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Jane never misses her appointment with her GP anymore, neither does she forget to take a dose of her antibiotic as prescribed – there’s an app to bring her up to speed on those details. Performing the role of a nurse, in the absence of one, this chatbot called Florence – named after Florence Nightingale, the founder of modern nursing, acts as a personal health assistant to remind Jane – and other users – to track their activity levels, body weight, pills, and their next doctor appointments.

And there are many more chatbots developed today to transform patient care.

Chatbots are software developed with machine learning algorithms including natural language processing (NLP) to stimulate and engage in a conversation with a user to provide real-time solutions and answers to questions.

Chatbots have already gained traction in other industries including retail, news media, social media, banking, and customer service departments. Many people engage with chatbots every day on their smartphones without even knowing. From engaging popular news chatbot thescore for the latest sports information, navigating through your bank’s online app, to playing chatbot games on Facebook Messenger, chatbots are revolutionizing the way we live.

Healthcare payers and providers are also beginning to leverage these AI-enabled tools to simplify patient care and cut unnecessary costs.

Here are some chatbots gaining widespread use in healthcare.

 

AI in healthcare


1.Your.Md

This free AI-enabled application allows you to input your symptoms and get the most likely diagnoses. Trained with machine learning models that enable it to give accurate or near-accurate diagnoses, YourMd provides useful health tips and information about your symptoms as well as verified evidence-based solutions.

Your. Md also offers information about local health service providers including diagnostic centers and clinics you could visit.

Your.Md is available on iOS, Facebook Messenger, Slick, Android, KIK, and Telegram.

 

chatbot ADA


2. Ada Health

Ranked as the fastest-growing health app in Europe in 2017, Ada Health has attracted more than 1.5 million users, who use it as a standard diagnostic tool to provide a detailed assessment of their health based on the symptoms they input.

Ada asks the user simple questions and runs their answers on a dataset of thousands of similar inputs and cases to provide the most approximate evaluation of their health and offer relevant solutions. Ada also provides users with detailed information about medical conditions, treatments, and procedures and connects them to healthcare providers near them.

 

Babylon Health


3. Babylon Health

Babylon Health offers AI-driven consultation with a virtual doctor, the chatbot, as well as a real doctor.

With the chatbot, users say what their symptoms are and the app runs these symptoms against a database of thousands of conditions that fit the mold. This is followed by a display of possible diagnoses and relevant steps the user should take to eliminate the symptoms. The app is built with speech recognition and natural language processing to analyze speech and text to produce relevant outputs.

The platform also enables video and text consultations with a real doctor, who provides relevant information about disease diagnosis and treatment options.

 

buoy health


4. Buoy Health

Trained in clinical data from more than 18,000 medical articles and journals, Buoy Health provides users with their likely diagnoses and accurate answers to their health questions. This chatbot matches a user’s questions against a large repository of evidence-based medical data to provide simple, easy-to-understand answers. The chatbot also offers information for every symptom you input.

Buoy Health was built by a team of doctors and AI developers through the Harvard Innovation Laboratory.

 

forksy chatbot


5. Forksy

Forksy is your go-to digital nutritionist to help you track your eating habits, diet choices, and caloric intake. The chatbot tracks a user’s diets, provides automated feedback to improve your diet choices, and offers useful information about every food you eat – including the number of calories it contains, as well as its health benefits and risks.

Forksy helps users create and maintain a healthy diet plan, and is useful as a nutrition advice automation for weight loss programs, fitness trackers, and sports teams.

 


6. CancerChatbot

The CancerChatbot by CSource is a useful tool for information on cancer, cancer treatments, prognosis, and related topics. This chatbot provides users with up-to-date information on cancer-related topics, running users’ questions against a large dataset of cancer cases, research data, and clinical trials.

This chatbot helps patients get all the information they need about a cancer-related topic in one place. It also assists healthcare providers in providing information to cancer patients and their families.

Patients and providers are not the only ones who find this chatbot useful: Families and friends of cancer patients can also get information about the care of cancer patients, what to expect, and how they can contribute to their treatment and recovery.

 

CancerChatbot


7. SafedrugBot

Doctors also have a chatbot that provides them information – Safedrugbot. This chatbot offers healthcare providers with the right information on drug dosage, adverse drug effects, and the right therapeutic option for various diseases.

