Healthcare Data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain point geographic files of healthcare organizations, providers, and hospitals and an boundary file of Primary Care Service Areas.

Health professionals, especially primary care physicians, are in high demand in many parts of the U.S. Some areas are experiencing health professional shortages. This map shows the ratio of population to primary care physicians in the U.S. Areas in dark red show where there are less primary care physicians per person.The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here.County data are suppressed if, for both years of available data, the population reported by agencies is less than 50% of the population reported in Census or less than 80% of agencies measuring crimes reported data.

This file contains provider data, including details and address, for the Medi-Cal Managed Care Plans (MCPs.) This data is submitted to DHCS monthly via X12 274 Transaction files, each of the MCPs submit one file per County in which they operate. As of October 2023, there are 26 Managed Care Plans. There are elements within the file that are not submitted by the plan, but are looked up from the Managed Care Annual Network Certification (ANC) Provider Taxonomy crosswalk. The submitted taxonomy is looked up and the Managed Care Provider Category and ANC Provider Type fields are returned. The most recent version the APL and other ANC related information can be found at: https://www.dhcs.ca.gov/formsandpubs/Pages/AllPlanLetters.aspx.

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

description:

The National Health Service Corps (NHSC) Jobs Center helps doctors and nurses who are interested in working at areas where there is the highest need find out more about opportunities in a particular area or healthcare discipline. The Job Center provides information on the work locations and area. Job opportunities can be searched and identified by a combination of search parameters. The user views the search results on a map and as text and both views provide links to get more detailed information for each returned opportunity.

The National Health Service Corps (NHSC) Jobs Center helps doctors and nurses who are interested in working at areas where there is the highest need find out more about opportunities in a particular area or healthcare discipline. The Job Center provides information on the work locations and area. Job opportunities can be searched and identified by a combination of search parameters. The user views the search results on a map and as text and both views provide links to get more detailed information for each returned opportunity.

A 20 m spatial resolution geotiff of travel time to level III health facilities in Uganda. Level III facilities typically have Qualified nurses, Nurse aids and Clinical officers (physicians assistants) present withing them. The services that Level III facilities typically provide are; Basic laboratory services, Maternity care, Inpatient care. Hospitals have Medical Doctors, Medical Nursers and paramedical officers present and offer the following services: preventative and general medical and surgical services as well as limited specialist services. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds and has been reduced by 22% to reflect the fact that children do not walk as fast adults. A full description will be available in a paper that has been submitted for review.

Locations of hospitals and medical clinics on Kaua'i. Does not include all health and medical services or providers.

The Medical Terminology Software market is experiencing robust growth, projected to reach a substantial size by 2033. A compound annual growth rate (CAGR) of 18.90% from 2019 to 2033 indicates a significant increase in demand driven by several key factors. The rising adoption of electronic health records (EHRs) and the increasing need for standardized medical terminology across healthcare systems are primary drivers. Improved interoperability, facilitated by consistent terminology, reduces medical errors, improves patient care, and streamlines administrative processes, thus boosting market adoption. Furthermore, the growing focus on healthcare data analytics and the need for efficient data management are creating significant opportunities for medical terminology software providers. The market is segmented by deployment (cloud-based and on-premise), by end-user (hospitals, clinics, research organizations, and pharmaceutical companies), and by geography. The competitive landscape includes both established players like Wolters Kluwer N.V. and 3M Company, and emerging innovative companies like Apelon Inc and Clinical Architecture LLC. The continued development of artificial intelligence (AI)-powered features within these software solutions, such as automated coding and natural language processing, further contributes to market expansion. The forecast period (2025-2033) anticipates continued growth, albeit potentially at a slightly moderated pace compared to the historical period. Factors such as regulatory changes and the need for ongoing software updates and maintenance may influence the growth trajectory. Nevertheless, the overall market outlook remains positive, driven by the enduring need for improved healthcare data management and interoperability. The market is geographically diverse, with North America and Europe currently representing significant market shares, but developing economies are also expected to contribute substantially to growth in the coming years, fueled by increased investment in healthcare infrastructure and technology. Competition will likely intensify as companies strive to offer increasingly sophisticated and comprehensive solutions. The focus will remain on user-friendliness, integration capabilities, and advanced analytical features to cater to the evolving needs of healthcare providers. Recent developments include: In March 2022, Thomas H. Lee Partners, L.P. acquired Intelligent Medical Objects for USD 1.5 billion to support IMO's product development and expand commercial relationships with hospitals and other healthcare providers., In January 2021, Wolters Kluwer partnered with Henry Schein to integrate Henry Schein MicroMD, practice management, and electronic medical record (EMR) solution, with Health Language Clinical Interface Terminology (CIT) to quickly map over a million medical abbreviations, typos, incomplete terms, and acronyms to standardize terminology.. Key drivers for this market are: Rising Focus on Minimizing Medical Errors, Government Initiatives for HCIT Adoption; Disparity and Fragmentation in the Terminology Content of Healthcare Organizations. Potential restraints include: Rising Focus on Minimizing Medical Errors, Government Initiatives for HCIT Adoption; Disparity and Fragmentation in the Terminology Content of Healthcare Organizations. Notable trends are: Reimbursement is Expected to Witness High Growth Over the Forecast Period.

