Innovation is considered essential to the quality and sustainability of healthcare systems. However, the path from innovative idea to adopted reality is complex and fraught with barriers. The way in which healthcare innovations are financed is often mentioned as a major stumbling block, but a comprehensive overview of the role payment mechanisms play in innovation processes is lacking. To fill this knowledge gap, we conducted an extensive literature review, combining a systematic data search with textual narrative synthesis. We contextualize the literature on the role of funding and reimbursement in the process of healthcare innovation in relation to stage-gate models of innovation processes. This results in a ‘financial fugle model’ in which the role of funding and reimbursement is analyzed in three consecutive phases of the innovation process: development, translation, and implementation. From the review of 157 included articles, four key findings stand out: 1) shortcomings in national reimbursement systems result in local fragmentation in the implementation of innovations; 2) lack of evidence on costs and benefits in financial decision-making may harm the development and implementation of potentially value-enhancing innovations; 3) more disruptive innovations encounter larger financial barriers; and 4) non-financial factors, including innovator characteristics and institutional support, are essential in overcoming financial barriers. Based on these key findings, we develop a research agenda for further investigation of the influence of payment mechanisms on the process of healthcare innovation.

OBJECTIVE:To construct a data-driven composite from (a subset of) currently used quality indicators for oesophagogastric cancer surgery and to evaluate whether this approach enhances the reliability of between-hospital comparisons on outcome relative to the expert-driven composite indicator 'textbook outcome (TO)'. DESIGN:In this retrospective cohort study, we applied Item Response Theory (IRT) to construct a data-driven continuous composite indicator reflecting a single latent variable-the quality of surgical care-and estimated latent variable scores for all individual patients. Reliability was compared between the expert-driven (TO) and data-driven (IRT) composite indicators. SETTING:All Dutch hospitals providing oesophagogastric cancer surgery. PARTICIPANTS:All patients who underwent oesophagectomy (n=3588) or gastrectomy (n=1782) between 2018 and 2022 as registered in the Dutch Upper GI Cancer Audit (DUCA). PRIMARY AND SECONDARY OUTCOME MEASURES:We evaluated the reliability of between-hospital comparisons using 'rankability', which quantifies the proportion of observed variation in indicator scores between hospitals not attributable to chance. RESULTS:Seven out of 15 quality indicators were included in the IRT composite indicator. Most of the patients were assigned the artificial maximum of the continuous quality score (ie, ceiling effect), resulting in similar average hospital scores. Relative to TO, rankability increased when using the IRT composite for oesophagectomy (57% vs 41%) but declined for gastrectomy (38% vs 47%). CONCLUSIONS:The selected seven quality indicators for oesophageal and gastric cancer surgery represent a single latent variable but are not yet optimal for differentiating surgical care quality due to ceiling effects. Despite using fewer indicators, the continuous IRT score showed a promising increase in rankability for oesophagectomy, suggesting that data-driven composite indicators may enhance hospital benchmarking reliability.

Alternative payment models (APMs) aim to improve efficiency and fairness in healthcare by shifting financial responsibility from payers to providers. Given their prospective nature, APMs require effective risk adjustment (RA) to prevent risk-selection incentives. RA design comes with complex trade-offs between risk selection, cost control and gaming. In the light of these trade-offs, thorough ex-ante evaluation of RA models is crucial. Traditionally, RA-model evaluation in the context of APMs has heavily relied on statistical metrics like R-squared. While useful for assessing model fit, these metrics often fail to capture the full spectrum of relevant incentives. This study therefore addresses the question: “What do meaningful incentive metrics for ex-ante evaluation of RA models look like in the context of prospective APMs for healthcare providers?” We conducted a literature review and consulted experts to synthesize existing work on RA evaluation. This informed the development of a conceptual framework for defining incentive metrics, distinguishing among risk-selection, cost-control, and gaming incentives. We applied our framework in a simulation of prospective payments to primary care practices (PCPs) in the Netherlands, using 2019 claims data from 346 PCPs (N = 1.4 M patients). The analysis focused on selection incentives, comparing traditional statistical metrics with metrics derived from our framework. Results show that statistical metrics like R-squared fall short in assessing selection incentives compared to our incentive metrics. This highlights the need for tailored incentive metrics for the ex-ante evaluation of RA models that are grounded in a thorough understanding of relevant provider behaviors in the light of APM goals.