Safedrugbot functions as a virtual assistant for doctors, running doctor’s queries against a large database of drug information. This chatbot also helps healthcare providers with up-to-date information on drug prescription and overall health tips for breastfeeding mothers.

Are these chatbots causing significant paradigm shifts in healthcare? Yes! Recently, Northwell Health, an AI company developing chatbots that will help patients navigate cancer care, says more than 96 percent of patients who used its post-discharge care chatbots found it very helpful, demonstrating the increased client engagement.

Chatbots also drive cost savings in healthcare delivery, with experts estimating that cost savings by healthcare chatbots will reach $3.6 billion globally by 2022. Chatbots are gradually reducing hospital wait times, consultation times, unnecessary treatments, and hospital readmissions by connecting patients with the right healthcare providers and helping patients understand their conditions and treatments even without visiting a doctor.

Machine learning applications are beginning to transform patient care as we know it. Although still in its early stages, chatbots will not only improve care delivery, but they will also lead to significant healthcare cost savings and improved patient care outcomes in the near future

 

types of chatbots


Types of Chatbots

To develop chatbots that will engage and provide solutions to users, chatbot developers need to determine what type of chatbots would most effectively achieve these goals. Two things, therefore, that the chatbot developer needs to consider are: the intent of the user and the best help the user needs; then, we can design the right chatbot to address these.

There are three basic types of chatbots – informative, conversational, and prescriptive chatbots. These chatbots vary in their conversational style, the depth of communication, and the type of solutions they provide.

Informative Chatbots

Informative chatbots provide helpful information for a user, often in the form of pop-ups, notifications, and breaking stories. Generally, informative bots provide automated information and customer support.

If you look up articles about flu symptoms on WebMD, for instance, a chatbot may pop up with information about flu treatment and current outbreaks.

Health news websites and mental health websites also use chatbots to help them access more detailed information about a topic. For example, while reading about alcohol addiction and withdrawal, a chatbot may pop-up with this: “Do you need help with alcohol addiction? Speak with any of our mental health professionals”

Conversational Chatbots

Conversational chatbots are built to be contextual tools that provide responses based on the intentions of the user. However, there are different levels of maturity to a conversational chatbot – not all types provide the same conversational depth.

For instance a Level 1 maturity chatbot only provides pre-built responses to clearly-stated questions without the capacity to follow through with any deviations.

        For example:

conversational chatbots

These responses are pre-built and at this level of maturity, the chatbot can not generate answers if John asks a further question. A level 2 intelligence chatbot can do this, in contrast.

 

health chatbotsHigher levels of intelligence for a conversational chatbot are able to understand context better and provide more than pre-built answers. This is because these sorts of chatbots are able to look at a conversation as a whole rather than looking at the proceeding sentence soley. The higher the intelligence level of the chatbot, the more personalized the responses and the more the conversations feels natural.

Conversational chatbots use natural language processing (NLP) and natural language understanding (NLU), applications of AI that enable computers understand human language and intent.


Prescriptive Chatbots

Although prescriptive chatbots are conversational by design, they are built not just to provide answers or direction, but to offer therapeutic solutions.

One example of a prescriptive chatbot is Woebot. Woebot is a chatbot designed by researchers at Stanford University to provide mental health assistance using cognitive behavioral therapy (CBT) techniques. People who suffer from depression, anxiety disorders, or mood disorders can converse with this chatbot, which, in turn, helps people to treat themselves by reshaping their behavior and thought patterns.

For instance, Peter suffers social anxiety and has a chat with Woebot.

woebot

First, the chatbot helps Peter relieve the pressure of his perceived mistake by letting him know its not out of the ordinary, which may restore his confidence; then it provides useful steps to help him deal with it better.

Chatbot developers should employ the variety of chatbots to engage and provide value to their audience. The key, however, is to know your audience and what best suits them and which chatbots work for what setting.

Any chatbot you develop that aims to give medical advice should deeply consider the regulations that govern it. There are things you can and cannot say, and there are regulations how you can say things. Navigating yourself through this environment will require legal counsel to guide as you build this portion of your chatbot.

