streaming. The first experimental edition is likely to be released in late spring 2022 and include workforce data for all staff groups for September and December 2021. We are continually working to improve our publications to ensure their contents are as useful and relevant as possible for our users. We welcome feedback from all users to PrimaryCareWorkforce@nhs.net.

The Activated Clotting Time (ACT) Cartridge market is an essential segment of the medical diagnostics industry, primarily utilized for monitoring anticoagulation in patients undergoing surgical procedures or those on anticoagulant therapy. This specialized cartridge measures the effectiveness of blood coagulation, e

In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. This data represents number of active recipients who received benefits under a medical benefit plan in that calendar year and month. A recipient may have received benefits from multiple plans in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) 2021 is a partial year. For privacy considerations, a count of zero is used for counts less than five. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, corrections in the ImpaCT system for January and February 2019 caused the addition of around 2000 and 3000 recipients respectively, and the counts for many types of assistance (e.g. SNAP) were adjusted upward for those 2 months. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree.\ NOTE: On February 14 2019, the enrollment

The acute-care pathway (from the emergency department (ED) through acute medical units or ambulatory care and on to wards) is the most visible aspect of the hospital health-care system to most patients. Acute hospital admissions are increasing yearly and overcrowded emergency departments and high bed occupancy rates are associated with a range of adverse patient outcomes. Predicted growth in demand for acute care driven by an ageing population and increasing multimorbidity is likely to exacerbate these problems in the absence of innovation to improve the processes of care.

Key targets for Emergency Medicine services are changing, moving away from previous 4-hour targets. This will likely impact the assessment of patients admitted to hospital through Emergency Departments.

This data set provides highly granular patient level information, showing the day-to-day variation in case mix and acuity. The data includes detailed demography, co-morbidity, symptoms, longitudinal acuity scores, physiology and laboratory results, all investigations, prescriptions, diagnoses and outcomes. It could be used to develop new pathways or understand the prevalence or severity of specific disease presentations.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

Electronic Health Record: University Hospital Birmingham is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All patients with a medical emergency admitted to hospital, flowing through the acute medical unit. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards and readmissions), physiology readings (NEWS2 score and clinical frailty scale), Charlson comorbidity index and time dimensions.

Available supplementary data: Matched controls; ambulance data, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

The Time-Lapse IVF incubators market has become a pivotal segment within the assisted reproductive technology (ART) industry, revolutionizing the way embryologists culture and monitor embryos during in vitro fertilization (IVF) procedures. These specialized incubators utilize advanced imaging technology to capture c

Please cite the following paper when using this dataset: N. Thakur, “Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions,” Preprints, 2022, DOI: 10.20944/preprints202206.0383.v1 Abstract The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and use cases in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a dataset is necessary. The Internet of Everything era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by mining relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. Therefore, this work presents a dataset of about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. Instructions: This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data only for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. The Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets) or any similar application may be used for hydrating this dataset. Data Description This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. Filename: Exoskeleton_TweetIDs_Set1.txt (Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022) Filename: Exoskeleton_TweetIDs_Set2.txt (Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021) Filename: Exoskeleton_TweetIDs_Set3.txt (Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020) Filename: Exoskeleton_TweetIDs_Set4.txt (Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020) Filename: Exoskeleton_TweetIDs_Set5.txt (Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019) Filename: Exoskeleton_TweetIDs_Set6.txt (Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019) Filename: Exoskeleton_TweetIDs_Set7.txt (Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017) Here, the last date for May is May 21 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets.

The data contained in this dataset shows the overall cumulative number of elapsed days (including processing time for transit, technical determinations, medical determinations, and quality review) from the date of filing through the date payment is made or the denial notice is issued for all Title XVI initial claims that require a medical determination for fiscal years 2013 and later.

The Medical Fluoroscopy Equipment market is a pivotal segment of the healthcare industry, dedicated to enhancing diagnostic imaging and therapeutic procedures through real-time imaging technology. Utilized extensively in various medical disciplines such as cardiology, orthopedics, and gastrointestinal diagnostics, f

The Intravascular Ultrasound (IVUS) market has emerged as a vital segment within the global medical imaging industry, providing clinicians with detailed views of vascular structures during interventional procedures. Traditionally used in cardiology, IVUS utilizes high-frequency sound waves to create real-time, cross

Objective: PICO (Participants, Interventions, Comparators, Outcomes) analysis is vital but time-consuming for conducting systematic reviews (SRs). Supervised machine learning can help fully automate it, but a lack of large annotated corpora limits the quality of automated PICO recognition systems. The largest currently available PICO corpus is manually annotated, which is an approach that is often too expensive for the scientific community to apply. Depending on the specific SR question, PICO criteria are extended to PICOC (C-Context), PICOT (T-timeframe), and PIBOSO (B-Background, S-Study design, O-Other) meaning the static hand-labelled corpora need to undergo costly re-annotation as per the downstream requirements. We aim to test the feasibility of designing a weak supervision system to extract these entities without hand-labelled data. Methodology: We decompose PICO spans into its constituent entities and re-purpose multiple medical and non-medical ontologies and expert-generated ru...

