Customer Chat: Maccabi Healthcare Services (part 2)

Welcome to part two of excerpts from our conversation with Yael Rotem-Sher, Organizational Development Manager, and Ifat Alfasi, Head of Learning and Development, Maccabi Healthcare Services. In this post, we’ll focus on skills development and career-pathing. If you’re not caught up on part one, you can read about the keywords approach in our previous post, and for a deeper dive check out our Skills Extraction whitepaper to learn how semantics, not keywords, are the ROI differentiators.

If you’re interested in viewing the vidcast in its entirety, you can do that here.


How do you view the recent focus on skills-based talent acquisition, talent management, and L&D? 


In the past, or in some cases still, most organizations look at career management in terms of jobs. You go from job to job to job to job. Nowadays, we look at this through a different lens–a skills lens. Which skills are required for multiple jobs, and can also be beneficial in other things? And for your specific job, which skills do you need to be able to continue doing it? We’re looking at everything from a 3D perspective. Not just, “here’s the job, and here’s the ladder” that we were used to. It’s a much more complex climbing wall. 

Building on the climbing wall idea, we wanted to give employees more tools to be in charge of their own career paths and to strengthen their employability. The first way we do that is by moving people within the organization, giving people different roles. The second is focusing on learning–personal and professional development. Maccabi has some amazing projects that enable employees to experiment with managing a project from start to finish, not as part of your job, but as a separate project. This gives them experience and skills that contribute to their value as an employee in our organization–and outside of it. Today it’s not a bad word to think of your value both in and out of your organization.


How does Maccabi build on that idea to help employees grow from skills development to career-pathing? 


When we look at learning & development for an employee, we first focus on specific skills: Which skills should be strengthened? Which skills should you invest time in as you build your career path? The process isn’t immediate gratification–it won’t give employees available positions immediately. Our aim is that it will help us with long-term planning. If as an employee I’m in point A, I’ll set point B and point C as my goals. Then I’ll plan my way there: These are the areas I should move forward to that match my skills. Which other skills do I need to upgrade and work on?

In practice, we want to create a visual marketplace; a website, or page on our website where our employees can go through retrain’s intake process, then click on jobs they find interesting, get to the landing page of our recruitment department and see available positions. If the position they fit into isn’t available now, maybe it will open up six months or a year from now. In the meantime, they can see which skills to work on and enter our online learning system to see L&D opportunities curated by’s system. It’s an image of the future. 

It’s a lot of work to create this but this is our goal– to give our employees and our managers the ability to plan their climbing wall strategically, based on skills. It’ll come into play in feedback and evaluation talks, employees will be more career-oriented and many parts of the organization will work with the recruitment department. And wow, the sky is the limit with this process.


How does the marketplace look from an employee perspective? From an organizational perspective?


At the end of the day, it’s like an employee’s navigation tool–like Waze for the career world. They are able to use the tool based on their skills, their wants and aspirations, combined with the current market demand and their company’s goals. They can enter the portal, see existing options, see whether–or when–they’re available and in essence take the wheel on steering their career. If they see a role they like, they can upskill for it by purchasing courses. For example, if you want to become a data analyst, you must learn python; here are relevant python courses. You don’t need to get confused by the variety. It’s a win-win for the organization and the employee.

From the business side, gives our organization the ability to generally map and detect the low-skill vulnerabilities to see where we need to develop. It can give sectorial maps, geographical and regional maps– it betters our organizational vision.

For example, ‘analyst’ is a growing profession all over the world including within Maccabi. We need more and more people skilled in this field. So if I plan to develop an analyst program, I’ll use this system and it will tell me who has the right skills and potential to become a data analyst. Then, I can very quickly pick them without a huge process of mapping out people. Based on that, I can also plan for the needs of the recruitment department. It opens a lot of opportunities and potential. is an AI-powered matching engine already prepped for the future. Structured first and foremost around Responsible AI, our talent intelligence platform connects the right talent to your open roles and career pathways by tapping into their skills, capabilities, and aspirations, making sure you reduce attrition and retain the right talent. To see it in action, request a demo

Customer Chat: Maccabi Healthcare Services (part 1 of 2)

More than two years since the start of the pandemic, hospitals around the world are still grappling with burnout, battling some of the worst staffing shortages in decades. Even before Covid-19, the U.S. public health system was under strain; a recent McKinsey report found that the public health workforce shrank by more than 15% over the past 10 years.

Maccabi Healthcare Services faced similar staffing and attrition challenges when they turned to Together, we helped Maccabi create a harmonized view of their workforce, clarifying what deployable skills they already had in-house, the talent they could quickly upskill to fit their short- and long term needs, and the skills they would need to begin developing and hiring for going forward.

