MJ/ ESP-ULB/ MPH/ Lecture on
classification 2002-2003 back to
docpatient.net/class
Health information system, indicators, data collection and
classifications
1. ROUTINE DATA IN THE HEALTH INFORMATION SYSTEMS
One can present the HIS structures in different schematic ways depending on the point of view. In any case, it is crucial to create the link between collected data and the objectives assigned to its collection.
According to a classic approach such as in fig 1, the central core of the system encompasses the “health status indicators”. Around the core, one finds the indicators related to the determinants among which health systems and health services have a special place. This diagram is organised through a conceptual approach, taking the nature of the indicators into account.
Fig 1. Diagram of the HIS and the various health indicators according to their kind
Belonging to the “core”, the health status indicators classically comprise mortality, morbidity and, more recently, positive health indicators.
Evidently, the collection system for the mortality data is a routine system. The collected data is exhaustive, covers the entire population as well as all lethal phenomena facing the population.
In terms of morbidity data, the problem is considerably more complex. The first and main difficulty is a conceptual one: what morbidity are we dealing with? Various authors (Chen et Bryant, 1975, Lévy et al., 1975, Goldberg et al., 1979) have insisted on the fact that the evaluation of morbidity by practising doctors is only one aspect of morbidity. If one wants to have a general overview of morbidity, one must design a more complex model, such as fig 2., modified from Goldberg (1979).
Fig. 2.2. Conceptual diagram of the various dimensions of morbidity
The various dimensions of morbidity are linked with different and complementary uses:
objective morbidity reflects what one would consider as the “real” health needs, that is to say those that are completely independent from the subjective point of view of both the patient and the examiner. This is the classic incidence and prevalence data concerning the “real” health problems (diseases) as they are described by the medical science at that time.
Perceived (or subjective) morbidity, on the contrary, is completely linked to the patient’s personal feelings, independently from any objectivation or diagnosis. This clearly reflects the “need that is felt” and hence the potential demand for intervention and for the health system to take charge.
Finally, diagnosed morbidity, which only intervenes at the final stages of the process, reflects both the results of the previous steps (hence the need and the demand), as well as the functioning of the health system itself. For this very reason, it is also an indicator of the socio-economic implication of morbidity.
Obviously, it appears that one needs different techniques and procedures to access the data pertaining to these various aspects of morbidity. Objective morbidity data can only be gathered through standardised health examination surveys conducted on representative sample of the population[1]. For perceived morbidity as well as for self-reported morbidity, the only way to collect data is through standardised health interview surveys organised within the same type of representative samples[2]. Regarding diagnosed morbidity, it is possible to obtain the data from health providers: the data can be based either on probabilistic samples of practices (the sentinel practices network is one example), or on a continuous basis in an integrated routine system[3].
In recent years, and especially since the exponential development of computer techniques in gathering and storing information, we face a true explosion of local information systems (at the hospital level, practice level, etc.) with not only a local objective but also a intended uses such as financing services, continuous evaluation, research, epidemiological goals, etc.
Three different settings: primary health care, secondary health care, and occupational health care. allow the health personnel to collect medical and administrative data concerning the patients or the workers in their service, and for whom a diagnosis may be established.
It is essential to more clearly define our concept of “routine data” which is so important in the conceptualisation of health information systems (Lippeveld et al., 2000): “Data can be collected through a variety of methods. We have found it useful to classify these methods into two groups: routine methods and non-routine methods.”
In
one of the rare books published on this topic, Stroup et al. (1994) devote a
chapter to the “sources of routinely collected data for surveillance”.
Beginning historically from the systematic recording of notifiable diseases
which was restricted to infectious diseases, and insisting in the interest of
gathering existing data in other domains (non infectious diseases, chronic
diseases, accidents,…), these authors make the distinction between six
categories of sources in the filed of routine data:
1.
-Notifiable
diseases
2.
-Vital
statistics
3.
-Sentinel
surveillance, including the sentinel networks
4.
-Registries
5.
-Health
surveys
6.
-“Administrative”
data collection systems
When
considering this classification, it appears it becomes clear how the definition
of the routine data concept can vary from one author to the next. If one adopts
a more strict understanding, the notion of “routine collection” refers to the
systematic character of the collection during the “normal” course of a usual
practice.
routinely collected medical data, i.e. data collected systematically
within the entire consulting population in the medical practice framework (at
three different levels: primary care, secondary care, and occupational
medicine), relating mainly to morbidity data or morbidity-related data (such as
determinants or risk factors), and allowing the elaboration of health
indicators and epidemiological use.
