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Adverse drug reactions (ADR) and interactions for patients who use substances and have a mental health problem is an area that lacks attention in research, practice and policy. The purpose of this paper is to examine what is currently known about this topic.

The paper explores the available literature and specialist information available on this subject.

This is a complex area, which is compounded by limited empirical evidence. However, information is available which when combined with individual patient assessment there is the potential to reduce the risk of detrimental ADR and improve concordance to medication.

The paper offers pragmatic suggestions which aim to improve the knowledge of practitioners and lessen the potential harm to patients.

According to Joint Commission standards, patients should be educated about drug-nutrient interactions. Because nurses are well-suited to educating patients, this paper aims to assess their knowledge of ACE inhibitor drugs, nutrient interactions and high- and low-potassium foods.

Licensed nurses from a teaching hospital in the US south eastern Atlantic region completed a self-administered questionnaire (n=83). Means, standard deviations and 95 percent confidence intervals were calculated for continuous data and frequency and percentage distribution for discrete data. Student's t-test was used to evaluate responses by ACE inhibitor patient load and nursing education.

Mean nurse knowledge of ACE inhibitors and potassium was 62±16 percent and identifying high- and low-potassium foods was 32±23 percent. Most identified five from 12 high-potassium foods and did not know the designation of six, one from 14 low-potassium foods and did not know the designation of 11. Knowledge scores and identifying high- and low-potassium foods were similar regardless of ACE inhibitor patient load and nursing education.

ACE inhibitors are the fourth most commonly used drug class in the USA. Nurses are well positioned to recognize potential drug-nutrient interactions owing to changing or adding a drug, dose delivery method, dietary change or a patient's physical or clinical status that may indicate nutrient deficiency. The findings suggest that the nurses surveyed were proficient in identifying ACE inhibitors pharmacology, but that most were unable to identify foods that increase drug-nutrient interaction risk, and thus this is an area in which additional training might be beneficial.

Case menus were used to portray real-life scenarios in which healthcare practitioners can provide patient education about ACE inhibitor drug and dietary potassium interactions.

To enlighten the food consumers and drug users as to some of their incompatibilities.

Examples from the literature covering compositions of food‐drug, food‐drug interactions, and dietary habits were collected from former works.

Major side‐effects of some diet(food) on drugs include alteration in absorption by fatty, high protein and fiber diets. Drugs such as methateiate, pyrimethamine, isonicotinic acid and asprin alter the transportation of some nutrients. Nutrient supplementations was found to be beneficial.

Possible factors affecting the reactions need to be identified.

It helps food consumers and drug users to avoid using some of these materials and also to adopt nutrient supplementation as a better alternative where possible.

The knowledge helps food technologists, nutritionists, physicians and pharmacists to serve the public better.

The purpose of this paper is to determine the prevalence of polypharmacy and drug–drug interactions (DDIs) in older and younger prisoners, and compared if age group is associated with risks of polypharmacy and DDIs.

For 380 prisoners from Switzerland (190 were 49 years and younger; 190 were 50 years and older), data concerning their medication use were gathered. MediQ identified if interactions of two or more substances could lead to potentially adverse DDI. Data were analysed using descriptive statistics and generalised linear mixed models.

On average, older prisoners took 3.8 medications, while younger prisoners took 2.1 medications. Number of medications taken on one reference day was higher by a factor of 2.4 for older prisoners when compared to younger prisoners (p = 0.002). The odds of polypharmacy was significantly higher for older than for younger prisoners (>=5 medications: odds ratio = 5.52, p = 0.035). Age group analysis indicated that for potentially adverse DDI there was no significant difference (odds ratio = 0.94; p = 0.879). However, when controlling for the number of medication, the risk of adverse DDI was higher in younger than older prisoners, but the result was not significant.

Older prisoners are at a higher risk of polypharmacy but their risk for potentially adverse DDI is not significantly different from that of younger prisoners. Special clinical attention must be given to older prisoners who are at risk for polypharmacy. Careful medication management is also important for younger prisoners who are at risk of very complex drug therapies.

This paper aims to examine whether a computerised system for medication reviews can support physicians' decisions and improve the quality of drug treatment in the elderly.

This is a descriptive intervention study. The study included 275 patients living in community settings and nursing homes in Stockholm, Sweden. Patient data were analysed using computer software and scrutinised by a clinical pharmacologist. Pharmaco‐therapeutic advice was sent to the physician responsible for each patient. The main outcome measures were initiation and discontinuation of drugs, changes of doses and rates of identified drug‐related problems.

Expert opinions were given by the clinical pharmacologist, for 275 patients, mean age 85 years; 70 per cent female. An average of 3.3 remarks was given concerning unsuitable drugs, unclear indication, dosing when the kidney function was decreased, drug‐drug interactions and quality indicators. On average 1.5 drug‐related problems (DRP) per patient were attended to by the responsible physician at each unit. The most common action taken was withdrawal of a drug (n=208). On average the drug use decreased from 10.4 to 9.5 drugs per patient, and several quality indicators were met. The drug costs decreased, and resulted in a more cost‐effective drug therapy.