Objectives Financial barriers are widely perceived as a major obstacle for translating innovative medical devices from prototype to practice. However, a clear overview of relevant financial barriers, their perceived urgency, and promising solutions is lacking. Therefore, this study aims to identify and prioritize the multitude of barriers and solutions from the perspective of various stakeholders involved in the development and financing of innovative medical devices. Methods We performed a Delphi study with three consecutive questionnaires sent to 72 experts from five stakeholder groups in the Netherlands: innovators, (social) venture capital investors, health insurers, healthcare providers, and (semi)governmental agencies. Results The response rate was 71% in the first round and decreased to 46% in the third round, with each stakeholder group being well-represented. We identified 33 distinctive barriers and 183 associated solutions. Although respondents assigned a consistently high priority to each of these barriers, eight barriers stand out in terms of high priority and degree of consensus. In addition, 22 solutions were considered most promising to solve these barriers. For both the barriers and the solutions, differences in the degree of consensus were larger within than between stakeholder groups. Conclusions Our study has identified and prioritized a diverse set of financial and related challenges and potential solutions to translate innovative medical devices, as jointly faced by the stakeholders. Improvement efforts should first focus on addressing the consistently high-priority barriers, using the solutions perceived as being most suitable. Public Interest Summary To progress from an innovative prototype to a medical device in practice, products must be able to pass through a critical phase in the innovation process. This phase is called the valley of death, because a lack of financial opportunities kills many innovative technologies at this stage. The present study provides insight into the multitude of financial barriers that play a role in this innovation phase, and the priorities assigned to these barriers by various groups of relevant stakeholders. In addition, stakeholders were asked to suggest promising solutions to address these barriers. Consequently, this study has shown the prioritized need for financial support of a co-creation process of innovations between innovators and users. In addition, the stakeholders provided suitable solutions focusing on timely communication, alternative payment models, and disincentivizing low-value care. Finally, opinions strongly diverged about solutions that require radical changes towards a more centrally governed innovation system.

Importance Efficient care processes are crucial to minimize treatment delays and improve outcome after endovascular thrombectomy (EVT) in patients with ischemic stroke. A potential means to improve care processes is performance feedback. Objective To evaluate the effect of performance feedback to hospitals on treatment times for EVT. Design, Setting, and Participants This cluster randomized clinical trial was conducted from January 1, 2020, to June 30, 2022. Participants were consecutive adult patients with ischemic stroke who underwent EVT in 13 Dutch hospitals. No patients were excluded. Data analysis took place from March to May 2023. Intervention The intervention consisted of feedback on hospital performance using structure, process, and outcome indicators. Indicator scores were based on data from a national quality registry and compared with a benchmark. Performance feedback was provided through a dashboard for local quality improvement teams who developed and implemented improvement plans based on the feedback. Every 6 months, 3 to 4 randomly selected hospitals switched to the intervention condition. Main Outcome and Measures The primary outcome was time from door to groin puncture for all patients treated with EVT. Secondary outcomes included door-to-needle time, National Institutes of Health Stroke Scale (NIHSS) score at day 2, expanded Treatment in Cerebral Infarction (eTICI) score, and modified Rankin Scale (mRS) score at 3 months. The effect of the intervention was estimated with multivariable linear mixed models. Results A total of 4747 patients were included (intervention: 2431; control: 2316). Their mean (SD) age was 72 (13) years; 2337 (49.2%) were female and 2410 (50.8%) were male. The median (IQR) baseline NIHSS score was 14 (8-19). Median (IQR) door-to-groin puncture time under the intervention condition was 47 (25-71) minutes, compared with 52 (29-75) minutes under the control condition. The adjusted absolute reduction was 5 minutes (beta = -4.8; 95% CI, -9.5 to -0.1; P = .04), corresponding to a relative reduction of 9.2% (95% CI, -18.3% to -0.2%). Conclusion and Relevance This study found that performance feedback provided through a dashboard used by local quality improvement teams reduced door-to-groin puncture time for EVT. Implementation of performance feedback in hospitals providing EVT can improve the quality of care for ischemic stroke. Trial Registration The Netherlands Trial Register: NL9090

