As promised in my first article (Transformation and convergence in the health and life science ecosystem), this is the second article in a series of five articles. This article focuses on the digitalization in Health & Life Sciences (HLS) and the necessity this has for data integration and standardization. Even though it might not be the most exhilarating topic, data integration and standardization is a crucial foundation and precondition for the digitalization in life sciences.

A peek into the future

Before elaborating on some of the ongoing data standardization initiatives in life sciences, let's put things in perspective and take a sneak peek into the future:

Sometime in the near future, each individual will have access to all his/her own personal health and medical data. Just like we have bank accounts today containing all our financial data (i.e. savings, loans, investments, etc.), sometime in the future, we will have individual ‘health accounts’ containing all our health and medical data (i.e. electronic health records, usage of medication, individual DNA and gene profile data, data from wearables, personal health data generated via health apps, etc.). This sensible data will be stored in a secure cloud, and we will have access to this data as well as grant other stakeholders (i.e. doctors, medical advisors, family members, etc.) access to (parts of or all of) this data. We will expect this data to be used proactively for preventive purposes to provide more precise medical diagnoses and counselling.

Actually, elements of the above already exist. A good example of this is The Danish e-Health Portal (Sundhed.dk) which enables all Danish citizens to access their personal health data (EHRs, electronic medicine profile, personal medical history, organ donor registration, etc.). Expanding this portal with i.e. genetic data and the ability for each citizen to be able to upload data from their mobile eHealth apps/devices – and that all this data is actually used for preventive and more precise diagnoses and treatment – could be the next step. I presume several other countries have similar e-Health portals under development.

This type of development will accelerate as mature eHealth technologies combined with an increased macro trend of individuals take more control of their own health, treatments and general well-being. As Dr. Bertalan Mesko very well describes it in this recent McKinsey article, “Healthcare will be driven much more by consumers than physicians, with patients increasingly coming to their doctors with more information, parameters they measured at home, and an informed opinion about how they should be treated”.

As individuals (or patients), we are more well-informed. Looking at the pharmaceutical industry, we see the initial steps of ‘Data being the new pill’. As Michelle Longmire, CEO of Medable Inc., writes “…most drugs will have both a chemical and digital component, as every pill will have a companion mobile app that collects patient-specific data”. Michelle has shared this perspective in her articles ”Data as a Drug” Will Define the Next Decade of Medicine” and “Beyond the Pill: Data is the New Drug”. In an increased competitive sector and in order to get closer to the patients, life science companies are focusing on developing apps and devices to improve patients’ adherence to medication, improve the overall treatment, improve patient loyalty to the brand, and ultimately provide patients with a better combined ‘value proposition’.

Think about it – if you had the option, would you say ‘no’ to more proactive health monitoring, where your individual healthcare data are integrated, standardized, analyzed and compared with millions of other individuals’ healthcare data, with the benefit of detecting serious diseases as early as possible, and based on that information be treated optimally?

The core of above digital evolution is that data from many different and diversified sources (electronic health records, data from mobile health apps, wearables and devices, etc.) must be integrated and standardized to be able to analyze and get a meaning out of it. Benchmarking and analysis are not possible without comparative (standardized) data – no use in comparing ‘apples with pears’ – and that requires some kind of standardization of data.

Moving towards increased standardization for better utilization of data

The pharmaceutical industry and regulators have been working towards standardization for decades to standardize shared information between them; primarily driven by the regulators pushing the pharmaceutical industry. Both regulators and the pharmaceutical industry want to be able to look, analyze and compare safety and efficacy across clinical trials, medicinal products and/or treatments. With payers being more cost-sensitive, and the pharmaceutical market being more competitive, this pushes for increased comparative analysis between medications/treatments to justify price and reimbursement rates. And from the general public, there is an expectation that the industry utilizes data more optimally in order to provide better and more cost-efficient medicinal products and treatments. As part of the global harmonization and standardization within life sciences, and for the purpose of this article, I will share three data standardization initiatives that currently have very high focus and are being adopted within the pharmaceutical industry:

CDISC – In drug development and clinical research, Clinical Data Interchange Standards Consortium (CDISC) is an international non-profit organization that provides clinical research data standards. The main purpose is to provide global and platform-independent data standards that will enable information systems to operate together to share information about clinical trials. Several of the data standards are now ‘de facto’ industry standards and are becoming mandatory by the regulatory authorities. This standardization within clinical research has been an ongoing endeavor during the last 15 years or more (see figure below regarding adoption of different CDISC standards). To avoid any legal problems and to maximize adoption, the standards are vendor- and platform-neutral and made freely available via the CDISC website.

