Intelligent Chatbots in Health

Naveen Ashish, PhD                                                                               

Chatbots or Conversational Intelligent Agents, such as Siri, Alexa, Cortana, has gained a significant presence in the modern society. In addition to their impacts on personal life, intelligent agents/devices, as well as conversational chat agents for applications such as customer support and travel reservations, have transformed the way modern businesses are being conducted. By definition, “a chatbot (also known as a talkbot, chatterbot, Bot, chatterbox, Artificial Conversational Entity) is a computer program which conducts a conversation via auditory or textual methods.”

Outside of the personal and business setting, a potential area for positive implementations of Chatbots is healthcare! Chatbots can be instrumental in enabling and/or optimizing multiple key health care and medical research applications; which have been proven to be beneficial for patients, clinicians, caregivers, insurers, responders and researchers.

One example of the first implementation of operative bots in the healthcare research context took place half a century ago. ELIZA, one of the first operative bots, was created to emulate a Rogerian psychologist, who asks patients questions by rearranging patients’ response.

So in what ways/ areas can Chatbots be utilized to make important contribution to healthcare? There are multiple key areas that we highlight.

Information and Support

PERSONALIZATION is defined as the key aspect in the upcoming evolution of patient care and medical treatment advancement.

Recent surveys suggest that nowadays, people are relying on comprehensive internet search platform like Google for health information/advice. The scope of information could range from common colds to severe chronic diseases. However, without sufficient medical training/background, one of the challenges that users face is how to effectively identify information that provides the most potent (efficient) means for understanding and addressing the conditions. In this scenario, a Chatbot kind interface, equipped with medical diagnosis procedures and treatment plans algorithms, can act as an intermediary to bridge the gap in knowledge between the users and the search engine. Specifically, a Chatbot interface can provide a) NL Interface, b) Filter and focus search, c) Personalized and organized search results and interactions. Chatbots can also provide assistance with tasks such as medication reminders, prescription refills, advice on medications and administrative reminders, such as scheduling appointments.

Chatbots for Doctors and Medical Staff

Optimization of service, costs, and the overall efficiency of health care delivery and management, especially in the United States, is another area that can benefit from the implementation of Chatbots. A major contributing factor for the current inefficiency and high costs is the lack of engagement and following-up care with patients. A scalable and cost effective Chatbots/ Virtual Assistant can be deployed to follow up and engage with patients after they left the clinic or hospital facility. Recent studies have found that a Chatbot or Virtual Assistant that was used for following-up care with patients can result in a better patient care experience outside of the clinic/hospital setting and prevent future readmissions. Additionally, doctors/healthcare providers can review the information that is collected by Chatbot from its interactions with patients, including medication intake, overall adherence, treatment visits, tests, ER visits, etc. to intervene with patients’ treatments if necessary.


Depend on the severity and requirements of the health conditions, intervention may be required for patients’ overall health improvement and prevention of future readmission. Some apps-based examples of effective intervention mediums that have yield successful results in recent years are in areas, such as smoking reduction, alcohol or substance usage, anger management, sedentary behavior, and diet management. While patients can now be proactive in achieving their health goal through continuous interactions with guidelines, interactive, menu-driven interface via an app on their smart devices, the success of these apps provides an evidence suggesting that there are significant demands and opportunities for Chatbots/ Virtual Assistant to create a more personalized experience for the users of these interventions.

TeraCrunch Chatbot Development

TeraCrunch has significant core technology as well as solution development expertise in the Chatbot space. Specifically:

  • Automated Natural Language Processing (NLP) and Semantic Text Understanding are at the core of any Chatbot, which is powered by TeraCrunch’s patent pending Socratez text understanding engine.
  • TeraCrunch has developed and deployed custom Chatbots in other domains including home automation and travel assistance.
  • TeraCrunch data scientists have solution development expertise and knowledge of the health and biomedical domain, particularly in cancer.

Lifestyle Attributes Database & Analysis for Cancer Research

Naveen Ashish, PhD

Lifestyle and Cancer

Researchers have traditionally analyzed patient data that is genetic, patient medical record data, and other data such as patient MRI images or Pathology reports in cancer research. Cancer has largely been considered as emanating “from genetic factors”. It is becoming increasingly clear however, that the primary factors towards cancers of various kinds are not solely genetic, but that environment and lifestyle are key contributing factors as well.

By ‘lifestyle’ factors, we imply aspects such as diet, exercise habits, other physical activity, weight and body mass index (BMI), alcohol tobacco or other substance use, sleep habits, stress and anxiety, etc. Many interesting studies, evaluating the impact of some such lifestyle factors, have appeared in recent years. For instance in breast cancer, a number of risk factors have been identified in the pathogenesis of breast tumors and among these, a great number are linked to nutrition and life-style such as alcohol consumption, obesity, and eating patterns (Study Link). A number of epidemiological studies (Study Link, Study Link) have provided convincing evidence that alcohol consumption is an important risk factor for the incidence and mortality of breast cancer. On the other hand, soybean products act as cancer preventive agents as shown in rodents and other animals (Study Link).

Interestingly soy products have been a staple part of the Asian diet for centuries (they are the predominant source of isoflavones, which belong to the family of phytoestrogens) and studies that investigate the relationship between soy food intake after the diagnosis of breast cancer and health status reported a slightly protective effect especially among the Asian population (Study Link). There have been almost 200 publications in the last 10 years, with the scope of research spanning genetic risk factors, late effects from treatment, comorbidities, second malignant neoplasms, reproductive health, psychosocial outcomes, long-term health, and lifestyle behaviors.

Getting Lifestyle Data: Social-Media

While lifestyle attributes are important, this is data that is typically hard to obtain as it is typically not part of patient medical records or other data. Many attributes, for a particular individual, are also dynamic and evolving – for instance exercise schedules, diet or other habits can and do change with time, place and other context. A promising source for gathering such information is social-media ! A person’s posts, conversations comments, likes and dislikes, and other expressions often provide valuable information from which lifestyle attributes can be derived. However, gathering such information in scalable fashion is a data science challenge.

TC-PAD: Lifestyle Data in a Database

TeraCrunch has developed TC-PAD (for Personal Attributes Database) which is comprehensive solution that provides a structured database of key personal attributes of an individual’s lifestyle. TC-PAD requires authorized access from individuals for the social-media feeds and profile, which it then accesses for content. It then applies sophisticated natural language and semantic understanding algorithms (part of the TeraCrunch Socratez text understanding suite) to synthesize a variety of lifestyle attributes, per individual and deliver this data as a structured database. This is an “on-demand” solution where such a lifestyle database can be created virtually instantly for a cohort of individuals, once authorization credentials have been provided.