Ik Bindoff

DOCUMENT: a system for classifying drug-related problems in community pharmacy

Background Drug-related problems (DRPs) are a major burden on the Australian healthcare system. Community pharmacists are in an ideal position to detect, prevent, and resolve these DRPs. Objective To develop and validate an easy-to-use documentation system for pharmacists to classify and record DRPs, and to investigate the nature and frequency of clinical interventions undertaken by Australian community pharmacists to prevent or resolve them. Setting Australian community pharmacies. Method The DOCUMENT classification system was developed, validated and refined during two pilot studies. The system was then incorporated into software installed in 185 Australian pharmacies to record DRPs and clinical interventions undertaken by pharmacists during a 12-week trial. Main outcome measure The number and nature of DRPs detected within Australian community pharmacies. Results A total of 5,948 DRPs and clinical interventions were documented from 2,013,923 prescriptions dispensed during the trial (intervention frequency 0.3%). Interventions were commonly related to Drug selection problems (30.7%) or Educational issues (23.7%). Pharmacists made an average of 1.6 recommendations per intervention, commonly relating to A change in therapy (40.1%) and Provision of information (34.7%). Almost half of interventions (42.6%) were classified by recording pharmacists as being at a higher level of clinical significance. Conclusion The DOCUMENT system provided pharmacists with a useful and easy-to-use tool for recording DRPs and clinical interventions. Results from the trial have provided a better understanding of the frequency and nature of clinical interventions performed in Australian community pharmacies, and lead to a national implementation of the system.

Development of an intelligent decision support system for medication review.

Background and objective:  The aim was to develop and evaluate a pilot version of a knowledge‐based system that can identify existing and potential medication‐related problems from patient information. This intelligent system could directly support pharmacists and other health professionals providing medication reviews.

A Comparison of Prescribing Criteria When Applied to Older Community-Based Patients

BackgroundStudies have compared prescribing criteria for older people in general terms, reporting the findings without true side-by-side comparisons of the frequency and type of potential drug-related problems (DRPs).ObjectiveThe aim of this study was to compare the frequency and type of DRPs identified by several prescribing criteria. Additionally, original pharmacist DRP findings were compared with DRPs identified using the prescribing criteria.MethodThree prescribing criteria were automated: Beers 2012 (Beers), Screening Tool of Older Person’s Prescriptions/Screening Tool to Alert doctors to Right Treatment (STOPP/START), and Prescribing Indicators in Elderly Australians (PIEA). The criteria were applied to medication reviews of 570 ambulatory older Australian patients. DRPs identified by each set of criteria were recorded. Each DRP was assigned a descriptive term which highlighted mainly drug classes and/or diagnoses to provide a meaningful common language for comparison between recorded DRPs. Descriptive terms were used to compare the frequency and type of DRP identified by each set of criteria, as well as against original pharmacists’ findings.ResultsBeers identified 399 DRPs via 21 different descriptive terms, STOPP/START identified 1,032 DRPs via 42 terms, and PIEA identified 1,492 DRPs via 33 terms. The various types of DRPs identified by all of the three prescribing criteria were represented by 53 different terms. When constrained to the same 53 different terms, pharmacists identified 862 DRPs.ConclusionEach set of criteria displayed relevance through mutual agreement of known high-risk medication classes in older people. The number and scope of DRPs identified by pharmacists was best represented by STOPP/START. The application of STOPP/START may be further augmented with relevant criteria from PIEA and Beers.

Drug-Related Problems Detected in Australian Community Pharmacies: The PROMISe Trial

Background Drug-related problems (DRPs) are a major burden on health care systems. Community pharmacists are ideally placed to detect, prevent, and resolve these DRPs. Objective: To determine the number and nature of DRPs detected and clinical interventions performed by Australian community pharmacists, using an electronic system. Methods: An electronic documentation system was designed and integrated into the existing dispensing software of 186 pharmacies to allow pharmacists to record details about the clinical interventions they performed to prevent or resolve DRPs. Participating pharmacies were randomly allocated to 3 groups: group 1 had documentation software, group 2 had documentation software plus a timed reminder to document interventions, and group 3 had documentation software, a timed reminder, and an electronic decision support prompt. Pharmacists classified DRPs, entered recommendations they made, and estimated the clinical significance of the intervention. An observational substudy that included pharmacies without any documentation software was completed to verify intervention rates. Results: Over 12 weeks, 531 participating pharmacists recorded 6230 clinical interventions from 2,013,923 prescriptions, with a median intervention rate of 0.23% of prescriptions. No significant differences were seen between the 3 groups that used documentation software; as expected, however, the pharmacies that used this software had a significantly higher documentation rate compared to the pharmacies without documentation software. The most common interventions were related to drug selection problems (30.8%) and educational issues (24.4%). Recommendations were often related to a change in therapy (40.0%), and 41.6% of interventions were self-rated as highly significant. Drug groups most commonly subject to an intervention included antibiotics, glucocorticoids, nonsteroidal antiinflammatory drugs, and opioids. Conclusions: The documentation system allowed for the determination of the frequency and types of DRPs, as well as the recommendations made to resolve them In community pharmacy practice. Use of the software, including its electronic prompts, significantly increased the documentation of interventions by pharmacists.