We sat down with Yael Rotem-Sher, Organizational Development Manager, and Ifat Alfasi, Head of Learning and Development, both from Maccabi, to talk about our collaboration. In the first of a two-part blog series, we’ll share excerpts from that conversation.

The full vidcast can be viewed here.


This is a journey into this very interesting project. Where did the need stem from? 


The first step was dreaming, visualizing what we wanted the future of Maccabi to look like. We looked at the current state, what problems we were facing, we looked at the world and asked ourselves which changes and adaptations were required. Then, we began building our dream of career management. 

Maccabi is a very large organization: 9,000 employees, 6,500 independent contractors; with subsidiaries, we’re over 120,000 people. At the end of the day, it’s a challenge because one can’t see the forest for the trees. 

Our second challenge was narrow pyramids in some roles. Most of our employees are caregivers; in that capacity, the pyramid you climb up is relatively narrow. Like everyone, we also began experiencing the trends in the labor market; although the percentage of people leaving Maccabi is relatively low compared to other organizations, it was still becoming a challenge. 

Thirdly, we looked at futuristic skills and understood we needed to develop our employees for the changes coming. In the past, an organization was in charge of its employees’ careers, but now, the power is in the hands of the employees. We asked ourselves,  how do we that shift? How do we make it accessible to them? How can we help them understand where and how they can develop? 

A big part of it also has to do with curatorship. Curatorship and accessibility were big challenges. Because everything is big in our organization, when we talk about personal and professional development, we’re talking about development in roles but also through learning and experimenting. And we wanted to make it all accessible. 

While we were starting to visualize this future, we mentioned our ideas to a vendor who put us in touch with  When presented their concept to us, I felt like my dream was coming true. It was like… Where did you come from? With every word, we looked at one another in awe. They talked about everything we visualized in our future.


How was your collaboration as a fast-paced startup alongside a huge and perhaps a bit traditional organization? 


Maccabi may be defined as a very large, traditionally bureaucratic healthcare organization, but in terms of Learning & Development, when we look ahead, we think, where do we want to bring our organization? The ambitious goals we set ourselves are based on the organization’s strategic goals. We want to be a more agile, fast-paced, adaptable organization. We don’t want every process to take two years. This is what we have in common with, and that’s how we make it happen. Our partnership means we have backup. This doesn’t mean it will happen instandly across the whole organization, but it’s starting to sink in in many areas, enabling us to move forward quickly.

We also try to be as innovative as possible, which is why it’s no surprise that we met We’re currently building training plans based on the fundamentals of where we want to go, and we progress by listening to the needs of our clients–in this case, participants in the program or even their managers. We always remain agile to modify and adapt.  and we build it as we go while listening to our clients’ needs. In this case,  the participant in the program or even their managers. We always remain agile enough to modify and adapt things.


How was the process of incorporating’s platform?  


We had always understood these to be long, time-consuming processes; by the time our database was updated, we’d have to do it again, as it will be outdated and irrelevant. Working in a very large organization, such processes aren’t easy; they take time and are impacted by bureaucracy and acquisitions. 

With, we brought them into our department’s management meetings because not only did their solution meet our needs as an organizational learning & development department, it also related to mobilization, recruitment, employee retention, employer branding–all fields of HR. From that, we were able to recruit quite quickly and see the benefits. In terms of learning & development, we had already been offering a multitude of opportunities but didn’t have the accessibility and curatorship we needed. These two elements come into play in’s solution, and our need was met. It was amazing. 

In our second in this two-part series, we’ll find out how supported Maccabi’s efforts around skills-based development and employee career pathing. is an AI-powered matching engine already prepped for the future. Structured first and foremost around Responsible AI, our solution connects the right talent to your open roles and career pathways by tapping into their skills, capabilities, and aspirations, making sure you reduce attrition and retain the right talent. To see it in action, request a demo

Extracting Skills from Text: Semantics–Not Keywords–Is the ROI Differentiator (part 2 of 2)

In our previous post, we talked about the difference between explicit skills and implied skills, and explained the Keyword Approach to skills extraction from text documents like CVs and job posts. We also outlined the importance of this automation. The right mix of precision and speed in AI deployment can:

  • Accurately connect talent with the right skills to your open roles
  • Achieve best-fit matches quickly, lowering cost and speed to hire
  • Reduce bias
  • Broaden the talent pool

Let’s now look at the second methodology.


Keywords vs. Semantics: The Semantics Approach


Semantic Analysis of text is the ability to construct logical representation of the meaning of the text as a whole, the same way we as humans understand natural language. A key factor in constructing the meaning of words is the ability to understand them based on context. For example, the word “bank” can have a different meaning depending on the context in which it appears. If a friend picks up their paycheck and says he’s going to the bank, you know he’s headed to a financial institution, not a large pile of snow, or snow-bank. 

