Development and Initial Validation of a Frontline Health Worker mHealth Assessment Platform (MEDSINC®) for Children 2-60 Months of Age

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 generated 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. This figure appears in color at www.ajtmh.org.

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. This figure appears in color at www.ajtmh.org.

Figure 4.

A comparison of the percent distribution of “standard–immediate–urgent” triage recommendations for respiratory distress, dehydration, 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. This figure appears in color at www.ajtmh.org.

Figure 5.

The distribution of responses to usability and acceptability surveys by frontline health workers. This figure appears in color at www.ajtmh.org.