Family Practice Vol. 18, No. 3, 288-291
© Oxford University Press 2001
Health Services Research |
Towards improvement of the accuracy and completeness of medication registration with the use of an electronic medical record (EMR)
Department of General Practice, University of Groningen, Ant. Deusinglaan 4, 9713 AW Groningen, The Netherlands.
Hiddema-van de Wal A, Smith RJA, van der Werf GTh and Meyboom-de Jong B. Towards improvement of the accuracy and completeness of medication registration with the use of an electronic medical record (EMR). Family Practice 2001; 18: 288–291.
Received 25 February 2000; Accepted 8 January 2001.
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Abstract |
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Background. Approximately 80% of GPs use a GP information system (GIS) and an electronic medical record (EMR) in their daily practice. To reap the full benefits of an EMR for patient care, post-graduate education and research, the data input must be well structured and accurately coded.
Objectives. The quality and user-friendliness of the software positively influence the completeness and reliability of the data recorded in the GIS. To assess this in actual practice, this study examined whether or not an increase occurred in the accuracy and completeness of indication-related medication registration after the GIS's software package was upgraded.
Method. GPs recorded data for the Registration Network Groningen (RNG) concerning four medication groups: insulin, trimethoprim, the contraceptive pill and ß-blocking agents. The completeness and accuracy of the registered data were assessed both before and after the change to the new software package. The completeness is evaluated on the basis of the indications missing for the prescribed medications. To assess accuracy, a check was made to determine whether the indications corresponded to those deemed relevant for that particular medication according to National Pharmaceutical Guidelines.
Results. The percentage of missing indications decreased notably, especially in the chronically prescribed medication groups. For insulin, the percentage decreased from 40.5 to 3% and for the contraceptive pill from 34.5 to 1%. For trimethoprim, the percentage decreased from 10 to 1%, and for ß-blocking agents from 22 to 1.5%. Of the indications present, the percentage of relevant indications showed a slight increase, with the largest increase observed for the contraceptive pill where the percentage rose from 86 to 96%.
Conclusions. The completeness of recorded indications improved considerably after the change of software. This is due mostly to the efforts of the GPs, their practice assistants and the support of the RNG organization involved in the conversion procedure. Accuracy improved slightly, especially due to the software modifications which ensured that non-existent codes could not be entered. To summarize, with increased user-friendliness of the software, combined with the training of motivated GPs, the quality of recorded data improved.
Keywords. Computerized patient record, electronic medical record, family practice, record accuracy..
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Introduction |
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The utilization of a GP information system (GIS) and an electronic medical record (EMR) is stimulated by professional organizations and by financial contributions from health insurance organizations. Approximately 80% of GPs are presently using an EMR in their daily practice. About 25% of these 80% use the software package MicroHIS.1 The Working Group on Coordination of Information Automation (WCIA), instituted by the professional association and the College of General Practitioners, has drawn up specifications with which a GIS must comply. These are referred to as the WCIA-GIS Reference Model.2 In order to qualify for the automation allowance issued by the health insurance organizations, the GIS used by the GP must comply with the specifications in the WCIA-HIS Reference Model; the current version is Reference Model 1995.
The medical data input must be both structured and coded for the full advantage to be gained from the use of an EMR. This applies to patient care, post-graduate education and research. It is necessary to access information systematically, i.e. to obtain a survey of one's own diagnostic and treatment procedures, for peer review of data and for the comparison of these data with national or regional pharmacotherapeutical guidelines3; for the selection of patient groups for prevention programmes; and for reliable medication monitoring.
Discipline and extra energy are required from the GP in order to maintain a structured medical registration system. Particularly the conversion from paper records to an electronic data system requires a great deal of initial effort. Physicians also have to become accustomed to working with a classification system. In the Dutch GISs, this is the International Classification of Primary Care (ICPC).4 To be as efficient and effective as possible, a GIS must be user-friendly. In other words, the GP must be able to enter the medical data systematically, easily and rapidly in a manner which causes a minimum of disruption in his/her normal working method. The computer in the consultation room should be a supporting factor, and not a disturbing element. For these reasons, the user-friendliness of the various GISs should be a continuing priority for all persons involved in their design.
