Implementing a socio-technical system for computer-aided tuberculosis diagnosis in Peru: A field trial among health professionals in resource-constraint settings.
Cesar Ugarte-GilMaria IcocheaJuan Carlos Llontop OteroKaterine VillaizanNicola YoungYu CaoBenyuan LiuTerence GriffinMaria J BrunettePublished in: Health informatics journal (2020)
A major challenge of tuberculosis diagnosis is the lack of universal accessibility to bacteriological confirmation. Computer-aided diagnostic interventions have been developed to address this gap and their successful implementation depends on many health systems factors. A socio-technical system to implement a computer-aided diagnostic tuberculosis diagnosis was preliminary tested in five primary health centers located in Lima, Peru. We recruited nurses (n = 7) and tuberculosis physicians (n = 5) from these health centers to participate in a field trial of an mHealth tool (eRx X-ray diagnostic app). From September 2018 to February 2019, the nurses uploaded images of chest X-rays using smartphones and the physicians reviewed those images on web-based platforms using tablets. Both completed weekly written feedback about their experience. Each nurse participated for a median duration of 12 weeks (interquartile range = 7.5-15.5), but image upload was only possible at a median of 58 percent (interquartile range = 35.1%-84.4%) of those weeks. Each physician participated for a median duration of 17 weeks (interquartile range = 12-17), but X-ray image review was only possible at a median of 52 percent (interquartile range = 49.7%-57.4%) of those weeks. Heavy workload was most frequently provided as the reason for missing data. Several infrastructural and technological challenges impaired the effective implementation of the mHealth tool, irrespective of its diagnostic accuracy.
Keyphrases
- primary care
- healthcare
- mycobacterium tuberculosis
- deep learning
- mental health
- pulmonary tuberculosis
- hiv aids
- gestational age
- public health
- study protocol
- quality improvement
- phase iii
- high resolution
- convolutional neural network
- adverse drug
- emergency department
- phase ii
- optical coherence tomography
- electronic health record
- physical activity
- machine learning
- mass spectrometry
- magnetic resonance imaging
- dual energy
- magnetic resonance
- artificial intelligence
- big data
- open label
- placebo controlled
- data analysis
- human health