Quality Ratings and Patient Satisfaction with Norwegian GPs

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Quality Ratings and Patient Satisfaction with Norwegian GPs

An Analysis of Municipal Capacity & Other Predictors of Overall GP Satisfaction &

Waiting Time Satisfaction

Evelyne Auer

Master Thesis

as part of the double-degree program European Master in Health Economics and Management

UNIVERSITY OF OSLO

Department of Health Management and Health Economics

June 19, 2017

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“Today, patient satisfaction ratings are important indicators of the efficacy, quality, and feasibility of healthcare services.” (Boquiren, Hack, Beaver, & Williamson, 2015)

Quality Ratings and Patient Satisfaction of Norwegian GPs Evelyne Auer

http://www.duo.uio.no/

Print: Reprosentralen, Universitetet i Oslo

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Abstract

Background: Quality of care and patient satisfaction with health services and providers play an ever increasing role and has been the focus of previous policy changes in Norwegian healthcare. In light of the 2012 Coordination Reform and the 2013 GP Regulation that introduced competition elements into primary care, the question arises whether patient satisfaction measures are affected by municipal capacity and reflect previously observed quality improvements.

Objective: The aim of this analysis is to investigate whether patient satisfaction ratings obtained from the DIFI Citizen and GP User Surveys combined with data on municipal capacity measures correspond to existing knowledge gained from previous research. More specifically, we investigate users’ satisfaction levels in the survey periods 2010, 2012 and 2015, and examine whether overall GP satisfaction and waiting time satisfaction are associated with socio- demographic variables, self-assessed health, municipal capacity, and satisfaction measures.

Method: The study employs descriptive statistics, bivariate analysis and hierarchical binomial logistic regression to investigate associations between various predictor variables with overall GP satisfaction and waiting time satisfaction.

Results: We find that age, general life satisfaction and waiting time satisfaction are associated with the odds of respondents’ Overall GP Satisfaction. In determining Waiting Time Satisfaction, we detected a consistently significant positive association with age, municipal GP supply satisfaction and general life satisfaction. Municipal capacity was negligible in its effect on Overall GP Satisfaction and Waiting Time Satisfaction in 2010 but became increasingly influential and significant in 2013 and 2015. Particularly in 2015, we find that high capacity yields the highest odds of users to express high Overall GP Satisfaction and Waiting Time Satisfaction.

Conclusion: The results of the present analysis are consistent with previous findings and add to the theoretical framework of overall GP satisfaction and waiting time satisfaction. The results suggest that municipal capacity has become increasingly important in determining patient satisfaction and that high capacity and thus increased competition among primary care physicians influences users’ satisfaction levels.

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IV

Acknowledgements

Great gratitude I owe to my friends, who never stopped believing in my abilities and offered mental support when the way towards the goal became rough. I am particularly thankful to Karin Kraus for the countless encouraging and inspiring skype conversations and her proofreading efforts.

Deep gratitude I owe to Paulius Olševskas for his understanding, his ceaseless encouragement and infallible optimism, who supported me in so many ways during my time in Oslo. Without him, a lot would not have been possible.

I am also grateful to my supervisor Tor Iversen for his academic expertise and productive criticism. Special thanks I would like to express to Nils Mevenkamp for his expertise in statistics and SPSS that were instrumental in my handling the data in a suitable, professional way.

Last but not least, I would like to voice my deep gratitude to my family, who always supported me in my decisions and next steps.

