How Do Health System Features Influence Health System Performance?

Annex A. The Health Systems Characteristics survey

The OECD survey on Health Systems Characteristics (HSC) aims to collect key policy and institutional characteristics by which heath care systems can be meaningfully differentiated from one another (Paris, Devaux and Wei, 2010[1]).

The HSC survey provides a structured snapshot of the key characteristics that underpin healthcare insurance and healthcare delivery in a given country. It describes arrangements to organise population coverage, the financing of healthcare insurance and delivery, the organisation of healthcare delivery, focusing on the public/private mix of healthcare provision, provider payment schemes, user choice and competition among providers, as well as the regulation of healthcare supply and prices. The survey also describes key aspects of governance and resource allocation in health systems, such as decentralisation in decision-making and the nature of budget constraints.

The Secretariat collected information on health system characteristics through surveys carried out in 2008, 2012, 2016, and 2023. The 2008 survey included 81 questions, often with multiple items and sub-questions for further details. The 2012, 2016, and 2023 surveys comprised 91, 78, and 77 questions, respectively.

The survey was filled in by 29 OECD member states in 2008. For the 2012 round, the then four new OECD member countries – Chile, Estonia, Israel and Slovenia – filled in the survey too, for a total of 33 countries. 28 OECD member countries and Costa Rica and South Africa filled in the survey in 2016 and 32 OECD countries and Bulgaria replied to the survey in 2023. Twenty-one Latin American and Caribbean countries filled in the survey in 2018 (Lorenzoni et al., 2019[2]).

Countries reviewed responses to the different rounds of the OECD surveys on health system characteristics to also verify the consistency of responses over time. Furthermore, the main characteristics of the health systems of OECD countries are publicly available in two datasets on the OECD data platform (https://data-explorer.oecd.org/). These datasets consist of results for the 2023 round as well as results for the 2016 and 2012 rounds of the survey and can be searched according to country, version of the survey, and question. The data can be displayed online or downloaded in an excel format file.

The raw responses to the survey are of interest for certain issues, but for the purpose of providing input to performance analyses, a more manageable set of indicators is necessary. A set of health system characteristics indicators that can help identify cross-country differences in health system performance was developed, informed by discussion with countries (Table A A.1) (de la Maisonneuve et al., 2016[3]; Lorenzoni et al., 2018[4]; Joumard, André and Nicq, 2010[5]). Additional data sources, such as the System of Health Accounts (SHA), were used to complement responses as appropriate, with the implicit assumption that these captured well a policy dimension.

