What do we do with all this information? A consequent major challenge of data explosion is the risk of cognitive overload; here, a plethora of raw data, rather than synthesized medical knowledge, confounds the generation of needed information and obscures decision making ().32,38,39
However, an evidence-enhanced health care ecosystem will increasingly address and yield appropriate information to appropriate users within the context of the delivery process, provide cognitive and decision support, and interdigitate with service quality, evidence generation, and novel payment mechanisms. Challenges along the path toward creating this ecosystem include data representation, standardized nomenclature, data formats and standards, federated data access, data mining and evidence synthesis approaches, evidence retrieval, reporting, and feedback on use of evidence. Health care optimization through use of HIT is possible (), but we must strategically coordinate and test national approaches, or we risk swimming in data soup (or drifting in data clouds) without transforming the health care system into a rational learning system.
Increase in data required for medical decision making relative to human cognitive capacity. PB, petabytes; Yr, year; SNPs, single-nucleotide polymorphisms.
Health care optimization through use of technology.
The NCI's caBIG has created many of the tools necessary to support an interoperable IT infrastructure and offers standard rules, a unified architecture, and a common language with which to share information and to collect, analyze, integrate, and disseminate information associated with cancer research. Founded on the core principles of open access, open development, open source, and federation of resources for either local or shared control, caBIG has convened a growing network and established connectivity; it is now working toward widespread deployment and adoption, implementing use cases in the cancer research community.
Although caBIG intentions align directly with our national need for an evidence-enhanced health care ecosystem, its development has underscored the need—associated with any technology change—to simultaneously address cultural and logistical factors. Failure to address cultural barriers imperils large-scale efforts at developing a coordinated data-driven health care system; perhaps at the core, the culture of science, long organized around the needs of the single investigator, is driven by incentives for the individual rather than the collective scientific community.
The culture change required for a national HIT infrastructure requires, first, commitment from all levels within, and often across, organizations.19
Second, community participation in the infrastructure's development is a critical success factor; concept ownership entails community input into system requirements, definition of products, and conventions for sharing information and restricting access. Third, much of what prevents data sharing is not technical challenge, but (1) the affiliation of stakeholders with silos (eg, by discipline, location, or sector), or (2) concerns about intellectual capital and acknowledgment. Federation may provide an acceptable and durable solution for retaining local control of data while permitting aggregation of data from multiple sites into an integrated research data set. Connecting across complex domains demands both leadership and active management; the exchange and use of information requires more than the ability to electronically move data from point to point—it requires human systems to convene to ensure effective and appropriate action. Federal standards will be necessary to require that data can be integrated. Finally, of fundamental importance, we must satisfactorily address data governance and patient privacy concerns.
How do we move from information and data interoperability to evidence generation? In the rapid-learning health care system, evidence is generated at the patient level and available for application to individualized patient care or to evaluation across populations, aggregated at the clinic, institution, health system, or national level. CER and health care quality assessment occur through aggregated analyses; modeling exploits multiple interconnected networks; and finally, personalized CER matches individual patient characteristics to best available evidence in the ecosystem. To make sense of available data, we require tools that include mathematical approaches, software systems, visualization platforms, and education. Existing nascent but evolving systems, such as Adjuvant! Online, model an individual's risk and likely benefit from adjuvant breast cancer treatment options. In a rapid-learning health care system, myriad increasingly complex software tools will connect the large number of personalized patient scenarios with available evidence and ongoing research, and will feed outcomes of treatment back through the system to clinicians and patients, dynamically building the evidence base and guiding clinical decisions.