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 [10]. 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 [11]. 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 [12].
References
[1] 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.
[2] Olsen, L., Aisner, D., et al., eds. (2007). The Learning Healthcare System – Workshop Summary. Washington, DC. National Academies Press.
[3] Friedman, C., Wong, A., et al. (2010). Achieving a nationwide learning health system. Science Translational Medicine, 2(57): 57cm29.
[4] Walker, J., Richards, F., et al., eds. (2006). Implementing an Electronic Health Record System New York, NY. Springer.
[5] Davenport, T. and Harris, J. (2007). Competing on Analytics : The New Science of Winning. Cambridge, MA. Harvard Business School Press.
[6] Davenport, T., Harris, J., et al. (2010). Analytics at Work: Smarter Decisions, Better Results. Cambridge, MA. Harvard Business Review Press.
[7] Adams, J. and Klein, J. (2011). Business Intelligence and Analytics in Health Care – A Primer. Washington, DC, The Advisory Board Company.
[8] Anonymous (2012). Needles in a haystack: Seeking knowledge with clinical informatics, PriceWaterhouseCoopers.
[9] Anonymous (2012). Obama Administration Unveils “Big Data” Initiative: Announces $200 Million in New R&D Investments. Washington, DC, White House.
[10] 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.
[11] Fisher, E., McClellan, M., et al. (2011). Building the path to accountable care. New England Journal of Medicine, 365: 2445-2447.
[12] Hersh, W. (2009). A stimulus to define informatics and health information technology. BMC Medical Informatics & Decision Making, 9: 24.
This article post first appeared on The Informatics Professor on April 1, 2012. Dr. Hersh joins us as our guest tomorrow on MU Live!, April 3rd at 2 pm Eastern. Learn more here on how to listen in and join the conversation.