Understanding older adults' medication decision making and behavior: A study on over-the-counter (OTC) anticholinergic medications

Background

Older adults purchase and use over-the-counter (OTC) medications with potentially significant adverse effects. Some OTC medications, such as those with anticholinergic effects, are relatively contraindicated for use by older adults due to evidence of impaired cognition and other adverse effects.

A structural design of clinical decision support system for chronic diseases risk management

In clinical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take thei ntervention, given the costs of diagnosis and therapeutics, is of fundamental importance This paper develops a possible structural design of clinical decision support system (CDSS) by considering the sensitivity analysis as well as the optimal prior and posterior decisions for chronic diseases risk management. Indeed, Bayesian inference of a nonhomogeneous Poisson process with three different failure models (linear, exponential, and power law) were considered, and the effects of the scale factor and the aging rate of these models were investigated. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. The proposed structural design of CDSS facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert’s opinions and the sampling information which will furnish decision makers with valuable support for quality clinical decision making.     

Measuring the benefits of clinical decision support: return on investment

In an effort to provide high quality care in a more cost-effective manner, health care providers have found it necessary to implement a series of decision support strategies designed to improve outcomes of care. While each of these strategies has measurable benefits, they each come along with additional costs. This article will describe a methodology for measuring the costs and direct and indirect benefits from decision support activities.     

The utility of a computerised clinical decision support system intervention in home medicines review: A mixed-methods process evaluation

BackgroundUse of high-risk medications is common and associated with adverse effects in older adults, including those living with dementia. A Computerised Clinical Decision Support System (CCDSS) called G-MEDSS© (Goal-directed Medication Review Electronic Decision Support System) contains tools that identify medications with anticholinergic and sedative effects and align older adult's health and medication goals of care with medication management strategies.ObjectivesTo describe the utility of a CCDSS called G-MEDSS in Home Medicines Review (HMR) by an accredited clinical pharmacist (ACP) using a mixed-methods process evaluation method.MethodsA mixed-methods process evaluation was conducted as part of a nation-wide cluster-randomised clinical trial evaluating the effectiveness of implementing G-MEDSS in HMR. Data were collected from ACPs and HMR recipients (the patient) using a feedback survey (ACPs and HMR recipients) and one-on-one telephone interviews (HMR recipients). Quantitative and qualitative data were combined at the start of analysis. Content analysis of the qualitative data was conducted, and emerging categories were linked to quantitative data.ResultsFeedback surveys were completed by eight ACPs and 26 HMR recipients; 11 HMR recipients completed one-on-one telephone interviews. Overall, ACPs and HMR recipients who received the intervention reported a positive attitude towards the utility of G-MEDSS in HMR. ACPs reported that G-MEDSS enhanced their ability to make evidence-based recommendations about anticholinergic and sedative medications, and to better align HMR recommendations with HMR recipients’ goals of care. However, ACPs described that it was difficult to obtain goals of care from HMR recipients. HMR recipients gave mixed reports about the ACPs consultations: some HMR recipients reported that they were engaged in discussions about their views on medications, whilst other participants reported that discussions about their concerns towards medications and goals was limited.ConclusionsThis study suggests that a CCDSS, such as G-MEDSS, demonstrates good utility to assist ACPs to identify and assess anticholinergic and sedative medications. Given the mixed feedback from the ACPs on the different components of the G-MEDSS, further refinement of these tools and their use in combination in practice may be needed.     

A critical evaluation of clinical decision support for the detection of drug-drug interactions

INTRODUCTION: Incorporation of clinical decision support systems (CDSSs) into computerized physician order entry assists prescribers with medication dosing, identification of duplicate therapies, drug-allergy alerts and drug-drug interactions (DDIs). The generation of DDI alerts is one aspect of CDSS that may improve patient safety and reduce adverse drug events. AREAS COVERED: Currents issues with the generation of DDI alerts, such as alert fatigue, unclear clinical significance and database inconsistencies are a few of the problems that have been identified with DDI alerting. Research has shown that DDI alerting may be improved through the tiering of alerts, generation of patient-specific alert and directing some alerts to clinicians other than physicians. More research in this area, such as how to decrease the variability of database rating systems, improve the identification of clinically significant alerts and increase the patient specificity of the generated DDI alerts, should be conducted. EXPERT OPINION: DDI knowledgebases need to take into account more patient-specific information. Strategies to avoid alert fatigue, such as DDI tiering and reducing signal:noise ratios, are important areas for future study. End-user participation and clinician feedback should be incorporated in the development of DDI knowledgebases to increase alert compliance.     

