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BackgroundThe medication regimen for critically ill patients is complex and dynamic, leading to a high incidence of drug-related problems. This study aimed to assess the effectiveness and economic efficiency of pharmaceutical care for these patients.MethodsIn this prospective cohort study conducted in a tertiary hospital, adult patients were assigned either to a clinical pharmaceutical care group or a control group based on existing clinical grouping rules. Health outcomes and economic indicators were collected, followed by a cost-effectiveness analysis.ResultsThe acceptance rate for clinical pharmacist interventions was 89.31%. The pharmaceutical care group exhibited significant reductions in the rate of medication errors (40.65% vs. 61.69%, P < 0.001) and adverse drug events (44.52% vs. 56.45%, P = 0.020). The usage rates for special-grade antibiotics (85.16% vs. 91.13%, P = 0.009) and proton pump inhibitors (77.42% vs. 88.71%, P = 0.002) were also lower in the pharmaceutical care group. Secondary outcomes did not show significant differences in total hospital stay (21 days vs. 22 days, P = 0.092). However, ICU stay was significantly shorter (9 days vs. 11 days, P = 0.003) in the pharmaceutical care group. Cost-effectiveness analysis demonstrated that each 1% reduction in adverse drug events associated with ICU pharmaceutical care saved $226.75 in ICU hospitalization costs and $203.42 in total ICU drug costs. A 1% reduction in the medication error rate saved $128.57 in ICU hospitalization costs and $115.34 in total ICU drug costs.ConclusionsPharmaceutical care significantly reduces adverse drug events and medication errors, promotes rational use of medications, decreases the length of ICU stay, and lowers treatment costs in critically ill patients, establishing a definitive advantage in terms of cost-effectiveness.

ObjectivesTo evaluate the implementation effect of hepatitis C medical insurance reimbursement policy in China from the view of medical institutions.MethodsThe electronic medical record of a top tertiary hospital in Chengdu from January 2014 to December 2020 were extracted, and the interrupted time series model was used to analyze the changes in diagnosis and treatment behavior and disease burden of hepatitis C patients after the implementation of HCV insurance reimbursement policy.ResultsIn terms of diagnosis and treatment, the number of visits (β2 = 19.290, P < 0.001) and treatments (β2 = 14.291, P < 0.01) increased instantaneously after the implementation of the outpatient reimbursement policy in Chengdu in 2018, and there was no significant change after the implementation of the single line payment policy for oral direct antiviral (DAA) drugs in 2019 (P > 0.05); in terms of medical expenses, the total treatment cost (β2 = 21439.3, P < 0.001), out-of-pocket expenses (β2 = 6109.44, P < 0.001) and drug expenses (β2 = 21889.8, P < 0.001) of hepatitis C patients have been significantly reduced after the implementation of the single-line payment policy.ConclusionHepatitis C medical insurance reimbursement policy can promote hepatitis C patients to actively seek medical treatment, promote the widespread use of DAA scheme, reduce the burden of patients, and improve the treatment efficiency of hepatitis C.

Objectives: To survey, analyze, and ascertain the preferences for specialty pharmacy services among patients requiring complex care and to provide evidence to support specialty pharmacy service decision-making in China.Methods: To identify essential service attributes and levels, a review of the literature, discussions with specialty pharmacy managers and a pilot questionnaire were conducted. A D-efficient fractional factorial design was used to generate the discrete-choice experiment (DCE) questionnaire. A face-to-face survey of patients with chronic illness and their families or friends was conducted at three specialty pharmacies in Chengdu and Qingdao, China. A mixed logit model was used for estimation.Results: Six relevant attributes were identified and incorporated into the DCE questionnaire. A total of 417 participants completed the survey (mean age 43 years, 45.1% males), and 32.1% had lung cancer. The conditional relative importance showed that the most critical attribute was “frequency of telephone follow-up to monitor adverse drug reactions (ADRs), “followed by “mode of drug delivery,” “provider of medication guidance services,” and “availability of medical insurance consultation”; the least important attribute was “business hours.” A 1 min increase in time spent led to a 0.73% decrease in the probability that a service profile would be chosen. Negative preferences were noted for ADR monitoring by telephone follow-up once a year (β = −0.23, p < 0.001) and business hours [8:30–20:00 (Monday to Friday), 8:30–17:30 (weekend)] (β = −0.12, p < 0.001). Compared with women, men had a higher preference for service monitoring ADRs once every 3 months.Conclusions: Preference measurements showed that “frequency of telephone follow-up to monitor ADRs” had the most critical impact on decisions, followed by “mode of drug delivery.” Specialty pharmacies in China need to take these findings into account to improve their design to increase uptake and patient loyalty.