Table A A.1. List of core indicators by domain

Domain	Indicator	Description
Health financing and coverage arrangements	Degree of user choice for basic coverage	A: residence‑based; B: insurance‑based, single insurer; C: insurance‑based, multiple insurers without choice; D: insurance‑based, multiple insurers with choice
	Level of financial protection for healthcare users	Share of healthcare spending financed by household out-of-pocket spending in total health spending
	Patient choice among providers	Whether individuals are free to choose any primary care physician, specialist, or hospital to seek care, face incentives to choose a specific primary care physician, specialist, or hospital, or have limited choice. A higher score means that individuals have more options when selecting their primary care physician, specialist, or hospital
	Role of primary care in the health system (gatekeeping)	Categorical (ordered). Financial incentives or referral obligation to access specialist care. A indicates no requirement and no incentive for referral form PHC; B indicates financial incentives and C indicates that referral form PHC is required.
	“Over the basic” coverage	Role played by private health insurance offering complementary, supplementary or duplicative coverage on a voluntary basis. Based on the share of private voluntary health insurance spending in total health expenditure.
	Continuity of care	It captures the share of the population that has a regular doctor to go for care. A: limited part of the population; B: the majority of the population; C: almost the whole population
Healthcare delivery systems	Incentives for volume increase in physicians’ payment methods	Evaluates the predominant mode of payment for physicians in primary care, community, and hospitals (i.e. fee‑for-service, capitation, salary), as a proxy for incentives to generate volumes of services. A higher value of the indicators means a larger use of fee‑for-service
	Incentives for volume increase in hospitals’ payment methods	Predominant mode of payment of hospitals. The higher the score the stronger the incentive to generate volumes. A higher value of the indicators means a larger use of fee‑for-service.
	Regulation of medical staff in hospitals	Reflects conditions for recruitment and remuneration of medical staff in hospitals. A: Not regulated; B: Partial regulation; C: Highly regulated.
	Financial incentives to improve quality of care	A higher score reflects a system with stronger incentives to improve quality for primary care physicians, specialists and hospitals.
	Degree of private provision of primary care and outpatient specialist services	Evaluates the degree of private provision in primary care and outpatient specialist care. A higher value of the indicators means that the predominant provision of primary and outpatient specialist care is private.
	Advanced roles of nurses	Shows whether nurses independently provide immunisation, health promotion, routine checks for chronically ill patients and minor procedures. A greater number of positive responses – each adding a score of 1 – triggers a higher score that indicates greater responsibilities for nurses.
	Financial incentives for primary care physicians to improve quality of care	A higher score reflects a system with stronger incentives to improve quality of care for primary care physicians.
Governance and resource allocation	Definition of the health benefit basket	Describes how the benefits covered by basic primary health insurance are defined for medical procedures, pharmaceuticals, and implantable devices. A higher score reflects the definition of a benefit basket at central level by a positive list.
	Use of Health Technology Assessment	Existence and use of health technology structure and capacity to determine benefit coverage, reimbursement level/prices and clinical guidelines. A: Little use; B: Somehow used; C: Highly used.
	Regulation of prices/fees for primary care physicians’ services, specialists’ services and hospitals’ services paid by third-party payers	A score is assigned by adding points for the degree of regulation by institutions financing of basic primary coverage. The indicator considers the average of the scores for physicians’ services, specialists’ services and hospitals’ services paid by third-party payers. A higher score indicates a higher degree of regulation.
	Use of electronic health records	Indicates the extent to which primary care physicians use electronic health records by evaluating eight functions: making appointments, keeping records of consultations, ordering laboratory tests, issuing drug prescriptions, sending drug prescriptions to a pharmacy, sending referral letters to medical specialists, receiving alerts or prompts about a potential problem with drug dose or drug interaction, storing diagnostic test results. A greater number of positive responses – each adding a score of 1 – triggers a higher score that indicates greater information continuity.

Qualitative responses provided to the questionnaire were transformed into quantitative indicators using the same scoring system for the different rounds of the survey. Scores were assigned to reflect the country variance rather than distributing countries evenly across categories (Lorenzoni et al., 2018[4]).

For the indicator “financial incentives to improve quality of care” (Table A A.2), the highest score was assigned when bonuses for physicians – both primary care and specialists – related to preventive care, management of chronic diseases, patient satisfaction, population risk factors, uptake of IT services and data quality and linkage were in place, and bonuses for hospitals related to clinical outcomes, use of appropriate processes, patient satisfaction, patient experience and uptake of IT services were at play. The lowest score was assigned when there were no bonuses for physicians and hospitals. The final score is the simple average of the three providers’ scores.

Table A A.2. Scoring system for financial incentives to improve quality of care

Primary care physicians	Score
No incentives	0
Yes, incentives related to:	3
Preventive care	(+0.6)
Management of chronic disease	(+0.6)
Patient satisfaction	(+0.6)
Population risk factors	(+0.6)
Uptake of IT services	(+0.6)
Specialists	Score
No incentives	0
Yes, incentives related to:	3
Preventive care	(+0.6)
Management of chronic disease	(+0.6)
Patient satisfaction	(+0.6)
Uptake of IT services	(+0.6)
Data quality and linkage	(+0.6)
Hospitals	Score
No incentives	0
Yes, incentives related to:	3
Clinical outcomes	(+0.6)
Use of appropriate processes	(+0.6)
Patient satisfaction	(+0.6)
Patient experience	(+0.6)
Uptake of IT services	(+0.6)

In this analysis, a health system was qualified as reporting “Weak” financial incentives for quality if the score was < 3, “Limited” incentives if the score was => 3 and < 4, and “Strong” incentives if the score was = > 4.