Evelyne Auer Oslo, June 2017

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Abbreviations

BLR Binomial Logistic Regression

DIFI Direktoratet for Forvaltning og IKT (Norwegian Agency for Public Management and eGovernment)

GLM Generalized Linear Models GP General Practitioner

HC Healthcare

LKU Norwegian Survey of Living Conditions (Levekårsundersøkelse) OLS Ordinary Least Square

PS Patient Satisfaction PE Patient Experience TGPS Overall GP Satisfaction WTS Waiting Time Satisfaction

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List of Tables

Table 1: Norwegian data sources assessing various dimensions of quality, patient satisfaction & patient experience

Table 2: Variables before transformation Table 3: Transformed variables

Table 4: Satisfaction variables for longitudinal view on satisfaction development Table 5: Mann-Whitney U Test for group differences in TGPS and WTS 2015 Table 6: Model 1.1 TGPS_1 2015

Table 7: Significant variables & coefficients in TGPS_1 2015 Table 8: Model 1.2 TGPS_2 2015

Table 9: Model 1.3 TGPS_3 2015 Table 10: Model 2.1 WTA_1 2015

Table 11: Significant variables & coefficients in WTA_1 2015 Table 12: Model 2.2 WTA_2

Table 13: Model 2.3 WTA_3

Table 14: Mann-Whitney U Test for group differences in TGPS and WTS 2013 Table 15: Model 1.1 TGPS_1 2013

Table 16: Model 1.2 TGPS_2 2013 Table 17: Model 1.3 TGPS_3 2013 Table 18: Model 2.1 WTA_1 2013 Table 19: Model 2.2 WTA_2 Table 20: Model 2.3 WTA_3

Table 21: Mann-Whitney U Test for group differences in TGPS and WTS 2010 Table 22: Model 1.1 TGPS_1 2010

Table 23: Model 1.2 TGPS_2 2010

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VII Table 24: Model 1.3 TGPS_3 2010

Table 25: Model 2.1 WTA_1 2010 Table 26: Model 2.2 WTA_2 2010 Table 27: Model 2.3 WTA_3 2010

Table 28: Municipal capacity (absolute numbers)

Table 29: Mann-Whitney U Test on Satisfaction Variables 2015 – 2013

Table 30: Mean Ranks of Mann-Whitney U Test on Satisfaction Variables 2015 – 2013 Table 31: Mann-Whitney U Test on Satisfaction Variables 2013 – 2010

Table 32: Mean Ranks of Mann-Whitney U Test on Satisfaction Variables 2013 – 2010 Table 33: Mann-Whitney U Test on Satisfaction Variables 2015 – 2010

Table 34: Mean Ranks of Mann-Whitney U Test on Satisfaction Variables 2015 – 2010 Table 35: Mean Statistics for Satisfaction Variables 2015, 2013, 2010

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List of Figures

Figure 1: Frequency Distribution of Satisfaction Variables 2015 (in %) Figure 2: Frequency Distribution of Satisfaction Variables 2013 (in %) Figure 3: Frequency Distribution of Satisfaction Variables 2010 (in %) Figure 4: Overall GP Satisfaction 2015, 2013, 2010

Figure 5: Waiting Time Satisfaction 2015, 2013, 2010

Figure 6: Satisfaction with Referrals to Specialists 2015, 2013, 2010 Figure 7: Satisfaction with GP’s Medical Competence 2015, 2013, 2010 Figure 8: Satisfaction with Referrals to Other Services 2015 & 2013

Figure 9: Satisfaction with Time to Explain/Consultation Length 2015 & 2013 Figure 10: Level of Trust in the GP 2015 & 2013

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1 Introduction

Quality of care and patient satisfaction with health services and providers play an ever increasing role in health care and have received particular attention since the dawn of the new millennium.

Norway reacted to this trend by adopting the continuous improvement of quality of care into national law. After the introduction of the patient list system for GPs in 2001 and various other laws on quality improvement in health care, the 2012 Coordination Reform (“Samhandlings- reformen”) delegated more responsibility and governance to the municipal level in matters of primary care. Targeting to strengthen primary care, competition elements were introduced that incentivized quality of service provision. In addition, the 2013 GP Regulation (“Fastlegeforskrift 2013”) added another layer to improve primary care by increasing access through reducing waiting time.

