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Facts To Know About Cloud Innovation in China

Ø  Healthy China Initiative

Ø  China is pushing to invest in strong health companies, especially domestically, while also developing their own research into solutions on providing healthcare for an aging population. Calls by Beijing to increase access to healthcare coverage while reducing coast barriers has brought on a significant increase in investments and the push fo innovation domestically. Cloud computing, telemedicine, artificial intelligence,and the substantial proliferation of diagnostic imaging are among the areas of focus.

 Ø  Expansion of Alibaba Cloud

Ø  Alibaba has become the second largest cloud provider in the Asia Pacific only Amazon Web Services. According to CNBC, “ Alibaba is also helping Chinese companies that want to expand abroad and also increasingly winning business from large customers elsewhere that are looking to crack the china market, which has long been a challenge for outsider.”

 Ø  Doctor Shortage Paves Way for Telehealth Adoption

Ø  The motivation for artificial intelligence growth and development could, in part, be explained by doctor shortages. Physicians report the need to automate some of their more repetitive work, and increase their availability, productivity, and workflow. According to a McKinsey Study, digitization impact up to 45% of revenue within the country’s healthcare industry.

 Ø  Growth in Utilization of Diagnostic Imaging

Ø  China is dealing with an aging and rising population due to the implementation and abolishment of the one-child policy, respectively, setting the stage for an increase in the number of patients requiring high-value medical services such MRIs and CTs. China has a 5 year plan dedicated to providing imaging technology for rural hospitals.

  Source: https://www.dcmsys.com/.Information shared above is the personal opinion of the author and not affiliated with the website.

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Data De-identification

Automating Data De-identification

 One aspect of medical technology innovation that is massively relevant to DICOM Systems at the moment is the concept of data de-identification. De-identification is a crucial aspect of attempts to advance medical technology at this time, as billions of dollars find their way to firms promising huge bounds forward in AI for diagnosis in various fields. Half of all surveyed chief information officers in health enterprises are planning to deploy artificial intelligence in some form either this year or next year. It's a field with incredible potential, restricted by the essential need to protect patients' personal information. Any way of automating the data de-identification process will massively boost productivity and give the whole field of medical AI a real shot in the arm.

 The Necessity of Mass Data De-identification

 Humans are in. a number of ways, easier to teach than artificial intelligence. That's because humans have been wired and conditioned over millions of years of evolution to recognize patterns, extrapolate, and intuit from incomplete data. AIs have a much shorter development time, and need to be taught from the ground up what conclusions they should draw from the data they receive. Since they haven't reached the level of sophistication necessary to teach them the flashes of human inspiration and intuition that serve many medical professionals so well, we have to resort to sheer brute force rote learning. Any AI that wants to learn even a minute amount about medical imaging needs to be fed a vast amount of data before it can be relied upon to make accurate assessments of medical imaging files. At a minimum, 100,000 samples are required. This enormous need for clean data renders manual attempts to de-identify medical files completely impractical. That's where we come in.

 DICOM's Data De-identification System

 DICOM does not itself deal in artificial intelligence algorithms, but we specialize in piping in the gallons of data needed to form a data lake from which an AI can draw to develop. To convert healthcare providers' imaging data into safe, de-identified data that the AI handlers can use, we have two scripts. The first works on the metadata of the file, finding and stripping out identifying information: 18 different kinds, including name, religion, and age. With this data securely eliminated, there is no way a patient can be identified from the metadata. The second script goes to work on the image file itself, neutralizing data (for example dates, patient number, hospital location etc.). There are several options that are available, depending on one's preference: scrambling identifying data in the file, or masking it entirely. It's important to note that, since the script goes to work on enormous sets of files indiscriminately, the data fed into it needs to be uniform. It can't detect if there are, for example data from two different hospitals with different notation policies, with five thousand images having their notations on the top, and five thousand having them on the right. It will merely scramble or mask the section of the image it is told to regardless of whether the notations are there or not. Be sure of the content of your image files before you set this script to work. Luckily, this process comes with its own QA stage, so as to ensure that any human oversights can be corrected before final dispatch.

  Source: https://www.dcmsys.com/.Information shared above is the personal opinion of the author and not affiliated with the website.

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Al In Healthcare

Interest in artificial intelligence (AI) is exploding!

Here are some predictions:

 Al in health care will grow to $6.6 billion in a few short years, at a 40% annual compounded growth rate.

 Al will enable an opportunity for $150 billion in industry savings.

What's Happening with Al Today?

  Algorithms are replacing some clinical tasks.

Advances in clinical analytics and machine learning have the potential to drive medical discovery at a pace never seen before.

Al is also being used today to detect diabetic retinopathy in adults diagnosed with diabetes who had not previously received a diagnosis of diabetic retinopathy.

An AI Transformation Is Happening.

 Source: https://www.dcmsys.com/.Information shared above is the personal opinion of the author and not affiliated with the website.

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The Value of Enterprise Imaging

Enterprise imaging can do much to improve workflow and optimize systems for a healthcare provider.

● Unify workflow - Connect information repositories remotely so that disparate healthcare enterprises can transfer data more effectively.

● Connect to remote sites - Safely connect with remote sites on an enterprise level, while encrypting all outgoing traffic.

● Automate data transfer - Automate the transfer of information from HIS/RIS/EMR to multiple modalities.

Source: https://www.dcmsys.com/enterprise-imaging-solutions/, Information shared above is the personal opinion of the author and not affiliated with the website.

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Patient Data De-identification and its Requirements

The utility of de-identified patient data in medical research is hard to overstate, but ensuring patient privacy must always be at the heart of the operation.

● Quality Assurance - Before protected healthcare information (PHI) is shared outside of the client hospital’s infrastructure, it must be properly de-identified, which requires sufficient QA to ensure data security.

● Independent Review - Objective third-party reviewers are required to review the methodology, code, and de-identification algorithms, to ensure that the integrity of PHI has not been compromised.

● Adaptive Framework - Because not all healthcare providers have the same perspective or research requirements, de-identification processes will necessarily differ. A scalable, adaptive framework is vital to meet the two goals of preserving patients’ privacy while using imaging data to further medical science.

● Airtight Data Protection - Messages, images, and associated metadata must be de-identified in a way which will prevent re-identification by anyone, except the source healthcare provider.

Source: https://www.dcmsys.com/patient-data-security/dicom-data-de-identification-and-re-identification/, Information shared above is the personal opinion of the author and not affiliated with the website.