NOTE: This dataset has been retired and marked as historical-only.

Select locations offering COVID-19 vaccination in Chicago. This is not an exhaustive list of all providers currently offering COVID-19 vaccine. Many providers are not listed on the map because they will start by providing vaccine to their current patients before expanding to others. Your first contact should be your health care provider, including your primary care provider, health clinic, or hospital where you have gotten medical care in the past. Check your provider’s website, electronic application (e.g., MyChart), or office recorded message for details on vaccine availability. Providers are also reaching out directly to schedule appointments with their existing patients, prioritizing those who are older with more underlying conditions. Check the details associated with each provider prior to calling or showing up at the office or pharmacy.

There is a phased roll-out of the COVID-19 vaccine in Chicago with a very limited supply, so certain groups are prioritized. Currently, vaccination is available by appointment only for eligible individuals. The vaccine will be offered at no cost to all Chicagoans who want it, but patience is needed while vaccine quantities increase. Learn more about Chicago's vaccination phases and who is currently eligible here: https://www.chicago.gov/city/en/sites/covid19-vaccine/home/vaccine-distribution-phases.html

For a map of these locations, see https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Locations-Map/4shi-izjg

The City of Chicago does not endorse any of the listed organizations. This list is provided only as a convenience. See the full disclaimer: https://www.chicago.gov/content/dam/city/sites/covid-19-vaccine/Documents/Disclaimer.pdf

For more information on COVID-19 vaccine, see https://www.chicago.gov/city/en/sites/covid19-vaccine/home.html.

To get the latest updates on Chicago's vaccination plan and be notified when different groups are eligible to receive COVID-19 vaccine, sign up for Chi COVID Coach: https://covidcoach.chicago.gov/

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

Neither rural areas nor urban areas predominate with respect to any specific range of the family physician ratios. The Federal and Provincial Advisory Committee on Health Manpower recommended that a ratio of 1307:1 would be a suitable target for family physicians. In contrast to the distribution of physician specialists, family physicians are more prevalent in terms of their presence in virtually all areas of Canada.

