For most patients, going to the ER is time-consuming, expensive, and often unnecessary. Emergency Rooms, by their very nature, have to triage patients by order of need, which means that if a patient’s life is not in immediate danger, they may have to wait several hours at least to be seen.
Additionally, unscheduled visits to the ER are not cheap. If the patient is uninsured, has a high deductible, or accidentally goes to an out-of-network provider, they can be stuck with a hefty bill after their visit — which is especially painful if the condition did not turn out to be a serious one.
These factors often increase patient hesitancy to seek care, which leads to worse health outcomes for the patient, and to more challenging, labour intensive cases for physicians when patients finally do feel bad enough to seek treatment even despite the above concerns.
This is why organizations are increasingly adopting AI Triage Assistants also know as Virtual Medical Assistants, which patients can access remotely. These assistants can increase efficiency for organizations and lead to better health outcomes for patients.
And according to McKinsey, this trend isn’t going away soon, AI Triage Assistants and other telehealth systemrs are a post-pandemic reality.
AI Triage Models And Implementation
There are a few different models of AI Triage out there, and which one is best for your organization will depend on a variety of factors — but most importantly the size and scale of your business, and whether you operate an emergency room yourself or not. Though no matter what type of assistant you use, all AI Triage assistants share come common features and tech stacks. We’ll take a look at some of these key features below.
Big Data
Big Data underpins all AI Triage assistants. That said, you don’t have to reinvent the wheel here. There are already AI models out there, based on millions of EHRs (Electronic Health Records) that you can license for your own assistant.
This doesn’t mean you shouldn’t also collect your own data though — as this can allow you to spot areas where you can improve efficiency and performance.
The Internet of Things, or IoT is one area where you especially cannot afford to neglect data collections. If your patients are using wearables, or the various different care devices in your hospital are connected to the internet — they are generating a huge amount of data that you can use to learn about your patients.
Your app should also be designed to automatically collect data about how patients interact with it, and what kind of ailments they tend to report. This will give you a clearer picture of your own patient population, which you can use to improve on the model that you have licensed.
Data Storage
When collecting large amounts of patient data, you’ll need a place to store it, and unless you happen to have spare room for a gigantic server farm on your premises, this means offsite storage in practice.
Luckily, there are cloud storage options available from the biggest names in the computer industry, including Google, Microsoft and Amazon.
Cloud Computing
As with storage, analyzing huge amounts of data with Machine Learning requires a huge amount of computing power that you probably don’t have onsite. But you can get cloud computing services from the same provider as cloud storage — there are plenty of options out there.
Data Security
Anytime that you transmit patient data over the internet, it creates privacy risks. This includes using a smart triage app.
Luckily, there are things you can do to mitigate this.
The first thing you need to do is that when building your AI triage assistant, you design it in a HIPAA compliant way. If you are working with a custom software development solutions provider, be sure that you’ve chosen an experienced partner that has developed HIPAA compliant systems before.
Beyond that, you should train your staff on cybersecurity and compliance best practices. While this is traditionally done in the form of annual training sessions, you may find that newer techniques, such as gamification, keep your employees more alert.
But no matter how much and how often you train, especially in a large organization, you don’t want to rely only on your employees.
The best way to guard against human error is to implement well-written access rules, that control who has access to HIPAA protected data and under what circumstances. These rules should be built into your systems, to prevent employees from accidentally violating data regulations.
“AI Triage Assistants are increasingly necessary for overloaded healthcare providers to more quickly asses patients, reducing wait times and improving patient and organisational outcomes. At Proxet, we have deep experience in evaluating the needs of specific healthcare providers and building smart solutions that address pain points directly.”
— Vlad Medvedovsky, Founder and CEO at Proxet (ex – Rails Reactor), a software development solutionscompany
Choosing A Tech Stack: Talk To Your Development Team
The tech stacks underlying AI triage assistants are quite complex, which is why you your best bet is to consult with an experienced software development solutions partner.
