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      <image:title>Where We Work - Improving the quality of malaria testing data</image:title>
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      <image:title>Where We Work - Improving the quality of malaria testing data</image:title>
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    <loc>https://www.auderenow.org/where-we-work/incentivizing-consumer-and-pharmacist-testing-for-malaria</loc>
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      <image:title>Where We Work - AI-powered result validation to aid in malaria diagnosis</image:title>
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      <image:title>Where We Work - Last mile digitization to improve health outcomes</image:title>
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      <image:title>Where We Work - WhatsApp chatbot to enable COVID-19 self-testing</image:title>
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      <image:title>Where We Work - Enabling swab and send testing for COVID-19</image:title>
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      <image:title>Where We Work - Enabling swab and send testing for COVID-19</image:title>
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      <image:title>Where We Work - Home testing and linkage to care for high mortality conditions</image:title>
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      <image:title>Where We Work - Home testing and linkage to care for high mortality conditions</image:title>
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      <image:title>Where We Work - Home testing and linkage to care for high mortality conditions</image:title>
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      <image:title>Where We Work - WhatsApp chatbot to enable HIV self-testing</image:title>
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      <image:title>Where We Work - WhatsApp chatbot to enable HIV self-testing</image:title>
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      <image:title>Where We Work - WhatsApp chatbot to enable HIV self-testing</image:title>
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    <loc>https://www.auderenow.org/where-we-work/hiv-testing-and-prevention-via-telehealth-for-msm-and-transgender-women-populations</loc>
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    <loc>https://www.auderenow.org/case-studies/case-study-thinkmd-kano</loc>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/7086fa45-7bf8-4a35-9894-fec271270ea1/thinkmd_cs_ai_accuracy.png</image:loc>
      <image:title>Case Studies - Enhancing Malaria RDT Interpretations with AI in Kano State, Nigeria - Make it stand out</image:title>
      <image:caption>HealthPulse AI achieved a high overall interpretation accuracy, supporting and enhancing FHW performance which showed a 95.3% accuracy.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/ab27f55c-13c7-4e8b-9ca0-2a66358e7e42/thinkmd_cs_fp_ai_accuracy.png</image:loc>
      <image:title>Case Studies - Enhancing Malaria RDT Interpretations with AI in Kano State, Nigeria - Make it stand out</image:title>
      <image:caption>AI outperformed FHWs by over 14% in detecting faint positives, ensuring early and accurate malaria diagnosis.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/a9e3052f-d8d9-4241-8cac-4b18c5439100/thinkmd_cs_ai_fw_concordance.png</image:loc>
      <image:title>Case Studies - Enhancing Malaria RDT Interpretations with AI in Kano State, Nigeria - Make it stand out</image:title>
      <image:caption>Strong alignment between AI and FHWs in positive mRDT results, indicating that the majority of FHWs are accurately interpreting RDTs.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/e2e1280c-d718-4ee7-948e-aeb21a0f5d7c/thinkmd_cs_user-flow.png</image:loc>
      <image:title>Case Studies - Enhancing Malaria RDT Interpretations with AI in Kano State, Nigeria - Make it stand out</image:title>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/73dcb01c-061c-421c-b736-f56e6ff42b50/thinkmd_cs_consistent.png</image:loc>
      <image:title>Case Studies - Enhancing Malaria RDT Interpretations with AI in Kano State, Nigeria</image:title>
      <image:caption>The AI achieved a weighted F1 score of 96.4%, surpassing the FHWs' score of 95.3% and demonstrating the AI's reliability in supporting frontline health workers. AI correctly interpreted 97.12% of negative mRDTs and 96.38% of positive mRDTs.</image:caption>
    </image:image>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/725251b5-1bba-4a96-9bd2-8edeb11647fa/thinkmd_cs_enhances_fp.png</image:loc>
      <image:title>Case Studies - Enhancing Malaria RDT Interpretations with AI in Kano State, Nigeria</image:title>
      <image:caption>One of the critical areas where AI showed remarkable value was in the identification of faint positives. A crucial capability for early and accurate diagnosis of malaria. AI correctly classified 90.2% of faint positive mRDTs compared to only 76.1% by FHWs.</image:caption>
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      <image:title>Case Studies - Enhancing Malaria RDT Interpretations with AI in Kano State, Nigeria</image:title>
      <image:caption>The AI performed as well as or better than experienced and well trained FHWs, indicating AI’s strength in elevating FHW performance.  AI matched FHWs with 97.52% agreement on positive results and 93.38% on negative results.</image:caption>
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      <image:title>Case Studies - Enhancing Malaria RDT Interpretations with AI in Kano State, Nigeria</image:title>
    </image:image>
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    <loc>https://www.auderenow.org/case-studies/case-study-chw-rwanda</loc>
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    <lastmod>2024-08-13</lastmod>
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      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study</image:title>
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      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study</image:title>
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      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study</image:title>
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      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study</image:title>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/3dbf7d82-340e-4135-a35b-cd756955a47e/workflow.png</image:loc>
      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/11d426e6-732f-4f3d-89a2-b1acb845d4f0/RwandaCaseStudy_community_rev_12.7.23%403x.png</image:loc>
      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study</image:title>
      <image:caption>The App was feasible, accepted, and desired by CHWs. CHWs reported increased self-confidence and community trust in their activities. 99% agreed app instructions were clear and easy to follow 93.8% agreed time was saved compared to using paper registers 93% found the app very user-friendly</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/c9c7abc6-da3d-46da-b911-a310161a351d/RwandaCaseStudy_RDTadmin_rev_12.7.23%403x.png</image:loc>
      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study</image:title>
      <image:caption>App use significantly improved CHW’s mRDT knowledge, administration adherence, and interpretation accuracy. Identification of positive results improved by 72% (from 13% to 85.2%) for very faint visible lines and RDT read window knowledge improved by 17% (79.9% to 97%).</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/a28ec6f9-8b0c-49a2-b374-e53bc51528d8/RwandaCaseStudy_surveillance_rev_12.7.23%403x.png</image:loc>
      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study</image:title>
      <image:caption>AI achieved 95.5% accuracy in interpreting mRDTs for malaria diagnosis, outperforming CHWs’ 94% accuracy. AI detected 25.4% more Pan lines than CHWs. AI is potentially valuable for identifying new malaria strains and essential for multiplex tests like Flu/COVID, where precise RDT line presence matters.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/64ff6a6dd00b77132a60f99b/2d8a1c55-f37a-49a9-8492-c56156cd54a8/RwandaCaseStudy_supervision_rev_12.7.23%403x.png</image:loc>
      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study</image:title>
      <image:caption>AI is a valuable tool for identifying CHWs who need support. 9% of CHWs in the study had an mRDT interpretation accuracy rate below 85%, indicating they could likely benefit from additional guidance in their daily testing.</image:caption>
    </image:image>
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      <image:title>Case Studies - Digitally-Enhanced Malaria Testing using AI to Support Quality Control with Health Workers – A Rwandan Pilot Study</image:title>
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