The STRIPassistant: a Digital Tool to Optimize Polypharmacy

The Structured Tool to Reduce Inappropriate Prescribing (STRIP) is a method to perform a medication review. Part of this method is the pharmaceutical analysis that is digitalized into the STRIPassistant (demo: www.ephor.eu). The effectiveness and time-efficiency of the STRIPassistant will be presented

Usability aspects of medication-related decision support systems in the outpatient setting: A systematic literature review

In this study, we evaluated the usability aspects of medication-related clinical decision support systems in the outpatient setting. Articles published

Optimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM): cluster randomised controlled trial.

OBJECTIVE To examine the effect of optimising drug treatment on drug related hospital admissions in older adults with multimorbidity and polypharmacy admitted to hospital. DESIGN Cluster randomised controlled trial. SETTING 110 clusters of inpatient wards within university based hospitals in four European countries (Switzerland, Netherlands, Belgium, and Republic of Ireland) defined by attending hospital doctors. PARTICIPANTS 2008 older adults (≥70 years) with multimorbidity (≥3 chronic conditions) and polypharmacy (≥5 drugs used long term). INTERVENTION Clinical staff clusters were randomised to usual care or a structured pharmacotherapy optimisation intervention performed at the individual level jointly by a doctor and a pharmacist, with the support of a clinical decision software system deploying the screening tool of older person's prescriptions and screening tool to alert to the right treatment (STOPP/START) criteria to identify potentially inappropriate prescribing. MAIN OUTCOME MEASURE Primary outcome was first drug related hospital admission within 12 months. RESULTS 2008 older adults (median nine drugs) were randomised and enrolled in 54 intervention clusters (963 participants) and 56 control clusters (1045 participants) receiving usual care. In the intervention arm, 86.1% of participants (n=789) had inappropriate prescribing, with a mean of 2.75 (SD 2.24) STOPP/START recommendations for each participant. 62.2% (n=491) had ≥1 recommendation successfully implemented at two months, predominantly discontinuation of potentially inappropriate drugs. In the intervention group, 211 participants (21.9%) experienced a first drug related hospital admission compared with 234 (22.4%) in the control group. In the intention-to-treat analysis censored for death as competing event (n=375, 18.7%), the hazard ratio for first drug related hospital admission was 0.95 (95% confidence interval 0.77 to 1.17). In the per protocol analysis, the hazard ratio for a drug related hospital admission was 0.91 (0.69 to 1.19). The hazard ratio for first fall was 0.96 (0.79 to 1.15; 237 v 263 first falls) and for death was 0.90 (0.71 to 1.13; 172 v 203 deaths). CONCLUSIONS Inappropriate prescribing was common in older adults with multimorbidity and polypharmacy admitted to hospital and was reduced through an intervention to optimise pharmacotherapy, but without effect on drug related hospital admissions. Additional efforts are needed to identify pharmacotherapy optimisation interventions that reduce inappropriate prescribing and improve patient outcomes. TRIAL REGISTRATION ClinicalTrials.gov NCT02986425

Risk identification-based association rule mining for supply chain big data

© Springer Nature Switzerland AG 2018. Since most supply chain processes include operational risks, the effectiveness of a corporation’s success depends mainly on identifying, analyzing and managing them. Currently, supply chain risk management (SCRM) is an active research field for enhancing a corporation’s efficiency. Although several techniques have been proposed, they still face a big challenge as they analyze only internal risk events from big data collected from the logistics of supply chain systems. In this paper, we analyze features that can identify risk labels in a supply chain. We propose defining risk events based on the association rule mining (ARM) technique that can categorize those in a supply chain based on a company’s historical data. The empirical results we obtained using data collected from an Aluminum company showed that this technique can efficiently generate and predict the optimal features of each risk label with a higher than 96.5% accuracy

Conversion of STOPP/START version 2 into coded algorithms for software implementation: A multidisciplinary consensus procedure

Background: The rapid digitalization of medical practice has attracted growing interest in developing software applications for clinical guidelines and explicit screening tools to detect potentially inappropriate prescribing, such as STOPP/START criteria. The aim of the current study was to develop and provide logically unambiguous algorithms of STOPP/START criteria version 2, encoded with international disease and medication classification codes, to facilitate the development of software applications for multiple purposes. Methods: A four round multidisciplinary consensus and validation procedure was conducted to develop implementable coded algorithms for software applications of STOPP/START criteria version 2, based on ICD, ICPC, LOINC and ATC classification databases. Results: Consensus was reached for all 34 START criteria and 76 out of 80 STOPP criteria. The resulting 110 algorithms, modeled as inference rules in decision tables, are provided as supplementary data. Conclusion: This is the first study providing implementable algorithms for software applications based on STOPP/START version 2, validated in a computer decision support system. These algorithms could serve as a template for applying STOPP/START criteria version 2 to any software application, allowing for adaptations of the included ICD, ICPC and ATC codes and changing the cut-off levels for laboratory measurements to match local guidelines or clinical expertise

'Optimising PharmacoTherapy In the multimorbid elderly in primary CAre' (OPTICA) to improve medication appropriateness: study protocol of a cluster randomised controlled trial.

INTRODUCTION Multimorbidity and polypharmacy are major risk factors for potentially inappropriate prescribing (eg, overprescribing and underprescribing), and systematic medication reviews are complex and time consuming. In this trial, the investigators aim to determine if a systematic software-based medication review improves medication appropriateness more than standard care in older, multimorbid patients with polypharmacy. METHODS AND ANALYSIS Optimising PharmacoTherapy In the multimorbid elderly in primary CAre is a cluster randomised controlled trial that will include outpatients from the Swiss primary care setting, aged ≥65 years with ≥three chronic medical conditions and concurrent use of ≥five chronic medications. Patients treated by the same general practitioner (GP) constitute a cluster, and clusters are randomised 1:1 to either a standard care sham intervention, in which the GP discusses with the patient if the medication list is complete, or a systematic medication review intervention based on the use of the 'Systematic Tool to Reduce Inappropriate Prescribing'-Assistant (STRIPA). STRIPA is a web-based clinical decision support system that helps customise medication reviews. It is based on the validated 'Screening Tool of Older Person's Prescriptions' (STOPP) and 'Screening Tool to Alert doctors to Right Treatment' (START) criteria to detect potentially inappropriate prescribing. The trial's follow-up period is 12 months. Outcomes will be assessed at baseline, 6 and 12 months. The primary endpoint is medication appropriateness, as measured jointly by the change in the Medication Appropriateness Index (MAI) and Assessment of Underutilisation (AOU). Secondary endpoints include the degree of polypharmacy, overprescribing and underprescribing, the number of falls and fractures, quality of life, the amount of formal and informal care received by patients, survival, patients' quality adjusted life years, patients' medical costs, cost-effectiveness of the intervention, percentage of recommendations accepted by GPs, percentage of recommendation rejected by GPs and patients' willingness to have medications deprescribed. ETHICS AND DISSEMINATION The ethics committee of the canton of Bern in Switzerland approved the trial protocol. The results of this trial will be published in a peer-reviewed journal. MAIN FUNDING Swiss National Science Foundation, National Research Programme (NRP 74) 'Smarter Healthcare'. TRIAL REGISTRATION NUMBERS Clinicaltrials.gov (NCT03724539), KOFAM (Swiss national portal) (SNCTP000003060), Universal Trial Number (U1111-1226-8013)