For the indicator “financial incentives for primary care physicians to improve quality of care” (Table A A.3), the highest score was assigned when bonuses for physicians – both primary care and specialists – related to preventive care, management of chronic diseases, patient satisfaction, population risk factors, uptake of IT services and data quality and linkage were in place The lowest score was assigned when there were no bonuses for physicians. The final score is the simple average of the scores for the two types of providers.

Table A A.3. Scoring system for financial incentives for primary care physicians to improve quality of care

Primary care physicians	Score
No incentives	0
Yes, incentives related to:	3
Preventive care	(+0.6)
Management of chronic disease	(+0.6)
Patient satisfaction	(+0.6)
Population risk factors	(+0.6)
Uptake of IT services	(+0.6)
Specialists	Score
No incentives	0
Yes, incentives related to:	3
Preventive care	(+0.6)
Management of chronic disease	(+0.6)
Patient satisfaction	(+0.6)
Uptake of IT services	(+0.6)
Data quality and linkage	(+0.6)

In this analysis, a health system was qualified as reporting “Limited” financial incentives to physicians for quality if the score was < 3, and “Strong” incentives if the score was = > 3.

The indicator “Degree of private provision of primary care and outpatient specialist services” reports information on private provision of primary care and outpatient specialists’ services. The score is computed as the average of the two sub-scores: private provision of primary care services and private provision of out-patient specialists’ services. The highest score was assigned when the predominant provision of primary care and out-patient specialist services was private only (Table A A.4).

Table A A.4. Scoring system for the degree of private provision of primary care and outpatient specialist services

	Predominant mode	Second mode	Score
Provision of primary care services
Public healthcare centres only	Public centres		0
Mixed of public and private provision	Public centres	Private clinics, private groups/ solo practice	3
	Private/ solo practice	Public centres
Private provision only	Private clinics, private groups/ solo practice		6
	Private clinics, private groups/ solo practice	Private clinics, private groups/ solo practice
Provision of out-patient specialist services
Public healthcare centres only	Public centres, public hospitals		0
	Public centres, public hospitals	Public centres, public hospitals
Mixed of public and private provision	Public centres, public hospitals	Private clinics, private groups/ solo practice, private hospitals	3
	Private clinics, private groups/ solo practice, private hospitals	Public centres, public hospitals
Private provision only	Private clinics, private groups/ solo practice, private hospitals		6
	Private clinics, private groups/ solo practice, private hospitals	Private clinics, private groups/ solo practice, private hospitals

The indicator “patient choice among providers” is based on responses to questions asking whether individuals are free to choose any doctor or hospital, face incentives to choose a specific doctor or hospital, or have a limited choice. The highest score was assigned when individuals had the possibility to choose any provider, whereas the lowest score was assigned when individuals didn’t have the freedom to choose a provider (Table A A.5).

Table A A.5. Scoring system for patient choice among providers

Provider	Degree of choice	Score (additive)
Primary care physician	Free choice	2
	Incentives to choose	1
	Limited choice	0
Specialist	Free choice	2
	Incentives to choose	1
	Limited choice	0
Hospital	Free choice	2
	Incentives to choose	1.33
	Limited choice but with exceptions (e.g. waiting times)	0.67
	Limited choice	0

The indicator “incentives for volume increase in physicians’ payment methods” is based on responses to questions on primary care physicians and specialists’ predominant mode of payment. A score is assigned according to physicians’ and specialists’ incentives to generate volumes of services, from 0 (salary) to 6 (fee‑for-services) (Table A A.6). The final score is a simple average of the two sub-scores for primary care physicians and for specialists (which in turn is the mean of outpatient and inpatient predominant mode of payment for specialists). The higher the score the stronger the incentives to generate volume.

Table A A.6. Scoring system for incentives for volume increase in physicians’ payment methods

Predominant mode of payment of primary care physicians	Score
Salary	0
Mix of salary and capitation	1
Capitation	2
Mix of fee‑for-service and salary	3
Mix of fee‑for-service, salary and capitation	4
Mix of fee‑for-service and capitation	5
Fee‑for-service	6
Predominant mode of payment of specialists	Score
Salary	0
Mix of fee‑for-service and salary	3
Fee‑for-service	6

In this analysis, a health system was qualified as reporting “Limited” use of fee‑for-service if the score was < 3, and “Large” use of fee‑for-service if the score was = > 3.