Outcomes of a decision support prompt in community pharmacy dispensing software to promote step-down of proton pump inhibitor therapy

AIM To evaluate the effect of a computerized decision support prompt regarding high-dose proton pump inhibitor (PPI) therapy on prescribing and medication costs. METHODS A prompt activated on dispensing high-dose esomeprazole or pantoprazole was implemented in 73 of 185 pharmacies. Anonymized prescription data and a patient survey were used to determine changes in prescribing and associated medication costs. RESULTS The pharmacist-recorded PPI intervention rate per 100 high-dose PPI prescriptions was 1.67 for the PPI prompt group and 0.17 for the control group (P < 0.001). During the first 28 days of the trial, 196 interventions resulted in 34 instances of PPI step-down, with 28 of these occurring in PPI prompt pharmacies. Cost savings attributable to the prompt were AUD 7.98 (£4.95) per month per PPI prompt pharmacy compared with AUD 1.05 (£0.65) per control pharmacy. CONCLUSION The use of electronic decision support prompts in community pharmacy practice can promote the quality use of medicines.

The potential for intelligent decision support systems to improve the quality and consistency of medication reviews

What is known and Objective:  Drug‐related problems (DRPs) are of serious concern worldwide, particularly for the elderly who often take many medications simultaneously. Medication reviews have been demonstrated to improve medication usage, leading to reductions in DRPs and potential savings in healthcare costs. However, medication reviews are not always of a consistently high standard, and there is often room for improvement in the quality of their findings. Our aim was to produce computerized intelligent decision support software that can improve the consistency and quality of medication review reports, by helping to ensure that DRPs relevant to a patient are overlooked less frequently. A system that largely achieved this goal was previously published, but refinements have been made. This paper examines the results of both the earlier and newer systems.

Applying MCRDR to a multidisciplinary domain

This paper details updated results concerning an implementation of a Multiple Classification Ripple Down Rules (MCRDR) system which can be used to provide quality Decision Support Services to pharmacists practicing medication reviews (MRs), particularly for high risk patients. The system was trained on 126 genuine cases by an expert in the field; over the course of 19 hours the system had learned 268 rules and was considered to encompass over 80% of the domain. Furthermore, the system was found able to improve the quality and consistency of the medication review reports produced, as it was shown that there was a high incidence of missed classifications under normal conditions, which were repaired by the system automatically. However, shortcomings were identified including an inability to handle absent data, and shortcomings concerning standardization in the domain, proposals to solve these shortcomings are included.

A clinical knowledge measurement tool to assess the ability of community pharmacists to detect drug‐related problems

Introduction  Drug‐related problems (DRPs) are associated with significant morbidity and mortality, with most DRPs thought to be preventable. Community pharmacists can detect and either prevent or resolve many of these DRPs. A survey‐based clinical knowledge measurement tool was designed and validated to estimate a community pharmacists clinical knowledge and ability to detect and appropriately resolve DRPs.

Expert-Driven Knowledge Discovery

Knowledge discovery techniques find new knowledge about a domain by analysing existing domain knowledge and examples of domain data. These techniques typically involve using a human expert and automated software analysis (data mining). Often the human expertise is used initially to choose which data is processed, and then finally to determine which results are relevant. However studies have noted that some domains contain data stores too extensive and detailed, and existing knowledge too complex, for effective data selection or efficient data mining. A different approach is suggested which involves the human expert more pervasively, taking advantage of their expertise at each step, while using data mining techniques to assist in discovering data trends and in verifying the experts findings. Preliminary results suggest that the approach can be successfully applied to discover new knowledge in a complex domain, and reveal many potential areas for research and development.

Intelligent decision support for medication review

This paper examines an implementation of a Multiple Classification Ripple Down Rules system which can be used to provide quality Decision Support Services to pharmacists practicing medication reviews (MRs), particularly for high risk patients. The system was trained on 84 genuine cases by an expert in the field; over the course of 15 hours the system had learned 197 rules and was considered to encompass around 60% of the domain. Furthermore, the system was found able to improve the quality and consistency of the medication review reports produced, as it was shown that there was a high incidence of missed classifications under normal conditions, which were repaired by the system automatically.