Technology Revolution in Home Care
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Machine learning is a type of artificial intelligence that is concentrated on developing algorithms that analyze data and predict future events with tremendous accuracy. It is a technology that is well suited to identify hidden trends and patterns in vast amounts of data, not visible to the human eye. Jonathan Vallée, CTO of AlayaCare, an end-to-end home healthcare software solution, discusses the importance of collecting and analyzing data, provide real examples of feasible tech solutions organizations could implement today, explore the insights gained from utilizing machine learning, and present the results of two studies on applying machine learning to streams of incoming remote patient monitoring vitals for decision support.
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Technology Revolution in Home Care
- 2. Technology Revolution in Home Care Jonathan Vallée, VP Engineering, AlayaCare
- 3. 14% increase in global GDP in as a result of Intelligence (AI) ($15.7
- 4. The highest potential impact sector is healthcare with 37% of the provider that are expected to adopt AI within 3 years.
- 5. Main areas of AI potential in healthcare Supporting Diagnosis Pandemics Imaging Diagnostics
- 6. The building blocks of AI Sensors The Cloud Machine Learning
- 7. Sensors are the building blocks of AI
- 10. Cloud computing provides a simple way to access servers, storage, databases and a broad application services over internet.
- 13. We Care - The Re-ACT program • More than a million dollars of savings for 210 clients • 70% decrease in client falls • 80% of clients believe that Re-ACT made a positive difference in their lives
- 14. SmartCoach Remote Monitoring Program • 73% reduction in emergency department visits • 64% reduction in the number of hospitalizations for patients • A 35% drop in patients who need at least one hospitalization • Estimated cost-savings to the health-care system for 74 patients of over $150,000
- 15. What’s your AI strategy?
- 16. Parting Questions • Are you or your technology partners gathering the right data for AI? • Do you have access to the skillset required to build business value out of data?
- 17. How will we achieve better outcomes in 2030?
Notes de l'éditeur
- § The highest potential impact sector is healthcare with 37% of the provider that are expected to adopt AI within 3 years § Main areas of AI potential □ Supporting diagnosis -> who should I focus on ® Use the patient’s unique history as a baseline and act on deviations □ Pandemics □ Imaging diagnostics
- Main areas of AI potential □ Supporting diagnosis -> who should I focus on ® Use the patient’s unique history as a baseline and act on deviations □ Pandemics □ Imaging diagnostics
- Building pieces ○ Sensors § Everything is readily available □ I’m talking to my speakers □ My phone manages my lights □ Etc.
- Remote monitoring sensors
- Infrastructure § The cloud makes data gathering feasible
- ○ Machine Learning Explained Video https://www.youtube.com/watch?v=rXGqKJoQ4qM
- § Algorithms are being developed everyday to predict the future from the past, to identify patterns invisible to the human eye, to automate tasks that are more and more complex, etc.
- • Real examples ○ We Care § The Re-ACT program □ More than a million dollars of savings for 210 clients □§ Ensuite avec nous □ Nombre d’alertes / 2 □ Nombre d’événement identifiés + 10%
- ○ Southlake hospital § With “frequent flyers” § A 73% reduction in emergency department visits and a 64% reduction in the number of hospitalizations for patients § A 35% drop in patients who need at least one hospitalization § Estimated cost-savings to the health-care system for 74 patients of over $150,000 § An overall 65% drop in patients’ health system utilization
- • Parting thoughts ○ What’s your AI strategy § Are you or your technology partners gathering the right data for AI § Do you have access to the skillset required to build business value out of data