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CHATBOTS AND VOICEBOTS IN THE PHARMACEUTICAL INDUSTRY – OPPORTUNITIES AND ADDED VALUE

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BRAIN STATION-COMPANY PROFILEBrain Station 51 in Germany and its sister company Brain Station 23 in Bangladesh are a team of experienced business IT professionals and professional developers. We are proud to be one of the most innovative IT leaders in the industry. Through the implementation of leading-edge technologies, we work on challenging projects to provide comprehensive solutions that meet all of our clients’ requirements.

We combine the finest technical craftsmanship with elegant and functional design to realise digital experiences. Our focus is on the pharmaceutical industry with its high requirements for safety, compliance and pharma-specific regulations and specifications, such as GCP (good clinical practice), GLP (good laboratory practice) and GMP (good manufacturing practice). In addition, the emphasis is on CDP (Customer Data Platform), BI (Business Intelligence) and reporting. Brain Station has been successfully developing tailor-made solutions for the pharmaceutical industry for many years.

Dr. Holger Kramer, Brain Station 51

Author:

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COGNIGY-COMPANY PROFILECognigy is a global leader in Conversational AI to support customer service automation. Its low-code platform, Cognigy.AI, enables enterprises to automate contact centres for customer and employee communications using intelligent voice- and chatbots.

With precise, reliable intent recognition, human-like dialogs and seamless integration into backend systems, Cognigy.AI creates superior user experiences and helps companies reduce support costs. Cognigy.AI is available in SaaS and on-premise environments and supports conversations in any language and on any channel including phone, webchat, SMS and mobile apps.

Cognigy’s worldwide client portfolio includes Daimler, Bosch, Henkel, Lufthansa, Salzburg AG and many more. For more information, please visit: www.cognigy.com.

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SOCIETAL CHANGE

At the same time, a vaccine against Covid-19 is expected to become available in many European countries and the USA at the end of 2020. A vaccine that has been developed in record time and passed all levels of testing. Emergency approvals are speeding up the market transition in the UK.

But the whirlwind of the pandemic has resulted in a boom in other industries. Amazon, delivery services such as the post office, but also other delivery services are experiencing record sales. Companies that were “born” as platform companies seem to have recognised the signs of the times and can hardly fill the flood of orders. However, the pandemic is merely a magnifying glass or a catalyst for what experts (Dueck, 2017) have been predicting for a long time: The disruption of existing business models through digitalisation. Those who still believe that this “new normal” is only a temporary phenomenon should know better by know.

Societal change stemming from digitalisation and new technologies has experienced an unprecedented level of acceleration due to the Corona pandemic in 2020. All sectors are affected – both public and private. Retail, F&B, and tourism have been plunged so severely into crises by lockdowns that many livelihoods are on the verge of ruin. Ideas are desperately being sought to avert the threat of bankruptcy: The F&B industry is providing delivery services, the retail sector is attempting to remain available online, and the travel industry is hoping for rebooking instead of cancellations. In the private sector, home schooling and distance learning are becoming the buzzwords, but with Germany’s lagging digitalisation, lessons have practically ground to a halt. Governments are indebting their countries at the expense of future generations in order to be able to pay compensations to affected industries.

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CHANGE IN THE PHARMACEUTICAL AND HEALTHCARE INDUSTRYChange in the pharmaceutical industry is characterised by two major trends: The HCP (Health Care Professional) is becoming a consultant and service provider, and the patient is becoming a consumer and customer. Informed and self-determined patients will no longer see the doctor as an all-knowing authority who is to be believed merely due to his professional ethics. The self-determined patient will obtain his information via the internet, i.e., networking groups, medical portals and chat rooms. The HCP – e.g., a doctor – will be confronted, compared and judged with and by this knowledge. These “sages in white” will no longer exist in their current form in the future, and the health market is transforming into a consumer market with the laws of a market economy, demand, and supply. Customers, who used to be patients, now need to be convinced and competed for. The HCP’s competitors are no longer lone clinics in the neighbourhood or in the nearest town, but can be accessed worldwide and are able to build up trusting relationships across physical distances through information, advice and references, something which was previously considered inconceivable (telemedicine, on-demand services). The relaxation of the ban on remote treatment in Germany is already a strong indication of the shift towards video consultations. For one, in Germany, the Telematics Infrastructure (TI) and the Digital Healthcare Act (DVG), supported by the federal government and gematik GmbH, are gaining momentum. Specific digital health applications (DiGA), such as the electronic patient record (ePA), must be made available from 2021 onwards. The reason for this is that providers of healthcare services and medical products are currently also developing and making available interfaces for data exchange, for example based on the near-standard FHIR (Fast Healthcare Interoperability) (in the B2B context). On the B2C market of “health platforms”, it is Apple and Google in particular which stand out. Recently, the Federal Cartel Office approved the establishment of a digital health platform by Poenix and Noventi (see press release from the Federal Cartel Office dated 21/12/2020).

