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delivering early and targeted interventions where they are critically needed.
Data mining and signal detection: Research pertaining to the use of data
mining approaches does provide an interesting commentary on the ability to
assess quality over time rather than at a fixed point; such an approach would
be an invaluable asset for regulators who are seeking to track the ability of a
service to achieve sustained improvement and /or regulatory compliance.
Achieving dynamic and relevant regulatory interventions is predicated on
mining data in a contemporaneous fashion and examining trends over time. A
potential also exists for empowering health providers and consumers through
the discerning application of data mining; such a strategy could perhaps foster
a culture of learning and development within an organisation; it is noteworthy
that with a limited amount of analysis, routine data analysis can yield
invaluable insights into patient safety and staff learning.
The human element: The literature suggests that the provision of data alone
is not a sufficient agent of change within clinical settings; rather, a culture and
ethos of improvement needs to be fostered so that meaningful improvement
can be achieved and maintained. In addition, when seeking to gather data
more efficiently, the necessity and value of including the element of
professional judgement in any form of dynamic and efficient decision making
by regulators was noted.
Conclusions
The question posed by this systematic narrative review is whether routine data
can be used to improve the quality of health and social care regulation. The
interrogation and use of such data lies at the heart of effective regulatory
decision making so that resources can be directed at where lies the greatest
risks. In a world of increasing accountability and finite resources, regulators
must clearly demonstrate that they act in a proportionate and targeted manner
by optimising the use of any data they hold.
While it is a widely accepted tenet of health and social care that the provision
of quality care is a noble aim, producing a definition of quality per se can be
frustratingly elusive. While the weight of literature urges caution against
applying a 'one size fits all' approach when attempting to encapsulate what
quality means, it also advocates the need for more effective use of data in
order to detect good and poor quality care. It can be seen that administrative
data extant within health and social care, if gathered precisely and analysed
using evidenced based methodologies, can help improve the quality of