 


Designing the Conversation Pathway

Before chatbots, we had text messages, which provided a convenient interface to communicate with friends, loved ones, and business partners. In fact, the survey findings reveal that more than 82% of people keep their messaging notifications on and an average person has at least 3 messaging apps on their smartphones. These reveal the importance of textual conversations in our daily lives.

According to Erika Hall in her book Conversational Design, humans are fond of this mode of conversation, not necessarily because of the complex features in these social media platforms, but simply because of the convenience and simplicity of maintaining and accessing social connections through brief, unpredictable conversations.

But as simple as text messaging may seem, there are a number of rules that guide it for an effective conversation, such as the relevance, tone, quantity, speed, and context of responses. Therefore, in leveraging conversational pathways for chatbots, AI developers must factor in those principles that guide effective and productive conversations, especially within the context of healthcare.

 

Conversation Design


Principles of Conversation Design

Before designing a conversational pathway for a chatbot, one must first understand what makes an effective conversation between two people. One of these principles is the principle of cooperation.

Conversational Cooperation and Intent

The principle of cooperation, as phrased by Philosopher Paul Grice in 1975, holds that a conversation between two or more persons can only be effective if there is an underlying contextual agreement or cooperation. This background advances the conversation in an agreed direction and maintaining the proper context to achieve a unanimous purpose.

For instance, take this conversation.

John: “Do you have an idea what books I should read for this exam?”
Greg: “Yes, I do”

In this example, Greg does not adhere to the principle of cooperation. This is because his response does not answer John’s question: it leaves John with literally no answers to his question, hence a conversational disagreement. John’s question, although literally asking if Greg knew any books needed for the exam, was not intended as a Yes-or-no question. John was indeed asking to know the books he needed for the exam, and Greg did not provide that.

The same goes for this conversation with a healthcare chatbot.

User: What do I do if my newborn’s eyes have become yellow?
Bot: Yellow discoloration of the eyes is called Jaundice, and it is caused by excess bilirubin…

While it is true that yellow discoloration of the eyes – and skin – is jaundice, it does not answer the user’s question.

In designing an effective conversational flow for a chatbot, therefore, understanding intent is key. Chatbot developers can employ Natural Language Understanding (NLU) platforms such as Google Dialogflow, IBM’s Watson, Facebook’s wit.ai, Amazon’s Lex, and Microsoft Cognitive Services Language Understanding LUIS.ai

These platforms match utterances and their variants with appropriate intents before generating responses. This maintains natural and free-flowing conversations between the user and the chatbot.

Context

Just as effective human-to-human conversations largely depend on context, an effective conversation with a chatbot also heavily depends on the context of the user.

This leads to one of the most important elements of a conversational pathway: Defining your audience. This requires extensive research into who your audience is, their problems and needs, how they want their problems solved, the appropriate language to use for them, and their socio-economic contexts.

people using health apps

 

You do not design a conversational pathway the way you perceive your intended users, but with real customer data that shows how they want their conversations to be.

For a drug bot for doctors and other healthcare professionals, answering questions about drug dosages and interactions should not be structured the same way it would for a patient. These pieces of information should contain essential information delivered in the appropriate medical lexicon.

Similarly, conversational style for a healthcare bot for people with mental health problems such as depression and anxiety must maintain sensitivity, respect, and the appropriate use of words with these patients.

For instance:

User: “My panic symptoms have been getting more frequent recently, how do I resolve this?
Bot: It appears you might not have been using your drugs correctly

While this may be correct, it comes off as an insensitive response for a user with anxiety disorders. Furthermore, it may not even be correct at all, as there may be other factors predisposing the user to frequent panic attacks.

Identifying the context of your audience also helps to build the persona of your chatbot. The chatbot persona gives a human feel to these conversations. A chatbot persona embodies the character and visual representation of a chatbot.

For example, for chatbots for doctors, an image of a doctor with a stethoscope around his neck fits best, rather than an image of a man wearing a suit or a lady wearing a casual outfit. Similarly, an image of a doctor wearing a stethoscope may fit best for a symptom checker chatbot. This relays to the user that the responses they will receive have been verified by medical professionals.

On the other hand, the image of a doctor wearing a white coat and stethoscope may impede effective communication for patients seeking mental health advice or support. These patients may easily converse with someone without these perceived barriers – someone who they perceive can relate to them. In this case, an image of a casually dressed man or woman may help.

