8 Must-Have Features For AI Assistants On Healthcare Websites

Considering the growing prominence of virtual assistants and AI-powered chatbots in various healthcare settings, it is important to underscore the critical need for AI in healthcare website usage. Recent data indicates that this brief engagement duration poses a significant challenge for companies that have made substantial investments in these online platforms.

Given the sensitivity of medical information on healthcare websites, incorporating AI assistants is a challenging task. Balancing the demand for fast assistance with the complexities of healthcare privacy legislation and the necessity for accuracy is no easy task.

In this blog, we’ll examine the critical features that need to be addressed while implementing AI assistants on your healthcare website.

  • Data Encryption: The AI chatbot should ensure data is always secure with robust security measures to safeguard data during storage, transmission, and processing.
  • Data Privacy: Ariya prioritizes data privacy by embedding it into our AI model’s design and development from the start. We carefully anonymize or pseudonymize personal and sensitive data, protecting individuals’ identities throughout the dataset.
  • Data Archive Management: Ariya ensures compliance and preserves historical data for future needs. It also establishes secure data deletion protocols when data is no longer required.
  • Compliant: With Ariya, you can navigate the complex landscape of compliance and legal considerations with confidence, knowing that your organization is well-equipped to meet regulatory standards, including GMP, and operate within the bounds of the law.

Ready to integrate conversational AI chatbots on your healthcare website?

8 reasons Medical Affairs teams require an AI-augment solution

Medical Affairs performs a vital function in how pharma companies develop new drugs and therapies. These teams coordinate how a new drug moves from product development to commercialization.

Additionally, Medical Affairs will identify market gaps to exploit, liaise with KOLs, oversee clinical trials, conduct research, and work as a source of medical expertise to support marketing and commercial teams.

The primary objective of Medical Affairs is the creation of a new product’s value proposition. These teams work closely with various stakeholders, including key opinion leaders, regulatory bodies, and internal research and development departments, to ensure that the proposed value proposition aligns with the latest medical insights and industry standards. In this process, they meticulously analyze clinical data, conduct comprehensive market research, and collaborate with cross-functional teams to ensure that the value proposition is robust and addresses unmet medical needs effectively.

Therefore, Medical Affairs are crucial to the success of an HCP companionship. We are all aware that it’s challenging, complex, and intensive work, and this blog discusses how Artificial Intelligence (AI) may boost the efficiency of the Medical Affairs team and create further added value.

Reducing The Volumes

Medical Affairs gather and collate an extensive volume of medical information and data. Given today’s accelerated pace of medical innovation, many databases are needed to stay on top of the latest advances. AI assists medical affairs teams in exploiting these growing resources driving out deeper insights, strengthening the medical community engagement, and finding positive outcomes for the whole organization.

Speed Up Literature Reviews

Literature, by nature, is unstructured data. It’s almost impossible to link data points in an array of text documents, for example, data presented in a spreadsheet. However, AI can quickly and efficiently connect undifferentiated written sources and extract information that helps Medical Affairs understand the entire content. AI enables KOL data, medical information, and open medical sources to be rapidly utilized to supplement search results or merged into a single source to provide a complete picture.

Leverage existing content repositories

Large biopharma companies store scientific information, KOL logs, and data from clinical trials across multiple teams over several geographies. This situation is a barrier to insight generation as the data may exist in various formats or languages, and sharing methods may be inefficient. These fragmented systems cause the Medical Affairs team to miss vital trends that promote the rapid development of therapies that will help patients. 

AI cuts through this undifferentiated morass effortlessly circumventing language issues and technical challenges to give Medical Affairs teams a global view to inform their work.

Real-Time Data Analysis

AI can assist medical affairs teams in gathering and analyzing data uncovering patterns and trends that analysts consume lot of their valuable time. These hitherto unknown links can assist in many areas including clinical trial design, patient population identification and inform product development and commercialization.

Improve HCP Experience

Medical Affairs teams use CRMs like Veeva or Salesforce to capture the conversations they have with HCPs while conducting field research. This knowledge is frequently underutilized. Medical Affairs teams may gather these high-value client encounters in one place by using AI tools to extract value from them. As a result, Medical Affairs can regularly monitor key topics, views, data gaps, and other relevant areas. Additionally, the medical strategies of pharmaceutical firms continue to evolve by incorporating HCP feedback.

Enable Better Patient Experience

Today, with the growing integration of digital health tools, NLP-driven AI Assistants on patient websites have a profound impact on the delivery of product-related information to patients.

Natural Language Processing (NLP) has revolutionized the way patient websites deliver product-related information, enhancing the accessibility and comprehension of medical content. Through its advanced linguistic analysis, NLP enables patient websites to offer personalized and user-friendly interactions, empowering patients to gain comprehensive insights into various products and treatment options. This streamlined approach not only fosters a deeper understanding of medical products but also promotes patient engagement and informed decision-making, ultimately improving the overall patient experience and healthcare outcomes.

Data-Driven Decision Making

Medical affairs curate a large volume of medical information and data. But because most datasets are curated manually, this ends with a complicated data structure. The knock-on effect is ongoing difficulties engaging KOLs in clinical trials and identifying relevant information, leaders, or individuals that offer beneficial support. AI makes it simpler and faster to uncover hidden associations and visualize the data in these resources by generating interactive graphs. As a result, Medical Affairs teams have additional resources to make better data-driven decisions.

Power Through Complex Processes

AI can assist medical affairs teams with complicated procedures such as regulatory compliance.  AI systems that have been properly instructed will be able to spot potential adverse events, keep track of negative outcomes, and ensure regulatory compliance. Pharmaceutical products must be entirely compliant, safe, and effective while promoting patient health. Currently, the process is manual and relies on an Excel spreadsheet or similar uncertain tool, making mistakes more likely (and with more serious consequences). AI streamlines this procedure by getting rid of plain text files and structuring the data in a usable, searchable manner.

Conclusion

As we have seen, AI has enormous potential to impact Medical Affairs throughout their value chain. Of course, as with all new technologies realizing this promise requires careful consideration of ethical and legal issues and a comprehensive understanding of the capabilities and limitations of AI algorithms. But the advantages of gaining this knowledge are plain to see commercially.

Medical affairs teams can use AI to enhance patient engagement, support regulatory compliance, and drive innovation in the pharmaceutical business while saving resources and, essentially, developing drugs and therapies that improve the lives of HCP and patients.

At phamax, we’ve trained Ariya, our AI digital assistant, to serve everyone in healthcare firms, from marketing, medical, leadership, or scientific teams. And especially the always diligent Medical Affairs Team.

