Purpose Medication management is a complex process, at high risk of error with life threatening consequences. The focus should be on devising strategies to avoid errors and make the process self-reliable by ensuring prevention of errors and/or error detection at subsequent stages. The purpose of this paper is to use failure mode effect analysis (FMEA), a systematic proactive tool, to identify the likelihood and the causes for the process to fail at various steps and prioritise them to devise risk reduction strategies to improve patient safety. Design/methodology/approach The study was designed as an observational analytical study of medication management process in the inpatient area of a multi-speciality hospital in Gurgaon, Haryana, India. A team was made to study the complex process of medication management in the hospital. FMEA tool was used. Corrective actions were developed based on the prioritised failure modes which were implemented and monitored. Findings The percentage distribution of medication errors as per the observation made by the team was found to be maximum of transcription errors (37 per cent) followed by administration errors (29 per cent) indicating the need to identify the causes and effects of their occurrence. In all, 11 failure modes were identified out of which major five were prioritised based on the risk priority number (RPN). The process was repeated after corrective actions were taken which resulted in about 40 per cent (average) and around 60 per cent reduction in the RPN of prioritised failure modes. Research limitations/implications FMEA is a time consuming process and requires a multidisciplinary team which has good understanding of the process being analysed. FMEA only helps in identifying the possibilities of a process to fail, it does not eliminate them, additional efforts are required to develop action plans and implement them. Frank discussion and agreement among the team members is required not only for successfully conducing FMEA but also for implementing the corrective actions. Practical implications FMEA is an effective proactive risk-assessment tool and is a continuous process which can be continued in phases. The corrective actions taken resulted in reduction in RPN, subjected to further evaluation and usage by others depending on the facility type. Originality/value The application of the tool helped the hospital in identifying failures in medication management process, thereby prioritising and correcting them leading to improvement.

Continuous-monitoring applications in sensor network applications require periodic data transmissions to the base-station (BS), which may lead to unnecessary energy depletion. The energy-efficient data aggregation solutions in sensor networks have evolved as one of the favorable fields for such applications. Former research works have recommended many spatial-temporal designs and prototypes for successfully minimizing the data-gathering overheads, but these are constrained to their relevance. This work has proposed a data aggregation technique for homogeneous application set-ups in sensor networks. For this, the authors have employed two ways of model generation for reducing correlated spatial-temporal data in cluster-based sensor networks: one at the Sensor nodes (SNs) and the other at the Cluster heads (CHs). Building on this idea, the authors propose two types of data filtration, first at the SNs for determining temporal redundancies (TRs) in data readings by both relative deviation (RD) and adaptive frame method (AFM) and second at the CHs for determining spatial redundancies (SRs) by both RD and AFM.

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Most ecological management applications use Wireless Sensor Networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, a significant amount of energy is used transmitting redundant data, making it tremendously problematic to attain a satisfactory network lifetime, which is a bottleneck in enduring such environmental monitoring applications. A two-vector prediction model based on Normalized Quantile Regression (NQR) for Data Aggregation is proposed to proficiently accomplish energy reduction in synchronized data collecting cycles. The introduced NQR algorithm provides high-accuracy prediction. With accurate estimates, energy usage is reduced.Furthermore, it extends the network's lifetime. In intracluster transmissions, NQR uses a two-vector data-prediction algorithm to coordinate the anticipated sensor's reading and, as a result, minimize cumulative inefficiencies from unin-terrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to existing methods, the suggested NQR methodology is shown to have high energy efficiency.The results show greater prediction accuracy, more positive predictions with high data quality, which help the network last longer.

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An essential design concern in a resource‐constraint sensor network is optimizing data transmission for each sensor node (SN) to prolong the network lifetime. Many research works cited that the dual prediction technique remains the most efficient technique for data reduction. A large amount of redundant data is usually transmitted across the network, leading to collisions, loss of data, and energy dissipation. This article proposes a data transmission reduction method (DTRM) to solve these problems, implemented on the cluster heads and operates in rounds. DTRM is lightweight in processing, has low complexity costs, and needs a limited memory footprint, but it is robust and effective. It can be combined with any form of cluster‐based data aggregation. We have incorporated the proposed DTRM with the data aggregation‐adaptive frame method (DA‐AFM), implemented on the SNs within the clusters. DA‐AFM can eliminate temporal redundancies and correlations in the sensor's time‐series readings. This helps the SN take fewer readings, which improves the efficiency of reducing data transmission and decreases the amount of energy spent during sensing. The proposed DTRM approach decreases the average transmission rates of data while maintaining data quality. This study is evaluated on real data obtained from the Intel Berkeley Lab and compared with three recent data reduction techniques focused on prediction. The results show that DTRM consumes up to 70% less energy while preserving the expected quality of data and reducing transmission.

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