The indicator “incentives for volume increase in hospitals’ payment methods” is based on responses to questions on hospitals’ predominant mode of payment. The score is assigned based on the likely impact of a payment method on volumes of care – the higher the incentive to increase volumes the higher the score (Table A A.7). The higher the score the stronger the incentive to generate volume.

Table A A.7. Scoring system for incentives for volume increase in hospitals’ payment methods

Predominant mode of payment	Score
Line‑item budgets	0
Per diem	1.5
Prospective global budget	3
Payment per case (DRG-like)	4.5
Payment per procedure or service	6

The indicator “definition of the health benefit basket” is based on how the benefits covered by primary health coverage are defined for medical procedures and for pharmaceuticals. The highest score was assigned when the benefit basket was defined at central level by a positive list. If the benefit basket was not considered as the preferred tool for priority setting (benefit basket is not defined), the lowest score was assigned for the indicator (Table A A.8). The final score is the simple average of the scores for medical procedures and pharmaceuticals.

Table A A.8. Scoring system for the definition of the health benefit basket

Medical procedures	Score
Benefit basket is not defined	0
Benefit baskets are defined at local level by providers under budget constraints	1.2
Benefit baskets defined by health insurance funds by a negative list	2.4
Benefit baskets defined by health insurance funds by a positive list	3.6
Benefit baskets are defined at central level by health insurance funds by a negative list	4.8
Benefit baskets are defined at central level by health insurance funds by a positive list	6
Pharmaceuticals	Score
Benefit basket is not defined	0
Benefit baskets are defined at local level by providers under budget constraints	1.2
Benefit baskets defined by health insurance funds by a negative list	2.4
Benefit baskets defined by health insurance funds by a positive list	3.6
Benefit baskets are defined at central level by health insurance funds by a negative list	4.8
Benefit baskets are defined at central level by health insurance funds by a positive list	6

The indicator “regulation of prices/fees paid by third-party payers” captures the degree of price/fee regulation for primary care physicians’ and hospitals’ services. The higher the degree of prices/fees regulation the higher the score assigned (Table A A.9). The final score is the simple average of the scores for primary care physicians and hospitals.

Table A A.9. Scoring system for the regulation of prices/fees paid by third-party payers

Primary care physicians’ services	Score
Third-party payers are “price takers”	0
Prices/fees are negotiated between individual third-party payers and providers	1
Prices/fees are negotiated between collective third-party payers and providers at central or local level, but individual insurers have the ability to negotiate prices for some services	2
Prices/fees are negotiated between collective third-party payers and providers at local level	3
Prices/fees are negotiated between collective third-party payers and providers at central level	4
Prices/fees are set unilaterally by the central government or third-party payers	5
Mix of FFS and capitation. Mix of salary and FFS. Mix of salary, FFS and capitation	5.5
Salaries (or capitation formula) are set at central level by the government	6
Hospital services	Score
Third-party payers are “price takers”	0
Prices are always negotiated by individual third-party payers and providers	2
Most of prices are set or negotiated at central or local level, but some prices can be negotiated between individual purchasers and providers	3
Prices/rates are negotiated by collective third-party payers and providers at the local level	4
Prices/rates are negotiated by collective third-party payers and providers at the central level	5
Prices/rates or budgets are set unilaterally by the government	6

For the indicator “use of electronic health records”, the highest score was assigned when electronic health records were used for evaluating eight functions: making appointments, keeping records of consultations, ordering laboratory tests, issuing drug prescriptions, sending drug prescriptions to a pharmacy, sending referral letters to medical specialists, receiving alerts or prompts about a potential problem with drug dose or drug interaction, storing diagnostic test results (Table A A.10). The lowest score was assigned when electronic health records were not used.