The paper develops and tests a method for intervention in the care of elderly patients. The method is based on a computerised expert support system for medication reviews at a distance and on education of the staff. A safer drug therapy with improved quality and cost‐effectiveness is thus provided.

My object in compiling this bibliography has been to list reference materials that will be useful to pharmacy students, faculty, and reference librarians working with them. While the bibliography is not intended to be comprehensive, I have covered the essential reference sources as well as some additional titles which are certainly desirable. Some of the books have been around for many years and are now in new editions; others are just appearing on the scene. I included periodicals only when they served a special purpose such as to offer a source of statistical or marketing information. Several of the references could have appeared in more than one place and, in the interest of brevity, I arbitrarily chose the category that seems most appropriate to me.

The purpose of this paper is to explore the implications of registered nurses’ (RNs) use of a computerized decision support system (CDSS) in medication reviews.

The paper employs a quasi-experimental, one-group pre-test/post-test design with three- and six-month follow-ups subsequent to the introduction of a CDSS. In total, 11 RNs initiated and prepared a total of 54 medication reviews. The outcome measures were the number of drug-related problems (DRPs) as reported by the CDSS and the RNs, respectively, the RNs’ views on the CDSS, and changes in the quality of drug treatment.

The CDSS significantly indicated more DRPs than the RNs did, such as potential adverse drug reactions (ADRs). The RNs detected additional problems, outside the scope of the CDSS, such as lack of adherence. They considered the CDSS beneficial and wanted to continue using it. Only minor changes were found in the quality of drug treatments, with no significant changes in the drug-specific quality indicators (e.g. inappropriate drugs). However, the use of renally excreted drugs in reduced renal function decreased.

The RNs’ use of a CDSS in medication reviews is of value in detecting potential ADRs and interactions. Yet, in order to have an impact on outcomes in the quality of drug treatment, further measures are needed. These may involve development of inter-professional collaboration, such as established procedures for the implementation of medication reviews, including the use of CDSS.

This is, to the best of the authors’ knowledge, the first study to explore the implications of medication reviews, initiated and prepared by RNs who use a CDSS. The paper adds further insight into the RNs’ role in relation to quality of drug treatments.

The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.

Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.

This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.

This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.

The purpose of this paper is to evaluate the utility of clinical processes in healthcare institutions of different sizes. The implications of adoption rate of computerized physicians order entry (CPOE) and electronic medical/health records (EMRs/EHRs) in different sized healthcare institutions in the USA were studied in terms of understanding its impact on enhancement of quality of patient care.

This study has used secondary data to obtain insights on the processes and technologies used in hospitals of different sizes in the USA and enlighten those in the developing countries to adopt a strategy that would be most appropriate for them. The Dorenfest Institute for H.I.T. Research and Education Analytics database (The Dorenfest Institute, 2011) provided the data for 5,038 US hospitals. Logistic regression was performed to study the impact of the different types of processes and technologies on institutions of different sizes, classified based on the number of beds, physicians, and nurses.

The findings show that small sized hospitals had a positive relationship with drug dosing interactions process and nursing and clinician content process. On the contrary, medium sized hospitals had a negative relationship with the usage of CPOE for entering medical records, i.e. <25 percent (p<0.05). In order to be effective, these institutions should increase the usage of EMRs by more than 25 percent to get positive outcomes. Large hospitals showed a positive relationship with the usage of >75 percent of CPOE to enter medical records and usage of medical records >75 percent.

The authors demonstrate the need for an evaluation of utility of acute care hospitals based on hospital size in terms of number of physicians, and nurses, which have not been dealt earlier by the past studies. Moreover, there is also a need for an evaluation of utility of acute care hospitals for implementation of CPOEs and EMRs that are integrated with clinical decision support systems.

Although the data are US-centric, the insights provided by the results are very much relevant to the Indian scenario to support the improvement of the quality of care. The findings may help those implementing processes in healthcare institutions in India. No study has addressed the measurement of the positive and negative outcomes arising due to the implementation of different percentages of CPOEs and EMRs in different sized institutions. Further the number of physicians and nurses have not been considered earlier. Therefore, the authors have classified the hospitals based on physicians and nurses and studied their impact on the adoption of CPOEs, clinical decision support systems, and EMRs.

The health movement that has been sweeping this country has given rise to increased consumer awareness of both prescription and nonprescription medications. One of several responses to this heightened interest has been a plethora of drug information publications aimed at the general public audience. Some of these publications became so popular they attained positions on the best‐seller list. At the same time books, such as the PDR, that had generally been available only to health professionals began to find their way into popular bookstores.