BIG DATA OPPORTUNITIES IN HEALTHCARE.
HOW CAN MEDICAL AFFAIRS CONTRIBUTE?
AS OPORTUNIDADES DOS BIG DATA NOS CUIDADOS DE SAÚDE.
QUE CONTRIBUTO PODEM DAR OS ASSUNTOS MÉDICOS?
Abstract
The topic of Big Data has been gathering more and more attention, namely in the healthcare sector. Recen-tly, while participating in a conference where this topic was extensively discussed1, it became clear that
there is still need for awareness around the evolution and opportunities associated with the management of large datasets and how the information generated from these sources can change our lives. Big Data has the potential to transform medical practice by integrating different data sources and making use of real time data, to increase patient´s involvement and improve the efficiency of care. Some major challenges are related with understanding its value, ensure security and governance in a way that research, health-care management and patient’s outcomes can be improved. One thought it could be helpful to bring up this discussion to a larger audience, focusing on two main dimensions: the potential impact of Big Data on healthcare and the specific contribution of Medical Affairs (MA). On this regard MA can raise awareness, stimulate adoption of digital tools and electronic data management, support partnerships while mitigating potential safety and commercial risks.
Keywords: Big Data, Medical Affairs, healthcare, governance, patient’s outcomes.
Resumo
O tema «Big Data» está na ordem do dia, atraindo a atenção das mais variadas áreas, nomeadamente do setor
da saúde1. Porém, o conhecimento sobre a evolução alcançada, bem como as perspetivas futuras, carecem
ainda de ser aprofundados. De facto, as reais potencialidades associadas à gestão coordenada de grandes bases de dados que permitam integrar de forma compreensiva os elementos sobre a saúde de cada um de nós ainda geram desconfiança e receio. De salientar o papel do cidadão na gestão da sua própria saúde, através da monitorização de um conjunto alargado de parâmetros biológicos, e a relevância deste assunto para as entidades que desenvolvem atividades no domínio da investigação clínica e de dispositivos médicos, bem como para as autoridades de saúde. Para que tudo isto seja possível, importa assegurar uma gestão coordenada e segura dessa informação, assim como acordar numa legislação robusta mas pragmática e no reconhecimento do seu valor acrescentado para a gestão do sistema de saúde e para o bem-estar dos cidadãos. Os Assuntos Médicos da indústria farmacêutica podem dar aqui um contributo essencial, nomeadamente nos domínios da sensibilização para a adoção da gestão digital de dados e de aplicações de saúde, particularmente no âmbito da investigação, harmonização e compatibilidade dos sistemas, diferenciação face aos processos convencionais, estímulo a parcerias e minimização dos riscos de segurança e comerciais.
Palavras-chave: Big Data, Assuntos Médicos, cuidados de saúde, governança, resultados dos doentes. José Aleixo Dias
MD, MSc Epid, MBA, Medical Director, Pfizer Biofarmacêutica, Lda., Invited Auxiliary Professor, Aveiro University Paulo Duarte
MBA, Multichannel Marketing Lead, Pfizer Biofarmacêutica, Lda.
The Big Data Concept
At the beginning this concept was associated with the collection and analysis of large amounts of data. A dataset is considered big if it ranges from a few tera-bytes to many petatera-bytes2. A byte is a basic unit of
information containing 8 bits, which can include 28 or 256 values. Currently, it describes how using the evolving technology, this vast amount of data trans-lates into information and knowledge about the laws of nature, genomics and human behavior, which can be used in monitoring, predicting and planning.
Big Data in Health
It was already 70 years ago that the computer entered our lives. This helped the collection and organiza-tion of data that has grown exponentially since then. Though it was mainly focused on data mining during the eighties and nineties, business analytics, customer relationship and healthcare management, brought us to a new development stage. The current amount of data being collected and stored is vast and expanding. We see a widespread uptake of electronic health records
and a certain “chaos” on how to manage them. Some of these records contain quantitative data. However, about 80% of the data is qualitative and registered in an unstructured format (text based).
Together with massive improvements regarding the decision making process, disease prevention and Healthcare monitoring, significant challenges also arise. These are mainly related with complex-ity (compatibilcomplex-ity, readabilcomplex-ity, reliance) and data privacy concerns. The technological advances have change the way we collect, analyze and store data, transforming a complex process into an easy solu-tion. But the challenge is to combine all the available sources in an organized way, allowing for its usage in real time and extract information that may lead to meaningful decisions.