ISO IDMP – The ISO Identification of Medicinal Products (ISO IDMP) is a global standard that provides the basis for the unique identification of medicinal products. The European Medicines Agency (EMA) is the first regulatory authority to mandate ISO IDMP in 2017/2018 (expected first iteration deadline), and other agencies will follow. ISO IDMP is a standardization within regulatory submission and pharmacovigilance and mandates pharmaceutical companies to integrate and standardize product data from multiple disperse internal sources (including Regulatory Affairs, Clinical/Drug Development, CMC, Product Supply/Manufacturing and Marketing). Integrating and standardizing this data to IDMP is a comprehensive data management task, and for some organizations the initial journey towards Master Data Management. For more elaboration on ISO IDMP and the related data standardization challenges, I recommend the white paper “Crossing the IDMP Data Chasm” as well as EMA’s IDMP webpage.

OMOP – With Real World Evidence (RWE) being a very hot topic for pharmaceutical companies as a way to gain new insight and revenue streams from available internal and especially external (big) data sources (patient registries like Optum, CPRD, Truven, as well as data from apps and patient wearables, etc.), this raises the demand for standardization across these observational data. This is where the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) comes into play. This data model is increasingly seen as the ‘leading’ data model for observational and ‘Real World Data’ and is often used as a basis for the data model behind RWE analytical platforms (like SAS’ real-world data and analytics platform). One of the advantages with OMOP CDM is that it combines cost and clinical information in one data model. For more information on OMOP, please visit the OMOP website.

As an example of the data maturity and ‘interconnectivity’ of these data standards in the industry, it is interesting to see assessment work has already been made in mapping data between OMOP and CDISC’s BRIDG model. “The harmonization between OMOP’s Common Data Model and the BRIDG model will establish the link between prospective clinical research and life sciences studies and retrospective active surveillance studies utilizing observational data such as those carried out by the OMOP investigators” quoted from the poster ‘Harmonization of the OMOP Common Data Model with the BRIDG Model’. There is also harmonization work being done between CDISC and ISO IDMP, with the aim of standardizing clinical data, so less data management effort is needed to make it ready for IDMP submissions. In my opinion, these efforts provide some interesting perspectives and a step in the right direction in utilizing the data more optimally. Perhaps a recommendation for all e-Health App and wearable developers would be, to take a peek at these data standards when developing new apps/wearables, to ensure that the data generated by the e-Health Apps/wearables can be efficiently integrated and used in a broader context.

A common denominator for all mentioned standards is that they all require extensive change management efforts, comprehensive data management expertise and technology, as well as resources to be adopted, maintained and used appropriately in the organizations. Pharmaceutical companies can spend up to several years to adopt just one CDISC data standard into their end-to-end clinical processes. The same is foreseen regarding ISO IDMP; an implementation phase will take 9-18 months and is followed by a maintenance/operational phase to ensure regulatory compliance. Working in SAS, I’m proud to be part of a software company with strong domain expertise and data management solutions for the three highlighted data standards.

Undoubtedly, Health & Life Sciences (HLS) is transforming and becoming more digital. In an era where pharmaceutical companies are taking experimental and the first initial digital steps to ‘get closer’ to their patients (i.e. with mobile eHealth apps), and where regulators and patients demand increased openness and transparency, the underlying and implicit precondition to all this is standardized data. In this article, I have highlighted three important data standards that are currently being adopted, and their importance and impact of the digitalization in life sciences. If we agree that ‘data is the new pill’ and that it fuels the digitalization of life sciences, the ability to efficiently standardize and integrate the data for analytical purposes and regulatory submission is foundational and a core capability. I strongly believe that what we see now is the early contours of the next data wave in life sciences.

(This was the second article in a series of five articles)