In addition, users of mobile devices are willing to make their data available for scientific purposes. In the future, medical care, which is currently fragmented, will above all be digitised and personalised, which is diametrically opposed to the current approach to medical care (Seebach, Nils, et al., 2021, p.37).

Even pharmaceutical companies will be confronted with a new competitive structure, as powerful players who today still stand for a completely different business model will enter the market (Amazon intends to enter the healthcare sector: https://lnkd.in/dHuZpdH). The balance of power between the players threatens to tilt in favour of the patient or consumer.

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For the pharmaceutical industry, it is important to remain vigilant and observe the changes on the market. The internet – as a synonym for digitalisation – will serve as a marketplace in the future. Sales activities, product promotion and the collection of information will become virtualised and lead to radical change. The novel, digitalised marketing of medicines, also in combination with mandatory prescriptions, will revolutionise the entire market. In this context, the following aspects are particularly salient for the pharmaceutical industry (adapted from Fischer, Dagmar et al., 2013, p.274 – p.275):

MOTIVATIONThe pharmaceuticals market is (still) a special market with its unique challenges, particularly where communication is concerned. In particular, the following play a role here (adapted from Fischer, Dagmar et al., 2013, p.276 – 277):

From the perspective of information processing, the first point falls into the well-known category of “big data”. Enormous quantities of data are generated on pharmaceutical products, their production, active ingredients and side effects. Ongoing research findings yield new insights, which in turn need to be communicated. Patients, but also HCPs, are on the brink of being overwhelmed and must be provided with exactly the right information at the right time from the perspective of the pharmaceutical company (Saghaei, Abbas, 2017, p.7). Unlike on the consumer goods market, pharmaceutical products serve to alleviate suffering and diseases, or even to eliminate them. On a classic consumer market, the main goals of consumption are of a joyful nature, such as enjoyment and increasing zest for life.

Bringing a pharmaceutical product onto the market and establishing it generally entails enormous entrepreneurial risk. The high expenditures for research and patents that expire after a certain period of time can lead to high losses if the pharmaceutical preparation is unable to establish itself on the market. Uncertainty concerning side effects and long-term consequences cannot always be completely eliminated at the time of market approval. The risk is often greater than for many products from the consumer goods industry. The decision-making risk for the market entry of a product in the pharmaceutical sector must therefore be given special consideration in order to minimise failure at market entry.

1. Expansion of the internet as a channel for sales and purchasing

4. Assisting HCPs with the collection of information

7. Price as a market instrument (generics / substitutes)

2. Direct addressing of target groups

5. Building service offerings focused on the pharmaceutical product

8. Establishment as a premium brand / brand messages

3. Profitable pricing structure

6. Focusing on the brand, not individual products

Complexity and information overload

Decision-making risks

Decision-making situation

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Despite a highly regulated pharmaceutical market (Medicines Act (AMG), licensing requirements, etc.), people are free to choose their physician, chemist/pharmacy, health insurer, and may also opt for generics and substitutes (Seebach, Nils et al., 2021, p.35).

Unlike the consumer goods market, pharmaceutical products are bought when this becomes necessary due to an illness or other health-related circumstances. It is less of an experiential world, such as in the consumer goods industry, which encourages emotional purchasing decisions when shopping. Medicines and other pharmaceutical products are used out of necessity or even due to an emergency situation, and rarely when the target group happens to be looking such a product or because it is currently being advertised. Furthermore, the healthcare market has so far developed into a seller’s market, whereas the consumer goods market is more of a buyer’s market (Seebach, Nils, et al., 2021, p.45).

Lifestyle products, wellness preparations, dietary supplements and drugstore items are more characteristic of a classic consumer goods market; in particular, products in the OTC (over the counter) segment are suitable for being advertised in a manner similar to products in the consumer goods industry.

