I am occasionally asked whether the work of informatics will be "done" when everyone is finishing implementing electronic health record (EHR) systems. Sometimes the query is further qualified by, "once everyone gets their HITECH money."
My answer is always an emphatic "No!" There is no question that some informatics implementation activity may slow down when healthcare organizations are no longer fueled by pursuit of HITECH incentive dollars. These activities may be impacted even further by bottom line woes that are likely to impact healthcare no matter what the outcome of healthcare reform, or whatever other distractions come along, such as ICD-10.
I often further qualify my answer by noting that for many of us, the real interesting work of informatics begins when the EHR platform is in place and we can truly start to do interesting things with the data. These are the so-called "secondary uses" or "reuses" of clinical data , things like quality measurement and improvement, improved clinical research, or indeed the "learning health system" first envisioned by the Institute of Medicine  and put in the context of the HITECH investment by Friedman et al. . Some call this the "optimization" stage of EHR implementation .
One buzzword that is used increasingly in healthcare (and was already in use outside of healthcare over the last few years) is analytics. As with all buzzwords, there is a copious volume of material that has been written. I find a couple books by Tom Davenport and associates [5, 6] to provide good overviews. Davenport is Research Director for a company in Portland called the International Institute for Analytics. A recent primer by The Advisory Board Company, a healthcare consulting firm, gives a good overview of analytics in the context of healthcare . Another recent report comes from PwC, which paints a similar picture of the near future, although (to my content!) describes this as clinical informatics (rather than analytics) , The phrase business intelligence is sometimes used to describe this work, and I suspect we will see another phrase, big data, appearing more frequently, especially with the recent Obama Administration initiative in this area .
The Advisory Board Company primer nicely paints an overview of the use of analytics and business intelligence in healthcare. They distinguish between different uses of the data, each requiring a higher level of analysis and complexity:
- Descriptive - reporting and querying of data to identify problems and solutions
- Predictive - modeling, forecasting, and simulating outcomes based on the data
- Prescriptive - recommend the best course of action based on the data
Of course, those of us who work in clinical informatics know that gleaning value from clinical data is challenging. Indeed, those who have learned from implementation in the trenches may be best qualified to understand the limitations of their data. As I often say, documentation is not usually the highest priority for busy clinicians. Indeed, it is often what stands between a tired clinician at the end of the day and being able to go home for dinner. Clinical data also suffers from the lack of standards in structure and terminology of data, and it is often fragmented across different systems, both within and across different healthcare organizations.
Nonetheless, the growing platform of electronic clinical data, fueled initially by EHR adoption and now augmented by efforts at health information exchange in the proposed rules for Stage 2 of meaningful use, point the way forward . Regardless of one's political views of healthcare reform, it is clear that the system needs to change to become more accountable and efficient. This will be drawn out with the move to new delivery systems, such as accountable care organizations . Thus, analytics and related activities are the future of clinical informatics, realizing the goal of my definition of the field, which is the use of information to improve individual health, healthcare, public health, and biomedical research .
 Safran, C., Bloomrosen, M., et al. (2007). Toward a national framework for the secondary use of health data: an American Medical Informatics Association white paper. Journal of the American Medical Informatics Association, 14: 1-9.
 Olsen, L., Aisner, D., et al., eds. (2007). The Learning Healthcare System - Workshop Summary. Washington, DC. National Academies Press.
 Friedman, C., Wong, A., et al. (2010). Achieving a nationwide learning health system. Science Translational Medicine, 2(57): 57cm29. http://stm.sciencemag.org/content/2/57/57cm29.full.
 Walker, J., Richards, F., et al., eds. (2006). Implementing an Electronic Health Record System New York, NY. Springer.
 Davenport, T. and Harris, J. (2007). Competing on Analytics : The New Science of Winning. Cambridge, MA. Harvard Business School Press.
 Davenport, T., Harris, J., et al. (2010). Analytics at Work: Smarter Decisions, Better Results. Cambridge, MA. Harvard Business Review Press.
 Adams, J. and Klein, J. (2011). Business Intelligence and Analytics in Health Care - A Primer. Washington, DC, The Advisory Board Company. http://www.advisory.com/Research/IT-Strategy-Council/Research-Notes/2011/Business-Intelligence-and-Analytics-in-Health-Care.
 Anonymous (2012). Needles in a haystack: Seeking knowledge with clinical informatics, PriceWaterhouseCoopers. http://www.pwc.com/us/en/health-industries/publications/needles-in-a-haystack.jhtml.
 Anonymous (2012). Obama Administration Unveils “Big Data” Initiative: Announces $200 Million in New R&D Investments. Washington, DC, White House. http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_press_release_final_2.pdf.
 Copoulos, M., Raiford, R., et al. (2012). The Next Chapter - First Look at the Proposed Rule on Stage 2 of Meaningful Use. Washington, DC, The Advisory Board Company. http://www.advisory.com/Research/IT-Strategy-Council/Research-Notes/2012/~/media/Advisory-com/Research/ITSC/Research-Notes/2012/The-Next-Chapter-Stage-2.pdf.
 Fisher, E., McClellan, M., et al. (2011). Building the path to accountable care. New England Journal of Medicine, 365: 2445-2447.
 Hersh, W. (2009). A stimulus to define informatics and health information technology. BMC Medical Informatics & Decision Making, 9: 24. http://www.biomedcentral.com/1472-6947/9/24/.