Exploring the Future of the Pharmaceutical Industry and the Healthcare System 17 partners throughout the organisation.49,50 When the experts need to
improve treatment in rural areas, they can use the platform to send tools to the community health workers such as educational material or data-collection forms. The health workers can run these interactive tools on their local 3G-enabled phones.
Another example51
shows how a clinical decision support system has greatly improved the ability of community health workers to diagnose diseases in rural areas in Andhra Pradesh in India. The study showed how the involvement of health workers in the diagnosis process greatly increased the amount of patient history and symptom data collected in the EHR. As such, as and when the patient is forwarded to a more expert physician for further diagnosis, the physician can target his or her intervention and expertise based on the additional data collected. Whereas the system used in this study was based on a thin client application, current projects with a mobile phone-based version of the application are under way in Kenya, Mozambique and India.
In support of their mission to bring healthcare to rural villages in underdeveloped regions, the approach taken by World Health Partners (WHP) builds on a platform that allows the remote capture of a broad range of diagnostic data. The data are then transmitted to a centralised call centre with expert physicians.52
This system allows the physician to
listen to a patient’s heart sounds, conduct blood pressure checks, view an electrocardiogram and obtain laboratory results and patient medical records while speaking to the patient in realtime.
All of these solutions rely on the ability to effectively communicate high-quality EHR data between the different layers of the healthcare system, thereby establishing a true continuum of care for patients. Such use of EHR-based systems mobilises and educates additional and peripheral layers of the healthcare system. The capacity of that system is thereby increased and its reach is extended to remote populations.
Interestingly enough, this is the same solution that developed societies are looking for in their attempts to contain healthcare spending and ensure enduring healthcare benefits for future generations. Technology is being used to drive out high-quality decision-making from highly expert, centralised healthcare facilities to smaller private specialists, general practitioners, home nurses and, ultimately, patients. This is believed to have two main consequences for the healthcare system: it will reduce the total cost of care and it will spark or even disrupt innovation in the healthcare sector.53
Moreover, as developing countries typically have to deal with a lower amount and complexity of EHR legacy infrastructure, they face less of an integration challenge in terms of system integration, data standardisation and semantic interoperability. For these reasons they may well leapfrog healthcare systems in developed societies in terms of adopting mobile health technologies.
Putting it All Together – A Commodity Environment for Secondary use of Electronic Health Record Data The last step in building on these opportunities for sustainable and enduring benefits for the overall patient population is the
1. 2.
Adams CP, Brantner VV, Spending on new drug development, Health Econ, 2010;19(2):130–41.
Sundgren M, New needs for interoperability in clinical research, presented at the 2010 World Health IT
3.
development and adoption of evidence-based healthcare policies (phase III translational research or community-to-policy).39
While an
in-depth review of healthcare policy development is beyond the scope of this article, it is important to note that such evidence-based policies involve making (different) use of exactly the same EHR data as used in the examples above.39,54
In other words, what is required for EHR data to provide real and sustainable benefits to the pharmaceutical and healthcare system at large is a connected environment combining EHR data from all its possible sources. This system (also termed a ‘dynamic and collaborative evidence continuum’)54
should be commonly used by
pharmaceutical companies, patients, providers, payers and policy makers. The flow of information needs to be controlled by utility, privacy and ethics and unhampered by technical, legal or organisational boundaries. Considerable barriers have to be overcome, for example in terms of standards for adoption, semantic interoperability, legacy system integration, development of legal policies and emergence of new collaborative models between various stakeholders in the pharmaceutical industry. However, because all of those stakeholders, as illustrated throughout the examples provided, stand to gain tremendous benefits from the secondary usage of EHR data, those barriers can be overcome as long as common standards and compatible approaches are used in respective systems.
How to Capitalise on Electronic Health Record Opportunities
The secondary use of EHR data holds tremendous promise all along the pharmaceutical value chain. It can help pharmaceutical companies develop personalised therapies and reduce the cost and timelines required to bring these therapies to market. It can help patients develop therapy-adherent behaviours, help providers focus on preventative strategies and help payers and policy-makers adopt evidence-based policies for sustained and enduring health benefits to the overall population.
This presents a quite compelling future for the pharmaceutical industry and healthcare system overall. However, it will require further development, the adoption of technical interoperability standards, legal interoperability frameworks and – perhaps most dauntingly – new collaborative models between stakeholders from across the healthcare system. If this occurs, a common and connected environment for secondary use of EHR data will be established that is in the best interests of the patients collectively served. n
Joris Van Dam is an IT Director of Health Information Technology at Janssen Research & Development. He is developing and executing a strategy for adopting public–private healthcare information technology platforms to improve overall treatment outcomes for patients. Dr Van Dam has 14 years of experience in a wide range of information management and consulting positions across the various industry segments. He has been involved in developing a knowledge management strategy and environment across the various research and development functions and supporting the overall information management needs of global clinical trials management. Dr Van Dam holds a PhD in Artificial Neural Networks from the University of Amsterdam.
Conference, Barcelona, Spain, 2010.
US Bureau of Labor Statistics, Healthcare, Bureau of Labor Statistics, 2 February 2010. Available at:
www.bls.gov/oco/cg/cgs035.htm
4. Goodman C, Comparative Effectiveness Research and Personalized Medicine: From Contradiction to Synergy, The Lewin Group Centre for Comparative Effectiveness Research, 2009.
DRUG DEVELOPMENT
61
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68