The Medical Terminology Software Market is experiencing robust growth, projected to reach a substantial size driven by several key factors. The market's Compound Annual Growth Rate (CAGR) of 18.90% from 2019 to 2024 indicates a significant upward trajectory. This expansion is fueled by the increasing need for accurate and standardized medical terminology across healthcare settings. The rising adoption of electronic health records (EHRs) and the growing emphasis on interoperability necessitate reliable medical terminology software solutions for seamless data exchange and improved patient care. Furthermore, the expansion of telehealth services and the increasing volume of healthcare data are driving demand for software capable of efficiently managing and analyzing this information. Government initiatives promoting the use of standardized terminology in public health surveillance and clinical research further contribute to market growth. The market is segmented by application (data aggregation, reimbursement, public health surveillance, data integration, decision support, clinical trials, quality reporting, clinical guidelines), product & service (services, platforms), and end-user (healthcare providers, healthcare payers, healthcare IT vendors). North America currently holds a significant market share due to advanced healthcare infrastructure and high technology adoption, although the Asia-Pacific region is expected to show rapid growth in the coming years driven by increasing healthcare spending and technological advancements. The competitive landscape is comprised of both established players and emerging companies offering a range of solutions, from basic terminology management systems to comprehensive platforms integrating various functionalities. Key players are strategically focusing on developing advanced features like natural language processing (NLP) capabilities for enhanced data analysis and improved user experience. Partnerships and acquisitions are common strategies adopted by companies aiming to expand their market reach and product portfolio. While regulatory hurdles and the complexities associated with integrating different systems could pose challenges, the long-term outlook for the Medical Terminology Software Market remains positive, underpinned by the sustained growth of the global healthcare IT sector and the growing emphasis on data-driven healthcare decision-making. The market is expected to continue its robust growth through 2033, benefiting from ongoing technological innovations and increasing demand for efficient and accurate medical terminology management. Recent developments include: In March 2022, Thomas H. Lee Partners, L.P. acquired Intelligent Medical Objects for USD 1.5 billion to support IMO's product development and expand commercial relationships with hospitals and other healthcare providers., In January 2021, Wolters Kluwer partnered with Henry Schein to integrate Henry Schein MicroMD, practice management, and electronic medical record (EMR) solution, with Health Language Clinical Interface Terminology (CIT) to quickly map over a million medical abbreviations, typos, incomplete terms, and acronyms to standardize terminology.. Key drivers for this market are: Rising Focus on Minimizing Medical Errors, Government Initiatives for HCIT Adoption; Disparity and Fragmentation in the Terminology Content of Healthcare Organizations. Potential restraints include: IT Infrastructural Constraints in Developing Countries, Interoperability Issues. Notable trends are: Reimbursement is Expected to Witness High Growth Over the Forecast Period.

This point datalayer contains the location of community health centers (CHCs) in Massachusetts. The layer was produced by the Massachusetts Department of Public Health (MA DPH) Center for Environmental Health (CEH) GIS program. The source material was provided by Tina Ford Wright, Publications and Marketing Assistant, Massachusetts League of Community Health Centers, a.k.a. "the League," (http://www.massleague.org). The League defines a community health center as a non-profit community-based organization that offers comprehensive primary and preventive health care, including medical, social and/or mental health services, to anyone in need regardless of their medical status, ability to pay, culture or ethnicity.CHCs are grouped into Main and Satellite locations. Main CHCs may have one or more satellite locations (also known as access points). The MCHC_CODE item defines the affiliation between main CHCs and their satellites.

CHCs vary by both the facility and/or building type in which they are located, scope of clinical services offered, and target patient population(s). The CEH GIS program used the MassGIS Hospitals, Schools, Colleges and Universities, and Prisons datalayers, and Internet Web sites in the case of homeless shelters, to derive the locations of health centers in these facilities. Health centers known to be administrative offices are attributed accordingly. With respect to clinical services, this GIS datalayer makes no distinction among CHCs. An exception is eye care and dental service providers that are indicated in the EYE and DENTAL fields. No information regarding target patient populations is explicitly defined, though assumptions may be based on health center name and/or location.

In all cases, patients seeking care should contact the CHCs directly to verify availability of clinical services, hours, etc., rather than rely on the information contained in this GIS datalayer, as such information is subject to change.