From
the aforementioned categories, category 2 (vital statistics) has a clear
routine approach. Our definition excludes category 3 (sentinel surveillance,
including the sentinel networks), which implies a selection of indicators and
the gathering of data through a probabilistic sample of practices. In Category
5 (health survey) even when repeated,
the surveys do not systematically collect the information from the routine
medical activities.
Notifiable
diseases (category 1) ; These collection systems are generally well organized
and function properly in most industrialized countries. Examples are given by
the MMWR from the CDC-Atlanta[4], or on a worldwide level by the
Weekly Epidemiological Report created by the WHO in Geneva[5]. The objective of these systems is
more restricted, aiming at careful and sensitive surveillance of contagious and
epidemic diseases in order to set up emergency actions, if necessary. This
defined objective is well covered by WHO activities.
2. A specific example : mental health
Mental
health problems are very common in European countries. They have an important
load on the socio-economic conditions of the population, due to their impact on
employment, working abilities, earnings, but also intra-familial difficulties,
including divorces, violence etc. The emphasis has been put specially this year
2001 on the priority to be given to mental health promotion, and subsequently
to elaborate indicators in this field.
-
Mental
health needs to be recognised as a priority and integrated in the
general scope of public health, due to its importance in terms of suffering
(mortality, morbidity and consequences on the working and living conditions)
-
There
are different concepts which need to be clearly defined in order to
avoid discrepancies between countries in the international comparisons:
examples are mental health, psychological well-being, psychological distress,
mental disorder, mental illness, etc. Theses variations in the definitions will
lead to differential classifications in the statistics, but even more to
differential attitudes from the patients, conducting to a different use of the
specific pathways in the mental health services
-
The organisation
of the mental health and psychiatric sector is very different from country
to country. Hence, the different tasks of the services are performed by
different structures, with different rhythms and intensities. All this makes
the comparison very hazardous between countries. One example is given by the
proportional distribution of the psychiatrists and psychologists in the taking
in charge of the mental health patients. Institutionalisation or domiciliary
treatment is another example of the great variations within EU.
Nevertheless,
in this field as in others, indicators founded on medico-administrative routine
data should be able to allow comparisons between countries or to follow the
trends. Due to the limitations which
have been briefly summarised above, and to the very nature of morbidity data collected
from within the caring system, one must be very cautious in the conclusions to
be drawn from such comparisons.
As
they are elaborated on the basis of data linked with the health system
functioning, such indicators may only reflect the complex interaction between
certain health needs and the available resources, the way they are functioning,
their activities, and sometimes their performances. They are unable to reflect
directly and with accuracy the actual health status (the “objective” morbidity)
of a population, nor the true prevalence
of certain mental conditions.
For
example, the increasing trend of the demand for mental health consultations
which is very generally observed in Europe nowadays, does not imply necessarily
an increase in the incidence of the mental conditions noticed at the level of
these consultations. Despite this warning, the indicator is not without
interest, as it permits to assess some specific trends and hence to anticipate prospectively the demand, which is
undeniably a valuable input for planning purposes in this sector.
This
is true for all morbidity indicators collected from within the caring system,
but, it is even more so than elsewhere in the mental health sector : the nature
of the health problems concerned here make them closer to the social problems
the patients can face. For this reason, in the perspective of mental health
indicators “working on the long term”, the question of the broadness of the
domain covered by these indicators cannot be eluded.
Beyond
the many problems we shall face with the gathering of the routine data
gathering at any level of the system (see below chap. 3 to 5), beyond the quality problems linked with the
previous ones, the real challenge is here to set up a coherent set of
indicators allowing health systems researchers, managers and decision makers to
make a good use of the information made available. Therefore, definitions,
standards, norms, values and criteria,
which all structure the administration
of this specialised care in the various countries, should be
systematically studied, analysed and compared in such a way that one should be
able to enlighten the similarities and the differences, and to allow a sound
interpretation of the indicators.
It
is noteworthy to mention that three European countries are on the way to set up
a national system for the mental health (or psychiatric) health information :
-
in
Belgium : the Minimum Psychiatric Summary (RPM)
(http://mpg-www.uia.ac.be/mpg/fr/index.html)
-
in
France, the PMSI is mainly oriented towards hospital data.
( http://www.le-pmsi.fr/commun/glossaire/gloss.html )
-
in UK,
the Mental Health Minimum Data Set, also covers ambulatory care episodes when
occurring within the NHS
(http://www.nhsia.nhs.uk/mentalhealth/dataset/pages/default.asp)
and the Integrated Mental Health Electronic Record (IMHER) takes part in the
national strategy to constitute an individual electronic medical file for each
patient
(http://www.doh.gov.uk/nhsexipu/strategy/nsf/imhercb.pdf)
The
variability of classification systems is great, specially at the primary care
level (see below), but even in the hospital settings where the ICD codes are in
use, problems arise for the comparison between countries, due to the different
versions in use at the same time.
