A framework for learning about improvement: embedded implementation and evaluation design to optimize learning

Abstract Improving health care involves many actors, often working in complex adaptive systems. Interventions tend to be multi-factorial, implementation activities diverse, and contexts dynamic and complicated. This makes improvement initiatives challenging to describe and evaluate as matching evaluation and program designs can be difficult, requiring collaboration, trust and transparency. Collaboration is required to address important epidemiological principles of bias and confounding. If this does not take place, results may lack credibility because the association between interventions implemented and outcomes achieved is obscure and attribution uncertain. Moreover, lack of clarity about what was implemented, how it was implemented, and the context in which it was implemented often lead to disappointment or outright failure of spread and scale-up efforts. The input of skilled evaluators into the design and conduct of improvement initiatives can be helpful in mitigating these potential problems. While evaluation must be rigorous, if it is too rigid necessary adaptation and learning may be compromised. This article provides a framework and guidance on how improvers and evaluators can work together to design, implement and learn about improvement interventions more effectively

Effectiveness of in-service training plus the collaborative improvement strategy on the quality of routine malaria surveillance data: results of a pilot study in Kayunga District, Uganda.

BACKGROUND: Surveillance data are essential for malaria control, but quality is often poor. The aim of the study was to evaluate the effectiveness of the novel combination of training plus an innovative quality improvement method-collaborative improvement (CI)-on the quality of malaria surveillance data in Uganda. METHODS: The intervention (training plus CI, or TCI), including brief in-service training and CI, was delivered in 5 health facilities (HFs) in Kayunga District from November 2015 to August 2016. HF teams monitored data quality, conducted plan-do-study-act cycles to test changes, attended periodic learning sessions, and received CI coaching. An independent evaluation was conducted to assess data completeness, accuracy, and timeliness. Using an interrupted time series design without a separate control group, data were abstracted from 156,707 outpatient department (OPD) records, laboratory registers, and aggregated monthly reports (MR) for 4 time periods: baseline-12 months, TCI scale-up-5 months; CI implementation-9 months; post-intervention-4 months. Monthly OPD register completeness was measured as the proportion of patient records with a malaria diagnosis with: (1) all data fields completed, and (2) all clinically-relevant fields completed. Accuracy was the relative difference between: (1) number of monthly malaria patients reported in OPD register versus MR, and (2) proportion of positive malaria tests reported in the laboratory register versus MR. Data were analysed with segmented linear regression modelling. RESULTS: Data completeness increased substantially following TCI. Compared to baseline, all-field completeness increased by 60.1%-points (95% confidence interval [CI]: 46.9-73.2%) at mid-point, and clinically-relevant completeness increased by 61.6%-points (95% CI: 56.6-66.7%). A relative - 57.4%-point (95% confidence interval: - 105.5, - 9.3%) change, indicating an improvement in accuracy of malaria test positivity reporting, but no effect on data accuracy for monthly malaria patients, were observed. Cost per additional malaria patient, for whom complete clinically-relevant data were recorded in the OPD register, was $3.53 (95$3.03, $4.15). CONCLUSIONS: TCI improved malaria surveillance completeness considerably, with limited impact on accuracy. Although these results are promising, the intervention's effectiveness should be evaluated in more HFs, with longer follow-up, ideally in a randomized trial, before recommending CI for wide-scale use

A new fifteen-switch inverter topology for two five-phase motors drive

The multimotor drives supplied from reduced switch-count converter topologies are potentially employed in different applications such as EVs/HEVs and traction systems. Much research work has been done to investigate the feasibility of different series/parallel-motor connections in an appropriate manner while being supplied by a single Voltage Source Inverter (VSI). In this paper, a new Fifteen-Switch Inverter (15-SI) topology for supplying two independent five-phase motors is proposed. This topology can be considered as an extension to the well-known nine-switch converter topology, which can be used to independently control two three-phase motor drives. First, the proposed inverter topology and its mathematical model are presented. Then, three carrier-based Pulse Width Modulation (PWM) schemes to control the converter and their operational constraints are investigated under Common Frequency (CF) and Variable Frequency (VF) modes of operation. The proposed system is validated by simulating the proposed 15-SI feeding two identical five-phase induction motors using Matlab/Simulink.Scopu