Table A A.10. Scoring system for electronic health records

Primary care physicians	Score
No use of electronic health records	0
Yes, electronic health records used for:	1
Making appointments	(+ 1)
Keeping records of consultations	(+ 1)
Ordering laboratory tests	(+ 1)
Issuing prescription drugs	(+ 1)
Sending drugs prescriptions to a pharmacy	(+ 1)
Sending referral letters to medical specialists	(+ 1)
Receiving alerts or prompts about a potential problem with drug dose or drug interaction	(+ 1)
Storing diagnostic test results	(+ 1)

Table A A.11 to Table A A.13 report the score by indicator by country for two rounds of the OECD survey on health systems characteristics.

Table A A.11. Score by indicator by country, 2016 and 2023

Country	Year	Degree of user choice for basic coverage	Level of financial protection for healthcare users (%)	Patient choice among providers (6 = largest choice)	Role of primary care in the health system (gatekeeping)	“Over the basic” coverage (%)
Australia	2016	A	16.9	5.3	C	14.3
Australia	2023	A	14.9	4.3	C	13.2
Austria	2016	C	19.2	5.3	A	6.8
Austria	2023	C	15.8	5.3	A	6.4
Belgium	2016	D	18.5	6.0	B	4.7
Belgium	2023	D	17.9	6.0	B	4.5
Canada	2016	A	15.6	2.7	C	14.5
Canada	2023	A	14.9	2.7	C	13.8
Chile	2016	D	34.8	2.7	C	6.8
Chile	2018	D	33.2	2.7	C	6.4
Chile	2023		29.8			8.0
Colombia	2016		15.6			8.3
Colombia	2018	D	15.1	0.0	C	7.6
Colombia	2023		13.7			8.0
Costa Rica	2016	B	22.0	0.0	C	2.9
Costa Rica	2018	B	22.4	0.0	C	3.8
Costa Rica	2023	B	20.7	0.0	C	4.8
Czechia	2016	D	14.8	6.0	A	0.9
Czechia	2023	D	12.7	5.0	A	0.9
Denmark	2016	A	13.5	4.0	B	2.3
Denmark	2023		12.9			2.3
Estonia	2016	B	23.7	4.3	C	1.6
Estonia	2023	B	22.1	4.3	C	2.0
Finland	2016	A	19.3	0.7	C	4.5
Finland	2023	A	16.1	0.7	C	4.1
France	2016	C	9.6	5.0	B	7.4
France	2023	C	8.9	5.0	B	6.3
Germany	2016	D	12.8	5.3	B	2.8
Germany	2023	D	11.0	5.3	B	2.5
Greece	2016	B	34.3	3.3	A	4.1
Greece	2023	B	33.3	2.7	A	4.4
Hungary	2016		27.7			4.2
Hungary	2023	B	27.6	6.0	A	2.9
Iceland	2016	A	16.9	4.0	A	1.6
Iceland	2023	A	14.9	4.0	A	1.8
Ireland	2016	A	13.1	6.0	C	14.4
Ireland	2023	A	10.7	6.0	C	12.0
Israel	2016	D	22.8	4.7	B	12.5
Israel	2023	D	19.8	0.7	B	11.0
Italy	2016	A	23.3	4.0	C	2.3
Italy	2023		21.4			2.7
Japan	2016	C	12.9	6.0	A	3.1
Japan	2023	C	11.1	6.0	A	3.2
Korea	2016	B	34.1			8.1
Korea	2023	B	28.0	6.0	A	9.3
Latvia	2016	A	43.3	3.3	B	0.8
Latvia	2023	A	27.0	3.3	B	3.6
Lithuania	2016	B	32.3	6.0	C	1.1
Lithuania	2023	B	30.2	6.0	C	1.3
Luxembourg	2016	B	10.7	6.0	A	4.3
Luxembourg	2023	B	8.8	6.0	A	4.2
Mexico	2016	C	41.4	0.0	B	7.3
Mexico	2018	C	42.3	0.7	C	8.1
Mexico	2023		41.4			8.4
Netherlands	2016	D	11.3	4.3	C	7.5
Netherlands	2023	D	9.8	4.3	C	5.7
New Zealand	2016		13.6			7.8
New Zealand	2023		12.9			7.9
Norway	2016	A	14.3	6.0	C	0.4
Norway	2023	A	14.1	6.0	C	0.4
Poland	2016	B	22.8	6.0	C	7.9
Poland	2023	B	18.0	6.0	C	7.1
Portugal	2016	A	29.4	0.7	C	8.9
Portugal	2023	A	28.6	0.7	C	8.0
Slovak Republic	2016		18.2			1.4
Slovak Republic	2023	C	19.4	2.3	C	0.9
Slovenia	2016	B	12.0	6.0	C	15.3
Slovenia	2023	B	12.7	6.0	C	13.0
Spain	2016	A	22.0	0.7	C	6.5
Spain	2023	A	21.0	0.7	C	7.4
Sweden	2016	A	14.5	6.0	B	1.2
Sweden	2023	A	13.1	6.0	B	1.0
Switzerland	2016	D	23.0	3.3	B	9.1
Switzerland	2023	D	22.3	3.3	B	8.6
Türkiye	2016	B	16.5	3.3	A	5.1
Türkiye	2023	B	16.3			4.9
United Kingdom	2016	A	14.5	4.0	C	5.0
United Kingdom	2023	A	13.9	4.0	C	4.5
United States	2016		11.7			5.5
United States	2023	D	10.7	0.0	B	5.7