The size of the Medical Terminology Software market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 18.90% during the forecast period.Medical terminology software is the unique kind of software that will enable medical professionals to understand and make use of medical terms and codes. It provides a complete reference tool that enables the coding of medical diagnoses, procedures, and treatments in a highly accurate manner. Among these are medical dictionaries, thesaurus, and coding guidelines.Medical terminology software is mainly to enhance the accuracy and productivity of health care coding. With the use of software solutions, it is possible to automate coding with accuracy to medical files by healthcare providers, which will prevent errors, increase the effectiveness of billing, and reduce work on administrative tasks. With such software, effective healthcare provider communication can be established since medical terminology software uses standardized vocabularies and definitions among health care professionals.Technological advancements have driven demand in the medical terminology software industry. Health care systems with expanding demands bring about compelling complexity levels in a quest for as efficiently achievable codeings on an entire medical system with effective documentation within recordation and revenue enhancement on regulated systems. Besides this, integration of AI and ML technologies is further enhancing the capabilities of medical terminology software, which is why it becomes possible to add features like automated coding and natural language processing. Recent developments include: In March 2022, Thomas H. Lee Partners, L.P. acquired Intelligent Medical Objects for USD 1.5 billion to support IMO's product development and expand commercial relationships with hospitals and other healthcare providers., In January 2021, Wolters Kluwer partnered with Henry Schein to integrate Henry Schein MicroMD, practice management, and electronic medical record (EMR) solution, with Health Language Clinical Interface Terminology (CIT) to quickly map over a million medical abbreviations, typos, incomplete terms, and acronyms to standardize terminology.. Key drivers for this market are: Rising Focus on Minimizing Medical Errors, Government Initiatives for HCIT Adoption; Disparity and Fragmentation in the Terminology Content of Healthcare Organizations. Potential restraints include: IT Infrastructural Constraints in Developing Countries, Interoperability Issues. Notable trends are: Reimbursement is Expected to Witness High Growth Over the Forecast Period.

A 20 m spatial resolution geotiff of travel time to the nearest hospital in Zimbabwe. Hospitals here included mission hospitals, district hospitrals and provincial hospitals, these facilities all have medical doctors present and offer refeeral services from primary health care, can attend to emergencie and provincial hospitals can offer specialist health care. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds and has been reduced by 22% to reflect the fact that children do not walk as fast adults. A full description will be available in a paper that has been submitted for review.

Analysis of ‘COVID-19 Vaccination Locations’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f9f47694-ac38-4808-ba6c-34956ab270e3 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Select locations offering COVID-19 vaccination in Chicago. This is not an exhaustive list of all providers currently offering COVID-19 vaccine. Many providers are not listed on the map because they will start by providing vaccine to their current patients before expanding to others. Your first contact should be your health care provider, including your primary care provider, health clinic, or hospital where you have gotten medical care in the past. Check your provider’s website, electronic application (e.g., MyChart), or office recorded message for details on vaccine availability. Providers are also reaching out directly to schedule appointments with their existing patients, prioritizing those who are older with more underlying conditions. Check the details associated with each provider prior to calling or showing up at the office or pharmacy.

There is a phased roll-out of the COVID-19 vaccine in Chicago with a very limited supply, so certain groups are prioritized. Currently, vaccination is available by appointment only for eligible individuals. The vaccine will be offered at no cost to all Chicagoans who want it, but patience is needed while vaccine quantities increase. Learn more about Chicago's vaccination phases and who is currently eligible here: https://www.chicago.gov/city/en/sites/covid19-vaccine/home/vaccine-distribution-phases.html

For a map of these locations, see https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Locations-Map/4shi-izjg

The City of Chicago does not endorse any of the listed organizations. This list is provided only as a convenience. See the full disclaimer: https://www.chicago.gov/content/dam/city/sites/covid-19-vaccine/Documents/Disclaimer.pdf

For more information on COVID-19 vaccine, see https://www.chicago.gov/city/en/sites/covid19-vaccine/home.html.