The character of a chatbot also depends on the context of the audience. A friendly and funny chatbot may work best for a chatbot for new mothers seeking information about their newborns, but may not work for a doctor seeking information about drug dosages or adverse effects.

Just as patients seeking information from a doctor would be more comfortable with and better engaged by a friendly and compassionate doctor, conversational styles for chatbots have to also be designed to embody these personal qualities.

Turn-Taking

One of the key elements of an effective conversation is turn-taking, and many bots fail in this aspect.

Here’s one of the examples used above.

User: My newborn’s eyes have become yellow, what do I do?
Bot: Yellow discoloration of the eyes is called Jaundice, and it is caused by excess bilirubin in the bloodstream, which may result from excess production of bilirubin from the breakdown of red blood cells or accumulation of bilirubin due to obstruction in its flow pathway. Jaundice is…

The chatbot goes on and on describing jaundice and leaves no space for the user to respond. Good communication flow involves switching the conversation: allowing the user and the chatbot to take turns in speaking.

Not only do these responses defeat the purpose of the conversation, but they also make the conversation one-sided and unnatural.

This concept is described by Paul Grice in his maxim of quantity, which depicts that a speaker gives the listener only the required information, in small amounts. Doing the opposite may leave many users bored and uninterested in the conversation.

Now that you have understood the basic principles of conversational flow, it is time to outline a dialogue flow for your chatbot. This forms the framework on which a chatbot interacts with a user, and a framework built on these principles creates a successful chatbot experience.

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choosing the right chatbot


Choosing the Right UIs for your Healthcare Chatbot

Chatbots are revolutionizing social interactions on a large scale, with business owners, media companies, automobile industries, and customer service representatives employing these AI applications to ensure efficient communication with their clients. But humans rate a process not only by the outcome, but also by how simple and comfortable the process is; Similarly, a conversation between man and machine is not nearly as judged by the outcome than by the ease with which the interaction is done – this is where a good User Interface (UI) comes in.

A user interface is the meeting point between man and computer; the point where a user interacts with designs. Depending on the type of chatbots, developers could use graphical user interfaces (GUIs), voice-controlled interfaces, and gesture-based interfaces, all of which use different machine learning models to understand human language – including speech recognition and bodily motions – to generate appropriate responses.

Common platforms for developing chatbot UIs include Alexa API, Facebook Messenger, Skype, Slack, Google Assistant, and Telegram.

These platforms have varying elements that developers could use in creating the best UI for their chatbots. Almost all of these platforms have rich graphical cards, for instance, which provides information in the form of texts, buttons, and imagery to make navigation and interaction effortless.

Skype, for example, supports several types of cards including sign-in card, video cards, thumbnail card, and adaptive cards, each with different functions.

All these platforms, except slack, provides “Quick Replies” as suggested actions in Skype or as keyboard (callback) buttons in Telegram which disappear once clicked. Users can also use quick replies to ask for locations, contact address, email address, or simply to end a conversation. Once the button is clicked, it is posted immediately to the conversation as a message.

Some of these platforms such as Telegram also provide custom keyboards, which pops up any time a Telegram app receives a message. These keyboards come with predefined reply buttons to make the conversation seamless.

Each of these platforms have unique merits over others – and demerits of course – so choosing the right platform may seem daunting. However, let these simple rules guide you for selecting the best UI for your chatbot.

  1. Make UI elements perform predictably, so users can easily navigate through the platform.
  2. Let elements be clearly labelled and indicated to improve usability.
  3. Design layout to improve readability: Ways to do this include avoiding excessive colors and buttons, and using fonts, capitals, letters, and italics appropriately.
  4. Avoid numerous tasks on a single page. This can wear the user out and cause a lot of confusion. Restrict the number of tasks to one per page. Furthermore, complex tasks should be divided into subtasks to improve the usability of the bot.
  5. Finally, make the design simpler to use.

The goal of an effective UI is to make chatbot interactions as close to a natural conversation as possible, and this involves using design elements in simple patterns to make navigation easy, comfortable, and give users the best experience.

In addition to the UI consideration, you have to consider privacy closely. While it won’t be something we will dive into too deep in this guide, you should still look into what information is shared during the conversation and if that specific channel supports the level of privacy you and your patients will feel comfortable with!

