are gps’ electronic health records suitable for use in ...multiannual information society support...

Project financed by the Belgian Federal Public Planning Service Science Policy in the framework of the multiannual information society support programme 2001-2008 Contrat n° I2/AE/207and n° I2/2F/207

IPH/EPI REPORTS Nr. 2008-001

Société Scientifique de Médecine Générale

Domus medica

Université Catholique

de Louvain

ResoPrim Primary health care research network

Are GPs’ Electronic Health Records suitable for use in Public Health Research?

Consolidated quantitative results phase 1

Version 1

January 2008

Etienne De Clercq Viviane Van Casteren

Pascale Jonckheer Peter Burggraeve

Marie-France Lafontaine Karen Degroote

Caroline Artoisenet Vincent Lorant

ResoPrim (Primary health care research network) Report Phase 2, 2006-2008

Epidemiology – December 2007 Brussels Scientific Institute of Public Health IPH/EPI Reports Nr. 2008 - 001 D/2008/2505/03

ResoPrim Primary health care research network

Are GPs’ Electronic Health Records suitable for use

in Public Health Research?

Consolidated quanti tative results phase 1 Version 1

C on tac t address : Et ienne De Clercq : Univers i té Cathol ique de Louvain V . V a n C a s t e r e n Viv iane Van Casteren : Sc ient i f ic Inst i tu te of Publ ic Heal th S c i e n t i f i c I n s t i t u t e o f P u b l i c H e a l t h Pascale Jonckheer : Soc ié té Sc ient i f ique de Médecine Généra le R u e J . W y t s m a n s t r a a t 1 4 Peter Burggraeve : Domus Medica 1 0 5 0 B r u x e l l e s – B r u s s e l Marie France Lafonta ine : Sc ien t i f i c Ins t i tu te o f Publ ic Hea l th B e l g i q u e – B e l g i ë Karen Degroote : Sc ient i f ic Inst i tu te of Publ ic Heal th T e l : 0 2 6 4 2 5 0 2 3 Carol ine Ar to isenet : Un ivers i té Cathol ique de Louvain F a x : 0 2 6 4 2 5 4 1 0 Vincent Lorant : Univers i té Cathol ique de Louvain v . v a n c a s t e r e n @ i p h . f g o v . b e

Tabel of content 5

TABLE OF CONTENT ABSTRACT............................................................................................................................................................... 7 1. INTRODUCTION .......................................................................................................................................... 8 2. MATERIAL AND METHODS..................................................................................................................... 9 3. RESULTS ...................................................................................................................................................... 12

• 3.1. HEALTH RESEARCH INFORMATION SYSTEM ASSESSMENT ................................................................. 12 • 3.2. DENOMINATOR ISSUE .......................................................................................................................... 15 • 3.3. QUALITY OF CARE................................................................................................................................ 15 • 3.4. EPIDEMIOLOGY .................................................................................................................................... 16 • 3.5. SOCIO-ECONOMY ................................................................................................................................. 16

4. DISCUSSION AND CONCLUSION.......................................................................................................... 17 ACKNOWLEDGMENTS...................................................................................................................................... 19 REFERENCES ....................................................................................................................................................... 20 LIST OF ABBREVATIONS ................................................................................................................................. 23 APPENDIX: ADDITIONAL TABLES................................................................................................................ 24

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Are GPs’ Electronic Health Records suitable for use in Public Health Research?

Keywords: Computerized Patient Records, Primary Health Care, Data Collection.

This report is an updated and extended version of the published paper:

De Clercq E, Van Casteren V, Jonckheer P, Burggraeve P, Lafontaine M-F, Vandenberghe H, Lorant V, Artoisenet C, Degroote K. Research networks: can we use data from GPs’ Electronic health Records. Studies in Health Technology and Informatics, 2006 ; 124: 181-186.

Some additional tables are provided in the appendix.

Abstract

7

Abstract As widely discussed in the literature, there are many potential scientific usages of data

extracted from the primary care Electronic Health Records (EHR), such as for quality of care,

epidemiological or socio-economical studies. Yet, can we use the current available data in the

EHR for such purposes? In this paper, our objective is to report on the preliminary findings of

the Belgian ResoPrim project (2003-2005) to answer the question. We set up a semi-

anonymous network involving 26 current practices (28 volunteer GPs), 3 different EHR

software systems and two Trusted Third Parties. Based on a literature overview we identified

27 research questions to be answered using 50 indicators. The study design includes

retrospective (2002 – 2004) and prospective (6 weeks) data collection processes around the

theme of “Hypertension and cardiovascular risk factors”. For some data sets, the data

extraction was a full automatic procedure, for some others, the data extraction was related to

an input from the GPs allowing some comparisons between both procedures. At this stage, we

performed an extended descriptive analysis of our data. Retrospectively we collected data

related to 22,730 patients and 203,128 contacts. Prospectively we collected data for 9,472

patients and 13,814 contacts. Our main findings are briefly presented and discussed in this

paper. The most promising fields seem to be the Health Research Information Systems

assessment and the quality of care studies. Such network also appears as a potential tool to

assess or to manage health information policy related to the Electronic Health Records. It can

also improve the content of the GPs’ clinical information systems. It is quite too soon to reach

the expected theoretical benefits for epidemiologic and socio-economic studies, yet some

possibilities to progress were observed in relation with the denominator issue.

Introduction

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1. Introduction In Belgium, as in other countries, there is an increasing demand of information from primary

health care for various research purposes such as epidemiological studies, health care quality

assessment, socio-economical studies [1-5]. This increases the number of dedicated networks

such as sentinel and other morbidity networks, pharmaco-vigilance networks, networks to

assess the quality of care. For these purposes, the Electronic Health Records (EHR) can be a

rich source of information.

Many problems however have already been documented when using data from EHR for

research purposes: secondary usage of the data, great variation in completeness of the EHR

content, issues related to structured or coded data entry, missing data, etc. [6-8]. Yet

numerous advantages have also been described: recording on long term period, potential

availability of numerous kinds of data, possibility to collect data from many GPs’ practices

and about many patients, etc. [9-12].

The Belgian ResoPrim project (phase I, 2003-2005) aimed at describing and analysing the

potential, limits and difficulties in the implementation and use of a General Practitioners’

(GP) research network regarding the daily clinical practice and the management of patient

records. To preserve some of the advantages mentioned above, we wanted to allow not

specifically well-trained GPs to collaborate to the network. The particular Belgian context had

also to be taken into account: there are many GPs’ software packages (+/- 20) [13], patients

may freely choose their GP (no list of patients), there are many home visits (+/- 40% of GPs’

contacts) [14].

The research objective developed in this paper is the assessment of the usefulness for the

researchers of the currently available data in the GPs’ Electronic Health Records (EHR). This

was done for three main fields: Quality of care, Epidemiology and Socio-economy. We also

highlighted some potential usefulness of the network for the management or for the

assessment of health information policy such as the introduction of Global Medical Records

for Belgian citizens. We present hereafter the global method applied, most of our restricted

research questions related to the three main fields and some hypotheses (findings).

Material and methods

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2. Material and methods Based on previous experiences in Belgium and abroad [11, 12, 15], we set up a semi-

anonymous network to collect data from General Practitioners’ EHR. In a semi-anonymous

network, the researchers can only indirectly contact the collaborating GPs through a trusted

third party. While preserving GPs’ anonymisation, this structure may be useful for quality

assurance in operational network or for feedbacks to GPs after data collection. Three different

software producers collaborated to the network and 28 volunteer GPs (26 practices) were

recruited partly at random (671 GPs contacted out of the 1700 GPs using the collaborating

software systems). We also applied some technical criteria thought to be critical for the

research network: “data not anymore put in a paper record”, “start using software <2004”,

“use of coding system for diagnosis”.

To reach our research objective, we firstly identified, based on our previous experience and

expertise, 8 major research axes: Quality of Care, Epidemiology, Socio-Economy, Health

Research Information System Assessment, denominator and sampling issues (as a basis for

epidemiological, socio-economical and quality of care studies), GPs’ education and GPs’

benefits. Within these axes, as a result of an extended literature review and multidisciplinary

working meetings, we identified more than 50 long and short terms objectives. Finally, related

to these objectives, we defined 36 restricted research questions for the first phase of the

ResoPrim project.

To answer all the 36 restricted research questions, 4 methods were identified: a quantitative

research based on the data extraction from the EHR, a qualitative research, questionnaires sent

to the GPs (sampling questionnaire, satisfaction survey) and an analysis of data collected

during previous Belgian projects. In this paper we focus on the quantitative research,

appropriate for 27 restricted research questions.

As an example in the quality of care domain, we can mention the restricted research question

numbered R016 : “How many hypertensive patients have an undefined cardio vascular risk

because of missing data?” (cfr table 1), which is related to the short term research question

“Can we appraise patients’ risks and their management?”. This short term research question is

itself related to a long term research question: “Could the research network be a tool for GPs

to manage their proper quality of care?”.

We implemented automatic extraction tools for the EHRs. We also set up manual procedures

(questionnaire filled in by the GPs at the end of the contacts) and procedures for semi-


10

automatic data extraction (manual validation by the GPs of extracted data before sending

them). A comprehensive recording method would have implied a too heavy workload for the

GPs. Moreover a theme specific method seemed more reassuring and more motivating for the

GPs. Therefore we selected a theme that best suited our short term research questions:

“hypertension and cardiovascular risk factors”.

To answer the 27 restricted research questions, we identified 50 indicators, such as “number

of patients with a diagnosis code for hypertension / number of patients with hypertension

encountered during the data collection period” (related to RO3 in table 1).

To implement the 50 indicators we defined 8 data sets to be collected in an automatic, semi-

automatic or manual way. Two data sets are retrospective (2002 – 2004) ; the 6 remaining

ones are prospective (data extracted at the end of each contact included in the data collection

period of 6 weeks). A specific data set was dedicated to patients who refused to take part in

the study. Some data sets were extracted for each contact with any other patient:

demographic data and data related to diagnosis, referrals or prescribed drugs. For each of

these contacts, 4 questions were asked: “location of the contact?”, “educational attainments of

the patient?”, “civil status of the patient?” and “hypertensive patient?”. Some additional data,

mainly related to drugs prescribed and to cardio-vascular risk factors (CVR) were extracted

for each contact with an hypertensive patient (semi-automatic and manual procedures).

Starting from these data sets, from our restricted research questions and taking into account

the specificities of each software package (through software analysis and 2 meetings with

each software developer) we defined the attributes of the various data (values, formats), we

refined our definitions (such as the definition of a “contact”), we defined mapping rules

between our data values and the values within each software package (for instance “divorced”

in a software package became “not married” in our data set), we identified the various

locations from where to extract the data in each software. We also defined the XML messages

to send the various data. At last we had 114 variables, each of them belonging to one or

several data sets.

All these activities were conducted within an iterative process: for instance, defining the

possible values of a variable may have an influence on the content of the data sets (e.g.

additional data required). For each variable, we are able to justify its use according to our

indicators and research objectives. The whole procedure to define the variables used in the

quantitative research is summarized in Figure 1.


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Figure 1: defining research questions and variables for the ResoPrim (phase 1) quantitative research

R e sea rch A xe s L o n g te rm re se a rc h o b jec tiv es re la te d to E P R d a ta

S ho rt te rm re sea rc h o b je c tiv es (2 – 4 ye a rs)

F irs t p ha se re se a rc h q u es tio n s (3 6 )

O the r M e tho d s Q u a n tita tiv e m e th o d (2 7 re sea rc h q u estio n s)

Ind ica to rs (5 0 )

D a ta se ts (8 ) an d flo w ch a rts

V ariab le s (1 1 4 ) – d e ta iled sp ec ific a tio n s

Quality control (4 weeks) and quality assessment procedures (using a dummy patients

technique) were conducted for the extraction modules developed by each software package.

The results presented hereafter are based on an extended descriptive analysis of the collected

data.

Results

12

3. Results Prospectively, we got data for 26 practices, 9,472 patients and 13,814 contacts. Retrospective

data were only obtained for 2 software systems (for the period 2002 – 2004: 18 practices,

22,730 patients and 203,128 contacts; for the year 2004: 18 practices, 16,813 patients and

74,878 contacts). Our 27 restricted research questions and preliminary findings (answers) are

presented in table 1. For some of them, to improve readers’ understanding, additional

information is provided in the text. In our results, the term “diagnosis” includes all the ICPC

codes not related to a procedure (01 to 29 and 70 to 99), i.e. related to diagnoses, symptoms

and complaints.

Table 1(a): restricted research questions and preliminary findings (not representative sample)

Nr Restricted Research questions Preliminary findings/Hypotheses

Health Research Information System Assessment R01 Are some indicators (tracers) for data quality (ATC codes

– ICPC codes) usable within the Resoprim network? Yes (Cf. table 2)

RO2 Is it possible to extract a link between a specific drug or referral and a diagnosis?

Yes. 48% of identified referrals and 27% of drug prescriptions were explicitly linked to a diagnosis.

RO3 What is the sensitivity of using automatically extracted data (coded diagnoses) against question posed to GPs (“golden standard”) for finding cases of hypertension?

Sensitivity: 0.54; specificity: 0.99; PPV: 95%; NPV: 86%

R04 Does the participation to Resoprim influence some tracers?

Yes, an increase. (to be compared with R05)

RO5 Has ResoPrim any impact on the coded content of the EHR?

Yes, an improvement

RO6 Does the PDA improve the number of home-contacts registered?

Yes (an increase ranging from 3% to 23% of all the contacts registered)

RO7 R08

Does the PDA improve the identification of hypertensive patients or the number of documented blood pressure?

No

R09 Which is the potential impact of the GMR on the documented care?

A potential improvement (but this needs further analysis)

Denominator issue RO10 Is it possible to produce Yearly Contact Groups (age and

sex? Yes

RO11 Is it possible to build broader prospective denominators (Yearly Contact Groups by age, sex, diagnosis of hypertension and socio-economical status)?

Perhaps but mainly prospectively and based on semi-automatic extraction or on questionnaires.

• 3.1. Health Research Information System Assessment

(RO1, RO4) Tracers are couples of drugs and diagnoses (ATC & ICPC codes). When a drug

is given then the diagnosis should be found somewhere in the EHR. For some tracers, it is

when a diagnosis is found that a drug should be given. This could be used later as a measure

of the global quality of the Clinical Information System. Seven tracers have been tested (cf.

table 2). The first three tracers could be calculated for more than 20 practices in the network

Results

13

and are therefore considered as “usable”. All the tracers increased their score when 2004 data

(retrospective) are compared with 2005 data (prospective).

(R02, R03) See table 1(a)

Table 1(b): restricted research questions and preliminary findings (not representative sample)

Nr Restricted Research questions Preliminary findings/Hypotheses Quality of care RO12 Which antihypertensive drugs are prescribed to patients

without hypertension?

RO13 with hypertension? The global GPs’ prescribing pattern is preserved. RO14 for hypertension (link)? RO15 How many hypertensive patients with antihypertensive

drugs? Widely underestimated by automatic data extraction vs questionnaire (57% vs 96%)

R016 How many hypertensive patients have an undefined CVR because of missing data?

38% when considering anamnestic and clinical parameters (semi-automatic procedure); 69% when adding biological parameters

RO17 R018

How many hypertensive patients with a high CVR actually have some individual risk cardiovascular factors under control and receive accurate treatment and accurate follow-up?

54% (of identified high risk patients) had their blood pressure under control (≤ 140/90 mmHg); 38% took statins; 54% had received a cholesterol check-up in the past year.

RO19 How many hypertensive patients receive accurate treatment according to some associated pathologies?

Only 40% of hypertensive patients with type 2 diabetes were taking ACE-inhibitors.

Epidemiology RO20 Can we disentangle prevalent and incident cases of

hypertension? No (17.9% of new cases among hypertensive patients using automatic extraction vs 6.5% using a questionnaire)

RO21 Can we calculate a prevalence of patients with a high/low/undefined CVR among hypertensive patients?

Hardly. Only 62% of hypertensive patients could roughly be classified (automatic & semi-automatic procedure) and among hypertensive patients we had 9.8% obvious high risk patients.

Socio-economy RO22 Can we obtain patterns and determinants (patient’s and

GP’s characteristics) of hypertensive drug prescribing and referral behaviour for hypertensive patients?

Hardly. Many data are missing. This requires further investigations.

RO23 R025

For hypertensive patients, are referrals and antihypertensive drug prescriptions related to socio-demographic status? (Equity in GP care)

Married and higher educated hypertensive individuals are more frequently referred to specialists and treated with antihypertensive drugs.

RO24 Is the diagnosis of hypertension related to socio-demographic status?

Surprisingly no difference in the risk of hypertension by educational level. Married individual tends to have a higher prevalence of hypertension.

RO26 Can we evaluate whether the assessment, magnitude or tackling of some cardiovascular risk factors among hypertensive patients is related to socio-demographic status? (Equity in GP care)

Not yet assessed. It requires more data (cf. R021)

RO27 Is it possible to perform a cost assessment of hypertension related drug prescriptions? (Pharmaco-economy)

Not yet assessed. It requires additional data.

(RO5) When we compared 2005 and 2004 data, the number of coded drug prescriptions by

contact recorded in the EHR increased (0.29 vs 0.12), the percentage of prescriptions linked

with a diagnosis increased from 2% to 36%; the number of coded diagnoses by contact

Results

14

increased with a factor 2 (0.42 vs 0.18). The small number of referrals recorded in 2005 and

2004 (respectively 162 and 1,136) and the large variations between GPs did not allow us to

highlight any increase either of the number of coded referrals by contact or of the proportion

of referrals linked with a diagnosis (75% in 2005, 65% in 2004).

