Technology changes the labor market rapidly. In health care, too, robotization, big data and artificial intelligence lead to an unprecedented revolution in the workplace. Think of Google and endless start-ups that are already active in health care. How does this work out for recruiting and binding staff?
Who wants to know what the influence of technology is on the labor market, has to look into Uber. There, a handful of people with smart algorithms keep millions of taxi drivers at work. Due to the innovation of autonomous cars of Tesla and Google, the change that taxi drivers will be unnecessary is real. Robotization, big data and artificial intelligence are also being forced into health care. Consequence? In health care, a number of jobs will be in danger. Is that a bad thing? And how does health care stay appealing to human employees?
Algorithms replace people
According to Oxford researchers Frey and Osborne, there is a real chance that 47% of jobs in America will be taken over by computer algorithms in the coming decades. In fifteen years telemarketeers, referees, cashiers and juridical assistants will be unnecessary.
The medical and pharmaceutical sectors are also facing major changes, as do health care service companies. Algorithms will change and replace human actions with regard to diagnosing, as well as curing and prevention.
Not far from now, as it seems, super computers like Watson of IBM will be able to flawlessly diagnose illnesses. Due to his artificial intelligence, Watson saves all information about familiar illnesses and medicine. This computer is able to consult this information any minute. Not just for findings of new research. But with existing data and medical statistics of all connected hospitals and doctors around the world.
Because of this, the role of a doctor changes. At work, doctors only have ten minutes per appointment for their most important task, to correctly diagnose the patient and to give advice for the best possible treatment. Within this limited time slot, it is impossible to recall on all the illnesses the patient dealt with and to compare this information to all diseases and medicine around the world. Moreover, a doctor is human. And people can make mistakes. Even if it regards one of the best doctors.
This is why doctors can even be replaced with diagnosing cancer. During a recent experiment with lung cancer, a computer algorithm made the correct diagnosis in 90 percent of cases, compared with a 50 percent success rate among “ordinary,” human doctors. CT-scans and mammograms are often checked by specialized algorithms to give doctors a second opinion. These algorithms are capable of finding tumors that doctors could have missed.
High-tech does not only change the job of a doctor, but also the existence of apothecary. Human apothecaries make an average of 1,7 mistakes per prescription. In the US, that is 50 million mistakes per year – with all the consequences that go along with it. In 2011, a pharmacy was opened in San Fransisco that is staffed by a robot. Within seconds, all prescriptions came in, in addition to all detailed information about other medicine that the patient was taking or even any allergies that were important. A sensitive defeat for man; in the first year, the robot pharmacist processed two million prescriptions flawlessly.
Big data for prevention.
This kind of big data is only appropriate to prevent an illness. The company that creates a big genetic data base first, is able to control this market completely. That is familiar information to Google. The Internet giant works on Google Baseline Study. It tries to produce a huge data base of human health. Based on this data, a profile of the ‘perfect health’ can be designed. With help of a ‘baseline-profile’, people can be warned in time for emerging health issues and diseases.
Google is not the only one who is working on a database for prevention. The company 23andme, a name that refers to the 23 chromosome pairs that encode the human genome, focuses on prevention based on chromosome-characteristics. Anyone who sends a saliva sample to the company in Silicon Valley, receives information about possible risks of his health and about possible genetic susceptibility through the internet. Since information is based on statistics, is the size of the data base of a company crucially important in order for the predictions to be accurate.
In addition, biological tech-companies in the US are worried that the upcoming power of China will take over the genetic market. The privacy law in the US is very strict, when China deals with this more lightheartedly. But if only Google and many American start-ups get free access to several biomedical devices, DNA-scans and medical information, an omniscient American tinted digital medical health service is more likely to arise. An oligopoly that is able to conquer epidemics and that can protect people against cancer, heart attacks and Alzheimer. Does everyone actually want this?