Big-Data Revolution
After collecting data for so long, some critical ques-tions remain to be answered, such as:
• Why do we need to collect and store such large
amounts of data?
• Who is analyzing all that information?
• For which purposes?
• Who is ensuring the security and reliability of such
information?
• What is the added value for the patient and for the
healthcare system?
The coordination of efforts between Healthcare Providers and Pharma Industry could gain momen-tum and be used to ensure better health outcomes. An important part of this goodwill is lost during the patient’s journey3. A more collaborative model
between stakeholders will ensure the added value brought by innovation and the commitment to pro-vide better health to our population.
Figure 1 – The innovation cycle
Adapted from McKinsey analysis4.
1. Demand 2. Supply 3. Technology 4. Innovation 5. Data Mgt
1. Demand for Better Data and Insights
There’s a huge pressure from governments and pay-ers on cost containment measures. Pharma wants to make a more efficient use of the available data to bet-ter understand disease distribution, safety patbet-terns, and patient’s compliance and to evaluate product im-pact and health outcomes. In this process, patients are being more empowered while involved in moni-toring their own health. More and more patients are digitally skilled and willing to play a role in this op-timization process. While sharing their experiences, they provide relevant insights that can be used to fos-ter research and improve health outcomes.
2. Supply and Governance of Relevant Data
Data sources are increasing and regularly used by a great variety of stakeholders. Several devices such as wearables and software are collecting data that could be of relevant use in healthcare management. How-ever, it is frequently unclear who is managing it, if this is stored? Where? For which purpose? Despite a large number of potential users, the lack of confi-dence on what will happen to personal data remains to be clarified.
3. Technology – Data Sharing
New players are becoming more important, while new companies are emerging and taking on a new and important role in the Healthcare environment. Technology is more and more friendly, easy to use and readily available. Despite security and integra-tion challenges most companies are favorable to data sharing and collaboration3.
4. Innovation & Co-creation
There are a lot of players in managing health tech-nology and Healthcare data. Customer insights and unmet needs are being screened and analyzed by cre-ative companies and startups, to develop new ways of collecting patient’s health outcomes and respond to unmet medical needs. Innovation can be the result of bringing complementary parties together (co-cre-ation), in order to jointly produce a mutually valued outcome.
5. Data Management & Legislation
The technology screening and regulation should be improved in the future, allowing for better decision making process for both patients and Healthcare providers. Appropriate and pragmatic legislation is needed for data protection, data storage and data analysis.
The Example of Managing
our Own Health
How much time out of the 8760 hours of a year, do we dedicate to evaluate and monitor our own health? For most people, perhaps only a couple of hours at the outmost, if one has considered booking a yearly check-up, and some additional exams.
Physicians hold patient’s health records but are these being managed in a way to monitor some health in-dicators regularly like: weight, blood pressure, heart rate, medication, treatment compliance, vaccination or physical activity? If one has a chronic condition like asthma, is this person being alerted for the pol-len’s level in advance, so that, one can take appropriate preventive measures? Is our heart function or glycemic control being regularly assessed? Suppose instead that our physician had timely access to our physical activ-ity, our glycaemia or microbiome, etc… then, recom-mendations would be personalized, early diagnosis could be more often made and prevention of complica-tions could be more promptly implemented.
Medicine was ahead of many other areas in recogniz-ing the value of data, to support the decision makrecogniz-ing
process. However, technology is quickly evolving and healthcare has lagged in adopting techniques to lever-age the rich information contained in these databas-es, namely in a coordinated way. Despite the growing tendency for the use of real world data (RWD) gen-erated both from field studies and registries, as well as, the concomitant proliferation of structured data-bases, a large amount of health or disease related in-formation is still of unstructured or semi-structured nature. That is the case of patient’s files, namely in what concerns complementary exams reports. In Portugal, examples of data used in Oncology Reg-istries5, Cardiology Registries6 or Rheumatology
Registries7 are collected from several sources,
vali-dated, coded and structured before being uploaded into the systems. However, this requires reading and interpretation before processing, once most of the data is semi or unstructured at origin.