Which conclusions can be drawn from this? Marketing in the pharmaceutical industry must take into account the special requirements of the market and implement corresponding measures alongside modernisation that are in line with the changes. Approaching customers, determining the target group, and the provision of information must be strategically astute, future-oriented, and technology-based in times of big data, data analysis (BI) and personalisation.

The following diagram serves to schematically illustrate various measures in reach measurement and the influence (impact) on the HCP. Online marketing and personalised communication not only lead to a higher degree of maturity in digitalisation, but in particular also increase reach and influence so that the success of marketing measures is significantly improved, the customer’s (patient, HCP) wishes and needs are truly understood from the perspective of the pharmaceutical company, and the market entry of new pharmaceutical products are based on a solid (digital) data foundation. Classical marketing measures alone are not helpful here, simply due to the massive amounts of data, evaluation options, speed and quality requirements, as well as the security aspects.

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In the following, particular emphasis will be placed on the aspect of communication. Modern, technology-based communication in pharmaceutical marketing is supportive in nature and fulfils the need to filter out important and correct data from huge quantities of data and making it available to the patient, the doctor or other HCPs in a usable format. In addition, modern solutions such as conversational AI ensure a constant exchange of information with the stakeholders in the pharmaceutical market, who wish to remain informed regardless of the time and place. The provision of high-quality information and the establishment of trust in the pharmaceutical markets constitute central aspects in this regard.

What are the next steps in your company?

Fig.: Maturity model of digital marketing (Source: AcrossNavigator 2015).

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CHATBOTS IN B2C AND B2BEveryone knows them: “Alexa”, “Cortana”, “Google Assistant” and many others. Digital voice assistants that are a simple and quick way to access information, but also to execute commands. These voice-controlled virtual identities have already found their way into our everyday lives. Natural language input and processing is an essential building block and driver of research on artificial intelligence (Buchkremer 2020, p.29). NLP and NLG are closely related terms here, and stand for Natural Language Processing and Natural Language Generation. In the background, text analysis plays a decisive role for these bots; whereby the processing of questions is referred to as “Natural Language Processing” and the generation of speech and texts is called “Natural Language Generation”. Today’s bots are syntax-oriented rather than semantics-oriented. In the future, voice assistants will know how to interpret other components in addition to semantics, such as humour and irony (Buchkremer 2020, p.30). In the B2B segment, professional solutions such as conversational AI need to establish themselves to ensure customer access and loyalty. The triad for future success is therefore: customer access, creating added value for the customer, and establishing customer loyalty (Seebach, Nils et al., 2020, p.48).

Expert interview with Sebastian Glock, Senior Technology Evangelist at Cognigy, on the possibilities of automation in customer service.

Brain Station:

Sebastian Glock:

Why are chat- and voicebots being used?

With well thought-out applications and smart implementation, companies improve customer service by being available to customers at any time and in numerous languages, while also helping to solve problems. Furthermore, this reduces costs. According to a study by IBM, companies around the world spend more than 1.3 trillion US dollars handling 256 billion calls from their customers every year. In other words, each customer service call costs an average of 30 US dollars. Even healthcare / pharmaceutical / chemical companies are looking for ways to provide good service at a lower cost, around the clock. Availability, even outside business hours, is extremely important – particularly for sensitive topics such as healthcare. When in doubt, customers want to receive sound information concerning their individual issues, even at night. Ideally, they would be able to talk to someone who is well-versed in the subject and possesses all the relevant background knowledge. This is what conversational AI can make possible today.

However, such customer service bots should not be lumped together with assistants like Alexa, Siri or Google Home. Naturally, they make using one’s voice as a user interface acceptable to a large section of the population. But the

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applications differ greatly. One is entertainment and a solution of convenience for the home. The other is a serious dialogue for solving problems with customers.

Brain Station:

Sebastian Glock:

What would such a solution look like?

As customers, we expect problems to be solved 1. quickly, 2. competently, 3. at any time of the day, and 4. across all channels where possible. These are four hurdles that customer service needs to overcome. Many simple chatbots are based on rudimentary software that can only provide ready-made answers to questions worded in an exact fashion. They work using pre-written FAQs or rules with keywords. They offer no real added value compared to information from the website or in an app.