BACKGROUND:Clinical and pathological outcomes of oesophagogastric cancer surgery are used for benchmarking hospital performance. The extent to which case-mix adjustment is required for valid hospital comparisons is unknown. This study aimed to develop distinct case-mix adjustment models for multiple outcomes of oesophageal and gastric cancer surgery, and to assess the impact of case-mix adjustment on between-hospital comparisons. METHODS:We included all patients who underwent oesophagogastric cancer resections in the Netherlands between 2017 and 2022. We developed distinct case-mix adjustment models for ten outcomes. Model performance was evaluated with the area-under-the-receiving-operator-curve (AUC) and pseudo-R-squared, representing how strongly case-mix factors predict the outcomes. We used the Wald χ2 test to assess relative predictor importance per model. The impact of case-mix adjustment on between-hospital comparisons on outcome was quantified using unadjusted and adjusted observed/expected ratios. RESULTS:In total, 4354 oesophageal cancer patients and 2109 gastric cancer patients were included. The most informative predictors in the models for oesophageal cancer were ASA-score, salvage surgery, peripheral vascular disease/aortic aneurysm, chronic lung disease, and tumour histology. For gastric cancer these were age, preoperative weight loss, tumour location, and clinical M-category. All case-mix models showed low to moderate performance, with AUCs ranging between 0.58 and 0.73 and between 0.58 and 0.74 for oesophageal and gastric cancer, respectively. Overall, case-mix adjustment had a limited impact on between-hospital comparisons, but more pronounced for 30-day mortality, failure-to-cure and failure-to-rescue. CONCLUSION:Given low to moderate model performance and the limited impact on between-hospital comparisons, case-mix adjustment may not always be necessary for valid benchmarking on outcomes in oesophagogastric cancer surgery.

In July 2017, a Dutch health insurer and primary care organization jointly implemented the All-In Contract (AIC), a population-based payment model for general practitioners (GPs). Affiliated GP-practices received a capitated payment per enrolled patient covering all GP care and multidisciplinary primary care for chronic conditions. Additionally, the care organization shared in savings and losses on total healthcare spending, contingent upon meeting quality targets. This study investigates the AIC’s impact on spending, quality indicators, and provider experiences 2.5 years after implementation. We employed a difference-in-differences approach comparing individual-level claims spending from enrollees of participating GP-practices (N = 16,425) with a control group (N = 212,251). Changes in indicators of chronic care management and patient satisfaction were investigated in a before-after analysis due to limited data availability. To contextualize the findings and explore provider experiences, focus groups were conducted with stakeholders involved in the development and/or implementation of the AIC. The AIC was associated with an insignificant 1.2

Prospective payments for health care providers require adequate risk adjustment (RA) to address systematic variation in patients' health care needs. However, the design of RA for provider payment involves many choices and difficult trade-offs between incentives for risk selection, incentives for cost control, and feasibility. Despite a growing literature, a comprehensive framework of these choices and trade-offs is lacking. This article aims to develop such a framework. Using literature review and expert consultation, we identify key design choices for RA in the context of provider payment and subsequently categorize these choices along two dimensions: (a) the choice of risk adjusters and (b) the choice of payment weights. For each design choice, we provide an overview of options, trade-offs, and key references. By making design choices and associated trade-offs explicit, our framework facilitates customizing RA design to provider payment systems, given the objectives and other characteristics of the context of interest.