Note: The indicators “Level of financial protection for healthcare users” and “Over the basic” coverage use the latest measure available in the OECD Data explorer. Degree of user choice for basic coverage: A: residence‑based; B: insurance‑based, single insurer; C: insurance‑based, multiple insurers without choice; D: insurance‑based, multiple insurers with choice. Role of primary care in the health system (gatekeeping): A: no requirement and no incentive for referral form PHC (“Weak” gatekeeping); B: financial incentives (“Medium”); C: referral form PHC is required (“Strong”).

Source: Health System Characteristics Surveys; OECD Health Statistics.

Table A A.12. Score by indicator by country, 2016 and 2023 (continued)

Country	Year	Incentives for volume increase in physicians’ payment methods (6 = largest incentives)	Incentives for volume increase in hospitals’ payment methods (6 = largest incentives)	Regulation of medical staff in hospitals	Financial incentives to improve quality of care (6 = largest incentives)	Degree of private provision of primary care and outpatient specialist services (6 = largest private provision)	Definition of the health benefit basket (6 = defined at central level through a positive list)	Use of Health Technology Assessment	Regulation of prices/fees paid by third-party payers (6 = strongest degree of regulation)
Australia	2016		4.5	A	1.6	6.0	5.2	C	5.5
Australia	2023	4.0	4.5	A	0.8		5.2	C	5.5
Austria	2016	2.7	4.5	B	0.0	4.5	5.7	C	4.2
Austria	2023	2.7	4.5	B	0.0		5.7	C	4.2
Belgium	2016	6.0	4.5		0.0	6.0	6.0	C	5.0
Belgium	2023	6.0	4.5		0.0		6.0	C	5.0
Canada	2016	5.3	3.0		1.6	4.5	5.0	C	4.0
Canada	2023	5.3	3.0		1.8		4.7	C	4.0
Chile	2016	0.0	3.0	C	4.6	3.0	5.3	C	4.9
Chile	2018	0.0	3.0	C	4.6		5.3	C	4.9
Chile	2023								4.9
Colombia	2016
Colombia	2018	1.0	0.0	A	0.0	3.0	6.0	B	4.3
Colombia	2023
Costa Rica	2016	0.0	0.0	C	0.0			C	6.0
Costa Rica	2018	0.0	0.0	C	0.0	1.5		C
Costa Rica	2023	0.0	0.0	C	0.0		6.0	C	6.0
Czechia	2016	3.7	4.5	B	1.6	6.0		B	4.7
Czechia	2023	3.7	4.5	B	4.2				4.7
Denmark	2016	3.0	3.0	A	0.6	4.5	2.3	C	4.7
Denmark	2023
Estonia	2016			B	1.4	3.0	6.0	C	5.3
Estonia	2023	1.7	6.0	B	1.6		6.0	C	5.3
Finland	2016	0.0	4.5	C	0.0	0.0	3.0	C	6.0
Finland	2023	0.0	4.5	B	0.0		3.0	C	6.0