 


Fusing the best of Man and AI – Hybrid Healthcare Chatbots

When customers interact with businesses or navigate through websites, they want quick responses to queries and an agent to interact with in real time. Inarguably, this is one of the key factors that influence customer satisfaction and a company’s brand image. With standalone chatbots, businesses have been able to drive their customer support experiences, but it has been marred with a lot of flaws, quite expectedly.

For example, it may be almost impossible for a healthcare chatbot to give an accurate diagnosis of a user’s symptoms, especially for complex conditions. While healthcare chatbots that serve as symptom checker could accurately generate differential diagnoses of an array of symptoms, it will take a doctor, in many cases, to investigate or query further to reach the accurate diagnosis.

In emergency situations after running your symptoms against a large database of information it is trained with, the bot will immediately advise the user to see a healthcare professional for treatment.

This is why hybrid chatbots – combining artificial intelligence and human intellect – can achieve better results than standalone bots.

GYANT, HealthTap, and Babylon Health and several other medical chatbots use hybrid chatbots that provide an interface for patients to speak with doctors. The users may engage in a live video or text consultation on the platform, bypassing the barriers of hospital visits.

Furthermore, hospitals and private clinics use medical chatbots to triage and clerk patients even before they come into the consulting room. These bots ask relevant questions about the patients’ symptoms, with automated responses that are aimed at producing a sufficient history for the doctor. Subsequently, these patient’s history are sent via a messaging interface to the doctor, who triages to determine which patients need to be seen first and which patients require the shortest consultation time.

Talk about efficiency!

Florence, a popular healthcare chatbot, gets its name from deputizing for a nurse where one is absent. This chatbot sends reminders to patients about their medications, tracks their body weight, activity levels, and mood, and also sends these reports to the user’s doctor.
This enables the doctors to monitor their patient’s progress, offer further health advice when necessary, and make dose adjustments if needed. These are done without the patient even moving a muscle to the doctor’s office.

The advantages of using hybrid chatbots in healthcare are enormous – and all stakeholders share the benefits.

For one, these chatbots reduce workload on healthcare professionals by reducing hospital visits, reducing hospital admissions and readmissions as treatment compliance and knowledge about their symptoms improve, and reducing unnecessary treatments and procedures.

For patients, this comes with a lot of benefits: less time spent commuting to the doctor’s office, less money spent on unnecessary treatments and tests, and easy access to the doctor at the push of a button.

The truth is chatbots can not replace a doctor’s expertise, neither can they take over patient care; however, combining the best of both worlds improves the efficiency of patient care delivery, simplifying and fast-tracking care without compromising quality.

healthcare chatbot technology


Building Intelligent Chatbots: Using Rasa NLU for Intent Classification and Entity Extraction.

For an effective chatbot application and good user experience, chatbots must be designed to make interactions as natural as possible; and this requires machine learning models that can enable the bot to understand the intent and context of conversations. This is where natural language processing and understanding tools come in.

Rasa NLU is an open-source library for natural language understanding used for intent classification, response generation and retrieval, entity extraction in designing chatbot conversations. The NLU component of Rasa used to be separate but has been merged with Rasa Core into a single framework.

The NLU is the library for natural language understanding that does the intent classification and entity extraction from the user input. This breaks down the user input for the chatbot to understand the user’s intent and context. The Rasa Core is the chatbot framework that predicts the next best action using a deep learning model.

In this article, we shall focus on the NLU component and how you can use Rasa NLU to build contextual chatbots.

Before going further, you must understand a few keywords.

Intent: This describes exactly what the user wants.

Take this example:

“Where can I buy Insulin in Denver, Colorado?”

In this statement, the “intent” will be: buy_insulin

Entity: An entity is a useful unit of data that provides more information about the user’s intent. The entity answers the questions “when” and “where” about the user’s intent.

In that example, the entities will be: “location”: “Denver”, “Pharmacy”

To build this structure for a conversational chatbot using Rasa, here are steps you should take:

  1. Installation and setup
  2. Training and testing

 

Installation and Setup

The first step is to set up the virtual environment for your chatbot; and for this, you need to install a python module. Once this has been done, you can proceed with creating the structure for the chatbot.