Introduction The predominant provider payment models in healthcare, particularly fee-for-service, hinder the delivery of high-value care and can encourage healthcare providers to prioritise the volume of care over the value of care. To address these issues, healthcare providers, payers and policymakers are increasingly experimenting with alternative payment models (APMs), such as shared savings (SS) and bundled payment (BP). Despite a growing body of literature on APMs, there is still limited insight into what works in developing and implementing successful APMs, as well as how, why and under what circumstances. This paper presents the protocol for a study that aims to (1) identify these circumstances and reveal the underlying mechanisms through which outcomes are achieved and (2) identify transferrable lessons for successful APMs in practice.Methods and analysis Drawing on realist evaluation principles, this study will employ an iterative three-step approach to elicit a programme theory that describes the relationship between context, mechanisms and outcomes of APMs. The first step involves a literature review to identify the initial programme theory. The second step entails empirical testing of this theory via a multiple case study design including seven SS and BP initiatives in Dutch hospital care. We will use various qualitative and quantitative methods, including interviews with involved stakeholders, document analysis and difference-in-differences analyses. In the final step, these data and the applicable formal theories will be combined to test and refine the (I)PT and address the research objectives.Ethics and dissemination Ethical approval has been granted by the Research Ethics Review Committee of Erasmus School of Health Policy and Management (Project ID ETH2122-0170). Where necessary, informed consent will be obtained from study participants. Among other means, study results will be disseminated through a publicly available manual for stakeholders (eg, healthcare providers and payers), publications in peer-reviewed scientific journals and (inter)national conference presentations.

OBJECTIVES:Bundled payments (BPs) are increasingly being adopted to enable the delivery of high-value care. For BPs to reach their goals, accounting for differences in patient risk profiles (PRPs) predictive of spending is crucial. However, insight is lacking into how this is done in practice. This study aims to fill this gap. METHODS:We conducted a systematic review of literature published until February 2024, focusing on BP initiatives in the Organization for Economic Cooperation and Development countries. We collected data on initiatives' general characteristics, details on the (stated reasons for) approaches used to account for PRP, and suggested improvements. Patterns within and across initiatives were analyzed using extraction tables and thematic analysis. RESULTS:We included 95 documents about 17 initiatives covering various conditions and procedures. Across these initiatives, patient exclusion (n = 14) and risk adjustment (n = 12) of bundle prices were the most applied methods, whereas risk stratification was less common (n = 3). Most authors stated mitigating perverse incentives as the primary reason for PRP accounting. Commonly used risk factors included comorbidities and sociodemographic and condition/procedure-specific characteristics. Our findings show that, despite increasingly sophisticated approaches over time, key areas for improvement included better alignment with value and equity goals, and enhanced data availability for more comprehensive corrections for relevant risk factors. CONCLUSIONS:BP initiatives use various approaches to account for PRP differences. Despite a trend toward more sophisticated approaches, most remain basic with room for improvement. To enable cross-initiative comparisons and learning, it is important that stakeholders involved in BPs be transparent about the (reasons for) design choices made.

Abstract Background Clinical outcomes of esophageal and gastric cancer surgery are used for internal and external benchmarking in clinical auditing. For true hospital comparisons, proper case-mix adjustment is required. This study aimed to develop distinct models that allow for case-mix adjusted quality assessment of esophageal and gastric cancer surgery separately and assessed the impact on between-hospital comparisons. Methods This study included all patients undergoing esophagogastric cancer surgery in the Netherlands between 2017-2022 registered in the Dutch Upper Gastrointestinal Cancer Audit. We developed distinct case-mix adjustment models for ten quality indicators (QIs) using backward selection. Model performance of each individual model was evaluated with area-under-the-receiving-operator-curve (AUC) statistics, representing the impact of case-mix on the QI scores. The impact on between-hospital comparisons was quantified using unadjusted and adjusted O/E ratios. Results A total of 4,354 esophageal cancer and 2,109 gastric cancer patients were included. The most frequently selected case-mix variables in the models for all QIs for esophageal cancer surgery were ASA-score, salvage surgery, peripheral vascular disease/aortic aneurysm, chronic lung disease, and tumor histology, whereas for gastric cancer these were age, preoperative weight loss, tumor location, and clinical M stage. The case-mix adjustment models for esophageal and gastric cancer surgery showed low to moderate performance, with internally validated AUCs of 0.58-0.73 and 0.58-0.74, respectively. Case-mix adjustment had low impact on between-hospital comparisons. For both types of cancer, case-mix adjustment had most pronounced impact on between-hospital comparisons for QIs 30-day/in-hospital mortality and failure-to-rescue. Conclusion This study showed that correct quality assessment requires different case-mix models per quality indicator for clinical outcomes of esophageal and gastric cancer surgery. The case-mix models had low to moderate performance scores, suggesting that case-mix has limited impact in between-hospital comparisons of oesopagogastric cancer surgery.