Research in England has shown that well-motivated physicians achieve increased accuracy and completeness in medical registration using such a system, particularly when it concerns data which they regard as relevant; i.e. the diagnoses, medication regimes and referral information.5–8 However, their scores on the registration of data concerning physical examination and life-style habits such as smoking and drinking were considerably lower.5,7 Specific feedback and discussions concerning the recorded results in the group of participating GPs improved the completeness of the registration information, but only in the aforementioned areas deemed relevant by the physician.5
The quality of the software and its user-friendliness influence the completeness and reliability of data recorded in the GIS. Innovations and software updates are introduced regularly, with the goal of continued improvement in the quality of the GIS. Whether or not these goals are being realized remains an important question. To evaluate this in actual practice, the conversion of one particular software package, MicroHIS 6.1 to MicroHIS 7.0, was studied, using data from the Morbidity and Medication Registration Network Groningen. The recorded medication is examined by consideration of the following question: have the accuracy and completeness of indication-related medication registration increased after the conversion?
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Method |
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The Registration Network Groningen (RNG) was established in 1989 by the Department of General Practice at the University of Groningen with the objective of collecting data in automated general practice, for the benefit of education, research and management development. All personal patient contacts with the participating GPs including the nature of the contact, the diagnosis relevant to that contact and any resulting referrals are recorded. Furthermore, all indication-related prescriptions are also recorded. Three group practices and three solo practices participated in this study.
Indication-related prescription data were only collected from the three group practices. Similar data from the solo practices were not used as medication registration was incomplete during the study period. Approximately 21 000 patients were registered from the three group practices.
The prescribed medication is coded with an ATC code, and the indication/diagnosis is coded with an ICPC code.4,10
The software package MicroHIS was used, which is one of the Dutch GIS packages. It complies with the WCIA standard. Conversion from the old software package MicroHIS 6.1 to MicroHIS 7.0 took place during the period from June to October of 1994.
A number of registration changes resulted from this conversion. These changes were as follows:
- In MicroHIS 6.1, there was no standardization of medication, dosage codes and medication indications. Each was entered according to the physician's own consideration. This caused the RNG many problems in processing the data. After the conversion took place, the standardized data of the KNMP (Royal Dutch Pharmacists Society) was used.
- ICPC codes, entered as indications, remained unchecked in the MicroHIS 6.1 program. Therefore, data entry errors went unnoticed. MicroHIS 7.0 does include such verification checks.
- ICPC codes, entered as indications, remained unchecked in the MicroHIS 6.1 program. Therefore, data entry errors went unnoticed. MicroHIS 7.0 does include such verification checks.
The periods from 1 December 1993 to 1 June 1994 and from 1 December 1994 to 1 June 1995 were chosen for the comparison of the results obtained before and after the conversion. Much attention was devoted to the supervision of the conversion in the RNG practices, before and after the conversion actually took place.
GPs and their assistants were given plenty of support before and during the actual conversion as they learned to work with the new program. Problems were discussed during the regular RNG registration consultation, and new registration agreements were made.
This study addresses four specific medication groups. (i) Insulin, a typical chronic medication, ATC code A10AA, relevant indication code T90.1: insulin prescribed for diabetes mellitus; (ii) trimethoprim, a typical temporary medication, ATC code J0IE-A01, relevant indication U71 (urinary tract infections) and also Y73 (prostatitis/seminal vesiculitis). The most obvious indications were selected, with the rarer ones, such as ‘upper bronchial infections on the basis of culture results’ were excluded; (iii) the contraceptive pill, which is commonly used chronically and only sometimes temporarily, ATC codes G03A and G03HB, relevant indication W11 (oral contraception), but also acne (code S96), menstruation disorders (codes X02, X05, X06, X07, X08, X09, X10, X11 and X89) and the morning-after pill (code W10); and (iv) the group of ß-blockers prescribed for temporary as well as for chronic use, ATC code starting with C07, with multiple relevant indications: pain/constriction in the chest, angina pectoris, myocardial ischaemia (codes K01, K02, K74 and K76), hypertension (codes K85, K86 and K87), arrhythmias (codes K04, K05, K78, K79 and K80), cardiovascular symptomatic hyperthyroidism (code T85), secondary prevention of myocardial infarction (code K75), slight to moderate heart failure in idiopathic dilated cardiomyopathy (codes K77.1 and K77.2), atherosclerosis (excluding coronary and cerebral atherosclerosis) (code K91), other heart diseases (code K84), migraine prophylaxis (code N89), essential tremor (code N06) and oesophageal varices (code K99).
Analysis
For each of the included medication groups, the completeness of the recorded indications was calculated using the percentage of present indications with respect to the percentage of missing indications.
