Improving the quality of primary care through tailored ...

Improving the quality of primary care through tailored interventions and customizable software linked to electronic medical records S. Treweek, S. Flottorp and A. Oxman

Dr Shaun Treweek Research Fellow Dr Signe Flottorp Project Leader Dr Andy Oxman Head of Section Department of Population Sciences Health Services Research Unit National Institute of Public Health Ngdalen N-0403 Oslo Norway Tel: +47 2204 2271 Fax: +47 2204 2595 Address for correspondence: Shaun Treweek Email: [email protected]

The principal information system used in Norwegian primary healthcare – the electronic medical record (EMR) – is a rich but underused source of data for quality improvement purposes. This paper describes two systems that aim to make better use of the EMR. First, ‘Best Possible Practice’ provides general practitioners with online support for the treatment of urinary tract infections and sore throats, tailored to the patient via the EMR. The second system, ‘QTools’, will allow health professionals, researchers and others to create a wide range of quality improvement tools. Studies with both systems are under way. Early results from ‘Best Possible Practice’ provide information on antibiotic use, use of laboratory tests and use of telephone consultations. These data will be used to assess the ability of the system to change the behaviour of health professionals. ‘QTools’ is under development but several elements of the system are in place, primarily a flexible data extraction tool. EMR systems offer many possibilities for quality improvement systems tailored to individual patients. Although working in this area is a challenge, such systems have the potential to chart and change practice at the local, regional and national levels. keywords: electronic medical record, quality improvement, tailored interventions, primary care

INTRODUCTION The need for continuous quality improvement in healthcare is now well recognized and reflected in the World Health Organiza-

tion’s strategy document ‘Health21: Health for all in the 21st century’ [1]. This document emphasizes the importance of information systems in the quality improvement process and several of WHO’s 21 targets require improved information systems if the target is to be met. These aims are also reflected in the Norwegian national strategy for quality improvement in healthcare [2]. However, the overall goal of this strategy – that all healthcare providers will have established effective quality systems in their organizations by the year 2000 – has not yet been achieved in primary care [3]. A major reason for this is that the principal information system used in Norwegian primary healthcare – the electronic medical record – is currently difficult to use as a quality improvement tool. Although some reporting tools are provided by these systems, they are generally considered inadequate for effective quality improvement work [4]. Electronic medical record (EMR) systems do, however, offer many possibilities for monitoring and improving the quality of care [5] [6] [7] [8] [9]. Systems linked to the EMR can, for example, be used to provide guidelines, online patient educational materials and decision support tools. However, these systems often do not give health professionals and researchers the flexibility to develop tailored data collection and quality improvement tools for specific needs: users must always return to the EMR supplier. EMR systems are a good source of data for epidemiological, public health monitoring and administrative purposes as well as for quality improvement and offer the possibility of studying population data that are collected at patient level. Such data could, for example, be used to compare the patient profiles of individual practices, assess the health needs of various populations or to monitor the implementation of health policy. EMR systems can also be used to increase patient involvement in decisions affecting their health by providing online, up-to-date, personalized educational materials.

BACKGROUND TO THE PROJECTS Around 90 per cent of general practitioners (GPs) in Norway use an EMR. There are essentially only three EMRs being used in Norway: Winmed (20 per cent of market share), Profdoc (50 per cent) and Infodoc (30 per cent). The Health Services Research Unit (HSRU) at the Norwegian National Institute of Public Health wanted to explore the potential offered by these systems for improving the quality of primary healthcare. HSRU has several projects in this area and two are described in this paper:

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Improving the quality of primary care

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‘Best Possible Practice’ (BPP) uses an online system to support the treatment of urinary tract infections (UTI) in women and sore throats ‘QTools’ is a software package that will allow the user to create a wide range of data collection and quality improvement tools, all of which are linked to the EMR

GPs, practice nurses and other practice staff can access these systems and data can be exported by both for use by researchers and healthcare managers.

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oping special software for all of HSRU’s (and others’) planned projects would be slow and prohibitively expensive. QTools aims to overcome these problems by providing the user with a collection of tools that will allow them to build their own quality improvement systems. Third-party systems can also be embedded within the QTools system. The project is a collaboration between EMR suppliers, third-party software developers, researchers, health professionals and others with an interest in EMR-based information.