BACKGROUND:Early recognition, which preferably happens in primary care, is the most important tool to combat cardiovascular disease (CVD). This study aims to predict acute myocardial infarction (AMI) and ischemic heart disease (IHD) using Machine Learning (ML) in primary care cardiovascular patients. We compare the ML-models' performance with that of the common SMART algorithm and discuss clinical implications. METHODS AND RESULTS:Patient-level medical record data (n = 13,218) collected between 2011-2021 from 90 GP-practices were used to construct two random forest models (one for AMI and one for IHD) as well as a linear model based on the SMART risk prediction algorithm as a suitable comparator. The data contained patient-level predictors, including demographics, procedures, medications, biometrics, and diagnosis. Temporal cross-validation was used to assess performance. Furthermore, predictors that contributed most to the ML-models' accuracy were identified. The ML-model predicting AMI had an accuracy of 0.97, a sensitivity of 0.67, a specificity of 1.00 and a precision of 0.99. The AUC was 0.96 and the Brier score was 0.03. The IHD-model had similar performance. In both ML-models anticoagulants/antiplatelet use, systolic blood pressure, mean blood glucose, and eGFR contributed most to model accuracy. For both outcomes, the SMART algorithm was substantially outperformed by ML on all metrics. CONCLUSION:Our findings underline the potential of using ML for CVD prediction purposes in primary care, although the interpretation of predictors can be difficult. Clinicians, patients, and researchers might benefit from transitioning to using ML-models in support of individualized predictions by primary care physicians and subsequent (secondary) prevention.

Introduction:While the benefits of integrated care are widely acknowledged, its implementation has proven difficult. Together with other factors, financial factors are known to influence progress towards care integration, but in-depth insight in their influence on the envisioned outcomes of integrated care projects is limited. Methods:We conducted a multiple case study of four integrated care projects in the Netherlands. The projects were purposely sampled to be representative of integrated care in its different forms. A total of 29 semi-structured interviews were held with project members, both medical and non-medical staff. In addition, 141 documents were analyzed, including scientific publications and minutes of meetings. Based on elaborate project descriptions we deduced the synergistic influences of financial and other factors on the outcomes of the projects. Results:Financial factors have an important influence on integrated care projects, though this influence is neither deterministic nor isolated. This is because the likelihood of realizing a positive outcome is affected by the degree to which four key conditions are fulfilled: 1) willingness to change, 2) alignment of interests and uniformity goal, 3) availability of resources to change, and 4) effectiveness of management of external actors. Conclusion:Financial factors have an impact on the outcomes of integrated care projects and must be viewed in synergy with interrelated other factors. Crucial for realizing success in integrated care, a balance must be struck between the level of ambition set in a project and the reality of the prevailing key conditions.

