Retrospective Analyses of End-of-Life Care Using Large Data Sets

Abstract

In an increasingly tight financial climate for public services, there is a drive to identify models of service delivery that can reduce demand for expensive hospital care while maintaining or improving the quality and experience of patient care. Yet in many instances, prospective randomised studies cannot deliver the sort of evidence required to support decisions about investment or disinvestment in new services. There is therefore a reliance on observational studies using existing data. Though we have made great strides in the better use of aggregate statistics, these still have limitations in that they are often unable to look in detail about specific care services. More recently methods of using anonymised person-level data sets have enables us to look in much more detail at individual pathways through care. In this chapter, we describe some examples of how linking person-level data from existing data sets can be can be used to identify costs of care across services. The data can also be used to undertake evaluations of the impacts of services by using statistical methods to identify matched control cases; it is possible to gain a much more robust assessment of whether services are working as intended.