Health Care Quality Indicators

(HCQI) project: overview of

results from the 2010-2011

data collection and next steps

Joint session of

OECD Health Data National Correspondents

and Health Accounts Experts

Paris, 4 October 2011

The HCQI work programme

• Measuring quality of care

(Health Care Quality Indicators)

• Exploring the reasons behind country variations

(cancer, primary health care)

• Improving the national data infrastructure

to measure quality of care

• Links with other strategies

Measuring quality of care

• Health Care Quality Indicators:

- primary care (avoidable admissions),

- acute care (30-day fatality rates for AMI and stroke),

- cancer (screening, survival, mortality),

- mental health care (unplanned readmission rates),

- patient safety (obstetric trauma, post-op complications),

- patient experiences (evaluation phase).

• Data collection 2010/11 - 34 countries

Primary care

Chronic conditions like asthma are either preventable or manageable

through proper prevention or primary care interventions.

Acute care

Case-fatality rate is a good measure of acute care quality because it reflects the processes

of care for AMI, including timely transport of patients and early treatment (i.e. thrombolysis).

Cancer

Cancer screening and survival rates reflect advances in public health interventions,

such as greater awareness of the disease, screening programmes, and improved treatment.

Mental health care

Unplanned hospital re-admissions for mental disorders are an indicator of

the quality of care because patients who receive appropriate and coordinated

follow-up care after discharges are not usually readmitted to hospital.

Patient safety

Although differences in procedural or postoperative patient safety indicators

may reflect differences in recording practices, these indicators show that

numerous patients have been affected by sentinel and adverse events.

Patient safety methodological issues

The effect of:

• Variation in definition

• Average LOS

• Average SDX

Postoperative Sepsis – impact of length of stay (LOS) and admission type exclusions

All non-obstetric surgical hospital discharges of one year, age =15 years or >

15 years, exclusions on immunocompromised state

or cancer and infections are performed

LOS

is < 48 hours or

< 2 days?

yesLOS

is < 24 hours or

< 1 day?

Count cases and report

denominator (elective) (14)

Count cases and

report denominator

(elective) (13)

SDx is

identical to the

numerator def.?

Count cases and

report numerator

(elective) (13)

LOS

is < 96 hours or <

4 days?

LOS

is < 72 hours or

< 3 days?

yes yes

yes yesyes

Count cases and

report denominator

(elective) (11)

Count cases and

report denominator

(elective) (12)

Count cases and

report denominator

(9)

yes

Count cases and

report numerator

(elective) (12)

Count cases and

report numerator

(elective) (11)

Count cases and

report numerator (9)

yes

yes

Count cases and

report

denominator (8)

Count cases and

report denominator

(7)

Count cases and

report denominator (6)

SDx is

identical to the

numerator def.?

SDx is

identical to the

numerator def.?

SDx is

identical to the

numerator def.?

yesyes yes

SDx is

identical to the

numerator def.?

SDx is

identical to the

numerator def.?

SDx is

identical to the

numerator def.?

SDx is

identical to the

numerator def.?

Count cases and report

numerator (elective) (14)

Count cases and report

numerator (8)

Count cases and report

numerator (7)Count cases and report

numerator (6)

Count cases and

report denominator (5)

Admission type elective?

SDx is

identical to the

numerator def.?

Count cases and report

numerator (5)yes

proceed with

flowchart

„Postoperative

Sepsis“

yes yes yesyes

NoExclude

LOS

is < 96 hours or <

4 days?

LOS

is < 72 hours or

< 3 days?

LOS

is < 48 hours or

< 2 days?

LOS

is < 24 hours or

< 1 day?

yes

yesyesyes no

LOS

is >= 96 hours or

>= 4 days?

yes

SDx is

identical to the

numerator def.?

Count cases and report

denominator (10)

Count cases and report

numerator (10)

yes

The effect of admission type exclusion

Post-operative sepsis (admission type distribution)

Patient experiences

• Evaluation of data collection tools:

- translation (CZE, JPN, KOR, LUX, NLD, SGP)

- cognitive testing (NLD, SGP)

• Evaluation of data collection methods:

- sampling methods

- data collection modes

• Evaluation of data:

- psychometric properties (5-level response categories,

internal consistency and correlations, the same construct)

• Questions likely to be included in the existing

national surveys (CAN, CZE, FRA, DEU, JPN, NLD,

SGP,GBR).

Exploring the reasons behind country variations

The objectives of Cancer Care Study

•To explore the characteristics of systems of cancer care in

OECD countries,

•To assess the relative effect of the main domains of the system

of care, in particular governance, on survival outcome of patients

with breast, cervical, colorectal and lung cancers.

The objectives of Primary Care Study

•To better understand primary care quality variations,

•To investigate how these variations relate to the way in which

primary care services are organised.

Cancer care - resources

• Almost a half of differences in cancer survival

may be explained by the available resources.