A smart solution is obtained through the combination of AI-based speech comprehension and the bots being connected to back-end systems. Such a smart, networked bot can then access an enormous wealth of knowledge about products, services or even personal information on the customer from the customer database in real time, and is not simply limited to a catalogue of questions and answers. This is read access; it gives the bot a great deal of knowledge.

However, the smart bot or virtual assistant also has written access to the back-end, i.e., it can, for example, change an address or an order quantity, create an appointment, cancel a booking, schedule a call back, etc. Our platform Cognigy.AI has already been used for this purpose in many instances. One could also say that, at first, bots were able to provide general information, and somewhat later on, more customised information. Today’s bots can autonomously initiate complex business processes. Just like a real employee in a call or contact centre.

Fig.: Easy chat flow design.

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Brain Station:

Sebastian Glock:

To what extent can bots automate customer service?

There will always be people managing communications and processes in customer service. Particularly in the highly regulated and sensitive pharmaceutical sector, humans are needed to provide customised assistance in the event problems arise. But at the end of the day, a lot of things involve standard business processes. Bots are good helpers and are becoming increasingly capable. In our projects, they can now independently respond to 30 to 60 per cent of all enquiries. This greatly increases the effectiveness of customer service and improves the customer experience for the user.

For one, however, the platform has to be set up and the response and business logic behind the bots needs to be configured. And this is exactly where partners such as Brain Station 51 come in. They will always be required for cases or problems where decisions have to be made, for example. But a bot can receive enquiries outside business hours, process them, and forward them to the right agent in the contact centre, who then takes care of them the next morning. Customers are left with a good feeling and the agent is able to work more effectively.

Serving important B2B customers should also continue to be possible face to face with a dedicated contact person. However, if the business customer simply wants to change an order, enquire about a delivery date, or receive specific product information, the virtual agent can also be of assistance – immediately and around the clock.

Often, a bot is connected upstream in order to directly forward enquiries to the right agent. Or it might operate entirely in the background. For example, it could be listening to a conversation and providing the human in customer service with information on the customer history or detailed specifications on the product which is currently the subject of the conversation. It makes the human smarter and improves the quality of his responses. The customer receives qualified responses more quickly. Called agent assist solutions, they are in high demand at our company.

One could also say that, at first, bots were able to provide general information, and somewhat later on, more customised information. Today’s bots can autonomously initiate complex business processes. Just like a real employee in a call or contact centre.

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Brain Station:

Sebastian Glock:

What is the technology behind a smart bot?

A smart bot can remember things and carry out dialogues like a human being. The technology behind it is called conversational AI. At its core is Natural Language Understanding, i.e., AI-based “understanding” of human language. Understanding here means that the input does not have to be exact in nature; instead, a wide range of input variants can be recognised. For this purpose, a bot is trained in advance. Once such groundwork has been carried out, the AI can quickly be scaled to other languages and markets. This is immensely important for our healthcare clients, most of whom are internationally active.

But the AI alone is not enough for this. You also need to be able to manage and adapt the bot responses easily and clearly in any language, design structured dialogues, connect back-end systems, and implement bots in channels. In the past, this was a complicated affair and could only be realised with a great deal of IT know-how and programming.

Fig.: Intelligent-Service-Ecosystem.

Fig.: Plug and play backend connection.

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REQUIREMENTS FOR CONVERSATIONAL AI IN THE HEALTH SECTORConversational AI or virtual agents assist human users with their concerns via a dialogue interface. This goes beyond mere conversation by executing tasks issued in the form of voice or text commands for the benefit of the user, and subsequently presenting the results (Stucki, Toni et al., 2020, p.4).

The implementation of a conversation strategy with chatbots requires the creation of a classic IT project, or integration into such an IT project. Once the technical area of application for the chatbot and specifications have been defined, the technology for implementation is selected. Special quality assurance measures apply to artificial intelligence projects. Furthermore, all regulatory requirements (GCP: Good clinical practices) must be fulfilled.

The requirements for the chatbot should be defined with as much precision as possible in order to consider the aspects described above. The following diagram shows an example and excerpt of how the requirements can be visualised in practice via a “solution design”.

However, today’s technology is so far along that even persons without a technical background can independently create and refine the control mechanisms behind the dialogues and the logic behind the virtual assistants.

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Fig.: Excerpt of conversational concept with HCP using conversational AI: solution design (custom project).

