Evaluating Portuguese primary healthcare through Prevention Quality Indicators (PQIs)
The National Program for Quality Indicators In Community Healthcare: Methodological Issues
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Transcript of The National Program for Quality Indicators In Community Healthcare: Methodological Issues
The National Program for Quality
Indicators In Community
Healthcare: Methodological
Issues
Orly Manor7th meeting of the Eastern Mediterranean Region of the
International Biometric Society (EMR-IBS)
Tel-Aviv April 22-25, 2013
2
“Efforts to improve
quality require
efforts to measure it”
(Casalino, 2000:NEJM)
Healthcare in Israel3
National Health Insurance law (1995) Universal healthcare
Standard basket of medical services
Four health plans (kupot cholim)
“Justice, equity and solidarity…medical services will be
offered based on medical considerations, with
reasonable quality.”
Healthcare in Israel4
Health Tax - progressive , paid to the National Insurance Institute
(funds are distributed to the health plans according to a capitation
formulae) + modest copayment directly to health plan for specific
services
Open enrollment, no option to reject applicant, annual option to
switch plans
Managed competition between plans (uniform benefits)
Competition is based on quality and nature of services
5
Israel National Program for Quality Indicators
in Community Healthcare (QICH)
Supervisory bodies were established “to follow and
assess the influence of the NHI law on health services in
Israel, their quality, effectiveness and cost"
QICH started as a research project (Porath & Rabinovitz,
2002) and later adopted as a national program.
Full cooperation and support by all four health plans
Mission
To provide the public and policy-makers information
on the quality of community healthcare provided in
Israel. This information covers various health
categories and is intended to promote and improve
the standard of healthcare in Israel.
6
Main Product
Annual report presenting national results
of quality indicators in community healthcare
Enables
the evaluation of developments and changes in healthcare over time
the early identification of risk factors in the Israeli population and in
sub-populations
the comparison of healthcare quality in Israel with other countries
7
8
"Not everything that
counts can be
counted,
and not everything
that can be counted
counts."
Einstein
Quality Indicators- Methods9
Measures of clinical performance (structure, process, outcome)
Based on electronic health records from the four health
plans
All indicators are rates
Some indicators are conditional on others
Covariates: age, sex, SEP (proxy)
10
Quality indicators (2013)
Example: Diabetes11
Blood glucose levels of individuals with diabetes are
directly related to the development of complications:
cardiovascular disease, blindness, kidney failure
Monitoring blood glucose by periodic hemoglobin A1c
testing and achieving adequate glycemic control
Example: Diabetes12
Prevalence measure:
Rate of individuals with diabetes mellitus from the entire population (overall
and by age and gender)
Process measure:
Rate of individuals with diabetes with documented levels of hemoglobin
A1c (HbA1c)
Outcome (intermediate) measure:
Rate of individuals with controlled levels of HbA1c from patients with
diabetes with documented levels of hemoglobin A1c (HbA1c)
Methodological Issues
1. Criteria
2. Population coverage
3. Data quality-measurement
error
4. Data sources
5. Consistency of measures
6. Reporting
13
14
Criteria
Implementation
Population Coverage
Population-based, near-complete coverage (not
sample)
Transfers between health plans
Births/deaths
Other populations: e.g., soldiers
15
Data Quality- misclassification error
Estimating the prevalence of a medical condition in the absence of neither
a gold standard nor an additional classification.
We wish to estimate - prevalence, sensitivity and specificity , yet df=1.
