Can we Really Use Facial Recognition to Deter Medical Scammers?

Facial Recognition to Deter Medical Scammers

From train tickets and concert shows, to medical appointments, swindlers can be found in many places in China. While some residents benefit from their services by being able to secure some of the services they need, albeit, at a higher price. Others feel annoyed and do not appreciate these professional scammers, who essentially sell you the same goods or services at higher price.

It gets especially sensitive when it comes to medical appointments. In a country without universal healthcare and relatively poor social benefits, good-quality medical resources are always in scarcity. Millions of patients flood the centralized hospitals for a chance to cure their ailments, only to discover that these precious appointment slots have been taken by the scalpers who are charging higher prices for the already pricey services.

Scalping in the Chinese medical industry has received the attention of the Chinese government. Starting in February, Beijing local health officials started to run a pilot project that combines facial recognition technology in the hospitals to identify and capture scalpers. According to Chinese state media Xinhua, Beijing officials had already registered more than 2100 scalpers into the system.

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The Xinhua report suggests that the 2100 scalpers were previously detained by local police and new scalpers are now facing a harsher punishment. They are not allowed to take high-speed trains, register their own companies, or apply for loans. The Chinese ministry of public security also called for more stringent measures against these scalpers in the near future.

China’s facial recognition system has been the country’s cutting-edge technology in recent years. The system is reportedly an effective tool to combat against criminal activists and capturing suspects at large. Earlier stories suggest that the facial recognition cameras successfully captured fugitives at Jacky Cheung’s concerts multiple times.

There is no doubt that the system is more advanced than the traditional police patrolling duties and could have a positive effect in the fight against scalping. The latest actions have received growing support on social media, and many argue that the pilot project should be adopted by hospitals and police services in the whole country.

In addition to combating medical scalpers, the system could also be used to solve other issues and deter criminals in other areas. Facial recognition alerts could also be used to detect potential violent aggressors and scammers that are in the hospital. It sounds like a system with a lot of benefits and few drawbacks. It only requires the hospitals to invest into these devices and train security staffs to use them to ensure the public safety within the premises.

However, the larger problems of the system were not addressed by the state media or the supporters of the technology. There is no question that facial recognition could identify individuals who enter the hospital premises, and there is no questions that the technology is reliable and attainable. The larger issues are with the usage of the system, and also, its potential for abuse.

While scalping medical appointment slots is a business that is deemed to be illegal, the penalties that the convicted scalpers receive remain to be a question in controversy. Their presence in the hospitals is not illegal. While their past behaviors may be problematic. There is a likelihood that they are coming to the hospital as a regular patient to seek medical treatment or that they are visiting friends who are in the hospital for legitimate medical care.

Furthermore, it remains unclear who is in charge of managing the database or the verification of the system. In order to have an effective strategy against these scalpers. Security personnel need to have a constantly updating database to detect newly recruited scalpers to ensure the effectiveness of the system. For scalpers who are already banned by the system, they are unlikely to personally show up to the hospitals at risk. They are more likely going to recruit individuals who are not captured by the facial recognition system to conduct tasks in the hospitals for them. Eliminating the old scalpers would not solve the issue of scalping, as the demand will remain, and those who are willing to take the risk will try to fulfill the gap left by the old scalpers.

More importantly, the effectiveness of the facial recognition system comes at the expense of the public’s own privacy. A facial recognition system does not only identify the targeted individuals but also regular patients who show up in hospitals for medical treatment. These patients may not want people to know that they are coming into the hospital. The individuals’ medical history and their whereabouts are part of their own private business and that should be respected. The hospitals who are in charge of these facial recognition systems, and the security companies who have access to this information, should be responsible for the safety and confidentiality of this sensitive data. However, previous new reports are suggesting that China is having more data privacy issues revealed, and the integrity of Chinese firms holding users’ data are consequently decreasing dramatically.

Would facial recognition really help the hospitals and local police in capturing scalpers? Past results and advanced technology seem to yield a positive answer. However, we all need to be aware of the larger social costs and the potential red flags that the system could bring to the external and internal stakeholders in the hospital. The effectiveness of the pilot program remains unclear at the moment. But it is evident that the hospitals and local health officials need to think carefully before starting to implement the facial recognition system in a hospital setting. In order to have a responsible, effective, and valid system that deters scalping, local officials need to have measures and policies to ensure public privacy and public interest at all times.

Featured photo credit to Sohu