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Another real-world example from the United International University in Dhaka, Bangladesh, shows a healthcare chatbot system called “Disha” (signpost). It is a system that assists the user with recognising diseases and maintaining a healthy lifestyle.

Fig.: System diagram for “Disha” from Dhaka (Rahman, Md. Moshiur et al., 2019).

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A chatbot that processes a massive amount of data which is used to detect commonly known diseases. In order to capture various possibilities in textual input, a Named Entity Recognition (NER) algorithm is used. The user can enter individual symptoms, although at least three symptoms must be entered for diseases to be recognised. The inputting of values such as blood pressure, editing of master data, and reminder features such as reminding the user daily to take their medication have also been implemented. The system is based on a generic machine learning algorithm. For making further improvements, a deep learning (DL) algorithm will subsequently be employed (Rahman, Md. Moshiur et al., 2019).

On the technical and conceptual side, it is generally important to define the corresponding environment and requirements. In this context, particular attention should be paid to

• Tower definition and architecture (runtime environment, build and deployment process, code repository, volumetric)

• Solution design (data model, intentions, GDPR)• APIs, access to existing resources, interfaces with core systems• Entry points (entry points for communication)• Communication for various chatbots (chatbot architecture)• Data basis, algorithm for learning (knowledge base and learning algorithms)• Quality assurance & testing approach• Operational concept

The list above is not exhaustive and should be considered as an excerpt.

“Intentions” here refers to the intention of the user – in this case the HCP – to access the chatbot, submit his queries, and request the corresponding information. This might include:

• Subscription / newsletter (regular provision of information).• Sample request (samples of medicines)• Search for dosing information / medication management (information on dosage)• Self-anamnesis• Reminding and notifying (reminder feature with notification)• HCP / patient education (continuing education for doctors and patients)• Self-service

The intentions must be defined in a process chart and agreed upon with the party responsible for the project. In this case, it is important to map both the positive and negative cases so that the chat bot can competently map every situation. Messages such as “an error has occurred” must not appear to prevent the loss of trust in the virtual identity. Bear in mind that the chatbot operates in a manner more closely resembling an abstract algorithm. It is practically a representative and helper from within the pharmaceutical company – a digital professional (much like a digital HCP). Errors and shortcomings (customer experience) are liable to be personalised and transferred to the pharmaceutical company (Stucki, Toni, et. al, p.27-28). This form of social intent can be intercepted by separate bots, which then work together with the specialist bots (chatbot architecture).

Branding (brand experience) via this digital channel is not only of key importance, but must be managed with great care and precision. The following diagram serves to show the scope and current status of chatbots in the healthcare sector in terms of their field of application (Mladan, Jovanovic et al., 2019, p.7):

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Fig.: Current status of chatbots in the health sector with regard to the analytical framework (L – Low, M – Medium, H – High), (Mladan, Jovanovic et al., 2019).

A SWOT analysis helps with the fundamental design of the chatbot for the chosen use case. Applying the SWOT analysis to chatbots in the health sector means that strengths and weaknesses directly pertain to the chatbot itself or are considered as internal features (characteristics). Opportunities, possibilities, and threats are external effects that complicate or even prevent the realisation of chatbots. Sample questions in the fields of the SWOT analysis for supporting a decision are listed here.

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Strengths

• What is unique about our chatbot?• What capabilities do the chatbots

implemented possess?• What are the advantages / benefits

of the system?• What are the greatest benefits of

the chatbot and what will they be in the future?

Weaknesses

• What needs to be prevented during real-world implementation?

• Is the knowledge base of the system sufficient?

• What improvements to the chatbot system are necessary?

• What are the disadvantages of the chatbot?

Opportunities

• Which external changes bring opportunities?

• What are the current trends in the health sector?

• What is lacking in the health sector? Can the chatbot fill this gap?

• Changes

Threats

• What negative aspects currently exist in the health sector?

• Is political instability having an impact?

• Is a change in consumer behaviour expected?

• What are the hurdles expected in the implementation of the system?

• Are there standards, regulations, laws, or new ordinances which negatively influence the use of chatbots?

Overall, it can be said that chatbots in the healthcare sector are highly adept at controlling, guiding, and supporting the patient pathway. Chatbots will become truly successful when a connection to health systems (e.g., ePA, electronic patient record) and other stakeholders such as health insurance companies can be established. This will enable a holistic view of the health data and make it possible to derive targeted diagnoses, therapies and treatments from it. In this context, trust in the technology and how it is handled, along with the security of the patient data, are of utmost importance for a chatbot to be a success.

