MSM = men who have sex with men; IDU = injection drug users.§Age was determined at the time of acquisition of the first chronological sample collected from an individual patient that was included in the analysis.

Note. Groups: SA =  subacute, CH =  chronic, CG =  control group. Pt =  patient; M/F =  male/female. NIHSS: National Institutes of Health Stroke Scale. Stroke etiology: i =  ischemic, h =  hemorrhagic stroke. V&TDS: visual and tactile double stimulation. CAV screen: CAV visual field screening. CAV-ET: CAV extinction test. NET Score: for subtests 1 to 8 and for the whole test battery. Mean (M) and standard deviation (SD) given for patients and healthy controls.

Socio-demographic and health-related characteristics of the patient sample by county.

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Population Health Management Market Size and Forecast 2025-2029

The population health management market size estimates the market to reach by USD 19.40 billion, at a CAGR of 10.7% between 2024 and 2029. North America is expected to account for 68% of the growth contribution to the global market during this period. In 2019 the software segment was valued at USD 16.04 billion and has demonstrated steady growth since then.

The market is experiencing significant growth, driven by the increasing adoption of healthcare IT and the rising focus on personalized medicine. Healthcare providers are recognizing the value of population health management platforms in improving patient outcomes and reducing costs. The implementation of these systems enables proactive care management, disease prevention, and population health analysis. However, the market faces challenges as well. The cost of installing population health management platforms can be a significant barrier for smaller healthcare organizations. Additionally, ensuring data security and interoperability across various systems remains a major concern.
Effective data management and integration are essential for population health management to deliver its full potential. Companies seeking to capitalize on market opportunities must address these challenges and provide cost-effective, secure, and interoperable solutions. By focusing on these areas, they can help healthcare providers optimize their population health management initiatives and improve patient care.

What will be the Size of the Population Health Management Market during the forecast period?

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The market continues to evolve, driven by advancements in technology and a growing focus on value-based care. Risk adjustment models, which help account for the variability in health risks among patient populations, are increasingly being adopted to improve care coordination and health outcome measures. For instance, a leading healthcare organization implemented risk stratification models, resulting in a 20% reduction in hospital readmissions. Remote patient monitoring, public health surveillance, and disease outbreak response are crucial applications of population health management. These technologies enable real-time health data collection, allowing for early intervention and improved health equity initiatives. Chronic disease management, a significant focus area, benefits from electronic health records, care coordination models, and health information exchange.

Value-based care programs, predictive modeling healthcare, and telehealth platforms are transforming the landscape of healthcare delivery. Healthcare data analytics, interoperability standards, and population health dashboards facilitate data-driven decision-making, enhancing health intervention efficacy. Behavioral health integration and preventive health services are gaining prominence, with health literacy programs and clinical decision support tools supporting personalized medicine strategies. The market is expected to grow at a robust rate, with industry growth estimates reaching 15% annually. This growth is fueled by the ongoing need for healthcare cost reduction, quality improvement initiatives, and the integration of technology into healthcare delivery.

How is this Population Health Management Industry segmented?

The population health management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


End-user

  Large enterprises
  SMEs


Delivery Mode

  On-Premise
  Cloud-Based
  Web-Based


End-Use

  Providers
  Payers
  Employer Groups
  Government Bodies


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market's software segment is experiencing significant growth and innovation, driven by various components that enhance healthcare organizations' capacity to manage and enhance the health outcomes of diverse populations. Population health management platforms aggregate and integrate data from multiple sources, including electronic health records, claims data, and patient-generated data. Advanced analytics are employed to generate valuable insights, enabling healthcare providers to identify at-risk populations, address chronic conditions, and improve overall patient outcomes. These platforms facilitate seamless data exchange between stakeholders, ensuring harmonious care coordination and enhancing the overall effectiveness of healthcare services.

