How Healthcare Training Data is Driving Healthcare AI to the Moon?
Data procurement has always been an organizational priority. More so when the concerned data sets are used to train autonomous, self-learning setups. Training intelligent models, especially the ones that are AI-powered, takes a different approach than preparing standard business data. Plus, with healthcare being the vertical of focus, it is important to focus on data sets that have a purpose to them and aren’t simply used for record-keeping.
But why do we even need to focus on training data when gargantuan volumes of organized patient data are already residing on medical databases and servers of retirement homes, hospitals, medical clinics, and other healthcare organizations. The reason is that standard patient data isn’t or cannot be used to build autonomous models, which then require contextual and labeled data to be able to take perceptive and proactive decisions in time.
This is where Healthcare Training data comes into the mix, projected as annotated or labeled datasets. These medical datasets are focused on helping machines and models identify specific medical patterns, the nature of diseases, prognosis of specific ailments, and other important aspects of medical imaging, analysis, and data management.
What is Healthcare Training Data- A Complete Overview?
Healthcare training data is nothing but relevant information that is labeled with metadata for the machine learning algorithms to recognize and learn from. Once the data sets are labeled or rather annotated, it becomes possible for the models to understand the context, sequence, and category of the same, which helps them make better decisions in time.
If you have a penchant for specifics, training data relevant to healthcare is all about annotated medical images, which ensure that intelligent models and machines become capable in time to recognize ailments, as a part of the diagnostic setup. Training data can also be textual or rather transcribed in nature, which then empowers models to identify data extracted from clinical trials and take proactive calls pertaining to drug creation.
Still a tad too complex for you! Well, here is the simplest way of understanding what healthcare training data stands for. Imagine a purported healthcare application that can detect infections based on the reports and images you upload onto the platform and suggest the next course of action. However, to make such calls, the intelligent application needs to be fed curated and aligned data that it can learn from. Yes, that is what we call ‘Training Data’.
What are the Most Relevant Healthcare Models that require Training Data?
Training data makes more sense to autonomous healthcare models that can progressively impact the life of commoners, without human intervention. Also, the escalating emphasis on amplifying the research capabilities in the healthcare domain is further fueling the market growth of data annotation; an indispensable and unsung hero of AI that is instrumental in developing accurate and case-specific training data sets.
But which healthcare models are in most need of training data? Well, here are the sub-domains and models that have picked up pace in recent times, beckoning the need for some high-quality training data:
- Digital Healthcare Setups: Focus areas include Personalized Treatment, virtual care for patients, and data analysis for health monitoring
- Diagnostic Setups: Focus areas include early identification of life-threatening and high-impact ailments like any form of cancer and lesions.
- Reporting and Diagnostic tools: Focus areas include developing a perceptive breed of CT Scanners, MRI detection, and X-Ray or imagery tools
- Image Analyzers: Focus areas include identifying dental issues, skin ailments, kidney stones, and more
- Data Identifiers: Focus areas include analyzing clinical trials for better disease management, identification of new treatment options for specific ailments, and drug creation
- Record-Keeping Setups: Focus areas include maintaining and updating patient records, following up periodically on patient dues, and even pre-authorizing claims, by identifying the nitty-gritty of an insurance policy.
These Healthcare models crave accurate training data to be more perceptive and proactive.
Why Healthcare Training Data is Important?
As seen from the nature of models, the role of machine learning is incrementally evolving when the healthcare domain is concerned. With perceptive AI setups becoming absolute necessities in healthcare, it comes down to NLP, Computer Vision, and Deep Learning for preparing relevant training data for the models to learn from.
Also, unlike the standard and static processes like patient record keeping, transaction handling, and more, intelligent Healthcare models like virtual care, image analyzers, and others cannot be targeted using traditional data sets. This is why training data becomes even more important in healthcare, as a giant step into the future.
The importance of healthcare training data can be understood and ascertained better by the fact that market size concerning the implementation of data annotation tools in healthcare to prepare training data is expected to grow by at least 500% in 2027, as compared to that in 2020.
But that’s not all, intelligent models that are properly trained in the first place can help healthcare setups cut additional costs by automating several administrative tasks and saving up to 30% of residual costs.
And yes, trained ML algorithms are capable of analyzing 3D scans, at least 1000 times quicker than they get processed today, in 2021.
Sounds promising, isn’t it!
Use Cases of Healthcare AI
Honestly, the concept of training data, used to empower AI models in healthcare, feels a bit bland unless we take a closer look at the use cases and real-time applications of the same.
Digital Healthcare Setup
AI-powered healthcare setups with meticulously trained algorithms are geared towards providing the best possible digital care to the patients. Digital and virtual setups with NLP, Deep Learning, and Computer Vision tech can assess symptoms and diagnose conditions by collating data from different sources, thereby reducing treatment time by at least 70%.
Resource Utilization
The emergence of the global pandemic did pinch most medical setups for resources. But then, Healthcare AI, if made a part of the administrative schema, can help medical institutions manage resource scarcity, ICU utilization, and other aspects of scarce availability, better.
Locating High-Risk Patients
Healthcare AI, if and when implemented in the patient record section, allows hospital authorities to identify high-risk prospects that have the chance of contracting dangerous diseases. This approach helps with better treatment planning and even facilitates patient isolation.
Connected Infrastructure
As made possible by IBM’s in-house AI, i.eWatson, modern-day healthcare setup is now connected, courtesy of Clinical Information Technology. This use case aims at improving interoperability between systems and data management.
In addition to the mentioned use cases, Healthcare AI has a role in play in:
- Predicting patient stay limit
- Predicting no-shows to save hospital resources and costs
- Predicting patients that might not renew health plans
- Identifying physical issues and the corresponding remedial measures
From a more elementary perspective, Healthcare AI aims at improving data integrity, the ability to implement predictive analytics better, and the record-keeping capabilities of the concerned setup.
But to make these use cases successful enough, the Healthcare AI models must be trained with annotated data.
The Role of gold-standard datasets for Healthcare
Training models are fine but what about the data? Yes, you do need datasets, which then have to be annotated to make sense to the AI algorithms.
But you cannot just scrap data out from any channel and still keep up with the standards of data integrity. This is why it is important to rely on service providers like Shaip who offer a wide range of reliable and relevant datasets for enterprises to make use of. If you are planning to set up a healthcare AI model, Shaip lets you choose from human-bot percepts, conversational data, physical dictation, and physician notes.
Plus, you can even specify use cases to make the datasets aligned towards core healthcare processes or conversational AI to target the administrative functions. But that’s not all, experienced annotators and data collectors even offer multi-lingual support when it comes to capturing and deploying open datasets for training models.
Coming back to what Shaip offers, you, as an innovator, can access relevant audio files, text files, verbatim, dictation notes, and even medical image dataset, depending on the functionality you want the model to have.
Wrap-Up
Healthcare, as a vertical, is on an innovating spree, more so in the post-pandemic era. However, enterprises, health entrepreneurs, and independent developers are constantly planning out new applications and systems that are intelligently proactive and can considerably minimize human effort by handling repetitive and time-consuming tasks.
This is why it is crucial to first train the setups or rather models to perfection by using precisely curated and labeled datasets, something that is better outsourced to reliable service providers to achieve perfection and accuracy.
