The average for 2020 based on 36 countries was 4.44 hospital beds. The highest value was in South Korea: 12.65 hospital beds and the lowest value was in Mexico: 0.99 hospital beds. The indicator is available from 1960 to 2021. Below is a chart for all countries where data are available.

In 2023, Singapore dominated the ranking of the world's health and health systems, followed by Japan and South Korea. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The health and health system index score of the top ten countries with the best healthcare system in the world ranged between 82 and 86.9, measured on a scale of zero to 100.

Global Health Security Index  Numerous health and health system indexes have been developed to assess various attributes and aspects of a nation's healthcare system. One such measure is the Global Health Security (GHS) index. This index evaluates the ability of 195 nations to identify, assess, and mitigate biological hazards in addition to political and socioeconomic concerns, the quality of their healthcare systems, and their compliance with international finance and standards. In 2021, the United States was ranked at the top of the GHS index, but due to multiple reasons, the U.S. government failed to effectively manage the COVID-19 pandemic. The GHS Index evaluates capability and identifies preparation gaps; nevertheless, it cannot predict a nation's resource allocation in case of a public health emergency.

Universal Health Coverage Index  Another health index that is used globally by the members of the United Nations (UN) is the universal health care (UHC) service coverage index. The UHC index monitors the country's progress related to the sustainable developmental goal (SDG) number three. The UHC service coverage index tracks 14 indicators related to reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, service capacity, and access to care. The main target of universal health coverage is to ensure that no one is denied access to essential medical services due to financial hardships. In 2021, the UHC index scores ranged from as low as 21 to a high score of 91 across 194 countries. 

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

ContextResearch-oriented cancer hospitals in the United States treat and study patients with a range of diseases. Measures of disease specific research productivity, and comparison to overall productivity, are currently lacking.HypothesisDifferent institutions are specialized in research of particular diseases.ObjectiveTo report disease specific productivity of American cancer hospitals, and propose a summary measure.MethodWe conducted a retrospective observational survey of the 50 highest ranked cancer hospitals in the 2013 US News and World Report rankings. We performed an automated search of PubMed and Clinicaltrials.gov for published reports and registrations of clinical trials (respectively) addressing specific cancers between 2008 and 2013. We calculated the summed impact factor for the publications. We generated a summary measure of productivity based on the number of Phase II clinical trials registered and the impact factor of Phase II clinical trials published for each institution and disease pair. We generated rankings based on this summary measure.ResultsWe identified 6076 registered trials and 6516 published trials with a combined impact factor of 44280.4, involving 32 different diseases over the 50 institutions. Using a summary measure based on registered and published clinical trails, we ranked institutions in specific diseases. As expected, different institutions were highly ranked in disease-specific productivity for different diseases. 43 institutions appeared in the top 10 ranks for at least 1 disease (vs 10 in the overall list), while 6 different institutions were ranked number 1 in at least 1 disease (vs 1 in the overall list).ConclusionResearch productivity varies considerably among the sample. Overall cancer productivity conceals great variation between diseases. Disease specific rankings identify sites of high academic productivity, which may be of interest to physicians, patients and researchers.

Timor-Leste TL: Hospital Beds: per 1000 People data was reported at 5.900 Number in 2010. Timor-Leste TL: Hospital Beds: per 1000 People data is updated yearly, averaging 5.900 Number from Dec 2010 (Median) to 2010, with 1 observations. Timor-Leste TL: Hospital Beds: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Timor-Leste – Table TL.World Bank: Health Statistics. Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included.; ; Data are from the World Health Organization, supplemented by country data.; Weighted average;

Long-term quantitative series for 20 Latin American countries, spanning from 1960 to 2020, on the number of hospital beds, physicians, nurses and healthcare expenditure.

Matus-Lopez, M. and Fernández Pérez, P. 2023. "Transformations in Latin American Healthcare: A Retrospective Analysis of Hospital Beds, Medical Doctors, and Nurses from 1960 to 2022". Journal of Evolutionary Studies in Business.

The information was extracted from official reports and cross-country databases. Official reports were available in digital format in the Institutional Repository for Information Sharing (IRIS) of Pan American Health Organization (PAHO). They were summary of four-year reports on Health Conditions in the Americas (PAHO 1962, 1966, 1970, 1974, 1978, 1982, 1986, 1990, 1994, 1998, 2002a), annual reports of Basic Indicators (PAHO 2002b, 2007, 2008, 2010, 2013), Health in South America (PAHO 2012) and Core Indicators (PAHO 2016). Databases were Open Data Portal of the Pan American Health Organization (PLISA) (PAHO 2023), Core Indicator Database provided directly by PAHO (PAHO 2022), Data Portal of National Health Workforce Accounts of the World Health Organization (NHWA) (WHO 2022), and the Global Health Expenditure Database of the World Health Organization (GHED) (WHO 2023).