The use of think-aloud protocols to identify a decision-making process of community pharmacists aimed at improving CMS Star Ratings scores

Background

The Center for Medicare and Medicaid Services (CMS) created the Star Rating system based on multiple measures that indicate the overall quality of health plans. Community pharmacists can impact certain Star Ratings measure scores through medication adherence and patient safety interventions.

A decision support system for the management of accidental mushroom and plant poisoning

In this paper the discussion focuses on a decision support system to be used as a tool in the treatment of poisoning cases attributable to mushrooms, plants and animals. In this first release, attention is focused on the risks related to fungi. Problems involved with mushroom poisoning and identification are analyzed to highlight which elements or characters must be taken into account in devising a computerized expert system. The components of such a system are presented, the different approaches are discussed and the choices made are motivated. Some preliminary results are also presented.     

A look at the safety profile of over-the-counter naproxen sodium: a meta-analysis

As the trend for Americans to self-medicate continues to increase, it becomes important to review the safety of over-the-counter (OTC) medications. This article will review the safety of an OTC analgesic, Aleve (naproxen sodium). The objective of this meta-analysis is to evaluate the frequency of occurrence of all adverse events in subjects taking various doses of OTC naproxen sodium as compared to placebo. These varying doses and dosage regimens were studied individually and consisted of 220 to 880 mg administered in single, multiple, and PRN (as needed) doses of naproxen sodium. This meta-analysis confirmed the favorable safety profile of naproxen sodium at OTC doses and established that the overall occurrence of adverse events with naproxen sodium was comparable and in some cases significantly lower than placebo. These results indicate that treatment with naproxen sodium may be highly beneficial from a clinical and economical perspective and safe when adhering to labeled directions.     

Use of path modeling to inform a clinical decision support application to encourage osteoporosis medication use

BackgroundOsteoporosis medication use is suboptimal. Simple interventions personalized to a patients’ stage of readiness are needed to encourage osteoporosis medication use.ObjectivesTo estimate interrelationships of sociodemographic factors, perceived fracture risk, health literacy, receipt of medication information, medication trust and readiness to use osteoporosis medication; and apply observed relationships to inform design specifications for a clinical decision support application that can be used for personalized patient counseling.MethodsData from a national sample of older women (n = 1759) with self-reported history of fractures and no current use of osteoporosis medication treatment were used to estimate an acceptable path model that describes associations among key sociodemographic characteristics, health literacy, perceived fracture risk, receipt of osteoporosis medication information within the past year, trust in osteoporosis medications, and readiness to use osteoporosis medication. Path model results were used to inform an application for personalized patient counseling that can be easily integrated into clinical decision support systems.ResultsIncreased age (β = 0.13), trust for medications (β = 0.12), higher perceived fracture risk (β = 0.21), and having received medication information within the past year (β = 0.21) were all positively associated with readiness to use osteoporosis medication (p < 0.0001). Whereas, health literacy (β = ?0.09) was inversely associated with readiness to use osteoporosis medication (p < 0.0001). Using these results, a brief 6-item question set was constructed for simple integration into clinical decision support applications. Patient responses were used to inform a provider dashboard that integrates a patient's stage of readiness for osteoporosis medication use, predictors of readiness, and personalized counseling points appropriate to their stage of readiness.ConclusionContent of counseling strategies must be aligned with a patient's stage of readiness to use treatment. Path modeling can be effectively used to identify factors for inclusion in an evidenced-based clinical decision support application designed to assist providers with personalized patient counseling and osteoporosis medication use decisions.