To discover how Ariya phamax’s AI digital assistant can enhance the value for Medical Science Liaisons, schedule a demo with our expert.

References

https://within3.com/resources/artificial-intelligence-medical-affairs

https://f.hubspotusercontent10.net/hubfs/8423710/Downloadables%20(to%20send%20to%20customers)/White%20Papers/W3_Whitepapter_4Tech_Disrupts_InsightsManagement_020922.pdfhttps://www.papercurve.com/post/how-artificial-intelligence-will-transform-medical-affairs-and-commercial-teams

https://www.linkedin.com/pulse/potential-ai-medical-affairs-digital-health-exploring-trenton/

https://f.hubspotusercontent10.net/hubfs/8423710/Downloadables%20(to%20send%20to%20customers)/White%20Papers/W3_Whitepapter_4Tech_Disrupts_InsightsManagement_020922.pdf

https://www.papercurve.com/post/how-artificial-intelligence-will-transform-medical-affairs-and-commercial-teams

Generative AI: A Force For Good?

The potential of GAI is exciting, but at this stage, the casual user doesn’t fully know what these tools are capable of or how they work.

The Evolution of AI-Language Models

Over the past decade, our capacity to use software to extract meaningful information from textual data has grown significantly.

Understanding, processing, and generating natural language are now essential concepts in conversational Artificial Intelligence (AI), mainly due to the emergence of next- generation Transformer language models.

Fundamentally, language models use statistical and probabilistic methods to predict the sequence of words or phrases in any given language. Simply put, a language model is an algorithm that has learned to read and write in a specific language, including but not limited to English, German or Spanish.

To do this with perfect accuracy, an AI language model is trained on large amounts of textual data to the point where it can reliably recognise and replicate patterns and relationships between the relevant words and phrases.

And the latest breakthroughs in the field of Natural Language Processing are helping to drive the growth in AI and underpin how language models will drive future developments in this field.

Language Models, A Short History

Language models have moved through several iterations since the 1970s, with each new approach driven by the increasing hardware sophistication and the ongoing research of language model software engineers.

Rule-Based Models emerged in the early days of language modelling research. Here software designers relied on rule-based systems to generate and understand language based on predefined rules to determine the meaning of a sentence and then generate appropriate responses.

Rule-based models are used in various fields and applications where a set of predefined rules can be used to classify or process data. Here are some examples of where rule-based models are used:

  1. Spam filtering: Rule-based models can be used to detect and filter spam emails based on certain trigger words or patterns in the content.
  2. Chatbots: Rule-based models can be used to create simple chatbots that can respond to specific user inputs or queries based on predefined rules.
  3. Fraud detection: Rule-based models can be used to identify potential fraudulent transactions based on certain rules or patterns in the data.
  4. Information retrieval: Rule-based models can be used to extract specific information from text documents or web pages based on predefined rules.
  5. Medical diagnosis: Rule-based models can be used in medical diagnosis to identify certain symptoms or conditions based on predefined rules or decision trees.

Overall, rule-based models are useful in situations where a set of explicit rules can be defined to automate decision-making or classification tasks. However, they may not be suitable for tasks that require more complex reasoning or handling of uncertain or noisy data.

These rules would be programmed into the model, and when it encounters a new email, it would analyze the email according to these rules to determine whether it’s spam or not.

Statistical Models developed in the 1980s and 1990s used probabilistic techniques to estimate the likelihood of observing a particular sequence of words or phrases. An early example was the n-gram model. This approach models sequences of words using the Markov Process, which determines the probability of observing a series of ‘n’ words in simple text. For example, a bigram model would calculate the probability of the next word given only the previous word, while a trigram model would calculate the probability of the next word given the previous two words.

Neural Models emerged in the 2000s, based on artificial neural networks. These simulate the structure and function of the human brain to perform complex tasks. They use Artificial Neural Networks (ANNs) to learn and improve over time by analysing large amounts of data. Examples of neural models include Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and more. These models were able to learn the underlying structure of language, allowing them to generate more realistic and natural-sounding text. LSTM or Long Short-Term Memory is the most commonly used type of RNN, that is designed to capture long-term dependencies in sequential data. LSTMs are popular because they are effective in capturing long-term dependencies in sequential data, a challenge that commonly arises in language modeling and other tasks.

Transformer Models have emerged as a bleeding-edge approach to language modelling in recent years. Unlike traditional neural systems, which process sentences sequentially, Transformers are advanced neural models that use a self-attention mechanism to identify important relationships between words in a sentence.

A Transformer model can efficiently process all the words in a phrase simultaneously, allowing it to comprehend the overall relationships between the words. In this way, Transformers generate more coherent and contextually relevant responses, significantly improving the quality of language generation and machine translation.

The evolution of transformer dates back to 2017, when the first transformer was introduced, which was an upgrade on previous neural network models like the RNN and CNN, in capturing long-range dependencies in sequential data.

BERT (Bidirectional Encoder Representations from Transformers) was introduced in 2018 which was trained on large corpus of text data and achieved state-of-the-art performance on several natural language processing tasks such as question answering, text classification, and named entity recognition.

Refer the following link for explanation of BERT

Following BERT was XLNet model which is another transformer-based model which captures dependencies in both forward and backward directions, similar to BERT.

BERT is a bidirectional model that is pre-trained using two main objectives: masked language modeling (MLM) and next sentence prediction (NSP). MLM randomly masks some of the words in a sentence and trains the model to predict the original word given its context, while NSP trains the model to predict whether two sentences are consecutive or not. BERT uses a fixed sequence of tokens as input and has a fixed order of processing the input tokens. BERT uses a fixed sequence of tokens as input and has a fixed order of processing the input tokens.

On the other hand, XLNet is a generalized autoregressive model that is pre-trained using permutation language modeling (PLM). Unlike BERT, which uses a fixed sequence of tokens, XLNet uses all possible permutations of the input tokens and predicts the probability of each token given its previous tokens, regardless of their order.

The GPT-3 (Generative Pre-trained Transformer 3) model family from Open AI gained prominence in 2020. The renowned ChatGPT is powered by this exact variant. It is a unidirectional autoregressive model with 175B parameters that only uses the encoder. (much bigger than BERT). The only GPT-3 versions that can currently be fine-tuned are the Da Vinci, Curie, Ada, and Babbage models. phamax’s Ariya AI-powered digital assistant employs the DaVinci model of GPT-3 to recognize entities and intentions.

Following GPT-3 is GPT-3.5, which performs task with better quality, longer output, and consistent instruction-following than the curie, babbage, or ada models.

Next is GPT-4, which is a large multimodal model (accepting text inputs and emitting text outputs today, with image inputs coming in the future) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities.

