Potential drug-drug interactions in pediatric outpatient prescriptions for newborns and infants.

OBJECTIVES: To surveyed the quantities, types, and related information of potential drug-drug interactions (DDIs) and estimate the off-label use percentage of pediatric outpatient prescriptions for newborns and infants from the National Health Insurance Research Database (NHIRD) of Taiwan. BACKGROUND: Adverse drug reactions (ADR) may cause morbidity and mortality, potential drug-drug interactions (DDI) increase the probability of ADR. Research on ADR and DDI in infants is of particular urgency and importance but the related profiles in these individuals are not well known. METHODS: All prescriptions written by physicians in 2000 were analyzed to identify potential DDIs among drugs appearing on the same prescription sheet. RESULTS: Of a total of 150.6 million prescription sheets, with 669.5 million prescriptions registered in the NHIRD of Taiwan, six million (3.99%) prescription sheets were for 2.1 million infants with 19.4 million (2.85%) prescriptions. There were 672,020 potential DDIs in this category, accounting for 3.53% per prescription; an estimated one DDI in every three patients. The interactions between aspirin and aluminum/magnesium hydroxide were most common (4.42%). Of the most significant drug-drug interactions, the interaction of digoxin with furosemide ranked first (20.14%), followed by the interactions of cisapride with furosemide and erythromycin (6.02% and 4.85%, respectively). The interactions of acetaminophen and anti-cholinergic agents comprised most types of drug-drug interactions (6.62%). CONCLUSION: Although the prevalence rates of DDIs are low, life-threatening interactions may develop. Physicians must be reminded of the potential DDIs when prescribing medications for newborns and infants.

[Drug safeguards to ensure the ambulatory medication safety of elderly people].

Medication safety is a primary patient safety goal. Medication safety addresses a comprehensive process that includes prescription issuance, medication dispensing, administration, and patient consumption. The application of mobile communication technology to healthcare products and to the extension of medication safety beyond the hospital into daily life are important trends. The elderly are a high risk group for adverse drug events due to age and higher incidences of chronic disease, multiple medication use, and improper medication behavior. Drug safeguards delivered through mobile services can enhance the medication safety of elders. Such services provide an education, care giving, and response link among institutions, homes, and the community, making it easier to integrate medication safety technology into daily life.

Physicians' responses to computerized drug-drug interaction alerts for outpatients.

INTRODUCTION: Adverse drug reactions (ADR) increase morbidity and mortality; potential drug-drug interactions (DDI) increase the probability of ADR. Studies have proven that computerized drug-interaction alert systems (DIAS) might reduce medication errors and potential adverse events. However, the relatively high override rates obscure the benefits of alert systems, which result in barriers for availability. It is important to understand the frequency at which physicians override DIAS and the reasons for overriding reminders. METHOD: All the DDI records of outpatient prescriptions from a tertiary university hospital from 2005 and 2006 detections by the DIAS are included in the study. The DIAS is a JAVA language software that was integrated into the computerized physician order entry system. The alert window is displayed when DDIs occur during order entries, and physicians choose the appropriate action according to the DDI alerts. There are seven response choices are obligated in representing overriding and acceptance: (1) necessary order and override; (2) expected DDI and override; (3) expected DDI with modified dosage and override; (4) no DDI and override; (5) too busy to respond and override; (6) unaware of the DDI and accept; and (7) unexpected DDI and accept. The responses were collected for analysis. RESULTS: A total of 11,084 DDI alerts of 1,243,464 outpatient prescriptions were present, 0.89% of all computerized prescriptions. The overall rate for accepting was 8.5%, but most of the alerts were overridden (91.5%). Physicians of family medicine and gynecology-obstetrics were more willing to accept the alerts with acceptance rates of 20.8% and 20.0% respectively (p<0.001). Information regarding the recognition of DDIs indicated that 82.0% of the DDIs were aware by physicians, 15.9% of DDIs were unaware by physicians, and 2.1% of alerts were ignored. The percentage of total alerts declined from 1.12% to 0.79% during 24 months' study period, and total overridden alerts also declined (from 1.04% to 0.73%). CONCLUSION: We explored the physicians' behavior by analyzing responses to the DDI alerts. Although the override rate is still high, the reasons why physicians may override DDI alerts were well analyzed and most DDI were recognized by physicians. Nonetheless, the trend of total overrides is in decline, which indicates a learning curve effect from exposure to DIAS. By analyzing the computerized responses provided by physicians, efforts should be made to improve the efficiency of the DIAS, and pharmacists, as well as patient safety staffs, can catch physicians' appropriate reasons for overriding DDI alerts, improving patient safety.

Predicting hospital-acquired infections by scoring system with simple parameters.

BACKGROUND: Hospital-acquired infections (HAI) are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR) and validated by Artificial Neural Networks (ANN) simultaneously. METHODOLOGY/PRINCIPAL FINDINGS: A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR) and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507) to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447). The scoring system also performed extremely well in the internal (AUC: 0.965) and external (AUC: 0.871) validations. CONCLUSIONS: We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction outcome that can be utilized in different clinical settings.

Cross-domain probabilistic inference in a clinical decision support system: examples for dermatology and rheumatology.

INTRODUCTION: Maintaining a large diagnostic knowledge base (KB) is a demanding task for any person or organization. Future clinical decision support system (CDSS) may rely on multiple, smaller and more focused KBs developed and maintained at different locations that work together seamlessly. A cross-domain inference tool has great clinical import and utility. METHODS: We developed a modified multi-membership Bayes formulation to facilitate the cross-domain probabilistic inferencing among KBs with overlapping diseases. Two KBs developed for evaluation were non-infectious generalized blistering diseases (GBD) and autoimmune diseases (AID). After the KBs were finalized, they were evaluated separately for validity. RESULT: Ten cases from medical journal case reports were used to evaluate this "cross-domain" inference across the two KBs. The resultant non-error rate (NER) was 90%, and the average of probabilities assigned to the correct diagnosis (AVP) was 64.8% for cross-domain consultations. CONCLUSION: A novel formulation is now available to deal with problems occurring in a clinical diagnostic decision support system with multi-domain KBs. The utilization of this formulation will help in the development of more integrated KBs with greater focused knowledge domains.

Satisfaction of nurse aides with pre-job training programs.

Services provided by nurse aides (NAs) directly influence quality of care. Consequently, NA training programs are critical in providing the qualified personnel who carry the bulk of the workload in long-term care facilities. Because studies related to NA pre-job training programs and student satisfaction are limited, we examined NA pre-job training programs and student satisfaction in Taiwan. The highest satisfaction levels were with lecturers and clinical applications. The lowest satisfaction levels were with tuition, class size and practice hours. General hospitals and nursing homes were the preferred sites for providing lectures and clinical practice instruction. The results of this study provide government departments and health care professionals data pertinent to designing more effective NA training programs.

Assessing the impact of user interface to the usability of a clinical decision support system.

In a clinical decision support system (CDSS), the outcome of the system is related to the user interface directly. A successful CDSS should offer an efficient user interface to clinicians in order to get the most proper consultation results. This study is to assess the impact of user interface to the usability of CDSS. Two different types of input user interfaces were integrated into a well-established CDSS, namely, keyword-based interface and menu-based interface. The operating time of each interface was assessed, and the efficiency of the interfaces, effectiveness of the systems, and enjoyment of the users were also evaluated for degree of usability.