• Key explanatory variables:

- financing (total national expenditure on health),

- investment in new cancer drugs (clinical use of

10 selected drugs),

- investment in technology (CT scanners/1M/GDP),

- existing infrastructure resources (comprehensive

cancer centres/1M).

Cancer care - process quality of the delivery

• Process quality of the delivery of cancer care may

explain approximately one third of differences in

cancer survival.

• The key explanatory variables:

- early detection through a screening programme

(national rollout, nationwide coverage, interval),

- easy access to cancer services

(waiting time from diagnosis to initial treatment),

- provision of optimal treatment (combined surgery,

radiotherapy, chemotherapy).

Cancer care - governance

• Approximately one quarter of differences in cancer

survival may be explained by governance.

• Key explanatory variables:

- NCCP fully implemented, or

- cancer specific targets,

- stewardship,

- timeframes,

- monitoring,

- guidelines,

- case management,

- coordination,

- quality assurance.

Cancer care – conclusions

• Which policy choices and elements within the three

domain groups lead to improved survival outcomes.

• The better-performing countries have established

cancer policy priorities, implemented key elements of

cancer control, introduced integrated care processes

and actively worked on the delivery of cancer services.

Cancer care – next steps

• Several weaknesses which imply an agenda for

getting better information on outcomes and relevant

policies in the future:

- the need for more up-to-date survival data,

- survival rates estimated by using period analysis,

- information on staging at the time of diagnosis,

- information on waiting times,

- the level of compliance with guidelines,

- information on screening programmes (colorectal)

- cancer-specific expenditure data (SHA project)

Primary care - variation across countries

Australia

Austria

Belgium

Canada

Czech Republic

Denmark

Estonia

Finland

France

Germany

Hungary

Iceland

Ireland

Israel

Italy

Japan

Korea

Luxembourg

Mexico

Netherlands

New Zealand

Norway

Poland

Portugal

Slovakia

Slovenia

Spain

Sweden

United Kingdom

United States

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

z-scores

Amenable mortality – dir. std. PYLL

Primary care – cluster analysis

• The selected indicators combine a mix of macro level system descriptors of funding,

efficiency and equity; closer to the issues that matter to people who use health

services such as accessibility, affordability and choice.

• The findings from cluster analysis: organisational features of primary care are highly

variable; countries can be grouped based on system characteristic information.

- Level of copayment for primary care services

- Density of family physicians per 100 000 population

- TNEH as a proportion of GDP

- Extent of GP gate keeping

- Ability to choose own GP

- Predominant practice structure (solo, mixed, multi)

• To collect nationally verifiable primary care system characteristic

information from all OECD countries.

• To assess the utility of factoring in socio economic data to adjust

for their confounding effects on mortality and morbidity.

• To explore more fully the utility of statistical clustering and relative

benchmarking in the context of health system comparative quality

monitoring.

• To potentially develop an enhanced suite of primary care quality

measures (multi morbidity, cost effective prescribing, PYLL indicators).

Primary care – next steps

Improving the national data infrastructure

• Work activities:

- balance between privacy and data protection

and the need for reliable and valid information

- unique patient identifiers

- adequate secondary diagnostic coding

- present-on-admission flags

- electronic health records

- longitudinal information on patient experience

• Balance between privacy and data-protection concerns

vs. the need for reliable and valid information for quality

led governance => HCQI Expert Group meeting on 18 Nov 2011

• Registries/administrative databases used for measuring

quality of care: unique patient identifiers, secondary

diagnostic coding, present-on-admission flags.

Improving the national data infrastructure – cont.

• Comprehensive use of electronic health records for

measuring quality of care as part of population-based

statistics => HCQI Expert Group meeting on 18 Nov 2011

• National systems to collect longitudinal information on

patient experience => PE subgroup meeting on 17 Nov 2011

Improving the national data infrastructure – cont.

Links with other strategies

• Work activities:

- consistency and linkage of quality measurement

efforts

- learning from other countries’ experiences in terms of quality improvement practice

- synergy with other organisations, projects,

initiatives

• Consistency and linkage of quality measurement efforts

with national quality policies on:

- health system input (professionals, hospitals, technologies),

- health system design (responsibilities for quality),

- monitoring (standards, guidelines, information-infrastructure), and

- improvement (safety programmes, quality incentives).

• Learning from other countries’ experiences in terms of

good quality improvement practice.

• Example: country quality review (Korea)

Links with other strategies – cont.

• Synergy in work on common definitions, data collection,

indicators reporting, data infrastructure, analytical work.

• Example: European Partnership for Action Against Cancer (EPAAC)

- Work Package 9 (cancer data and information):

- recommendation to consider the use of fractional polynomials

for study between socio-economic variables and cancer survival

in Europe,

- recommendation to use the System of Health Accounts (SHA)

framework to collect comparable estimates of cancer care

expenditure at the national level.

Links with other strategies – cont.