The COVID-19 pandemic and subsequent expansion of telehealth may be exacerbating inequities in ambulatory care access due to institutional and structural barriers. We conduct a repeat cross-sectional analysis of ambulatory patients to evaluate for demographic disparities in the utilization of telehealth modalities. The ambulatory patient population at Oregon Health & Science University (Portland, OR) is examined from June 1 through September 30, in 2019 (reference period) and in 2020 (study period). We first assess for changes in demographic representation and then evaluate for disparities in the utilization of telephone and video care modalities using logistic regression. Between the 2019 and 2020 periods, patient video utilization increased from 0.2% to 31%, and telephone use increased from 2.5% to 25%. There was also a small but significant decline in the representation males, Asians, Medicaid, Medicare, and non-English speaking patients. Amongst telehealth users, adjusted odds of video participation were significantly lower for those who were Black, American Indian, male, prefer a non-English language, have Medicaid or Medicare, or older. A large portion of ambulatory patients shifted to telehealth modalities during the pandemic. Seniors, non-English speakers, and Black patients were more reliant on telephone than video for care. The differences in telehealth adoption by vulnerable populations demonstrate the tendency towards disparities that can occur in the expansion of telehealth and suggest structural biases. Organizations should actively monitor the utilization of telehealth modalities and develop best-practice guidelines in order to mitigate the exacerbation of inequities.

Methods A repeat cross-sectional study was conducted of patients who utilized the ambulatory clinics at Oregon Health & Science University (OHSU) from June 1 through September 30, in 2019 (reference period) and 2020 (study period). The study period was chosen because it exhibited a relatively stable rate of in-person, telephone, and video ambulatory visits. The initial months of the pandemic in March through May 2020 were marked by shifting state and institutional policies that affected appointment availability. By the summer of 2020, clinics were more open to scheduling in-person visits. We chose to investigate a later, more stable time-frame for disparities because we believe that the analysis would be more indicative of ongoing trends.

Unique patient counts were extracted from ambulatory provider-led visits, defined as outpatient visits with physicians, nurse practitioners, or physician assistants. Visits modalities included in-person, video, or telephone, the latter two comprising telehealth. Patient demographics included ethnicity, race, preferred language, payer, age, and sex. The encounter-level data was aggregated by unique patient identifier into patient counts for the study period of June 1 through Sept 30, 2020. Table 1 displays unique patient counts of ambulatory care modality utilization (in-person, video, telephone, and any telehealth) for each demographic group (race, ethnicity, sex, preferred language, insurance, and age). There is also a column for total patients in that demographic group. In the main article, we performed logistic regression to evaluate the association of patient demographics with telehealth utilization. Table 2 displays unique patient counts of ambulatory care modality utilization for each demographic group only within primary care clinics.

Table 3 displays unique patient counts for each demographic group within the time periods before and during the COVID-19 pandemic: June 1 through Sept 30, 2019 and June 1 through Sept 30, 2020. In the study, we compared the proportional representation of demographic groups between before and during the pandemic to assess for overall changes in our patient population.

Summary statistics for gender, age, source of admission, discharge status, primary payer and local health district of patient residence of patients who had a reportable ambulatory surgical procedure.For state-level ambulatory reportable procedures, please see https://opendata.utah.gov/Health/2013-Utah-State-Level-Ambulatory-Surgery-Procedure/upk4-zczr. For facility-level information, please visit: http://health.utah.gov/hda/report/outpatient.php

This dataset is grouped by service provider specialty, and provides information about the number of recipients, number of claims, and dollar amount for given diagnosis claims. Restricted to claims with service date between 01/2012 to 12/2017. Restricted to claims with a primary diagnosis only. Restricted to top 100 most frequent diagnosis codes that are marked as primary diagnosis of a claim. Provider is the rendering provider marked in the claim. Provider specialty is the primary specialty of the rendering provider. This data is for research purposes and is not intended to be used for reporting. Due to differences in geographic aggregation, time period considerations, and units of analysis, these numbers may differ from those reported by FSSA.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) 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 & 100 ITU beds. 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”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

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.