(R06, RO7, RO8) Personal Device Assistant (PDA) and current software systems using it do

not seem suitable for daily data entry into the EHR (e.g. diagnosis code of hypertension or

blood pressure). During the prospective data collection many technical problems prevented us

to highlight any significant result, except an increase of the number of home-contacts

registered.

(RO9). The Global Medical Record (GMR) is a health policy measure (i.e. a contextual

factor) aiming at stimulating patients to centralize their health care information in one point of

care (the GP). Patients and GPs receive small financial incentives for opening a GMR. For

patients with a GMR (3266), the sensitivity of using automatically extracted data (coded

diagnoses) against question posed to GPs (used as “golden standard”) for finding cases of

hypertension is higher than for patients without GMR (41% vs 33%). Among patients with a

GMR the prevalence of the diagnosis codes for hypertension (K85-7 ICPC codes) was twice

as high (14.5%) as for patients without GMR (7.8%). At the level of the GP, there is no

relation between the proportion of all recorded patients with a GMR and the proportion of all

recorded contacts with at least one coded diagnosis.

Table 2: tracers for data quality

Drug-class ATC code Diagnosis ICPC code UsabilityAnti-diabetics A10 Diabetes (type 1 + 2) T89, T90 Yes Thyroxin or Strumazol H03 Thyroidal dysfunction T85, T86 Yes Allopurinol M04 Gout T92 Yes Anti-epileptics N03 Epilepsy N88 No Drugs for glaucoma and miotics S01E Glaucoma F93 No Drugs for migraine N02C Migraine N89, N90 No Acyclovir and derivates JO5AB-AD,

excl. JO5AB03-04 Herpes Y72, X90,

S70, S71, F85 No

Results

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• 3.2. Denominator issue

(R010, R011). See table 1(a).1

• 3.3. Quality of care

(R012 - RO15) As already studied elsewhere [16], the number of hypertensive patients under

prescription is widely underestimated by the automatic data extraction (57% vs 96% for the

questionnaire). Yet the global GPs’ prescribing pattern for hypertension seems preserved.

(RO16 - RO18) To calculate the global cardiovascular risk (CVR) we used an algorithm [17]

where a first assessment can be done using so-called “anamnestic and clinical” variables (age,

gender, smoking status, dyslipidemia, diabetes, personal cardiovascular event, familial

cardiovascular event, BMI, hypertension). A consecutive fine-tuning is realized using

biological measurements (total cholesterol, HDL, LDL, triglycerides, glycemia). For hardly

67% of hypertensive patients all requested anamnestic and clinical data were gathered with

the semi-automatic and manual data extractions. This fell to 42% when the complete

parameter set was used (anamnestic and clinical + biological). The full-automatic extraction

method (including identification of hypertensive patients) still needs further investigation. It

was possible to roughly and automatically classify 964 hypertensive patients (out of 1554) in

two categories: ‘obvious high risk’ and ‘definable risk’ using the anamnestic and clinical

model. For 41% (329) of the 811 patients classified as ‘definable risk’, the biological

parameters needed for a further classification were available. Since CVR was only assessed

for hypertensive patients there were no cases in the low risk category. Based on the

anamnestic and clinical model 153 out of 964 hypertensive patients (16%) for whom it was

possible to calculate an anamnestic and clinical CVR were classified into the category

‘obvious high risk’. Seventy one patients (22%) out of the 329 patients classified as ‘definable

risk’ with all the parameters available, had to be considered as “false negative obvious high

risk” patients, which could lead to an underestimation of their CVR. Evidence-Based

Medicine guidelines state that patients at high risk should take statins to reduce high

cholesterol-levels which should be measured on a regular basis. Using the semi-automatic

extraction it was assessed that out of the 209 patients at high risk 38% took statins (this

1 See also : Van Casteren V, De Clercq E, Vandenberghe H, Burggraeve P, Jonckheer P, Lorant V, Artoisenet C, Lafontaine MF. ResoPrim. Report Phase 1 2003-2005. Preliminary findings October 2005, project financed by the Belgian Federal Public Planning Service Science Policy, Scientific Institute of Public Health ed, Brussels, April 2006, pp. 30-31.

Results

16

decreased with 58% when using automatic data extraction alone) and that 54% had received a

cholesterol check-up in the past year.

(R019) See table 1(b)

• 3.4. Epidemiology

(R020 – R021) See table 1(b)

• 3.5. Socio-economy

(R022) See table 1(b)

(R023, R025) Married and higher educated hypertensive individuals tended to be more

frequently referred to specialists (married: 29% vs 22%; higher educated: 35% vs 26%) or

treated by antihypertensive drugs (married: 88% vs 77%; higher educated: 88% vs 84%). Yet

these differences disappear when we only take into account data for people attending GPs’

offices (excluding home visits).

(RO24) There is no difference in the risk of hypertension by educational level: in both

educational groups (patients with higher/lower educational level), hypertension hit about 26%

of the patients seen during the period of six weeks (prospective data collection).

(R026 – R027) See table 1(b)

Discussion and conclusion

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4. Discussion and conclusion All these results require further investigation during the second phase of the ResoPrim project

and later. At this stage, our preliminary results are only valid for our sample (not

representative). Yet these results already provide us with interesting findings for further work

to build research hypotheses and research questions related to the usefulness of currently

available data in most GPs’ EHRs.

To build a representative sample of the GPs’ population is a challenging issue, for a research

network using electronic health records. First, only volunteer GPs are participating. Second,

there are many technical constraints: an agreement of the concerned software developers is

always needed (to build a dedicated extraction module), a sufficient number of participating

GPs per software system is required (to ensure an effective anonymisation of the GPs), some

technical criteria have to be applied to ensure to get some data from the EHRs (e.g.: not to

note consultation data in a paper record; to presently use the software prescription module, to

presently use a codification system for coding diagnoses, …). A fair balance between IT skill

of the GPs and representative dimension of the sample should be found.

Representativity of a GPs’ sample should be tested according to some items: gender, age,

university of graduation, type of practice and level of activity. Our experience within the first

phase of the ResoPrim project suggested that when “contacted GPs” are selected based on a

few specific software systems, there could be significant differences in age, type of practice

and university between the contacted GPs and all the Belgian GPs (for instance, there were

more GPs aged 30 to 49 in the contacted group). Yet, there were no difference (age, gender,

university) between contacted GPs and GPs willing to participate. When selecting GPs from

those willing to participate, some technical criteria (e.g. “to presently use the prescription

module” or “not to note data in a paper record”) seemed to have little impact on GPs

characteristics; some GPs’ characteristics looked rather stable (e.g. age groups or level of

activity).

In the near future, the most promising fields for scientific usages of data extracted from the

primary care HER seem to be the Health Research Information Network assessment (RO1–

RO9) and the quality of care studies (RO12–RO19). In both fields, some research hypothesis

could be drawn from our results such as “Does the improvement of GPs’ coding behavior last

after the end of the recording period, increasing the potential for secondary usage?” “Can we


18

assess changes in GPs’ prescribing patterns?”, keeping in mind that we are only dealing with

documented care.

The network also appears to be a potential tool to assess or to manage some health

information policy, for instance: the Belgian labeling procedure or the impact of the Belgian

Global Medical Record (GMR). In Belgium, since 2002, the primary care software producers

may apply to get an annual quality label if their product conforms to a set of functional

criteria. The result of this procedure can be considered as a structural indicator for the quality

of care. Networks such as ResoPrim can provide through process indicator of quality of care,

an answer to the question: Do GPs use in daily practice the functions provided by their

labeled software systems, such as the implementation of national thesaurus or the ability to

link explicitly a drug prescription to one or several problems?

Another example of using our network as a tool to assess some impact of health policy

measures is related to the introduction of the Global Medical Record (GMR) in the Belgian

primary healthcare system. Our results suggest that the EHRs of patients with a Global

Medical Record (GMR) could include more ICPC codes. Although further analysis is

required for confounding factors (such as patients’ age), a part of the gap could be attributed

to a difference in GPs’ coding behaviour between the two patients’ groups (a more stringent

update of the EHR for patients with a GMR, which appears to be a contextual factor

influencing data quality).

Some of our findings (R04, R05) generate the hypothesis that participating in a primary care

research network such as Resoprim can stimulate GPs to use some functions of the EHR, such

as coding diagnoses or linking a drug prescription and a problem. This effect could last after

the end of the data collection period. Therefore, such network can be seen as a tool to improve

the content of the GPs’ clinical information systems and to support a health information

policy.

For epidemiologic studies (R020, R021), our preliminary findings tend to show that, for a

ResoPrim like network, it is rather too soon to attain such goals as studying the incidence and

prevalence of relevant health problems in the general population or providing policy makers

with relevant information to assess the health needs and to commission services (e.g.

vaccination programs or alert systems). Yet some possibilities to progress could be observed

regarding the denominator of epidemiologic rates (R010, R011), which could also bring

benefits for socio-economical and/or quality of care studies.


19

In the Socio-economic field (R022–R027), much work remains to be done to get useful

additional information from the EHR to enhance research addressing some important issues

such as 1) the use of health care resources by the GP through prescription, referral to specialty

care, request of additional examinations (particularly medical imaging and clinical biology)

and hospitalization requests, 2) the employment and activity of GPs, such as the number of

patients and contacts, number of activities and moonlighting, proportion of home visits and 3)

last but not least equity in GP care (preventive procedures, drug prescriptions and referrals

among different socio-economic and ethnic groups).

In our data (R024), we did not observe any difference in the risk of hypertension by

educational level, which is a bit surprising given the well-known relationship between

hypertension and socio-economic status and the results of the 2001 Belgian Health Interview

Survey: 6.5% of the individuals with higher education had hypertension compared to 15% in

the lower educated.

Data capture from home visit still remains a problem (R06–R08). Yet, sometimes, for some

research objectives, we could get it round [16].

Setting up a reference framework, such as ResoPrim, for primary care data networks is a

long-standing process. We think however that our method and our preliminary findings can be

of interest for other research teams as well.

Acknowledgments We thank all the GPs and the industrial partners involved in the data collection. The ResoPrim

project is supported by grants of the Belgian Federal Public Planning Service Science Policy

(Nr I2/AE/207 and Nr I2/2F/207).

References

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Usability of Data for Medical Practice Assessment. Int J Qual Health Care

2002;14(2):127-37.

[7] Wilson AE, Pollock C, Weekes T, and Dowell A. Can General Practice Provide Useful

Information?--Evaluation of a Primary Health Care Information Project in Northern

England. J Epidemiol Community Health 1995;49(3):227-30.

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[8] Hiddema-van de Wal A, Smith RJ, van der Werf G T, Meyboom-de Jong B. Towards

Improvement of the Accuracy and Completeness of Medication Registration With the

Use of an Electronic Medical Record (EMR). Fam Pract 2001;18(3):288-91.

[9] Dresser MV, Feingold L, Rozenkranz SL, Coltin KL. Clinical Quality Measurement.

Comparing Chart Review and Automated Methodologies. Medical Care 1997; 35(6):

539-52.

[10] Okkes IM, Groen A, Oskam SK, Lamberts H. Advantages of Long Observation in

Episode-oriented Electronic Patient Records in Family Practice. Method Inform Med

2001; 40: 229-35.

[11] Pearson N et al. Collecting morbidity data in general practice: the Somerset morbidity

project. BMJ 1996; 312: 1517-20.

[12] Vlug AE, van der LJ, Mosseveld BM, van Wijk MA, van der Linden PD,

Sturkenboom MC et al. Postmarketing surveillance based on electronic patient records:

the IPCI project. Method Inform Med 1999; 38(4-5): 339-44.

[13] Federal Public Service for Health, Food Chain Safety and Environment – Health

informatics & Telematics Unit. Homologation de logiciels de gestion de dossiers

patient. http://www.health-telematics.be/. (accessed 12.2005).

[14] Vandenberghe H, Van Casteren V, Jonckheer P, Lafontaine MF, De Clercq E. Quality

of Care Assessment using GPs’ Electronic Patient Records: Do We Need Data from

Home Visits? Stud Health Technol Inform 2004 ;110 :35-41.

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[15] De Clercq E, Vandenberghe H, Jonckheer P, Bastiaens H, Lafontaine MF, Van Casteren

V. Assessment of a three-year experience with a Belgian Primary Care data Network.

Stud Health Technol Inform 2002; 93:163-9.

[16] Vandenberghe HE, Van Casteren V, Jonckheer P, Bastiaens H, Van der Heyden J,

Lafontaine MF, De Clercq E. Collecting information on the quality of prescribing in

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algorithm. Acta Cardiol 2004; 59(6): 598-605.

List of abbreviations

23

List of abbrevations ATC Anatomical Therapeutic Chemical Classification: widely used system to classify

drugs

CVR Cardiovascular risk

EHR Electronic health record: an electronic file of clinical and administrative patient data at the cabinet of the doctor in order to storage all necessary patient information a physician finds relevant for care delivery

GMR Global medical record: a health policy measure aiming at stimulating patients to centralize their health care information in one point of care (the GP)

GP General practitioner: primary care physician, family physician

HRIS Health Research Information System: the most comprehensive system extending the CIS with the database containing extracted information for research.

ICPC International Classification of Primary Care: international classification of complaints, symptoms, diagnoses and procedures in primary care

PDA Personal Device Assistant

SMMS Secured Medical Message Systems: systems providing secured exchange of medical information between health care professionals (hospitals, labs, specialists, general practitioners…) through telephone line or cable

TSP Trust service provider: an intermediate station in the data itinerary where all nominative attributes of the sender (GP) are deleted before the data is further forwarded to the research unit

XML Extensible Markup Language

Appendix : additional tables

24

Appendix: additional tables List of tables:

• Health Research Information System Assessment: R01, R04 P 26

Tracer 1 = All patients on anti-diabetic drugs (whether insulin or OAD) should have the diagnosis of diabetes coded in the EPR p 26 Tracer 2 = All patients with a diagnosis code of epilepsy should have anti-epileptic drug prescriptions recorded in the EPR p 27 Tracer 3 = All patients on typical drugs for migraine should have the diagnosis of migraine coded in the EPR p 28 Tracer 4 = All patients with a diagnosis code of glaucoma should have typical drug prescriptions for glaucoma recorded in the EPR and vice versa p 29 Tracer 5 = All patients with a diagnosis code of thyroidal dysfunction should have typical drug prescriptions recorded in the EPR and vice versa p 30 Tracer 6 = All patients on acyclovir and derivates should have the diagnosis of herpes (simplex or zoster) coded in the EPR p 31 Tracer 7 = All patients on allopurinol (Zyloric°) should have the diagnosis of gout coded in the EPR p 32

• Health Research Information System Assessment: R02 P33

Table 1: % of linked-referrals and linked-drugs (2004 - 2005) p 33 Table 2: % of linked referrals, drugs and hypertensive drugs (HTdrugs) for all patients and hypertensive patients p 33 Referrals and linked referrals p 34 New prescribed drugs and linked drugs p 35


Prevalence of Hypertension (question & automatic extraction) p 36


Impact of the prospective data collection process on the GPs' coding behavior: Diagnoses and symptoms p 37 Impact of the prospective data collection process on the GPs' coding behavior: Drugs p 37 Impact of the prospective data collection process on the GPs' coding behavior: Referrals p 37 Stability of the coding behavior p 38 Coding behavior 1 p 39 Coding behavior 2 p 40 Contacts with at least one code or one diag. p 41 New drug prescriptions p 42 Referrals p 43

• Health Research Information System Assessment: R06, R07, R08 P44

Home visits and PDA p 44

Appendix : additional tables

25


Global medical record (GMR) and coding behavior p 45

• Quality of care: R012-15 P46

Drugs prescribed: automatic extraction (AE) vs question (Q) p 46 Drugs, linked drugs, hypertension linked drugs p 46

• Quality of care: R016 P47

Definable cardiovascular risk for hypertensive patients – Automatic extraction (AE) p 47 Definable cardiovascular risk for hypertensive patients – Question (diabetes and/or CV personal past event p 48

• Quality of care: R017-R018 P49

Extracted and validated blood pressure (BP) p 49 Extracted and validated BMI p 51 Cholesterol p 53 All the high risk patients with a statin – Automatic extraction (AE) vs question (Q) p 55 All the high risk patients with ace_inib+aspirine+statine (aas) – Automatic extraction (AE) vs question (Q) p 56

• Quality of care: R019 P57

Diabetes hypertensive patients with ACE inhibitors – Automatic extraction (AE) vs question (Q) p 57

• Quality of care: additional tables P58

Cholesterol p 58 Extracted and validated BMI p 59 Extracted and validated blood pressure (BP) p 60 Hypertensive patients with ace_inib+aspirine+statine (aas) – Automatic extraction (AE) vs question (Q) p 61

• Epidemiology: R20 R21 P62

New cases of hypertension – Automatic extraction (AE) vs questions (Q) p 62

• Socio-economy : R023, R025 P63

Impact of higher education and married status on treatment and referrals p 63

• Socio-economy : R024 P64

Prevalence of hypertension p 64

26

• Health Research Information System Assessment: R01, R04

D48 Tracer 1 = All patients on anti-diabetic drugs (whether insulin or OAD) should have the diagnosis of diabetes coded in the EPR