NLP Capabilities

Language models play a vital role in Natural Language Processing (NLP), a subfield of Artificial Intelligence that focuses on understanding and processing everyday human language inputs, either spoken or via text. Applications made possible by NLP include interactive chatbots, sentiment analysis, named entity recognition, and text classification.

  • Speech Recognition models that help computers understand spoken language, enabling applications such as speech-to-text transcription, voice assistants, and speech analytics.
  • Machine Translation models that can translate text from one language to another. Their training in multiple languages helps to bridge the gap between differing grammar rules and word orders.
  • Text Generation models can create new original text based on a given prompt. These skills are helpful for chatbots, virtual assistants, and content generation, as recently demonstrated by ChatGPT.
  • Text Summarisation models assimilate large amounts of text into a shorter, more digestible form, making them useful for accurately summarising news articles, legal documents, and research papers.

Language Modelling Use Cases

Language models can be used to build chatbots and virtual assistants to converse with users in natural language. Uses include providing customer service, conducting simple diagnoses or answering queries. Furthermore, language models can be domain trained on custom data to fit specific user or industrial purposes.

Modern language models can analyse text data and identify its underlying sentiment (using sentiment analysis). This is useful for monitoring social media commentary, customer reviews, and user feedback.

Machine translation models can translate text from one language to another. This is useful for communication and business purposes, such as translating documents or websites or breaking down language-based geographical barriers in global organisations.

Here are some trending examples:

  1. Drug discovery: Language models can be used to analyze large volumes of scientific literature and patent documents to identify potential drug targets or predict drug interactions.
  2. Clinical trial design: Language models can help researchers design more effective clinical trials by analyzing patient data and identifying patterns that could indicate optimal dosing regimens or patient cohorts.
  3. Medical information retrieval: Language models can be used to extract information from electronic medical records and other medical documents, making it easier for healthcare professionals to find relevant information quickly.
  4. Adverse event reporting: Language models can help automate the identification and reporting of adverse events related to drug use, improving patient safety and speeding up the reporting process.
  5. Drug safety monitoring: Language models can be used to monitor social media and other online sources for mentions of adverse events related to drugs, providing early warning of potential safety issues.
  6. Patient engagement: Language models can help personalize patient communications by analyzing patient data and tailoring messages to individual needs and preferences.

Overall, language modelling has the potential to revolutionize many aspects of the pharmaceutical industry, from drug discovery to patient engagement, by leveraging the power of natural language processing and machine learning.

If proof were needed that NLP language modelling has hit the mainstream, look no further than the recent launch of Microsoft 365 Copilot, which uses ChatGPT’s large language model to offer a conversational AI productivity tool based on a graphical analysis of a user’s MS 365 usage.

More Babel Towers To Topple

Language models are foundational for today’s conversational AI applications.

The evolution of this specialist branch of computer science has moved language modelling from rule-based models to probabilistic techniques like the n-gram model to neural networks through to today’s state-of-the-art Transformer models. This groundbreaking research has resulted in today’s pioneering language generation and machine translation capabilities.

With future advancements in language technologies and the immense amount of data collected from current users, future language models will undoubtedly become even more powerful. Soon exciting new applications and use cases will emerge not only in commerce but in how everyone will conduct their lives.

We Need To Talk About Dashboards

An Unintended Controversy

A few weeks ago, I suggested to a colleague that I may write a blog on the decline (I might have even said death) of dashboards. Of course, this elicited a degree of shock. My heresy, it seems, strikes at the heart of anyone who can’t envisage life without their business intelligence (BI) dashboards. Let’s face it; it’s how the corporate world has managed its performance for years.

Even in my company, we have many dashboards. The phamax team have created a fair few over the last decade, but their true efficacy is something I’ve been thinking about for some time. In my view, new technologies are pushing hard to make these corporate shibboleths a thing of the past.

But for the time being, I again pose the question. Are dashboards still the answer to your information needs?

It’s not just me

When I talk to healthcare leaders, many express their frustration with how hard it is to get on-the-spot business information to make timely decisions. Having to rely on a team member, SharePoint or a dashboard to access critical data for a report or meeting takes too much time and delays making a decision.

In truth, leaders don’t actually need dashboard data. Instead, they want to know the overall story the data is telling them. How is the business progressing? Do I need to concentrate on specific things? What do I need to say to shareholders? These are the real corporate stories, and in telling them, future progress lies.

A recent 2021 Gartner Analytics & Business Intelligence poll showed that 25% of leaders view the skill of corporate storytelling as one of the most critical when selecting a new analytics solution.

While I would caution that data generated by BI algorithms are not comparable to the narratives humans create. But they can help us find and understand the insights we, as leaders, need to craft our stories.

I’m not sure dashboards do this as well as we might hope. So we need something else, something more.

We’ll talk about this later.

The dawn of a new (Data) age

Let’s start by getting our ducks in a row and defining what a dashboard is. The name derives from the idea that busy corporate players need complex business information laid out efficiently in visualisations like graphs, infographics and charts to promote understanding and get the evidence they need to prompt appropriate responses.

The name ‘dashboard’ derives from a car cockpit’s dials and gauges. As you drive, you have all the information you need to assess the quality of your driving and the vehicle’s health. In addition, the layout makes it easy to read and understand, allowing you to make any required adjustments as you progress to your destination.

It’s the same principle here. Performance data is captured on a bespoke digital dashboard and delivered quickly and simply to users.

Today BI dashboards tend to fall into four broad categories:

Strategic BI dashboards help senior management to get an overall view of how the business strategy is performing and derive new plans for the future.

Operational BI dashboards concentrate on real-time metrics such as website analytics, marketing response rates or call centre performance. These dashboards allow operational staff to make situational decisions.

Analytical BI dashboards tend to be more complex, using many data types to perform various corporate analytical functions.

Tactical BI dashboards blend operational and strategic data and indicate how functional teams contribute to strategic aims.

Naturally, many sub-variants like ’emergency’, ‘new-because-we-don’t-like-the-original’ or ‘a rosier outlook’ supplement these definitions too.

As access to computing technology expanded during the 1990s, the use and complexity of digitised BI dashboards, allied with advances in data visualisation, transformed these tools into the ubiquitous management tool we know today.

Microsoft’s Digital Nervous System was an early example of a tech-led approach to corporate performance data. From there, automated BI dashboards spread throughout the corporate sector, driven by academics that honed the concept by introducing new-fangled ideas like Balanced Scorecards, RAG reports, KPIs, heat maps and bubble charts.

Dashboards soon became the currency of executive oversight and, for converts, were seen as a panacea—an all-seeing eye into the corporate heart.