Nowadays, online product reviews play a crucial role in the purchase decision of consumers. A high proportion of positive reviews will bring substantial sales growth, while negative reviews will cause sales loss. Driven by the immense financial profits, many spammers try to promote their products or demote their competitors' products by posting fake and biased online reviews. By registering a number of accounts or releasing tasks in crowdsourcing platforms, many individual spammers could be organized as spammer groups to manipulate the product reviews together and can be more damaging. Existing works on spammer group detection extract spammer group candidates from review data and identify the real spammer groups using unsupervised spamicity ranking methods. Actually, according to the previous research, labeling a small number of spammer groups is easier than one assumes, however, few methods try to make good use of these important labeled data. In this paper, we propose a partially supervised learning model (PSGD) to detect spammer groups. By labeling some spammer groups as positive instances, PSGD applies positive unlabeled learning (PU-Learning) to study a classifier as spammer group detector from positive instances (labeled spammer groups) and unlabeled instances (unlabeled groups). Specifically, we extract reliable negative set in terms of the positive instances and the distinctive features. By combining the positive instances, extracted negative instances and unlabeled instances, we convert the PU-Learning problem into the well-known semisupervised learning problem, and then use a Naive Bayesian model and an EM algorithm to train a classifier for spammer group detection. Experiments on real-life Amazon.cn data set show that the proposed PSGD is effective and outperforms the state-of-the-art spammer group detection methods.

Next-app prediction is the task of predicting the next app that a user will choose to use on the smartphone. It helps to establish a variety of intelligent personalized services, such as fast-launch UI app, intelligent user-phone interactions, and so on. Since app names only provide limited semantic information, the intrinsic relation among apps cannot be fully exploited. Meanwhile, next-app to be used is largely determined by a sequence of apps that a user used recently. To address these challenging problems, this paper first enriches the semantic information of apps by extracting descriptive text of each app from the app store and thus proposes a topic model to transform apps as well as user preferences into latent vectors. Then, a set of nearest neighbors can be constructed based on the similarity of latent vectors and it is employed for training the prediction model. Furthermore, our prediction scheme is built on the temporal sequential data and is modeled by using the chain-augmented Naive Bayes model. Experimental results with a real smartphone application log data have demonstrated that our method achieves higher recall and DCG values compared with several baseline next-app prediction methods.

In a cloud environment, the primary way to optimize physical resources is to reuse a physical machine (PM) by consolidating complementary multiple virtual machines (VMs) on it. When considering VMs' dynamically changing resource demands, one hot research topic revolves around reusing VM migration resources more efficiently. The challenge here is finding the best tradeoff between the VM migration optimization performance and complexity. On one hand, to improve the migration efficiency, VM migration planning is adopted to achieve efficient resource reuse while minimizing the number of VM migrations. On the other hand, the huge number of PMs and VMs in a cloud datacenter often adds considerable complexity to migration planning, which hampers the decision-making process in VM migration. To address these issues, this paper proposes a hybrid resource pool model to reduce the complexity of VM migration planning by limiting the scope of VM migration decisions. Then, based on this model, we use our novel resource-reuse optimization mechanism (called HirePool) to improve efficiency by maximizing resource usage with only a few VM migrations. Finally, we perform simulation tests and actual experiments running on a real cloud platform to evaluate HirePool. Results show that HirePool improves average resource usage by 13%, saves the number of PMs used by 12%, and reduces the average number of migrations (compared with contrast mechanisms) by 31%.

The backbone is the natural abstraction of a complex network, which can help people understand a networked system in a more simplified form. Traditional backbone extraction methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency—the exhaustive search of all nodes or edges is often prohibitively expensive. In this paper, we propose a backbone extraction heuristic with incomplete information (BEHwII) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way, which only relies on the local/incomplete knowledge rather than the global view of the network. Experimental results on four real-life networks demonstrate the advantage of BEHwII over the classic disparity filter method by either effectiveness or efficiency validity.