It may sound impersonal that computer algorithms play a major role in deciding over human health, but it could be in favor of the patient. Diabetics already use sensors that automatically check their sugar levels, that alarm the patient if dangerous levels have been diagnosed and they can activate the pump of insulin or glucagon through an app on their iPhone.
|Algorithms as doctors
Algorithms in directing
Algorithms can play a distinctive role on administrative level, next to a medical level. Deep Knowledge Ventures, a participation company specialized in health care from Hong Kong , added the algorithm VITAL to the board of directors in 2014. The algorithm got equal as much votes compared to other board members about investing in companies.
VITAL creates recommendations based on the analysis of gigantic amount of information about financial situation, clinical tests and intellectual property of health care companies. Other algorithms also contributed to groundbreaking innovations. For example to science, that there are more than two hundred different types of cancer that all have a different course of disease and a different survival rate.
Personal patient profile
By using data, it is enabled to offer customized treatment to patients. That in itself is wonderful, for example with treatment of cancer. Due to diversity of types of cancer, therapies that are customized to patients increase, that are focused on specific characteristics of tumors. For this, a unique personal profile of the patient will be created. This is called goal oriented therapy. A therapy that mostly has more positive effects and less side effects.
On the other hand, pharmaceutical companies see salvation in so called ‘Personalized Health care’. In particular, the ranking of genetic material (DNA sequencing) will play a major part in the analysis of genetic profiles, the detection of diseases and the determination of customized treatment.
Whether therapy for cancer treatment does indeed have an effect is still difficult to predict. The patient who hears that he or she has cancer will be dealt with emotionally when the diagnosis becomes known. That feeling of insecurity keeps existing if the doctor prescribes medication. It makes sense, because it is unsure whether the treatment is working well.
Jeske Timmermans, head of Roche Foundation Medicine in the Netherlands, one of the pioneers in the field of Personalized Health care, said; ‘That sounds unqualified, but the truth is often not very different.’. In ‘NRC Handelsblad’, Timmermans pleads in favor of personalized treatments, because these are tailored on the personal situation of the patient at the exact right moment. ‘But, we do need to be able to use more data from the real world (real-world data, outside of clinical studies)’, states Timmermans. She pleads for more available information, in order that researchers will retrieve more knowledge about the genetic profile and characteristics of tumors.
The final thresholds of data revolution
Unfortunately, not all data is accessible for research. To enable personalized treatment on individual level, more medical data of more people is necessary. And that multiplicity of data is not or inadequately available.
When coordinating treatment to the genetic profile of the tumor, one specific mutation and the use of one drug are still often looked at. The combination of multiple resources can offer perspective. But for instance, with 300 different medicine, there are 45.000 possible combinations of two resources. Such investigations take time and the patient suffers from it. Mission impossible.
”Less than 10 percent of all patients take place in clinical studies concerning medicine,” states Jeske Timmermans of Roche. “There is a large amount of knowledge hidden in the data of patients being treated outside clinical trials,” she says. “We are so deficient in ourselves if we do not use that data or not enough for the benefit of all future patients, our children and grandchildren.”
The collection, the use and the analysis of big data in health care is being hindered by social views, ethical, juridical and logistic challenges. This data has to meet standards to become interchangeable. Safety and discretion must be guaranteed, with guarantees against misuse. And last but not least: patients must be prepared to make their (anonymized) data available. In order to develop more and more personalized medicine, or to use existing medicine more personalized, these boundaries need to be challenged. But if real-world data becomes available to research, unimaginable break through lies ahead in health care, also in the development of medicine.
Admittedly, Watson from IBM is also not finished, who sometimes suffers from technical problems. So it will take some time for doctors to be replaced by this kind of super computers. But technical problems – as hard as they may be – mostly need to be fixed only once. Which will lead to not only one doctor, but an endless amount of doctors that are available day in day out – in every part of the world. Even if it costs hundred billion euros to make this system work, it will be much cheaper at the end compared to training all the individual doctors that are needed.
Changes for mankind
The question remains if all human doctors will disappear some day at all. Will the tasks that require more creativity than just standard diagnoses in the near future be pointed to people or will these consequently be carried out by artificial intelligent computers? Since a super computer does not know emotion or feelings, thus no empathy, more than enough professions will remain that need the human factor, right?