To get the latest updates on Chicago's vaccination plan and be notified when different groups are eligible to receive COVID-19 vaccine, sign up for Chi COVID Coach: https://covidcoach.chicago.gov/

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

--- Original source retains full ownership of the source dataset ---

The global Patient Self-Service Kiosk market is experiencing robust growth, driven by the increasing need for improved patient experience, reduced operational costs for healthcare providers, and the accelerating adoption of digital health technologies. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors, including the rising prevalence of chronic diseases necessitating frequent check-ins, the increasing demand for convenient healthcare access, particularly among younger demographics, and the integration of these kiosks with Electronic Health Records (EHR) systems for streamlined data management. The shift towards value-based care and the pressure on healthcare providers to enhance operational efficiency are further bolstering market expansion. Self-check-in kiosks, in particular, are gaining traction due to their ability to expedite the registration process and reduce wait times. Floor map kiosks offering wayfinding assistance within large healthcare facilities also contribute to positive patient experiences. The market is segmented by kiosk type (self-check-in, floor map, prescription order refilling, others) and application (specialty clinics, hospitals, others), offering diverse opportunities for vendors. The integration of advanced technologies such as telehealth and artificial intelligence within these kiosks is expected to propel growth further in the coming years. The North American market currently holds a significant share, driven by high technology adoption rates and well-established healthcare infrastructure. However, the Asia-Pacific region is poised for substantial growth, fueled by increasing healthcare spending and the expanding middle class seeking improved healthcare services. Europe is also expected to witness steady market expansion, driven by government initiatives promoting digital healthcare and increasing demand for efficient healthcare delivery systems. Key players in the market are strategically investing in research and development to enhance kiosk functionalities, expand their product portfolios, and improve user interfaces to capture market share. Competition is expected to remain intense, with mergers and acquisitions likely playing a pivotal role in shaping the market landscape. While regulatory hurdles and concerns about data security could pose some challenges, the overall market outlook remains positive, indicating significant growth potential throughout the forecast period.

Surface rendering showing significant increase of neural activity associated with the deviation from the Target.

Collection description

Healthcare providers often underestimate patients’ pain, sometimes even when aware of their reports. This could be the effect of experience reducing sensitivity to others pain, or distrust towards patients’ self-evaluations. Across multiple experiments (375 participants), we tested whether senior medical students differed from younger colleagues and lay controls in the way they assess people’s pain and take into consideration their feedback. We found that medical training affected the sensitivity to pain faces, an effect shown by the lower ratings and highlighted by a decrease in neural response of the insula and cingulate cortex. Instead, distrust towards the expressions’ authenticity affected the processing of feedbacks, by decreasing activity in the ventral striatum whenever patients’ self-reports matched participants’ evaluations, and by promoting strong reliance on the opinion of other doctors. Overall, our study underscores the multiple processes which might influence the evaluation of others’ pain at the early stages of medical career.