The complexity of adapting these data has to do with the nature of the information, once this could be based on numbers, text, graphs or images. Recent ad-vances in technology such as Hadoop MapReduce8 or
tranSMART9 make it possible. However the
chal-lenge resides on how to extract knowledge from them.
The “Six Vs” to Value
Certain authors highlighted the Big Data character-istics in “three Vs”: Volume, Variety and Velocity10.
Oth-ers proposed adding a fourth dimension11,12: Veracity.
Reflecting on these, we are proposing not 3 or 4 but, 6 dimensions:
1. Volume
The amount of Big Data generated and stored across the World in 2012 was said to be over 3500 petabytes (1015)
in the US, while above 2000 in Europe. However, more recent estimates point to at least 5 zettabytes (1021),
potentially growing to 35 zettabytes by 202012.
2. Variety
There is a large variety of channels supporting elec-tronic interactions. Last year 42012 million of
porta-ble health monitors were sought to operate, mostly in three different manners:
- People to people – networks, virtual communities,
we-binars/WebEx’s;
- People to machine – archives, computers, mobile
ap-plications;
- Machine to machine – bar code scanners, monitor
de-vices, scientific research.
3. Velocity
The speed of data transfer is incredible high. Around the World, about 3 million emails10 are sent every
sec-ond, while in YouTube 20 hours of video are stored every hour and 50 million tweets are exercised per day.
4. Veracity
Despite its many advantages, a large amount of data cannot guarantee accurate results, unless quality is ensured. The poor quality of the data is estimated to cost 3,1 trillion11 dollars a year in the US, while
deci-sions are frequently taken with large uncertainty on the quality of the data they were based upon.
5. Versatility
Concerns the capability to adapt and flexibility to use available data generated through the different systems. It requires availability and compatibility.
6. Viability
Is the data accessible and available to be used, both by personal consent and by law? While processing and protecting the right to privacy, one should not avoid technological progress and innovation which could be useful to people.
The combination of these six dimensions leads to = VALUE
The opportunities are no longer bound only to data capture and management but on how data are col-lected and analyzed to create value, having in view disease prevention and precision treatments.
The Value of Big Data in Healthcare
Big Data is a big opportunity to develop patient driv-en outcomes, and sustainable models of healthcare delivery, to maximize the potential of scientific de-velopments.
Experts provide their perspective on how one can use Big Data in a manner consistent with patients’ rights, to improve healthcare systems to that end. The European Federation of Pharmaceutical Industries (EFPIA) considers that a focus on outcomes can ad-dress many current healthcare challenges13. Systems
are struggling to spend their money where it has the highest impact and Big Data might help these systems to allocate spending where it really makes a differ-ence. However, there is a risk that overly-restrictive data privacy laws and consent requirements could
adversely affect our ability to realize the potential of Big Data for research, especially in healthcare. By fo-cusing on interventions that really work and moving away from those that don’t, we can improve health and make health systems more financially sustainable. The Big Data strategies to better inform decision making could generate high savings in the healthcare system, by optimizing innovation, improving the efficiency of research and clinical trials, and building new tools for physicians, consumers, insurers, and regulators to meet the promise of more individualized approaches. This opportunity is driving Healthcare providers in complex business environments experiencing a grow-ing trend in the types and volumes of available data. The healthcare data growth is generated from several sources, including the R&D process itself, retailers, patients, and caregivers.
Big Data in Pharma
The uses for Big Data in Pharma are limitless. Clini-cal, Commercial and Market Access teams can all benefit from Big Data insights. Most important ob-jectives being:
- Understand and assess patient needs;
- Develop better clinical targeting, once the end-point measures of human physiology are no longer enough to guide the new generation of medicines and to evaluate drug response;
- Gather Patient Related Outcomes (PROs);
- Explore sensor technology to further contextualize disease and drug response through continuous phys-iological readouts;
- Develop better payer messaging; - Develop better market segmentation.
Part of taking advantage of Big Data involves making data capture as electronic as possible. Companies are increasingly using electronic systems of capturing and transmitting PRO responses by using comput-ers, tablets and mobile devices to record patient an-swers. Recently FDA approved a PRO-Diary for use as a medical device worn on the wrist and used to record patient data. This approval will drive its use in clinical trials as a way to collect PROs quickly and more reliably from the patient. These and similar de-vices will improve data capture and increase datasets to yield more reliable results.
