This week, I attended Healthcare Computing 2008 to get an update on the current state of e-health in the UK and to explore how grid technology can contribute. Health Informatics is a broad subject and it isn't possible to engage with the whole field, but I see three main areas of potential engagement.
The path is most followed by the academic community to date is that of linking together data used in clinical trials or in health research. This is a natural fit for the e-science community as it extends existing work on secure access to distributed research data. The medical world imposes more security constraints, which adds academic interest, but is otherwise familiar to the e-scientists. It is also of a scale that is manageable in research projects. Successful projects include Psygrid, which is now deployed across the NHS research centres in mental health and in bioinformatics. So this is the first area of engagement.
A natural question is whether this experience with research data grids is also applicable to clinical systems. Clinical practice is a much larger enterprise. Major initiatives exist to share data such as patient records and medical images. These are either provided by small-scale systems implemented by individual health trusts, or by specialist commercial solutions. These systems face the same problems as data grids - single sign-on, access control, metadata management, data vocabularies - but engagement with the grid world seems virtually non-existent. There is probably scope for mutual learning between the two communities, but the healthcare community is understandably focussed on providing solutions now. So I regard the chances of successful engagement here as slight.
A more likely area is the computing aspect of grids. Although this has also made little impression on the clinical community to date, there are successful R&D projects in the areas of data analysis and diagnosis support, including GIMI and Healthcare at Home. These use pattern matching technologies to detect suspicious lumps in medical images or to warn of possible problems in patient monitoring systems. These are rather specialised systems, which would make it easier to arrange pilot deployments to text their clinical effectiveness.
The third area of promise is the most straightforward. The NHS is one of the world's biggest organisations and runs a lot of IT systems. As a result, there are plenty of opportunities for deploying modern IT infrastructure techniques, with virtualisation as an easy quick win. The NHS has yet to consider the effect of CO2 emissions and electricity costs from its myriad systems and it will have to do so soon.
Taking this to a further stage, I believe that the NHS would benefit greatly from adopting service-oriented techniques. There is currently a developing interest in lean management techniques in NHS hospitals, with great opportunities for improving processes for patients and employees while saving money for hard-pressed hospital trusts. Such process optimisations can be delivered more easily if the IT layer is equally flexible. The business management should lead, but the IT infrastructure must be agile to follow the dance.
The path is most followed by the academic community to date is that of linking together data used in clinical trials or in health research. This is a natural fit for the e-science community as it extends existing work on secure access to distributed research data. The medical world imposes more security constraints, which adds academic interest, but is otherwise familiar to the e-scientists. It is also of a scale that is manageable in research projects. Successful projects include Psygrid, which is now deployed across the NHS research centres in mental health and in bioinformatics. So this is the first area of engagement.
A natural question is whether this experience with research data grids is also applicable to clinical systems. Clinical practice is a much larger enterprise. Major initiatives exist to share data such as patient records and medical images. These are either provided by small-scale systems implemented by individual health trusts, or by specialist commercial solutions. These systems face the same problems as data grids - single sign-on, access control, metadata management, data vocabularies - but engagement with the grid world seems virtually non-existent. There is probably scope for mutual learning between the two communities, but the healthcare community is understandably focussed on providing solutions now. So I regard the chances of successful engagement here as slight.
A more likely area is the computing aspect of grids. Although this has also made little impression on the clinical community to date, there are successful R&D projects in the areas of data analysis and diagnosis support, including GIMI and Healthcare at Home. These use pattern matching technologies to detect suspicious lumps in medical images or to warn of possible problems in patient monitoring systems. These are rather specialised systems, which would make it easier to arrange pilot deployments to text their clinical effectiveness.
The third area of promise is the most straightforward. The NHS is one of the world's biggest organisations and runs a lot of IT systems. As a result, there are plenty of opportunities for deploying modern IT infrastructure techniques, with virtualisation as an easy quick win. The NHS has yet to consider the effect of CO2 emissions and electricity costs from its myriad systems and it will have to do so soon.
Taking this to a further stage, I believe that the NHS would benefit greatly from adopting service-oriented techniques. There is currently a developing interest in lean management techniques in NHS hospitals, with great opportunities for improving processes for patients and employees while saving money for hard-pressed hospital trusts. Such process optimisations can be delivered more easily if the IT layer is equally flexible. The business management should lead, but the IT infrastructure must be agile to follow the dance.
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