Serie 1. Hospital Beds per 1,000 inhabitants

Serie 2. Physicians per 10,000 inhabitants

Serie 3. Nurses per 10,000 inhabitants

Serie 4. Government spending on health, per capita. Constant US dollars of 2020

Cite as:

Association of rurality of patients’ residence with patient experience.

During the COVID-19 pandemic, hospitals were challenged to provide both COVID-19 and non-COVID treatment. A survey questionnaire was designed and distributed via email to hospitals empanelled under the Ayushman Bharat–Pradhan Mantri Jan Arogya Yojana(AB-PMJAY), the world’s largest National Health Insurance Scheme. Telephonic follow-ups were used to ensure participation in places with inadequate internet. We applied support vector regression to quantify the hospital variables that affected the use vs. non-use of hospital services (Model-1), and factors impacting COVID-19 revenue and staffing levels (Model-2).We quantified the statistical significance of important input variables using Fisher’s exact test. The survey, conducted early in the pandemic, included 461 hospitals across 20 states and union territories. Only 55.5% of hospitals were delivering emergency care, 26.7% were doing elective surgery and 36.7% providing obstetric services. Hospitals with adequate supplies of PPE, including N95 masks, and separate facilities designated for COVID-19 patients were more likely to continue providing emergency surgeries and services effectively. Data analysis revealed that large hospitals (> 250 beds) with adequate PPE and dedicated COVID-19 facilities continued both emergency and elective surgeries. Public hospitals were key in pandemic management, large private hospital systems were more likely to conduct non-COVID-19 surgeries, with not-for-profit hospitals performing slightly better. Public and large private not-for-profit hospitals faced fewer staff shortages and revenue declines. In contrast, smaller hospitals (< 50 beds) experienced significant staff attrition due to anxiety, stress and revenue losses. They requested government support for PPE supplies, staff training, testing kits, and special allowances for healthcare workers. The inclusion of COVID-19 coverage under AB-PMJAY improved access to healthcare for critical cases. Maintaining non-COVID-19 care during the pandemic indicates healthcare system resiliency. A state-wide data-driven system for ventilators, beds, and funding support for smaller hospitals, would improve patient care access and collaboration.

The Outpatient Hospital Services market was valued at USD 46.75 Billion in 2024 and is expected to reach USD 89.79 Billion by 2030 with a CAGR of 11.45%.

Hospital Food Services Market size was valued at USD 40.7 Billion in 2022 and is projected to reach USD 56. 4 Billion by 2030 growing at a CAGR of 12.8% from 2024 to 2030.

Global Hospital Food Services Market Drivers

Patient-Centered Care: As the importance of patient-centered care grows, hospitals are paying more attention to accommodating patients' food preferences, nutritional requirements, and cultural demands. Hospital food services must accommodate a wide range of dietary requirements, such as those of patients with certain medical problems, allergies, and dietary restrictions based on religion or culture.

Wellness and Nutrition: People are becoming more conscious of the connection between healthy eating and positive health outcomes. Hospitals are realizing how critical it is to feed patients wholesome meals to aid in their recuperation, strengthen their immune systems, and avoid complications. As a result, hospital food services are facing an increasing demand for items that are fresher, healthier, and locally produced.

Technology and Innovation: The market for hospital food services is changing as a result of technological advancements. Meal quality and consistency are being improved by technology, which is simplifying operations and increasing efficiency. Examples of this include automated meal ordering systems and smart kitchen equipment. Digital platforms are also making it possible for patients to give real-time feedback and modify their meal preferences, making eating more customized.

Efficiency and Cost Containment: Healthcare providers under pressure to keep costs under control without sacrificing quality standards. Hospital food services have to balance keeping costs down with providing patients with the nourishment and satisfaction they require. This frequently entails putting effective procurement procedures into place, streamlining kitchen operations, and reducing food waste by improving portion control and inventory management.

Regulatory Compliance and Food Safety: To guarantee that patients get safe and hygienic meals, hospital food services are subject to strict regulatory regulations and food safety standards. It is crucial to follow rules like the Food Safety Modernization Act (FSMA) and directives from agencies like the Food and Drug Administration (FDA). To ensure compliance and reduce risks, hospitals need to make investments in quality assurance programs, staff training, and sanitary procedures.