In the light of these policy changes, the question arises if the desired change can be measured in terms of quality of, and patient satisfaction with, primary care services. To that end, multiple studies have traced the development of quality and patient satisfaction over the past 15 years, basing the studies on the two most widely used data sources, the Norwegian Survey of Living Conditions (Levekårsundersøkelse) and the website Legelisten.no. While the first one provides important insight into objective quality data such as actual waiting time and referrals to specialist care, Legelisten is rather subjective, less specific on satisfaction measures, and self-sampled. Relatively little is known regarding patients’ satisfaction with primary care physicians, particularly in relation to previous policy changes that affected municipal capacity and competition in primary health care.

For this reason, the present study aims to depict the current status quo of patients’ perceived quality of, and satisfaction with, GPs in relation to municipal capacity. The analysis is based on the DIFI GP User Survey that includes extensive satisfaction measures, in combination with municipal capacity data obtained from the Norwegian Directorate of Health (Helsedirektoratet). The main focus will be on determining in how overall satisfaction with the GP and waiting time satisfaction are influenced by potential predictors such as socio-demographic indicators, self-assessed health status, other satisfaction measures and, most notably, municipal capacity. In tracing the development of these measures before and after the introduction of the Coordination Reform (utilizing the survey periods 2010, 2012, 2015), the study will examine to what extent patient

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4 satisfaction measures and various related predictors reflect previous findings that were based on different data sources. In this way, the analysis adds to the ongoing validation process and strives to grant deeper insight into patients’ perception.

The present thesis is structured in the following way: The first part will provide an overview over the current theory on quality and patient satisfaction, including determinants and limitations, followed by an outline of the Norwegian primary healthcare setting and previous research related to competition, quality and patient satisfaction with GPs in Norway, before stating the study aim.

The second part relates to the methodology and introduces the data and statistical tests that were employed. In part three, we will present the results of the analysis. The fourth section will summarize the results, discuss the main findings and implications and state further research options as well as the final conclusion.

1.1 Quality & Patient Satisfaction

1.1.1 Quality

According to Donabedian (Donabedian & Bashshur, 2003), quality in health care results from “the science and technology of health care” on the one hand, and “the application of that science and technology”, on the other. Since it is impossible to guarantee quality itself, we can only strive for an increase in probability of good or high-quality care, by taking actions “to establish, protect, promote, and improve the quality of care”. George and Sanda (2007) define quality of health care more concisely as “the degree to which health services for individual or populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.”

In judging the quality as good or bad, improved or deteriorated, Donabedian (Donabedian &

Bashshur, 2003) identifies three information types of quality: structure, process and outcome.

These three types are interdependent as structure influences the process, and the process, in turn, affects the outcome, which we judge as good or bad. Under structure, Donabedian (ibid) subsumes

“the conditions under which care is provided, including material resources, human resources and

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5 organizational characteristics.” Process is defined by “the activities that constitute health care” as carried out by professionals, patients and their families, such as diagnosis, treatment, rehabilitation, and prevention. Outcome as the third information type of quality refers to “changes (desirable or undesirable) in individuals and populations that can be attributed to health care” (ibid) including changes in health status, changes in knowledge acquired by patients and family members, changes in behavior of patients or family members that may influence future health, and the satisfaction of patients and their family members with the care received and its outcomes.”

In Donabedian’s understanding of outcome, we find both an objectively measurable dimension in the form of change in health status and change in a patient’s acquired knowledge or behavior, as well as a subjective dimension through patient satisfaction. Accordingly, George and Sanda (2007) distinguish between the patient perspective and the provider perspective with quality of care.

Patient-reported outcomes are consequently measured in terms of patient satisfaction.

1.1.2 Patient Satisfaction

Despite its widespread international use and its application in various healthcare contexts, the concept of patient satisfaction has no clear, uniform definition nor a clear relation to the technical quality of health services (Junewicz & Youngner, 2015). One of the most popular definitions is Pascoe’s (1983), which establishes PS as “a health care recipient’s reaction to salient aspects of the context, process, and result of their service experience.” And according to Boquiren et al. (2015), PS measures the perceived quality of care, and as such forms the basis for evaluation and improvement of health services, which shall serve as the preferred definition in the present thesis.

