Pay for Quality

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10 Pay for Quality KCE Reports 118 2.1.1.4 Data Extraction and construction of evidence tables Subsequently 2 reviewers (DDS and PVH) performed the data-extraction process. The following data were extracted and summarized in evidence tables: citation, country, primary vs. hospital care, health system characteristics, payer characteristics, provider characteristics, patient characteristics, quality goals and targets, P4Q incentives, implementing and communicating the programme, quality measurement, study design, sampling/response/drop out, comparison, analysis, effectiveness evaluation, safety evaluation, access and equity evaluation, cost effectiveness evaluation, continuity and integration evaluation, co interventions, relationship results with regard to health system, payer, provider and patient. The results of this process are described extensively in chapter 3 (conceptual grounding of P4Q) and chapter 4 (the evidence base of P4Q). Meta analysis of these results is not performed, because of a high level of clinical heterogeneity (differences in setting, population, intervention, and outcome measures used). Figure 1: Flow chart for the identification of relevant, high quality primary conceptual and empirical papers Potentially relevant papers identified and screened for retrieval from electronic search: 491 in medline, 1555 in embase, 912 in cochrane, 612 in psycinfo, 213 in econlit, 1661 in web of science, 32 based on reference list and citation tracking, 32 from the review process. (n = 5517) Studies reviewed in detail (Reviewer 1 n = 331) (Reviewer 2 n = 486) Studies included in final analysis empirical papers (Reviewer 1 n =146) (Reviewer 2 n = 165) conceptual papers (Reviewer 1 n = 143) (Reviewer 2 n= 139) Studies included in systematic review empirical papers (after consensus led by the third reviewer n =104 of which 32 are related to equity) conceptual papers (after consensus led by the third reviewer n=143) Papers excluded on the basis of title review, combined with abstract review in case of unclarity (Reviewer 1 n = 5184) (Reviewer 2 n = 5049) Papers excluded on the basis of full text review (Reviewer 1 n = 42) (Reviewer 2 n =86) Reasons: Population (R1 = 8) (R2 = 1) Intervention (R1 = 30) (R2 = 81 ) Outcome (R1 = 4) (R2 = 0 ) Design (R1 = 4) (R2 = 4 ) Empirical papers excluded after quality appraisal (reviewer 1 n = 47) (reviewer 2 n =70) Reasons: Poor quality (R1 = 47) (R2=70) additional empirical papers included with regard to equity n=1
KCE Reports 118 Pay for Quality 11 2.1.2 Selection of and data-extraction from articles related to equity To describe the equity dimension of P4Q models a selection of publications from the main database was selected from the above evidence table. Afterwards two reviewers (SW and PB) performed an independent full text analysis. Publications labelled as “doubtful relevance concerning the impact on equity” by one of the two reviewers, were discussed between the reviewers until consensus was reached. In addition, all references of relevant equity articles were once again screened for additional material and one publication was added. In a second phase the researchers (SW and PB) assessed how equity is conceptualized in the studies (to what extent did the selected papers address different domains of equity and did they conceptualize equity as vertical or horizontal or both?). To do this systematically, the researchers developed a grid which includes all equity-related concepts identified as essential (see appendix 10). 2.1.3 Revision of the conceptual framework into an evidence based state of the art framework After the results were available from the conceptual framework construction and from the systematic review of the P4Q evidence base (equity dimension as well as all other quality dimension), both were integrated by relating the evidence base to the theoretical assumptions. The initial framework was assumed to be a set of hypotheses to be confirmed or disconfirmed by empirical findings. When confronting theory with evidence, it is important to consider both the internal validity as the external validity of the findings. The internal validity is guarded by the previous ‘hard’ approach using a systematic review process. However, as suggested by Leontien et al (2008), this is combined with a Realist Evaluation Approach (see publications by Pawson & Tilley) to address external validity 33 . This approach addresses the question of what works for whom in what circumstances? Measures are expected to vary in their impact depending on the conditions in which they are introduced. Realistic evaluation assumes that mechanisms supporting an intervention are activated by conditions and can explain observed patterns in outcomes. These configurations are also known as context mechanism regularities which can be explored to uncover the ‘black box’ behind an intervention as P4Q. Both underlying mechanisms (more practically than fundamentally behaviourally approached) and contextual factors are identified in the conceptual framework. During the revision process these were specifically related to evidence on outcomes. Missing links are also identified. The results can be consulted in Chapter 5. 2.1.4 Consultation of international experts as P4Q involved country representatives For the international comparison study (chapter 6) four countries are taken into account, namely the USA, the UK, the Netherlands and Australia. 6 experts were chosen, based on their expertise with P4Q. For the USA and the UK, 2 key experts per country were invited to participate. For the Netherlands and Australia, two countries which are still in a starting phase of implementing P4Q, with only a few P4Q schemes operational, only 1 key expert per country was invited to participate. Prof. Rosenthal and Prof. Damberg acted as US experts. The last 10 years Prof. Rosenthal has been working as a health care economist at the Harvard school of public health. Most of her work is related to provider-payer incentives. Her research focuses primarily on Pay For Quality and other payment mechanisms like capitation and FFS. Dr. Damberg is a health services researcher, who works at a non profit US think-thank, named RAND. In the past she has worked for both the federal government doing health policy work, as well as for the private industry. Her main areas of interest are measuring quality and performance of the health system, patient safety, consumerdirected health plans, and assessing the impact of health care reform initiatives on quality and costs. Since 1992 most of her research has focused on Pay For Quality.
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