Creating Seamless Customer Experiences | DHC Summit 2019
The DHC convened a panel of diverse subject matter experts to discuss the idea of “threading the needle” between creating user-friendly, seamless customer experiences and still recognizing and acknowledging the complexities of healthcare data privacy concerns.
We have summarized their key points below.
Ryan Billings, formerly AMAG
Brian Simmons, SVP, Client Relations, imre Health
Sara Gerke, Ethicist and Law Research Fellow of Harvard
Summary of Expert Insights
Understand Customer Expectations
Customers’ expectations are not shaped by competitors in this industry. One can order coffee on their phone and pick it up at Starbucks without talking to anyone, or they can have a car pick them up in minutes. These are the expectations, and the competition is not just with industry peers, but with companies like Amazon, Uber, Netflix, that are meeting expectations so seamlessly and quickly.
There are certainly lessons that can be learned from these companies and how they operate. On the topic of telemedicine — there are other ways to create a more seamless experience. Whether it’s maybe partnering with a pill packer and offering some overnight shipping on products in partnership with a company offering that.
Don’t Overlook Digital Customer Service
Another area that’s overlooked in pharma is customer service, certainly on digital. There’s very few pharmas that are addressing patient and customer complaints and issues and that sort of thing on social, publicly and proactively. Having the capabilities and the content and the resources to be proactively looking for those things, because that does speak volumes to ease customers’ concerns and things publicly on social; airlines and the like are doing that, as well.
On helping pharma companies with MLR concerns — the good thing about today is that there is tons of precedent, especially in social. There are ways and concrete examples that show MLR what’s out there, what’s possible. The next step is figuring out how to be more authentic with one’s content. How can one be more dynamic, more personalized, more real with people and lift some of the stipulations around robotic content because that is certainly not going to stand out anymore.
A lot of platforms, with Facebook as an example, are moving towards a much more transparent place. Facebook, given the Cambridge Analytic issue, one would think that would kill any other business but no one really cared. Facebook has still taken actions to very purposefully say “This is what our advertisers are advertising”, and they continue to roll out these transparency updates.
Be Mindful When Collecting Data
One should be mindful of the data that they’re collecting and think, by collecting all this data, are they creating a barrier for more registrants, from a marketing perspective. Do they need to collect a phone number if they don’t have an SMS program in place? Probably not, and from a compliance perspective, they probably don’t want to do that. But it comes back to: do they need that technology?
Facebook, Instagram are a ‘necessary evil’. A few consumers enjoy how targeted the ads are to them but, again, most consumers don’t always think about that.
SVP, Marketing and Business Development at imre
Realize Consumer Expectation is Huge
The expectation on the side of the brands is the investment of human capital. Finding the right people to drive pharma brands. So, mixing up the team by bringing in people who’ve been at big agencies, both inside and outside of pharma, can bring a good balance. As an example, AbbVie hired half their team from jetBlue, because jetBlue understands seamless customer experiences. Investing in the right people on the brand team at the pharma company makes a huge difference.
The trick with MLR, or the success factor, is bringing them in early and getting their advice. Using Nexium as an example, in the early days because the patient population was on Facebook, in order to be a brand community, the “Purple Pill” on Facebook, they needed to have open comments.
Build Trust Through Transparency
In future there will likely be more pricing transparency. There’s a hope that this year and in years to come, there will be more transparency and creation of understanding of all of the work that goes into drug development. A lot of people fail to understand that there’s a lot of costly science investment that happens in order to make these drugs possible and to bring them to market. Hopefully, going forward, that transparency takes place and allows people to see the passion and the hard workers, the scientists, the marketers and others that are behind the brands.
The hardest part is building more trust for the pharma industry at large. That way, as consumers, as patients feel a little bit more comfortable that companies are going to act ethically with all their data, and they can trust them for the betterment of the ad units they’re getting served for the betterment of their health.
Research Fellow, Medicine, AI, and Law at Harvard
Assess Legal and Ethical Issues Around Data Privacy
It’s always interesting to look at both sides, ethical and legal issues around customer data transparency expectations. From the data perspective, Amazon, Facebook, etc., have a lot of data which is valuable. On compliance — one should not only look to see what they need to be in compliance with the law, because law is lagging behind in the US. There will hopefully be a change when it comes to data privacy protection, at both a state and federal level.
To get HIPAA compliance right from the beginning is very important. When one is developing medical devices, it needs to be in contact with the FDA. The FDA is currently reforming everything. They are working on a pre-certification process, and so it will be very important to show that one has robust ethical standards and compliance criteria in their company.
It’s important to bring in different stakeholders, and especially also the patient because patients and patient involvement are often forgotten about. It’s also important to talk to the people for whom someone wants to develop something. Often, AI data scientists who are developing something for some developing countries haven’t even talked to these people; whether they need it, how it works there, if it can be feasible to implement in practice. That’s why it’s important to talk to different people in the field, to the stakeholders, and not to forget the patients.
Be Transparent with Data Collection Usage
Trust is very important, one needs to build trust. The question is how?
It is very difficult and it will take time, but one should start to become more transparent. It always depends — from a company perspective, one can be transparent to a specific point and then there are IP rights, patents, or a trade secrecy which can make it difficult to be very transparent. But one should start to, nevertheless, be transparent and say clearly what data they are going to collect, what is going to happen with the data, with whom will the data be shared. There is a big lag at the moment, where this is not being done.
On Facebook users not caring about the Cambridge Analytics issue — it’s not that they don’t care but they have no other choice because Facebook is the only way to stay connected nowadays. If there were a similar platform but with better ethical guidelines, everyone would likely prefer to go on that platform instead of Facebook. Everyone wants to have the confidence that their data is used in a way which is ethical and fair, but many people don’t think about what’s going to happen with their data. A lot of people don’t know that money can be made with their data and that the data is very valuable to every company. That’s something which is not talked about, and that’s the point of transparency.
What companies should try to think more about is themselves, because they are also consumers. They should think about what they would like done with their own data, and also start to think about data breaches; brainstorm, with whom do they want to share data, with whom do they want to partner. That will also become more critical as one needs to think about data sharing and data sharing agreements.