Artificial Intelligence in Pharma: What it Means for Patient Trust - The unexpected ways that AI can increase patient trust in pharma
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Artificial Intelligence in Pharma:
What it Means for Patient Trust
The unexpected ways that AI can increase
patient trust in pharma
AI-driven transformation has come at the right time
for the pharmaceutical industry.
Historically, public opinion toward the pharmaceutical Human outcomes and accuracy
industry has tended to be dominated by controversy. From
the hiking of drug prices to pharma’s role in the current Ultimately, the pharmaceutical industry is publicly tasked
North American opioid crisis, a 2017 survey by Ipsos/MORI with saving and improving patient lives. This means the
shows that only 48% of more than 18,000 people across single most effective way to earn patient trust is to drive
23 countries believe pharmaceutical companies will treat and demonstrate better health outcomes. As Thom et. al.
them fairly [1]. Within healthcare, pharmaceuticals is the state, trust can be defined as "the acceptance of a vulnerable
only sector without an upward trajectory for public trust [2]. situation in which the truster believes that the trustee will act
In light of this, pharmaceutical executives are now forced to in the truster’s best interests", and AI highlights the connection
actively manage publicly broken trust. between pharma and patient interests [4]. AI-powered data
analytics uses real world evidence to reveal patterns in
Meanwhile, pharmaceutical executives are under intense data which cannot be discovered by the human eye. This
pressure to compete with innovative new technologies in a data-driven pattern recognition is used to form evidence-
rapidly shifting market, driving greater efficiency and returns based predictions, and enables a move from aggregated to
on investment. Artificial Intelligence (AI), with its potential for personalised analysis, which can deliver effectively tailored
big data and predictive analytics, has become a central focus treatments and drastically improve patient outcomes. AI
in this move towards efficiency in pharma. According to a moves us from asking “how are patients responding to
recent report, the combined applications of AI can create treatment X?” to “how will patient X respond to treatment Y in
$150 billion in annual savings for the US healthcare economy the future?” or “which treatment will be best for this patient,
by 2026 [3]. However, recent high-profile data breaches and why? ”. And this technology is no longer in the future - it’s
have made it painfully clear that successful adoption of AI- here. Already in 2012, OKRA Technologies’ CEO Dr Loubna
powered analytics must take customer trust into careful Bouarfa was predicting surgeons’ movement to improve
consideration. In 2019, AI and trust management are fully surgical workflows in the operating room. Her subsequent
able to positively reinforce each other, in unexpected ways. OKRA analytics engine has already produced more than a
million predictions for global top 10 pharma companies, to
So, how can pharma adopt AI whilst maintaining - and even improve efficiency and accuracy.
improving - patient trust?
In this sense, the basic promise of pharma and the
basic promise of AI are highly compatible.
In 2019, AI and
trust management
are fully able to
positively
reinforce each
other, in
unexpected The OKRA platform,
ways.
answering real world
and market questions
in real time.
Explainability, transparency and provability and there is some room to question the level of transparency
that consumers wish for. A recent study in the Harvard
The pharmaceutical industry, and healthcare in general, Business Review demonstrated that transparency exists
is firmly grounded in evidence based reasoning. This on a scale, and while users will not trust “black box” models,
means that consumer trust is usually grounded in reliability they also do not want full levels of transparency. Consumers
and explainability - of being able to explain, motivate do not require deep mathematical insight into an algorithm
and reproduce results. At first sight, AI technology might - merely basic insights on the factors driving algorithmic
counteract evidence-based trust; it is often described as decisions [5]. This can easily be demonstrated through
a “black box”, where users are kept from knowing why an reason codes, as mentioned above, which is also in line with
algorithm proposes a particular decision. To ensure that EU GDPR stipulations on the “right to explanation of decisions
AI-supported decision-making is trusted, it must clearly made by automated systems”. By finding the best level of
demonstrate what its recommendations are based on, and transparency, AI adopters can demonstrate an evidence-
at what level of certainty. based approach, whilst avoiding unnecessary technical and
communicative difficulty.
Firstly, the ability to “explain” AI recommendations is here -
OKRA provides so called reason codes, a set of short natural Data access and security
English sentences that explain why a given recommendation
is made. We include what data sources the model has used, A central component to an AI workflow is access to data.
and the level of accuracy attached to a specific prediction or Artificial intelligence uses mathematical models to recognise
analysis. Furthermore, AI-powered analytics engines such patterns in data, beyond what humans can perceive. These
as OKRA have vast processing power that allows for more patterns are then used to make relevant evidence-based
evidence to be processed. In combination with human analyses and predictions of the future. AI needs data to
interpretation of outputs, AI supports pharmaceutical function, which means AI adopters must consider data-
employees to process more data points in shorter amounts related concerns.
of time, with replicable and more precise results.