These systems combine big data, Machine Learning, Data security, and also will have different demands for both iOS, Android, and web. Often this will be based on a commonly used framework like tensor flow python, keras python, scikit-learn python or numpy python
The main decision that you will need to make when building your AI Triage Assistant is about triage methodologies, will it use multiple choice forms only, or allow patients to enter and describe their symptoms in writing, which is stored as free text and analysed with Natural Language Processing.
Multiple choice form based systems are simpler to build, but patients may feel more engaged and listened to with a triage assistant that can handle free text.
More On Data Safety And Protection
As we mentioned earlier, any time you are working with large amounts of patient data, you have to be very careful and ensure that you are doing you work in a compliant manner. In the United States, the main legislation governing patient data is HIPAA, or Health Insurance Portability and Accountability Act.
To stay compliant with HIPAA, these are the main bases that you need to cover.
- Access to stored data: HIPAA law requires that you put access management rules in place to safeguard protected health information (PHI). Access should only be granted to those who need it to do their job, and no one else.
- Data encryption: Any time you send data it passes through a server. Sending data outside your organization means that it passes through a third-party server. Although data sent within your organization does not need to be encrypted it is highly
- Deidentifying data: when conducting research (this is where Machine Learning comes in), HIPAA law does not require patient permission if the data is adequately deidentified. This means that the data used cannot be connected to an individual in any way. If there is even a slight chance that the data can be tied to a specific individual, then it is not in accordance with HIPAA regulations.
- Updated policies and procedures: HIPAA law changes often. When implementing new technology an organization must look to their internal policies to ensure that their procedures are HIPAA compliant.
- Business associate agreement (BAA): If you’ll be working with a custom software development solutions partner to develop your AI Triage Assistant, you’ll need to have Business Associate Agreement in place. It is legally required to sign a BAA before giving technology partners access to patient data.
User Interface
Another aspect you cannot neglect when building your AI Triage assistant is the user interface. Think of it as the equivalent of “bedside manner” but for your app. Ideally patients will perceive it not as a machine, but as a virtual triage nurse.
The app should be intuitive and easy to use, and the chat bot aspect especially should be written according to best practices. Some of these are general and apply to any chat bot but some are more industry specific.
For example, where a customer support bot might be cheerful, casual, and humorous, when it comes to a healthcare chat bot your don’t want to over do this.
People’s health is not a joke, and some people who consult your AI triage assistant may already be scared and in pain, so the best tone to go for is calm, serious, and respectful, although still friendly.
This consideration is also important because senior citizens, surveys show, are already hesitant to use telehealth and similar technologies, so trying to be “cool” or using slang in your chatbot may be off putting for them.
The main priority is clear, effective communication that helps the user understand exactly what actions they need to take next.
The AI Triage Assistant should also include the option to speak to a live human, whether through customer service or by calling the clinic directly — as AIs may not always understand everything.
Verification Of Results
A concern that many organizations have when it comes to implementing AI Triage Assistants is “How Do I Verify The Results?”
This is not something that you need to worry about however, results analysis from a recent Stanford study in the Found that Machine Learning models demonstrate a comparable level of diagnosis accuracy to human doctors.
The bigger challenge here for organizations is not deciding whether or not AI triage assistants are reliable, but convincing patients that they are.
If your organization or practice is implementing an AI triage assistant or other telehealth systems for the first time, its important to accompany this with a well coordinated communications campaign, telling patients what they can expect from the system, how it works, why its trustworthy, and highlighting the benefits and convenience of the AI assistant as compared to calling the clinic and waiting an hour or more on the phone before speaking to a practitioner.
Developing Your AI Triage Assistant
If your organization is looking for a custom software development solutions partner to help roll out an AI assistant for your patients, consider Proxet.
Proxet has deep experience in developing HIPAA compliant smart healthcare systems for Enterprise and SME, and our team of experts is ready to put your data to work for your patients.