Nbr Patient

ICPC or ATC ICPC ATC

Overlap ICPC and

ATC TracerIdentified by ICPC

Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Total 7831 513 422 272 181 66.5% 82.3% Total (1+2) 5090 385 338 178 131 73.6% 87.8% 10605 193 59 147 13 8.8% 30.6% 16813 329 131 218 20 9.2% 39.8%Soft 1 3763 297 266 143 112 78.3% 89.6% 6932 159 26 142 9 6.3% 16.4% 10468 243 46 211 14 6.6% 18.9%Soft 2 1327 88 72 35 19 54.3% 81.8% 3673 34 33 5 4 80.0% 97.1% 6345 86 85 7 6 85.7% 98.8%Soft 3 2741 128 84 94 50 53.2% 65.6% 1_01 277 14 15 12 515 80.0% 82.4% 515 17 7 12 2 16.7% 41.2% 787 24 12 16 4 25.0% 50.0% 1_02 303 39 18 16 453 88.9% 95.1% 453 15 3 13 1 7.7% 20.0% 609 19 3 17 1 5.9% 15.8% 1_03 209 15 11 9 309 81.8% 88.2% 309 12 0 12 0 0.0% 0.0% 482 23 3 22 2 9.1% 13.0% 1_04 395 23 15 11 720 73.3% 85.2% 720 30 2 30 2 6.7% 6.7% 1128 50 6 47 3 6.4% 12.0% 1_05 287 35 12 9 493 75.0% 92.1% 493 9 3 6 0 0.0% 33.3% 723 23 3 20 0 0.0% 13.0% 1_06 560 46 5 5 1592 100.0% 100.0% 1592 1 1 0 0 100.0% 2497 3 3 0 0 100.0% 1_07 269 19 10 7 447 70.0% 86.4% 447 2 2 0 0 100.0% 622 4 3 1 0 0.0% 75.0% 1_08 110 21 19 13 136 68.4% 77.8% 136 14 1 14 1 7.1% 7.1% 164 17 1 17 1 5.9% 5.9% 1_09 542 13 14 9 874 64.3% 72.2% 874 30 3 29 2 6.9% 10.0% 1268 36 3 35 2 5.7% 8.3% 1_10 410 21 11 10 888 90.9% 95.5% 888 17 3 15 1 6.7% 17.6% 1234 26 7 20 1 5.0% 26.9% 1_11 214 5 3 3 198 100.0% 100.0% 198 0 0 0 0 452 2 0 2 0 0.0% 0.0% 1_12 66 5 4 3 93 75.0% 83.3% 93 4 1 3 0 0.0% 25.0% 172 6 2 4 0 0.0% 33.3% 1_13 121 10 6 5 214 83.3% 90.9% 214 8 0 8 0 0.0% 0.0% 330 10 0 10 0 0.0% 0.0% 2_01 218 18 10 10 358 100.0% 100.0% 358 4 4 0 0 100.0% 456 29 29 0 0 100.0% 2_02 15 0 0 712 712 0 0 0 0 1169 0 0 0 0 2_03 532 0 15 0 979 0.0% 0.0% 979 0 0 0 0 1464 0 0 0 0 2_04 309 3 0 0 893 100.0% 893 2 2 0 0 100.0% 2312 2 2 0 0 100.0% 2_05 253 51 10 9 731 90.0% 98.1% 731 28 27 5 4 80.0% 96.4% 944 55 54 7 6 85.7% 98.2% 3_01 146 10 11 8 72.7% 76.9% 3_02 455 31 40 21 52.5% 62.0% 3_03 25 0 3 0 0.0% 0.0% 3_04 329 11 2 2 100.0% 100.0% 3_05 317 7 0 0 100.0% 3_06 124 2 6 2 33.3% 33.3% 3_07 317 3 0 0 100.0% 3_08 492 16 17 15 88.2% 88.9% 3_99 536 4 15 2 13.3% 23.5%Tracer % of all patients on anti-diabetic drugs that have a diagnosis of diabetes coded in their EPR

** Some GPs have upgraded their systems during the summer 2004. This could have had an impact on their coding behavior (improvement of the coding module).

2005 2004 - last 4 months** 2004

27

D49 Tracer 2 = All patients with a diagnosis code of epilepsy should have anti-epileptic drug prescriptions recorded in the EPR

Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Total 7831 128 22 117 11 50.0% 17.2% Total (1+2) 5090 90 16 81 7 43.8% 17.8% 10605 64 6 58 0 0.0% 9.4% 16813 119 13 106 0 0.0% 10.9%Soft 1 3763 72 9 68 5 55.6% 12.5% 6932 57 0 57 0 0.0% 10468 105 0 105 0 0.0%Soft 2 1327 18 7 13 2 28.6% 38.9% 3673 7 6 1 0 0.0% 85.7% 6345 14 13 1 0 0.0% 92.9%Soft 3 2741 38 6 36 4 66.7% 15.8% 1_01 277 8 1 8 1 100.0% 12.5% 515 7 0 7 0 0.0% 787 8 0 8 0 0.0% 1_02 303 5 2 4 1 50.0% 40.0% 453 3 0 3 0 0.0% 609 7 0 7 0 0.0% 1_03 209 5 0 5 0 0.0% 309 5 0 5 0 0.0% 482 9 0 9 0 0.0% 1_04 395 9 1 9 1 100.0% 11.1% 720 12 0 12 0 0.0% 1128 18 0 18 0 0.0% 1_05 287 8 0 8 0 0.0% 493 4 0 4 0 0.0% 723 11 0 11 0 0.0% 1_06 560 7 3 4 0 0.0% 42.9% 1592 0 0 0 0 2497 0 0 0 0 1_07 269 5 0 5 0 0.0% 447 0 0 0 0 622 0 0 0 0 1_08 110 0 0 0 0 136 0 0 0 0 164 1 0 1 0 0.0% 1_09 542 10 0 10 0 0.0% 874 13 0 13 0 0.0% 1268 27 0 27 0 0.0% 1_10 410 5 2 5 2 100.0% 40.0% 888 6 0 6 0 0.0% 1234 13 0 13 0 0.0% 1_11 214 5 0 5 0 0.0% 198 1 0 1 0 0.0% 452 3 0 3 0 0.0% 1_12 66 3 0 3 0 0.0% 93 5 0 5 0 0.0% 172 5 0 5 0 0.0% 1_13 121 2 0 2 0 0.0% 214 1 0 1 0 0.0% 330 3 0 3 0 0.0% 2_01 218 4 2 3 1 50.0% 50.0% 358 3 3 0 0 0.0% 100.0% 456 6 6 0 0 0.0% 100.0% 2_02 15 0 0 0 0 712 0 0 0 0 1169 0 0 0 0 2_03 532 7 0 7 0 0.0% 979 0 0 0 0 1464 0 0 0 0 2_04 309 0 0 0 0 893 0 0 0 0 2312 0 0 0 0 2_05 253 7 5 3 1 20.0% 71.4% 731 4 3 1 0 0.0% 75.0% 944 8 7 1 0 0.0% 87.5% 3_01 146 7 0 7 0 0.0% 3_02 455 11 4 9 2 50.0% 36.4% 3_03 25 0 0 0 0 3_04 329 0 0 0 0 3_05 317 0 0 0 0 3_06 124 5 0 5 0 0.0% 3_07 317 0 0 0 0 3_08 492 10 2 10 2 100.0% 20.0% 3_99 536 5 0 5 0 0.0% Tracer % of all patients with a diagnosis code of epilepsy that have anti-epileptic drug prescriptions recorded in their EPR


2005 2004 - last 4 months** 2004

28

D50 Tracer 3 = All patients on typical drugs for migraine should have the diagnosis of migraine coded in the EPR

Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Total 7831 93 69 35 11 31.40% 74.20%

Total (1+2) 5090 64 47 24 7 29.20% 73.40% 10605 26 11 15 0 0.00% 42.30% 16813 55 16 39 0 0.00% 29.10%Soft 1 3763 40 23 23 6 26.10% 57.50% 6932 25 10 15 0 0.00% 40.00% 10468 49 10 39 0 0.00% 20.40%Soft 2 1327 24 24 1 1 100.00% 100.00% 3673 1 1 0 0 100.00% 6345 6 6 0 0 100.00%Soft 3 2741 29 22 11 4 36.40% 75.90% 1_01 277 3 3 0 0 100.00% 515 0 0 0 0 787 1 0 1 0 0.00% 0.00% 1_02 303 6 1 5 0 0.00% 16.70% 453 3 1 2 0 0.00% 33.30% 609 6 1 5 0 0.00% 16.70% 1_03 209 2 1 1 0 0.00% 50.00% 309 0 0 0 0 482 1 0 1 0 0.00% 0.00% 1_04 395 1 1 0 0 100.00% 720 2 0 2 0 0.00% 0.00% 1128 14 0 14 0 0.00% 0.00% 1_05 287 2 2 1 1 100.00% 100.00% 493 3 2 1 0 0.00% 66.70% 723 4 2 2 0 0.00% 50.00% 1_06 560 0 0 0 0 1592 0 0 0 0 2497 0 0 0 0 1_07 269 5 3 4 2 50.00% 60.00% 447 0 0 0 0 622 0 0 0 0 1_08 110 3 0 3 0 0.00% 0.00% 136 2 0 2 0 0.00% 0.00% 164 2 0 2 0 0.00% 0.00% 1_09 542 4 2 2 0 0.00% 50.00% 874 4 0 4 0 0.00% 0.00% 1268 8 0 8 0 0.00% 0.00% 1_10 410 8 7 1 0 0.00% 87.50% 888 8 7 1 0 0.00% 87.50% 1234 10 7 3 0 0.00% 70.00% 1_11 214 5 3 5 3 60.00% 60.00% 198 1 0 1 0 0.00% 0.00% 452 1 0 1 0 0.00% 0.00% 1_12 66 0 0 0 0 93 0 0 0 0 172 0 0 0 0 1_13 121 1 0 1 0 0.00% 0.00% 214 2 0 2 0 0.00% 0.00% 330 2 0 2 0 0.00% 0.00% 2_01 218 6 6 0 0 100.00% 358 1 1 0 0 100.00% 456 5 5 0 0 100.00% 2_02 15 0 0 0 0 712 0 0 0 0 1169 0 0 0 0 2_03 532 0 0 0 0 979 0 0 0 0 1464 0 0 0 0 2_04 309 0 0 0 0 893 0 0 0 0 2312 0 0 0 0 2_05 253 18 18 1 1 100.00% 100.00% 731 0 0 0 0 944 1 1 0 0 100.00% 3_01 146 0 0 0 0 3_02 455 5 0 5 0 0.00% 0.00% 3_03 25 0 0 0 0 3_04 329 8 8 0 0 100.00% 3_05 317 3 3 0 0 100.00% 3_06 124 1 1 0 0 100.00% 3_07 317 0 0 0 0 3_08 492 12 10 6 4 66.70% 83.30% 3_99 536 0 0 0 0Tracer % of all patients on typical drugs for migraine that have the diagnosis of migraine coded in their EPR


2005 2004 - last 4 months** 2004

29

D51 Tracer 4 = All patients with a diagnosis code of glaucoma should have typical drug prescriptions for glaucoma recorded in the EPR and vice versa

Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Total 7831 52 35 26 9 17.3% 67.3% Total (1+2) 5090 43 33 19 9 20.9% 76.7% 10605 30 12 20 2 6.7% 40.0% 16813 63 27 42 6 9.5% 42.9%Soft 1 3763 39 29 19 9 23.1% 74.4% 6932 29 11 20 2 6.9% 37.9% 10468 60 24 42 6 10.0% 40.0%Soft 2 1327 4 4 0 0 0.0% 100.0% 3673 1 1 0 0 0.0% 100.0% 6345 3 3 0 0 0.0% 100.0%Soft 3 2741 9 2 7 0 0.0% 22.2% 1_01 277 2 1 2 1 50.0% 50.0% 515 3 1 2 0 0.0% 33.3% 787 4 2 3 1 25.0% 50.0% 1_02 303 11 7 7 3 27.3% 63.6% 453 5 1 5 1 20.0% 20.0% 609 13 3 12 2 15.4% 23.1% 1_03 209 7 6 3 2 28.6% 85.7% 309 2 0 2 0 0.0% 0.0% 482 5 0 5 0 0.0% 0.0% 1_04 395 0 0 0 0 720 3 0 3 0 0.0% 0.0% 1128 7 1 7 1 14.3% 14.3% 1_05 287 6 5 2 1 16.7% 83.3% 493 1 1 0 0 0.0% 100.0% 723 3 1 2 0 0.0% 33.3% 1_06 560 1 1 0 0 0.0% 100.0% 1592 0 0 0 0 2497 3 3 0 0 0.0% 100.0% 1_07 269 2 1 2 1 50.0% 50.0% 447 2 2 0 0 0.0% 100.0% 622 3 3 0 0 0.0% 100.0% 1_08 110 4 2 2 0 0.0% 50.0% 136 2 0 2 0 0.0% 0.0% 164 2 0 2 0 0.0% 0.0% 1_09 542 0 0 0 0 874 2 1 1 0 0.0% 50.0% 1268 4 1 3 0 0.0% 25.0% 1_10 410 1 1 0 0 0.0% 100.0% 888 6 3 4 1 16.7% 50.0% 1234 10 4 7 1 10.0% 40.0% 1_11 214 0 0 0 0 198 0 0 0 0 452 0 0 0 0 1_12 66 2 2 0 0 0.0% 100.0% 93 0 0 0 0 172 2 2 0 0 0.0% 100.0% 1_13 121 3 3 1 1 33.3% 100.0% 214 3 2 1 0 0.0% 66.7% 330 4 4 1 1 25.0% 100.0% 2_01 218 1 1 0 0 0.0% 100.0% 358 0 0 0 0 456 1 1 0 0 0.0% 100.0% 2_02 15 0 0 0 0 712 0 0 0 0 1169 0 0 0 0 2_03 532 0 0 0 0 979 0 0 0 0 1464 0 0 0 0 2_04 309 0 0 0 0 893 0 0 0 0 2312 0 0 0 0 2_05 253 3 3 0 0 0.0% 100.0% 731 1 1 0 0 0.0% 100.0% 944 2 2 0 0 0.0% 100.0% 3_01 146 1 0 1 0 0.0% 0.0% 3_02 455 2 0 2 0 0.0% 0.0% 3_03 25 0 0 0 0 3_04 329 2 2 0 0 0.0% 100.0% 3_05 317 0 0 0 0 3_06 124 1 0 1 0 0.0% 0.0% 3_07 317 0 0 0 0 3_08 492 0 0 0 0 3_99 536 3 0 3 0 0.0% 0.0% Tracer % of all patients identified as having glaucoma (either by ICPC or ATC) that have both the diagnosis and the prescription of typical drugs recorded in their EPR


2005 2004 - last 4 months** 2004

30

D52 Tracer 5 = All patients with a diagnosis code of thyroidal dysfunction should have typical drug prescriptions recorded in the EPR and vice versa

Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Total 7831 202 65 161 24 11.9% 32.2% Total (1+2) 5090 170 52 136 18 10.6% 30.6% 10605 139 7 135 3 2.2% 5.0% 16813 264 21 250 7 2.7% 8.0%Soft 1 3763 136 27 125 16 11.8% 19.9% 6932 134 6 131 3 2.2% 4.5% 10468 247 7 245 5 2.0% 2.8%Soft 2 1327 34 25 11 2 5.9% 73.5% 3673 5 1 4 0 0.0% 20.0% 6345 17 14 5 2 11.8% 82.4%Soft 3 2741 32 13 25 6 18.8% 40.6% 1_01 277 11 3 10 2 18.2% 27.3% 515 14 2 12 0 0.0% 14.3% 787 20 3 18 1 5.0% 15.0% 1_02 303 15 1 14 0 0.0% 6.7% 453 12 0 12 0 0.0% 0.0% 609 17 0 17 0 0.0% 0.0% 1_03 209 13 1 12 0 0.0% 7.7% 309 17 0 17 0 0.0% 0.0% 482 26 0 26 0 0.0% 0.0% 1_04 395 18 1 18 1 5.6% 5.6% 720 28 0 28 0 0.0% 0.0% 1128 60 0 60 0 0.0% 0.0% 1_05 287 18 1 17 0 0.0% 5.6% 493 8 1 8 1 12.5% 12.5% 723 19 1 19 1 5.3% 5.3% 1_06 560 6 1 5 0 0.0% 16.7% 1592 0 0 0 0 2497 0 0 0 0 1_07 269 8 1 7 0 0.0% 12.5% 447 0 0 0 0 622 0 0 0 0 1_08 110 2 1 1 0 0.0% 50.0% 136 5 0 5 0 0.0% 0.0% 164 9 0 9 0 0.0% 0.0% 1_09 542 17 6 16 5 29.4% 35.3% 874 24 3 23 2 8.3% 12.5% 1268 47 3 47 3 6.4% 6.4% 1_10 410 11 2 10 1 9.1% 18.2% 888 18 0 18 0 0.0% 0.0% 1234 32 0 32 0 0.0% 0.0% 1_11 214 7 1 6 0 0.0% 14.3% 198 1 0 1 0 0.0% 0.0% 452 3 0 3 0 0.0% 0.0% 1_12 66 3 2 3 2 66.7% 66.7% 93 1 0 1 0 0.0% 0.0% 172 5 0 5 0 0.0% 0.0% 1_13 121 7 6 6 5 71.4% 85.7% 214 6 0 6 0 0.0% 0.0% 330 9 0 9 0 0.0% 0.0% 2_01 218 8 8 1 1 12.5% 100.0% 358 1 1 0 0 0.0% 100.0% 456 11 11 0 0 0.0% 100.0% 2_02 15 0 0 0 0 712 0 0 0 0 1169 0 0 0 0 2_03 532 8 0 8 0 0.0% 0.0% 979 0 0 0 0 1464 0 0 0 0 2_04 309 0 0 0 0 893 0 0 0 0 2312 0 0 0 0 2_05 253 18 17 2 1 5.6% 94.4% 731 4 0 4 0 0.0% 0.0% 944 6 3 5 2 33.3% 50.0% 3_01 146 5 3 4 2 40.0% 60.0% 3_02 455 4 1 4 1 25.0% 25.0% 3_03 25 0 0 0 0 3_04 329 4 4 0 0 0.0% 100.0% 3_05 317 0 0 0 0 3_06 124 2 0 2 0 0.0% 0.0% 3_07 317 1 1 0 0 0.0% 100.0% 3_08 492 8 3 8 3 37.5% 37.5% 3_99 536 8 1 7 0 0.0% 12.5% Tracer % of all patients identified as having thyroidal dysfunction that have both the diagnosis and the prescription of typical drugs recorded in their EPR


