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Helping midwives conduct safe deliveries in developing countries

Hacker News

Helping midwives conduct safe deliveries in developing countries

Hi HN! We have developed a solution that helps midwives in low resource healthcare setting to conduct safe deliveries. More than 80% deliveries occur with the assistance of staff nurses in LMICs (Low Middle-Income Countries). Most of these midwives are unaware of the standard protocol for monitoring the labor during pregnancy. We visited more than 100 such healthcare settings and found that existing paper-based protocols are complicated for unskilled health workers. Our solution helps them in monitoring the delivery and making timely interventions to save lives. It is a Mobile tablet based intelligent labor monitoring tool which not only allows the staff nurse to register and enter vital signs of a pregnant woman but also reminds to monitor the labor vitals, as per the standard intrapartum protocol. It also generates alerts in case of complications, based on an inbuilt algorithm. The doctor at a remote location can also view the live labor progress and guide the staff nurse. We have recently released a narrow down version of our product in play store for public use. You can find it on Google PlayStore: https://play.google.com/store/apps/details?id=in.janitri.war... You can find more about our solution on our website: https://janitri.in (Product name is "Daksh" - Hindi word for Competent). We have been piloting our solution in 35 government hospitals in four Indian states with positive results. We have observed an increase in protocol adherence in all these healthcare settings. Now we want to offer our solution to other developing countries with a similar problem. We know HN has a lot of people who have experience in developing or selling software to hospitals & governments. We'd love to hear your thoughts on hospital/government sales, entering a different country with an existing product, and anything else that you've seen come up on your journey!

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90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, apps · Missing: mac, agents, macos
19%19% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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