Introduction and Digitalization
Digitalization is without doubt dramatically transforming all industries, with some industries more on the forefront than others. With health & life science (HLS) being a more regulated (and some would say conservative) environment, the digital transformation has perhaps had a slower adoption. This is dramatically changing now, and will accelerate during the next 5-10 years, as part of the ongoing convergence of the ecosystem. Some of the trends, hype’s and opportunities that will accelerate the digital transformation of health & life sciences are topics like wearables (mobile health apps and tracking devices), ‘Patients like me’ concepts, quantified self, personal genomics, personalized medicine, collaborative research environments, Real-World Evidence, and the like.
Use of technology has grown and spread across HLS. For example, in medical records, the use of paper records was more or less standard fifteen or twenty years ago, and it is all but unthinkable now. Similarly within drug development, many pharmaceutical companies conducted clinical trials on paper just 10-15 years ago…, even though many pharmaceutical companies still conduct a (small) ratio of their clinical trials paper-based, this will soon end and be captured 100% electronically.
HLS has though been slower to change. Providers plead privacy or patient safety, as reasons. Increasingly, though, these are starting to look like excuses.
The Wider Picture
The pressure on the global healthcare system is increasing. Demographic development and aging populations, expensive development of new drugs and treatments, increasing regulatory requirements from health authorities, and (during the recent years) the general impact of austerity and worldwide recession have all taken their toll. The downward pressure on rising costs is unlikely to stop any time soon. In the UK, for example, the NHS is expected to be running a deficit of around £20 billion within a very few years if delivery models do not change dramatically. In my birth country Denmark, these challenges are also well known, where the rise of drug expenses has recently been debated in public media (note: article in Danish). Danish hospital expenses for medicine has doubled within the last 9-10 years, primarily due to newer improved medicines that are more tailored (personalized) to each patient. This triggers a debate regarding affordability for the welfare state. Similar challenges and debates on medicine costs are seen in other EU countries, US, Japan and…, it’s fair to assume the rest of the world. Efficiency savings, or ‘salami slicing’ of small bits of big healthcare budgets to afford new medicine, is simply not going to solve the problem.
There are other macro trends (read: issues). In a world of social media and instant shopping, we are as individuals used to living in a connected and digital world. As a customer we expect, and usually get, a seamless experience whether we shop online, in store or both. We are therefore conditioned to expect the same of healthcare. But there, the seams are much more obvious. In a world where we can talk remotely and instantly to friends via SMS, Skype, Facebook, What’s App and any number of other messaging systems, we expect instantaneous responses from healthcare providers and professionals. And that just doesn’t happen at the moment. Patient satisfaction is likely to become an increasingly important source of competitive advantage, yet many providers cannot even measure it today, as a recent McKinsey & Co article highlights.
Convergence in the Health & Life Science Ecosystem
When taking an evolutionary and closer look at the health & life science ecosystem, a convergence is ongoing bringing the core stakeholders within the ecosystem closer connected and triggering more collaborative behavior. The main fuel energizing this convergence is the digitalization and increased availability, abundancy and accessibility of data within the ecosystem, providing new opportunity between previously separated (siloed) areas.
My model illustrates this and has three circles. In the outermost circle are all the organizations, loose confederations and individuals who are the stakeholders in healthcare. These stakeholders include pharmaceutical companies, healthcare providers & hospitals, medical device manufacturers, government & authorities, academia, individual patients, patient groups, and individual practitioners.
All of these stakeholders produce or generate data of some sort, and have a stake in how it might be used and analyzed. But none of them has complete data. It is by bringing the data together, through collaboration, integration and standardization, that new insights can be developed, ultimately for the benefit of the patient.
Many recent developments are evidence of this convergence. Take for example collaborative research environments (such as Project Data Sphere) where life science companies, hospitals, and institutions, as well as independent researchers share, collaborate and join efforts in identifying new treatments within a specific therapeutic area.
Another example is the ongoing clinical trial data transparency revolution, where pharmaceutical companies provide researchers access to analyze de-identified clinical trial data on the most granular level (individual patient-level). With this, researchers now have the possibility to access and combine clinical trial data from multiple pharmaceutical companies within a specific therapeutic area, and can thereby generate and provide new insights regarding comparative efficacy or safety issues related to the products, and potentially discover new scientific breakthroughs. What was unthinkable just a few years ago, is now a fact!…, and imagine the news impact some day in the near future, when a new breakthrough has been discovered due to this availability of data.
A third example of the convergence, is the concept of ‘Big Data’ in life sciences. In life sciences ‘Big Data’ is primarily referred to as Real-World Evidence (RWE) and when talking about convergence in this context, I refer to the convergence that will inevitably occur within pharmaceutical companies, once they are onboarding the RWE journey. RWE is about harnessing existing internal and especially available external data (patient registries, social media, data from patient wearables, etc.) to gain new insights, whether it is to improve clinical, commercial, financial, operational or regulatory decision making. This requires a new mindset, a new approach, and the need to combine competencies across internal siloes.
The Core Aspects of the New Model
Transformation is essential and ongoing, and I’m convinced that the core of this transformation will be centered on the following 4 important key aspects (the inner circle of my model, previous diagram). I will cover each one in a separate blog article. They are:
- Integration & standardization, since data is the ‘main fuel’, the ability to efficiently standardize and combine the data is fundamental. Core examples of industry standardization waves are CDISC (drug development) and ISO IDMP (regulatory and pharmacovigilance);
- Analytics, advancements in analytics and the ‘democratization of analytics’ enabling more to digest, analyze, make sense and discover new insights of the vast volumes of data that already exist, and which will just grow in the future;
- Collaboration, stakeholders in the ecosystem have always collaborated, but when siloes are removed, this triggers new models of working together as well as stronger focus on potential ownership of new IP (intellectual property);
- Patient-centric, a genuine desire to put patients at the heart of healthcare. This goes far beyond buzz-words about patient-centered care, and recognizes that empowered patients have better health outcomes. It requires understanding that advances in genetics and wearables can enable more personalized treatment.
The ongoing convergence and transformation of healthcare & Life Sciences requires new thinking and mindset. All those involved will have to work across previously separated areas, and share data that might well have been considered totally confidential just a few years ago. But only in this way can we generate the shared data and insights that will really make a difference to patients. Welcome to the era of shared data and collaboration around analytics in healthcare.
Please provide your reflections, comments and perspectives on the evolution and direction of the Health and Life Science ecosystem.
(This is first article in a five article series...)