We can use a Bayesian approach- simultaneous inferences of the
prevalence, sensitivity and specificity and positive and negative predictive
value (Joseph et al 1995)
Selecting priors- experts’ opinion, understanding sources of data and
errors (Greenland 2009)
16
Data Quality- Consistency of Measures
Uniform definitions across health plans
Membership
Data collection period
Numerator and denominator
17
Data Sources
1. Medical records (e.g., documentation of BMI)
2. Nurse’s records (e.g., documentation of vaccination)
3. Pharmacy claim records (e.g., medication purchase)
4. Laboratory results (e.g., HbA1c levels)
5. Hospital procedure codes (e.g., CABG)
6. Other (e.g., mammography)
18
Data Quality – Sources of Data and Sources of Error
1. Medical records (e.g., documentation of BMI)
2. Nurse’s records (e.g., documentation of vaccination)
3. Pharmacy claim records (e.g., medication purchase)
4. Laboratory results (e.g., HbA1c levels)
5. Hospital procedure codes (e.g., CABG)
6. Other (e.g., mammography)
19
Automated vs manual data input (variation between and within health plans)
Pop-up options vs typing in vaccine name, historical data
(variation between health plans)ATC vs YARPA/LARGO(variation between health plans)
Standardized values for calibration(variation between laboratories)
MOH codes used for billing (too broad)
Self reported, billing-based
Data Quality – Checks and Audit
1. Internal (health plans) Data checks (BI) Feedback loops/criterion validity
2. Quasi-external (directorate) Between and within health plan data checks (outliers)
Comparison with existing national data
3. External (independent auditor) Process audit of infrastructure changes Process audit of indicator implementation
20
Reporting21
Transparency + court ruling of the public
reporting of indicators by health plan
Case mix: substantial differences between
health plans by SEP
Limited available data on SEPFriedberg et al. (2011). Rand Corporation: Methodological Issues in Public Reporting
Reporting - Adjusting for Case Mix
22
Israel is divided into statistical areas.
The Israel Central Bureau of Statistics calculates SEP
scores for each statistical area using recent census
information
Currently-using GIS each person’s address (unidentified
data) is linked to his statistical area and the respective
SEP score
Reporting -Benchmarking
23
Setting benchmarks: setting, testing, comparing??
Diabetes care24
Diabetes care25
Diabetes care includes routine monitoring and
proper control of:
• Blood glucose levels (93%)
• Cholesterol levels (90%)
• Blood pressure measurement (92%)
• BMI assessment (86%)
• Eye examination (65%)
• Influenza (55%) and pneumococcal vaccination (77%)
Diabetes care26
International comparisons – Diabetes care (2010)
27
*US data from the National Committee for Quality Assurance, HEDIS data set for 2010
Diabetes care – Israel and England (QOF) (2009)
Thank you29
Clalit
Chaim Bitterman
Orit Ya’akobson
Arnon Cohen
Margalit Goldsprecht
Tamara Koren
Meuhedet
Liora Valinsky
Yossi Zini
Alon Yaffe
External auditor: Aliza Lukach
Israel National Institute for Health Policy Research
Advisory boards
Leumit
Daniel Vardi
Eran Matz
Doron Dushnitzky
Nirit Peretz
MaccabiYair BirenboimEinat ElranNesya GordonRachel MaromGuy Levy
Directorate: Orly Manor, Arie Ben-Yehuda, Amir Shmueli, Ora Paltiel, Ronit Calderon and
Dena Jaffe
Directorate staff: Wiessam Abu Ahmad, Galit Shefer.
Importance and Relevance30
Evidence – Moving Target
HbA1c control
31
“Intensive therapy was stopped after a mean of 3.5 years due to increased mortality” The Action to Control Cardiovascular Risk in Diabetes Study Group N Engl J Med 2008; 358:2545-2559
.
Ability to Quantify – Defining Diabetes
32
Reporting33
Quasi-external Audit34
Comparison old v. new 2009 Three-year trend 2008-2010
New ReportRelative
difference
Measure nameOld
2009New 2009DiffRel Diff200820092010
2008-2009
2009-2010
Influenza vaccination0.6050.6050.000.00% 0.600.600.580.92%-4.60%
Pneumococcal vacciation0.5000.796 0.810.790.78-1.62%-1.75%
HbA1c documentation0.9310.9310.000.00% 0.920.930.930.93%-0.06%
Controlled HbA1c (0-74 yrs) 0.4770.455-0.02-4.61% 0.440.460.443.01%-2.50%
Uncontrolled HbA1c0.0960.0960.000.00% 0.100.100.09-6.62%-8.55%
Absolute value >3%
Absolute value 1-3%