The experience of physicians in pharmacogenomic clinical decision support within eight German university hospitals.

AIM: The aim of this study was to assess the physicians' attitude, their knowledge and their experience in pharmacogenomic clinical decision support in German hospitals. MATERIALS & METHODS: We conducted an online survey to address physicians of 13 different medical specialties across eight German university hospitals. In total, 564 returned questionnaires were analyzed. RESULTS: The remaining knowledge gap, the uncertainty of test reimbursement and the physicians' lack of awareness of existing pharmacogenomic clinical decision support systems (CDSS) are the major barriers for implementing pharmacogenomic CDSS into German hospitals. Furthermore, pharmacogenomic CDSS are most effective in the form of real-time decision support for internists. CONCLUSION: Physicians in German hospitals require additional education of both genetics and pharmacogenomics. They need to be provided with access to relevant pharmacogenomic CDSS.

Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development.

BACKGROUND: Pharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS. METHODS: We searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified. RESULTS: Alerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient's genotype. CONCLUSIONS: Within the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who are suitable for preemptive genotyping.

Comparison of estimated medical costs among patients who are defined as having rheumatoid arthritis using three different standards.

Accurate estimation of medical care costs raises a host of challenging issues. We examined whether pure administrative claims data without clinical validation of diagnosis allow reasonable estimation of disease-related costs in rheumatoid arthritis (RA). Three patient groups were examined: patients with clinically confirmed RA (group A, n=338), patients with likely RA (administrative claims data reported the diagnosis of RA and patients were treated with disease modifying antirheumatic drugs, DMARDs; group B, n=303), and patients with possible RA (same as group B but patients had no DMARD treatment; group C, n=685). The payer's perspective was taken for this analysis. Only direct costs were included in the analysis. Cost data and data for several covariates were obtained from a major German statutory health insurance plan, the AOK Niedersachsen. A patient-per-patient microcosting approach was performed. A repeated measures, fixed effects model was applied to examine differences between the three study groups. Mean+/-SEM annual RA-related direct costs were euro 2,017+/-183 per patient-year in group A, euro 1,763+/-192 in group B, and euro 805+/-58 in group C. Outpatient (inpatient) costs were euro 1,636 (328) in group A, euro 1344 (340) in group B, and euro 546 (136) in group C. DMARD costs were by far the single most important cost driver in groups A and B. The difference in total RA-related direct cost between groups A and B was not significant whereas the differences between groups A and C (group B and C respectively), were significant. Pure administrative claims data allowed for an accurate estimate of disease-related costs in RA patients that received DMARD therapy. However, the high number of patients for whom administrative claims data listed the diagnosis RA, but no DMARD treatment was given poses a challenge for further research.

[Quality management of treatment of rheumatoid arthritis in a rheumatological setting]. / Das Qualitätsmanagement der Therapie der chronischen Polyarthritis in der rheumatologischen Praxis.

Rheumatoid arthritis is a chronic inflammatory rheumatic disease affecting about 1% of the general population world-wide. It is characterised by severe pain and a reduction of functional capacity leading to a reduced quality of life. The initiation of an early, effective, continuous and long-term treatment is essential for preventing or delaying progression of disease as long as possible. The implementation of a comprehensive and structured quality management program including both general practitioners and specialists in rheumatology will help to support structural, procedural and outcome quality based on special indices that can be used for benchmarking. The Swiss Clinical Quality Management (SCQM) and the regional model project of the Regional Co-operative Rheumatology Centre in Hanover, Germany (Regionales Kooperatives Rheumazentrum Hannover e.V.) are two examples for total quality management (TQM) of inflammatory rheumatic diseases.