Now we know everything, we thought.

Even as I write, dashboards still sound utilitarian. As a technologist and business leader, I can see precisely why dashboards have become so popular. Today they offer dizzying degrees of sophistication since their inception in the early 1970s. And, on the face of it, they seem to solve many informational dilemmas in an automated easy-to-access format.

But that’s not the end of the story. For all their understandable appeal, BI dashboards are not infallible Delphic oracles.

If you build it…

Will they come? Not necessarily. Building dashboards can be a lengthy process. Scoping and prioritising conflicting user requirements is only the beginning. The build takes weeks as datasets are sourced and reformatted, developers scratch their heads over a raft of formulae, and filters are hastily cobbled together. And after all that, user testing goes over schedule after discovering many problems.

Even after launch, corporate dashboard use remains limited. Despite everything, no one knows how to filter their data from the dashboard, and some find that what they need is no longer available, so the requests for further dashboards begin.

Marketing needs a dashboard for this. The Clinical Team requires a new dashboard for that. Drop everything! The MD needs a strategy update dashboard for next week’s board meeting. Soon enough, different iterations circulate ad infinitum through the corporate structure feeding data-hungry appetites wherever they go, but satisfying few.

As an analogy, we can liken dashboards to swans: seemingly elegant as they glide serenely over the water but with much frantic thrashing below the waterline to keep things afloat.

The definition of truth

I must also mention some conceptual issues with dashboards that worry me. I recently read this excellent article by Angela Meharg on LinkedIn and found that I agreed with much of her thinking.

Meharg notes several fundamental issues: ‘you don’t know what to measure in your enterprise.’ You might think this is an obvious point, but I wonder how many dashboard builds set off without a clear idea of what they are measuring and its use for effective decision-making.

Meharg also thinks many dashboards concentrate on the wrong metrics focusing on outcomes (effectively the past) instead of ‘measuring the actions you have pre-determined ought to produce those results.’ The eternal battle between lagging and leading indicators…

I also found this helpful summary in the Harvard Business Review by Joel Shapiro. In his view, there’s a tendency to overstate the capabilities of dashboards to include the ability to make accurate predictions of the future and help to shape ongoing business strategy. He eloquently states:

“Moving from description to prediction to action requires knowledge of how the underlying data was generated, a deep understanding of the business context, and exceptional critical thinking skills on the part of the user to understand what the data does (and doesn’t) mean. Dashboards don’t provide any of this. Worse, the allure of the dashboard, that feeling that all the answers are there in real time, can be harmful. The simplicity and elegance can tempt managers to forget about the all-important nuances of data-driven decision making.”

The message is clear, despite their alluring utility, we must use dashboards carefully and critically to ensure they help us in our efforts to develop our organisations and the stories we want to tell.

Despite all the above, dashboards remain an essential part of corporate analytics. But rather than being a quick-fix solution, their value depends on their quality, how easy they are to use, and where a company is on its data journey.

For example, a specific website tracking dashboard has much to offer to marketers grappling to make sense of how effective their efforts are. But a more dynamic approach might be recommended when setting a high-stakes corporate strategy.

Maybe it’s time to look at dashboards as a contributory part of new data discovery technologies. Ones that give a clearer idea of what corporate data is saying so we can make informed decisions before taking action.

On life support?

So let’s return to my theme: the sad, slow decline of the BI dashboard.

Maybe I’m being a bit preemptive because, for all their faults, dashboards remain a valuable way to assess performance. This is especially true when used by experienced professionals with great instincts, natural curiosity, and the ability to look further into what the data is saying so they can craft stories appropriate to the business circumstances.

Happily, there are many such individuals in modern commerce.

And this is where advances in AI and conversational digital assistants, like Ariya, will help breathe new life into BI dashboards, enhancing their utility to everyone in the business.

This process will advance in stages as AI technology develops and embeds into tech ecosystems and leveraging existing data infrastructure and toolsets.

The first stage is adopting a well-integrated AI-powered conversational layer into an existing BI dashboard suite. Preferably a domain-trained industry-specific product like Ariya designed to work within a specific business context (in Ariya’s case, it’s healthcare).

Now users can find BI data using an intuitive UI on their phones, tablets and PCs. This becomes additionally powerful for team and project work when the AI is installed in group collaboration tools like MS Teams, Sharepoint or Slack. For example, a team can ask for supplementary snippets of info to address their ongoing queries and concerns during their discussions.

Information access and use become increasingly fluid as users freely explore corporate data repositories to find new ideas and connections and share them seamlessly with colleagues and stakeholders. Whether in business meetings or client interactions, the data is always there.

The next stage is where digital assistants become trusted everyday partners, simplifying how we work with data and business information. The reliance on complex static BI dashboards wanes. Users now use fewer dashboards but with increasing speed and accuracy to extract, filter and present disconnected corporate (and none corporate) data to achieve their objectives.

On this theme, the phamax team is now exploring use cases that integrate Ariya into existing BI tools offering a dynamic user experience where the dashboard plays an increasingly minimal role.

Instead, a domain-trained algorithm proactively directs BI data straight to subject matter experts based on their habitual data needs. Imagine the convenience of having all the metrics you need to meet your objectives delivered daily to your inbox by Ariya – no effort required.

How much more productive would you become as a result?

And now the end is near?

Readers know how times have changed over recent years and have challenged how and where we work. Likewise, the advances in AI mean how we use dashboards will also change. To my mind, this is a growing truth.

But as we have seen, there’s still a place for at least some dashboards.

In effect, we need to cut the number of dashboards, increase their quality, and amplify the ease of use of what remains. The key is to create as few as needed and strengthen their utility by using new access tools, like Ariya, to manage the interactions efficiently and creatively.

The phamax team is working on a dashboard-lite approach to BI. Using Ariya, our team has made it easy to create a set of predefined user-specific dashboards that require zero maintenance and operate using Ariya’s sophisticated conversational layer.

So we can conclude, at least for the time being, (some) dashboards will live to fight another day. Indeed with AI tools, they have the potential to perform better than ever.

Until…

A visit to the museum

Finally, let’s cast our minds forward for a moment. A couple of colleagues from a future company are visiting the Museum Of Old School Business Practice. They stop to examine the BI dashboard exhibit, studying how it worked. They look at each other, and simultaneously, they both say:

‘Wow, working with data was difficult back then!”

And yes, by then, the BI dashboard will have sadly left us for good.

Contact the phamax team by email or via the contact section to discuss how our conversational digital assistant Ariya can transform how you work with BI data using cutting-edge AI.