Sustainability and Environmental Awareness: Hospitals are adopting more and more environmentally friendly methods in their food service operations. This entails obtaining foods from nearby and organic growers, putting energy-efficient kitchen appliances into use, and minimizing food waste through composting and recycling programs. Sustainability programs support patient preferences for meals that are ethically sourced and environmentally friendly, while also helping to conserve the environment.

Hospitals frequently provide food to specialty patient groups, such as children, the elderly, and people with long-term diseases, all of whom have particular dietary requirements. Hospital food services must adjust their menus and meal plans to meet these unique needs, which may include therapeutic diets, meals with added nutrients that are suited to certain patient demographics, and diets with changed textures.

Integrated Geodatabase: The Global Catholic Foortprint of Care for the Vulnerable and ChildrenBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.GoodLands created a significant new data set of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data was extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare around the world using data mined from the Annuarium Statisticum Eccleasiea.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at the bottom of this page for details).

Belgium's Number of hospital beds per 1,000 people iswhich is the 1st highest in the world ranking. Transition graphs on Number of hospital beds per 1,000 people in Belgium and comparison bar charts (Canada vs. Denmark vs. Belgium) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

The average for 2020 based on 1 countries was 2.49 hospital beds. The highest value was in New Zealand: 2.49 hospital beds and the lowest value was in New Zealand: 2.49 hospital beds. The indicator is available from 1960 to 2021. Below is a chart for all countries where data are available.

Appearances by institution.

Ranks by specific disease.

