MOL TOOL: “Keeping Talents Engaged With Your Business.” Since No one except Us, bothered to create Multilevel Operational Learning (MOL) #tools that prompts talents towards asking good #Questions , #Companies will spend considerable time in figuring out questions for #AI ; even joining #Expensive seminars outside of their industry for “question scoping”. more here:- https://searchenterpriseai.techtarget.com/news/252452490/An-AI-project-should-start-with-a-question-not-an-insight?track=NL-1816&ad=924240&src=924240&asrc=EM_NLN_103538717&utm_medium=EM&utm_source=NLN&utm_campaign=20181115_Microsoft%20gives%20Power%20BI%20more%20enterprise%20BI%20features;%20AI%20lessons%20learned%20from%20Gartner;%20SQL%20Server%20security%20best%20practices
We’ve invented MOL tool or Multilevel Operational Learning tool to create Healthcare Operational sensitivity and efficiencies in fresh undergrads and graduate student. With experienced IT professionals without healthcare and/or analytics experiences, our MOl tool generates rich communications- easing their integration needs into Analytics and with employer. Hence MOL tool improves your AI Training data close to general AI Intelligence. Analytics now is more than Data Scientist’s skills. It is about diversity of experiences who are trained effectively in analytics and contribute to AI training data. MOL Tool is built from Machine Learning algorithms; performs classification, segmentation, clustering and various unsupervised and supervised learning. Helps talent learn Complex Healthcare data and Industry use cases, for example, Re-admissions, ER care, Hospital Acquired Infections (HAI), Home-health care and Healthcare Fraud etc without intimidation.
Most companies struggle with employee churn and engagement. Since 2000, 52% of #fortune 500 companies have vanished or good #talent solution makes companies 52% more profitable. According to KPMG survey, unfilled jobs cost USA companies $160 Billion per year. #AI hype has created enormous demand for human labor; sprouting upstream #Companies such as data cleaning and now – data labeling for downstream Analytics. Many companies jumped into #AI #hype without realizing huge scale of #talent needed. more here:- https://venturebeat.com/2018/11/16/hive-taps-a-workforce-of-700000-people-to-label-data-and-train-ai-models/?utm_source=VentureBeat&utm_campaign=401263088b-VB_Daily&utm_medium=email&utm_term=0_89d8059242-401263088b-33454215 . So don’t just start with use case for #AI ; start with #business case. That means asking lots of good questions and scoping at least two to three business cases from those #Questions . more here:-https://searchenterpriseai.techtarget.com/feature/Lessons-learned-from-3-AI-use-cases?track=NL-1816&ad=924240&src=924240&asrc=EM_NLN_103538714&utm_medium=EM&utm_source=NLN&utm_campaign=20181115_Microsoft%20gives%20Power%20BI%20more%20enterprise%20BI%20features;%20AI%20lessons%20learned%20from%20Gartner;%20SQL%20Server%20security%20best%20practices
Working with data makes learning fast because it starts working on curiosity inside you. Curiosity is number currency of talent but 95% companies globally don’t know how to hire and train for industry business knowledge. Bay Area has incredible hardcore IT Boot-camps! For talent with fresh undergrad and graduate quantitative degrees, there is no tool globally to find their curiosity. How will they perform in jobs within two weeks on self learning tool and self-creating questions and hypotheses? Answer helps company as much as talent. There was always a need for a tool that technical and business talent can use to learn because there is a need to figure out data sets. There is a need to turn AI black-box to bright-box. And they require Curiosity! We invented MOL Tool after 6 years of rigorous machine Learning Algorithm research; offers summarization, visualization – questions comes to you as next step, not the other way around. Our MOL tool enhances curiosity, business knowledge of healthcare at levels and hence Engagement of talent regardless of industry experiences.
Talent in global corporate environment is looking for critical thinking and problem skills and Universities globally have responded by MBA- Analytics like programs. In USA, while admission in all other programs is at 2.4%, in Analytics related education enjoys 7.4% admission rate with very high tuition fees. Unfortunately, they are MBA Lite programs – not much Actionable Learning. Employers are not happy (https://www.wired.com/story/impatient-with-colleges-employers-design-their-own-courses/?mbid=nl_121817_daily_list3_p4 ). So no one except us, researched with machine Learning algorithms and thought to create a tool that addresses both – Employee and Employer’s pain points.
Almost two hours of lecture at Golden Gate University MBA – Analytics program on 12/11/2017 in San Francisco by founder, Navin Sinha on Car Injury Fraud Predictive Analytics, then was helpful to students. Reflecting back, MBA programs in
#USA #universities don’t teach healthcare business (CPT, ICD). Takes #talents 6 months training to get up to speed and next 6 months working on various projects to gain 100% confidence. Healthcare #INDUSTRY looses great #talents as they prefer healthcare #experiences . We also reflected on this experience and questioned how to reduce this one year growing pain? So we tuned our Machine learning algorithms further in our MOL tool since 12/11/2017. We also developed many Machine Learning algorithms to find if the talent is curious, then help such talents ask smart questions, create testable hypotheses, learn healthcare fast and forget about next employer(s). No one year growing pain makes employees happy, reduces churn, improves productivity of diverse team – all drivers of lower costs for employers!