The KNMGP Model allows for a more systematic and multifaceted call and recall system than traditional practices. The model blends traditional patient generated requests for follow up “call me in a week” with online access to services and pivotally, telemonitor triggers (Seto et al., 2012). The data mining function is central to the practice model. Although initial data sources are likely to be in house data, captured from contacts in consultation, online service use and telemonitoring triggers there is huge potential for “big data” approaches (Murdoch and Detsky, 2013) to inform and assist in initiating call and recall. The field of data mining is still an emergent one(Marschollek et al., 2012)(Amini et al., 2013) but collation of data within an organisational footprint offers the use of tools such as PARR (Billings et al., 2012) to proactively schedule attention from physicians and telemonitoring services.
At this stage the architecture of the data mining element is uncertain. In the initial stages it may simply be a bank of regular searches producing a task list for contacts. As the technology evolves it is likely to become a proactive warning system based on real time intelligence.
As referenced above the model has supporting evidence for each of its components. Many are at work in different parts of the NHS, but, to date, no single area has been able to combine the various components into a single model of primary care.
Currently primary care is under scrutiny for a model which has changed little since the inception of the NHS (Mathers, 2012) and both the Royal College of GPs and NHS England are completing major consultations on the future of General Practice (RCGP, 2012).
Initial cacluations within the LCD/TP organisation suggested that the model would be resource neutral given sufficient size and resource pooling to enable staffing and response issues to be met.
Within Wingate there has been an uncovering of significant levels of unmet need which mean this assumption is no longer likely to hold true. Further detailed modelling will be essential to validate the feasibility.
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