2005 2004 - last 4 months** 2004

31

D53 Tracer 6 = All patients on acyclovir and derivates should have the diagnosis of herpes (simplex or zoster) coded in the EPR

Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Total 7831 74 71 11 8 72.7% 95.9% Total (1+2) 5090 42 40 4 2 50.0% 95.2% 10605 20 12 12 4 33.3% 60.0% 16813 46 19 32 5 15.6% 41.3%Soft 1 3763 14 12 4 2 50.0% 85.7% 6932 12 4 9 1 11.1% 33.3% 10468 31 4 29 2 6.9% 12.9%Soft 2 1327 28 28 0 0 100.0% 3673 8 8 3 3 100.0% 100.0% 6345 15 15 3 3 100.0% 100.0%Soft 3 2741 32 31 7 6 85.7% 96.9% 1_01 277 1 0 1 0 0.0% 0.0% 515 0 0 0 0 787 0 0 0 0 1_02 303 1 1 0 0 100.0% 453 3 1 3 1 33.3% 33.3% 609 7 1 7 1 14.3% 14.3% 1_03 209 0 0 0 0 309 0 0 0 0 482 3 0 3 0 0.0% 0.0% 1_04 395 5 5 1 1 100.0% 100.0% 720 3 2 1 0 0.0% 66.7% 1128 5 2 4 1 25.0% 40.0% 1_05 287 1 1 1 1 100.0% 100.0% 493 1 0 1 0 0.0% 0.0% 723 3 0 3 0 0.0% 0.0% 1_06 560 2 2 0 0 100.0% 1592 0 0 0 0 2497 0 0 0 0 1_07 269 1 1 0 0 100.0% 447 0 0 0 0 622 0 0 0 0 1_08 110 0 0 0 0 136 1 0 1 0 0.0% 0.0% 164 1 0 1 0 0.0% 0.0% 1_09 542 1 0 1 0 0.0% 0.0% 874 2 0 2 0 0.0% 0.0% 1268 5 0 5 0 0.0% 0.0% 1_10 410 0 0 0 0 888 1 1 0 0 100.0% 1234 4 1 3 0 0.0% 25.0% 1_11 214 1 1 0 0 100.0% 198 0 0 0 0 452 0 0 0 0 1_12 66 1 1 0 0 100.0% 93 0 0 0 0 172 0 0 0 0 1_13 121 0 0 0 0 214 1 0 1 0 0.0% 0.0% 330 3 0 3 0 0.0% 0.0% 2_01 218 5 5 0 0 100.0% 358 2 2 0 0 100.0% 456 4 4 0 0 100.0% 2_02 15 0 0 0 0 712 0 0 0 0 1169 0 0 0 0 2_03 532 0 0 0 0 979 0 0 0 0 1464 0 0 0 0 2_04 309 0 0 0 0 893 0 0 0 0 2312 0 0 0 0 2_05 253 23 23 0 0 100.0% 731 6 6 3 3 100.0% 100.0% 944 11 11 3 3 100.0% 100.0% 3_01 146 1 1 0 0 100.0% 3_02 455 6 6 3 3 100.0% 100.0% 3_03 25 0 0 0 0 3_04 329 4 4 0 0 100.0% 3_05 317 0 0 0 0 3_06 124 0 0 0 0 3_07 317 1 1 0 0 100.0% 3_08 492 17 16 4 3 75.0% 94.1% 3_99 536 3 3 0 0 100.0% Tracer % of all patients on acyclovir and derivates that have the diagnosis of herpes (simplex or zoster) coded in their EPR


2005 2004 - last 4 months** 2004

32

D54 Tracer 7 = All patients on allopurinol (Zyloric°) should have the diagnosis of gout coded in the EPR

Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Nbr Patient


Overlap ICPC and


Total 7831 599 542 112 55 49.1% 90.5% Total (1+2) 5090 544 509 74 39 52.7% 93.6% 10605 107 24 91 8 8.8% 22.4% 16813 184 58 144 18 12.5% 31.5%Soft 1 3763 505 473 67 35 52.2% 93.7% 6932 100 21 86 7 8.1% 21.0% 10468 166 44 137 15 10.9% 26.5%Soft 2 1327 39 36 7 4 57.1% 92.3% 3673 7 3 5 1 20.0% 42.9% 6345 18 14 7 3 42.9% 77.8%Soft 3 2741 55 33 38 16 42.1% 60.0% 1_01 277 16 12 8 4 50.0% 75.0% 515 9 6 7 4 57.1% 66.7% 787 19 15 10 6 60.0% 78.9% 1_02 303 13 11 3 1 33.3% 84.6% 453 3 2 1 0 0.0% 66.7% 609 8 3 6 1 16.7% 37.5% 1_03 209 11 4 10 3 30.0% 36.4% 309 13 1 13 1 7.7% 7.7% 482 18 2 18 2 11.1% 11.1% 1_04 395 5 2 5 2 40.0% 40.0% 720 11 1 10 0 0.0% 9.1% 1128 17 4 16 3 18.8% 23.5% 1_05 287 73 72 8 7 87.5% 98.6% 493 15 7 10 2 20.0% 46.7% 723 22 8 16 2 12.5% 36.4% 1_06 560 191 191 1 1 100.0% 100.0% 1592 1 1 0 0 100.0% 2497 4 4 0 0 100.0% 1_07 269 20 17 4 1 25.0% 85.0% 447 1 0 1 0 0.0% 0.0% 622 2 1 1 0 0.0% 50.0% 1_08 110 34 34 3 3 100.0% 100.0% 136 9 2 7 0 0.0% 22.2% 164 10 2 8 0 0.0% 20.0% 1_09 542 89 84 11 6 54.5% 94.4% 874 15 0 15 0 0.0% 0.0% 1268 26 0 26 0 0.0% 0.0% 1_10 410 37 34 9 6 66.7% 91.9% 888 12 1 11 0 0.0% 8.3% 1234 23 2 21 0 0.0% 8.7% 1_11 214 5 4 1 0 0.0% 80.0% 198 0 0 0 0 452 2 1 2 1 50.0% 50.0% 1_12 66 5 4 2 1 50.0% 80.0% 93 2 0 2 0 0.0% 0.0% 172 4 1 3 0 0.0% 25.0% 1_13 121 6 4 2 0 0.0% 66.7% 214 9 0 9 0 0.0% 0.0% 330 11 1 10 0 0.0% 9.1% 2_01 218 6 6 0 0 100.0% 358 2 2 0 0 100.0% 456 7 7 0 0 100.0% 2_02 15 0 0 0 0 712 0 0 0 0 1169 0 0 0 0 2_03 532 2 0 2 0 0.0% 0.0% 979 0 0 0 0 1464 0 0 0 0 2_04 309 0 0 0 0 893 0 0 0 0 2312 0 0 0 0 2_05 253 31 30 5 4 80.0% 96.8% 731 5 1 5 1 20.0% 20.0% 944 11 7 7 3 42.9% 63.6% 3_01 146 3 0 3 0 0.0% 0.0% 3_02 455 13 8 12 7 58.3% 61.5% 3_03 25 0 0 0 0 3_04 329 8 7 1 0 0.0% 87.5% 3_05 317 3 3 0 0 100.0% 3_06 124 3 0 3 0 0.0% 0.0% 3_07 317 1 1 0 0 100.0% 3_08 492 18 13 14 9 64.3% 72.2% 3_99 536 6 1 5 0 0.0% 16.7% Tracer % of all patients on allopurinol that have the diagnosis of gout coded in their EPR


2005 2004 - last 4 months** 2004

33

• Health Research Information System Assessment: R02

Table 1: % of linked-referrals and linked-drugs (2004 - 2005) 2005 2004 Software N referrals N linked-ref. % N referrals N linked-ref. %

Soft. 1 114 92 80.70% 839 501 59.71% Soft. 2 48 30 62.50% 297 236 79.46% Soft. 1+2 162 122 75.31% 1 136 737 64.88% Soft. 3 182 43 23.63% / / / Total 344 165 47.97%

N drugs N linked-drugs % N drugs N linked-drugs % Soft. 1 1 523 383 25.15% 8 693 1 0.01% Soft. 2 564 367 65.07% 255 208 81.57% Soft. 1+2 2 087 750 35.94% 8 948 209 2.34% Soft. 3 860 41 4.77% / / / Total 2 947 791 26.84% Table 2: % of linked referrals. drugs and hypertensive drugs (HTdrugs) for all patients and hypertensive patients 2005 - All patients 2005 - Hypertensive patients Software N referrals N linked-ref. % N referrals N linked-ref. %

Soft. 1 114 92 80.70% 37 33 89.19% Soft. 2 48 30 62.50% 22 14 63.64% Soft. 1+2 162 122 75.31% 59 47 79.66% Soft. 3 182 43 23.63% 29 6 20.69% Total 344 165 47.97% 88 53 60.23%

N drugs N linked-drugs % N drugs N linked-drugs % Soft. 1 1 523 383 25.15% 1 009 271 26.86% Soft. 2 564 367 65.07% 336 216 64.29% Soft. 1+2 2 087 750 35.94% 1 345 487 36.21% Soft. 3 860 41 4.77% 533 17 3.19% Total 2 947 791 26.84% 1 878 504 26.84%

N HTdrugs N linked-HTdrugs % N HTdrugs N linked-HTdrugs %

Soft. 1 870 219 25.17% 668 189 28.29% Soft. 2 359 248 69.08% 230 147 63.91% Soft. 1+2 1 229 467 38.00% 898 336 37.42% Soft. 3 485 27 5.57% 345 12 3.48% Total 1 714 494 28.82% 1 243 348 28.00%

34Referrals and linked referrals All patients 2005 All patients 2004 All patients 2004 (4 months) Referrals Referrals Referrals

N ref N reflink % N ref N reflink % N ref N reflink % GPs Soft 1 1_01 1 0.00% 1_02 12 10 83.33% 67 58 86.57% 19 13 68.42% 1_03 17 16 94.12% 121 89 73.55% 47 34 72.34% 1_04 4 2 50.00% 54 42 77.78% 11 5 45.45% 1_05 8 6 75.00% 63 42 66.67% 34 17 50.00% 1_06 1_07 6 4 66.67% 4 2 50.00% 2 0.00% 1_08 7 5 71.43% 108 64 59.26% 58 30 51.72% 1_09 26 20 76.92% 174 93 53.45% 80 29 36.25% 1_10 33 28 84.85% 245 109 44.49% 110 25 22.73% 1_11 1_12 1_13 1 1 100.00% 2 2 100.00% Total 114 92 80.70% 839 501 59.71% 361 153 42.38%Soft 2 2_01 16 12 75.00% 123 112 91.06% 51 47 92.16% 2_02 2_03 2_04 14 0.00% 6 0.00% 5 2_05 18 18 100.00% 168 124 73.81% 53 36 67.92% Total 48 30 62.50% 297 236 79.46% 109 83 76.15% Total 1+2 162 122 75.31% 1 136 737 64.88% 470 236 50.21%Soft 3 3_01 5 2 40.00% 3_02 3_03 3_04 89 41 46.07% 3_05 3_06 5 0.00% 3_07 13 0.00% 3_08 42 0.00% 3_99 28 0.00% Total 182 43 23.63% Total 344 165 47.97%

35New prescribed drugs and linked drugs All patients 2005 All patients 2004 All patients 2004 (4 months) New treatment New treatment New treatment

N drug N druglink % N Drug N druglink % N drug N druglink % GPs Soft 1 1_01 200 11 5.50% 219 0.00% 160 0.00% 1_02 185 27 14.59% 1 434 0.00% 445 0.00% 1_03 169 52 30.77% 1 158 1 0.09% 381 1 0.26% 1_04 156 51 32.69% 1 104 0.00% 317 0.00% 1_05 92 18 19.57% 569 0.00% 199 0.00% 1_06 1 0.00% 1_07 121 43 35.54% 11 0.00% 3 0.00% 1_08 116 5 4.31% 932 0.00% 327 0.00% 1_09 208 29 13.94% 1 444 0.00% 539 0.00% 1_10 107 75 70.09% 854 0.00% 277 0.00% 1_11 44 27 61.36% 120 0.00% 23 0.00% 1_12 62 26 41.94% 251 0.00% 75 0.00% 1_13 63 19 30.16% 596 0.00% 219 0.00% Total 1 523 383 25.15% 8 693 1 0.01% 2 965 1 0.03%Soft 2 2_01 196 185 94.39% 2_02 1 0.00% 2_03 183 0.00% 2_04 2_05 185 182 98.38% 255 208 81.57% 216 176 81.48% Total 564 367 65.07% 256 208 81.25% 216 176 81.48% Total 1+2 2 087 750 35.94% 8 949 209 2.34% 3 181 177 5.56%Soft 3 3_01 29 15 51.72% 3_02 256 26 10.16% 3_03 34 0.00% 3_04 3_05 3_06 128 0.00% 3_07 4 0.00% 3_08 207 0.00% 3_99 202 0.00% Total 860 41 4.77% Total 2 947 791 26.84% 8 949 209 2.34% 3 181 177 5.56%

36


Prevalence of Hypertension (question & automatic extraction) All the patients with hypertension (Q) and attending GPs’office (Q). AE :automatic extraction of codes introduced by the GP Total Q HTA AE HTA Q HTA Prevalence + + + - Q AE Sensitivity Specificity PPV NPV AE HTA AE HTA + - + - Soft/GPs N pat. N pat. N pat. N pat. N pat. N pat. N pat. Total 7434 1980 1119 1062 918 57 5397 26.6% 15.1% 0.536 0.990 0.949 0.855 Soft 1 3367 869 626 602 267 24 2474 25.8% 18.6% 0.693 0.990 0.962 0.903 Soft 2 1326 516 216 200 316 16 794 38.9% 16.3% 0.388 0.980 0.926 0.715 Soft 3 2741 595 277 260 335 17 2129 21.7% 10.1% 0.437 0.992 0.939 0.864 1_01 261 47 42 40 7 2 212 18.0% 16.1% 0.851 0.991 0.952 0.968 1_02 303 147 117 113 34 4 152 48.5% 38.6% 0.769 0.974 0.966 0.817 1_03 209 69 29 29 40 0 140 33.0% 13.9% 0.420 1.000 1.000 0.778 1_04 395 61 31 25 36 6 328 15.4% 7.8% 0.410 0.982 0.806 0.901 1_05 287 88 83 82 6 1 198 30.7% 28.9% 0.932 0.995 0.988 0.971 1_06 342 56 40 37 19 3 283 16.4% 11.7% 0.661 0.990 0.925 0.937 1_07 259 62 47 47 15 0 197 23.9% 18.1% 0.758 1.000 1.000 0.929 1_08 110 60 44 44 16 0 50 54.5% 40.0% 0.733 1.000 1.000 0.758 1_09 433 106 67 65 41 2 325 24.5% 15.5% 0.613 0.994 0.970 0.888 1_10 410 74 60 59 15 1 335 18.0% 14.6% 0.797 0.997 0.983 0.957 1_11 176 28 21 21 7 0 148 15.9% 11.9% 0.750 1.000 1.000 0.955 1_12 61 20 11 10 10 1 40 32.8% 18.0% 0.500 0.976 0.909 0.800 1_13 121 51 34 30 21 4 66 42.1% 28.1% 0.588 0.943 0.882 0.759 2_01 218 99 77 71 28 6 113 45.4% 35.3% 0.717 0.950 0.922 0.801 2_02 15 14 0 0 14 0 1 93.3% 0.0% 0.000 1.000 0.067 2_03 532 142 0 0 142 0 390 26.7% 0.0% 0.000 1.000 0.733 2_04 309 126 6 6 120 0 183 40.8% 1.9% 0.048 1.000 1.000 0.604 2_05 252 135 133 123 12 10 107 53.6% 52.8% 0.911 0.915 0.925 0.899 3_01 146 47 14 13 34 1 98 32.2% 9.6% 0.277 0.990 0.929 0.742 3_02 455 122 55 50 72 5 328 26.8% 12.1% 0.410 0.985 0.909 0.820 3_03 25 25 10 10 15 0 0 100.0% 40.0% 0.400 1.000 0.000 3_04 329 62 53 49 13 4 263 18.8% 16.1% 0.790 0.985 0.925 0.953 3_05 317 60 51 50 10 1 256 18.9% 16.1% 0.833 0.996 0.980 0.962 3_06 124 52 22 21 31 1 71 41.9% 17.7% 0.404 0.986 0.955 0.696 3_07 317 50 12 12 38 0 267 15.8% 3.8% 0.240 1.000 1.000 0.875 3_08 492 81 36 32 49 4 407 16.5% 7.3% 0.395 0.990 0.889 0.893 3_99 536 96 24 23 73 1 439 17.9% 4.5% 0.240 0.998 0.958 0.857

37


Impact of the prospective data collection process on the GPs' coding behavior: Diagnoses and symtoms

2005 (7 831 pat. ; 10 914 contacts) 2004 (16 813 pat.; 74 878 contacts) Population :

All patients Software

Nbr diag & sympt

/ contact

Nbr diag / contact

Nbr diag / patient -year

Nbr of contacts with a diag.