Big Data applications vary slightly among pharma-ceutical companies and consultants. Pharma compa-nies are more likely to focus Big Data initiatives on current products to grant the success of drugs already
on the market14. Consultants are more likely to
con-tribute to the development of new products. Overall, the prevalence of these broad Big Data-driven mar-ket intelligence initiatives will continue to increase, as data become more useable and the benefits more obvious.
Stakeholders
This approach in Healthcare through data creation and engagement, sets-up new roles and responsi-bilities, requiring careful management of the differ-ent interests involved3. Some of the most relevant
stakeholders include: Providers of clinical/medical data, Payers, Researchers, Developers, Marketers, Government and Consumers. Patients are requested to participate more intensively, namely by using de-vices and digital applications. They are empowered to gain more control of their own health parameters and medication compliance. Moving from a patient centric approach to a medicine oriented outcome, processes of data integration and management are needed to reach a more efficient outcome. Research should be supported by credible tools, fully compli-ant and operating under the adequate algorithms. To predict opportunities, Pharma needs to collect data that is relevant for the research purpose, conduct the monitoring according with the best clinical practices, ensuring drug safety reporting and systematically evaluate patient’s outcomes. The implementation of these processes will hopefully allow technicians to make a more efficient use of the available information, aiming to support self-care and healthcare provid-ers. The data captured by medical devices should be stored and integrated in healthcare systems, allow-ing for a robust patient journey. Payers should make appropriate use of this information to better allocate resources and decide based on real life data. They can also predict the occurrence of certain pathologies and invest in awareness campaigns. Governments hope to transform traditional medicine through a more personalized patient’s approach. Understanding the relevance of Big Data will help them to reinforce the need of data capture and utilization, to prioritize sys-tems integration and better allocate resources to ad-dress human and financial challenges.
Prevention and Early Treatment
Earlier is better when it comes to detecting diseases like: cardiovascular diseases, cancer or diabetes, be-cause we have a better chance to fight back. Analyz-ing real world data, while explorAnalyz-ing the potential for
mainstream use of sophisticated sensoring devices, such as: the nano particles detector, the portable in-direct calorimeter (measures oxygen consumption and determine resting metabolic rate) and the con-tinuous glucose monitoring chips. These will help Pharmaceutical companies to better and more quick-ly identify medicines that will be offering patients more personalized options. For example, people with fibromyalgia which causes widespread pain, fatigue and cognitive issues, can cycle from doctor to doctor for up to five years before getting an accurate diag-nosis. Using a large Electronic Medical Record da-tabase of de-identified patient data, Pfizer scientists created a model to help clinicians identify patients which might be suffering from fibromyalgia earlier so that, patients can get effective care.
The Long and Winding Road
We have come a long way, information is currently more quickly available but frequently, not the one we really need. As mentioned, there are already avail-able and operating a lot of electronic health records and registries, but most of them are not representa-tive, compatible or sufficiently exhaustive. The Big Data approach differs from traditional decision sup-port tools, once suggestions are mostly drawn by real-time patient data analysis. It may help to trans-late personalized medicine into clinical practice.
Opportunities
- Big data greatly expand the capacity of generating new knowledge;
- It also helps with knowledge dissemination;
- Contributes to bringing personalized medicine into clinical practice;
- It may allow healthcare transformation by deliver-ing information directly to patients;
- The era of value-based payments or reimbursements may provide a positive stimulus;
Potential Obstacles
- Limited number of initiatives to champion its wide-spread use within clinicians groups or hospitals; - Most champions for Big Data use (healthcare re-searchers, pharmaceutical companies, public health and other governmental organizations) do not de-liver directly individual patient care;
- There will be considerable privacy concerns which perhaps require more attention than those related with financial data;
- Overreliance on electronic systems.
In order to overcome these obstacles, one needs to: - Understand the value of Big Data;
- Balance ownership and data sharing;
- Ensure compatibility, combine different data sets; - Network;
- Grant proper coordination;
- Defend data privacy without being over-restrictive; - Polling data for better health results.