Part 1- NHS prescribing of unlicensed cannabis-based medicines (March 2023-March 2024) Part 2- Private prescribing of unlicensed cannabis-based medicines (March 2023-September 2023) Part 3- Number of Unique Identified Prescribers for unlicensed cannabis-based medicines (NHS & Private) NHS Prescription Services process prescriptions for Pharmacy Contractors, Appliance Contractors, Dispensing Doctors and Personal Administration, The information is then used to make payments to pharmacists and appliance contractors in England for prescriptions dispensed in primary care settings (other arrangements are in place for making payments to Dispensing Doctors and Personal Administration). This involves processing over 1 billion prescription items and payments totalling over £9 billion each year. The information gathered from this process is then used to provide information on costs and trends in prescribing in England and Wales to over 25,000 registered NHS and Department of Health and Social Care users. Data Source When prescriptions are processed by NHSBSA data capture, prescriptions sometimes contain prescribing of medicines that were not populated on the NHSBSA drug database at the time. This type of order will be captured as an ‘unspecified drug.’ Data for prescribing of unlicensed cannabis-based medicines has been taken from data captured as unspecified prescribing. Unlicensed cannabis-based medicines are identified by an additional review process which occurs after the prescriptions have been processed. The items identified by this review are reported against the date that the prescription was written and not necessarily when they were submitted. Therefore, these figures may be subject to change if the prescription is submitted to the NHSBSA in a later month. This Dataset This dataset shows items and the number of unique prescribers for prescriptions of unlicensed cannabis-based products. Unlicensed cannabis-based medicines that fall into the ‘unspecified’ category are identified by an additional review process which occurs after the prescriptions have been processed. The items identified by this review are reported against the date that the prescription was written and not necessarily when they were submitted for payment. Therefore, these figures may be subject to change if the prescription is submitted to the NHSBSA in a later month. Time Period March 2023 to March 2024 (NHS prescribing), March 2023 to September 2023 (private prescribing). At present, private unlicensed cannabis data is only available until September 2023. Data is presented monthly for items data, and for the aggregated time-period for the number of identified licensed prescribers who have issued prescriptions.

On March 2, 2022 DC Health announced the District’s new COVID-19 Community Level key metrics and reporting. COVID-19 cases are now reported on a weekly basis. The data in this table includes overall COVID-19 statistics for the District of Columbia hospitals. The number of hospital beds and ventilators available. Due to rapidly changing nature of COVID-19, data for March 2020 is limited.General Guidelines for Interpreting Disease Surveillance Data during a disease outbreak, the health department will collect, process, and analyze large amounts of information to understand and respond to the health impacts of the disease and its transmission in the community. The sources of disease surveillance information include contact tracing, medical record review, and laboratory information, and are considered protected health information. When interpreting the results of these analyses, it is important to keep in mind that the disease surveillance system may not capture the full picture of the outbreak, and that previously reported data may change over time as it undergoes data quality review or as additional information is added. These analyses, especially within populations with small samples, may be subject to large amounts of variation from day to day. Despite these limitations, data from disease surveillance is a valuable source of information to understand how to stop the spread of COVID19.

The COVID-19 pandemic has put a huge strain on governments and medical professionals all across the world. To identify acceptable treatments, many clinical studies from the Indian system of Traditional Medicines [Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homoeopathy (AYUSH)] have been conducted. Objective of the study is determine the efficiency of the Traditional System of Indian Medicine (AYUSH system) in lowering the incidence, duration, and severity of COVID-19 through a living systematic review and meta-analysis. We will search the following databases e.g; Pubmed; the Cochrane central register of controlled trials (CENTRAL); the Clinical Trials Registry - India (CTRI); Digital Helpline for Ayurveda Research Articles (DHARA): AYUSH research portal; WHO COVID-19 database etc. Clinical improvement, WHO ordinal scale, viral clearance, incidences of COVID-19 infection, and mortality will be considered as primary outcomes. Secondary outcomes will be use of O2 therapy or mechanical ventilator, admission to high dependency unit or emergency unit, duration of hospitalization, the time to symptom resolution, and adverse events. The review will be updated bi-monthly with two updates. It will provide practitioners, guideline developers, and authorities with up-to-date syntheses on interventions on a regular basis to help them make health-care decisions about AYUSH therapies for COVID-19 management. Study is supported by World Health Organization, South East Asia Regional Office, New Delhi, India. Here, we shared the result of our search strategy of our project and data extraction tool developed.