Handling the transfer of power: a note on the 1964 origins of the Douglas‐Home rules

Opposition is rarely a good preparation for government. The only post‐war government to enter office confident, well‐acquainted with the Civil Service and with a fund of administrative experience to draw on was the Attlee administration formed in 1945. The longer a party spends in opposition the more these assets disappear. Labour, by the end of the long period of Conservative rule in 1951–64, was largely unfamiliar with the burdens of office. This formed the background to the formulation of the Douglas‐Home rules, whereby informal contact is permitted between the Civil Service and the Opposition in advance of a general election. Since 1964 this arrangement has gradually become more extensive (especially after Neil Kinnock complained that the period for contact was too brief during the run‐up to the 1992 election) and more formalised. In late 1993 John Major agreed that contacts could be made from early 1996 in advance of the next election, rather than only during the last six months of a parliament, as had by then become the convention.’ The object of this short paper is, however, to explain how these rules originated.

Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.