To gain the trust of consumers, there are a number of ways
Secondly, explainability and transparency are not absolutes, to improving the security of patient data, and further ways
By finding the best
level of transparency,
AI adopters can
demonstrate an evidence-
based approach, whilst
avoiding unnecessary
technical and
communicative difficulty.
of ensuring public perception of that security. Firstly, public Furthermore, from a technical viewpoint, AI models can
attitudes towards data sharing within the healthcare industry be optimised for privacy, for example by using “privacy-
are more optimistic that we might expect. A recent report preserving machine learning” models that reduce the risk of
suggests that as long as safety and security are perceived to re-identifying patients within aggregated data.
be safeguarded, consumers are likely to consent to sharing
their data. Secondly, we can assume that this confidence Conclusion
grows with a sense of control of one’s data [6]. The EU’s GDPR
is a significant step in this direction, building trust not by direct First movers on AI in pharma have the opportunity to
insight, but by a sense of regulatory control. As the European communicate these measures clearly, and not only win
Union is set to deliver its first-ever comprehensive framework trust but also significant savings through low-cost proofs of
on AI in Europe - to be launched in Q2 2019 - pharma concept. Pearson and Raeke conclude that patient trust is
companies have a unique opportunity to communicate supported by 5 key factors that mirror a successful patient-
compliance with upcoming EU legal frameworks, relying on physician relationship: competence, compassion, reliability,
established institutions to build trust with consumers. OKRA’s integrity, and open communication [7]. As AI vendors
CEO, Loubna Bouarfa, is a member of the European High- are establishing strong case studies for reliability and
Level Expert Group on Artificial Intelligence and represents competence, pharmaceutical adopters should communicate
the interests of both industry and patients. In a recent their commitment to human outcomes (compassion), data
multi-stakeholder workshop on AI in European healthcare, security strategies (integrity), with open communication at a
hosted by OKRA’s CEO and other EU High-Level Experts, a level of desired rather than complete transparency - for the
top concern was patient control over data. This area is set benefit of both patients and company bottom lines.
to figure prominently in the European Commission’s policy
recommendations, which pharma should choose to align Risking customer trust can be a key barrier to adopting
with. transformative AI technology in the pharmaceutical industry.
However, by taking the evidence-based approach that
Loubna Bouarfa working as part
of the European High-Level
pharma does so well in other areas, executives and marketers
Expert Group on can highlight AI’s role in driving precise, improved patient
Artificial intelligence
outcomes. Evidence suggests that AI can deliver $150 billion
in annual savings to serious adopters, and the healthcare
industry cannot delay their digital journeys. With the use of AI,
and in partnership with trusted vendors, patient and market
objectives converge.
Pharma
companies have a
unique opportunity
to communicate
compliance with
upcoming EU legal
frameworks, relying
on established
institutions to
Follow
OKRA Technologies Contact us
build trust with
on LinkedIn to read
our upcoming
to explore how AI can
drive results in 2019.
consumers.
2019 report series. okra.ai/contact-us
First movers on AI in pharma have the opportunity
to communicate these measures clearly, and not only
win trust but also significant savings through low-cost
proofs of concept.
Authors
Rasim Shah
OKRA Chief Revenue Officer
Ida Svenonius Toby Hackett
OKRA Marketing and Communications Manager OKRA UK Account Manager
About OKRA Technologies
At OKRA Technologies, we work with global pharmaceutical companies to drive competitive insight with validated gold
standard accuracy. We provide an artificial intelligence analytics tool, designed to learn what truly drives health and
market outcomes, and trigger instant action. OKRA combines all your data sources in one place and gives you one
evidence-based view of the truth, accessible across teams, time and space. With artificial intelligence, we answer not only
what happened before, but what will happen in future and why - all in real time.
References
[1] ‘A Crisis of Trust’ Ben Page (2017), Chief Executive Ipsos/MORI. https://www.ipsos.com/sites/default/files/ct/news/documents/2017-09/a-crisis-of-trust-ben-page_0.pdf
[2] ‘2018 Edelman Trust Barometer - Healthcare: Global’ Edelman (2018). https://www.edelman.com/sites/g/files/aatuss191/files/2018-10/Edelman_Trust_Barometer_Global_Healthcare_2018.pdf
[3] ‘Artificial Intelligence : Healthcare’s New Nervous System’ Accenture (2017). https://www.accenture.com/t20171215T032059Z__w__/us-en/_acnmedia/PDF-49/Accenture-Health-Artificial-
Intelligence.pdf#zoom=50
[4] Thom, David H. et.al. (2004). 'Measuring Patients’ Trust In Physicians When Assessing Quality Of Care', Health Affairs, 23 (4).
[5] ‘We Need Transparency in Algorithms, But Too Much Can Backfire’ Harvard Business Review (2018) https://hbr.org/2018/07/we-need-transparency-in-algorithms-but-too-much-can-backfire
[6] ‘Through the looking glass - A practical path to improving healthcare through transparency’ KPMG (2017) https://assets.kpmg.com/content/dam/kpmg/xx/pdf/2016/08/through-the-looking-glass.
pdf
[7] Pearson, D. Steven & Lisa H. Raeke (2001). 'Patients' Trust in Physicians: Many Theories, Few Measures, and Little Data', Journal of General Internal Medicine, 15 (7).
Contact us today
to explore how AI can
drive results in 2019.
okra.ai/contact-us
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