Background Efforts to mitigate unwarranted variation in the quality of care require insight into the 'level' (eg, patient, physician, ward, hospital) at which observed variation exists. This systematic literature review aims to synthesise the results of studies that quantify the extent to which hospitals contribute to variation in quality indicator scores.Methods Embase, Medline, Web of Science, Cochrane and Google Scholar were systematically searched from 2010 to November 2023. We included studies that reported a measure of between-hospital variation in quality indicator scores relative to total variation, typically expressed as a variance partition coefficient (VPC). The results were analysed by disease category and quality indicator type.Results In total, 8373 studies were reviewed, of which 44 met the inclusion criteria. Casemix adjusted variation was studied for multiple disease categories using 144 indicators, divided over 5 types: intermediate clinical outcomes (n=81), final clinical outcomes (n=35), processes (n=10), patient-reported experiences (n=15) and patient-reported outcomes (n=3). In addition to an analysis of between-hospital variation, eight studies also reported physician-level variation (n=54 estimates). In general, variation that could be attributed to hospitals was limited (median VPC=3%, IQR=1%-9%). Between-hospital variation was highest for process indicators (17.4%, 10.8%-33.5%) and lowest for final clinical outcomes (1.4%, 0.6%-4.2%) and patient-reported outcomes (1.0%, 0.9%-1.5%). No clear pattern could be identified in the degree of between-hospital variation by disease category. Furthermore, the studies exhibited limited attention to the reliability of observed differences in indicator scores.Conclusion Hospital-level variation in quality indicator scores is generally small relative to residual variation. However, meaningful variation between hospitals does exist for multiple indicators, especially for care processes which can be directly influenced by hospital policy. Quality improvement strategies are likely to generate more impact if preceded by level-specific and indicator-specific analyses of variation, and when absolute variation is also considered.PROSPERO registration number CRD42022315850.

Innovative eHealth technologies are becoming increasingly common worldwide, with researchers and policy makers advocating their scale-up within and across health care systems. However, examples of successful scale-up remain extremely rare. Although this issue is widely acknowledged, there is still only a limited understanding of why scaling up eHealth technologies is so challenging. This article aims to contribute to a better understanding of the complexities innovators encounter when attempting to scale up eHealth technologies and their strategies for addressing these complexities. We draw on different theoretical perspectives as well as the findings of an interview-based case study of a prominent remote patient monitoring (RPM) innovation in the Netherlands. Specifically, we create a cross-disciplinary theoretical framework bringing together 3 perspectives on scale-up: a structural perspective (focusing on structural barriers and facilitators), an ecological perspective (focusing on local complexities), and a critical perspective (focusing on mutual adaptation between innovation and setting). We then mobilize these perspectives to analyze how various stakeholders (n=14) experienced efforts to scale up RPM technology. We provide 2 key insights: (1) the complexities and strategies associated with local eHealth scale-up are disconnected from those that actors encounter at a broader level scale-up, and this translates into a simultaneous need for stability and malleability, which catches stakeholders in an impasse, and (2) pre-existing circumstances and associated path dependencies shape the complexities of the local context and facilitate or constrain opportunities for the scale-up of eHealth innovation. The 3 theoretical perspectives used in this article, with their diverging assumptions about innovation scale-up, should be viewed as complementary and highlight different aspects of the complexities perceived as playing an important role. Using these perspectives, we conclude that the level at which scale-up is envisaged and the pre-existing local circumstances (2 factors whose importance is often neglected) contribute to an impasse in the scale-up of eHealth innovation at the broader level of scale.