Outcomes
- Prediction data: identification of potential health problems; involvement of potential patients in health programs; benchmarking with similar realities, e.g. countries, cities, hospitals, patients, etc.;
- Better evaluation: identification of non-responders, potential related interactions, associated diseases, clinical data, clinical results, comparisons with real-world data; identification of the best health pro-grams and the best outcomes;
- Monitoring: impact of programs on patient behav-iors and health conditions, including side effects and potential interactions;
- Reporting – patient’s behaviors according with compliance outcomes, products misuse, (sleep mode, diet, healthy habits, etc.).
Big Data has the potential to transform medical
prac-tice, by using information generated on a daily basis, to improve the quality and efficiency of care.
The Contribution of Medical Affairs
Data integration enables comprehensive searches for subsets of data based on the linkages established, while smart algorithms linking laboratory and clini-cal data, for example, could raise red flags concern-ing safety or efficacy15. Traditionally Research &
Development in Pharma has been a secret activ-ity conducted within the walls of R&D. In recent years, the external collaboration with universities, other Pharma companies, consultants, health provid-ers and payprovid-ers, expanded. By breaking internal silos and enhancing collaboration with external partners, pharmaceutical companies can extend their knowl-edge and data networks.
Safety is not only a concern but a competitive advan-tage both when regulatory submissions take place but also, once the drug is on the market. Signals could be detected from a wide range of sources, namely, on web sites, webinars, online physician communities and consumer-generated media that can provide data on the reach and reputation of different medicines.
Customer insights can be captured and used to shape strategy throughout the medicine development pro-cess adding to experimental results, to identify oppor-tunities for improving patient’s response. Partnerships with payers, providers and other institutions are criti-cal to these efforts. Customer facing Medicriti-cal Affairs activities are critical to attain this objective. Much of the Medical Affairs work has to do with assessing data, both our own and data generated by third par-ties. However as described, Big Data refers to the man-agement of several sources, to reach a common goal:
“ask the right questions; provide the right answers”.
Medical Affairs can make important contributions to the adherence and rational use of Big Data in an ef-ficient and pragmatic way, namely by:
- Sensitizing to the adoption of digital healthcare data management;
- Training on systems;
- Defend the harmonization and compatibility of healthcare systems;
- Demonstrate the added value of Big Data in the de-cision making process (e.g. mobility, mortality, drug usage, compliance, safety, costs, etc…);
- Identify relevant sources of publicly available healthcare data integrate information;
- Show RWD advantages over traditional studies and clinical trials;
- Capture insights to explore specific heath oppor-tunities;
- Contribute to build know-how partnerships; - Define priority of actions;
- Mitigate potential safety and commercial risks; - Support publication of results (paper, poster, web…) and debate.
The Future of Big Data
Technology will help stakeholders to early diagnose, to predict diseases, to act and allocate resources in the critical areas in advance. The available data sets will differentiate procedures and drug outcomes, al-lowing authorities to invest better and prioritize on the most important areas. The use of social media tools and analytics allows decision makers to better understand trends and potential problems. Patient monitoring according with behaviors, demographic factors, etc., will help decision makers to better un-derstand the potential roadblocks and future expense trends. Every strategy will be monitored in real time, for example: clinical trial results, adverse events, lack of effect or adherence to treatments. Data integration
will allow moving from silos to a well-organized sys-tem, where all available data will contribute to a bet-ter health management. The future health model will be in essence a collaborative model, with full engage-ment of all participants, centered in the “patient” and in the “outcomes”. This new methodology will trans-form the way health professionals work, as well as, citizens contribute to their Healthcare management. Therefore, health professionals need to gain skills and digital competence to be able to collect, store, analyze and advice based on real life data and patient outcomes. Strategies behind-the-pill will increase dramatically during the following years while the drug will be only one small part of the whole treatment.
Concluding Remark
Big Data is more about the question than the technol-ogy. It is a direct outcome of the digitalization across the healthcare system, with very relevant impact in prevention and disease management. If used in com-pliance with privacy laws and these will not become over-restrictive, it may be extremely valuable for the health gains moving forward.
Acknowledgment
A word of appreciation to the University of Lisbon for its kind invitation to present and discuss the topic of Big Data with honorable guests from the Faculty, Research and Development Industry, Governmental Institutions, European Federation of Pharmaceutical Industries and Associations and Healthcare Providers representatives.
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