Estimated Costs of Drug-Related Problems Prevented by Pharmacist Prescription Reviews Among Hospitalized Internal Medicine Patients.

BACKGROUND AND OBJECTIVES: Data are lacking on the estimated costs of pharmacist prescription reviews (PPRs) for hospitalized internal medicine patients. This study investigates the estimated costs of drug-related problems (DRPs) prevented by PPRs among hospitalized internal medicine patients. METHODS: We reviewed all medication orders for patients at an academic teaching hospital in China for 2 years. DRPs were categorized using the Pharmaceutical Care Network Europe classification. The severity of the potential harm of DRPs was assessed by the Harm Associated with Medication Error Classification (HAMEC) tool. The estimated cost of PPRs was calculated. RESULTS: A total of 162426 medication orders for 4314 patients were reviewed, and 1338 DRPs were identified by pharmacists who spent 2230 hours performing PPRs. Among the 1080 DRPs that were prospectively intervened upon, 703 were resolved. The HAMEC tool showed that 47.1% of DRPs were assessed as level 2, 30.4% as level 3, 20.6% as level 1, and 0.6% carried a life-threatening risk. Pharmacist interventions contributed to the prevention of DRP errors and a reduction of $339 139.44. This resulted in a mean cost saving of $482.42 per patient at an input cost of $21 495.06 over the 2 years. The benefit-cost ratio was 15.8. CONCLUSION: PPRs are beneficial for detecting potential DRPs and creating potential cost savings among hospitalized internal medicine patients.

Bearing Performance Degradation Assessment Based on SC-RMI and Student's t-HMM.

Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student's t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student's t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method.