Oddvar Kaarboe

Multi‐tasking, quality and pay for performance

We present a model of optimal contracting between a purchaser and a provider of health services when quality has two dimensions. We assume that: (i) the provider is (at least to some extent) altruistic; (ii) one dimension of quality is verifiable (dimension 1) and one dimension is not verifiable (dimension 2); (iii) the two quality dimensions can be either substitutes or complements. Our main result is that setting the price equal to the marginal benefit of the verifiable quality dimension can be optimal even if the two quality dimensions are substitutes.

Career Concerns, Monetary Incentives and Job Design

We study optimal incentive contracts when commitments are limited, and agents have multiple tasks and career concerns. The agents career concerns are determined by the outside market. We show that the principal might want to give the strongest explicit incentives to agents far from retirement to account for the fact that career concerns might induce behavior in conflict with the principals preferences. Furthermore, we show that maximized welfare might be decreasing in the strength of career concerns, that optimal incentives can be positively correlated with various measures of uncertainty, and that career incentives have strong implications for optimal job design.

Prioritization and patients' rights: Analysing the effect of a reform in the Norwegian Hospital Sector

The right to equal treatment, irrespective of age, gender, ethnicity, socio-economic status and place of residence, is an important principle for several health care systems. A reform of the Norwegian hospital sector of 2002 may be used as a relevant experiment for investigating whether centralization of ownership and management structures will lead to more equal prioritization practices over geographical regions. One concern was variation in waiting times across the country. The reform was followed up in subsequent years by some other policy initiatives that also aimed at reducing waiting lists. We measure prioritization practice by a method that takes departure in recommended maximum waiting times from medical guidelines. We merge the information from the guidelines with individual patient data on actual waiting times for the period 1999-2005. This way we can monitor whether each patient in the available register of actual hospital visits has waited shorter or longer than what is considered medically acceptable by the guideline. The results indicate no equalization between the five new health regions, but we find evidence of more equal prioritization within four of the health regions. Our method of measuring prioritizations allows us to analyse how prioritization practice evolved over time after the reform, thus covering some further initiatives with the same objective. The results indicate that an observed reduction in waiting times after the reform have favoured patients of lower prioritization status, something we interpret as a general worsening of prioritization practices over time.

Distorted Performance Measures and Dynamic Incentives

Incentive contracts must typically be based on performance measures that do not exactly match agents’ true contribution to principals’ objectives. Such misalignment may impose difficulties for effective incentive design. We analyze to what extent implicit dynamic incentives such as career concerns and ratchet effects alleviate or aggravate these problems. Our analysis demonstrates that the interplay between distorted performance measures and implicit incentives implies that career and ratchet effects have real effects, that stronger ratchet effects or more distortion may increase optimal monetary incentives, and that bureaucratic promotion rules may be optimal.

Monitoring prioritisation in the public health‐care sector by use of medical guidelines. The case of Norway

This paper presents a new way to monitor priority settings in public health-care systems. We take departure in medical guidelines prescribing acceptable waiting times for different medical descriptions. Allocating ICD10 codes to the medical descriptions, we are able to compare actual waiting times to the recommended maximum waiting times. This way we use the medical guidelines as a tool for monitoring prioritisation in the health sector. In an application, using data from the Norwegian Patient Register, we test statistically for compliance with the guidelines. The results indicate that patients suffering from the most severe conditions are receiving too low priority in the Norwegian health-care sector relative to patients of lower priority.

WAITING TIMES AND SOCIOECONOMIC STATUS. EVIDENCE FROM NORWAY

We investigate whether socioeconomic status, measured by income and education, affects waiting time when controls for severity and hospital-specific conditions are included. We also examine which aspects of the hospital supply (attachment to local hospital, traveling time, or choice of hospital) matter most for unequal treatment of different socioeconomic groups. The study uses administrative data from all elective inpatient and outpatient stays in somatic hospitals in Norway. The main results are that we find very little indication of discrimination with regard to income and education when both severity and aspects of hospital supply are controlled for. This result holds for both men and women.

