The introduction posited that the life sciences field is changing in several important ways both content-wise and structurally. Emerging patient-driven health care models are influencing some of these changes and could contribute to shaping a positive future for life sciences and health care. Content-wise, one key life sciences change is the growth in information, both in the amount of general and scientific information, and in the type of information, as narrow and interdisciplinary scientific fields expand. Patient-generated content in emerging health care models is adding a new category and dimension of information. Consumers are not just creating information but also helping to make it meaningful and navigable, first by organizing themselves and the information into knowledge communities. Second, at some websites, individuals creating or interacting with the data can help to stratify it with relevancy and abstraction layers by actively engaging in collaborative filtering, tagging, voting and other standard Internet community data management techniques or passively, by having their attention recorded as page views. Third, individuals such as expert patients are becoming value-added health information resources themselves through their self-knowledge and community participation. Health social networks and other peer-based resources such as patient registries by condition could proliferate and be formalized into tools analogous to the Wikipedia which has emerged as a widely useful consumer-produced information resource.
Structurally, the life sciences field is changing in three ways, the concept of health, how science is being conducted, and the models by which health care is realized. In the first case, the notion of health is changing as patient-driven models help to expand the definition of health and health care as depicted in . Individuals may have the time, interest and utility to tinker with different tiers of the health concept, thinking creatively about and experimenting with cures, especially for pharmaceutically-uninteresting or complex orphan diseases, improving and resolving chronic conditions, measuring and reaching baseline normalization, increasing wellness, preventing disease and engaging in genetic and physical enhancement as possible.
In the second case, how science is being conducted and who is conducting it is also being influenced by patient-driven health care models. Individuals can focus on personally-relevant aspects of health, formulating important and possibly novel areas of inquiry, collecting data about their experiences and finding others with similar interests and conditions with whom to collaborate and mobilize resources. Rather than forming a hypothesis at the outset, individuals may engage in self-tracking, analyzing the resulting data and using self-experimentation as a tool for improvement; for the individual, understanding the underlying mechanism may be irrelevant if desired outcomes are obtained. Mathematical modeling, simulation and synthetic biology are also redefining and adding to the way that science is being conducted by traditional professionals and could potentially have an even more powerful impact if they were to be made available in easy-to-use consumer offerings. Many tools are freely available but not packaged in accessible ways for different user groups, for example Stanford’s SimTK biological structure simulation models (http://www.simtk.org
) and synthetic biology’s DNA parts database which contained over 3,500 standardized building blocks as of December 2008 (http://www.partsregistry.org
In the third case, the ways that health care is realized are also changing through patient-driven health care models. Early examples include health social networks, direct-to-consumer personalized medicine services, self-tracking communities and non-reimbursed clinics for preventive medicine and other interventions. The creative exploration of individuals focusing on a much wider concept of health could further add to the value chain of health services, extending them from the traditional model of general diagnostic and urgent care providers with high expertise (e.g.; physicians and hospitals) to new areas. An interesting array of alternative models and entrepreneurial services could arise, for example on-demand physician consultation as a standard service, personalized genomics interpretation and intervention offerings as genetic information becomes more clinically relevant, and fully automated tools for personal quantified self-tracking and environmental monitoring.
4.1. Summary and Opportunity
The growing presence of patient-driven health care models may be central to the evolving health ecosystem. Individuals are starting to better manage their health, independently, with peers, in large aggregated online affinity communities and in consultative co-care with medical professionals. Tools, demographics and financial incentives may combine to accelerate the achievement of improved health outcomes for all ages. Individuals and groups of individuals as new classes of participants in the health ecosystem could be beneficial at many levels from the practical, inspiring the launch of resources, services and businesses, to the theoretical, helping to inform the general inquiry of health and to supplement the traditional scientific method with empirical data.
More health resources and alternatives are starting to be available, consumers can control more of their own data and are becoming empowered to make their own choices; traditional medicine is no longer the exclusive source of health solutions. The individual can obtain relevant information more readily and act upon it. Health information databases and patient registries by condition are emerging as a significant public resource.
The emerging patient-driven technology-enabled health care models have focal points at every node of the wellness cycle, particularly at earlier stages, targeting prevention rather than therapy. Uptake could advance quickly given the more open attitudes of younger generations regarding trust and privacy and their facility in using Internet models for information-seeking, communication and action-taking. Self-collected digital data could be an input to quantitative analysis, predictive outcomes and biosimulation. Consolidated reflection on reductionist self-measurement activities could be extrapolated into new perspectives such as a shift in the overall conceptualization of health, and the meaning of wellness to the individual and society.
For both consumers and all manner of medical and public health and environmental research professionals, this could be a time of great opportunity. There is a potential chance to learn and apply the emerging models, to invent new tools, to reach out to a global peer audience in collaboration, to embrace technological change and to make progress on systemic challenges that may have previously appeared intractable.