Communication with a chatbot, on the other hand, can be very exhausting if the system has problems comprehending the user or requires too much interaction. To avoid this, it is advisable to establish a comprehensive and trustworthy information base which the chatbot uses to perform its work. For this purpose, the chatbot system can be supplemented with a self-learning system (machine learning). In order to make a chatbot appear intelligent, care must be taken to ensure corresponding variation in its vocabulary for one and the same intent. However, this makes the chatbot relatively complex and may lead to problems. On the other hand, the main focus can be on the stability of a chatbot. In this case, the user has little control over the conversation. Instead, the chatbot more or less takes over the flow of the conversation.

Table: Questions for the SWOT analysis (translated from Denecke, Kerstin et al., p.78).

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If we look at the healthcare market as a whole, there is a lack of robust and easy-to-use chatbots and applications that are able to capture the HCP’s or patient’s interest over the long term and inspire them with new experiences and real added value. The political regulation of the healthcare market as such is a challenging topic. Many regulations and rules need to be taken into account, such as data protection, requirements for medical devices at the national and EU levels, ordinances, and stipulations by health ministries. Integration with other healthcare systems will be indispensable in the long term.

Used correctly, chatbots can contribute significantly to the digitalisation of the healthcare market. Here, it is advisable to initially focus on the simplicity and robustness of the chatbot in order to build implicit trust on the part of the user. But in order to take concrete steps, it is necessary to identify the right use case. As a digital assistant, e.g. for obtaining information, monitoring the state of health, for making appointments and establishing doctor-patient communication, or also for issuing warnings when health parameters are exceeded, the chatbot is able to generate numerous useful services on a long-term basis (Denecke, Kerstin et al., p. 83).

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REFERENCES FROM THE PHARMACEUTICAL INDUSTRY

Bangladesh College of Physicians & Surgeons

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CONCLUSION AND OUTLOOKChanged consumer behaviour and the evolution of the HCP into a service provider in healthcare require modern, targeted – and above all individualised – marketing approaches. The target group is no longer really a group, but instead an individual with specific needs for product information and a wide spectrum of other information, such as research findings, studies and events. Pharmaceutical companies which have a strategic understanding of how to utilise technological advancements to serve their customers and consumers with news and information at the right place and at the right time clearly have strategic competitive advantages. Conversational AI can be the right key technology in this case for utilising virtual assistants to implement the targeted gathering of information in near real time and be available to the HCP in a personalised fashion around the clock.

In the future, the characteristics of consumer goods markets will also dominate the market for pharmaceutical products. Pharmaceutical companies must not be blind to these developments, and will need to implement corresponding measures for value creation as early as in sales and marketing measures. The patient, now a consumer on the pharmaceutical market, who has educated himself through obtaining information, behaves economically and with self-responsibility. In particular, this can be observed in wellness and lifestyle products, but also for dietary supplements and other drugstore products, and for chronic and preventive treatments. However, this does not mean that pharmaceutical companies should concentrate entirely on the marketing side of pharmaceutical products, but instead on the brand in order to establish a sort of “omnipresence” so as to permanently be on the minds of patients (consumers). Such brand establishment requires a highly strategic approach and technological implementation expertise in systems used in the pharmaceutical industry (IQVIA, Sitecore, AEM, Google Analytics, VeevaCRM, and many others). Special statutory requirements for the pharmaceutical industry, for example from the Federal Ministry of Health (BMG) or the Medical Association (Ärztekammer), must also be taken into account. At the European level, for example, it is the EMA (European Medicines Agency).

Modern means of communication such as conversational AI can maintain constant contact with consumers worldwide in the form of chat- and voicebots. In this context, classic marketing strategies alone, such as booths at trade fairs and pharmaceutical representatives, are outdated. Instead, the future of pharmaceutical marketing requires a combination of measures from the classical world and the latest measures from this new world. If this combination works, pharmaceutical companies will be able to establish themselves as brands, equipping them for the challenges of the future.

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Brain Station combines proven know-how specific to the pharmaceutical industry from numerous successfully completed IT projects with the necessary methodological, procedural, and regulatory competencies. This includes compliance with GCP, GLP, GMP and other national regulatory requirements of various countries.