3. Health indicators and data collection in general medicine
3.1.1. Introduction
Between the health needs expressed by the population and the actual morbidity data collected in hospitals at the end of a hospitalisation, there is an “information gap” that could be dealt with given the current possibilities of computer sciences.
Traditionally, one may estimate morbidity through hospital diagnoses or even through reported deaths. This morbidity, which is the object of medical procedures, is the base for morbidity indicators and can be used to estimate the health system’s needs.
The felt needs can be analysed by population surveys, but are merely a faint reflection of the demand for care expressed by the population. Besides these surveys, one has recently added the sentinel network of general practitioners (GPs). These networks in General Practice Family Medicine (GP/FM) have allowed, first on paper and then by electronic means, for an estimate on the prevalence of certain problems that are not necessarily featured as hospital diagnoses in the health statistics.
Figure 3.1 The various
sources of health information and the place of
GP/FM data collections6
Whatever the performances and praise deserved by the sentinel networks, they remain completely selective concerning the subjects they analyse, which are always decided by doctors based on their perception of the importance of the problems.
It is therefore information pre-sorted by the providers, which allow for extrapolations for an entire population on highly prevalent subjects. Nonetheless, the sentinel networks could not reflect the entire set of health issues approached by doctors, nor the demand expressed by the population at the health service level.
General practitioners and family physicians form what is commonly called the primary care level.
In several northern European countries, as well as in Italy, Portugal, and Spain, this level plays the role of gatekeeper of the health system. The patient is not authorised to consult a specialised care level if he/she has not first consulted a general practitioner with whom he or she generally has a record.
This ‘gate’ system is not present in Belgium, France, Germany, Luxemburg, and Greece. It is therefore difficult to obtain a common denominator to set an epidemiological rate.
The development of the Electronic Medical Record (EMR) allows the general practitioner (GP) to insert himself/herself in the health information chain[6] (Cf. Fig. 3.1).
3.1.2. General Medicine and Databases
It has been demonstrated that the GP receives and treats, at his/her level, more than 92% of the health problems presented by the consulting population.
The data is retrieved and, if it is adequately classified, it allows for the use of analytical as well as operational data, to be used by the GP.
The processed data can also be utilised when it is transferred from the patient to the hospitals. It can be aggregated and serve in the elaboration of databases that constitute “knowledge reservoirs” for the medical profession[7] (Cf. fig. 3.2)
These databases have been developed in Europe for several years and seem completely distinct in their constitution, content, and homogeneity. Some of them contain several tens of millions of years/patients in standardised information, which are at the start of intensive investigations. Data mining techniques are applied to these databases.
Figure 3.2 Information flow in GP/FM through EMRs use 7
The question of availability of morbidity indicators through these data collections is at the heart of the problem tackled by this analysis.
What are the existing databases in Europe? How are they organised? Can the information gathered by these various bases be aggregated and continuously serve as a source of information on the population’s demands for health care as much as on the answers from the health services?
We will attempt to answer these questions in the following report.
One must nonetheless realise that the degree of GP’s computerisation greatly varies throughout Europe. If more than 90% of British general practitioners are computerised and efficiently use an EMR, this is not true when we travel south or if we look at “disorganised” basic health systems such as in France, Germany, or Belgium.
The pivotal position of a GP is nevertheless constantly reaffirmed. Even if hospital and secondary services sometimes have a tendency to be exported towards the primary networks in Germany, Belgium, and France, the GP still remains the intermediary, the advocate and the preferred treatment for most Europeans.
GP’s see 75% of their consulting population over a single year, and 95% over a three years period.
They may have a professional information system and be aware of its importance in the emergence and the management of personal health data. If this is the case, they will become unavoidable sources for obtaining an overview of the supply and demand of care and the construction of efficient health indicators[8].
3.1.3. Making use of electronic means
In order to identify a comprehensive view of the European databases that continuously record morbidity in general medicine, one needs to consult many documentary means and new information and communication techniques.
The domain of health information is at the heart of general medicine research on the international level. This chapter’s author’s (M.J.) position as member of the WONCA International Classification Committee (WICC) and President of the Association of ICPC Users (CISP-Club) allowed for a quick and direct contact with a great number of researching GP’s in Europe.
The WICC, CISP-Club and GP WONCA[9] circulation lists, as well as a number of informal contacts, enabled to access information about dozens of data collections throughout Europe.