References

  • https://www.linkedin.com/pulse/6-reasons-dashboards-dont-work-what-do-angelameharg
  • https://hbr.org/2017/01/3-ways-data-dashboards-can-mislead-you
  • https://www.forbes.com/sites/brentdykes/2018/10/30/the-real-reason-most-dashboardsdont-tell-data-stories/?sh=19877bae1abb
  • https://towardsdatascience.com/dashboards-are-dead-b9f12eeb2ad2

Why high-quality performance reporting is so essential?

If you can’t measure it…

There’s an old management maxim: if you can’t measure it, you can’t manage it. Many will have heard this oft-used phrase during their careers and think it is somewhat of a cliche. But there’s a hard truth in this dusty old saying that encapsulates the vital nature of performance reporting.

By proactively comparing current performance against targets, goals and expected outcomes, organisations can identify strategic successes, innovations, quality improvements, corrective actions, or risk mitigations to enhance future business progress.

This is why high-quality performance reporting is so essential.

The importance of managing performance

Business reports provide a wealth of management information that helps firms to plan better and improve decision-making. With luck, these reports will indicate that things are going well, but the reports can also help find emerging trends, quality improvements or spot data irregularities that need addressing.

As well as guiding decision-making, business reports create a historical record to show how much progress has been made and gives vital clues as to where strategic choices could have been better. This information is also productively used as indicative data for new annual budgets, sales, forecasts and planning initiatives.

In many cases, business reporting is a regulatory imperative, especially for publicly funded organisations or firms, like healthcare, where an audit trail of the end-to-end processes is essential, especially in relation to product failures or concerns. 

Types of performance reports

In any modern business, you’ll find performance reports abound. They are the lifeblood of how a firm operates, covering areas such as strategy, finance, product, marketing, stock reports, health and safety, online interactions and, customer satisfaction, to name but a few.

Each report has its place in the corporate structure, helping departmental leaders to understand how their team is performing and to make interventions and improvements to achieve objectives or KPIs.

Performance reporting in healthcare

As mentioned above, it’s evident that performance reporting in the healthcare sector is of enormous importance. Whether it’s patient care, drug efficacy, treatment effectiveness, product sales or value for money, performance reporting is essential in identifying quality improvements and mitigating risks.

Without this data, urgent corrective actions could be missed, resulting in unintended outcomes for healthcare organisations and their clients.

Performance reporting best practice

Before undertaking a performance reporting project, it’s helpful for managers to understand the thought processes that promote an outcome that achieves its aims. In summary, these are:

Know your audienceunderstand who will use the report and what they need to get from it.

Define objectives: what does the report need to achieve? E.g. challenge existing practices or highlight future improvements.

Assessment: this is the core section of the report and includes data discovery and manipulation.

Visual Presentationusing visualisation techniques to help readers get to the heart of the report’s findings.

ProofreadingEnsuring spelling, grammar, and document flow are appropriate to avoid distracting or irritating readers.

An Executive Summary: a short précis for busy executives to rapidly understand the report and its broad conclusions.

Following these guidelines should make for a highly actionable report that decision-makers can use to guide their thinking.

Make data dashboards work harder

The data that supports performance reporting is usually captured and presented using Business Information (BI) dashboards.

Most businesses will develop a suite of dashboards representing different aspects of the organisation, each dealing with metrics important to every department. These will include marketing, clinical teams, research and development, sales and human resources, among others.

These digital tools are purpose-built to extract data from corporate datasets automatically and, using filters and algorithms to present the data as an illustrative single-page document replete with graphs, infographics and charts.

Most managers would agree that dashboards are undeniably helpful as corporate governance tools.

Once produced, this data can be fed directly into a company-wide or departmental performance report, and the report writer can draw appropriate conclusions from what they find.

Data discovery using conversational AI

Based on the above, the potential for dashboard overload is high. For example, the number of dashboards in complex healthcare organisations could be in the hundreds. So how can busy managers faced with this glut of data find the information they need to support their report compilation, let alone their decision-making?  

Here we find some of the weaknesses of using dashboards for performance reporting.

With a typical dashboard, bespoke data visualisation is an intricate process requiring users to hunt for and pull data relevant to their reporting needs. This will mean flipping between many different dashboards hunting for a specific data point necessary for the final report. Not only is this frustrating to users it can mean essential details are missed.

In some cases, too much information can be as detrimental as too little. When dashboard data is used to make decisions there’s an urgent need for innovative ways to make that data much easier to work with.

Ariya, phamax’s AI-powered digital assistant, is designed simplify your data discovery snd manipulation processes.

AI in healthcare performance reporting

Recent research indicates that digital assistants are growing in the healthcare sector. For example, a study by Juniper Networks states that an increase in the use of AI-powered digital assistants could deliver operational savings of up to $4 billion.

Installing Ariya, will enable healthcare organisations to capture, process and leverage internal information. And well-considered data always underpins quality improvement and world-class business management practices.

Ariya simplifies the way healthcare firms work with dashboard data. Our team has designed the AI to discover precise data points buried deep in the datasets and automatically visualise these as bite-sized snippets. As a result, managers can analyse or compare data points in real-time to add to their report or share with colleagues.

Ariya is more than a conversational digital assistant; she’s an ‘on-call analyst’, always ready to instantly find the data that makes for a genuinely actionable performance report.

Contact us at ariya@phamax.ch or schedule a demo to discuss how Ariya can transform your performance reporting processes.

Why Responsible AI Matters in the Healthcare Industry

Digital assistants and conversational AIs are a common part of our daily lives. Industries like banking, insurance, telecoms, and retail sectors all use AI to interact with clients, not to mention in emerging areas like autonomous vehicles.

Of course, we have all seen movies where artificial intelligence systems develop a sense of self and begin to devastate the planet. The AI will happily serve our needs one moment, and then send red-eyed cyborgs through time tunnels to end existence as we know it the next. Even though we are aware that these dystopian stories are the stuff of blockbuster films, it is also true to say that these dramas are, in some ways, influencing how we perceive what AI might be able to accomplish in the future.

In this blog, we will get to know

· What is meant by “responsible AI

· The significance of developing responsible AI

· The guiding principles of Responsible AI

· A Responsible AI for the healthcare sector

What Is Responsible AI?

Responsible AI means designing, developing, and deploying AI solutions with the core principles of empowering employees and businesses. In addition, the AI’s design should equitably impact customers and society, allowing companies to engender trust and scale their AI confidently.

Importance of making responsible AI

As the tech environment evolves from text input requests to voice-enabled AI-powered chatbots, how much thought do users give to the ethics that underpin how companies build their digital assistants?