Our automatic human understanding of the meaning of words comes from a concept called NLP–Natural Language Processing. Through NLP, we understand words based on their context; neighbor words are the most important influencers on context, but distant words can also have an effect.  

As such, if we want computers to understand words the way humans do, it means their ability to interpret a word based on the context in which it appears is a key factor.

NLP can be used to build machines that understand and respond to text or voice data in much the same way humans do. To extract skills from free-text documents like CVs, the Talent Intelligence Platform uses Deep Learning NLP models called transformers, a type of language model that processes each word in a sentence in relation to all the other words in the sentence, rather than processing each word individually. 

Unlike the Keyword Approach, in which text must appear in the exact same way within the CV in order to be extracted, Semantic Analysis automatically extracts skills both when they are explicitly written in the text and when they are implied by the tasks the individual describes in their CV.

For instance, using the financial analyst example from our previous post [[link]], the Semantics Approach will recognize the keyword “economics” and it will also interpret a sentence like “Maintaining and improving dashboards and calculation files of multiple reports using advanced Excel” to extract “create financial reports” as a skill. 

Conversely, Semantic Analysis will not extract a word that could be considered a skill, if it doesn’t appear in the right context. For example, if an individual describes working as an “Office Manager in the Economics Department,” Deep Learning NLP models will not detect that person has the skill “economics” just because the word is there.


Unique Needs of HR


Workforce roles and skills are evolving at breakneck speed, with new capabilities in demand and more open jobs than there are people to fill them. To optimize talent acquisition, management and retention, HR leaders need automation that speaks their ever-evolving language. 

Our Talent Intelligence Platform uses semantics-based machine learning models to provide the most accurate, actionable data possible. We empower enterprises to succeed through skills-based hiring, talent mapping capabilities, inner mobility and retention initiatives, and personalized learning and development programs for every single employee. 

If you’d like to see how our sophisticated, Responsible AI-driven Talent Intelligence platform transforms workforce planning, we’d love to show you. Book a Demo to participate in a tailored walk-through based on your organization’s specific needs for hiring, upskilling and retaining quality talent.

Extracting Skills from Text: Semantics–Not Keywords–Is the ROI Differentiator (part 1 of 2)

Automatically extracting skills from text documents like CVs and job posts is what enables successful talent mapping between individuals and potential roles. With the right mix of precision and speed, AI deployment can:

  • Accurately connect talent with the right skills to your open roles
  • Achieve best-fit matches quickly, lowering cost and speed to hire
  • Reduce bias 
  • Broaden the talent pool 

However – all AI skills extraction methods aren’t the same, and the difference matters.  

There are two main methodologies at play in the HR Tech space. In our next two blog posts, we’ll explain the ins and outs of each.


Explicit Skills vs. Implied Skills


First, let’s look at what’s in a document such as a CV. Throughout one’s job history description, there are titles, roles, tasks and skills. Parsing–the means by which data is separated into more easily processed components in order to produce a well-structured set of information–is what enables mapping ability; in this case, skills mapping.

However, CVs and job descriptions are written in a “free-text” manner, whereas a “structured” set of information would be usually in the form of a table or graph. The challenge becomes finding a platform that can scan a CV and identify both the skills that are explicitly indicated in the document as well as those not explicitly mentioned, but which are implied by the tasks the individual describes. 

Here’s an example. A financial analyst lists the following tasks on their CV: 


  • Maintaining and improving dashboards and calculation files of multiple reports using advanced Excel
  • Assessing credit risks  

From the description of these tasks, one can deduce the individual is proficient at the following skills:


  • Creating a financial report
  • Analyzing financial risk

But without those specifically-worded skills outlined in the CV, how can a machine learning platform infer such capabilities?


Keywords vs. Semantics: The Keywords Approach


Put simply, using the Keywords Approach to skills extraction means words on the keywords list must appear in the exact same way within the CV in order to be extracted. Conversely, any skills not listed on the keywords list will not be extracted. 

Here’s an example. If the skill “economics” is listed on the keyword list, it will only be extracted if it appears exactly that way on the CV. If instead, a CV includes variations of that word, such as “economist,” the keyword-focused platform will not detect it as a skill. 

Taken further, words that are included in the keywords list but appear in the CV not in the context of skills will also be extracted as skills, despite being mentioned in a different context.

Therefore, the deficiency of a keywords approach is that it can result in missing skills that aren’t explicitly mentioned in the text, or which aren’t included in the keywords list. It can also extract words as skills even though they were not mentioned in that context. In other words, keywords approach is context-free.


In our next blog post, we’ll explain the Semantics Approach to AI-enabled skills extraction. 


You can also check out our Skills Extraction whitepaper here.