MedStorm™: Innovative Patient Safety Software for Optimal Therapeutics (original) (raw)
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Computerized advice on drug dosage to improve prescribing practice
status and date: …, 2008
Data collection and analysis Two review authors independently extracted data and assessed study quality. We grouped the results from the included studies by drug used and the effect aimed at for aminoglycoside antibiotics, amitriptyline, anaesthetics, insulin, anticoagulants, ovarian stimulation, anti-rejection drugs and theophylline. We combined the effect sizes to give an overall effect for each subgroup of studies, using a random-effects model. We further grouped studies by type of outcome when appropriate (i.e. no evidence of heterogeneity). Main results Forty-six comparisons (from 42 trials) were included (as compared with 26 comparisons in the last update) including a wide range of drugs in inpatient and outpatient settings. All were randomized controlled trials except two studies. Interventions usually targeted doctors, although some studies attempted to influence prescriptions by pharmacists and nurses. Drugs evaluated were anticoagulants, insulin, aminoglycoside antibiotics, theophylline, anti-rejection drugs, anaesthetic agents, antidepressants and gonadotropins. Although all studies used reliable outcome measures, their quality was generally low. This update found similar results to the previous update and managed to identify specific therapeutic areas where the computerized advice on drug dosage was beneficial compared with routine care: 1. it increased target peak serum concentrations (standardized mean difference (SMD) 0.79, 95% CI 0.46 to 1.13) and the proportion of people with plasma drug concentrations within the therapeutic range after two days (pooled risk ratio (RR) 4.44, 95% CI 1.94 to 10.13) for aminoglycoside antibiotics; 2. it led to a physiological parameter more often within the desired range for oral anticoagulants (SMD for percentage of time spent in target international normalized ratio +0.19, 95% CI 0.06 to 0.33) and insulin (SMD for percentage of time in target glucose range: +1.27, 95% CI 0.56 to 1.98); 3. it decreased the time to achieve stabilization for oral anticoagulants (SMD-0.56, 95% CI-1.07 to-0.04); 4. it decreased the thromboembolism events (rate ratio 0.68, 95% CI 0.49 to 0.94) and tended to decrease bleeding events for anticoagulants although the difference was not significant (rate ratio 0.81, 95% CI 0.60 to 1.08). It tended to decrease unwanted effects for aminoglycoside antibiotics (nephrotoxicity: RR 0.67, 95% CI 0.42 to 1.06) and anti-rejection drugs (cytomegalovirus infections: RR 0.90, 95% CI 0.58 to 1.40); 5. it tended to reduce the length of time spent in the hospital although the difference was not significant (SMD-0.15, 95% CI-0.33 to 0.02) and to achieve comparable or better cost-effectiveness ratios than usual care; 6. there was no evidence of differences in mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, antirejection drugs and antidepressants. For all outcomes, statistical heterogeneity quantified by I 2 statistics was moderate to high. Authors' conclusions This review update suggests that computerized advice for drug dosage has some benefits: it increases the serum concentrations for aminoglycoside antibiotics and improves the proportion of people for which the plasma drug is within the therapeutic range for aminoglycoside antibiotics. It leads to a physiological parameter more often within the desired range for oral anticoagulants and insulin. It decreases the time to achieve stabilization for oral anticoagulants. It tends to decrease unwanted effects for aminoglycoside antibiotics and anti-rejection drugs, and it significantly decreases thromboembolism events for anticoagulants. It tends to reduce the length of hospital stay compared with routine care while comparable or better cost-effectiveness ratios were achieved. However, there was no evidence that decision support had an effect on mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimize the effect of computerized advice. Taking into account the high risk of bias of, and high heterogeneity between, studies, these results must be interpreted with caution.
Safety of the Patient from the Medication
Journal of Pharmacy and Pharmacology 5 (2017) 13-19, 2017
Currently, more than 7% of admissions to acute care hospitals are related with AEMs (adverse events to medications). AEMs are the sixth cause of death, causing a cost of over $5.6 million dollars (USD) per hospital per year. There is an estimate that between 19% and 23% of hospitalized patients will have an adverse effect within the first 30 days after being discharged, 14.3% will be re-admitted and 70% of these events will be related to a medication prescription. Fortunately, at least 58% of these AEMs are preventable, since they result from a lack of information on the medication, prescription and dosage errors and from the abuse and underuse of the same. Polymedicated patients, especially the elderly with multiple pathologies and/or chronic patients that need to be admitted into the hospital more frequently, usually to internal medicine, neurology, psychiatry, rehabilitation and intensive care, are precisely the most liable to suffer from medication errors. It must be the objective to aim for the increase in the patient safety standards when it comes to medications.
PharmEqui: a tool to improve the clinical practice regarding pharmacotherapy and drug utilization
Procedia Computer Science, 2018
Is extremely difficult to current methods to know how much intensive is the patient's pharmacotherapy and measure it. In this context, we have developed a method that can contribute to advances in clinical practice. The main objective was making this calculation method applicable to clinical practice, becoming it feasible in a computational program/applicative. The calculation method considers the maximum doses to treat a specific disease and the doses in use by the patient to provide a calculation of a specific correction factor. The software development technologies are as follows: Bulma, for responsiveness and style, and Angular Material, for style and searches on the database of disease and medication names. The logical development of the application was set up with Typescript language and Angular 5, a framework for multi-platform development for mobile and desktop. This software, basically, have three different pages. The result page shows a table with 4 columns: patient Identification, equivalence dose (obtained from the calculation quoted above), amount of medicines and, colour's classification of the patients from the highest doses to lowest doses. The software has achieved generate responses for the pharmacological treatment of different patients for a specific disease in different therapeutic levels.
