Electronic Differential Effectiveness in Tuberculosis (TB)

Differential electronic effectiveness

In the automotive industry an electronic differential (ED) is used in a dual wheel drive electric vehicle to avoid slippage between wheels. It consists of a shaft encoder that measures the rotational speed and angle of both the front and the rear wheels and an additional microcontroller based control system to distribute power to each motor according to different steering angles and vehicle speeds. The design of such a control system is a challenge due to the high cost of sensors required. In this paper, we propose a simple and effective model of the differential system with only one sensor and an economical control method that can work at different turning angles during cornering. The model and the proposed control approach are validated using experimental data.

During a tuberculosis (TB) treatment period, adherence to direct observed therapy (DOT) is vital for successful TB control. However, achieving a high level of adherence is challenging in rural settings where patients cannot receive face-to-face DOT. A locally developed and low-cost e-medication monitor (EMM) has been shown to be an effective way to support adherence through the provision of patient medication reminders. The EMM also provides dosing information that can be used to identify non-adherent patients and shift them to DOT. However, evidence from implementation studies is limited.

In this study we surveyed a sample of health care workers who had been trained to use the EMM to assess their acceptability and experiences with the device. All nine health care workers (four physicians and five nurses) surveyed considered the EMM to be acceptable and felt that it increased their workload, but not to an unreasonable degree. They found the adherence monitoring functions to be useful and convenient, and they believed that it would help them improve their patient management practices.

We also assessed the effectiveness of EDS by measuring the number and accuracy of diagnostic hypotheses generated before and after its use for each of 16 clinical cases. We found that EDS significantly improved both the number and the accuracy of diagnostic hypotheses across all levels of experience. However, the effect of EDS on diagnostic hypotheses was greater when it was used early in the diagnostic process compared to late in the process.

In a simulated TB case-mix system, the results of this study suggest that EDS can help to reduce the amount of time clinicians spend on diagnosis and increase the number of accurate diagnoses that are made. Further operational research is needed to evaluate coverage and adherence in the EMM under programmatic conditions in more areas, as well as the cost-effectiveness of this low-cost and user-friendly technology.