/contact

Nbr non HTA codes

/ contact

Nbr diag & sympt

/ contact

Nbr diag / contact

Nbr diag / patient -year

Nbr of contacts with a diag. /

contact

Nbr non HTA codes

/ contact Soft. 1 0.46 0.26 1.08 0.25 0.24 0.11 0.03 0.13 0.03 0.03 Soft. 2 0.32 0.17 0.86 0.14 0.15 0.27 0.16 0.79 0.11 0.13 Soft. 1 + 2 0.42 0.24 1.05 0.21 0.21 0.18 0.08 0.38 0.06 0.07 Soft. 3 0.44 0.25 / 0.23 0.22 / / / / / Total 0.42 0.24 1.05 0.22 0.21 0.18 0.08 0.38 0.06 0.07

Impact of the prospective data collection process on the GPs' coding behavior: Drugs

Population : All patients

2005 (7 831 pat. ; 10 914

contacts) 2004

(16 813 pat.; 74 878 contacts)

Software

Nbr Drugs / contacts

Nbr Drugs HTA /

contact

Nbr Drugs / contacts

Nbr Drugs HTA /

contact Soft. 1 0.30 0.17 0.20 0.12 Soft. 2 0.25 0.16 0.01 0.01 Soft. 1 + 2 0.29 0.17 0.12 0.07 Soft. 3 0.24 0.13 / / Total 0.27 0.16 0.12 0.07

Impact of the prospective data collection process on the GPs' coding behavior: Referrals

Population : All patients

2005 (7 831 pat. ; 10 914

contacts) 2004

(16 813 pat.; 74 878 contacts)

Software

Nbr Referrals

Nbr Referrals /

contact

Nbr Referrals Nbr Referrals /

contact Soft. 1 114.00 2.26 839.00 1.97 Soft. 2 48.00 2.16 297.00 0.92 Soft. 1 + 2 162.00 2.23 1136.00 1.52 Soft. 3 182.00 4.99 / / Total 344.00 3.15 1136.00 1.52

38

Stability of the coding behaviror All patients All patients All patients

2002 2003 2004

GPs Patient Contact Nbr contact / patient Patient Contact

Nbr contact / patient Patient Contact

Nbr contact / patient

Soft 1 1_01 90 304 3.38 114 368 3.23 787 2 305 2.93 1_02 622 3 024 4.86 602 3 091 5.13 609 2 989 4.91 1_03 515 2 117 4.11 496 1 914 3.86 482 1 773 3.68 1_04 907 2 571 2.83 1 401 4 747 3.39 1 128 3 918 3.47 1_05 833 3 536 4.24 766 2 843 3.71 723 2 626 3.63 1_06 2 201 9 440 4.29 2 393 10 986 4.59 2 497 10 580 4.24 1_07 564 2 521 4.47 595 2 671 4.49 622 2 722 4.38 1_08 145 1 443 9.95 155 1 460 9.42 164 1 385 8.45 1_09 1 212 3 945 3.25 1 230 4 047 3.29 1 268 4 137 3.26 1_10 1 279 6 615 5.17 1 197 6 585 5.50 1 234 6 871 5.57 1_11 1 4 4.00 204 298 1.46 452 957 2.12 1_12 183 640 3.50 173 577 3.34 172 570 3.31 1_13 347 2 455 7.07 324 2 287 7.06 330 1 833 5.55 Total 8 899 38 615 4.34 9 650 41 874 4.34 10 468 42 666 4.08Soft 2 2_01 429 3 519 8.20 438 3 696 8.44 456 3 777 8.28 2_02 1 338 6 725 5.03 1 286 6 199 4.82 1 169 5 419 4.64 2_03 1 315 7 752 5.90 1 277 7 879 6.17 1 464 10 256 7.01 2_04 2 312 5 941 2.57 2_05 945 5 965 6.31 952 6 026 6.33 944 6 819 7.22 Total 4 027 23 961 5.95 3 953 23 800 6.02 6 345 32 212 5.08Total 12 926 62 576 4.84 13 603 65 674 4.83 16 813 74 878 4.45

39

Coding behavior 1 All patients 2005 All patients 2004 All patients 2004 (last 4 months)

GPs

Nbr diag / patient-year

Nbr diag / contact


Nbr diag / contact

Nbr diag / patient- year

Nbr diag / contact

Soft 1 1_01 0.56 0.19 0.19 0.06 0.12 0.04 1_02 1.76 0.36 0.28 0.06 0.73 0.15 1_03 0.55 0.15 0.12 0.03 0.15 0.04 1_04 0.83 0.24 0.15 0.04 0.31 0.09 1_05 1.67 0.46 0.44 0.12 1.20 0.33 1_06 0.00 0.00 0.02 0.00 0.01 0.00 1_07 1.31 0.30 0.04 0.01 0.03 0.01 1_08 0.53 0.06 0.02 0.00 0.02 0.00 1_09 0.34 0.10 0.10 0.03 0.18 0.06 1_10 3.80 0.68 0.08 0.01 0.18 0.03 1_11 0.92 0.43 0.21 0.10 0.51 0.24 1_12 1.22 0.37 0.17 0.05 0.38 0.12 1_13 1.02 0.18 0.11 0.02 0.18 0.03 Total 1.08 0.26 0.13 0.03 0.25 0.06 Soft 2 2_01 2.68 0.32 6.36 0.77 2.65 0.32 2_02 0.00 0.00 0.00 0.00 0.00 0.00 2_03 0.01 0.00 0.00 0.00 0.00 0.00 2_04 0.00 0.09 0.00 0.01 0.00 0.01 2_05 3.95 0.55 2.20 0.31 1.82 0.25 Total 0.86 0.17 0.79 0.16 0.47 0.09 Soft 1+ 2 1.05 0.24 0.38 0.08 0.33 0.07 Soft 3c 3_01 0.00 0.47 3_02 0.00 0.13 3_03 0.00 0.19 3_04 0.00 0.05 3_05 0.00 0.54 3_06 0.00 0.54 3_07 0.00 0.18 3_08 0.00 0.32 3_99 0.00 0.19 Total 0.00 0.25 Total 1.05 0.24 0.38 0.08 0.33 0.07 Note 1: diag = codes 70 to 99. \{A97. A98. A99} Note 2 : Nbr diag / patient-year. is calculated by: (Nbr diag in the period) x (i / nbr contacts in the period); i = nbr contacts 2004 / nbr patients 2004

40

Coding behavior 2 All patients 2005 All patients 2004 All patients 2004 (last 4 months)

GPs

Nbr diag-Sympt / patient-year

Nbr diag - sympt /

contact

Nbr diag-Sympt / patient- year

Nbr diag - sympt /

contact

Nbr diag-Sympt / patient- year

Nbr diag - sympt /

contact Soft 1 1_01 1.02 0.35 0.50 0.17 0.45 0.15 1_02 2.26 0.46 0.57 0.12 1.47 0.30 1_03 1.91 0.52 0.94 0.26 1.05 0.29 1_04 1.74 0.50 0.33 0.09 0.70 0.20 1_05 2.17 0.60 0.59 0.16 1.61 0.44 1_06 0.49 0.12 0.23 0.05 0.47 0.11 1_07 2.45 0.56 0.47 0.11 0.91 0.21 1_08 1.71 0.20 0.25 0.03 0.59 0.07 1_09 1.12 0.34 0.43 0.13 0.92 0.28 1_10 4.99 0.90 0.75 0.14 1.99 0.36 1_11 1.28 0.60 0.54 0.25 1.17 0.55 1_12 2.49 0.75 0.59 0.18 1.12 0.34 1_13 1.40 0.25 0.58 0.10 0.91 0.16 Total 1.87 0.46 0.47 0.11 0.96 0.24 Soft 2 2_01 5.75 0.69 10.10 1.22 5.05 0.61 2_02 0.00 0.00 0.00 0.00 0.00 0.00 2_03 0.01 0.00 0.00 0.00 0.00 0.00 2_04 0.00 0.10 0.00 0.01 0.00 0.02 2_05 7.65 1.06 4.15 0.57 3.26 0.45 Total 1.64 0.32 1.35 0.27 0.85 0.17 Soft 1+ 2 1.86 0.42 0.80 0.18 0.92 0.21 Soft 3c 3_01 4.36 1.09 3_02 0.68 0.17 3_03 0.77 0.19 3_04 0.28 0.07 3_05 2.72 0.68 3_06 2.66 0.66 3_07 1.44 0.36 3_08 2.89 0.72 3_99 0.00 0.38 Total 0.00 0.44 Total 1.89 0.42 0.80 0.18 0.92 0.21 Note: Diag-sympt = codes 70 to 99. \{A97. A98. A99} + codes 1 to 29

41

Contacts with at least one code or one diag. All patients 2005 All patients 2004 All patients 2004 (last 4 months)

GPs

% of contacts with code

% of contacts with diag





Soft 1 1_01 0.29 0.17 0.15 0.06 0.14 0.04 1_02 0.44 0.34 0.11 0.05 0.30 0.14 1_03 0.51 0.15 0.28 0.03 0.36 0.04 1_04 0.50 0.24 0.09 0.04 0.20 0.09 1_05 0.54 0.42 0.22 0.12 0.60 0.31 1_06 0.11 0.00 0.05 0.00 0.11 0.00 1_07 0.57 0.30 0.15 0.01 0.29 0.01 1_08 0.19 0.06 0.06 0.00 0.16 0.00 1_09 0.34 0.10 0.16 0.03 0.35 0.05 1_10 0.81 0.61 0.16 0.01 0.37 0.03 1_11 0.57 0.42 0.26 0.10 0.56 0.24 1_12 0.59 0.35 0.25 0.05 0.47 0.11 1_13 0.25 0.18 0.14 0.02 0.27 0.03 Total 0.43 0.25 0.13 0.03 0.27 0.06 Soft 2 2_01 0.43 0.31 0.70 0.53 0.50 0.28 2_02 0.00 0.00 0.00 0.00 0.00 0.00 2_03 0.00 0.00 0.00 0.00 0.00 0.00 2_04 0.08 0.08 0.03 0.01 0.06 0.01 2_05 0.64 0.39 0.43 0.23 0.46 0.20 Total 0.20 0.14 0.18 0.11 0.17 0.08 Soft 1+ 2 0.36 0.21 0.15 0.06 0.23 0.07 Soft 3c 3_01 0.54 0.29 3_02 0.14 0.09 3_03 0.19 0.19 3_04 0.03 0.03 3_05 0.85 0.54 3_06 0.63 0.51 3_07 0.30 0.17 3_08 0.98 0.32 3_99 0.38 0.17 Total 0.46 0.23 Total 0.39 0.22 0.15 0.06 0.23 0.07

42

New drug prescriptions All patients 2005 All patients 2004 (last 4months) All patients 2004

GPs

Nbr Drugs / contact

Nbr Drugs HTA / contact

Nbr Drugs / contact


Nbr Drugs / contact


Soft 1 1_01 0.56 0.26 0.15 0.07 0.10 0.04 1_02 0.44 0.31 0.42 0.29 0.48 0.33 1_03 0.59 0.34 0.64 0.39 0.65 0.40 1_04 0.31 0.16 0.22 0.10 0.28 0.14 1_05 0.24 0.16 0.21 0.13 0.22 0.13 1_06 0.00 0.00 0.00 0.00 0.00 0.00 1_07 0.32 0.19 0.00 0.00 0.00 0.00 1_08 0.73 0.46 0.69 0.42 0.67 0.40 1_09 0.29 0.16 0.35 0.19 0.35 0.19 1_10

0.19 0.11 0.12 0.06 0.12 0.06 1_11 0.16 0.08 0.08 0.06 0.13 0.09 1_12 0.53 0.29 0.39 0.24 0.44 0.26 1_13 0.31 0.17 0.31 0.18 0.33 0.20 Total 0.30 0.17 0.19 0.11 0.20 0.12Soft 2 2_01 0.44 0.29 0.00 0.00 0.00 0.00 2_02 0.00 0.00 0.00 0.00 0.00 0.00 2_03 0.20 0.12 0.00 0.00 0.00 0.00 2_04 0.00 0.00 0.00 0.00 0.00 0.00 2_05 0.55 0.36 0.09 0.06 0.04 0.02 Total 0.25 0.16 0.02 0.01 0.01 0.01Soft 1+ 2 0.29 0.17 0.12 0.07 0.12 0.07Soft 3 3_01 0.16 0.13 3_02 0.30 0.17 3_03 1.31 0.92 3_04 0.00 0.00 3_05 0.00 0.00 3_06 0.98 0.53 3_07 0.01 0.01 3_08 0.33 0.18 3_99 0.33 0.18 Total 0.24 0.13 Total 0.27 0.16 0.12 0.07 0.12 0.07

43

Referrals

All patients 2005 All patients 2004 (last 4 months) All patients 2004

N ref N ref N ref GPs

N ref / 100 contact

N ref / 100 contact

N ref / 100 contact

Soft 1 1_01 0.00 0.00 1 0.04 1_02 12 2.86 19 1.79 67 2.24 1_03 17 5.92 47 7.87 121 6.82 1_04 4 0.80 11 0.76 54 1.38 1_05 8 2.07 34 3.67 63 2.40 1_06 0.00 0.00 0.00 1_07 6 1.60 2 0.20 4 0.15 1_08 7 4.43 58 12.21 108 7.80 1_09 26 3.67 80 5.17 174 4.21 1_10 33 5.94 110 4.65 245 3.57 1_11 0.00 0.00 0.00 1_12 0.00 0.00 0.00 1_13 1 0.49 0.00 2 0.11 Total 114 2.26 361 2.35 839 1.97Soft 2 2_01 16 3.57 51 3.99 123 3.26 2_02 0.00 0.00 0.00 2_03 0.00 0.00 0.00 2_04 14 2.69 5 0.17 6 0.10 2_05 18 5.37 53 2.24 168 2.46 Total 48 2.16 109 0.96 297 0.92Soft 1+ 2 162 2.23 470 1.76 1 136 1.52Soft 3 3_01 5 2.82 0.00 0.00 3_02 0.00 0.00 0.00 3_03 0.00 0.00 0.00 3_04 89 23.54 0.00 0.00 3_05 0.00 0.00 0.00 3_06 5 3.82 0.00 0.00 3_07 13 3.47 0.00 0.00 3_08 42 6.64 0.00 0.00 3_99 28 4.55 0.00 0.00 Total 182 4.99 0.00 0.00 0.00 0.00 0.00Total 344 3.15 470 1.76 1 136 1.52

44

• Health Research Information System Assessment: R06, R07, R08

Home visits and PDA

2004 >="2004-02-14" and <="2004-03-27" 2005 2005

Contact type Contact type Medium

Cabinet Home visit Other Total

Home visit Cabinet

Home visit Other Total

Home visit PDA

No PDA Total PDA PDA

Nbr Nbr Nbr Nbr % Nbr Nbr Nbr Nbr % Nbr Nbr Nbr Status With BP**** 1_01 67 6 0 73 8.22% 306 24 54 384 6.25% 23 361 384 New / Old* 0 1_02 249 120 16 385 31.17% 263 118 39 420 28.10% 0 420 420 1_03 196 0 16 212 0.00% 249 0 38 287 0.00% 0 287 287 1_04 469 0 20 489 0.00% 496 0 25 521 0.00% 0 521 521 1_05 368 0 29 397 0.00% 371 0 28 399 0.00% 0 399 399 1_06 664 645 0 1309 49.27% 428 341 4 773 44.11% 306 467 773 Old*** 1 1_07 350 0 1 351 0.00% 354 11 9 374 2.94% 11 363 374 New** 0 1_08 55 84 19 158 53.16% 77 81 5 163 49.69% 0 163 163 1_09 497 0 25 522 0.00% 525 171 25 721 23.72% 171 550 721 New** 6 1_10 687 139 24 850 16.35% 599 0 7 606 0.00% 0 606 606 1_11 107 0 1 108 0.00% 191 65 21 277 23.47% 65 212 277 Old*** 2 1_12 61 0 5 66 0.00% 81 18 19 118 15.25% 18 100 118 New** 1 1_13 167 85 5 257 33.07% 138 51 17 206 24.76% 0 206 206 Total 3937 1079 161 5177 20.84% 4078 880 291 5249 16.77% 594 4655 5249 10 * New / Old = GPs starting to use a new PDA during the project but having used another one previously ** New = GPs starting to use a PDA during the project *** Old = GPs already using their own PDA **** Number of contacts with a PDA and a documented blood pressure (BP).

