‘How do people, in particular doctors, process information and explicit knowledge they need in overload circumstances.’ (Smith, 1996).
Information comes from many sources – research, data analysis, innovation, creativity and exploration. There is evidence anecdotal, observational and empirical that the amount of data, and in turn information, being created and made available is increasing at exponential rates. The concept of “information overload” (Melinat et al, 2014) has long been with humankind (Rosenberg, 2003) and has attracted considerable interest in the literature including within healthcare (Hall et al, 2004).
There are many drivers for this – the emergence of the internet, increasing text, image and sound storage capability, digitalization in many forms of texts, profiling of ‘big data’, increased expenditure on research and development, the growth and accessibility of tertiary education and post graduate study with its associated research, publications and the seemingly insatiable market led drive for innovation.
In 1992, the concept of “Evidence –Based Medicine” (EBM) was formalised and since then has had a substantial influence within the healthcare research (Guyatt et al, 1992) and knowledge environment. EBM de-emphasized that intuition, failure to systematically review of clinical experience, and or a pathophysiologic rationale were sufficient grounds for making clinical decisions. There is a logical expansion and clear necessity to stress the importance of undertaking and examining clinical research (McGinn et al, 2000).
While the initial enthusiasm for EBM is not without critics (Goodman, 2008) the demand for effective clinicians to dig deep into new research has brought information and knowledge overload to academic attention. There is evidence that professionals are missing or ignoring large amounts of material simply due to the absence of sufficient time to absorb what is being produced. This is a complex area of future study. (Eppler et al, 2004).
The parallel growth of information science and systems management research as a discipline (Córdoba et al, 2012) has encouraged filtering and processing of the information flow by consumers, marketers and organisations to direct ‘the right’ information to consumers based on professional need, mined interests, market forces, consumer needs, wants and desires.
Clinical Decision Rules (CDR) have emerged as declarative and summative statements in some EBM based clinical articles promoting, at time of submission, current best practice advice for care delivery, diagnosis, prognosis, or treatment impacts (McGinn et al, 2000). The CDR is in many respects a filter for clinical information overload. Future research in to clinical knowledge overload and fatigue will be driven by the role of best practice and EBM in the healthcare literature. The overload is driving greater research into applying the methods and application of knowledge and information management within healthcare. The area is a difficult one to define though with impacts in nursing, allied health and clinical research and must cater for the explosion in genomics and new procedures and medications.
One future possibility to manage the future clinical information overload is the increasing use of Decision Support Systems (DSS). This site commences a scoping discussion exploring the integration of published decision rules with uniquely identified machine readable rules for incorporation into Clinical Decision Support Information Systems (CDSIS).
Where will we end up? That is not known and cannot be known at this time. What is known is that something like OSCAR will emerge over the next few decades, it is inevitable. The information tsunami is growing daily and the complexity of medical care is way beyond any one clinician.
It will be a long, complicated process and far from inexpensive, but this is eHealth and we would not expect anything less.
References and Bibliography
Córdoba, J. R., Pilkington, A., & Bernroider, E. W. (2012). Information systems as a discipline in the making: comparing EJIS and MISQ between 1995 and 2008. European Journal of Information Systems, 21(5), 479-495.
Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The information society, 20(5), 325-344.
Gable, G. G. (1994). Integrating case study and survey research methods: an example in information systems. European journal of information systems, 3(2), 112-126.
Keogh, C., Wallace, E., O’Brien, K. K., Murphy, P. J., Teljeur, C., McGrath, B., … & Fahey, T. (2011). Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish a Web-based register. Journal of clinical epidemiology, 64(8), 848-860.
Keogh, C., Wallace, E., O’Brien, K. K., Galvin, R., Smith, S. M., Lewis, C., … & Fahey, T. (2014). Developing an international register of clinical prediction rules for use in primary care: a descriptive analysis. The Annals of Family Medicine, 12(4), 359-366.
McGinn, T. G., Guyatt, G. H., Wyer, P. C., Naylor, C. D., Stiell, I. G., Richardson, W. S., & Evidence-Based Medicine Working Group. (2000). Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Jama, 284(1), 79-84.
Melinat, P., Kreuzkam, T., & Stamer, D. (2014, September). Information Overload: A Systematic Literature. In Perspectives in Business Informatics Research: 13th International Conference, BIR 2014, Lund, Sweden, September 22-24, 2014, Proceedings (Vol. 194, p. 72). Springer.
North, K. and Kumta, G., (2014). Knowledge Management, Springer Texts in Business and Economics, Switzerland