Best possible practice Clinical guidelines are increasingly being used to try and reduce unwanted variation in clinical practice and to help health professionals ensure that their practice is evidence-based [10]. Guidelines can lead to a change in clinical practice but passive dissemination of guidelines has little effect [11] [12] [13]. There is no single intervention that can effectively transfer knowledge into practice [13] and interventions to support guidelines need to be tailored to each guideline [14]. Moreover, the effect of the intervention must be evaluated with the same rigour as with other clinical interventions, preferably using a randomized controlled trial [15]. Sore throat and UTI are both common conditions, UTIs being found particularly in women. We developed evidence-based guidelines by systematically reviewing the current literature; these guidelines were developed in collaboration with, among others, the Norwegian Centre for Quality Assurance of Laboratory Tests in Primary Healthcare, the Norwegian Association for Primary Care and the General Practitioners’ Association. The project was approved by the regional ethics committee, the Norwegian Board of Health and the Data Inspectorate. Ways in which BPP uses the EMR are by: ● ● ● ● ●

providing intervention advice through the EMR linking to diagnosis, prescription and laboratory test information using information in the EMR as inclusion/exclusion criteria providing online patient information extracting EMR data for analysis

QTools, a quality improvement tool for primary healthcare The software developed to implement the sore throat and UTI guidelines was specially developed; it took over a year to write and was relatively expensive (around $22 000). Devel-

DESIGN AND EVALUATION OF BEST POSSIBLE PRACTICE Design In collaboration with Mediata AS, a medical software company, HSRU developed a Windows-based system (BPP) that was linked to a networked EMR, Winmed (Legedata AS, Oslo). BPP is triggered by particular diagnoses, prescription codes or laboratory tests entered in the EMR. These triggers are shown in Table 1. ICPC stands for the International Classification of Primary Care system of coding diagnoses and symptoms and is used by all Norwegian EMRs. The selected ICPC codes cover the likely codes used for patients with sore throat or UTI. ATC is the Anatomical Therapeutic Chemical classification system and is used to classify drug groups. This is also a standard in Norwegian EMRs. The selected ATC codes are again those most likely (though not exclusively) to be used in connection with sore throat and UTI. There is no standard for laboratory tests as yet so these are grouped under the headings given in Table 1 and each practice then links their own preferred test names to these groups. If one of the triggers shown in Table 1 is entered into the EMR, Winmed sends a Dynamic Data Exchange call to BPP. Once triggered, BPP collects symptom data via two data collection forms for sore throat treatment, or a single form for UTI. A GP, nurse or other member of practice staff can choose not to complete the form by clicking ‘Cancel’. This may be necessary if the trigger ICPC, ATC or laboratory test does not, in fact, concern a sore throat or UTI patient. Completing a form will generate on-screen interventions that are tailored to the patient’s symptoms and which follow the evidence-based guideline. An example intervention, and the symptoms that led to it, is shown in Figure 1 (the actual text of the intervention is in Norwegian).

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Table 1 Diagnosis, prescription and laboratory 'triggers' for the sore throat and UTI guideline system Diagnosis ICPC code

Prescription ATC

Laboratory tests

Sore throat

UTI

Sore throat

UTI

Sore throat

UTI

R21 R22 R72 R74 R76

U01 U02 U05 U06 U07 U13 U27 U29 U35 U71 U98

J01A J01C J01D A01 J01E E J01F

G04A B G04A C J01C A08 J01E A01 J01E C20 J01C A J01D A01 J01E J01M

Rapid strep. test Throat culture CRP Mononucleosis test White count Differential Other sore throat test

Rapid dipstick Urine microscopy Uricult Urine culture Other test for UTI

Note: Sore throat patients must be over three years old. UTI patients must be women between the age of 16 and 55 and not pregnant.

The data collected via these forms are stored in a database separate from the EMR, but practice staff can copy and paste a summary of them into the EMR. Online patient information is also available, which staff can print out and hand to the patient. All collected data are transferred to the HSRU on floppy disk because Norwegian general practices are currently not allowed to link their EMRs to the Internet.

Evaluation The most important question regarding BPP is: does it change practice? We have used a randomized control trial using a balanced block design with around 120 GP practices (80 per cent power to detect a 15 per cent change in laboratory test, antibiotic use and telephone consultations at the 5 per cent level of significance). Each practice uses the intervention system for one condition (UTI or sore throat) and is a control for the other. Although it is impossible to blind the practices to the intervention, the balanced block design reduces bias caused by staff behaving differently because they know they are in a study. Randomization is by practice. The BPP software is sent out in two versions. The first version of the software does not provide intervention information, it passively collects data for both sore throat and UTI Is the woman pregnant? Fever over 38.5°C? Pain in the sides?

