BTC has reached a high and is consolidating.
It feels like it's not going very well, probably going to pull back?
In any case, just avoid chasing highs and cutting losses #BTC走势分析 .
The market says these two sentences, talking about what I spent time researching today.
I specifically studied the possibilities of $NIGHT in medical data scenarios today. To be honest, I have a very personal reason for researching this scenario, as I have had a very uncomfortable experience myself, being referred between different hospitals. As a result, each hospital required a complete set of tests again because the data between hospitals cannot be shared, or the sharing process is so cumbersome that no one is willing to do it. At that time, I was wondering if this problem could be solved with technology.
After researching all day today, I feel that @MidnightNetwork the possibilities in this direction are real, but the road to implementation is much more complicated than I initially expected.
What is the core contradiction of medical data?
Let me clarify the issue first, as the challenges of medical data are not just technical problems; understanding its full scope is essential to determine which parts Midnight can address.
Medical data has a unique attribute: it possesses both high privacy and high circulation demand simultaneously. These two aspects do not appear as extreme in other types of data, but they coexist in medical data. #night
Privacy goes without saying; you don't want any unrelated people to know your medical history, your genetic information, or your mental health records. Once this data is leaked, the impact on your employment, insurance, and social relationships is very real.
However, the demand for circulation is also real. When you switch to a different hospital, the doctor needs to see your historical records to make accurate judgments; medical research institutions need a large amount of patient data for statistical analysis; clinical drug trials need to screen eligible patients; insurance companies need to assess health risks when pricing. If these demands cannot be met, the efficiency of the medical system and the quality of research will suffer greatly.
The current solution is to compromise between privacy and circulation demand, resulting in neither side being well addressed. Data circulation relies on centralized medical information systems, which carry risks of data breaches and cannot truly interoperate due to the competing interests and non-uniform standards among institutions. Patients have almost zero control over their own data, where it is, who is using it, and for what purpose, patients usually have no idea.
This problem truly exists; what can Midnight do here?
What can Midnight's mechanism solve in medical scenarios?
I think the application logic of Midnight's selective disclosure mechanism in the medical data scenario can be thought of in several layers.
The first layer is cross-institutional data sharing. Your medical records exist locally, encrypted for protection, and only encrypted proofs exist on the Midnight network. When a doctor at Hospital B needs the test results from Hospital A, you authorize locally to generate a proof that discloses only that specific test report, with all other records remaining hidden. The system at Hospital B verifies this proof, retrieves the data, and completes the consultation. You are fully informed and in complete control, and there is no need for any data-sharing agreement between Hospital A and Hospital B, nor a centralized medical information platform.
I believe this scenario is the closest to real needs among all of Midnight's application directions, as the pain points it addresses are ones I have personally experienced, not imagined.
The second layer is the data authorization for medical research. Research institutions need a large amount of patient data for statistical analysis, but what they really need is not patients' identity information, but anonymous statistical data that meets specific conditions. Midnight's zero-knowledge proof can allow patients to prove that their data meets a certain research condition without exposing their personal identity, allowing their data to be included in statistics anonymously. This is much stronger in protecting patient rights than the current model of simply signing an informed consent form and then the data belongs to the research institution.
The third layer is insurance pricing and risk assessment. Insurance companies need to assess your health risks when pricing, but you shouldn't have to hand over all your medical records to the insurance company permanently just to buy a policy. Midnight's mechanism allows you to only prove that you meet or do not meet certain health conditions, providing the information needed for pricing while your complete medical history never leaves your local device.
I believe the logic of these three layers of application is self-consistent and represents real demand scenarios, not examples constructed to demonstrate technical capabilities.
But I start to frown when I think about the practical level.
Having talked about the possibilities, let me mention the things that make me frown, and there are more of them than I initially anticipated.
The first issue is the complexity of legal compliance.
Medical data is one of the most strictly regulated types of data in most countries worldwide. The U.S. has HIPAA, the EU has specific clauses in GDPR targeting medical data, and China has the Personal Information Protection Law and the Data Security Law, along with more specific regulations for medical data management. These legal frameworks impose very specific requirements on the storage, transmission, and access control of medical data, and there are many inconsistencies between the requirements of different countries.
The immutability of blockchain data directly conflicts with certain legal requirements. The GDPR in the EU includes a right to be forgotten, where patients have the right to request the deletion of their personal data. However, if data has already been recorded on a blockchain in some form, even if encrypted, it is technically impossible to delete it. Whether destroying the decryption keys can be considered as meeting the legal requirements for the right to be forgotten currently lacks clear precedent in the European legal system.
If this issue is not resolved, Midnight will not be able to operate in compliance in the European medical scenario.
The second issue is the incentives for medical institutions.
For Midnight's medical data solution to truly take off, medical institutions need to be willing to integrate this system, develop corresponding interfaces, and modify existing workflows. This is purely a cost for medical institutions, at least in the short term, because their current centralized data systems, despite various problems, are at least operationally stable.
Why would medical institutions spend a lot of resources to integrate a new blockchain system? If the answer to this question is simply 'because it's better for patients,' the pace of advancement will be very slow, as institutional decisions are not driven by morality. Regulatory pressure or substantial commercial interests are needed for medical institutions to have the motivation to undertake such large-scale system transformations.
Currently, I do not see either of these driving forces clearly reflected in Midnight's roadmap.
The third issue is the technical threshold for patients.
Midnight's medical data solution relies on patients managing their local data and keys themselves, deciding who gets to see what. This empowers tech-savvy users, but for most patients, the complexity of this operation may drive them to give up.
The user base of medical data is not crypto-native users but ordinary people from all age groups and technical levels, including a large number of elderly and low-income individuals, whose ability to manage cryptographic keys is almost zero. If the system's ease of use is not foolproof, the actual coverage of this solution will be very low, becoming a niche tool used only by tech enthusiasts.
The role of NIGHT in medical scenarios
After clarifying the medical data scenario, I reconsidered what role NIGHT could play in this context.
If Midnight's medical data application really takes off, every authorization to view, every research data validation, and every insurance risk assessment will consume DUST. If the interaction frequency of medical data is high enough, it will become a very stable source of DUST consumption in the Midnight network, providing real support for the demand side of NIGHT.
But the premise of this scenario is that the medical data application is truly established, and establishing it requires overcoming the obstacles I found during my research today, each of which is not small.
My feelings after today's research
After researching all day today, my feeling about the medical data scenario is: this is the application direction of Midnight I most hope to see succeed, as it addresses the most real problems and has the most direct impact on ordinary people.
But it is also the one with the highest difficulty in implementation. The complexity of legal compliance, the motivation of medical institutions to participate, and the technical threshold for ordinary patients combined make me feel that even if Midnight's technology is fully mature, the large-scale implementation of medical data in this scenario will still take a very long time.
I'm not saying this can't be done, but accomplishing it requires far more than just a good technical solution.