Satisfaction in particular reflects in how far expectations were met, as “it is influenced by varying standards, different expectations, the patient’s disposition, time since care, and previous experience” (Crow et al., 2002). Patients’ expectations indeed seem to play a vital role in the subjective evaluation of health services. Apparently, high satisfaction ratings can be associated with low or non-existent patient expectations (Junewicz & Youngner, 2015).

Returning to Donabedian, we adopt his view on PS as evaluative outcomes, i.e. “client opinions about, and satisfaction with, various aspects of care, including accessibility, continuity, thoroughness, humaneness, informativeness, effectiveness, and cost” that do not only reflect the

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6 category of outcomes per se but links in with the structure and process as well (Donabedian &

Bashshur, 2003). Nowadays, PS measurement is largely seen in the light of the evolution of HC service provision – as a driver for and consequence of patient engagement and focus – while also adding to the ethical and legal obligations of institutions and providers, thus serving increased accountability.

1.1.2.1 Determinants of Patient Satisfaction

PS is multi-dimensional as surveys investigate characteristics of both exogenous and endogenous factors of the doctor-patient encounter, thus encompassing all the evaluative aspects summarized by Donabedian (Donabedian & Bashshur, 2003); overall satisfaction, organization/structure and accessibility/availability (relating to the facility), communication and interaction (between the HC professional and the patient), technical skills and competence of the medical staff or HC personnel as perceived by the patient or user. The following section shall provide a brief overview of the various dimensions and their effect on PS.

Communication has proven to play a key role in achieving patient satisfaction. Specifically the doctor’s ability to explain things in an understandable way, to listen effectively and to address questions and concerns are vital elements for providing information to the patient, and thus for creating a good doctor-patient relationship. For this reason, the majority of PS surveys include questions or statements regarding the available time spent for asking questions, explaining, and listening (Boquiren et al., 2015; Junewicz & Youngner, 2015).

In addition to communication, Boquiren et al. (2015) identified 4 more domains and key influencers of PS. Relational conduct or humanistic characteristics, i.e. the doctor’s interpersonal skills, respect, and shared decision-making together with the patient, form the second key domain.

Technical skills including expertise and competence are another major factor contributing to PS in addition to personal qualities such as empathy, concern, kindness and friendliness. Lastly, availability and accessibility are also important to patients. Consequently, high PS ratings correlate with competent (as perceived), easily accessible doctors who listen carefully, spend efficient time with the patient, treat the patient respectfully and explain well. Concurrently, the lowest PS ratings

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7 were found for physicians of the high control type, while the highest ratings referred to person- centered doctors (Flocke, Miller, & Crabtree, 2002).

PS is mainly but not exclusively defined by characteristics of the evaluated doctor or clinician.

Non-clinician related influencers of PS include organizational factors on a systemic or practice- specific level such as staff interaction, difficulty of getting an appointment, waiting times, equipment and accessibility, and also patient-related determinants such as socio-cultural beliefs, socio-demographics, personality, and previous experience. All of these factors influence PS and affect overall satisfaction ratings (Boquiren et al., 2015). Moreover, continuity of care is strongly associated with higher PS, particularly in the context of primary care. Various studies show that countries lacking continuity of care have generally lower satisfaction ratings than countries where patients seek the same general practitioner over a long period of time (Fan, Burman, McDonell, &

Fihn, 2005).

On the individual level, a patient’s perception and evaluation is influenced by demographic factors, socio-economic status, and self-assessed health status. Age has yielded the most consistent effect on satisfaction ratings. Numerous studies found a positive correlation of age and satisfaction (Carlin, Christianson, Keenan, & Finch, 2012; Hall & Dornan, 1990; Russell, Johnson, & White, 2015; Sitzia & Wood, 1997; Westaway, Rheeder, Van Zyl, & Seager, 2003), while there is some counterevidence (Kahana, Lee, Kahana, & Yu, 2015). With regard to patients’ health status, studies have been inconsistent in showing concrete one-sided associations with PS. Carlin et al.’s study (2012), for instance, reports higher overall satisfaction ratings from patients with one or more chronic conditions, while at the same time increased disease complexity combined with a good understanding of the condition and treatment options as explained by the physician correlated with lower overall satisfaction ratings. The influence of a patient’s health status on his/her satisfaction level is relatively small. Fan et al. (2005) found that only 7% of the variance in PS can be explained by the patient’s health status.