45


Global medical records (GMR) and coding behavior 2005

GPs N paitents N Contact % of contacts with a code

% of contacts with a diag. N GMR

% patients with GMR


Soft 1 1_01 277 354 0.29 0.17 0.00 0.00 0.56 1_02 303 420 0.44 0.34 99.00 0.33 1.76 1_03 209 287 0.51 0.15 36.00 0.17 0.55 1_04 395 498 0.50 0.24 57.00 0.14 0.83 1_05 287 386 0.54 0.42 178.00 0.62 1.67 1_06 560 702 0.11 0.00 211.00 0.38 0.00 1_07 269 374 0.57 0.30 56.00 0.21 1.31 1_08 110 158 0.19 0.06 64.00 0.58 0.53 1_09 542 709 0.34 0.10 54.00 0.10 0.34 1_10 410 556 0.81 0.61 176.00 0.43 3.80 1_11 214 277 0.57 0.42 7.00 0.03 0.92 1_12 66 117 0.59 0.35 47.00 0.71 1.22 1_13 121 206 0.25 0.18 54.00 0.45 1.02 Total 3 763 5 044 0.43 0.25 1039.00 0.28 1.08Soft 2 2_01 218 448 0.43 0.31 0.00 0.00 2.68 2_02 15 18 0.00 0.00 0.00 0.00 0.00 2_03 532 899 0.00 0.00 0.00 0.00 0.01 2_04 309 521 0.08 0.08 29.00 0.09 0.00 2_05 253 335 0.64 0.39 223.00 0.88 3.95 Total 1 327 2 221 0.20 0.14 252.00 0.19 0.86Soft 1+ 2 5 090 7 265 0.36 0.21 1291.00 0.25 1.05Soft 3 3_01 146 177 0.54 0.29 128.00 0.88 1.88 3_02 455 847 0.14 0.09 355.00 0.78 0.51 3_03 25 26 0.19 0.19 9.00 0.36 0.77 3_04 329 378 0.03 0.03 307.00 0.93 0.21 3_05 317 467 0.85 0.54 135.00 0.43 2.16 3_06 124 131 0.63 0.51 113.00 0.91 2.17 3_07 317 375 0.30 0.17 307.00 0.97 0.70 3_08 492 633 0.98 0.32 251.00 0.51 1.29 3_99 536 615 0.38 0.17 480.00 0.90 0.00 Total 2 741 3 649 0.46 0.23 2085.00 0.76 0.00 Total 7 831 10 914 0.39 0.22 3376.00 0.43 1.07

46

• Quality of care: R012-15

Drugs prescribed: automatic extraction (AE) vs questions (Q)

2005 All hypertensive patients (Q2) seen

on consulation (Q1) all patients Total Soft 1 Soft 2 Soft 3 Total Soft 1 Soft 2 Soft 3 Nr of patients 1554 730 354 470 7831 3763 1327 2741 AE hypertensive drug ? 57.34% 72.47% 35.88% 50.00% 18.47% 21.53% 15.37% 15.76% AE Diuretics 23.36% 29.73% 12.99% 21.28% 42.12% 42.72% 35.78% 43.98% AE Beta-blockers 32.18% 40.00% 19.77% 29.36% 55.33% 51.60% 55.88% 62.04% AE ACE-ihibitors 15.19% 15.62% 11.86% 17.02% 25.52% 20.86% 32.84% 30.79% AE Sartanes 9.97% 13.56% 4.80% 8.30% 13.62% 14.94% 11.27% 12.27% AE Central working agents 2.57% 3.01% 1.69% 2.55% 3.87% 3.70% 3.43% 4.40% AE Alpha-blockers 0.06% 0.14% 0.00% 0.00% 0.14% 0.25% 0.00% 0.00% AE Calcium_antagonist 9.59% 11.23% 7.91% 8.30% 17.63% 15.93% 22.06% 18.75% Q Hypertensive drug? 95.50% 96.16% 95.20% 94.68% 18.95% 18.66% 25.40% 16.23% Q Diuretics 44.59% 38.90% 47.46% 51.28% 46.70% 40.46% 49.85% 54.16% Q Beta-blockers 53.54% 51.37% 53.67% 56.81% 56.06% 53.42% 56.38% 60.00% Q ACE-ihibitors 25.68% 20.96% 30.79% 29.15% 26.89% 21.79% 32.34% 30.79% Q Sartanes 20.08% 20.00% 19.77% 20.43% 21.02% 20.80% 20.77% 21.57% Q Central working agents 3.54% 2.47% 3.95% 4.89% 3.71% 2.56% 4.15% 5.17% Q Alpha-blockers 3.93% 4.11% 3.67% 3.83% 4.11% 4.27% 3.86% 4.04% Q Calcium_antagonist 18.60% 20.55% 16.67% 17.02% 19.47% 21.37% 17.51% 17.98%

Drugs. linked drugs. hypertension linked drugs All patients. automatic extraction (2005) drugs HTA Drug HTA related Drug HTA linked HTA C02: central working agent & alpha-blockers 53 3% 11 2% 10 4%C03: Diuretics & associations 282 16% 83 17% 50 18%C07: Beta-blockers & associations 717 42% 196 40% 109 39%C08: Calcium-antagonists 199 12% 74 15% 35 13%C09: ACE-inhibitors. Sartanes & associations 463 27% 130 26% 74 27%TOTAL 1714 494 278

47

• Quality of care: R016

Definable cardiovascular risk for hypertensive patientsAutomatic extraction (AE) All the hypertensive patients (Q2) seen on consultation (Q1)

Not obvious high risk patients Patients with definable risk

Soft/GPs HTA

Obvious HR (diabetes and/or CV

personal past event)

with all parameters*

with 4 parameters**

with 4 validated parameters**

All parameters*

4 clin. and anamnestic

parameters**


validated parameters**

N

Pat. N

Pat.

% of HTA Pat.

N Pat.

% of HTA Pat. N Pat.

% of HTA Pat. N Pat.

% of HTA Pat.

% of HTA Pat.

% of HTA Pat.

% of HTA Pat.

Total 1554 153 9.8% 329 21.2% 699 45.0% 811 52.2% 31.0% 54.8% 62.0%Soft 1 730 65 8.9% 298 40.8% 362 49.6% 365 50.0% 49.7% 58.5% 58.9%Soft 2 354 63 17.8% 31 8.8% 102 28.8% 186 52.5% 26.6% 46.6% 70.3%Soft 3 470 25 5.3% 0 0.0% 235 50.0% 260 55.3% 5.3% 55.3% 60.6%1_01 44 6 13.6% 1 2.3% 2 4.5% 2 4.5% 15.9% 18.2% 18.2%1_02 101 14 13.9% 33 32.7% 37 36.6% 37 36.6% 46.5% 50.5% 50.5%1_03 64 5 7.8% 2 3.1% 3 4.7% 3 4.7% 10.9% 12.5% 12.5%1_04 56 8 14.3% 9 16.1% 16 28.6% 18 32.1% 30.4% 42.9% 46.4%1_05 56 2 3.6% 39 69.6% 45 80.4% 45 80.4% 73.2% 83.9% 83.9%1_06 55 1 1.8% 19 34.5% 19 34.5% 19 34.5% 36.4% 36.4% 36.4%1_07 62 4 6.5% 53 85.5% 54 87.1% 54 87.1% 91.9% 93.5% 93.5%1_08 31 3 9.7% 23 74.2% 26 83.9% 26 83.9% 83.9% 93.5% 93.5%1_09 106 4 3.8% 37 34.9% 53 50.0% 54 50.9% 38.7% 53.8% 54.7%1_10 74 13 17.6% 53 71.6% 58 78.4% 58 78.4% 89.2% 95.9% 95.9%1_11 27 1 3.7% 1 3.7% 1 3.7% 1 3.7% 7.4% 7.4% 7.4%1_12 18 1 5.6% 1 5.6% 17 94.4% 17 94.4% 11.1% 100.0% 100.0%1_13 36 3 8.3% 27 75.0% 31 86.1% 31 86.1% 83.3% 94.4% 94.4%2_01 48 12 25.0% 0 0.0% 1 2.1% 1 2.1% 25.0% 27.1% 27.1%2_02 14 0 0.0% 0 0.0% 0 0.0% 1 7.1% 0.0% 0.0% 7.1%2_03 75 0 0.0% 0 0.0% 0 0.0% 66 88.0% 0.0% 0.0% 88.0%2_04 123 2 1.6% 0 0.0% 63 51.2% 79 64.2% 1.6% 52.8% 65.9%2_05 94 49 52.1% 31 33.0% 38 40.4% 39 41.5% 85.1% 92.6% 93.6%3_01 8 2 25.0% 0 0.0% 5 62.5% 6 75.0% 25.0% 87.5% 100.0%3_02 79 5 6.3% 0 0.0% 25 31.6% 34 43.0% 6.3% 38.0% 49.4%3_03 24 0 0.0% 0 0.0% 23 95.8% 23 95.8% 0.0% 95.8% 95.8%3_04 62 0 0.0% 0 0.0% 39 62.9% 39 62.9% 0.0% 62.9% 62.9%3_05 57 4 7.0% 0 0.0% 21 36.8% 26 45.6% 7.0% 43.9% 52.6%3_06 52 1 1.9% 0 0.0% 43 82.7% 44 84.6% 1.9% 84.6% 86.5%3_07 27 0 0.0% 0 0.0% 23 85.2% 25 92.6% 0.0% 85.2% 92.6%3_08 79 10 12.7% 0 0.0% 4 5.1% 8 10.1% 12.7% 17.7% 22.8%3_99 82 3 3.7% 0 0.0% 52 63.4% 55 67.1% 3.7% 67.1% 70.7%

* All parameters enabling to calculate the CV risk: gender, age, (any) blood pressure, (any) BMI, smoking status & cholesterol** 4 (validated) parameters: gender, age, (any) blood pressure, (any) BMI Note: the smoking status had not been properly registered in the patient records (default value = never smoking)

48

Definable cardiovascular risk for hypertensive patientsQuestions (diabetes and/or CV personal past event) All the hypertensive patients (Q2) seen on consultation (Q1)

Not obvious high risk patients Patients with definable risk

Soft/GPs HTA

Obvious HR (diabetes and/or CV

personal past event)

with all parameters*

with 4 parameters**

with 4 validated

parameters** All

parameters*


parameters**


validated parameters**

N

Pat. N

Pat.

% of HTA Pat.

N Pat.

% of HTA Pat.

N Pat.

% of HTA Pat.

N Pat.

% of HTA Pat.

% of HTA Pat.

% of HTA Pat.

% of HTA Pat.

Total 1554 396 25.5% 258 16.6% 568 36.6% 648 41.7% 42.1% 62.0% 67.2%Soft 1 730 213 29.2% 212 29.0% 265 36.3% 266 36.4% 58.2% 65.5% 65.6%Soft 2 354 99 28.0% 46 13.0% 103 29.1% 163 46.0% 41.0% 57.1% 74.0%Soft 3 470 84 17.9% 0 0.0% 200 42.6% 219 46.6% 17.9% 60.4% 64.5%1_01 44 10 22.7% 1 2.3% 2 4.5% 2 4.5% 25.0% 27.3% 27.3%1_02 101 22 21.8% 27 26.7% 31 30.7% 31 30.7% 48.5% 52.5% 52.5%1_03 64 16 25.0% 2 3.1% 2 3.1% 2 3.1% 28.1% 28.1% 28.1%1_04 56 18 32.1% 6 10.7% 12 21.4% 13 23.2% 42.9% 53.6% 55.4%1_05 56 23 41.1% 21 37.5% 25 44.6% 25 44.6% 78.6% 85.7% 85.7%1_06 55 17 30.9% 10 18.2% 10 18.2% 10 18.2% 49.1% 49.1% 49.1%1_07 62 23 37.1% 35 56.5% 36 58.1% 36 58.1% 93.5% 95.2% 95.2%1_08 31 8 25.8% 18 58.1% 21 67.7% 21 67.7% 83.9% 93.5% 93.5%1_09 106 37 34.9% 25 23.6% 38 35.8% 38 35.8% 58.5% 70.8% 70.8%1_10 74 23 31.1% 43 58.1% 47 63.5% 47 63.5% 89.2% 94.6% 94.6%1_11 27 4 14.8% 1 3.7% 1 3.7% 1 3.7% 18.5% 18.5% 18.5%1_12 18 3 16.7% 1 5.6% 15 83.3% 15 83.3% 22.2% 100.0% 100.0%1_13 36 9 25.0% 22 61.1% 25 69.4% 25 69.4% 86.1% 94.4% 94.4%2_01 48 15 31.3% 0 0.0% 1 2.1% 1 2.1% 31.3% 33.3% 33.3%2_02 14 0 0.0% 0 0.0% 0 0.0% 1 7.1% 0.0% 0.0% 7.1%2_03 75 27 36.0% 0 0.0% 0 0.0% 43 57.3% 36.0% 36.0% 93.3%2_04 123 26 21.1% 0 0.0% 48 39.0% 63 51.2% 21.1% 60.2% 72.4%2_05 94 31 33.0% 46 48.9% 54 57.4% 55 58.5% 81.9% 90.4% 91.5%3_01 8 3 37.5% 0 0.0% 5 62.5% 5 62.5% 37.5% 100.0% 100.0%3_02 79 14 17.7% 0 0.0% 23 29.1% 30 38.0% 17.7% 46.8% 55.7%3_03 24 5 20.8% 0 0.0% 18 75.0% 18 75.0% 20.8% 95.8% 95.8%3_04 62 11 17.7% 0 0.0% 30 48.4% 30 48.4% 17.7% 66.1% 66.1%3_05 57 13 22.8% 0 0.0% 18 31.6% 20 35.1% 22.8% 54.4% 57.9%3_06 52 9 17.3% 0 0.0% 35 67.3% 36 69.2% 17.3% 84.6% 86.5%3_07 27 2 7.4% 0 0.0% 22 81.5% 24 88.9% 7.4% 88.9% 96.3%3_08 79 12 15.2% 0 0.0% 4 5.1% 8 10.1% 15.2% 20.3% 25.3%3_99 82 15 18.3% 0 0.0% 45 54.9% 48 58.5% 18.3% 73.2% 76.8%

* All parameters enabling to calculate the CV risk: gender, age, (any) blood pressure, (any) BMI, smoking status & cholesterol ** 4 (validated) parameters: gender, age, (any) blood pressure, (any) BMI Note: the smoking status had not been properly registered in the patient records (default value = never smoking)

49

• Quality of care: R017, R018

Extracted and validated Blood pressure (BP) All the patients (2005) with hypertension (Q) AND diabetes (Q) AND/OR cardiovascular personal past event (Q)

Soft/ GPs Total BP

extracted Extracted BP < 4

months Extracted BP < 12

months Validated

BP Validated BP < 4

months Validated BP < 12

months 130/85 140/90 130/85 140/90 130/85 140/90 130/85 140/90Total 312 276 257 89 166 264 89 169 299 282 99 179 288 99 182Soft 1 213 212 211 78 134 212 78 135 211 210 78 132 211 78 133Soft 2 99 64 46 11 32 52 11 34 88 72 21 47 77 21 491_01 10 9 9 2 7 9 2 7 9 9 2 7 9 2 71_02 22 22 22 12 17 22 12 17 22 22 11 15 22 11 151_03 16 16 16 2 7 16 2 7 16 16 2 7 16 2 71_04 18 18 18 10 16 18 10 16 18 18 10 16 18 10 161_05 23 23 22 5 10 23 5 11 23 22 5 9 23 5 101_06 17 17 17 4 10 17 4 10 17 17 5 11 17 5 111_07 23 23 23 7 13 23 7 13 23 23 7 13 23 7 131_08 8 8 8 1 1 8 1 1 8 8 1 1 8 1 11_09 37 37 37 18 29 37 18 29 37 37 18 29 37 18 291_10 23 23 23 14 16 23 14 16 23 23 14 16 23 14 161_11 4 4 4 0 1 4 0 1 3 3 0 1 3 0 11_12 3 3 3 1 1 3 1 1 3 3 1 1 3 1 11_13 9 9 9 2 6 9 2 6 9 9 2 6 9 2 62_01 15 15 14 4 11 15 4 12 15 14 4 11 15 4 122_02 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02_03 27 0 0 0 0 0 0 0 24 24 10 14 24 10 142_04 26 18 15 0 10 18 0 11 18 15 0 10 18 0 112_05 31 31 17 7 11 19 7 11 31 19 7 12 20 7 12

Note: figures in the table represents numbers of patients

50

Extracted and validated blood pressure (BP) All the patients (2005) with hypertension (Q) AND diabetes (Q) AND/OR cardiovascular personal past event (Q)