4 Reduced general ‘state of health’? 4

Painful urination? Frequent urination or increased feeling that she needs to urinate? Problems for less than three days? Blood visible in the urine? Treated for bladder catarrh earlier?

Intervention • Possible kidney infection • Ask the woman to take a urine sample at home - send this for bacteriological test • Give information on the correct way to take collect a sample

OK

Note that a tick means ‘Yes’

Note that a tick means ‘Yes’

Fig. 1 An example of an intervention with the UTI Best Possible Practice system

patients. These data are sent to HSRU after two months of using the software. The ‘registration’ database is now complete. The software also extracts baseline data on the previous two years’ prevalence of sore throat and UTI at each practice. During May 2000, HSRU ran two training courses in Oslo and one in Stavanger. The guidelines were published in the Journal of the Norwegian Medical Association [16][17] and all practices were encouraged to discuss the guidelines and their current practice. The second version of BPP was sent out in September 2000 and contained intervention software for either sore throat or UTI. Practices will again send data to HSRU on floppy and we expect that our intervention database will be complete in January 2001. These data will be used to test the six hypotheses in Box 1. In addition to the data collected via the BPP software, the perceptions of practice staff have been assessed via a questionnaire study.

DESIGN OF THE QTOOLS SYSTEM QTools builds on the experience gained through BPP. An overview of the QTools system is shown in Figure 2. The software is being developed in three phases by HSRU and Mediata AS. The communication module will be used by all three phases and uses a collection of ‘dictionaries’ to act as interpreters for different EMRs. Each dictionary will contain a list of locations (for example, where to find the diagnosis code in the EMR) and format information (how the diagnosis code is stored). QTools will have four core tools: an extraction tool, a patient educational materials tool,



Box 1 The six hypotheses for the sore throat and UTI guideline system Sore throat intervention system will lead to: 1. Reduced use of antibiotics 2. Reduced use of laboratory tests 3. Increased use of telephone consultations UTI intervention system will lead to: 1. Unchanged use of antibiotics 2. Reduced use of laboratory tests 3. Increased use of telephone consultations

a report generator and the QTools Tool Builder. The last two of these can be used to create custom tools. The extraction tool will allow the user to extract data from the EMR and export it to a new file for analysis in spreadsheet and statistical packages. This is part of the first phase of QTools development and the current user interface is shown in Figure 3. The user can extract a wide range of fields within the EMR and use selection criteria (such as age, diagnosis and prescription) to extract data on a subset of patients. Two export formats are currently supported (ASCII and Microsoft Access) and three levels of anonymization The fields available for extraction will vary depending on the security clearance of the user. The GP, for example, will have full clearance, a researcher or administrator will not. The patient educational materials tool is the second phase of QTools development and will allow the user to link diagnosis codes to stored patient education materials; these can then be printed out and given to the patient or

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made available online. The materials themselves can be created by either the user or a research/patient organization using a standard word processor. The report generator will be a flexible tool that allows the user to interrogate the data stored in the EMR and then produce a report of the results to the screen, printer or a file. The user will create a report ‘template’ by selecting fields from the EMR, setting limits on these fields if desired and doing basic statistics on other selected fields. Mediata AS is developing this tool independently of the HSRU. The most sophisticated core tool is the QTools Tool Builder. This will be the third phase of QTools. It will allow the user to create a wide range of data collection forms, guideline implementations, reminder systems and decision support tools. The user will create a template and can then choose to, for example: ● ● ● ● ●

Define new data fields to collect data not available in the EMR. Attach error and validity checking to new fields. Include fields from the EMR. Define a mechanism that triggers the user’s template. Link decision trees to the template. These could be used to implement guidelines or reminders that are triggered by defined combinations of responses.

By saving templates the user is able to build up a library of custom QTools modules.