Consider, for example, the findings from Accenture’s 2022 Tech Vision research, which showed that only 35% of consumers worldwide expressed confidence in how businesses implement AI. And roughly 77% of those surveyed believe that organisations should be held fully accountable for any misuse of AI.

Sundar Pichai, the CEO of Google, echoed this idea when he said, “There is no question in my mind that artificial intelligence needs to be regulated. The question is how best to approach this.” The EU is one organisation looking to answer Pichai’s question with its working framework called ‘Ethics Guidelines For Trustworthy AI.’

While the EU consultation is still a work in progress, the message is clear. As AI use accelerates, legal frameworks will compel developers to build their AI products based on ethical policies that belay users’ and society’s understandable concerns.

Guiding principles of Responsible AI

AI and the machine learning models that support it should be comprehensive, explainable, ethical, efficient, and built on well-documented principles.

Comprehensiveness — an AI has clearly defined testing and governance criteria to prevent machine learning from being hacked.

Explainable — ensuring AIs are programmed so we can describe their purpose, rationale and decision-making processes in a way the average end user can understand.

Ethical — AI initiatives have processes that seek out and eliminate bias in machine learning models to avoid undesirable distortions in outputs or intentions.

Efficient — AI can run continually and respond quickly to changes in the operating environment while minimising environmental impacts.

Responsible AI is vital in a Digital Assistant

AI has the potential to revolutionise how we lead our lives. It will increasingly change how we interact with business and make complex decisions on our behalf as the tech becomes increasingly sophisticated.

In short, we must trust that an AI digital assistant works in our best interests using optimised, unbiased data and in a scrupulously controlled data architecture. Key processes we must create are AI digital assistants that are interpretable, fair, safe and respectful of user privacy.

Responsible AI, therefore, is an aspiration aimed at doing precisely these and should include a focus on the following:

Design Criteria

A digital assistant’s functionality should consistently meet user needs. The principles of human-centered design and comprehensive user input in the build stage are crucial to this criterion.

At launch, implementing feedback loops, system metrics, confidence scores, and continuous learning ensure that the assistant continues to deliver against its core purpose.

When we at phamax began with Ariya as a pilot, it was all thanks to the innovators who partnered for trial. We were able to discover how crucial the user journey is. In addition to simplicity, the interface should take care of ease of access.

Compliance Frameworks

The criteria for any digital assistant must account for competing compliance frameworks, especially in complex areas like healthcare, where standards differ between geographies. This will inevitably lead to trade-offs.

With the increasing demand for information in digital space, it becomes inevitable to take care of sensitive information. Especially in channels and webpages that are patient-centric. For example, in some countries, users should be able to get information approved by their national regulatory bodies; therefore, a digital assistant’s design must consider these variations.

The process of content approvals and application logic to ensure the right content delivery may be difficult but is necessary when designing the embedded Ariya version.

Data Responsibility

Data warehouses are varied and businesses prefer their own data management systems. Especially in healthcare sectors where data sensitivity is more, organisations tend to resist any external systems that can breach privacy. Hence, digital assistants should be able to account for these challenges and should be able to connect to any format of client business data sources, including external datasets. A secure architecture is, therefore, an essential prerequisite to data integrity. Furthermore, the source data must be used objectively.

Ariya is designed to leverage existing data sources and can be hosted in client-preferred environments and channels. The recent introduction of Ariya on MS Teams has proved to be a boon as organisations can limit their access to internal as well as access and connect to their own ERP systems

Data Privacy

Ensuring AI digital assistant providers inform users of their rights to privacy in data storage and usage, including in improving performance and user experience. Unfortunately, the potential for a poorly designed digital assistant to contravene these rules are legion, so robust privacy protections is paramount. These rights are outlined in the EU’s General Data Protection Regulation, and failure to comply with it can result in severe financial penalties.

For example, what measures must be taken to protect people’s (employees, patients, and stakeholders like KOL/HCPs) privacy, given that ML models might recall or disclose details of the data they have been exposed to? What steps are needed to ensure users have adequate transparency and control of their data?

Fortunately, by applying several strategies in a precise, principled manner, the likelihood that ML models reveal underlying facts can be reduced.

Controlled Access & Administration

The rigorous control of access to a conversational AI across broad user populations is essential. Sales managers, for example, should be able to view all user data, but a sales rep, for example, cannot view data of other representatives or their territory.

Also, interactions and communications in the digital space have gained popularity amongst KOL and HCPs. It is crucial to distribute region-specific information. This process might include implementing a secure login for accessing patient data, such as DocCheck, that restricts access only to registered healthcare practitioners.

Clear Parameters

Communicating to stakeholders about the digital assistant’s limitations to users. The data models a chatbot uses should broadly reflect the patterns within the data used to train the AI. As a result, tech firms should be able to effectively communicate their data model’s scope and coverage, clarifying both its capabilities and limitations where required This will undoubtedly be a typical “moving feast” as new data enters the systems. Therefore, suitable levels of ongoing review and governance will also ensure a digital assistant performs reliably, maintains its objectivity and meets its core functionality.

A Responsible AI For the Healthcare Sector

As AI technology becomes more prevalent, we’ve learnt that the ethics of responsible AI should always stay front and centre on all things AI. phamax being at the forefront of conversational AI use in the healthcare sector, takes these concerns seriously and has ensured these principles remain a priority as Ariya, our AI-powered digital assistant, is being developed and upgraded.

Reporting The Easy Way, With Conversational AI

Few would disagree that reports are a feature of corporate life in companies worldwide. However, only those tasked with writing a report will know what a struggle it can be. Getting bogged down moving data from one system to another, scratching your head over Excel formulas, then making it look presentable in PowerPoint. What a malarkey!

While reporting is essential, it remains a time-consuming and stressful business.

Luckily, there’s now an easier way!

So Many Reports…

Whether it’s financial, HR, Marketing, or strategic reviews, reports are the mortar that joins the corporate structure together and helps the company function efficiently. But be in no doubt that reporting is a challenging and often undervalued process, and getting a report right can be a constant headache.

  • How long should it be?
  • How detailed should it be?
  • Whom is it pitched at?
  • When do they need it?

All are questions to consider before those tasked with reporting get to work.

Then, after that, there are more hurdles to surmount:

Sourcing The Right Materials

This is the first report-writing challenge. Collating data is always a frustrating and tedious business. Collecting these materials means accessing several data sources plus complex manipulation to get the information report-ready.

Using Conversational AI (CAI) frees you from this burden.

The data gathering process becomes automated no matter how many sources are involved. There’s no need to juggle between dashboards in different formats. Instead, you get the information you need with a simple voice or chat request into one AI powered interface. In a flash, your data arrives almost at the speed of sound!