Soft/GPs Total Extracted BP

BP extracted < 4 months

BP extracted < 12 months

Validated BP

Validated BP extracted < 4 months

Validated BP extracted < 12 months

130/85 140/90 130/85 140/90 130/85 140/90 130/85 140/90Total 312 88.5% 82.4% 28.5% 53.2% 84.6% 28.5% 54.2% 95.8% 90.4% 31.7% 57.4% 92.3% 31.7% 58.3%Soft 1 213 99.5% 99.1% 36.6% 62.9% 99.5% 36.6% 63.4% 99.1% 98.6% 36.6% 62.0% 99.1% 36.6% 62.4%Soft 2 99 64.6% 46.5% 11.1% 32.3% 52.5% 11.1% 34.3% 88.9% 72.7% 21.2% 47.5% 77.8% 21.2% 49.5%1_01 10 90.0% 90.0% 20.0% 70.0% 90.0% 20.0% 70.0% 90.0% 90.0% 20.0% 70.0% 90.0% 20.0% 70.0%1_02 22 100.0% 100.0% 54.5% 77.3% 100.0% 54.5% 77.3% 100.0% 100.0% 50.0% 68.2% 100.0% 50.0% 68.2%1_03 16 100.0% 100.0% 12.5% 43.8% 100.0% 12.5% 43.8% 100.0% 100.0% 12.5% 43.8% 100.0% 12.5% 43.8%1_04 18 100.0% 100.0% 55.6% 88.9% 100.0% 55.6% 88.9% 100.0% 100.0% 55.6% 88.9% 100.0% 55.6% 88.9%1_05 23 100.0% 95.7% 21.7% 43.5% 100.0% 21.7% 47.8% 100.0% 95.7% 21.7% 39.1% 100.0% 21.7% 43.5%1_06 17 100.0% 100.0% 23.5% 58.8% 100.0% 23.5% 58.8% 100.0% 100.0% 29.4% 64.7% 100.0% 29.4% 64.7%1_07 23 100.0% 100.0% 30.4% 56.5% 100.0% 30.4% 56.5% 100.0% 100.0% 30.4% 56.5% 100.0% 30.4% 56.5%1_08 8 100.0% 100.0% 12.5% 12.5% 100.0% 12.5% 12.5% 100.0% 100.0% 12.5% 12.5% 100.0% 12.5% 12.5%1_09 37 100.0% 100.0% 48.6% 78.4% 100.0% 48.6% 78.4% 100.0% 100.0% 48.6% 78.4% 100.0% 48.6% 78.4%1_10 23 100.0% 100.0% 60.9% 69.6% 100.0% 60.9% 69.6% 100.0% 100.0% 60.9% 69.6% 100.0% 60.9% 69.6%1_11 4 100.0% 100.0% 0.0% 25.0% 100.0% 0.0% 25.0% 75.0% 75.0% 0.0% 25.0% 75.0% 0.0% 25.0%1_12 3 100.0% 100.0% 33.3% 33.3% 100.0% 33.3% 33.3% 100.0% 100.0% 33.3% 33.3% 100.0% 33.3% 33.3%1_13 9 100.0% 100.0% 22.2% 66.7% 100.0% 22.2% 66.7% 100.0% 100.0% 22.2% 66.7% 100.0% 22.2% 66.7%2_01 15 100.0% 93.3% 26.7% 73.3% 100.0% 26.7% 80.0% 100.0% 93.3% 26.7% 73.3% 100.0% 26.7% 80.0%2_02 0 / / / / / / / / / / / / / / 2_03 27 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 88.9% 88.9% 37.0% 51.9% 88.9% 37.0% 51.9%2_04 26 69.2% 57.7% 0.0% 38.5% 69.2% 0.0% 42.3% 69.2% 57.7% 0.0% 38.5% 69.2% 0.0% 42.3%2_05 31 100.0% 54.8% 22.6% 35.5% 61.3% 22.6% 35.5% 100.0% 61.3% 22.6% 38.7% 64.5% 22.6% 38.7%

Note: percentages are calculated on the total numbers of patients.

51

Extracted and validated BMI All the patients (2005) with hypertension (Q) AND diabetes (Q) AND/OR cardiovascular personal past event (Q)

Soft/GPs Total Extracted

BMI Weight extracted

< 4 months Weight extracted

< 12 months Validated

BMI

Validated weight extracted < 4

months Validated weight

extracted < 12 months BMI < 25 BMI < 25 BMI < 25 BMI < 25Total 312 185 114 24 143 27 219 141 29 172 32Soft 1 213 138 71 13 97 16 140 72 14 99 17Soft 2 99 47 43 11 46 11 79 69 15 73 151_01 10 2 0 0 2 0 2 0 0 2 01_02 22 14 5 1 8 2 14 5 1 8 21_03 16 1 1 0 1 0 1 1 0 1 01_04 18 7 6 0 7 0 8 6 0 8 01_05 23 22 4 0 9 1 22 4 0 9 11_06 17 10 0 0 0 0 10 0 0 0 01_07 23 22 11 1 18 2 22 11 1 18 21_08 8 8 2 0 6 0 8 2 0 6 01_09 37 17 9 0 11 0 18 10 1 12 11_10 23 23 22 6 23 6 23 22 6 23 61_11 4 0 0 0 0 0 0 0 0 0 01_12 3 3 3 1 3 1 3 3 1 3 11_13 9 9 8 4 9 4 9 8 4 9 42_01 15 1 0 0 1 0 1 0 0 1 02_02 0 0 0 0 0 0 0 0 0 0 02_03 27 0 0 0 0 0 24 24 4 24 42_04 26 17 15 4 17 4 23 15 4 18 42_05 31 29 28 7 28 7 31 30 7 30 7


52

Extracted and validated BMI All the patients (2005) with hypertension (Q) AND diabetes (Q) AND/OR cardiovascular personal past event (Q)

Total Extracted BMI

Weight extracted < 4 months

Weight extracted < 12 months

Validated BMI

Validated BMI extracted < 4 months

Validated BMI extracted < 12 months

Soft/GPs BMI < 25 BMI < 25 BMI < 25 BMI < 25 Total 312 59.3% 36.5% 7.7% 45.8% 8.7% 70.2% 45.2% 9.3% 55.1% 10.3%Soft 1 213 64.8% 33.3% 6.1% 45.5% 7.5% 65.7% 33.8% 6.6% 46.5% 8.0%Soft 2 99 47.5% 43.4% 11.1% 46.5% 11.1% 79.8% 69.7% 15.2% 73.7% 15.2%1_01 10 20.0% 0.0% 0.0% 20.0% 0.0% 20.0% 0.0% 0.0% 20.0% 0.0%1_02 22 63.6% 22.7% 4.5% 36.4% 9.1% 63.6% 22.7% 4.5% 36.4% 9.1%1_03 16 6.3% 6.3% 0.0% 6.3% 0.0% 6.3% 6.3% 0.0% 6.3% 0.0%1_04 18 38.9% 33.3% 0.0% 38.9% 0.0% 44.4% 33.3% 0.0% 44.4% 0.0%1_05 23 95.7% 17.4% 0.0% 39.1% 4.3% 95.7% 17.4% 0.0% 39.1% 4.3%1_06 17 58.8% 0.0% 0.0% 0.0% 0.0% 58.8% 0.0% 0.0% 0.0% 0.0%1_07 23 95.7% 47.8% 4.3% 78.3% 8.7% 95.7% 47.8% 4.3% 78.3% 8.7%1_08 8 100.0% 25.0% 0.0% 75.0% 0.0% 100.0% 25.0% 0.0% 75.0% 0.0%1_09 37 45.9% 24.3% 0.0% 29.7% 0.0% 48.6% 27.0% 2.7% 32.4% 2.7%1_10 23 100.0% 95.7% 26.1% 100.0% 26.1% 100.0% 95.7% 26.1% 100.0% 26.1%1_11 4 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%1_12 3 100.0% 100.0% 33.3% 100.0% 33.3% 100.0% 100.0% 33.3% 100.0% 33.3%1_13 9 100.0% 88.9% 44.4% 100.0% 44.4% 100.0% 88.9% 44.4% 100.0% 44.4%2_01 15 6.7% 0.0% 0.0% 6.7% 0.0% 6.7% 0.0% 0.0% 6.7% 0.0%2_02 0 / / / / / / / / / / 2_03 27 0.0% 0.0% 0.0% 0.0% 0.0% 88.9% 88.9% 14.8% 88.9% 14.8%2_04 26 65.4% 57.7% 15.4% 65.4% 15.4% 88.5% 57.7% 15.4% 69.2% 15.4%2_05 31 93.5% 90.3% 22.6% 90.3% 22.6% 100.0% 96.8% 22.6% 96.8% 22.6%

Note: percentages are calculated on the total numbers of patients.

53

Cholesterol All the patients (2005) with hypertension (Q) AND diabetes (Q) AND/OR cardiovascular personal past event (Q)

Total Sex Age Total Chol.

Total cholesterol < 4 months


Soft/GPs F M Between 30 and 69 <190 <190

Total 312 118 193 164 219 78 30 167 58Soft 1 213 86 127 114 189 68 23 144 48Soft 2 99 32 66 50 30 10 7 23 101_01 10 5 5 6 6 2 0 6 21_02 22 8 14 11 20 9 4 14 61_03 16 8 8 5 14 5 1 10 21_04 18 10 8 13 16 4 2 9 51_05 23 7 16 14 21 9 3 17 61_06 17 7 10 10 17 1 0 13 21_07 23 10 13 9 22 11 5 16 81_08 8 3 5 5 8 2 1 4 21_09 37 14 23 21 31 17 6 28 71_10 23 7 16 10 22 4 1 18 61_11 4 3 1 2 4 2 0 3 01_12 3 0 3 2 0 0 0 0 01_13 9 4 5 6 8 2 0 6 22_01 15 8 7 10 0 0 0 0 02_02 0 0 0 0 0 0 0 0 02_03 27 10 17 14 0 0 0 0 02_04 26 5 20 11 0 0 0 0 02_05 31 9 22 15 30 10 7 23 10


54

Cholesterol All the patients (2005) with hypertension (Q) AND diabetes (Q) AND/OR cardiovascular personal past event (Q)

Patients Total Total chol.


Total cholesterol < 12

months

Cholesterol < 4 months /Total cholesterol

Cholesterol < 12 months /Total cholesterol

<190 <190 <190* <190**Total 312 70.2% 25.0% 9.6% 53.5% 18.6% 35.6% 38.5% 76.3% 34.7%Soft 1 213 88.7% 31.9% 10.8% 67.6% 22.5% 36.0% 33.8% 76.2% 33.3%Soft 2 99 30.3% 10.1% 7.1% 23.2% 10.1% 33.3% 70.0% 76.7% 43.5%1_01 10 60.0% 20.0% 0.0% 60.0% 20.0% 33.3% 0.0% 100.0% 33.3%1_02 22 90.9% 40.9% 18.2% 63.6% 27.3% 45.0% 44.4% 70.0% 42.9%1_03 16 87.5% 31.3% 6.3% 62.5% 12.5% 35.7% 20.0% 71.4% 20.0%1_04 18 88.9% 22.2% 11.1% 50.0% 27.8% 25.0% 50.0% 56.3% 55.6%1_05 23 91.3% 39.1% 13.0% 73.9% 26.1% 42.9% 33.3% 81.0% 35.3%1_06 17 100.0% 5.9% 0.0% 76.5% 11.8% 5.9% 0.0% 76.5% 15.4%1_07 23 95.7% 47.8% 21.7% 69.6% 34.8% 50.0% 45.5% 72.7% 50.0%1_08 8 100.0% 25.0% 12.5% 50.0% 25.0% 25.0% 50.0% 50.0% 50.0%1_09 37 83.8% 45.9% 16.2% 75.7% 18.9% 54.8% 35.3% 90.3% 25.0%1_10 23 95.7% 17.4% 4.3% 78.3% 26.1% 18.2% 25.0% 81.8% 33.3%1_11 4 100.0% 50.0% 0.0% 75.0% 0.0% 50.0% 0.0% 75.0% 0.0%1_12 3 0.0% 0.0% 0.0% 0.0% 0.0% / / / / 1_13 9 88.9% 22.2% 0.0% 66.7% 22.2% 25.0% 0.0% 75.0% 33.3%2_01 15 0.0% 0.0% 0.0% 0.0% 0.0% / / / / 2_02 0 / / / / / / / / / 2_03 27 0.0% 0.0% 0.0% 0.0% 0.0% / / / / 2_04 26 0.0% 0.0% 0.0% 0.0% 0.0% / / / / 2_05 31 96.8% 32.3% 22.6% 74.2% 32.3% 33.3% 70.0% 76.7% 43.5%

Note: percentages are calculated on the total numbers of patients. * % of patients with a cholesterol < 4 months that is < 190 ** % of patients with a cholesterol < 12 months that is < 190

55

All the high risk patients with a statinAutomatic extraction (AE) vs questions (Q) All the patients (2005) with hypertension (Q) AND diabetes (Q) AND/OR cardiovascular personal past event (Q) Total Q Statin AE Statin Q Statin Prevalence + + + - Q AE Sensitivity Specificfity PPV NPV AE Statin AE Statin + - + - Soft/GPs N Pat. N Pat. N Pat. N Pat. N Pat. N Pat. N Pat. Total 312 156 65 64 92 1 155 50.0% 20.8% 0.410 0.994 0.985 0.628Soft 1 213 99 51 50 49 1 113 46.5% 23.9% 0.505 0.991 0.980 0.698Soft 2 99 57 14 14 43 0 42 57.6% 14.1% 0.246 1.000 1.000 0.4941_01 10 5 4 4 1 0 5 50.0% 40.0% 0.800 1.000 1.000 0.8331_02 22 6 3 3 3 0 16 27.3% 13.6% 0.500 1.000 1.000 0.8421_03 16 9 7 7 2 0 7 56.3% 43.8% 0.778 1.000 1.000 0.7781_04 18 8 6 6 2 0 10 44.4% 33.3% 0.750 1.000 1.000 0.8331_05 23 8 7 7 1 0 15 34.8% 30.4% 0.875 1.000 1.000 0.9381_06 17 8 0 0 8 0 9 47.1% 0.0% 0.000 1.000 / 0.5291_07 23 17 5 5 12 0 6 73.9% 21.7% 0.294 1.000 1.000 0.3331_08 8 3 0 0 3 0 5 37.5% 0.0% 0.000 1.000 / 0.6251_09 37 23 14 14 9 0 14 62.2% 37.8% 0.609 1.000 1.000 0.6091_10 23 7 4 3 4 1 15 30.4% 17.4% 0.429 0.938 0.750 0.7891_11 4 1 1 1 0 0 3 25.0% 25.0% 1.000 1.000 1.000 1.0001_12 3 1 0 0 1 0 2 33.3% 0.0% 0.000 1.000 / 0.6671_13 9 3 0 0 3 0 6 33.3% 0.0% 0.000 1.000 / 0.6672_01 15 12 4 4 8 0 3 80.0% 26.7% 0.333 1.000 1.000 0.2732_02 0 0 0 0 0 0 0 / / / / / / 2_03 27 13 3 3 10 0 14 48.1% 11.1% 0.231 1.000 1.000 0.5832_04 26 18 0 0 18 0 8 69.2% 0.0% 0.000 1.000 / 0.3082_05 31 14 7 7 7 0 17 45.2% 22.6% 0.500 1.000 1.000 0.708

56

All the high risk patients with ace_inib+aspirine+statine (aas)Automatic extraction (AE) vs questions (Q) All the patients (2005) with hypertension (Q) AND diabetes (Q) AND/OR cardiovascular personal past event (Q) Total Q aas AE aas Q aas Prevalence + + + - Q AE Sensitivity Specificity PPV NPV AE aas AE aas + - + - Soft/GPs N Pat. N Pat. N Pat. N Pat. N Pat. N Pat. N Pat. Total 312 39 8 7 32 1 272 12.5% 2.6% 0.179 0.996 0.875 0.895 Soft 1 213 21 6 5 16 1 191 9.9% 2.8% 0.238 0.995 0.833 0.923 Soft 2 99 18 2 2 16 0 81 18.2% 2.0% 0.111 1.000 1.000 0.835 1_01 10 1 0 0 1 0 9 10.0% 0.0% 0.000 1.000 / 0.900 1_02 22 1 0 0 1 0 21 4.5% 0.0% 0.000 1.000 / 0.955 1_03 16 1 0 0 1 0 15 6.3% 0.0% 0.000 1.000 / 0.938 1_04 18 2 2 1 1 1 15 11.1% 11.1% 0.500 0.938 0.500 0.938 1_05 23 3 3 3 0 0 20 13.0% 13.0% 1.000 1.000 1.000 1.000 1_06 17 1 0 0 1 0 16 5.9% 0.0% 0.000 1.000 / 0.941 1_07 23 7 0 0 7 0 16 30.4% 0.0% 0.000 1.000 / 0.696 1_08 8 0 0 0 0 0 8 0.0% 0.0% / 1.000 / 1.000 1_09 37 3 0 0 3 0 34 8.1% 0.0% 0.000 1.000 / 0.919 1_10 23 2 1 1 1 0 21 8.7% 4.3% 0.500 1.000 1.000 0.955 1_11 4 0 0 0 0 0 4 0.0% 0.0% / 1.000 / 1.000 1_12 3 0 0 0 0 0 3 0.0% 0.0% / 1.000 / 1.000 1_13 9 0 0 0 0 0 9 0.0% 0.0% / 1.000 / 1.000 2_01 15 3 0 0 3 0 12 20.0% 0.0% 0.000 1.000 / 0.800 2_02 0 0 0 0 0 0 0 / / / / / / 2_03 27 3 0 0 3 0 24 11.1% 0.0% 0.000 1.000 / 0.889 2_04 26 7 0 0 7 0 19 26.9% 0.0% 0.000 1.000 / 0.731 2_05 31 5 2 2 3 0 26 16.1% 6.5% 0.400 1.000 1.000 0.897