This is not an exhaustive study – that would certainly require a great deal more time and effort – but the examined bases truly represent the field’s tendencies.
It was, for example, impossible to obtain the collaboration of the General Practice of the University of Amsterdam, whose competence on the matter is universally recognised.
Despite certain reservations, the Internet survey form (see chapter II) allowed us to gather essential information on twelve databases.
This form was greatly based on work achieved by Dr Job Metsemakers in Holland (*). In chapter II, one will find a summary of the information obtained, while the entirety of the information collected is available in Access (*.mdb), Excel (*.xls), and text format (*.txt) in the software aid annexed to this report.
Publishing the Internet site http://www.ulb.ac.be/esp/emd also allowed us to edit the tools needed to understand the survey form correctly.
The European geographic regions were thus republished in a specific site (http://www.ulb.ac.be/esp/emd/nuts.htm).
The various terminologies and classifications were the subject of a study published on the site and featured in the software aid annexed to this report.
After having explored the various databases in a qualitative and quantitative manner, and outlined the content (chapter 3.3), it seemed constructive to give an overview of the different terminologies and classifications currently available in Europe and in use in the EMR’s or other computerised health systems (see chapter 3.4).
Special attention was devoted to the use of ICPC (International Classification of Primary Care), which serves as a medium for many European studies (see chapter 3.5).
4. Working fields in GP/FM
First step in classification field. Delineating the working fields of the doctor and specially the general practitioner.
|
|
Doctor's knowledge, |
|
|
I |
II |
|
|
III |
||
tableau : four zones in the patient doctor encounter (© M Jamoulle)
Consider four fields in GP activity and consequently four types of prevention.
I: The patient is not sick and you
are in a health promotion process or in immunisation campaign,
II: the patient is not sick and you are screening for diseases,
III: unfortunately you find the disease and now the patient knows that he is
sick (but sometimes don't accept it),
IV: the last one, the fourth, is not the easiest, the patient feels himself
sick and you find nothing or you was wrong in finding something.
Health promotion, screening and medical activities could throw the patient into the fourth field (Monday morning 'cardiac' patient after saturday evening TV panel on ischemic heart disease, cancerophobic women after anxiogenic repetitive mammography campaign, terrified patient about 3 mm liver angioma found in a routine abdominal scan, high PSA level patient with negative biopsies)
Consultation, a meeting between science and conscience is also a deal between patient and doctor anxiety. Quaternary prevention would raise the best way to protect the patient. Its also a Chi Square joke. How to deal with false positive?
|
Conscience or Patient feeling |
Science or Doctor knowledge, disease natural
evolution |
|
|
I
Action taken to avoid or remove the cause of a
health problem in an individual or a population before it arises. Includes
health promotion and specific protection |
II Action taken to detect a health problem at an
early stage in an individual or a population, thereby facilitating cure, or
reducing or preventing it spreading or its long-term effects |
|
|
IV Quaternary Prevention: Action taken to identify
patient at risk of overmedicalisation, to protect him from new medical
invasion, and to suggest to him interventions, which are ethically
acceptable. |
III Action taken to reduce the chronic effects of a
health problem in an individual or a population by minimizing the functional
impairment consequent to the acute or chronic health problem |
|
This file
edited in april 2000
Some références
Jamoulle M. Information et
informatisation en médecine générale. [Computer and computerisation in general practice] in: Les
informa-g-iciens.: Presses Universitaires de Namur; 1986:193-209
presented at the WONCA congress in Hongkong :
Jamoulle M, (Wonca
Classification committee), Roland M,(Equip
Wonca Europe). Quaternary prevention and the glossary of general
practice/family medicine, Poster, Hongkong Wonca congress proceedings, June
6/9, 1995
The concept of
Quaternary prevention has been endorsed by the WONCA International
Classification Committeee during its Durham meeting in 1999. The four definitions
above are extracted from the WONCA dictionary, edited by Niels Bentzen, OUP,
2000 (publication in process)
Meador
CK. The art and sciences of non disease. N.Engl. J. Med. 1965;272:92-95
Pilowsky
I. Abnormal illness behaviour.Br J Med Psychol 1969 Dec;42(4):347-51
Grol R. (ed). To heal or to harm, the prevention of somatic
fixation in general practice, Royal College of General Practitioners, London ,
1981.
Snadden
B. Ethical dilemmas of cervical cancer screening. Canadian family physician,
1992, 331-333
Hellstrom
O W. Health promotion in general practice. Europ. J. Public Health.
1994;4:119-124
Guttman
N, Kegler M, McLeroy KR. Health promotion paradoxes, antinomies and conundrums,
Health education research,(editorial). (11) 1, march 1996; i-xii