DIY – Do It Yourself!

Report collation becomes extra irksome if it involves waiting for colleagues to deliver essential content elements. A delay from just one contributor adds to the stress and brings the reporting deadline worryingly closer.

A digital assistant is the only colleague you need to make your report writing straightforward. As the AI can access all company data, it can find everything you need and fetch it in your required format. So whether it’s a team’s performance, product sales, or an account update, a CAI enabled digital assistant like Ariya is designed to give you much-needed independence.

Instantaneous access to all the business information you need for your report creation is always in your gift.

Analysis Paralysis

Sometimes reporting data is complex. And in the healthcare sector, data can be very technical, requiring the input of a domain trained business analyst to interpret it correctly. In these cases, you can be sure your request is only one of many the analyst team is dealing with, so getting priority can be difficult.

This lack of availability can be especially acute when you present the report in a meeting or discussion. You might need an analyst to help re-interpret the data during your presentation. That takes a lot of planning and diary management, and for ad hoc sessions, it becomes impossible.

A digital assistant enabled with analytics code simplifies the information manipulation process. You always have access to on-demand business analytics so you can present your report safe in the knowledge that you can re-cut any element quickly without recourse to an in-situ analyst.

As a result, meetings become quicker and more productive. Plus, attendees can immediately use the report data to solve problems and implement actions. The phamax team have built Ariya to deliver rapid responses anytime, anywhere. With Ariya, there’s always a skilled analyst in the meeting room ready to re-visualise data in a meaningful, attendee-friendly format.

The Time It Takes

Reports take time, lots of it. So it’s not uncommon for managers, team leaders or data analysts to spend long evening hours toiling away to meet their reporting deadlines, usually when the rest of the team has gone home.

Even when the data’s ready, there’s still the re-formatting of the raw data and the ever-present curse of converting it into a PowerPoint presentation. All that copy-pasting, resizing and colouring – what a chore reporting can be.

So what if you could speed up the creation of your PowerPoint creations? The power of CAI will quickly transform raw data and speed the journey from uninspiring numbers to easily digestible charts and diagrams.

You can ask Ariya to autofill your reporting template with the latest numbers. Think of the time that will save. Then from there, Ariya helps you to convert your Excel reports into amazing PowerPoint slides.

So, maybe you can go home on time, after all.

With CAI, Reporting Becomes Easy

The phamax team have designed our CAI, Ariya, based on our many years of experience developing reports in the healthcare sector.

Our team has been-there-done-that and knows the effort involved in producing reports that inform, educate, and energise audiences throughout the corporate healthcare structure.

Love or loathe them; reports are the lifeblood of healthcare organisations worldwide, so making them as efficient and user-friendly as possible is vitally important.

Using a digital assistant gives everyone in a healthcare firm the ability to efficiently create fit-for-purpose reports that add value and meet the precise needs of your audience.

We hope you’ll agree, a domain trained CAI like Ariya will make reporting in any healthcare firm much, much easier.

If you’d like to learn more about Ariya’s reporting skillset or would like more information on our market- leading conversational AI or data solutions, please complete our contact form or email us at info@phamax.ch and our sales team will be in touch.

Information silos are hurting your business

When a management meeting is on the cards are you inspired into action? Let’s say you’ve checked in with stakeholders, run the numbers, and have started to notice promising growth that you can capitalize on.

You feel a level of satisfaction given the progress you’ve made, but acknowledge the difficulties in getting there. You’ve had to juggle various tools, wait for analysts, evaluate business performance, and push stakeholders to send information on time.

Many of today’s modern tools are designed to streamline data collection and insights sharing. However, tools can become so intertangled they can begin to look like spaghetti. When did tools which are supposed to be helpful start getting siloed?

By the end of this blog, you’ll understand:

  1. Information silos: What they are and what causes them
  2. How easy access to information is a necessity, not a luxury
  3. How to overcome information silos

One of the biggest challenges organizations face is the inability to make information available to employees using existing tech infrastructure. Also, often employees are unable to use information fully due to non-availability in the shape and format they want it to be in.

When access to information is not simple and easy, information silos can arise.

Why Are Organisations Facing Information Silos

Businesses intend to facilitate a free flow of information organization-wide, however, many of today’s systems haven’t been updated to align with evolving consumer needs and technology.

An information silo exists when management systems are unable to communicate with unrelated systems. Many information technology challenges can reduce the effectiveness of information when and where it’s most needed. Here are some of the pressing challenges organizations are facing today:

Dealing with Disconnected Systems is Stressful

Information silos in pharma

Data accessibility and availability are critical components to any progressive pharma company.

Despite modern tools generating many well-known benefits, there are also many teething difficulties to address. As modern information management systems continue to disrupt industries, technical guidance is crucial to empower users and optimize the way you apply tools, especially when manual efforts are not worth it.

Many organizations fail to realize the benefit of adopting a centralized information repository where employees can leverage information any time, anywhere. Failing to believe in the benefits of sharing information can be more harmful than you’d think.

Unfortunately, information is often stored in different places or with individuals, which makes information availability a bottleneck that’s difficult to overcome. Information is often duplicated in redundant ways that cause employees to lose time and operate with less efficacy.

Organizations typically use complex tools and systems which are disconnected. Many modern information systems leverage cloud technology to improve collaboration, increase accessibility, reduce costs, and increase data security. However, most pharma companies still use PDF and Word files as content repositories that sit with individuals or in departments. Many of these files contain sensitive information and are content-heavy, meaning it’s essential for information to be protected in an environment that makes sharing easy.

Not Everyone Is on the Same Page

Another huge organizational challenge, one that’s largely a carryover from traditional practices, is the dependency on an individual or silo to obtain time-sensitive information.

Let’s say, for example you’re relying on information from a quarterly marketing report from a team member/colleague. You’d be reliant on that individual being available when the information is needed, creating a dependency on another individual or information silo.

As a company grows, workers become assigned to teams to increase productivity. When different teams begin to focus on different priorities, this requires disparate systems and tools (like a CRM for your sales team and an analytics tool for your business team).

Creating departments to focus on specialized tasks is a vital step in scaling an organization. However, this fragmentation can create barriers that block the flow of information across teams.

Time Lost Gathering Information is Demotivating

Gathering information can consume significant resources, where when there’s so much data to evaluate staff can get demotivated with menial and repetitive tasks that consume lots of time and attention. Employees often lose considerable time looking for the information they need, something which can eat into productivity and efficiency.

It’s important to acknowledge that all information has an expiry date or time. With this being said, every decision that’s made is dependent on relevant data, levying greater expectations to do more in less time.