Fig. 3 The current QTools extraction tool user interface

DATA AND RESULTS Best possible practice Fig. 2 Overview of the QTools system

Figure 4 shows baseline prevalence data for sore throat and UTI diagnoses for the two-


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year period prior to our study. The sore throat and UTI data include more than 70 000 and 50 000 patients, respectively. These two graphs show aggregated data for 71 practices but it is also possible to look at individual practices and GPs. The project is ongoing. Prospective data are available for 395 sorethroat patients and 82 UTI patients. The use of laboratory tests and antibiotics are particularly interesting for the sore throat study and Figure 5 shows use of streptococcus tests with these patients and the percentage of positive results. Figure 6 shows the percentage of sorethroat patients receiving antibiotics and the percentage of these patients who also tested positive to streptococcus. The median length of a course of antibiotics for the 71 patients for whom course data are available is eight days with a 95 per cent confidence interval of seven to ten days (see Altman for confidence limit calculation, [18]). Telephone consultations account for 22 of the 395 patient contacts (6 per cent). The UTI data are more limited at present but Figure 7 gives an indication of antibiotic use among these patients. Of the 82 UTI patients, 58 (71 per cent) were prescribed antibiotics. The median course length (data

from 49 patients) is five days with a 95 per cent confidence interval of five to seven days. The rapid dipstick urine test is the most frequently used test and these account for 52/77 (68 per cent) of the tests taken in our current sample. Finally, telephone consultations account for five of the 82 patient contacts (6 per cent).

The QTools system The first phase of QTools – the communication module and the extraction tool – is still under development and only test data generated from 150 ‘virtual patients’ are available. A number of pilot projects involving the QTools data extraction tool are planned and these started in October 2000. These will primarily be aimed at using the extraction tool to assess the completeness (in other words, are data missing?) and accuracy (are the data correct?) of data in the EMR, along lines suggested by Hogan and Wagner [19]. However, we will also use the tool to get a ‘snapshot’ of the current prevalence and treatment of common conditions such as diabetes, hypertension, elevated cholesterol and clinical preventive services. For hypertension, elevated cholesterol and clinical

ICPC diagnosis code

Fig. 4 Baseline prevalence data for sore throat and UTI over a twoyear period

Fig. 5 Use of streptococcus tests with patients having a sore throat

ICPC diagnosis code

ICPC diagnosis code

Fig. 6 Use of antibiotics with patients having a sore throat

Fig. 7 Percentage of UTI patients receiving antibiotics. Diagnosis code U98 has been recorded just once and this patient received antibiotics. Codes U05, U06, U07, U13, U27 and U35 have not yet been recorded



preventive services we will also compare current practice against a ‘best practice’ guideline that is being developed in a parallel HSRU project. Data from these studies are expected to be available in early 2001.

DISCUSSION The use of EMRs in primary care has evolved rapidly in Norway. However, relatively little has been done to take advantage of their potential benefits for quality improvement, research and administration [20]. The two projects described here are, we believe, useful contributions to EMR-based quality improvement work in Norway. It is too early to discuss the data collected by BPP, except to state that they highlight exciting possibilities provided by systems linked to EMRs. However, having now installed the guideline software in around 120 GP practices, some general points may be helpful to others considering similar systems: 1. First and foremost, Norwegian general practices have so far been very positive towards the system. The most useful information is that which is delivered ‘just in time’ and a guideline linked to the patient’s EMR is able to do this. 2. A guideline must be based on a systematic overview of the best evidence available so that the content of the guideline does not become a major implementation issue. 3. The system should be simple to use and take a very short time to complete. 4. The software needs to be thoroughly tested before being sent out to practices, preferably by following a formal test protocol designed to generate fault situations. 5. The Windows operating system is not a single operating system. Windows comes in at least five versions and it is possible to find several versions running on the same network. Most of our technical problems have been related to mixed Windows networks. The test protocol should be tested with as many versions/combinations of Windows as possible. 6. Technical support is essential. At least 50 per cent of the practices involved in the guideline study have called our technical support at least once. Their questions were not always related to technical problems. Many GPs wanted to be talked through the installation process, others just wanted to double check something. If support is not available during working hours many practices will drop out

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because staff do not have the time to try and figure things out on their own. 7. The software is not a panacea. Two of the barriers to implementation of the sore throat and UTI guidelines, changes to fee scheduling and telephone routines, are not dependent on the software. Regular telephone, newsletter and in some case personal contact is essential to ensure that practices stay with the project.

CONCLUSION EMRs are a rich source of data in primary healthcare and they offer many possibilities for quality improvement systems tailored to individual patients. Although experience makes these systems easier to develop, distribute and support, working with hundreds of networked systems spread across a country will always be a challenge. The rewards, however, are potentially great and such systems have the potential to chart and change practice at the local, regional and national levels.

Funding Support for these projects has been provided by The Norwegian Medical Association, The Research Council of Norway and the Norwegian National Institute of Public Health. First published in the Proceedings of SHIMR 2000, the Fifth International Symposium on Health Information Management Research, Sheffield, June 2000.

REFERENCES [1] World Health Organization (1998) Health21: health for all in the 21st century. European Health for All series no. 6. [2] Oslo: Ministry of Health and Social Affairs og Statens helsetilsyn (1995) Nasjonal strategi for kvalitetsutvikling i helsetjenesten. IK-2482 [National strategy for quality improvement in healthcare]. [3] Oslo: Statens helsetilsyn (1997) Nasjonal strategi for kvalitetsutvikling i helsetjenesten. [National strategy for quality improvement in healthcare]. [4] Grimsmo A. Helsedataprosjektet. Report on the Helsedata project for the Information Technology Committee for Primary Medicine, Norway, 1998. [5] Oxman A D, Thomson M A, Davis D A, Haines RB. No magic bullets: a systematic review of 102 trials of interventions to help health care professionals deliver services more effectively or efficiently. Can. Med. Assoc. J. 1995; 153: 1423-31. [6] Sullivan F, Mitchell E. Has general practitioner computing made a difference to patient care? A systematic review of published reports. BMJ 1995; 311: 848-52. [7] Balas E A, Austin S M, Mitchel J A, Ewigman B G, Bopp K D, Brown G D. The clinical value of computerized information services: a review of 98 randomized clinical trials. Arch. Fam. Med. 1996; 5: 271-78.


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Health Informatics Journal [8] Shea S, DuMouchel W, Bahamonde L. A meta-analysis of 16 randomised controlled trials to evaluate computer-based clinical reminder systems for preventive care in the ambulatory setting. JAMIA 1996; 3: 399-409. [9] Hunt D L, Haynes B, Hanna S E, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998; 280: 1339-46. [10] Woolf S H, Grol R, Hutchinson A, Eccles M, Grimshaw J. Potential benefits, limitations, and harms of clinical guidelines. BMJ 1999; 318: 527-30. [11] Grimshaw J M, Russell I T. Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet 1993; 342: 1317-22. [12] Bero L A, Grilli R, Grimshaw J M, Harvey E, Oxman A D, Thomson M A. Closing the gap between research and practice: an overview of systematic reviews of interventions to promote the implementation of research findings. The Cochrane Effective Practice and Organization of Care Review Group. BMJ 1998; 317: 465-68. [13] NHS Centre for Reviews and Dissemination, University of York. Getting evidence into practice. Effective Health Care 1999; 5: 1-16. [14] Oxman A, Flottorp S. An overview of strategies to pro-

mote implementation of evidence based health care. In: Silagy C, Haines A, (Eds.) Evidence Based Practice in Primary Care. London: BMJ Books; 1998, pp. 91-109. [15] Granados A, Jonsson E, Banta H D, Bero L, Bonair A, Cochet C, Freemantle N, Grilli R, Grimshaw J, Harvey E, et al. EUR-ASSESS Project Subgroup Report on Dissemination and Impact. Int J Technol Assess Health Care 1997; 13: 220-86. [16] Flottorp S, Oxman AD, Cooper JG, Hjortdahl P, Sandberg S and Vorland LH. Retningslinjer for diagnostikk og behandling av akutte vannlatingsplayer hos kvinner (Guidelines for the diagnosis and treatment of sore throat.) Tidsskr. Nor. Laegeforen 2000; 120: 1748-53. [17] Flottorp S, Oxman AD, Cooper JG, Hjortdahl P, Sandberg S and Vorland LH. Retningslinjer for diagnostikk og behandling av sår hals (Guidelines for the diagnosis and treatment of urinary tract infections in women.) Tidsskr. Nor. Laegeforen 2000; 120: 1754-60. [18] Altman D G. Practical statistics for medical research. London: CRC Press; 1991. [19] Hogan W, Wagner M. Accuracy of data in computer-based patient records. JAMIA 1997; 4: 342-55. [20] Hofmann B, Torstad J. EDB-basert journalsystem i primærhelsetjenesten. [IT-based medical record systems in primary healthcare] Tidsskr. Nor. Lægeforen 1996; 116: 1470-73.
