Journal of evaluation in clinical practice
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Randomized Controlled Trial Multicenter Study
The Systematic Tool to Reduce Inappropriate Prescribing (STRIP): Combining implicit and explicit prescribing tools to improve appropriate prescribing.
Inappropriate prescribing is a major health care issue, especially regarding older patients on polypharmacy. Multiple implicit and explicit prescribing tools have been developed to improve prescribing, but these have hardly ever been used in combination. The Systematic Tool to Reduce Inappropriate Prescribing (STRIP) combines implicit prescribing tools with the explicit Screening Tool to Alert physicians to the Right Treatment and Screening Tool of Older People's potentially inappropriate Prescriptions criteria and has shared decision-making with the patient as a critical step. ⋯ In conclusion, the STRIP helps health care providers to systematically identify potentially inappropriate prescriptions and medication-related problems and to change the patient's medication regimen in accordance with the patient's needs and wishes. This article describes the STRIP and the available evidence so far. The OPERAM study is investigating the effect of STRIP use on clinical and economic outcomes.
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Randomized Controlled Trial Multicenter Study
Improving screening and brief intervention activities in primary health care: Secondary analysis of professional accuracy based on the AUDIT-C.
The ODHIN trial found that training and support and financial reimbursement increased the proportion of patients that were screened and given advice for their heavy drinking in primary health care. However, the impact of these strategies on professional accuracy in delivering screening and brief advice is underresearched and is the focus of this paper. ⋯ Although the use of AUDIT-C as a screening tool was accurate, a considerable proportion of risky drinkers did not receive advice, which was reduced with financial incentives.
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Randomized Controlled Trial
Identifying causal mechanisms in health care interventions using classification tree analysis.
Mediation analysis identifies causal pathways by testing the relationships between the treatment, the outcome, and an intermediate variable that mediates the relationship between the treatment and outcome. This paper introduces classification tree analysis (CTA), a machine-learning procedure, as an alternative to conventional methods for analysing mediation effects. ⋯ CTA may uncover mediation effects where conventional approaches do not, because CTA does not require any assumptions about the distribution of variables nor of the functional form of the model, and CTA will systematically identify all statistically viable interactions. The versatility of CTA enables the investigator to explore the theorized underlying causal mechanism of an intervention in a much more comprehensive manner than conventional mediation analytic approaches.
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Lean Six Sigma (LSS) has been recognized as an effective management tool for improving healthcare performance. Here, LSS was adopted to reduce the risk of healthcare-associated infections (HAIs), a critical quality parameter in the healthcare sector. ⋯ The implementation of an LSS approach could significantly decrease the percentage of patients with HAIs.
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Guidelines recommend screening for risk factors associated with chronic diseases but current electronic prompts have limited effects. Our objective was to discover and rank associations between the presence of screens to plan more efficient prompts in primary care. ⋯ Associations between the provision of important screens can be discovered and ranked. Rules with promising combinations of associated screens could be used to implement data driven alerts.