As soon as a CT-scan points out that someone has cancer, does that person prefer to hear this from a empathic doctor or from a anonymous computer? This leads to the question if a machine can be empathic, or can act empathically.
A super computer like Watson will eventually be able to analyze the emotional state of a person the same way that it detects tumors. The computer is able to measure the human blood pressure, but also to analyze brain activity and biometric data to exactly know how someone feels. The computer should potentially be able to tell a patient exactly what he or she needs to know in the right connotation due to statistics that the computer acquired during millions of other social interactions.
For example, an algorithm of Mattersight, a company that specializes in customer service, also confirms that machines are able to correctly respond to emotions. The algorithm interprets emotional state and personality type based on the tone of voice and the words that the caller uses. So if he is introvert or extrovert, rebellious, independent or obedient. The caller will be put through to the employee that matches your profile closely based on the analysis of the algorithm. Outcome? A happy customer.
Human resources is ready for the future.
Rest assured. There is a long way to go for the first physical amalgamations of man and machine (cyborgs) walking around the streets and doing virtually all human work, without ever being sick himself. However, algorithms have already taken over parts of human tasks and responsibilities in health care. For instance, the socially adapted talking robot in elderly care that activates and stimulates people.
Megalomaniac tech-giants like Google, as well as small start-ups that change rapidly with the market, tread the field of health care. Only because of that, the health sector is being challenged. The functions of doctors and decision makers in hospitals change rigorously. This affects all kind of jobs in companies that offer services or products to the health sector.
Think of medical advisors, accountants or product specialists who should have broad knowledge about medication and diseases, but who should also be able to talk about data analysis and new technological innovations. Will the waiting rooms soon be populated by data analysts and trend watchers to inform doctors of what’s coming at them? Or does the conversation move to the internet even more?
A new way of recruitment
The explained transformation is up and running Therefore, it is necessary for staff functionaries to already determine which profiles of employees will be needed in the future so that the organization does not fall behind. It is extremely important to make sure staff is ready for the new reality to keep the existence of a company save. It is advisable to recruit and bind the right people in time.
The first step is to explore what the current knowledge, skills and ambitions are within the existing staff. In addition, it is important to establish how the possible gap in knowledge and capabilities can be spanned to know which candidates need to be recruited. Highly educated staff is scarce and becomes scarcer in time.
It is important to look for people who are necessary in the future to stay or become top leader in the market. Employees which companies in the health sector can use to anticipate on changes that happen because of the application of high-tech and big data. The traditional way to recruit new talent does not exist anymore. In line with former described personalized health care, I plead for this method. Personalized Recruitment. This method supports flexibility with ongoing changes. It has been proved that this reduces 60 percent of process time of vacancies and reduces approximately 52 percent of all costs.
The following 5 steps will be completed:
- Analyze – Understand the employee
Analyze what your employees expect of a employer/employee relationship. What do your employees hear, see and think when your company is being discussed. What do their friends and other acquaintances say at other companies. Why do your employees get out of bed every morning to work for your company.
- Design – Insight in the message
Design an authentic and meaningful message with help of a team of your employees that is easy to get across. This message must be so clear that everyone can tell about it at a party, drink or other occasion and is immediately understood. This includes the Employee Value Proposition (EVP).
- Communicate – One clear message
Communicate the message of the company in a way that it echoes within existing and potential employees. Test the message within the company, discuss it and adjust it. Everyone that enjoys working at your company needs to be able to relate to it.
- Integrate – Personalized Recruitment® Strategy
Integrate and test the message found as a common “business as usual” concept that is regularly reflected by leaders and employees. Design the logical input of different techniques like: employer branding, job marketing, growth hacking, behavioral design and visual design thinking. All together it forms the Personalized Recruitment® strategy.
- Carry through – Activate latent job seekers
Measure, learn and improve your message – together with the employees – about ”why” you work with this particular company. Do this as a part of the ongoing strategic cycle. Build your own Personalized Recruitment strategy by constantly adjusting to the market.
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In this article, I mentioned some examples from the book Homo Deus, written by Yuval Noah Harari.
Other sources I reached out to are:
Jeske Timmermans PHC Roche Nederland