57

• Quality of care: R019

Diabetes hypertensive patients with ACE inhibitorsAutomatic extraction (AE) vs questions (Q) All the hypertensive patients (Q) with diabetes (Q)

Total Q ACE-inhibitors

AE ACE-inhibitors

Q ACE-inhibitors Prevalence

+ + + - Q AE Sensitivity Specificity PPV NPV AE ACE-inhibitors AE ACE-inhibitors + - + - Soft/GPs N Pat. N Pat. N Pat. N Pat. N Pat. N Pat. N Pat. Total 240 90 50 44 46 6 144 37.5% 20.8% 0.489 0.960 0.880 0.758Soft 1 105 29 20 16 13 4 72 27.6% 19.0% 0.552 0.947 0.800 0.847Soft 2 51 22 12 11 11 1 28 43.1% 23.5% 0.500 0.966 0.917 0.718Soft 3 84 39 18 17 22 1 44 46.4% 21.4% 0.436 0.978 0.944 0.6671_01 8 3 2 2 1 0 5 37.5% 25.0% 0.667 1.000 1.000 0.8331_02 13 1 2 1 0 1 11 7.7% 15.4% 1.000 0.917 0.500 1.0001_03 4 0 0 0 0 0 4 0.0% 0.0% / 1.000 / 1.0001_04 9 3 3 1 2 2 4 33.3% 33.3% 0.333 0.667 0.333 0.6671_05 15 6 6 6 0 0 9 40.0% 40.0% 1.000 1.000 1.000 1.0001_06 3 2 0 0 2 0 1 66.7% 0.0% 0.000 1.000 / 0.3331_07 13 3 2 2 1 0 10 23.1% 15.4% 0.667 1.000 1.000 0.9091_08 6 1 1 0 1 1 4 16.7% 16.7% 0.000 0.800 0.000 0.8001_09 16 3 1 1 2 0 13 18.8% 6.3% 0.333 1.000 1.000 0.8671_10 9 4 3 3 1 0 5 44.4% 33.3% 0.750 1.000 1.000 0.8331_11 3 1 0 0 1 0 2 33.3% 0.0% 0.000 1.000 / 0.6671_12 2 1 0 0 1 0 1 50.0% 0.0% 0.000 1.000 / 0.5001_13 4 1 0 0 1 0 3 25.0% 0.0% 0.000 1.000 / 0.7502_01 5 2 2 2 0 0 3 40.0% 40.0% 1.000 1.000 1.000 1.0002_02 17 7 4 4 3 0 10 41.2% 23.5% 0.571 1.000 1.000 0.7692_03 11 4 0 0 4 0 7 36.4% 0.0% 0.000 1.000 / 0.6362_04 18 9 6 5 4 1 8 50.0% 33.3% 0.556 0.889 0.833 0.6672_05 3 3 1 1 2 0 0 100.0% 33.3% 0.333 / 1.000 0.0003_01 14 5 4 3 2 1 8 35.7% 28.6% 0.600 0.889 0.750 0.8003_02 5 3 1 1 2 0 2 60.0% 20.0% 0.333 1.000 1.000 0.5003_03 11 8 1 1 7 0 3 72.7% 9.1% 0.125 1.000 1.000 0.3003_04 13 3 0 0 3 0 10 23.1% 0.0% 0.000 1.000 / 0.7693_05 9 4 3 3 1 0 5 44.4% 33.3% 0.750 1.000 1.000 0.8333_06 2 2 0 0 2 0 0 100.0% 0.0% 0.000 / / 0.0003_07 12 5 5 5 0 0 7 41.7% 41.7% 1.000 1.000 1.000 1.0003_08 15 6 3 3 3 0 9 40.0% 20.0% 0.500 1.000 1.000 0.7503_99 83 15 3 3 12 0 68 18.1% 3.6% 0.200 1.000 1.000 0.850

58

• Quality of care: additional tables

Cholesterol All the hypertensive patients (Q)

Total Sex Age Total chol.



Soft/GPs F M Mean between 30 and 69 <190 <190

Total 1084 531 546 62.1 708 657 181 57 425 112Soft 1 730 362 368 61.4 499 570 158 48 359 96Soft 2 354 169 178 63.6 209 87 23 9 66 161_01 44 26 18 58.5 36 17 8 1 17 51_02 101 47 54 61.3 77 83 24 7 44 121_03 64 33 31 62.2 33 51 15 5 29 81_04 56 33 23 61.3 42 44 6 3 22 91_05 56 24 32 60.7 41 46 15 5 32 91_06 55 24 31 57.2 43 51 7 1 37 81_07 62 30 32 63.7 36 59 23 9 40 151_08 31 14 17 62.1 20 27 7 1 13 21_09 106 50 56 59.5 79 73 26 7 51 111_10 74 37 37 63.4 44 69 11 6 41 121_11 27 16 11 58.3 18 19 8 0 13 01_12 18 8 10 64.7 12 1 0 0 0 01_13 36 20 16 68.3 18 30 8 3 20 52_01 48 20 23 63.4 29 0 0 0 0 02_02 14 9 5 61.3 5 0 0 0 0 02_03 75 40 35 66.3 44 0 0 0 0 02_04 123 56 65 60.5 80 0 0 0 0 02_05 94 44 50 65.8 51 87 23 9 66 16


59Extracted and validated BMI All the hypertensive patients (Q)

Total Extracted

BMI

Weight extracted < 4

months

Weight extracted < 12

months Validated

BMI

Validated BMI extracted < 4

months

Validated BMI extracted < 12

months Soft/GPs BMI < 25 BMI < 25 BMI < 25 BMI < 25Total 1554 806 575 99 708 116 946 667 122 798 138Soft 1 730 407 223 36 299 45 410 227 37 302 46Soft 2 354 154 145 32 170 37 262 225 51 249 55Soft 3 470 245 207 31 239 34 274 215 34 247 371_01 44 4 7 0 10 0 4 7 0 10 01_02 101 45 14 2 27 3 45 14 2 27 31_03 64 3 4 0 5 0 3 4 0 5 01_04 56 19 16 0 21 0 21 16 0 21 01_05 56 47 6 0 17 3 47 7 0 18 31_06 55 20 1 0 1 0 20 1 0 1 01_07 62 58 26 6 40 7 58 26 6 40 71_08 31 29 7 0 19 2 29 8 0 20 21_09 106 55 28 2 38 3 56 29 3 39 41_10 74 73 72 15 74 15 73 72 15 74 151_11 27 1 0 0 0 0 1 0 0 0 01_12 18 18 9 2 12 3 18 10 2 12 31_13 36 35 33 9 35 9 35 33 9 35 92_01 48 2 0 0 3 0 2 1 0 4 02_02 14 0 0 0 0 0 3 3 2 3 22_03 75 0 0 0 0 0 69 72 15 72 152_04 123 68 68 11 85 15 101 69 12 86 162_05 94 84 77 21 82 22 87 80 22 84 223_01 8 7 5 2 8 2 8 5 2 8 23_02 79 27 23 3 24 3 37 31 5 32 53_03 24 23 23 4 23 4 23 23 4 23 43_04 62 40 26 2 31 2 40 26 2 31 23_05 57 22 14 4 24 5 29 14 5 24 63_06 52 44 42 6 43 6 45 42 6 43 63_07 27 23 16 4 20 6 25 16 4 20 63_08 79 4 20 0 25 0 9 20 0 25 03_99 82 55 38 6 41 6 58 38 6 41 6


60

Extracted and validated blood pressure (BP) All the hypertensive patients (Q)

Total Extracted

BP BP extracted < 4

months BP extracted < 12 months Validated

BP Validated BP extracted

< 4 months Validated BP extracted

< 12 months

Soft/GPs 130/85 140/90 130/85 140/90 130/85 140/90 130/85 140/90Total 1554 1411 1330 372 788 1368 388 816 1482 1411 401 829 1447 418 857Soft 1 730 717 706 233 440 715 238 449 716 705 235 439 714 240 448Soft 2 354 229 181 46 115 196 48 122 302 263 73 158 276 76 165Soft 3 470 465 443 93 233 457 102 245 464 443 93 232 457 102 2441_01 44 41 38 15 26 41 17 29 41 38 15 26 41 17 291_02 101 101 99 39 68 100 39 69 101 99 39 65 100 39 661_03 64 64 63 3 24 64 4 25 64 63 3 24 64 4 251_04 56 56 55 26 48 56 26 49 56 55 26 48 56 26 491_05 56 56 55 19 31 56 19 32 56 55 19 31 56 19 321_06 55 54 53 14 31 54 15 32 54 53 15 32 54 16 331_07 62 62 61 18 39 62 19 40 62 61 18 39 62 19 401_08 31 31 31 1 5 31 1 5 31 31 1 5 31 1 51_09 106 106 104 48 82 104 48 82 106 104 48 82 104 48 821_10 74 71 74 30 41 74 30 41 71 74 30 41 74 30 411_11 27 22 20 2 6 20 2 6 21 19 3 7 19 3 71_12 18 18 18 8 12 18 8 12 18 18 8 12 18 8 121_13 36 35 35 10 27 35 10 27 35 35 10 27 35 10 272_01 48 48 47 11 31 48 11 32 48 47 11 30 48 11 312_02 14 0 0 0 0 0 0 0 1 4 0 0 4 0 02_03 75 0 0 0 0 0 0 0 68 72 24 39 72 24 392_04 123 88 83 20 57 91 21 60 92 85 23 59 93 25 632_05 94 93 51 15 27 57 16 30 93 55 15 30 59 16 323_01 8 8 7 2 5 8 3 6 8 7 2 4 8 3 53_02 79 79 77 8 51 79 9 53 79 77 8 51 79 9 533_03 24 24 24 10 15 24 10 15 24 24 10 15 24 10 153_04 62 60 58 6 14 60 7 15 60 58 6 14 60 7 153_05 57 56 52 7 17 55 8 19 56 52 7 17 55 8 193_06 52 52 52 11 31 52 11 31 52 52 11 31 52 11 313_07 27 27 25 12 15 26 13 16 27 25 12 15 26 13 163_08 79 78 76 21 43 77 21 44 77 76 21 43 77 21 443_99 82 81 72 16 42 76 20 46 81 72 16 42 76 20 46


61

Hypertensive patients with ace_inib+aspirine+statine (aas)Automatic extraction (AE) vs questions (Q) All the hypertensive patients (Q) Total Q aas AE aas Q aas Prevalence + + + - Q AE Sensitivity Specificity PPV NPV AE aas AE aas + - + - Soft/GPs N Pat. N Pat. N Pat. N Pat. N Pat. N Pat. N Pat. Total 1554 86 20 17 69 3 1465 5.5% 1.3% 0.198 0.998 0.850 0.955Soft 1 730 30 8 7 23 1 699 4.1% 1.1% 0.233 0.999 0.875 0.968Soft 2 354 27 2 2 25 0 327 7.6% 0.6% 0.074 1.000 1.000 0.929Soft 3 470 29 10 8 21 2 439 6.2% 2.1% 0.276 0.995 0.800 0.9541_01 44 3 1 1 2 0 41 6.8% 2.3% 0.333 1.000 1.000 0.9531_02 101 1 0 0 1 0 100 1.0% 0.0% 0.000 1.000 / 0.9901_03 64 1 0 0 1 0 63 1.6% 0.0% 0.000 1.000 / 0.9841_04 56 2 2 1 1 1 53 3.6% 3.6% 0.500 0.981 0.500 0.9811_05 56 4 3 3 1 0 52 7.1% 5.4% 0.750 1.000 1.000 0.9811_06 55 2 0 0 2 0 53 3.6% 0.0% 0.000 1.000 / 0.9641_07 62 8 0 0 8 0 54 12.9% 0.0% 0.000 1.000 / 0.8711_08 31 0 0 0 0 0 31 0.0% 0.0% / 1.000 / 1.0001_09 106 5 0 0 5 0 101 4.7% 0.0% 0.000 1.000 / 0.9531_10 74 3 1 1 2 0 71 4.1% 1.4% 0.333 1.000 1.000 0.9731_11 27 0 0 0 0 0 27 0.0% 0.0% / 1.000 / 1.0001_12 18 1 1 1 0 0 17 5.6% 5.6% 1.000 1.000 1.000 1.0001_13 36 0 0 0 0 0 36 0.0% 0.0% / 1.000 / 1.0002_01 48 5 0 0 5 0 43 10.4% 0.0% 0.000 1.000 / 0.8962_02 14 0 0 0 0 0 14 0.0% 0.0% / 1.000 / 1.0002_03 75 4 0 0 4 0 71 5.3% 0.0% 0.000 1.000 / 0.9472_04 123 12 0 0 12 0 111 9.8% 0.0% 0.000 1.000 / 0.9022_05 94 6 2 2 4 0 88 6.4% 2.1% 0.333 1.000 1.000 0.9573_01 8 1 0 0 1 0 7 12.5% 0.0% 0.000 1.000 / 0.8753_02 79 6 2 1 5 1 72 7.6% 2.5% 0.167 0.986 0.500 0.9353_03 24 1 0 0 1 0 23 4.2% 0.0% 0.000 1.000 / 0.9583_04 62 5 0 0 5 0 57 8.1% 0.0% 0.000 1.000 / 0.9193_05 57 3 0 0 3 0 54 5.3% 0.0% 0.000 1.000 / 0.9473_06 52 5 3 3 2 0 47 9.6% 5.8% 0.600 1.000 1.000 0.9593_07 27 1 0 0 1 0 26 3.7% 0.0% 0.000 1.000 / 0.9633_08 79 2 1 1 1 0 77 2.5% 1.3% 0.500 1.000 1.000 0.9873_99 82 5 4 3 2 1 76 6.1% 4.9% 0.600 0.987 0.750 0.974

62

• Epidemiology : R20 (for R021, cf. R016)

New cases of hypertension Automatic extraction (AE) vs questions (Q) All the patients (2005) AE Q Incidence HTA New HTA HTA New HTA AE Q + + + + Soft/GPs N Pat. N Pat. N Pat. N Pat. Total 1202 215 1554 101 17.9% 6.5% Soft 1 709 107 730 30 15.1% 4.1% Soft 2 216 17 354 27 7.9% 7.6% Soft 3 277 91 470 44 32.9% 9.4% 1_01 46 12 44 3 26.1% 6.8% 1_02 117 11 101 2 9.4% 2.0% 1_03 29 3 64 4 10.3% 6.3% 1_04 31 1 56 0 3.2% 0.0% 1_05 83 4 56 1 4.8% 1.8% 1_06 104 0 55 0 0.0% 0.0% 1_07 49 4 62 1 8.2% 1.6% 1_08 44 1 31 1 2.3% 3.2% 1_09 69 7 106 5 10.1% 4.7% 1_10 61 51 74 5 83.6% 6.8% 1_11 30 6 27 3 20.0% 11.1% 1_12 12 4 18 1 33.3% 5.6% 1_13 34 3 36 4 8.8% 11.1% 2_01 77 2 48 3 2.6% 6.3% 2_02 0 0 14 6 42.9% 2_03 0 0 75 6 8.0% 2_04 6 6 123 7 100.0% 5.7% 2_05 133 9 94 5 6.8% 5.3% 3_01 14 1 8 0 7.1% 0.0% 3_02 55 8 79 7 14.5% 8.9% 3_03 10 0 24 0 0.0% 0.0% 3_04 53 0 62 0 0.0% 0.0% 3_05 51 38 57 5 74.5% 8.8% 3_06 22 17 52 11 77.3% 21.2% 3_07 12 9 27 3 75.0% 11.1% 3_08 36 6 79 12 16.7% 15.2% 3_99 24 12 82 6 50.0% 7.3%

63

• Socio-economy : R023, R025

Impact of higher education and married status on treatment and referrals Hypertensive patients (n=2037) Treatment Referral + - Total + - Total

Higher education missing = 190 N % N % N missing = 190 N % N % N + 388 88.0% 53 12.0% 441 + 153 34.7% 288 65.3% 441 - 1178 83.8% 228 16.2% 1406 - 369 26.2% 1037 73.8% 1406 Total 1566 / 281 / 1847 Total 522 1325 1847

Married status Missing = 74 missing = 74 + 1303 87.7% 182 12.3% 1485 + 436 29.4% 1049 70.6% 1485 - 366 76.6% 112 23.4% 478 - 107 22.4% 371 77.6% 478

Total 1669 294 1963 Total 543 1420 1963 Hypertensive patients (Q) attending GP's office (n = 1554) Treatment Referral + - Total + - Total

Higher education missing = 145 N % N % N missing = 190 N % N % N + 364 96.0% 15 4.0% 379 + 153 40.4% 226 59.6% 379 - 991 96.2% 39 3.8% 1030 - 369 35.8% 661 25.5% 1030

Total 1355 54 1409 Total 522 887 1409 Married status missing = 55 missing = 74

+ 1145 96.2% 45 3.8% 1190 + 436 36.6% 754 63.4% 1190 - 295 95.5% 14 4.5% 309 - 107 34.6% 202 65.4% 309 Total 1440 59 1499 Total 543 956 1499

64

• Socio-economy : R024

Prevalence of hypertension All patients (7434) Hypertension + - Total

Higher education missing = 577 N % N % N

+ 441 26.0% 1256 74.0% 1697 - 1406 27.2% 3754 72.8% 5160 Total 1847 5010 6857

Married status missing = 282 + 1485 37.9% 2429 62.1% 3914 - 478 14.8% 2760 85.2% 3238 Total 1963 5189 7152

D/2008/2505/03

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