preprint available at:
https://psyarxiv.com/uz9rd/

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

emotion expression identification

Map type

T

Obesity is a major risk factor for development and worsening of osteoarthritis (OA). Managing obesity with effective weight loss strategies can improve patients’ OA symptoms, functionality, and quality of life. However, little is known about the clinical journey of patients with both OA and obesity. This study aimed to map the medical journey of patients with OA and obesity by characterizing the roles of health care providers, influential factors, and how treatment decisions are made. A cross-sectional study was completed with 304 patients diagnosed with OA and a body mass index (BMI) of ≥30 kg/m2 and 101 primary care physicians (PCPs) treating patients who have OA and obesity. Patients with OA and obesity self-manage their OA for an average of five years before seeking care from a healthcare provider, typically a PCP. Upon diagnosis, OA treatments were discussed; many (61%) patients reported also discussing weight/weight management. Despite most (74%) patients being at least somewhat interested in anti-obesity medication, few (13%) discussed this with their PCP. Few (12%) physicians think their patients are motivated to lose weight, but almost all (90%) patients have/are currently trying to lose weight. Another barrier to effective obesity management in patients with OA is the low utilization of clinical guidelines for OA and obesity management by PCPs. As the care coordinator of patients with OA and obesity, PCPs have a key role in supporting their patients in the treatment journey; obesity management guidelines can be valuable resources. Osteoarthritis (OA) is a disease where the soft tissue between joints wears out causing pain and swelling. Obesity, having unhealthy extra body weight, increases the chances of a person getting OA and can make their OA worse.We wanted to learn more about what patients with OA and obesity experience as they try to manage their OA, including the doctors they talked to, the treatments they used, and if their weight was discussed. To better understand this journey, 304 people with OA and obesity and 101 primary care doctors who treat people with OA and obesity took an online survey.We found that people with OA and obesity tried to manage their OA symptoms on their own for an average of five years before going to a doctor for help. Many (54%) talked with their primary care doctor first. When people with obesity were told by doctors that they had OA, most people (61%) said that they talked about weight and weight loss. Most people (72%) also talked with their doctors about OA treatments.Few doctors (12%) thought their patients were serious about losing weight but almost all patients (90%) said they had tried or were still trying to lose weight. About half of doctors followed guidelines for taking care of people with OA (51%) and obesity (61%).Primary care doctors play a key role in helping patients with OA and obesity. Doctors can follow guidelines and provide treatment options including referrals to other specialists to support weight loss efforts. Osteoarthritis (OA) is a disease where the soft tissue between joints wears out causing pain and swelling. Obesity, having unhealthy extra body weight, increases the chances of a person getting OA and can make their OA worse. We wanted to learn more about what patients with OA and obesity experience as they try to manage their OA, including the doctors they talked to, the treatments they used, and if their weight was discussed. To better understand this journey, 304 people with OA and obesity and 101 primary care doctors who treat people with OA and obesity took an online survey. We found that people with OA and obesity tried to manage their OA symptoms on their own for an average of five years before going to a doctor for help. Many (54%) talked with their primary care doctor first. When people with obesity were told by doctors that they had OA, most people (61%) said that they talked about weight and weight loss. Most people (72%) also talked with their doctors about OA treatments. Few doctors (12%) thought their patients were serious about losing weight but almost all patients (90%) said they had tried or were still trying to lose weight. About half of doctors followed guidelines for taking care of people with OA (51%) and obesity (61%). Primary care doctors play a key role in helping patients with OA and obesity. Doctors can follow guidelines and provide treatment options including referrals to other specialists to support weight loss efforts.

Medical technology: locations of manufacturing companies, dealers and service providers. Map type: Symbols. Spatial extent: Switzerland. Time: 2021

The incident locations represented are approximated and not the actual location of the incident (or individuals residence). A computer generated randomized distance adjustment is applied to each incident location to ensure data are anonymous. This approximated location data is also shown on the dashboard.Interacting with the DashboardMay 2018 Update: Click on one of the charts to filter the displayed data and drop down options. You can select multiple chart elements at a time (i.e. select male for gender and January and February for month). To clear the filters and return to seeing all the data, click on the selected chart elements to remove them.Click on one or more values in drop down to filter the data shown in the display. To clear filters and return to seeing all of the data, click on selected values in the drop down to remove them. For the date filter, select and then delete the text. The map legend is accessible through the navigation in the upper right hand cornerUse the map selection tool in the upper left corner or the map to select calls in specific areas. The following documents what data are collected and why they are being collected. Additional variables will be added to the dashboard in the next phase.Opioid Abuse ProbableA call may be coded as “opioid abuse probable” for many reasons, such asAre there are any medical symptoms indicative of opioid abuse?Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)?Are there witnesses or patient statements made that point to opioid abuse?Is there any other evidence that opioid abuse is probable with the patient?“Opioid abuse probable” is determined by Tempe Fire Medical Rescue Department’s Emergency medical technicians and paramedics on scene at the time of the incident. Narcan/Naloxone Given“Narcan/Naloxone Given” refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses.Groups“Groups” are used to determine if there are specific populations that have an increase in opioid abuse. The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well.The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempe’s Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well.The “unknown” group includes incidents where a patient is unable to answer or refuses to answer the demographic questions. GenderPatient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine.