International Journal of Medical Informatics
Patient-specific evidence-based care recommendations for diabetes mellitus: development and initial clinic experience with a computerized decision support system
Introduction
Providing patient-specific reminders at the time of a clinical encounter has been shown to improve compliance with recommendations 1, 2, 3, 4. Benefits have been observed for preventive health care interventions, cancer prevention, vaccinations, and many other aspects of medical care. This is encouraging because poor adherence with interventions of proven effectiveness, a factor that contributes to increased patient morbidity and mortality, has been observed for many conditions. For example, in patients with acute myocardial infarction, a recent study noted that while 81% of all patients received aspirin and 72% received thrombolytic therapy when appropriate, these values fell to 74 and 49%, respectively, among older patients [5]. A similar situation exists with many chronic diseases. While patients with congestive heart failure have been shown to benefit from angiotensin converting enzyme inhibitors, it is estimated that only a third of patients with this condition are prescribed these medications [6]. Diabetes is another example of a chronic condition in which there is evidence of suboptimal compliance with therapeutic interventions that have been shown to be beneficial [7]. The reasons for this include lack of awareness of new treatments by physicians, delayed recognition of complications, and poor patient adherence [8]. The situation is compounded by the fact that many therapeutic or screening interventions are only appropriate for specific subgroups of patients, based on various patient characteristics.
Computer-based reminder systems could help improve the quality of care by linking patient-specific information from an electronic patient record with an underlying set of rules based on high quality medical evidence concerning appropriate laboratory investigations, medications or other efficacious treatments. Indeed, Lobach and Hammond have shown that a computer-based reminder system improved diabetes care in a primary care setting [9]. In their study, compliance with care recommendations increased from 16% in the control group to 32% in the intervention arm.
While this is encouraging, there are a number of reasons why such reminder systems are not more widespread. Not least among these is that computerized decision support systems often depend on a comprehensive electronic medical record that includes information on each patient’s past medical history, current medical problems, current medications, and laboratory testing and results. While such electronic records are likely to become ubiquitous in due course, they are not in widespread use at present [10]. Additionally, even when such systems become more uniformly available, issues relating to keeping them up-to-date with patient-specific medical information and patient behaviours pertinent to chronic disease management will remain [11].
To address some of these issues and concerns, we have developed an automated self-administered questionnaire for adult patients with diabetes that assesses numerous patient-specific characteristics, without the need for linkage to an electronic medical record and then links the patient’s responses to specific recommendations for their care based on current evidence regarding optimal screening and therapy.
This paper reviews the development, preliminary evaluation and initial experience with using the automated questionnaire in a diabetes clinic.
Section snippets
Development
The initial development of the automated diabetes questionnaire has previously been described [12]. This involved creating a questionnaire and subsequently translating the paper questionnaire into an automated, computer version. Topics for the questionnaire were identified using numerous sources. These included a systematic selection of original and review articles in over 100 clinical journals in conjunction with the production of the evidence-based journals, ACP Journal Club and
Initial evaluation
To assess acceptance of the automated questionnaire, patients were initially asked to complete it using a hand-held lap-top device known as Health-Quiz [17]. This device had a small display screen and three buttons for inputting responses (yes, no, unsure). Patients found the questionnaire easy to complete and indicated few concerns regarding the content, terminology, or flow of the questionnaire.
Subsequently, a more formal evaluation of patient responses to the questionnaire was undertaken
Clinic experience
More recently, patients have been asked to complete the questionnaire before their physician or nurse appointments in the diabetes clinic at McMaster University. This has permitted us to assess patient satisfaction with the questionnaire and printouts, as well as health care practitioner satisfaction with the feedback forms that list the patient-specific recommendations. Consecutive patients scheduled to see a specific nurse or physician during a morning or afternoon clinic were invited by
Discussion
Computer-based reminder systems can improve the rate of adherence with appropriate interventions. Before such systems can be implemented in many settings, however, rapid, convenient and inexpensive approaches to acquiring patient-specific information are essential. For practices with electronic medical records already in place, linkage with pharmacies and laboratories will permit certain information to be acquired and updated on an on-going basis and thus address some of these requirements.
Acknowledgements
This work was supported by a grant from HEALNet, a Networks of Centres of Excellence Program, funded by the Medical Research Council of Canada and the Social Sciences and Humanities Research Council of Canada.
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