Demographics, characteristics and comorbidities of patients hospitalized with a SARS-CoV-2 infection or COVID-19 diagnosis, total and stratified by rural/urban zip codes.

PIONEER: The impact of ethnicity and multi-morbidity on COVID-related outcomes; a primary care supplemented hospitalised dataset Dataset number 3.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 65million cases and more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) and death. Evidence suggests that older patients, those from some ethnic minority groups and those with multiple long-term health conditions have worse outcomes. This secondary care COVID dataset contains granular demographic and morbidity data, supplemented from primary care records, to add to the understanding of patient factors on disease outcomes.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 and 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) 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 & 100 ITU beds. 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”. UHB has cared for >5000 COVID admissions to date.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records and clinic letters. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound).

Available supplementary data: Health data preceding and following admission event. Matched “non-COVID” controls; ambulance, 111, 999 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.

Background

Sarcomas are uncommon cancers that can affect any part of the body. There are many different types of sarcoma and subtypes can be grouped into soft tissue or bone sarcomas. About 15 people are diagnosed every day in the UK. 3 in every 200 people with cancer in the UK have sarcoma.

A highly granular dataset with a confirmed sarcoma event including hospital presentation, serial physiology, demography, treatment prescribed and administered, prescribed and administered drugs. The infographic includes data from 27/12/2004 to 31/12/2021 but data is available from the past 10 years+.

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

EHR. UHB 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 hospitalised patients from 2004 onwards, curated to focus on Sarcoma. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards and triage). Along with presenting complaints, outpatients admissions, microbiology results, referrals, procedures, therapies, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), and all blood results (urea, albumin, platelets, white blood cells and others). Includes all prescribed & administered treatments and all outcomes. Linked images are also available (radiographs, CT scans, MRI).

Available supplementary data: Matched controls; ambulance, 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.

Demographic and clinical characteristics of the study sample (n = 29).

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

Data are mean±SD or median [IQR].ERI, EPO Resistance Index, BP, Blood pressure; hsCRP, high sensitivity C Reactive Protein; IL-6, Interleukin 6.

The Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) contain the universe of emergency department visits in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SEDD consist of data from hospital-based emergency department visits that do not result in an admission. The SEDD include all patients, regardless of the expected payer including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

The SEDD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., gender, age, race), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SEDD, some include State-specific data elements. The SEDD exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers.

Restricted access data files are available with a data use agreement and brief online security training.

About this dataset

Context

The dataset tabulates the Meservey population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Meservey. The dataset can be utilized to understand the population distribution of Meservey by age. For example, using this dataset, we can identify the largest age group in Meservey.

Key observations

The largest age group in Meservey, IA was for the group of age 5-9 years with a population of 48 (15.74%), according to the 2021 American Community Survey. At the same time, the smallest age group in Meservey, IA was the 85+ years with a population of 1 (0.33%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Meservey is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Meservey total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meservey Population by Age. You can refer the same here

Lactate is a chemical produced by the body as cells consume energy - in times of stress more lactate is produced. In the past, we thought that lactate was just a waste product, but more recently we have learned that lactate has an important role to play in the body.

People suffering from certain severe illnesses may have a high ‘lactate’ level in their blood. This is particularly common in the following:

Severe infections which the body cannot properly control (sepsis)

People who have sustained severe injuries (traumatic injury)

People who are critically unwell with other illnesses (needing treatment in an intensive care unit)

Some patients will develop a high lactate level when they are in hospital. Doctors recognise that this indicates the patient is becoming more unwell, but it is often challenging to know exactly what is causing the lactate level to be raised.

Raised lactate level has been associated with worse outcome in other syndromes, including major trauma and undifferentiated critical illness; however healthy individuals may generate very high lactate levels during strenuous exercise from which they recover without any harm. It is unclear whether lactate in itself is harmful to patients. This dataset provides unique insight into the potential role of lactate as not only a biomarker but a therapeutic target in acute illness.

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

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

Scope: Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards), presenting complaint, physiology readings (BMI, temperature and weight), Sample analysis results (blood sodium level, lactate, haemoglobin, oxygen saturations, and others) drug administered and all outcomes.

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

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

About this dataset

Context

The dataset tabulates the Taneytown population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Taneytown. The dataset can be utilized to understand the population distribution of Taneytown by age. For example, using this dataset, we can identify the largest age group in Taneytown.

Key observations

The largest age group in Taneytown, MD was for the group of age 5-9 years with a population of 595 (8.28%), according to the 2021 American Community Survey. At the same time, the smallest age group in Taneytown, MD was the 85+ years with a population of 43 (0.60%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Taneytown is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Taneytown total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Taneytown Population by Age. You can refer the same here

SD: Standard Deviation, HCV: Hepatitis C Virus, AH: Alcoholic Hepatitis, NASH: Non-Alcoholic Steatohepatitis, M: Male, F: Female, AST: Aspartate Amino Transferase, ALT: Alanine Amino Transferase.

These are the statistics listed in the "Stats at a Glance" section of the City of Austin demographics website: https://demographics-austin.hub.arcgis.com/

The VA National Clozapine Registry tracks the health and demographics of patients who have been prescribed clozapine by the VA. Clozapine, or the brand name Clozaril, is a drug used to treat the most serious cases of schizophrenia. Unfortunately, clozapine may also affect portions of the blood, lowering the body's resistance to infection and sometimes creating life-threatening circumstances. Realizing the severity of the problem, the Food and Drug Administration (FDA) established guidelines for analysis of White Blood Cells and Neutrophils and set strict minimum limits. The FDA also mandated that any manufacturer of clozapine must maintain a Clozapine Registry. These registries are to track the location and the health of clozapine patients and to ensure 'weekly White Blood Cell testing prior to delivery of the next week's supply of medication'. To date, the clozapine manufacturer registries have been unable to develop sufficient controls to meet these requirements, especially the ability to prevent dispensing clozapine when blood results are abnormal. However, because of the unique structure of Veterans Health Information Systems and Technology Architecture, the Veterans Health Administration obtained permission from the FDA and clozapine manufacturers to use its in-place computer network to gather and evaluate weekly patient information, then export this data to manufacturer clozapine registries. The VA assigned functional administration of this effort to the National Clozapine Coordinating Center (NCCC) located in Dallas, Texas. Weekly data on each VA clozapine patient is processed at two locations. Facility Level --When a clozapine prescription is written, a computer program in each facility's internal computer system retrieves white blood cell count, neutrophil count, and clozapine dose and evaluates the information according to FDA guidelines. If an adverse blood condition is found, the computer may warn to trigger a physician reevaluation, or lock out entirely to prevent dispensing, depending on the severity. Weekly, this information, along with certain patient demographic information, is gathered locally and transmitted to Hines Office of Information & Technology Field Office for centralized storage. This data can only be accessed by the NCCC. Raw data is downloaded from the Hines OI Field Office database on a weekly basis. An ancillary computer program reformats the data and evaluates the information for inconsistencies and data gathering errors. The computer-corrected data is manually compared with hand-written facsimile information sent to the NCCC by each site. This manually corrected data is again reformatted for data storage in MS Access format at the NCCC. The corrected data is also reformatted into American Standard Code for Information Interchange fixed-length fields and transmitted via modem to the manufacturers' Clozapine Registry and, in turn, to the FDA.

Prospective registry and biorepository of consecutive patients who present to emergency departments in the Detroit Medical Center with (1) an established history of hypertension (HTN) or (2) elevated blood pressure with no history of HTN. Patients are surveyed using validated Survey instruments including the SF-12 Health Survey, Patient Activation Measure short form (PAM-13), Berlin Obstructive Sleep Apnea Scale, Social Support Questionnaire short form (SSQ-6), Perceived Stress Scale (PSS-14), MacArthur Scale of Subjective Social Status, Six-Item Cognitive Impairment Test (6-CIT), and Brief Coping Orientation to Problems Experienced (Brief COPE). Biological specimens are obtained from consenting individuals. Other collected data include patient demographics, vital signs, physical examination, basic medical history, medications, electrocardiography, x-ray, and lab results.

The global aseptic necrosis treatment market is projected to reach a value of $674.52 million by 2033, expanding at a CAGR of 5.64% during the forecast period of 2025-2033. The market growth is driven by increasing prevalence of osteoarthritis, rising geriatric population, advancements in treatment techniques, and growing awareness about the disease. The market is segmented based on treatment type, diagnosis method, patient demographics, indication, company, and region. Medication, surgical intervention, rehabilitation therapy, and physical therapy are the major treatment types. MRI, CT scan, X-ray, and bone scintigraphy are the commonly used diagnosis methods. Adult, pediatric, and geriatric patients are the target patient demographics. Osteonecrosis, avascular necrosis, and post-traumatic necrosis are the main indications for treatment. Key market players include Sanofi, AstraZeneca, AbbVie, Bristol Myers Squibb, Johnson&Johnson, Celgene, Bayer, Teva Pharmaceutical, Gilead Sciences, Pfizer, Eli Lilly, Merck, Roche, Novartis, and Amgen. The market is geographically distributed across North America, South America, Europe, Middle East & Africa, and Asia Pacific. Recent developments include: Recent developments in the Aseptic Necrosis Treatment Market have shown significant activity among key players such as Sanofi, AstraZeneca, and AbbVie. Notably, these companies are increasingly focusing on the research and development of innovative treatment options to address emerging medical needs, especially in the context of joint ailments. Current affairs indicate a strengthening collaboration between pharmaceutical giants, as evidenced by strategic partnerships to accelerate drug discoveries. In recent weeks, several companies have reported notable growth in their market valuations, with Pfizer and Merck showing impressive gains driven by their advanced therapies in clinical trials. Additionally, the acquisition landscape has seen movements involving prominent entities like Johnson & Johnson and Roche, who are actively pursuing acquisitions to enhance their capabilities in aseptic necrosis treatment. These expansions are aimed at broadening their product portfolios and improving patient outcomes. Furthermore, ongoing regulatory approvals for novel therapies are creating a competitive environment that is expected to shape the market landscape significantly. The influx of investment into research initiatives is also anticipated to drive innovations and diversify available treatment options, thereby boosting the overall market dynamics.. Key drivers for this market are: Growing elderly population demand, Advancements in minimally invasive techniques; Expansion of telemedicine services; Increased awareness of treatment options; Rising prevalence of obesity-related conditions. Potential restraints include: Rising prevalence of osteonecrosis, Advances in surgical techniques; Growing awareness of treatment options; Increasing healthcare expenditure; Demand for effective pain management.

Emergency department (ED) patients often present with undiagnosed complaints, and can exhibit rapidly evolving physiology. Therefore, data from continuous physiologic monitoring, in addition to the electronic health record, is essential to understand the acute course of illness and responses to interventions. The complexity of ED care and the large amount of unstructured multimodal data it produces has limited the accessibility of detailed ED data for research. We release Multimodal Clinical Monitoring in the Emergency Department (MC-MED), a comprehensive, multimodal, and de-identified clinical and physiological dataset. MC-MED includes 118,385 adult ED visits to an academic medical center from 2020 to 2022. Data include continuously monitored vital signs, physiologic waveforms (electrocardiogram, photoplethysmogram, respiration), patient demographics, medical histories, orders, medication administrations, laboratory and imaging results, and visit outcomes. MC-MED is the first dataset to combine detailed physiologic monitoring with clinical events and outcomes for a large, diverse ED population.