France	2016	3.3	4.5	B	4.6	6.0	6.0	C	5.0
France	2023	3.3	4.5	B	4.0		6.0	C	5.0
Germany	2016		4.5		0.0	6.0	4.7	C	2.7
Germany	2023	4.0	4.5	A	0.0		4.8	C	4.0
Greece	2016	2.7	4.5	C	0.0	4.5	4.7	C	3.7
Greece	2023	2.7	4.5	C	0.0		4.7	C	3.7
Hungary	2016					1.5
Hungary	2023	0.3	4.5	B	1.4		6.0	C	5.5
Iceland	2016	2.0	3.0	B	0.0	3.0	4.9	C	4.7
Iceland	2023	2.0	4.5	B	0.0		4.9	C	4.7
Ireland	2016		3.0			4.5	3.0	C
Ireland	2023		3.0		0.0		4.7	C
Israel	2016	0.0	0.0	B	0.0	1.5	6.0	C	1.0
Israel	2023	1.3	0.0	B	1.2		6.0	C	1.0
Italy	2016	0.7	4.5	B	0.0	0.0	5.5	C
Italy	2023
Japan	2016		0.0			6.0	6.0	C	4.3
Japan	2023		0.0				6.0	C	4.3
Korea	2016					6.0
Korea	2023	4.0	6.0	B	4.0		6.0	C	4.3
Latvia	2016	3.7	3.0	B	1.4	3.0	4.9	C	5.3
Latvia	2023	3.7	3.0	B	1.4		6.0	C	5.3
Lithuania	2016		4.5		1.4	3.0		C	5.3
Lithuania	2023	0.7	4.5	B	1.4		6.0	C	5.3
Luxembourg	2016	6.0	3.0	A	0.4	6.0	6.0	C	4.7
Luxembourg	2023	6.0	3.0	A	0.0		6.0	C	4.7
Mexico	2016		3.0	B	1.6	3.0	3.0	C
Mexico	2018	0.0	3.0	B	0.0		6.0	B	5.0
Mexico	2023
Netherlands	2016	5.7		B	3.0	6.0	3.0	C	2.3
Netherlands	2023	4.7		B	3.2		4.0	C	2.3
New Zealand	2016					6.0
New Zealand	2023
Norway	2016	1.7	3.0	B	2.0	3.0	5.5	C	5.0
Norway	2023	2.7	3.0	B	0.0		5.5	C	5.0
Poland	2016		4.5	A	3.4	0.0	6.0	C	5.5
Poland	2023	2.7	4.5	A	3.2		6.0	C	5.5
Portugal	2016	1.0	3.0	C		0.0	4.0	B	4.5
Portugal	2023	2.0	3.0	B	5.0		4.0	B	6.0
Slovak Republic	2016					4.5			3.0
Slovak Republic	2023	2.7	3.0	B	4.2		6.0	C	3.0
Slovenia	2016	0.0	4.5	B	0.0	1.5	6.0	C	4.3
Slovenia	2023	0.0	4.5	B	0.0		6.0	C	4.3
Spain	2016	1.3	3.0	B	4.2	3.0	6.0	C	4.2
Spain	2023	1.3	3.0	B	5.0		6.0	C	4.2
Sweden	2016	0.0	3.0	A	3.8	3.0	3.0	C	4.8
Sweden	2023	0.0	3.0	A	3.8		3.0	C	4.8
Switzerland	2016	5.0	4.5	A	0.0	4.5	4.9	C	1.7
Switzerland	2023	5.0	4.5	A	0.0		4.9	C	1.7
Türkiye	2016	3.0	3.0	C	0.0	1.5	6.0	C
Türkiye	2023						6.0
United Kingdom	2016	1.3	4.5	B	5.0	0.0	4.0	C	4.8
United Kingdom	2023	1.3	4.5	B	4.4		4.0	C	4.8
United States	2016
United States	2023	3.3		A	0.0		4.0	B	0.0

Note: Regulation of medical staff in hospitals: A: Not regulated; B: Partial regulation; C: Highly regulated. Use of Health Technology Assessment: A: Little use; B: Somehow used; C: Highly used.

Source: Health System Characteristics Surveys.

