Improve medical first step: check data
Currently, data analysts have not used a meaningful way to transform the health care industry. Of course, we have massive medical data on patients, doctors, tests and even treatment outcomes, and many of them are paperless and electronic. But still in the early stages of learning how to optimize electronic data.
Although there are also pocket innovations in the use of data in closed medical systems (such as Caesar), this is not a universal paradigm. The paradigm is that individuals receive treatment from many different medical institutions, but they cannot share each other. Lack of access to large data sets, data analyst innovation must be constrained (also subject to medical cooperation, this is another topic). What I want to explain in this article is that we are talking about the data in the medical records (written medical records), recording the actual clinical treatment, not the management data or billing data, which is the representative of medical services for payment and operation.
Barriers to data sharing
You might think that sharing data in the new digital age is a simple matter, but not so – technical flaws and lack of incentives lead to valuable data being blocked. Many public electronic health records (EHRs) are not scientifically designed, and they do not allow easy data sharing within and between systems. This is no accident. I suspect that some EHR companies have lost any meaning because interoperability medical records or data export functions are not reflected in their software.
In the reality of our service-based fee-for-service, there is no real financial support for doctors, clinics, and hospitals to allocate time, resources, and funds to share data. The payer and purchaser have the right to sign patient data for management, payment or treatment decisions, but this task falls to the supplier. What is the return of the supplier doing this? It's nothing. The health plan does not pay for additional expenses for the data sharing process or office visits. Without financial incentives in co-operative care, there is no hospital or clinic that is really willing to share data – because it burns money, is time-consuming, laborious, and many hospitals and clinics do not have the right technology or IT staff. If the organization really has the benefit of sharing medical records, EHR has the above characteristics and will be more helpful.
In the current situation, if a health plan or individual wants medical record data, then the document is printed or copied in paper. This is the data mobility that people want.
There is now a solution to the lack of interoperability in EHR by querying the database's directory or extracting, safely uploading and transferring files to another database. After that, people still need to explore the meaning found in the data written. Some health information science and policy leaders want medical records to be more structured for easier interoperability. However, do patients want their medical records to be just a collection of data? All the nuances and ins and outs are lost. So the key challenge still exists: how to extract meaning and value from the unstructured, textual part of the medical record analysis to support analytics efforts?
Use cognitive computing to understand data
Other industries have also attempted to extract meaning and value from unstructured textual data. Thinking about the information obtained from the text of the website has greatly promoted the development of research, advertising and e-commerce. Adaptive algorithms or learning algorithms have created learning patterns and inferred knowledge that are applied to website narratives. This is the core of cognitive computing. These algorithms are flooded with vast amounts of data in the network and dispatched to perform specific activities. The statistical techniques under these algorithms are considered machine learning and are now used to identify areas of sound, as well as to assist digital assistants such as Apple's Siri or Amazon's Alexa; integrate visual cues and types to assist with autonomous driving; even in Beat the world championship in the chess game.
Every year, the United States has to generate 1.2 billion patient documents, many of which have realistic data on health care. If you find it well, you will be able to provide valuable insights into the diagnosis and treatment of diseases. However, these documents are written in different texts by different clinical experts in different environments. If you try to come up with a rule-oriented way to tell the computer how to read and understand the information you write, trying to use a computer to decrypt the information can be difficult and error-prone. Even though these rules can explain and understand the language and templates used by clinicians in a medical setting, they are not necessarily effective in another institution or professional practice.
Conversely, the correct approach is to use adaptive or machine learning algorithms that are becoming more powerful and widely used when computers store a large number of rich data sets. Its performance has reached a level where it is possible to translate clinical records from an institution for the first time without customization.
How to use data to improve medical health
Aiming at individual patient conditions and treatment history, the use of adaptive algorithm data mining techniques to collect medical information is a promising technology. Then, using accurate and timely medical information, we can implement virtual treatment trials to understand what works for patients and what is useless. The explosive growth of treatment and medical choices makes it increasingly difficult for doctors to recall and apply all the evidence during patient care, and this knowledge will become increasingly critical. Moreover, as more and more expensive medical technologies are put into use, patients have to bear greater medical costs, and doctors must consider the cost-effectiveness of each treatment choice.
Only when we can use powerful cognitive techniques to mine unstructured data sources and information can data analytics begin to transform health care in a meaningful way. By acquiring knowledge from real-world clinical data, we are able to understand the optimal treatment options for each patient and provide true personalized care. In the process, we will also be able to subvert the standards that are widely accepted but based on flawed or unrepresentative scientific research. Drug treatment will be more direct and effective, treatment processes can help better outcomes, and will fundamentally change the way health care consumes and supplies.
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