Increasing complexities surrounding data access and analysis can affect your decision-making processes, eating into efficiency while demanding a competent use of modern technologies.

How to Overcome Information Silos: The Solution

By embracing advanced technology solutions, organizations can better channel the information needed to be successful. Here are some important steps to overcoming information silos and creating integrated data flows:

1. Create a One-Stop Information Hub

It’s crucial to generate a centralized source for employees to access so they can meet their everyday information needs. Isolated data sets in silos reduce the opportunities for data sharing and collaboration between users in different departments.

It’s harder to work together effectively when people don’t have visibility into siloed data. There should be one common interface which connects all information sources and makes it easier for departments to work in collaborative infrastructures.

2. Secure Easy and Simple Access to Data

Eliminating the need to manually search for information is a modern convention that can enhance efficiency dramatically. Managing information has become a challenge, especially since data is so abundant and has increased in complexity.

As technology continues to add to information, it’s crucial for data and crucial business information to be readily available at its disposal with ease.

3. User Experience Matters

Modern digital assistants enable users to navigate intended information flows or simply obtain critical information through conversational interfaces. As a versatile digital assistant, Ariya can be customised to an interactive text-based, voice-based, or button-based input to suit your preferences.

4. Enable Users to Access Information Anytime, Anywhere

Whether you’re in a meeting or on the go, it’s important to have advanced access to information. In today’s fast-pasted industries, solutions compatible with mobile devices are preferred. Ariya can be accessed any time, anywhere, having been designed for users on-field and for management teams to access concise or brief data points remotely.

Luckily, many of these challenges can be overcome by using a knowledge assistant like Ariya, as AI and big data continue to play a major role in optimizing pharma business processes.

Easy Access to Information is a Necessity, Not a Luxury

In a world where technology has created an abundance of data, it’s created a self-perpetuating cycle where the same technology is the solution. Despite many sectors adopting AI to enhance consumer experiences, the healthcare industry is a bit behind with the times when it comes to embracing the latest technology.

There is considerable evidence to suggest AI and big data will have a profound impact within the pharma industry going forward, where a GlobalData report has revealed it will play a prevalent role in drug discovery, development processes, sales and marketing, and many other processes.

Currently, AI-enabled solutions are generally customer-centric (patient-centric), meaning we’re yet to experience the full benefits from an organizational perspective. Many employees continue to struggle with information needs that can be addressed using AI conventions.

Information management issues continue to rear their ugly head. Organizations must buckle up to address the elephant in the room, including information silos, a lack of mobility, and the need for on-demand access to information.

Conversational AI solutions like Ariya democratize information and are designed to make your life simpler, not more complex. By investing in one today, you’ll be one step closer to making information more readily available.

Empower employees by making information readily available. Motivate them to be more productive, forward-thinking, and compelled to achieve greater things.

Conversational AI is boosting performance in the healthcare value chain

We’ve all become used to interacting with the conversational AI (CAI) capabilities built into our phones, tablets, and laptops.

This voice-enabled technology has become part of our everyday lives as it answers our questions, controls IoT devices, makes purchases or, just by asking, serves us the content we want.

It’s unsurprising, therefore, to find that CAI is also revolutionizing the commercial world. A recent study by McKinsey found that 56% of businesses use AI in at least one of their operational areas.

Many firms first saw the benefits that enterprise-level CAI tech could bring as they learned to cope with the impacts of the Covid pandemic.

For example, when lockdowns necessitated working from home, remote access to cloud-based company info helped keep the commercial world afloat. Now home workers can make a full contribution by using a digital assistant to access formatted data from their company’s legacy systems over secured broadband or mobile networks.

Perhaps this is why the value of CAI is becoming increasingly apparent to the healthcare sector, which now recognizes the potential of this tech as an enabler throughout their value chains.

By deploying enterprise-level CAI technology for use in sales, research, regulation and administration, healthcare firms will see a marked increase in accuracy, productivity and profitability.

End To End Process Improvements

Our recent blog outlined the critical differences between chatbots and CAI digital assistants. The following examples demonstrate just a few ways in which a true AI solution brings tangible performance uplifts in healthcare.

Speeding up essential administration. Healthcare sales reps, for example, are required to add data to CRM databases to support their sales activity. This work soaks up valuable time, which reps could use to make extra sales or find new prospects. Voice-enabled CAI can auto-fill pre-defined templates, and using dictation; sales staff can populate free form sections, thereby speeding up necessary but low-value admin.

Simplifying Online as well as Offline research processes

CAI tech is adept at speeding up and simplifying research processes. Healthcare firms habitually rely on research data to produce their products and services. This process necessitates the ongoing review of complex clinical, technical or academic papers. CAIs can undertake offline assessments of downloaded PDFs and other document types, condensing and compiling the content into the format requested by the user.

Firms can deploy CAI applications to undertake ‘social listening’. By integrating this tech with social media channels, areas such as patient comments, brand mentions, reporting adverse events, and competitor moves can be monitored.

Allied to the above, CAIs can perform online content reviews from platforms like PubMeds. This information will usually be lengthy, highly technical, and take a long time to review. Instead, users can ask a digital assistant to extract the main information points from these sources and present them concisely and accurately. This allows for greater comprehension of the content and enables follow-up work to start expediently.

Reporting is made easy

The APIs of a CAI digital assistant is most usefully integrated into all a firm’s legacy data sources. When reporting data is required from several datasets, a CAI will efficiently gather information from each location in a single request. As a result, the time to compile a sales, financial, or marketing report is significantly reduced. In addition, the output can be refreshed by the CAI on an ongoing basis for subsequent versions.

In addition, over time, the CAI will be able to recognize and collate emerging trends and patterns in the data, improving the report’s overall impact.

The CAI Edge, in the era of “contactless”

The potential of CAI technology to enable information and process efficiencies is only just beginning. Data processing mediated through a voice-enabled AI will transform the post-pandemic healthcare sector, where the demand for new ideas, innovations, and products will accelerate.

In designing, our CAI, Ariya, the phamax team used their extensive industry experience to develop a domain-trained digital assistant for health sector firms.

Our team has configured Ariya’s machine learning and natural language processing to meet the healthcare industry’s precise needs. This helps to optimize operational efficiencies and develop new competitive advantages. All while achieving a market-leading ROI for your investment in CAI technology.

CAIs like Ariya is already boosting the performance of companies in the healthcare value chain. Is now the time to look at how a CAI can do the same for your healthcare firm?

If you’d like to meet Ariya or would like more information on our conversational AI or data solutions, complete our contact form or email us at ariya@phamax.ch, and one of our team will be in touch.