Table A A.13. Score by indicator by country, 2016 and 2023 (continued)

Country	Year	Use of electronic health records (9 = largest use)	Advanced roles of nurses (4 = most advanced roles)	Financial incentives for primary care physicians to improve quality of care (6 = largest incentives)	Continuity of care
Australia	2016	9		4.8	C
Australia	2023	8	4	4.8	C
Austria	2016	8		0	B
Austria	2023	6		0	B
Belgium	2016	1	0	0	C
Belgium	2023	6	0	0	C
Canada	2016	4	2	4.8	B
Canada	2023	5	2	5.4	B
Chile	2016	8	4	4.8	A
Chile	2018	8	4	4.8
Colombia	2018	2	3	0
Costa Rica	2016	9	3	0	C
Costa Rica	2018	9	4	0
Costa Rica	2023	8	2	0	C
Czechia	2016	2	2	4.8	C
Czechia	2023	4	3	4.8	C
Denmark	2016	8	1	0
Denmark	2023
Estonia	2016	9	4	4.2	C
Estonia	2023	8	4	4.8	C
Finland	2016	9	4	0	C
Finland	2023	8	4	0	C
France	2016	1	2	4.8	B
France	2023	1	2	4.8	B
Germany	2016			0	C
Germany	2023	6	3	0	C
Greece	2016	9	3	0	A
Greece	2023	8	3	0	A
Hungary	2016				C
Hungary	2023			4.2	C
Iceland	2016	7	1	0	A
Iceland	2023	7	4	0	A
Ireland	2016	7	4	0	B
Ireland	2023	6	4	0	B
Israel	2016	8	4	0	C
Israel	2023	8	4	0	C
Italy	2016	5	0	0	B
Italy	2023				B
Japan	2016				A
Japan	2023	3	1	0	A
Korea	2016				A
Korea	2023	3	0	3.6	A
Latvia	2016	3	4	4.2	C
Latvia	2023	3	2	4.2	C
Lithuania	2016	0	1	4.2	C
Lithuania	2023	7	2	4.2	C
Luxembourg	2016	3	0	0	C
Luxembourg	2023	0	1	0	C
Mexico	2016			4.8
Mexico	2018	0	2	0
Mexico	2023
Netherlands	2016	8	4	3	C
Netherlands	2023	8	4	3.6	C
Norway	2016	7	0	0	C
Norway	2023	8	0	0	C
Poland	2016	1	4	3	B
Poland	2023	6	3	3	B
Portugal	2016	8	4	4.8	B
Portugal	2023	7	4	5.4	B
Slovak Republic	2023	2	1	5.4
Slovenia	2016	5	0	0	C
Slovenia	2023	5	0	0	C
Spain	2016	9	4	4.8	B
Spain	2023	8	4	4.8	B
Sweden	2016	9	4	5.4	B
Sweden	2023	8	4	5.4	B
Switzerland	2016	6	1	0	C
Switzerland	2023	3	1	0	C
Turkiye	2016	6	2	0
United Kingdom	2016	8	4	4.2	C
United Kingdom	2023	8	4	4.2	C
United States	2016				B
United States	2023	1	4	0	B

Note: Continuity of care: A: limited part of the population has a regular doctor to go for care (“Weak” continuity); B: the majority of the population has a regular doctor to go for care (“medium”); C: almost the whole population has a regular doctor to go for care (“Strong”).

References

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[5] Joumard, I., C. André and C. Nicq (2010), “Health Care Systems: Efficiency and Institutions”, OECD Economics Department Working Papers, No. 769, OECD Publishing, Paris, https://doi.org/10.1787/5kmfp51f5f9t-en.

[4] Lorenzoni, L. et al. (2018), “Which policies increase value for money in health care?”, OECD Health Working Papers, No. 104, OECD Publishing, Paris, https://doi.org/10.1787/a46c5b1f-en.

[2] Lorenzoni, L. et al. (2019), “Health systems characteristics: A survey of 21 Latin American and Caribbean countries”, OECD Health Working Papers, No. 111, OECD Publishing, Paris, https://doi.org/10.1787/0e8da4bd-en.

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