To assess the accuracy of the medication group to which an indication is added, the indications were split into two categories: (i) the relevant indications, i.e. indications having significance, as they are listed for the relevant indication groups in the National Pharmaceutical Guidelines of the Health Insurance Board;9 and (ii) the non-relevant indications, which include indications without significance and those which may be significant, but cannot be identified as such.
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Results |
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Figure 1a–d
shows that the percentage of missing indications greatly diminished after conversion to the new program. This difference is particularly pronounced for the chronically prescribed medications. For insulin, the percentage of missing indications dropped from 40.5 to 3%. For the contraceptive pill, the percentage dropped from 34.5 to 1%. For temporarily used medications, the percentage of missing indications also dropped significantly. For ß-blockers, the percentage dropped from 22 to 1.5%, and for the trimethoprim group, the percentage dropped from 10 to 2%.
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After conversion in all medication groups, the non-relevant indications decreased when the proportion of relevant to non-relevant indications was examined (Fig. 2a–d
). For insulin, the relevant indications increased from 96 to 99%, for trimethoprim from 95 to 97%, for the contraceptive pill from 86 to 96%, and for ß-blockers from 90 to 94%.
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Discussion and conclusions |
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TopAbstractIntroductionMethodResultsDiscussion and conclusionsReferences |
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This study shows a significant improvement in completeness and accuracy after the conversion. An impressive decrease in the percentage of missing indications was seen in all medication groups examined. A slight increase in the percentage of relevant indications was seen, though these were already rather high before conversion. This may be explained by the fact that before the new version of MicroHIS was initiated, the registration agreements were reviewed again and evaluated in the new system in all practices. The practice assistants, who usually deal with the chronic repeats, were fully involved in this phase of the project.
The new version of MicroHIS has a feature which only allows the entry of existing ICPC codes.
The morbidity registration, as it is used within the RNG, is demanding for GPs. They continually face the requirement to translate their daily work into a structured record. The increased user-friendliness of the program reduces this demand. More modifications are required before the system software will link up optimally with the thinking and handling of the individual GPs. The program conversion evidently contributed to the improvement in the quality of the recorded data.
In conclusion, the conversion has resulted in a significant improvement, particularly regarding completeness of data, but also in the accuracy of data recorded for the RNG. These improvements are partly due to the efforts of the GPs, their practice assistants and the organization of the RNG, and in part to the conversion itself, as well as its modifications.
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References |
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1 Althuis TP. Nut III, een studie naar automatisering van Nederlandse huisartsen 1997 in. [A survey of automatization of Dutch general practitioners in 1997]. Utrecht: NHG (Dutch College of General Practitioners), 1999.
2 WCIA-HIS-Referentiemodel 1995. Utrecht: Werkgroep Coördinatie Informatisering en Automatisering (WCIA) van NHG en LHV, 1996.
3
Kamps GB, Stewart RE, van der Werf GTh, Schuling J, Meyboom-de Jong B. Adherence to the guidelines of a regional formulary. Fam Pract 2000; 17: 254–260.
4 Lamberts H, Wood M (eds). International Classification of Primary Care (ICPC). Oxford: Oxford University Press, 1987.
5
Gilliland A, Mills KA, Steele K. General practitioner records on computer—handle with care. Fam Pract 1992; 9: 441–450.
6 Jick H, Jick SS, Derby LE. Validation of information recorded on general practitioner based computerised data resource in the United Kingdom. Br Med J 1991; 302: 766–768.
7 Pringle M, Ward P, Chilvers C. Assessment of the completeness and accuracy of computer medical records in four practices committed to recording data on computer. Br J Gen Pract 1995; 45: 537–541.[Web of Science][Medline]
8 Whitelaw FG, Nevin SL, Milne RM, Taylor MW, Watt AH. Completeness and accuracy of morbidity and repeat prescribing records held on general practice computers in Scotland. Br J Gen Pract 1996; 46: 181–186.[Web of Science][Medline]
9 van der Werf GTh, Smith RJA, Stewart RE, Meyboom-de Jong B. Spiegel op de huisarts. [The work of the GP reflected: a report on the registration of diseases, prescribed medication and referrals in the automatized practice]. Groningen: Universiteitsdrukkerij, 1998.
10 WHO Collaborating Centre for Drug Statistics Methodology. Guidelines for ATC Classification and DDD Assignment. Oslo, 1996.
11 Farmacotherapeutisch Kompas 1995 [National Pharmacotherapeutical Guidelines]. Amstelveen: Centrale Medisch Pharmaceutische Commissie van de Ziekenfondsraad, 1995.
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