Economic impact of pharmaceutical services on polymedicated patients: A systematic review

BackgroundPolypharmacy is commonly related to poor drug adherence, decreased quality of life and inappropriate prescribing in eldery. Furthermore, this condition also leads to a higher utilization of health services resources, due to the increased risk of adverse drug events, length of stays in hospitals and readmissions rates after discharge.ObjectiveThis Systematic Review aimed to synthesize the current evidence that evaluates pharmaceutical services on polymedicated patients, from an economic perspective.MethodsSystematic searches were conducted in MEDLINE, SCOPUS and Cochrane Library databases to identify studies that were published until January 2021. Experimental and observational studies were included in this review, using strict inclusion/exclusion criteria and were assessed for quality using the following tools: RoB and ROBINS-I. Two independent reviewers selected the articles and extracted the data.Results3,662 articles were retrieved from the databases. After the screening, 18 studies were included: 9 experimental and 9 observational studies. The studies reported that the integration of the pharmacist as a member of the healthcare team provides an optimized use of pharmacotherapy to polymedicated patients and contributes to health promotion, providing reduction of spending on medication, reduction of expenses related to emergency care and hospitalizations and other medical expenses. The ECRs made cost-effectiveness or cost-benefit analysis, and most of the Non Randomized studies had statistically significant cost savings even considering the expenses of pharmaceutical assistance. Experimental studies reported a cost reduction varying between US$ 193 to US$ 4,966 per patient per year. Furthermore, observational studies estimated a cost reduction of varying from US$ 3 to US$ 2,505 per patient per year. The cost savings are related to decrease in emergency visits and hospitalizations, through pharmacist intervention (medication review and pharmacotherapy follow-up).ConclusionsConsidering the set of studies included, pharmaceutical care services directed to polymedicated patients may cooperate to save financial resources. Most of the interventions showed positive economic trends and also contributed to improving clinical parameters and quality of life. However, due to the majority of the studies having exploratory or qualitative methodology, it is essential to carry out more robust studies, based on full economic evaluation.     

Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah

ObjectivesThis study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implementation of AI in healthcare, and additionally advise future HEEs.MethodsA systematic literature review was conducted in 2 databases (PubMed and Scopus) for articles published in the last 5 years. Two reviewers performed independent screening, full-text inclusion, data extraction, and appraisal. The Consolidated Health Economic Evaluation Reporting Standards and Philips checklist were used for the quality assessment of included studies.ResultsA total of 884 unique studies were identified; 20 were included for full-text review, covering a wide range of medical specialties and care pathway phases. The most commonly evaluated type of AI was automated medical image analysis models (n = 9, 45%). The prevailing health economic analysis was cost minimization (n = 8, 40%) with the costs saved per case as preferred outcome measure. A total of 9 studies (45%) reported model-based HEEs, 4 of which applied a time horizon >1 year. The evidence supporting the chosen analytical methods, assessment of uncertainty, and model structures was underreported. The reporting quality of the articles was moderate as on average studies reported on 66% of Consolidated Health Economic Evaluation Reporting Standards items.ConclusionsHEEs of AI in healthcare are limited and often focus on costs rather than health impact. Surprisingly, model-based long-term evaluations are just as uncommon as model-based short-term evaluations. Consequently, insight into the actual benefits offered by AI is lagging behind current technological developments.     

Residential greenness and risks of depression: Longitudinal associations with different greenness indicators and spatial scales in a Finnish population cohort

We carried out a longitudinal study on the associations between residential greenness and depression risk in urban areas in Finland. Residential greenness indicators were estimated within various buffer sizes around individuals' home locations (selected n = 14424) using time-series of normalized differential vegetation index (NDVI) and CORINE land cover data (CLC). We estimated individuals’ cumulative exposure to residential greenness over a 5-years and 14-years follow-up. We used doctor-diagnosed depression and Beck Depression Inventory for depression assessment. Our multi-logistic model showed an inverse association between residential greenness and depression, implying lowered depression risk for individuals with higher residential greenness. The association was particularly evident when using NDVI-based residential greenness (within a buffer of 100 m radius) and doctor-diagnosis depression data, adjusted with individual-level covariates. The odds ratio was 0.56 (95% CI 0.33 to 0.96) for the 5-years follow-up, and 0.54 (95% CI 0.30 to 0.98) for the 14-years follow-up. The associations between CLC-based total residential green space and depression varied across the different buffer sizes. In general, all the associations depended on the type of depression assessment, quality of greenness indicators, and the spatial scale of analysis. The associations also varied across the socio-demographic groups and neighborhood socioeconomic disadvantage level.     

