Authors: Barry A. Finette, Megan McLaughlin, Samuel V. Scarpino, John Canning, Michelle Grunauer, Enrique Teran,
Marisol Bahamonde, Edy Quizhpe, Rashed Shah, Eric Swedberg, Kazi Asadur Rahman, Hosneara Khondker, Ituki Chakma, Denis Muhoza, Awa Seck, Assiatta Kabore, Salvator Nibitanga, and Barry Heath

American Journal of Tropical Medicine & Hygiene

Received October 30, 2018. Accepted for publication March 11, 2019. Published online April 15, 2019.

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Abstract. Approximately 3 million children younger than 5 years living in low- and middle-income countries (LMICs)
die each year from treatable clinical conditions such as pneumonia, dehydration secondary to diarrhea, and malaria. A majority of these deaths could be prevented with early clinical assessments and appropriate therapeutic intervention. In this study, we describe the development and initial validation testing of a mobile health (mHealth) platform, MEDSINC®, designed for frontline health workers (FLWs) to perform clinical risk assessments of children aged 2–60 months. MEDSINC is a web browser–based clinical severity assessment, triage, treatment, and follow-up recommendation platform developed with physician-based Bayesian pattern recognition logic. Initial validation, usability, and acceptability testing were performed on 861 children aged between 2 and 60 months by 49 FLWs in Burkina Faso, Ecuador, and Bangladesh. MEDSINC-based clinical assessments by FLWs were independently and blindly correlated with clinical assessments by 22 local health-care professionals (LHPs). Results demonstrate that clinical assessments by FLWs using MEDSINC had a specificity correlation between 84% and 99% to LHPs, except for two outlier assessments (63% and 75%) at one study site, in which local survey prevalence data indicated that MEDSINC outperformed LHPs. In addition, MEDSINC triage recommendation distributions were highly correlated with those of LHPs, whereas usability and feasibility responses from LHP/FLW were collectively positive for ease of use, learning, and job performance. These results indicate that the
MEDSINC platform could significantly increase pediatric health-care capacity in LMICs by improving FLWs’ ability to
accurately assess health status and triage of children, facilitating early life-saving therapeutic interventions.

FIGURE 1. MEDSINC Bayesian/cluster-pattern algorithms use acquired clinical data points (see Table 1) that are given a numerical weighted score and then grouped based on clinical assessment patterns being processed. Severity assessments (none–moderate–severe) are then gen- erated by unique tolerance scores for respiratory distress, dehydration, sepsis risk, and acute malnutrition. Clinical risk for eight additional clinical conditions—malaria, urinary tract infection, measles, anemia, cellulitis, ear infection, meningitis, and dysentery—are based on individual-based scores. MEDSINC platform also generates patient-specific triage, treatment, and follow-up recommendations.
FIGURE 2. Validation study design and recruitment of subjects.
FIGURE 3. The overall correlation of MEDSINC-generated clinical assessments by non–health-care professionals with an average of 2 hours of training compared with local health-care professionals performing independent blinded clinical assessments of the same patient.
FIGURE 4. A comparison of the percent distribution of “standard–immediate–urgent” triage recommendations for respiratory distress, de- hydration, sepsis–systemic inflammatory response syndrome, and acute malnutrition by the MEDSINC platform generated by FLWs compared with local health professionals for Ecuador and Bangladesh field studies.

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