Prescribing Data in General Practice Demonstration (PDGPD) project--a cluster randomised controlled trial of a quality improvement intervention to achieve better prescribing for chronic heart failure and hypertension.

BACKGROUND: Research literature consistently documents that scientifically based therapeutic recommendations are not always followed in the hospital or in the primary care setting. Currently, there is evidence that some general practitioners in Australia are not prescribing appropriately for patients diagnosed with 1) hypertension (HT) and 2) chronic heart failure (CHF). The objectives of this study were to improve general practitioner's drug treatment management of these patients through feedback on their own prescribing and small group discussions with peers and a trained group facilitator. The impact evaluation includes quantitative assessment of prescribing changes at 6, 9, 12 and 18 months after the intervention. METHODS: A pragmatic multi site cluster RCT began recruiting practices in October 2009 to evaluate the effects of a multi-faceted quality improvement (QI) intervention on prescribing practice among Australian general practitioners (GP) in relation to patients with CHF and HT. General practices were recruited nationally through General Practice Networks across Australia. Participating practices were randomly allocated to one of three groups: two groups received the QI intervention (the prescribing indicator feedback reports and small group discussion) with each group undertaking the clinical topics (CHF and HT) in reverse order to the other. The third group was waitlisted to receive the intervention 6 months later and acted as a "control" for the other two groups.De-identified data on practice, doctor and patient characteristics and their treatment for CHF and HT are extracted at six-monthly intervals before and after the intervention. Post-test comparisons will be conducted between the intervention and control arms using intention to treat analysis and models that account for clustering of practices in a Network and clustering of patients within practices and GPs. DISCUSSION: This paper describes the study protocol for a project that will contribute to the development of acceptable and sustainable methods to promote QI activities within routine general practice, enhance prescribing practices and improve patient outcomes in the context of CHF and HT. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR), Trial # 320870.

Identification of features of electronic prescribing systems to support quality and safety in primary care using a modified Delphi process.

BACKGROUND: Electronic prescribing is increasingly being used in primary care and in hospitals. Studies on the effects of e-prescribing systems have found evidence for both benefit and harm. The aim of this study was to identify features of e-prescribing software systems that support patient safety and quality of care and that are useful to the clinician and the patient, with a focus on improving the quality use of medicines. METHODS: Software features were identified by a literature review, key informants and an expert group. A modified Delphi process was used with a 12-member multidisciplinary expert group to reach consensus on the expected impact of the features in four domains: patient safety, quality of care, usefulness to the clinician and usefulness to the patient. The setting was electronic prescribing in general practice in Australia. RESULTS: A list of 114 software features was developed. Most of the features relate to the recording and use of patient data, the medication selection process, prescribing decision support, monitoring drug therapy and clinical reports. The expert group rated 78 of the features (68%) as likely to have a high positive impact in at least one domain, 36 features (32%) as medium impact, and none as low or negative impact. Twenty seven features were rated as high positive impact across 3 or 4 domains including patient safety and quality of care. Ten features were considered "aspirational" because of a lack of agreed standards and/or suitable knowledge bases. CONCLUSIONS: This study defines features of e-prescribing software systems that are expected to support safety and quality, especially in relation to prescribing and use of medicines in general practice. The features could be used to develop software standards, and could be adapted if necessary for use in other settings and countries.

An electronic prompt in dispensing software to promote clinical interventions by community pharmacists: a randomized controlled trial.

UNLABELLED: What is already known about this subject. Computerized prompts and reminders have been shown to be effective in changing the behaviour of health professionals in a variety of settings. There is little literature describing or evaluating electronic decision-support for pharmacists. What this study adds. An electronic prompt in dispensing software for a targeted clinical intervention has a significant effect on pharmacists' behaviour. A markedly increased rate of recording and performing the targeted clinical intervention was found. The effect of the prompt reduces markedly once the prompt is deactivated. AIM: To evaluate the effect of an electronic prompt in dispensing software on the frequency of clinical interventions recorded by community pharmacists. METHOD: An electronic decision-support prompt identifying patients for a targeted proactive clinical intervention was developed and implemented. Each time an oral antidiabetic agent was dispensed, a prompt was displayed reminding pharmacists to discuss the suitability of aspirin therapy in eligible patients with diabetes. The prompt was randomly assigned to 31 of 52 metropolitan pharmacies in Melbourne (Australia) for 6 weeks, with the remaining pharmacies as controls. RESULTS: One hundred and fifty pharmacists in 52 pharmacies recorded a total of 2396 clinical interventions at an intervention rate of 0.92 interventions per 100 patients [95% confidence interval (CI) 0.58, 1.23]. Pharmacists recorded a total of 201 target interventions related to aspirin therapy in diabetes at an intervention rate of 2.55 interventions per 100 diabetic patients (95% CI 0.85, 4.24). All of the targeted clinical interventions were recorded in the prompt arm; no targeted interventions were recorded in the control group. The effect of the prompt decreased over the study period and was not maintained after prompt deactivation. CONCLUSION: An electronic prompt significantly increased pharmacists' recording of the targeted clinical intervention in diabetic patients. An electronic decision-support prompt has significant potential to promote community pharmacists' contribution to the quality use of medicines.

Quality of drug interaction alerts in prescribing and dispensing software.

OBJECTIVE: To investigate the quality of drug interaction decision support in selected prescribing and dispensing software systems, and to compare this information with that found in a range of reference sources. DESIGN AND SETTING: A comparative study, conducted between June 2006 and February 2007, of the support provided for making decisions about 20 major and 20 minor drug interactions in six prescribing and three dispensing software systems used in primary care in Australia. Five electronic reference sources were evaluated for comparison. MAIN OUTCOME MEASURES: Sensitivity, specificity and quality of information; for major interactions: whether information on clinical effects, timeframe and pharmacological mechanism was included, whether management advice was helpful, and succinctness. RESULTS: Six of the nine software systems had a sensitivity rate > or = 90%, detecting most of the major interactions. Only 3/9 systems had a specificity rate of > or = 80%, with other systems providing inappropriate or unhelpful alerts for many minor interactions. Only 2/9 systems provided adequate information about clinical effects for more than half the major drug interactions, and 1/9 provided useful management advice for more than half of these. The reference sources had high sensitivity and in general provided more comprehensive clinical information than the software systems. CONCLUSIONS: Drug interaction decision support in commonly used prescribing and dispensing software has significant shortcomings.