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72 Pay for Quality KCE Reports 118 However, on the various types of intermediate outcomes often also positive effects were found (S). This contrasts with long term outcome targets for which no significant effects were detected, whenever included (W). It is striking that most studies are focused on the correction of underuse of appropriate care, with varying P4Q results, whereas only two studies also focus on the overuse of inappropriate care (lab testing prescription, medical imaging prescription, drug prescription, etc.) 171 , 215 . The first study found positive results, the second reported mixed effects (some positive, some showed no significant difference and the effect seemed to decrease with time) (C). For the second study the behavioural independent nature of the P4Q scheme should be taken into account. GPs agreed beforehand, by means of a democratic majority decision at a regional meeting, to improve prescribing according to a one page evidence based formulary and to be more critical and efficient in their prescribing on antibiotics and gastric medicine in general. However, the bonus was performance independent, and given to all the GPs. Most P4Q studies are in compliance with SMART goals (specific, measurable, achievable, relevant, and timely), although it was often unclear why and by whom specific patient groups and targets were selected. If we look at P4Q results the reporting of a high involvement of stakeholders led more often to positive results, as compared to studies that did not report such an involvement (S). In addition, with regard to targets being relevant and timely, almost no study indicated that a previously detected quality problem (high variability or low performance) on a specific target was the reason to include it in a P4Q programme. Most studies referred to general lack of quality findings without assessing them in a local context as a first step, and to base indicator selection on those findings. Studies with a baseline measurement only used those results for comparison reasons, not for selection purposes. Especially in acute care conditions a number of baseline reports with already a high performance level are presented (e.g. 80 to 90% achievement on certain included measures). Looking at P4Q results this presence or absence for room for improvement seems to have been of influence on finding positive or no effects (S). A related lack in the reporting of studies concerns the dynamical aspects of quality enhancement. No studies report on a cyclical selection and refinement of indicators through time as a function of room for improvement. For the QOF a number of small scale changes to the indicator set have been made and others are planned, but their effects have not been studied yet. There is an evolution in the number of targets and indicators which are included in P4Q programmes. Programs during the nineties included often only one or a few targets. Later on, this number expanded gradually through the years with the start up of new programmes. The QOF with almost 150 indicators is one example. In terms of P4Q results a very limited target selection seems to correspond with lesser P4Q effects, especially in the domain of preventive care (S). 5.1.2 Quality measurement Data validity and its acceptance are reported as sufficient in most studies. With the aid of the Healthcare Effectiveness Data and Information Set (HEDIS), which is standardized over USA health plans to foster comparability, and based on strict data validity criteria many studies make use of existing databases originally intended for other purposes (although mostly also financing related). Differences in data collection method (automatic extraction, secondary data use, additional registration; sampling versus continuous) have not led to substantial differences in clinical P4Q results (S). However, the effect of the choice of the data source on cost effectiveness of the programme and on provider satisfaction (due to the level of paperwork and workload effects) remains unclear after reviewing empirical results. A few studies tested for data coding quality and the level of gaming the system. Their results show that coding quality was generally good, but that there was also some gaming present, however to a limited extent (W). How detected gaming is dealt with is often not described.
KCE Reports 118 Pay for Quality 73 The majority of P4Q evaluation studies include case mix adjustment in their analysis methods. It is however often unclear whether risk adjustment was only used for study purposes or whether it was used as an integral part of the P4Q quality measurement, e.g. to adjust intermediate outcome observations for patient characteristics to calculate performance and award incentives. Exception reporting was only used in UK studies. The level remained stable and has shown to fulfil its purpose in terms of guarding care equity (W). In terms of unintended consequences there is at present no evidence of a diversion of attention effect from other healthcare quality priorities. Studies that have included not incentivized control measures found an absence of effect or an improvement (W). Figure 3 : P4Q concepts: Quality Quality Different possible Quality dimensions (7 dimensions): Effectiveness (S) Equity of care (W) Integration and coordination (W) Provider experience (W) Generic applications (W) Other domains (N) Quality targets Structure, process, and/or outcome indicators: Structure (S) Process (S) Intermediate outcome (S) Long term outcome (W) Number of targets and indicators: Not too few (S) A lack of attention for inappropriate care SMART: A lack of attention for relevant and timely, based on room for improvement within a dynamical approach Quality measurement Data source and validity: No difference in clinical results (S) Other domains (N) Case-mix: A lack of distinction between study and programme risk adjustment utilization Exception reporting (W) Unintended consequences: At present not identified (W)
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