Line Probe Assay (LPA) is a widely used method for diagnosing drug-resistant tuberculosis (DRTB), but it is a time-consuming and labor-intensive process that requires expert interpretation. DRTB is a significant threat to global TB control efforts and its prompt diagnosis is critical for initiating appropriate treatment. In this paper, we present an automated LPA test interpretation solution that uses computer vision techniques to extract and analyze strips from LPA sheets and uses machine learning algorithms to produce drug sensitivity and resistivity outcomes with extremely high precision and recall. We also develop OCR models to eliminate manual data entry to further reduce the overall time. Our solution comprises a rejection module that flags ambiguous and novel samples that are then referred to experienced lab technicians. This results in increased trust in the solution. To evaluate our solution, we curate an extensive and diverse dataset of LPA strips annotated by multiple microbiologists across India. Our solution achieves more than 95% accuracy for all drugs on this dataset. The proposed solution has the potential to increase the efficiency, standardization of LPA test interpretation, and fast-tracking the dissemination of results to end-users via a designated Management Information System (MIS).

Ensuring that patients adhere to prescribed medication remains an important challenge in global health. While technology has been utilized to monitor and improve adherence, solutions to date have been too costly for large-scale deployment in developing regions. This paper describes 99DOTS, a low-cost approach for tracking adherence using a combination of paper packaging and low-end mobile phones. Every day, patients reveal an unpredictable phone number behind the pills and send a free call to that number to indicate that drugs were dispensed and taken. Within five years of its inception, 99DOTS has become a standard of care for tuberculosis in India and has enrolled over 200,000 patients. We provide a holistic account of the project's evolution, including its iterative design, scaled implementation, and lessons learned along the way. We hope this account will serve as a useful case study for anyone seeking to establish and scale new low-cost technologies for a global audience.

OBJECTIVE: Communication of abnormal test results in the outpatient setting is prone to error. Using information technology can improve communication and improve patient safety. We standardized processes and procedures in a computerized test result notification system and examined their effectiveness to reduce errors in communication of abnormal imaging results. DESIGN: We prospectively analyzed outcomes of computerized notification of abnormal test results (alerts) that providers did not explicitly acknowledge receiving in the electronic medical record of an ambulatory multispecialty clinic. MEASUREMENTS: In the study period, 190,799 outpatient visits occurred and 20,680 outpatient imaging tests were performed. We tracked 1,017 transmitted alerts electronically. Using a taxonomy of communication errors, we focused on alerts in which errors in acknowledgment and reception occurred. Unacknowledged alerts were identified through electronic tracking. Among these, we performed chart reviews to determine any evidence of documented response, such as ordering a follow-up test or consultation. If no response was documented, we contacted providers by telephone to determine their awareness of the test results and any follow-up action they had taken. These processes confirmed the presence or absence of alert reception. RESULTS: Providers failed to acknowledge receipt of over one-third (368 of 1,017) of transmitted alerts. In 45 of these cases (4% of abnormal results), the imaging study was completely lost to follow-up 4 weeks after the date of study. Overall, 0.2% of outpatient imaging was lost to follow-up. The rate of lost to follow-up imaging was 0.02% per outpatient visit. CONCLUSION: Imaging results continue to be lost to follow-up in a computerized test result notification system that alerted physicians through the electronic medical record. Although comparison data from previous studies are limited, the rate of results lost to follow-up appears to be lower than that reported in systems that do not use information technology comparable to what we evaluated.