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      Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?

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          Abstract

          The field of artificial intelligence (AI) has evolved considerably in the last 60 years. While there are now many AI applications that have been deployed in high-income country contexts, use in resource-poor settings remains relatively nascent. With a few notable exceptions, there are limited examples of AI being used in such settings. However, there are signs that this is changing. Several high-profile meetings have been convened in recent years to discuss the development and deployment of AI applications to reduce poverty and deliver a broad range of critical public services. We provide a general overview of AI and how it can be used to improve health outcomes in resource-poor settings. We also describe some of the current ethical debates around patient safety and privacy. Despite current challenges, AI holds tremendous promise for transforming the provision of healthcare services in resource-poor settings. Many health system hurdles in such settings could be overcome with the use of AI and other complementary emerging technologies. Further research and investments in the development of AI tools tailored to resource-poor settings will accelerate realising of the full potential of AI for improving global health.

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          Effect of a text messaging intervention on influenza vaccination in an urban, low-income pediatric and adolescent population: a randomized controlled trial.

          Influenza infection results in substantial costs, morbidity, and mortality. Vaccination against influenza is particularly important in children and adolescents who are a significant source of transmission to other high-risk populations, yet pediatric and adolescent vaccine coverage remains low. Traditional vaccine reminders have had a limited effect on low-income populations; however, text messaging is a novel, scalable approach to promote influenza vaccination. To evaluate targeted text message reminders for low-income, urban parents to promote receipt of influenza vaccination among children and adolescents. Randomized controlled trial of 9213 children and adolescents aged 6 months to 18 years receiving care at 4 community-based clinics in the United States during the 2010-2011 influenza season. Of the 9213 children and adolescents, 7574 had not received influenza vaccine prior to the intervention start date and were included in the primary analysis. Parents of children assigned to the intervention received up to 5 weekly immunization registry-linked text messages providing educational information and instructions regarding Saturday clinics. Both the intervention and usual care groups received the usual care, an automated telephone reminder, and access to informational flyers posted at the study sites. Receipt of an influenza vaccine dose recorded in the immunization registry via an electronic health record by March 31, 2011. Receipt was secondarily assessed at an earlier fall review date prior to typical widespread influenza activity. Study children and adolescents were primarily minority, 88% were publicly insured, and 58% were from Spanish-speaking families. As of March 31, 2011, a higher proportion of children and adolescents in the intervention group (43.6%; n = 1653) compared with the usual care group (39.9%; n = 1509) had received influenza vaccine (difference, 3.7% [95% CI, 1.5%-5.9%]; relative rate ratio [RRR], 1.09 [95% CI, 1.04-1.15]; P = .001). At the fall review date, 27.1% (n = 1026) of the intervention group compared with 22.8% (n = 864) of the usual care group had received influenza vaccine (difference, 4.3% [95% CI, 2.3%-6.3%]; RRR, 1.19 [95% CI, 1.10-1.28]; P < .001). Among children and adolescents in a low-income, urban population, a text messaging intervention compared with usual care was associated with an increased rate of influenza vaccination. However, the overall influenza vaccination rate remained low. clinicaltrials.gov Identifier: NCT01146912.
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            Mobile phone-delivered reminders and incentives to improve childhood immunisation coverage and timeliness in Kenya (M-SIMU): a cluster randomised controlled trial

            Summary Background As mobile phone access continues to expand globally, opportunities exist to leverage these technologies to support demand for immunisation services and improve vaccine coverage. We aimed to assess whether short message service (SMS) reminders and monetary incentives can improve immunisation uptake in Kenya. Methods In this cluster-randomised controlled trial, villages were randomly and evenly allocated to four groups: control, SMS only, SMS plus a 75 Kenya Shilling (KES) incentive, and SMS plus 200 KES (85 KES = USD$1). Caregivers were eligible if they had a child younger than 5 weeks who had not yet received a first dose of pentavalent vaccine. Participants in the intervention groups received SMS reminders before scheduled pentavalent and measles immunisation visits. Participants in incentive groups, additionally, received money if their child was timely immunised (immunisation within 2 weeks of the due date). Caregivers and interviewers were not masked. The proportion of fully immunised children (receiving BCG, three doses of polio vaccine, three doses of pentavalent vaccine, and measles vaccine) by 12 months of age constituted the primary outcome and was analysed with log-binomial regression and General Estimating Equations to account for correlation within clusters. This trial is registered with ClinicalTrials.gov, number NCT01878435. Findings Between Oct 14, 2013, and Oct 17, 2014, we enrolled 2018 caregivers and their infants from 152 villages into the following four groups: control (n=489), SMS only (n=476), SMS plus 75 KES (n=562), and SMS plus 200 KES (n=491). Overall, 1375 (86%) of 1600 children who were successfully followed up achieved the primary outcome, full immunisation by 12 months of age (296 [82%] of 360 control participants, 332 [86%] of 388 SMS only participants, 383 [86%] of 446 SMS plus 75 KES participants, and 364 [90%] of 406 SMS plus 200 KES participants). Children in the SMS plus 200 KES group were significantly more likely to achieve full immunisation at 12 months of age (relative risk 1·09, 95% CI 1·02–1·16, p=0·014) than children in the control group. Interpretation In a setting with high baseline immunisation coverage levels, SMS reminders coupled with incentives significantly improved immunisation coverage and timeliness. Given that global immunisation coverage levels have stagnated around 85%, the use of incentives might be one option to reach the remaining 15%. Funding Bill & Melinda Gates Foundation.
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              Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings.

              Widespread application of clinical natural language processing (NLP) systems requires taking existing NLP systems and adapting them to diverse and heterogeneous settings. We describe the challenges faced and lessons learned in adapting an existing NLP system for measuring colonoscopy quality.
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                Author and article information

                Journal
                BMJ Glob Health
                BMJ Glob Health
                bmjgh
                bmjgh
                BMJ Global Health
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2059-7908
                2018
                29 August 2018
                : 3
                : 4
                : e000798
                Affiliations
                [1 ] Spark Street Consulting , New York City, New York, USA
                [2 ] Fondation Botnar , Basel, Switzerland
                Author notes
                [Correspondence to ] Dr. Brian Wahl; bpwahl@ 123456gmail.com
                Author information
                http://orcid.org/0000-0002-0037-7364
                Article
                bmjgh-2018-000798
                10.1136/bmjgh-2018-000798
                6135465
                30233828
                c1804ccb-4c0c-4a75-94ca-8e21371314c3
                © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 24 February 2018
                : 20 July 2018
                : 27 July 2018
                Funding
                Funded by: Fondation Botnar;
                Categories
                Analysis
                1506
                Custom metadata
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                artificial intelligence,primary health,low- and middle-income countries

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