左乙拉西坦治疗小儿癫痫的影响分析

目的研究左乙拉西坦治疗小儿癫痫的疗效以及对智力的影响。方法在本院2017年4月-2019年5月收治的小儿癫痫患儿中选取74例开展研究,按照随机数表法分两组观察组和对照组,观察组37例,对照组37例,对照组采用奥卡西平进行治疗,观察组采用左乙拉西坦进行治疗,对比观察组与对照组组的治疗总有效率和智力评分变化。结果观察组与对照组比较,观察组的治疗总有效率较高,智力评分明显较高,两项对比差异有统计学意义(P <0.05)。结论左乙拉西坦治疗小儿癫痫有较好的治疗效果,治疗总有效率较高,且对患儿的智力有明显的改善作用,在实际临床小儿癫痫的治疗中具有较高的运用价值。     

The Importance of Quality Perioperative Patient Education

Surgical patients, both inpatient and outpatient, need perioperative education in order to know how to prepare for surgery and what they can expect in the days following their surgical procedure. Quality perioperative patient education not only improves postoperative patient outcomes, but contributes as well to high patient satisfaction scores, which are currently tied to Centers for Medicare and Medicaid Services (CMS) reimbursements. This paper will demonstrate the importance of patient education and describe some perioperative Internet-based resources.     

中度稳定期慢性阻塞性肺疾病疲劳与多维度指标的关系

目的 探讨中度稳定期慢性阻塞性肺疾病(慢阻肺)患者疲劳与营养指标、生活质量指标、炎症指标、精神情绪指标的关系。方法 采用前瞻性研究,根据一般疲劳定义,从慢阻肺患者中随机选取中度稳定期慢阻肺疲劳患者30例(疲劳组)与中度稳定期慢阻肺非疲劳患者26例(非疲劳组)。收集两组一般资料、营养指标、生活质量指标、炎症指标、精神情绪指标,并进行比较。结果 两组性别、年龄、吸烟指数等方面差异无统计学意义(均P>0.05);与非疲劳组相比,疲劳组白蛋白、血红蛋白、体重指数、第1秒用力呼气容积占预计值的百分比(percent predicted forced expiratory volume in one-second, FEV1%)、6 min步行距离(6-minute walk distance, 6MWD)均显著降低(P<0.05),慢性阻塞性肺疾病评估测试(chronic obstructive pulmonary disease assessment test, CAT)评分、C反应蛋白(C-reactive protein, CRP)、降钙素原(procalcitonin, PCT)、白细胞(WBC)计数、贝克抑郁和焦虑量表评分, 心理身体紧张松弛测试量表(PSTR)评分均较高(P<0.05)。 结论 疲劳与中度稳定期慢阻肺患者营养、生活质量、炎症、精神情绪有关,是建立慢阻肺患者多维度疲劳测量的重要部分。     

信息化平台在助产质量敏感指标中的应用

目的 建立助产质量敏感指标的信息化管理系统，并探讨其临床应用效果。方法 根据现有的助产质量指标，设计信息化管理系统，通过数据筛选-整合分析-持续改进-评价效果等环节，进行助产质量指标的监测及应用效果评价。结果 改进后质控结果汇总时长、图表生成时长、结果反馈时长均显著减少（P＜0.001），质控人员满意度提高（P＜0.01），助产质量敏感指标在进行持续改进的基础上有所改善（P <0.05或P <0.01）。 结论 助产质量指标的信息化管理系统建设和应用有效提高产房护理质量管理效率，有利于促进助产质量持续改进。