Fig.: Brain Station delivery model.

Brain Station’s internationally established delivery model yields high-quality project results for clients in the pharmaceutical industry and guarantees the realisation of chatbots

We look forward to hearing from you.

Dr. Holger KramerBusiness Area Manager D-A-CHBRAIN STATION 51Office: +49 4403 6999839Cell: +49 172 4516987Address: Rebhuhnweg 4, 26160 Bad Zwischenahn, Germany

KeywordsHCP, NLP/NLG, AI, conversational AI, pharmacy, artificial intelligence, virtual identities, change, strategy, online marketing, digital transformation, consumer goods market, pharmaceutical market, customer access, deep learning, knowledge base, healthcare systems.

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LITERATUREBuchkremer, Rüdiger, Heupel, Thomas, Koch, Oliver (Artificial Intelligence, 2020); Künstliche Intel-ligenz in Wirtschaft & Gesellschaft: Auswirkungen, Herausforderungen & Handlungsempfehlungen [Artificial Intelligence in Business & Society: Impacts, Challenges & Recommendations for Action], Wiesbaden, Springer, 2020.

Federal Cartel Office, press release dated 21/12/2020: https://www.bundeskartellamt.de/Shared-Docs/Meldung/DE/Pressemitteilungen/2020/21_12_2020_Gesundheitsplattform.html

Dahm, Markus H., Thode, Stefan (Strategy, 2019): Strategie und Transformation im digitalen Zeit-alter: Inspiration für Management und Leadership [Strategy and Transformation in the Digital Age: Inspiration for Management and Leadership], Wiesbaden, Springer, 2019.

Denecke, Kerstin, Tschanz, Mauro, Dorner, Tim Lucas, May, Richard: Intelligent Conversational Agents in Healthcare: Hype or Hope, Bern University of Applied Science, Swiss Post Ltd, 2019.

Dueck, Gunter (Change, 2017): Im Digitalisierungstornado [In the Digitalisation Tornado], Springer Vieweg, 2019.

Fischer, Dagmar, Breitenbach, Jörg (Pharmaceutical Industry): Die Pharmaindustrie: Einblick – Durchblick – Perspektiven [The Pharmaceutical Industry: Insight – Overview – Perspectives], 4th Edition, Wiesbaden, Springer Spektrum, 2013.

Mladan, Jovanovic, Baez, Marcos, Casati, Fabio (Healthcare Market): Chatbot a conversational healthcare services, Paper, 2019.

Matusiewicz, David, Startmann, Frank, Wimmer, Johannes (Healthcare, 2019): Marketing im Gesund-heitswesen: Einführung – Bestandsaufnahme – Zukunftsperspektiven [Marketing in Healthcare: In-troduction – Current status – Future prospects], Wiesbaden, Springer, 2019.

Rahman, Md. Moshiur, Amin, Ruhul, Khan Liton Md. Nazmul, Hossain, Nahid (Chatbot): Disha: An Im-plementation of Machine Learning Based Banga Healthcare Chatbot, Paper, 2019.

Saghaei, Abbas (Chatbot): Chatbot Chatter: More business exploring how chatbots can help save money, boost customer service, in The Progress Report, a digest of trends, research & late-breaking news, 2017.

Seebach, Nils, Wasilewski, Luisa (Digitalisation): Digitaler Puls: Warum der Gesundheitsmarkt jetzt digital handeln muss! [Digital Pulse: Why the Healthcare Market Needs to Take Digital Action Now!], hogrefe, Bern, 2021.

Stucki, Toni, D’Onofrio, Sara, Portmann, Edy (Real-World Examples): Chatbots gestalten mit Praxis-beispielen der Schweizerischen Post [Designing Chatbots With Real-World Examples from the Swiss Post], Springer, Wiesbaden, 2020.

© 2021 Brain Station is a reference of Brain Station 51, Germany and Brain Station 23, Bangladesh. All Rights Reserved. Brain Station believes the information in this document is accurate as of its publication; and such information is subject to change. Brain Station acknowledges the proprietary rights of other companies to the trademarks, product names and other intellectual property rights mentioned in this document. Unless expressly permitted, neither this document nor any part of it may be reproduced, altered, or shared in any form or by any means, without the consent of Brain Station and/or any named intellectual property under this document.

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