Design and development of an integrated mHealth platform to improve kangaroo mother care in Kenya

Background and Significance: There are 15 million preterm births a year. Premature babies suffer the highest rates of newborn mortality, occurring primarily in low/middle-income countries (LMICs). Neonatal hypothermia (low body temperature) is a life-threatening complication, which is prevented by Kangaroo Mother Care (KMC), but in Kenya, a profound shortage of health workers and lack of resources are barriers to KMC. Our international team has developed an integrated platform (educational and data collection apps + biomedical device) to improve the implementation of KMC in health facilities. Methods: From August 2020 – February 2021, a multi-disciplinary team from the United States and Kenya utilized agile development (weekly scrum meetings) and human-and user-centered design techniques to develop high-fidelity wireframes (Figma) of Android apps which are designed to integrate with a patented self-warming biomedical device (US10390630B2; NG/PT/IC/2016/053394) that utilizes wireless sensors to track KMC babies, continuously monitor infant vital signs, and display physiological data on mobile phones/tablets. Results: High-fidelity wireframes have been developed for two user interfaces of an integrated app, NeoRoo. The NeoRoo-Family app is for KMC parents; the NeoRoo-HealthWorker app is built for nurses and doctors. NeoRoo-Family provides parental caregivers with: (a) automated monitoring of key vital signs for their baby; (c) ability to alert a clinician as needed; (c) tracking of KMC metrics and goals, such as number of hours of skin-toskin care completed in a week; and (d) educational resources for evidence-based newborn care. The NeoRoo- HealthWorker app interface enables clinicians to: (a) simultaneously track breathing, heart rate, temperature, and oxygen saturation for multiple KMC infants in real-time; (b) review each infant’s past clinical history and vital signs trends; (c) receive automated and parent-generated alerts; (d) support harmonized dissemination of key educational messages to families. Conclusions: By providing education, continuous thermal support, and integrated, automated vital signs monitoring for premature babies, via the NeoRoo mHealth platform, we hope to better equip parents and health workers in Kenya to: (1) prevent hypothermia; (2) automatically monitor vital signs in newborns; (3) track key KMC metrics; (4) promote more effective task-sharing among KMC teams. On-going work includes participatory design interviews and a usability assessment

The NeoRoo mobile app: Initial design and prototyping of an Android-based digital health tool to support Kangaroo Mother Care in low/middle-income countries (LMICs).

Premature birth and neonatal mortality are significant global health challenges, with 15 million premature births annually and an estimated 2.5 million neonatal deaths. Approximately 90% of preterm births occur in low/middle income countries, particularly within the global regions of sub-Saharan Africa and South Asia. Neonatal hypothermia is a common and significant cause of morbidity and mortality among premature and low birth weight infants, particularly in low/middle-income countries where rates of premature delivery are high, and access to health workers, medical commodities, and other resources is limited. Kangaroo Mother Care/Skin-to-Skin care has been shown to significantly reduce the incidence of neonatal hypothermia and improve survival rates among premature infants, but there are significant barriers to its implementation, especially in low/middle-income countries (LMICs). The paper proposes the use of a multidisciplinary approach to develop an integrated mHealth solution to overcome the barriers and challenges to the implementation of Kangaroo Mother Care/Skin-to-skin care (KMC/STS) in LMICs. The innovation is an integrated mHealth platform that features a wearable biomedical device (NeoWarm) and an Android-based mobile application (NeoRoo) with customized user interfaces that are targeted specifically to parents/family stakeholders and healthcare providers, respectively. This publication describes the iterative, human-centered design and participatory development of a high-fidelity prototype of the NeoRoo mobile application. The aim of this study was to design and develop an initial ("A") version of the Android-based NeoRoo mobile app specifically to support the use case of KMC/STS in health facilities in Kenya. Key functions and features are highlighted. The proposed solution leverages the promise of digital health to overcome identified barriers and challenges to the implementation of KMC/STS in LMICs and aims to equip parents and healthcare providers of prematurely born infants with the tools and resources needed to improve the care provided to premature and low birthweight babies. It is hoped that, when implemented and scaled as part of a thoughtful, strategic, cross-disciplinary approach to reduction of global rates of neonatal mortality, NeoRoo will prove to be a useful tool within the toolkit of parents, health workers, and program implementors

Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence

Purpose Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Consolidated Standards of Reporting Trials, and the Checklist for Artificial Intelligence in Medical Imaging is increasing to improve the peer-review process and reporting of AI tools. However, no such standards exist for product-level review. Methods A review of clinical trials showed a paucity of evidence for radiology AI products; thus, the authors developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. The assessment tool was applied to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools. Results There is limited technical information on methodologies for FDA-approved algorithms compared with open-source products, likely because of intellectual property concerns. Furthermore, FDA-approved products use much smaller data sets compared with open-source AI tools, because the terms of use of public data sets are limited to academic and noncommercial entities, which precludes their use in commercial products. Conclusions Overall, this study reveals a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring actual performance of AI tools in clinical practice