In the age of AI and automation, if industries are not ahead of the curve, they are often falling behind. 

According to a May DeDeloitte survey that connected with 600 executives from large manufacturing companies based in the U.S., 90% of manufacturing leaders see smart factory initiatives as critical. Specifically, the integration of advanced automation, real-time data analytics, and AI-driven decision-making.

While companies realize the importance of this automation agility, few are actually equipped to take the next steps. Ninety-two percent of manufacturers surveyed said they believe smart manufacturing will be the main driver for competitiveness over the next three years, while 85% believe their smart manufacturing initiatives will transform how products are made, improve agility, and attract new manufacturing talent.

There is still some hesitancy in the medical lab world to lean too far into laboratory automation, as over half (52%) of lab professionals strongly or somewhat agree that automation is a threat to their jobs, according to a Siemens Healthineers survey. 

As younger workers begin to join the force, they have different priorities than their older counterparts, including an expectation for digital fluency. The majority (85%) of survey respondents agreed or strongly agreed that smart manufacturing initiatives will attract new talent to the manufacturing industry, showcasing it as a vibrant and viable career path.

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Healthcare manufacturing has a negative reputation among younger workers, as it is often painted as an archaic or outdated industry. In order to continue bringing in new and fresh talent, the industry must adapt. 

Currently, vacancy rates in medical laboratories are estimated to be 7–11%, and as high as 25% in some geographies, according to Siemens. 

Paavana Sainath, head of research and development for core laboratory solutions at Siemens Healthineers and a developer of lab technology used to reduce lab professionals’ hands-on time by up to 75% to get patients their test results faster, spoke with MD+DI about how automation is changing the healthcare manufacturing workforce, and what companies must do to stay ahead of the game. 

What does the new health tech workforce look like?

Sainath: I would start with the ability for the workforce and the people to think system of systems and anchor the notion of delivering value-based solutions. Increasingly there is a need for products that have clinical effectiveness while delivering cost efficiency and meeting patient satisfaction. The workforce itself needs to have multiplied domain expertise, knowledge of the clinical space, AI, data science, and user experience. And they must be able to live at that intersection of these fields. This will help the workforce to develop more robust solutions. Another feature would be digitally native skills, which is already a feature of the emerging workforce of the future, in a sense that they have a high degree of comfort with real time data analytics, population health, key machine learning, and Gen AI skills that deliver targeted solutions for clinical problems. The other big trend is a need to work in the virtual or digital environments. An example is system simulation and modeling. It is a huge focus for us, to be able to develop and learn with rapid iterations. There is less emphasis on physical development, and more on the virtual. There is a focus on things like model based engineering, computational dynamics, software modeling, code generation. The future of engineering for me is not physical, it is virtual. The last thing from a workforce perspective, which is not new, is the need for adaptability and lifelong learning. This has been true for a very long time, but it is increasingly important because of the fast moving technology cycles we are in. The ability to learn, adapt, and stay current is extremely important.

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What automation techniques/AI do you see as being the most pivotal in the next 5 years?

Sainath: There are many automation and AI technologies that will come to fruition in the next five years. We already discussed the need for workforce skills in Gen AI and machine learning. These will be very pivotal to building valued capabilities for helping customers and patients. Some other key techniques that will have a significant impact in the near term are digital twinning. We have the ability to create models of robots. Now that will help us bring rapid automation for key areas with technologies like collision free navigation. They will become more mature with the advent of autonomous cars. We can use that technology that’s really maturing in a different industry and apply it to health tech. The applications can be anything from simple tools to humanoid robots that will enable significant innovation in automation. We will also be able to leverage AI to train on multimodal data. We are now at the cusp of this new clinical insight where you can combine imaging and lab data with patient vitals, meta data of patients to provide more insight. A tangible example in the lab world is automatic reflex testing. You have high risk individuals who have routine testing done, and you can reflex test them for targeted biomarkers. You can arrange the right test for the right patient, driven by the knowledge of clinical conditions, and improve clinical care and wellness. Remove that friction that we currently have in the healthcare system.

How do you measure success when developing technology that aims to both improve efficiency and appeal to a new generation of workers?

Sainath: How we measure efficiency goes back to the value statement and value based care. Therein lies the appeal. When we focus on efficient delivery of the right healthcare to the right cohort of patients, that helps the new generation of workers have the flexibility to not have to over invest in healthcare. That is a major concern of the new generation; how much of a healthcare burden exists on the new generation for taking care of older generations. As we think of value based care, which should be a core part of what we do, how do we energize the workforce to work on topics that will help remove friction in the healthcare system, deliver the right test to the right patient, focus on wellness, those are the avenues to bring both cost efficiency and operational efficiency but also energize the workforce.

What suggestions do you have for manufacturers trying to attract the younger workforce?

Sainath: I think for the younger workforce, a mission is extremely important. It’s important to lean into the mission that the organization is focused on. The younger generation is very attuned to the risks of climate change and the need for more sustainable solutions. Second is showcasing innovation and impact. What we work on daily and what impact it is having on the broader world. It’s extremely important to have that sense of purpose and meaning for the newer generation. The last thing is to make things more attractive by way of flexibility, whether it’s the design of the work space, the community it builds, flexibility in virtual work, having that be part of the conversation and allowing that freedom to choose how you orchestrate your work life, similar to how you orchestrate your own life, is giving control to them to manage.

Do you have concerns about AI taking away jobs, specifically from Gen Z workers? 

Sainath: Do I have long term concerns? Perhaps. I do genuinely think human beings are very versatile and I have yet to see machines demonstrate even close to that level of versatility. While I am a technology optimist, I don’t think they will get to that level of versatility, even among humanoid robots or automation solutions. Human beings can easily flip to the next task that is completely different. Training a machine to do a versatile list of tasks is a challenging problem. The newer generation of workers will be capable of leveraging Gen AI to make themselves more capable. It is not going to be Gen AI taking roles away but more like people with Gen AI skills taking roles from people not practicing those skills. That digital skillset is a core part of literacy for younger generations.  

What are the biggest challenges clinical labs face when implementing automation and AI solutions?

Sainath: I think some of the challenges include natural concerns around automation and displacing workers. I do think this fear is overstated. Automation and AI are companions to enable productivity, but they aren’t competent at being independent. A second challenge is around credibility. Automation has been touted as a way to get more work done with fewer people. However I do not think it will impact workforce size significantly, because of the versatility issue. I do think the last issue is that trust is not a key feature. Building trust would require the people who generate these models to have robust model explainability tools, showing data, so you don’t have bias one way or another, and strong governance frameworks. It is important not to rapidly lose the trust you have built with consumers. 

source：mddionline

https://www.mddionline.com/manufacturing/manufacturing-ai-and-gen-z-the-new-healthcare-workforce

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