Start by defining the pipeline through which the data will flow and the intent classification and entity extraction can be done. Rasa recommends using a spaCy pipeline but there are several others, such as the supervised_embeddings pipeline which can be used.

To do this, activate the virtual environment and run this:

pip install rasa

 

Once this is completed, run the following command on your desired directory:

rasa init --no-prompt

This will generate several files including your training data, stories data, initial models, and endpoint files, using default data.

You now have an NLU training file where you can prepare data to train your bot. You may use your own custom data with a markdown or JSON format. Open up the NLU training file and modify the default data appropriately for your chatbot.

Let’s create a contextual chatbot called E-Pharm, which will provide a user – let’s say a doctor – with drug information, drug reactions, and local pharmacy stores where drugs can be purchased. The first step is to create an NLU training file that contains various user inputs mapped with the appropriate intents and entities. The more data is contained in the training file, the more “intelligent” the bot will be.

See examples

##Intent: greet
- Hello
- Hey
- Hi there
- Good day
## intent: ask_Amoxicillin_dosage

- How is Amoxicillin taken?
- What’s the correct dose of Amoxicillin?
- How should I use Amoxicillin?
## intent: Amoxicillin_interactions

- Is Amoxicillin safe to use with Insulin?
- What are the likely interactions with Metformin?
- Which drugs react with Amoxicillin?

These data will train the chatbot in understanding variants of a user input since the file contains multiple examples of single user intent.

To define entities and values, let’s use a previous example:

“Where can I buy Insulin in Colorado?”

The name of the entity here is “location” and the value is “colorado”. You need to provide a lot of examples for “location” to capture the entity adequately. Furthermore, to avoid contextual inaccuracies, it is advisable to provide these training data in lower case.

You may design a lookup table containing a list of values for a single entity. This is preferable to creating sample sentences for all values.

For example, if a chatbot is designed for users residing in the United States, a lookup table for “location” should contain all 50 states and the District of Columbia.

Once you have all your training data, you can move them to the data folder. Ensure to remove all unnecessary or default files in this folder before proceeding to the next stage of training your bot.

 

chatbot testing


Training and Testing

To train the nlu mode, run this command:

rasa train nlu

This command looks for training files in your data folder and creates a trained model. It then saves this model in the model folder. The model is named with a prefix nlu-, which indicates that is is an nlu-only type of model.

You can test a model by running this command:

rasa shell nlu

 

If you want to test a single model out of multiple models, run this command:

rasa shell -m models/nlu-20190515-144445.tar.gz

This interactive shell mode, which is used as the NLU interpreter, will return an output in the same format you ran the input indicating the capacity of the bot to classify intents and extract entities accurately.

The output will look something like this:

{'entities':[{'confidence':0.7756870940230542,

'end': 39,

'entity': 'location',

'extractor': 'ner_crf',

'start': 34,

'value': 'New York'}],

'intent': {'confidence': 0.7036955584872587, 'name': 'Pharmacy'},

'intent_ranking': [{'confidence': 0.7036955584872587, 'name': 'buy_drug'},

{'confidence': 0.08354613362606624, 'name': 'bye'},

{'confidence': 0.07291869896872455, 'name': 'fine_ask'},

'text': 'Where can I buy Insulin in New York?'}

After training your chatbot on these data, you may choose to create and run an nlu server on Rasa.

To do this, run this command:

rasa run --enable-api -m models/nlu-20190515-144445.tar.gz

The output it generates is modifiable to whatever parameters you choose. Once your server is running, you may test it using curl. This indicates the intent and confidence of your server.

That sums up our module on training a conversational model for classifying intent and extracting entities using Rasa NLU. Your next step is to train your chatbot to respond to stories in a dialogue platform using Rasa core. I shall explain this in subsequent articles.

 

HIPAA Compliant technology


Building HIPAA-Compliant Chatbots with Rasa Stack

Rasa stack provides you with an open-source framework with which to build highly intelligent contextual models giving you full control over the process flow. Conversely, closed-source tools are third-party frameworks that provide custom-built models through which you run your data files. With these third-party tools, you have little control over the design of the software and how your data files are processed; thus, you have little control over the confidential and potentially sensitive data your model receives.

This is why an open-source tool such as Rasa stack is best for building AI assistants and models that comply with data privacy rules, especially HIPAA.