With the recent Ebola epidemic, the flaws in Liberia’s medical infrastructure have been made painfully obvious. Liberia, a country of four million people, has only 37 practicing doctors according to health officials. This is evidence of a serious lack in the availability of medical services to the majority of Liberians. An American gynecologist who visited the country in 2012 to provide services with a team from the Mt. Sinai Hospital observed families of hospital patients supplying their own food and bed linens due to the medical facility they were working in lacking funds for basic necessities. The root issue at the heart of many of Liberia’s woes stems from the long civil war. In addition to damaging the medical infrastructure, the country’s only medical school was forced to close for long periods of time, resulting in medical students taking an average eight years to graduate. There has been a serious push for reform and revitalization with medical facilities being rebuilt and medical students now on track to spend only three years in school. Liberia is facing a number of issues, and prior to the current epidemic has not prioritized health expenditures. The government spends an estimated 16.8 percent of their GDP, the lowest in the world, on healthcare. The average GDP spending on healthcare systems in sub-Saharan Africa is ~50 percent. Liberia’s healthcare system is highly dependent on international aid. Donors finance 50 percent of total health expenditures. Approximately 80 percent of all health services are provided by non-governmental organizations (NGOs) and will continue to be so for the foreseeable future. However, the Ministry of Health and Social Welfare has been working with NGOs such as Health Systems 20/20 to improve their existing infrastructure. Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name ADM1_NAME - Administration level one identification / name ADM2_NAME - Administration level two identification / name NAME - Name of health facility TYPE1 - Primary classification in the geodatabase TYPE2 - Secondary classification in the geodatabase CITY - City location available SPA_ACC - Spatial accuracy of site location (1 – high, 2 – medium, 3 – low) COMMENTS - Comments or notes regarding themedical facility SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThe feature class was generated utilizing data from OpenStreetMap, Wikimapia, GeoNames and other sources. OpenStreetMap is a free worldwide map, created by crowd-sourcing. Wikimapia is open-content mapping focused on gathering all geographical objects in the world. GeoNames is a geographical places database maintained and edited by the online community. Consistent naming conventions for geographic locations were attempted but name variants may exist, which can include historical or less widespread interpretations.The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Aizenman, Nurith and Beemsterboer, Nicole. “Why Patients Aren’t Coming to Liberia’s Redemption Hospital.” August 27, 2014. Accessed September 26, 2014. www.npr.org.“Liberia: ArcelorMittal Folds Partly – Terminates Expansion Contract.” All Africa. August 14, 2013. Accessed September 26, 2014. allafrica.com. Cohen, Elizabeth. “Ebola Patients Left to Lie on the Ground.” CNN. September 23, 2014. Accessed September 26, 2014. www.cnn.com.“Kingdom Care Medical Center Reaches Rural Communities with Health Care.” Daily Observer. January 28, 2014. Accessed September 26, 2014. www.liberianobserver.com. DigitalGlobe, "DigitalGlobe Imagery Archive." Accessed September 24, 2014.“Eternal Love Winning Africa: ELWA Hospital.” Eternal Love Winning Africa. January 2014. Accessed September 26, 2014. www.elwaministries.org.Freeman, Colin. “One Patient in a 200-bed Hospital: How Ebola has Devastated Liberia’s Health System.” The Telegraph. August 15, 2014. Accessed September 26, 2014. www.telegraph.co.uk.“Lewin Reaches Out to River Gee, Maryland.” Gale Global Issues. March 4, 2013. . Accessed September 26, 2014. find.galegroup.com. Gbelewala, Korboi. “Liberia: Health Offical – Ebola Death Toll Hits 11 in Lofa.” All Africa. June 24, 2014. Accessed September 26, 2014. allafrica.com. GeoNames, "Liberia." September 23, 2014. Accessed September 23, 2014. www.geonames.org.Google, September 2014. Accessed September 2014. www.google.com.Kollie, Namotee P.M. “Liberia: C.B. Dunbar Hospital Receives Medical Supplies.” September 27, 2013. Accessed September 26, 2014. allafrica.com.“MSF Hands Over Last Hospitals to Ministry of Health after 20 Years of Emergency Aid in Liberia.” Medecins Sans Frontieres. June 25, 2010. Accessed September 26, 2014. www.msf.org. Nah, Vivian M. and Johnson, Obediah. “Liberia: Ebola Kills Woman at Duside Hospital in Firestone.” All Africa. April 4, 2014. Accessed September 26, 2014. allafrica.com. “Catholic Hospital Director Dies of Ebola in Liberia.” National Catholic Register. August 05, 2014. Accessed September 26, 2014. www.ncregister.com.OpenStreetMap, "Liberia." September 2014. Accessed September 18, 2014. www.openstreetmap.org.Senkpeni, Alpha Daffae. “No Ebola Gears for Clinic in Grand Bassa District #2.” Front Page Africa. August 12, 2014. Accessed September 26, 2014. www.frontpageafricaonline.com. “Seventh-day Adventist Cooper Hospital” Seventh-Day Adventist Church. November 18, 2004. Accessed September 26, 2014. www.adventistdirectory.org.“St. Benedict Menni Rehabilitation Centre, Liberia.” Sisters Hospitallers. January 2014. Accessed September 26, 2014. www.sistershospitallers.org. “Liberia – SOS Medical and Social Centres.” SOS Children’s Villages. January 2014. Accessed September 26, 2014. www.sos-medical-centres.org.“Liberia.” Sustainable Marketplace. January 2014. Accessed September 26, 2014. liberia.buildingmarkets.org. “Reconstruction of the Vinjama Hospital in Liberia.” Swiss Agency for Development and Cooperation (SDC). January 2014. Accessed September 26, 2014. www.sdc.admin.ch. Verdier, Lewis S. “Liberia: TB On the Rise in Pleebo.” All Africa. March 28, 2013. Accessed September 26, 2014. allafrica.com.Wikimapia, "Liberia." September 2014. Accessed September 22, 2014. wikimapia.org.“Snapper Hill Clinic.” Word Press. November 12, 2012. Accessed September 26, 2014. jbloodnc.wordpress.com.Sources (Metadata)Neporent, Liz. "Liberia's Medical Conditions Dire Even Before Ebola Outbreak." ABC News. August 4, 2014. Accessed October 3, 2014. abcnews.go.com."Liberia." Health Systems Strengthening: Where We Work:. January 1, 2014. Accessed October 3, 2014. www.healthsystems2020.org."Financing Liberia's Health Care." Health Systems Strengthening: News:. February 13, 2012. Accessed October 3, 2014. www.healthsystems2020.org.UNCLASSIFIED

Abstract

The survey was carried out by the Health Strategy and Policy Institute (HSPI) of the Ministry of Health in partnership with the World Bank. The survey was designed to be representative of six provinces in six distinct geographical regions. The provinces include Dien Bien, which has a large ethnic minority population and is one of the country’s poorest provinces, as well as Hanoi, one of the wealthiest areas in the country. The four other provinces (Binh Dinh, Dak Lak, Dong Nai, and Dong Thap) were selected because they have socioeconomic characteristics typical of their respective regions. Information was collected from a representative sample of commune health stations and district hospitals as well as patients who use those facilities. Elements of the information collected in the study include the availability of key inputs (infrastructure and medicines) at the facility, patient experiences, the qualifications and experience of doctors, the knowledge of doctors, and the actual practice of doctors as recorded in direct observations of clinical practice.

