Dave Pollard advocates a personalised approach to knowledge management that aims to increase individual worker productivity and effectiveness. In looking at the processes of knowledge management he advocates a bottom-up approach that is centered on the activities of the individual. He classifies these activities as involving information acquiring (finding data, reading, compiling information), information processing (review, analysis, interpretation, knowledge transfer) and social activities (finding experts, collaboration, conversation). He claims that knowledge management activities which have centred on capturing ever increasing amounts of content into storage systems have not helped the individual worker become more productive in the above activities.
So what are the alternatives? Pollard advocates that
a) greater emphasis be placed on social networks, communities and expertise location and tools that aid and support such activities.
b) personalised content management systems that filter and organise content needed by the individual.
Tools already do exist to support these approaches although many continue to require further development. But this development should be forthcoming and not pose a major obstacle to support the personalised knowledge management approach advocated.
What are the challenges for the pharma and life science industry in adopting such an approach?
Approaches such as community and expertise location systems are already being used to varying degrees inside many large pharma firms and it is reasonable to expect these approaches to continue to evolve inside the firm. The greater challenge may be in extending the approach beyond immediate teams and departments, to participants outside the firm, to partners, suppliers, doctors and patients. For smaller companies the participation in multi-organisational networks and communities of practice is also especially critical for knowledge and relationship development. The particularly restrictive regulatory, legal and patent environment that the industry works in, and the ensuing culture that has developed and is needed in reaction, probably provides the greatest challenges and obstacles to progress in this area.
Personalised content management systems in contrast face fewer fundamental environmental barriers in the industry and quicker progress here should be more forthcoming. However although huge amounts of data is collected in drug, life science and health research, there still remain many problems in collecting, classifying and distributing information so that it can be received in a personalised manner. The scope in time, people, place, volume of information and diversity of knowledge associated around the life science product life cycle is particularly large and challenging.
Let us take examples of what the following individuals around the product life cycle might desire, which in many cases is not yet possible:
Chemist – can I automatically obtain filtered alerts including structural information to my desktop from all newly published sources on chemical structures that I am interested in? Can I easily import all such structural information to my desktop application?
Biologist – can I easily access and understand all chemical and analytical information associated with a compound that was generated by our chemistry lab that I am testing in my cell screening?
Clinical Trial Manager – can I quickly source all expertise required for the rapid initiation and support of a trial? Can I recruit and maintain high quality investigator relationships? Can I find and recruit patients quickly and cheaply enough?
Pharmacovigilance Expert – is all the information that I need from R&D and clinical trial data available to enable me to make earlier, more pro-active, risk-lowering decisions and give best advice?
Doctor – is knowledge from R&D, clinical trials and data in the market place available and integrated into actionable real-time recommendations that I can use to improve the quality of my decision in a health situation where I have minutes to decide?
Patient – Is my genetic data sufficiently protected legally so that it can be disseminated to avail of personalised genetic-based treatment while maintaining my privacy? Is my health profile available to all clinical trial possibilities that I could avail of?
Several conceptual and systematic approaches to knowledge integration offer potential for improvements in the above situations to deliver personalised knowledge to the individuals concerned, including:
- Standardised description of research information, e.g., chemical structures and analytical information associated with drug candidates and drugs described by chemical markup language
- Fully electronic-based data capture using collaborative electronic R&D systems and electronic laboratory notebooks
- Development of Semantic Web and ontology approaches for the sharing of knowledge between expertise domains
- Maintenance of an extensive electronic life science product life cycle dossier
- Deployment of outsourcing and fully electronic support systems in clinical trial management
- Increased use of knowledge sharing, community, mentoring and conversation approaches supporting individuals in their activities and needs
To serve as a forum for the above topic we are currently launching a community forum and Web conference on the topic of “Integrating knowledge in the life science product life cycle” at http://innovationwell.net/
Barry Hardy
Douglas Connect
www.douglasconnect.com
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