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As of 2019

BackgroundMental health professionals play a crucial role in promoting the physical well-being of people with mental illness. Awareness of HIV status can enable professionals in mental health services to provide more comprehensive care. However, it remains uncertain whether mental health professionals consistently document HIV status in mental health records.AimsTo investigate the extent to which mental health professionals document previously established HIV diagnoses of people with mental illness in mental health records, and to identify the clinical and demographic factors associated with documentation or lack thereof.MethodsA retrospective cohort study was conducted using an established data linkage between routinely collected clinical data from secondary mental health services in South London, UK, and national HIV surveillance data from the UK Health Security Agency. Individuals with an HIV diagnosis prior to their last mental health service contact were included. Documented HIV diagnosis in mental health records was assessed.ResultsAmong the 4,032 individuals identified as living with HIV, 1,281 (31.8%) did not have their diagnosis recorded in their mental health records. Factors associated with the absence of an HIV diagnosis included being of Asian ethnicity, having certain primary mental health diagnoses including schizophrenia, being older, being with a mental health service for longer, having more clinical mental health appointments, and living in a less deprived area.ConclusionsA significant number of individuals living with HIV who are receiving mental healthcare in secondary mental health services did not have their HIV diagnosis documented in their mental health records. Addressing this gap could allow mental healthcare providers to support those living with HIV and severe mental illness to manage the complexity of comorbidities and psychosocial impacts of HIV. Mental health services should explore strategies to increase dialogue around HIV in mental health settings.

The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) is the largest publicly available all-payer inpatient care database in the United States. The NIS is designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. 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.

Starting with the 2012 data year, the NIS is a sample of discharges from all hospitals participating in HCUP, covering more than 97 percent of the U.S. population. For prior years, the NIS was a sample of hospitals. The NIS allows for weighted national estimates to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The NIS's large sample size enables analyses of rare conditions, such as congenital anomalies; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured. NIS data are available since 1988, allowing analysis of trends over time.

The NIS inpatient data include clinical and resource use information typically available from discharge abstracts with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, discharge status, patient demographics (e.g., sex, age), 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’. The NIS excludes data elements that could directly or indirectly identify individuals.

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

About this dataset

Context

The dataset tabulates the Excel 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 Excel. The dataset can be utilized to understand the population distribution of Excel by age. For example, using this dataset, we can identify the largest age group in Excel.

Key observations

The largest age group in Excel, AL was for the group of age 45 to 49 years years with a population of 74 (15.64%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.42%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Excel is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Excel 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 Excel Population by Age. You can refer the same here

Attendances and referrals by gender, GMS eligibility, age group, number of chronic illnesses and prescribed medications.

The 110th Congressional District Summary File (Sample) (110CDSAMPLE) contains the sample data, which is the information compiled from the questions asked of a sample of all people and housing units. Population items include basic population totals; urban and rural; households and families; marital status; grandparents as caregivers; language and ability to speak English; ancestry; place of birth, citizenship status, and year of entry; migration; place of work; journey to work (commuting); school enrollment and educational attainment; veteran status; disability; employment status; industry, occupation, and class of worker; income; and poverty status. Housing items include basic housing totals; urban and rural; number of rooms; number of bedrooms; year moved into unit; household size and occupants per room; units in structure; year structure built; heating fuel; telephone service; plumbing and kitchen facilities; vehicles available; value of home; monthly rent; and shelter costs. The file contains subject content identical to that shown in Summary File 3 (SF 3).

Data sample demographics. Note that AP refers to anti-psychotic medication, and AD refers to anti-depressive medication. Thus, AP and AD in this table refer to the percentage of patients taking anti-psychotic and anti-depressive medication, respectively. PANSS is a symptom scale for schizophrenia. We show its positive, negative, and composite scores.

Sample sizes of diabetes patients with COVID-19 hospitalization across different demographic groups.

BackgroundThe objective of this research was to conduct a qualitative study among a diverse group of providers to identify their clinical needs, barriers, and adverse safety events in the peripartum care of people with a body mass index (BMI) ≥ 50 kg/m2.MethodsObstetricians, anesthesiologists, certified nurse midwives, nurse practitioners, and nurses were invited to participate in focus group discussions if they were employed at the hospital for >6 months. Key concepts in the focus group guide included: (1) Discussion of challenging situations, (2) Current peripartum management approaches, (3) Patient and family knowledge and counseling, (4) Design and implementation of a guideline (e.g., checklist or toolkit) for peripartum care. The audiotaped focus groups were transcribed verbatim, uploaded to a qualitative analysis software program, and analyzed using inductive and constant comparative approaches. Emerging themes were summarized along with representative quotes.ResultsFive focus groups of 27 providers were completed in 2023. The themes included staffing (level of experience, nursing-patient ratios, safety concerns), equipment (limitations of transfer mats, need for larger sizes, location for blood pressure cuff, patient embarrassment), titrating oxytocin (lack of guidelines, range of uses), monitoring fetal heart rate and contractions, patient positioning, and communication (lack of patient feedback, need for bias training, need for interdisciplinary relationships). Providers gave examples of items to include in a “BMI cart” and suggestions for a guideline including designated rooms for patients with a BMI ≥ 50 kg/m2, defining nursing ratios and oxytocin titration plans, postpartum incentive spirometer, and touch points with providers (nursing, physicians) at every shift change.ConclusionsProviders discussed a range of challenges and described how current approaches to care may negatively affect the peripartum experience and pose threats to safety for patients with a BMI ≥ 50 kg/m2 and their providers. We gathered information on improving equipment and communication among providers.

Abstract

The 2013 Turkey Demographic and Health Survey (TDHS-2013) is a nationally representative sample survey. The primary objective of the TDHS-2013 is to provide data on socioeconomic characteristics of households and women between ages 15-49, fertility, childhood mortality, marriage patterns, family planning, maternal and child health, nutritional status of women and children, and reproductive health. The survey obtained detailed information on these issues from a sample of women of reproductive age (15-49). The TDHS-2013 was designed to produce information in the field of demography and health that to a large extent cannot be obtained from other sources.

Specifically, the objectives of the TDHS-2013 included: - Collecting data at the national level that allows the calculation of some demographic and health indicators, particularly fertility rates and childhood mortality rates, - Obtaining information on direct and indirect factors that determine levels and trends in fertility and childhood mortality, - Measuring the level of contraceptive knowledge and practice by contraceptive method and some background characteristics, i.e., region and urban-rural residence, - Collecting data relative to maternal and child health, including immunizations, antenatal care, and postnatal care, assistance at delivery, and breastfeeding, - Measuring the nutritional status of children under five and women in the reproductive ages, - Collecting data on reproductive-age women about marriage, employment status, and social status

The TDHS-2013 information is intended to provide data to assist policy makers and administrators to evaluate existing programs and to design new strategies for improving demographic, social and health policies in Turkey. Another important purpose of the TDHS-2013 is to sustain the flow of information for the interested organizations in Turkey and abroad on the Turkish population structure in the absence of a reliable and sufficient vital registration system. Additionally, like the TDHS-2008, TDHS-2013 is accepted as a part of the Official Statistic Program.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Children under age of five

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years and women age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design and sample size for the TDHS-2013 makes it possible to perform analyses for Turkey as a whole, for urban and rural areas, and for the five demographic regions of the country (West, South, Central, North, and East). The TDHS-2013 sample is of sufficient size to allow for analysis on some of the survey topics at the level of the 12 geographical regions (NUTS 1) which were adopted at the second half of the year 2002 within the context of Turkey’s move to join the European Union.

In the selection of the TDHS-2013 sample, a weighted, multi-stage, stratified cluster sampling approach was used. Sample selection for the TDHS-2013 was undertaken in two stages. The first stage of selection included the selection of blocks as primary sampling units from each strata and this task was requested from the TURKSTAT. The frame for the block selection was prepared using information on the population sizes of settlements obtained from the 2012 Address Based Population Registration System. Settlements with a population of 10,000 and more were defined as “urban”, while settlements with populations less than 10,000 were considered “rural” for purposes of the TDHS-2013 sample design. Systematic selection was used for selecting the blocks; thus settlements were given selection probabilities proportional to their sizes. Therefore more blocks were sampled from larger settlements.

The second stage of sample selection involved the systematic selection of a fixed number of households from each block, after block lists were obtained from TURKSTAT and were updated through a field operation; namely the listing and mapping fieldwork. Twentyfive households were selected as a cluster from urban blocks, and 18 were selected as a cluster from rural blocks. The total number of households selected in TDHS-2013 is 14,490.

The total number of clusters in the TDHS-2013 was set at 642. Block level household lists, each including approximately 100 households, were provided by TURKSTAT, using the National Address Database prepared for municipalities. The block lists provided by TURKSTAT were updated during the listing and mapping activities.

All women at ages 15-49 who usually live in the selected households and/or were present in the household the night before the interview were regarded as eligible for the Women’s Questionnaire and were interviewed. All analysis in this report is based on de facto women.

Note: A more technical and detailed description of the TDHS-2013 sample design, selection and implementation is presented in Appendix B of the final report of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two main types of questionnaires were used to collect the TDHS-2013 data: the Household Questionnaire and the Individual Questionnaire for all women of reproductive age. The contents of these questionnaires were based on the DHS core questionnaire. Additions, deletions and modifications were made to the DHS model questionnaire in order to collect information particularly relevant to Turkey. Attention also was paid to ensuring the comparability of the TDHS-2013 findings with previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In the process of designing the TDHS-2013 questionnaires, national and international population and health agencies were consulted for their comments.

The questionnaires were developed in Turkish and translated into English.

Cleaning operations

TDHS-2013 questionnaires were returned to the Hacettepe University Institute of Population Studies by the fieldwork teams for data processing as soon as interviews were completed in a province. The office editing staff checked that the questionnaires for all selected households and eligible respondents were returned from the field. A total of 29 data entry staff were trained for data entry activities of the TDHS-2013. The data entry of the TDHS-2013 began in late September 2013 and was completed at the end of January 2014.

The data were entered and edited on microcomputers using the Census and Survey Processing System (CSPro) software. CSPro is designed to fulfill the census and survey data processing needs of data-producing organizations worldwide. CSPro is developed by MEASURE partners, the U.S. Bureau of the Census, ICF International’s DHS Program, and SerPro S.A. CSPro allows range, skip, and consistency errors to be detected and corrected at the data entry stage. During the data entry process, 100% verification was performed by entering each questionnaire twice using different data entry operators and comparing the entered data.

Response rate

In all, 14,490 households were selected for the TDHS-2013. At the time of the listing phase of the survey, 12,640 households were considered occupied and, thus, eligible for interview. Of the eligible households, 93 percent (11,794) households were successfully interviewed. The main reasons the field teams were unable to interview some households were because some dwelling units that had been listed were found to be vacant at the time of the interview or the household was away for an extended period.

In the interviewed 11,794 households, 10,840 women were identified as eligible for the individual interview, aged 15-49 and were present in the household on the night before the interview. Interviews were successfully completed with 9,746 of these women (90 percent). Among the eligible women not interviewed in the survey, the principal reason for nonresponse was the failure to find the women at home after repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TDHS-2013 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TDHS-2013 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall

IntroductionOpioid overdose is a major public health challenge. We aimed to understand facilitators and barriers to engagement in medication for opioid use disorder (MOUD) among persons with OUD in Washington, DC.MethodsWe used a cross-sectional mixed-methods concept mapping approach to explore MOUD engagement between 2021–2022. Community members at-large generated 70 unique statements in response to the focus prompt: “What makes medication for opioid use disorder like buprenorphine (also known as Suboxone or Subutex) difficult to start or keep using?” Persons with OUD (n = 23) and service providers (n = 34) sorted and rated these statements by theme and importance. Data were analyzed with multidimensional scaling and hierarchical cluster analysis, producing thematic cluster maps. Results were validated by our community advisory board.ResultsSeven themes emerged in response to the focus prompt: availability and accessibility; hopelessness and fear; unmet basic needs; characteristics of treatment programs; understanding and awareness of treatment; personal motivations, attitudes, and beliefs; and easier to use drugs. “Availability and accessibility,” “hopelessness and fear,” and “basic needs not being met” were the top three identified barriers to MOUD among consumers and providers; however, the order of these priorities differed between consumers and providers. There was a notable lack of communication and programming to address misconceptions about MOUD’s efficacy, side effects, and cost. Stigma underscored many of the statements, showcasing its continued presence in clinical and social spaces.ConclusionsThis study distinguishes itself from other research on MOUD delivery and barriers by centering on community members and their lived experiences. Findings emphasize the need to expand access to treatment, dismantle stigma associated with substance use and MOUD, and address underlying circumstances that contribute to the profound sense of hopelessness and fear among persons with OUD–all of which will require collective action from consumers, providers, and the public.