Geographic coverage

The study consists of six provinces locating in six geographical regions of Vietnam: Dien Bien, Hanoi, Binh Dinh, Dak Lak, Dong Nai, and Dong Thap. Four and a half provinces (Binh Dinh, Dak Lak, Dong Nai, Dong Thap, and the new half of Hanoi which was the “formal Ha Tay” ) were selected as a “typical” of their corresponding regions based on criteria of provincial average income per capita and provincial poverty rates. To assess the equality of healthcare services, one poor and ethnic minority province (Dien Bien), and a major city (the original half of the capital Hanoi) were also included

Analysis unit

1. Module 1- Facility Questionnaire: facilities
2. Module 2- Health worker interviews: doctors (district hospitals) or health staff (commune health stations)
3. Module 3- Clinical vignettes: doctors or assistant doctors
4. Module 4- Clinical observations: interactions of doctors and patients
5. Module 5- Exit patient interviews: inpatients/outpatients

Universe

6 provinces: Dien Bien, Hanoi, Binh Dinh, Dak Lak, Dong Nai, Dong Thap

Kind of data

Sample survey data [ssd]

Sampling procedure

The Vietnam District and Commune Health Facility Survey 2015 was conducted in the same locations with the Household survey to assess inequity in health status and health service utilization in Vietnam (which was simultaneously conducted to collect information on demand side of Vietnam health system) to ensure the linkage in analyzing the relationship between the health seeking behavior and the quality of local providers.

The study consists of six provinces locating in six geographical regions of Vietnam: Dien Bien, Hanoi, Binh Dinh, Dak Lak, Dong Nai, and Dong Thap. Four and a half provinces (Binh Dinh, Dak Lak, Dong Nai, Dong Thap, and the new half of Hanoi which was the “formal Ha Tay” ) were selected as a “typical” of their corresponding regions based on criteria of provincial average income per capita and provincial poverty rates. To assess the equality of healthcare services, one poor and ethnic minority province (Dien Bien), and a major city (the original half of the capital Hanoi) were also included.

The sample of the health facility survey were commune health stations and district hospitals locating in the communes and districts that were corresponding with the selected enumeration areas (clusters) in the Household Survey (Household survey to assess inequity in health status and health service utilization in Vietnam). Specifically in urban areas of Hanoi, where multiple central level hospitals concentrate, some districts do not have district hospitals. In this case, the corresponding city level hospitals or polyclinics were selected. In each facility, besides facilities’ overall information, data of a sample of doctors and inpatients and outpatients were collected.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2015 Vietnam health survey consists of 5 components including: (1) facility questionnaire; (ii) health worker interviews; (iv) exit patient interviews (iv) clinical vignettes; (v) clinical observation. Except clinical observation, the core instruments of four remaining modules were modeled along the Service Delivery Indicators (SDI), with the integration of the Service Availability and Readiness Assessment (SARA) and 2001-2002 Vietnam National Health Survey tools, and adapted to Vietnam contexts. The module clinical observation, specifically, used Generalizable Reducible Metrics (GRM) method which was based on direct observation of clinical practice. The clinical observation analysis was mostly based on data collection instrument tools implemented successfully in other settings including India and Tanzania.

1.Facility Questionnaire: - Collected general information about the health facility, utilization, waste management, facility infrastructure, availability of equipment, materials, drugs and supplies, offered laboratory and diagnostic services. - Collected revenues and expenditure by source, information on clinical audits, supervision visits, availability of guidelines. 2. Health worker interviews - Collected data of district hospital doctors’ and commune health station all staff’s characteristics, training opportunities, income, dual practices, satisfaction, and policy suggestions. 3. Clinical vignettes
- Assessed the clinical knowledge of doctors and/or assistant doctors using medical vignettes. 4. Clinical observations - Assessed the practices of doctors and assistant doctors. Collected information on consultation time, number of history questions, performance of examinations, prescribed medicines, given treatments, given tests. 5.Exit patient interviews
- Collected information on patient experience (waiting time, services of receive, procedures carried out, payments, etc.), socio-economic characteristics, source of health financing, and provider preferences and expectations (reason for choosing facility.)

Cleaning operations

All completed forms were double entered by HSPI's data entry specialists; checked by HSPI's data managers and by the World Bank's technical staff.

Private hospitals of Belize District remained stable at 4 number over the last 1 years.