The Health Insurance and Portability and Accountability Act (HIPAA) of 1996 is United States regulation that sets the standards for using, handling, and storing sensitive healthcare data. The act outlines regulations for the use of protected health information (PHI). The act refers to PHI as all data that can be used to identify a patient, which was provided by the patient as part of a health care service.

HIPAA considers the following data protected health information:

  • Patient’s name, address, date of birth, and Social Security number
  • A patient’s health status: this includes medical or mental health condition
  • Any health service the patient has received or is currently receiving
  • Information regarding the payment for healthcare services that could be used to identify the patient.

Note: The HIPAA Privacy Rule does not consider employment and educational details as PHI. Furthermore, de-identified data – since it is not traceable to the owner of the data – does not fall under the HIPAA Privacy Rule.

Consequently, under the HIPAA Rule, every person involved in developing or managing your AI assistants that can access, handle, or store PHI at any given time must be HIPAA-compliant.


What Does It Mean to Be HIPAA-Compliant?

The HIPAA rule, under its Security Rule, provides three requirements a company must meet to be HIPAA-complaint. These are called safeguards and include administrative safeguards, physical safeguards, and technical safeguards.

Administrative Safeguards

The Security Rule defines administrative safeguards as all “administrative actions, policies, and procedures you need to set up to manage the implementation, development, and maintenance of security measures to protect electronic protected health information and to manage the conduct of the covered entity’s workforce in relation to the protection of that information.”

These safeguards include all the security policies you have put in place in your company, including designating a privacy official, to guide the use, storage, and transfer of patient data, and also to prevent, detect, and correct any security violations

Physical Safeguards

The Security Rule describes the physical safeguards as the physical measures, policies, and processes you have set in place to protect a covered entity’s electronic PHI from security violations.

This safeguard includes designating people, either by job title or job description, who are authorized to access these data, as well as electronic access control systems, video monitoring, and door locks restricting access to these data.

Technical Safeguards

These are the tech measures as well as the policy and procedures that protect and control access to electronic health data. These measures ensure only authorized persons have access to electronic PHI. Furthermore, this rule requires that workforce members must only have access to PHI as appropriate for their roles and job functions.

Technical safeguarding involves encryption of electronic PHI and the Rule requires that your company must design a mechanism that encrypts all electronic PHI when necessary, both at-rest or in-transit over electronic communication tools such as the internet. Furthermore, the Security Rule allows flexibility in the type of encryption covered entities may use.

The HIPAA Security Rule requires that you identify all the sources of PHI, including external sources, and all human, technical, and environmental threats to the safety of PHI in your company.

 

contextual chatbots


Compliance to HIPAA Rule for Building Contextual Chatbots

As long as your chatbot will be collecting PHI and sharing with a covered entity, such as healthcare providers, insurance companies, and HMOs, it must be HIPAA-complaint.

Using these safeguards, the HIPAA regulation requires that chatbot developers incorporate these models in a HIPAA-complaint environment. This requires that the AI conversations, entities, and patient personal identifiers are encrypted and stored in a safe environment.

This involves all the pipelines and channels for intent recognition, entity extraction, and dialogue management; all of which must be safeguarded by these three measures.

With Rasa Stack, an open-source tool that runs under Apache 2.0 license, building a HIPAA-complaint chatbot is possible.

Rasa offers a transparent system of handling and storing patient data since the software developers at Rasa do not have access to the PHI at any time. All the tools you use on Rasa are hosted in your HIPAA-complaint on-premises system or private data cloud, which guarantees a high level of data privacy since all the data resides in your infrastructure.

Furthermore, Rasa also allows for encryption and safeguarding of all data transition between its NLU engines and dialogue management engines, to optimize data security. As you build your HIPAA-compliant chatbot, it will be important to have 3rd parties audit your setup and give you advice where there could be vulnerabilities from their experience.

Rasa is also available in Docker containers so it is easy for you to integrate it into your infrastructure. If you need help with this, we can gladly help setup your Rasa chatbot quickly.

 

Bottom Line

In developing contextual chatbots, one must keep in mind that users provide sensitive and confidential information that must be kept private – at all times. In achieving this, therefore, Rasa’s open-source framework offers a transparent operation of data systems that gives your team the full control of and access to these health data to ensure HIPAA-compliance.

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