Drugs with narrow therapeutic index as indicators in the risk management of hospitalised patients
Pharmacy Practice (Internet), 2010
Drugs with narrow therapeutic index (NTI-drugs) are drugs with small differences between therapeutic and toxic doses. The pattern of drug-related problems (DRPs) associated with these drugs has not been explored. Objective: To investigate how, and to what extent drugs, with a narrow therapeutic index (NTI-drugs), as compared with other drugs, relate to different types of drug-related problems (DRPs) in hospitalised patients. Methods: Patients from internal medicine and rheumatology departments in five Norwegian hospitals were prospectively included in 2002. Clinical pharmacists recorded demographic data, drugs used, medical history and laboratory data. Patients who used NTI-drugs (aminoglycosides, ciclosporin, carbamazepine, digoxin, digitoxin, flecainide, lithium, phenytoin, phenobarbital, rifampicin, theophylline, warfarin) were compared with patients not using NTI-drugs. Occurrences of eight different types of DRPs were registered after reviews of medical records and assessment by multidisciplinary hospital teams. The drug risk ratio, defined as number of DRPs divided by number of times the drug was used, was calculated for the various drugs. Results: Of the 827 patients included, 292 patients (35%) used NTI-drugs. The NTI-drugs were significantly more often associated with DRPs than the non-NTI-drugs, 40% versus 19% of the times they were used. The drug risk ratio was 0.50 for NTI-drugs and 0.20 for non-NTI-drugs. Three categories of DRPs were significantly more frequently found for NTI-drugs: non-optimal dose, drug interaction, and need for monitoring. Conclusion: DRPs were more frequently associated with NTI-drugs than with non-NTI-drugs, but the excess occurrence was solely related to three of the eight DRP categories recorded. The drug risk ratio * Hege S. BLIX. PhD, MSc in Pharmacy. Lovisenberg is a well-suited tool for characterising the risk attributed to various drugs.
Detection of Patients at High Risk of Medication Errors: Development and Validation of an Algorithm
Basic & Clinical Pharmacology & Toxicology, 2015
Medication errors (MEs) are preventable and can result in patient harm and increased expenses in the healthcare system in terms of hospitalization, prolonged hospitalizations and even death. We aimed to develop a screening tool to detect acutely admitted patients at low or high risk of MEs comprised by items found by literature search and the use of theoretical weighting. Predictive variables used for the development of the risk score were found by the literature search. Three retrospective patient populations and one prospective pilot population were used for modelling. The final risk score was evaluated for precision by the use of sensitivity, specificity and area under the ROC (receiver operating characteristic) curves. The variables used in the final risk score were reduced renal function, the total number of drugs and the risk of individual drugs to cause harm and drug-drug interactions. We found a risk score in the prospective population with an area under the ROC curve of 0.76. The final risk score was found to be quite robust as it showed an area under the ROC curve of 0.87 in a recent patient population, 0.74 in a population of internal medicine and 0.66 in an orthopaedic population. We developed a simple and robust score, MERIS, with the ability to detect patients and divide them according to low and high risk of MEs in a general population admitted at acute admissions unit. The accuracy of the risk score was at least as good as other models reported using multiple regression analysis.
The term 'medication review' does not have a single well-defined meaning and is often found to include a wide range of interventions, from technical prescription review over interventions aimed at patient compliance to comprehensive medication management strategies. In studies of the effect of medication reviews, the tools used to perform the actual 'medication reviews' are vaguely often described or not described at all. A few validated tools to support medication reviews have been developed, such as the STOPP & START-criteria by Gallagher et al. or the Medication Appropriateness Index (MAI) by Hanlon et al. The process of performing a medication review should, however, not only be a one-track search for inappropriate use of selected high-risk drugs, a reconciliation of medicine lists, or a search for cost-savings. Rather, a full medication review should ensure that all drugs on a patient's list of medication are assessed, and that every diagnosis is treated according to guidelines, e.g. taking comorbidity and specific patient characteristics into consideration. While such considerations may be expressed on a general level, detailed descriptions and procedures for medication review is lacking. Such procedures should clearly outline how to conduct a medication review, among other things taking into consideration the setting, as the data sources differ widely between e.g. a pharmacy and hospital setting. No single procedure will ever be universally accepted as a gold standard or a one-size-fits-all solution, nor should it be. A discussion of the procedures used by clinical pharmacists is, however, important in order to ensure a continuous development of the quality of the pharmaceutical services offered to patients. This paper describes a practice model for pharmacist's medication review, tailored to the general practice setting. The model includes Collaboration with the general practitioner (GP) but does not include a patient interview, and was tested in a pilot study by conducting medication reviews on Poly pharmacy patients i.e. receiving 7 or more drugs for regular use.