Abstract Background Healthcare use by High-Need High-Cost (HNHC) patients is believed to be modifiable through better coordination of care. To identify patients for care management, a hybrid approach is recommended that combines clinical assessment of need with model-based prediction of cost. Models that predict high healthcare costs persisting over time are relevant but scarce. We aimed to develop and validate two models predicting Persistent High-Cost (PHC) status upon hospital outpatient visit and hospital admission, respectively. Methods We performed a retrospective cohort study using claims data from a national health insurer in the Netherlands—a regulated competitive health care system with universal coverage. We created two populations of adults based on their index event in 2016: a first hospital outpatient visit (i.e., outpatient population) or hospital admission (i.e., hospital admission population). Both were divided in a development (January-June) and validation (July-December) cohort. Our outcome of interest, PHC status, was defined as belonging to the top 10% of total annual healthcare costs for three consecutive years after the index event. Predictors were predefined based on an earlier systematic review and collected in the year prior to the index event. Predictor effects were quantified through logistic multivariable regression analysis. To increase usability, we also developed smaller models containing the lowest number of predictors while maintaining comparable performance. This was based on relative predictor importance (Wald χ2). Model performance was evaluated by means of discrimination (C-statistic) and calibration (plots). Results In the outpatient development cohort (n = 135,558), 2.2% of patients (n = 3,016) was PHC. In the hospital admission development cohort (n = 24,805), this was 5.8% (n = 1,451). Both full models included 27 predictors, while their smaller counterparts had 10 (outpatient model) and 11 predictors (hospital admission model). In the outpatient validation cohort (n = 84,009) and hospital admission validation cohort (n = 20,768), discrimination was good for full models (C-statistics 0.75; 0.74) and smaller models (C-statistics 0.70; 0.73), while calibration plots indicated that models were well-calibrated. Conclusions We developed and validated two models predicting PHC status that demonstrate good discrimination and calibration. Both models are suitable for integration into electronic health records to aid a hybrid case-finding strategy for HNHC care management.

BackgroundDespite sophisticated risk equalization, insurers in regulated health insurance markets still face incentives to attract healthy people and avoid the chronically ill because of predictable differences in profitability between these groups. The traditional approach to mitigate such incentives for risk selection is to improve the risk-equalization model by adding or refining risk adjusters. However, not all potential risk adjusters are appropriate. One example are risk adjusters based on health survey information. Despite its predictiveness of future healthcare spending, such information is generally considered inappropriate for risk equalization, due to feasibility challenges and a potential lack of representativeness.MethodsWe study the effects of high-risk pooling (HRP) as a strategy for mitigating risk selection incentives in the presence of sophisticated- though imperfect- risk equalization. We simulate a HRP modality in which insurers can ex-ante assign predictably unprofitable individuals to a 'high risk pool' using information from a health survey. We evaluate the effect of five alternative pool sizes based on predicted residual spending post risk equalization on insurers' incentives for risk selection and cost control, and compare this to the situation without HRP.ResultsThe results show that HRP based on health survey information can substantially reduce risk selection incentives. For example, eliminating the undercompensation for the top-1% with the highest predicted residual spending reduces selection incentives against the total group with a chronic disease (60% of the population) by approximately 25%. Overall, the selection incentives gradually decrease with a larger pool size. The largest marginal reduction is found moving from no high-risk pool to HRP for the top 1% individuals with the highest predicted residual spending.ConclusionOur main conclusion is that HRP has the potential to considerably reduce remaining risk selection incentives at the expense of a relatively small reduction of incentives for cost control. The extent to which this can be achieved, however, depends on the design of the high-risk pool.

In healthcare systems with a purchaser–provider split, contracts are an important tool to define the conditions for the provision of healthcare services. Financial risk allocation can be used in contracts as a mechanism to influence provider behavior and stimulate providers to provide efficient and high-quality care. In this paper, we provide new insights into financial risk allocation between insurers and hospitals in a changing contracting environment. We used unique nationwide data from 901 hospital–insurer contracts in The Netherlands over the years 2013, 2016, and 2018. Based on descriptive and regression analyses, we find that hospitals were exposed to more financial risk over time, although this increase was somewhat counteracted by an increasing use of risk-mitigating measures between 2016 and 2018. It is likely that this trend was heavily influenced by national cost control agreements. In addition, alternative payment models to incentivize value-based health care were rarely used and thus seemingly of lower priority, despite national policies being explicitly directed at this goal. Finally, our analysis shows that hospital and insurer market power were both negatively associated with financial risk for hospitals. This effect becomes stronger if both hospital and insurer have strong market power, which in this case may indicate a greater need to reduce (financial) uncertainties and to create more cooperative relationships.