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Getting costs down to patient level is complex for the same reason that finance and clinical departments have trouble communicating: a completely different approach is taken to the two sets of information that have to be used.
A cat’s cradle: illustration based on three main cost drivers, Medical, Nursing and Theatres, shows the mapping of ledger entries to clinical resources followed by allocation to patient records using appropriate weightings. Cost data takes the form of figures from the General Ledger associated with cost centres and subjective account codes. Some may match clinical categories, e.g. if a cost centre correspond to a specialty, while others may cut across clinical categories.
The first step in the Ardentia approach is to view costs as covering a series of components of care, resources such as ward nursing, diagnostic services, medical time, the different elements of theatre provision (nursing, surgeons, anaesthetists, consumables, premises), etc. The categories used should correspond to the cost buckets used for reference costing.
Bridging the gap and understanding hospital financial performance.
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Patient Level Costing delivers its reports over the web. This means that all you have to do is give any user you want access to your network, specifying what information and what reports he or she can see. The user needs no software other than astandard web browser.
All the usual functions of web consultation are available: clicking on reports to open them, drop down menus, drill down from level to level of reports. In addition, Arden- tia provides for ‘drill-through’ so that once a user with the appropriate access rights has identified a particular area of interest, say results for one specialty, practice and month, he or she can extract the corresponding individual patient activity records.
A series of reports allows analysis of the results at any level of aggregation from the individual patient upwards. This makes it possible to analyse income and expenditure by individual patients, by groups of patients (e.g. age, sex, post code of residence, etc.), by commissioner (e.g. by practice),by specialty, by consultant, by HRG, etc. or by any combination of all these.
At any point, users can drill-through from any value shown to bring back the underlying patient records associated with it for follow-up research. For example, such patient records will contain the patient identifier allowing casenotes to be pulled and examined in order to explain anomalies.
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