Norwegian priority guidelines: Estimating the distributional implications across age, gender and SES

OBJECTIVE Targeting hospital treatment at patients with high priority would seem to be a natural policy response to the growing gap between what can be done and what can be financed in the specialist health care sector. The paper examines the distributional consequences of this policy. METHOD 450000 elective patients are allocated to priority groups on the basis of medical guidelines developed by one of the regional health authorities in Norway. Probit models are estimated explaining priority status as a function of age, gender and socioeconomic status. RESULTS Women and older people are overrepresented among patients with low priority. Conditional on age, women with low priority have lower income and less education than women with high priority. Among men below 50 years, patients with low priority have less education than patients with high priority. CONCLUSION Targeting hospital treatment at patients with high priority, though sensible from a pure medical perspective, may have undesirable distributional consequences.

How do Hospitals Respond to Price Changes? Evidence from Norway

Many publicly funded health systems use activity-based financing to increase hospital production and efficiency. The aim of this study is to investigate whether price changes for different treatments affect the number of patients treated and the mix of activity provided by hospitals. We exploit the variations in prices created by the changes in the national average treatment cost per diagnosis-related group (DRG) offered to Norwegian hospitals over a period of 5 years (2003-2007). We use the data from Norwegian Patient Register, containing individual-level information on age, gender, type of treatment, diagnosis, number of co-morbidities and the national average treatment costs per DRG. We employ fixed-effect models to examine the changes in the number of patients treated within the DRGs over time. The results suggest that a 10% increase in price leads to about 0.8-1.3% increase in the number of patients treated for DRGs, which are medical (for both emergency and elective patients). In contrast, we find no price effect for DRGs that are surgical (for both emergency and elective patients). Moreover, we find evidence of upcoding. A 10% increase in the ratio of prices between patients with and without complications increases the proportion of patients coded with complications by 0.3-0.4 percentage points.

The impact of different prioritisation policies on waiting times: Case studies of Norway and Scotland

We investigate the distributional consequences of two different waiting times initiatives, one in Norway, and one in Scotland. The primary focus of Scotlands recent waiting time reforms, introduced in 2003, and modified in 2005 and 2007, has been on reducing maximum waiting times through the imposition of high profile national targets accompanied by increases in resources. In Norway, the focus of the reform introduced in September 2004, has been on assigning patients referred to hospital a maximum waiting time based on disease severity, the expected benefit and the cost-effectiveness of the treatment. We use large, national administrative datasets from before and after each of these reforms and assign priority groups based on the maximum waiting times stipulated in medical guidelines. The analysis shows that the lowest priority patients benefited most from both reforms. This was at the cost of longer waiting times for patients that should have been given higher priority in Norway, while Scotlands high priority patients remained unaffected.

The relationship between educational attainment and waiting time among the elderly in Norway

We investigate whether educational attainment affects waiting time of elderly patients in somatic hospitals. We consider three distinct pathways; that patients with different educational attainment have different disease patterns, that patients with different levels of education receive treatments at different hospitals, and that patient choice and supply of local health services within hospital catchment areas explain unequal waiting time of different educational groups. We find evidence of an educational gradient in waiting time for male patients, but not for female patients. Conditional on age, male patients with tertiary education wait 45% shorter than male patients with secondary or primary education. The first pathway is not quantitatively important as controlling for disease patters has little effect on relative waiting times. The second pathway is important. Relative to patients with primary education, variation in waiting time and education level across local hospitals contributes to higher waiting time for male patients with secondary education and female patients with secondary or tertiary education and lower waiting time for male patients with tertiary education. These effects are in the order of 15-20%. The third pathway is also quantitatively important. The educational gradients within catchment areas disappear when we control for travel distance and supply of private specialists.