The State Legislative District Summary File (Sample) (SLDSAMPLE) contains the sample data, which is the information compiled from the questions asked of a sample of all people and housing units. Population items include basic population totals; urban and rural; households and families; marital status; grandparents as caregivers; language and ability to speak English; ancestry; place of birth, citizenship status, and year of entry; migration; place of work; journey to work (commuting); school enrollment and educational attainment; veteran status; disability; employment status; industry, occupation, and class of worker; income; and poverty status. Housing items include basic housing totals; urban and rural; number of rooms; number of bedrooms; year moved into unit; household size and occupants per room; units in structure; year structure built; heating fuel; telephone service; plumbing and kitchen facilities; vehicles available; value of home; monthly rent; and shelter costs. The file contains subject content identical to that shown in Summary File 3 (SF 3).

This data package contains claims-based data about beneficiaries of Medicare program services including Inpatient, Outpatient, related to Chronic Conditions, Skilled Nursing Facility, Home Health Agency, Hospice, Carrier, Durable Medical Equipment (DME) and data related to Prescription Drug Events. It is necessary to mention that the values are estimated and counted, by using a random sample of fee-for-service Medicare claims.

adata missing for one patient; b 3 patients had concomitant mesial temporal atrophy/sclerosis and regional/local atrophy.

This dataset provides comprehensive customer data suitable for segmentation analysis. It includes anonymized demographic, transactional, and behavioral attributes, allowing for detailed exploration of customer segments. Leveraging this dataset, marketers, data scientists, and business analysts can uncover valuable insights to optimize targeted marketing strategies and enhance customer engagement. Whether you're looking to understand customer behavior or improve campaign effectiveness, this dataset offers a rich resource for actionable insights and informed decision-making.

Key Features:

Anonymized demographic, transactional, and behavioral data. Suitable for customer segmentation analysis. Opportunities to optimize targeted marketing strategies. Valuable insights for improving campaign effectiveness. Ideal for marketers, data scientists, and business analysts.

Usage Examples:

Segmenting customers based on demographic attributes. Analyzing purchase behavior to identify high-value customer segments. Optimizing marketing campaigns for targeted engagement. Understanding customer preferences and tailoring product offerings accordingly. Evaluating the effectiveness of marketing strategies and iterating for improvement. Explore this dataset to unlock actionable insights and drive success in your marketing initiatives!

Population: City: Age 0 to 14: Jiangsu data was reported at 6.392 Person th in 2023. This records an increase from the previous number of 6.010 Person th for 2022. Population: City: Age 0 to 14: Jiangsu data is updated yearly, averaging 3.119 Person th from Dec 1997 (Median) to 2023, with 27 observations. The data reached an all-time high of 5,972.581 Person th in 2020 and a record low of 1.922 Person th in 1999. Population: City: Age 0 to 14: Jiangsu data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: By Age and Region: City.

Despite decades of low utilization, telemedicine adoption expanded at an unprecedented rate during the COVID-19 pandemic. This study examined quantitative and qualitative data provided by a national online sample of 228 practicing physicians (64% were women, and 75% were White) to identify facilitators and barriers to the adoption of telemedicine in the United States (U.S.) at the beginning of the COVID-19 pandemic. Logistic regressions were used to predict the most frequently endorsed (20% or more) barriers and facilitators based on participant demographics and practice characteristics. The top five reported barriers were: lack of patient access to technology (77.6%), insufficient insurance reimbursement (53.5%), diminished doctor-patient relationship (46.9%), inadequate video/audio technology (46.1%), and diminished quality of delivered care (42.1%). The top five reported facilitators were: better access to care (75.4%), increased safety (70.6%), efficient use of time (60.5%), lower cost for patients (43%), and effectiveness (28.9%). Physicians’ demographic and practice setting characteristics significantly predicted their endorsement of telemedicine barriers and facilitators. Older physicians were less likely to endorse inefficient use of time (p < 0.001) and potential for medical errors (p = 0.034) as barriers to telemedicine use compared to younger physicians. Physicians working in a medical center were more likely to endorse inadequate video/audio technology (p = 0.037) and lack of patient access to technology (p = 0.035) as a barrier and more likely to endorse lower cost for patients as a facilitator (p = 0.041) than providers working in other settings. Male physicians were more likely to endorse inefficient use of time as a barrier (p = 0.007) than female physicians, and White physicians were less likely to endorse lower costs for patients as a facilitator (p = 0.012) than physicians of color. These findings provide important context for future implementation strategies for healthcare systems attempting to increase telemedicine utilization.