1.1.2.2 Measuring Patient Satisfaction

Conceivably because of its wide range of definitions and the influence of context, patient satisfaction is measured with several tools. How patient satisfaction data are gathered not only seems to depend on the use or underlying motive, the specific geographical context or the

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8 healthcare system. Various satisfaction surveys have been developed based on different approaches that are not always directly related to healthcare, such as defining the patient as user or customer receiving a service. The particular challenge in creating standardized, reliable patient satisfaction surveys lies in achieving comparability despite its multidimensional nature and in universal applicability for all patients in a given setting, regardless of their disease and the resulting differences in received care (George & Sanda, 2007).

1.1.2.3 Use of Patient Satisfaction Ratings

Measuring PS has multiple advantages. It can facilitate continuity of providers in that patients remain with a “good” doctor through creating a long-term doctor-patient relationship. This, in turn, increases the patient’s loyalty towards the doctor. PS ratings have also shown to correlate with fewer malpractice suits, and a higher tendency to recommend one’s doctor (Boquiren et al., 2015).

It has also become increasingly common to link PS as an indicator of healthcare quality with payment or reimbursement schemes to incentivize the quality improvement in healthcare provision (Godager, Hennig-Schmidt, & Iversen, 2016; Junewicz & Youngner, 2015). In addition, PS ratings are also used for marketing and quality assessment purposes and thus function as competitive device, affecting patient volumes and thus also profits (Godager, Iversen, & Ma, 2015).

Performance standards and efficiency measures aid the patient in choosing, and potentially in remaining with, a provider, which results in increased revenue and other financial benefits (Boquiren et al., 2015; Junewicz & Youngner, 2015). Regarding its multifarious application, the use of PS can be summarized as follows (Boquiren et al., 2015): “Today, patient satisfaction ratings are important indicators of the efficacy, quality, and feasibility of healthcare services.” Although it is essential to know how to achieve PS, particularly for GPs in markets with financial incentives, there are also potential disadvantages stemming from an extensive focus on PS. Boquiren et al.

(2015) point out three elements necessary to satisfy a patient: 1) providing necessary care, thus achieving positive outcomes; 2) granting the patient’s medically unnecessary wishes; and 3) displaying good interpersonal skills, such as respect and communication skills.

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9 In the Norwegian context, PS ratings are used to provide information about the perceived quality of care (particularly by patients for patients) to facilitate the choice of a suitable GP (Biørn &

Godager, 2010; Godager et al., 2015). However, during the past 10 to 15 years, multiple reforms in different countries linked quality measures to financial incentives in order to achieve improved quality of care (Godager, Iversen, & Ma, 2009; Godager & Wiesen, 2013). More details on quality ratings and PS with primary care in Norway will be given in section 1.3.

1.1.2.4 The Relation of Patient Satisfaction & Quality of Health Services

How is PS related to quality and does PS help improve the quality of care? Based on its use as quality indicator, benchmarking tool for choice of provider, and incentive for quality improvement, PS is often regarded a direct proxy for quality. However, the relationship between PS and quality has not yet been completely clarified. Furthermore, the potential effect of PS on quality of care improvement is not established (Junewicz & Youngner, 2015).

George & Sanda (2007) for instance, argue that PS reflects the care process (regarding waiting time, provision of information, access, speed of treatment) but also correlates directly with outcome of care. Junewicz & Youngner (2015), by contrast, warn against the conflation of PS and quality, particularly with regard to the technical quality of care, due to patients’ lack of knowledge and the prevalent information asymmetry in healthcare. Detsky & Shaul (2013) corroborate that PS does not correlate with the technical quality of care since evidence-based standards are largely unknown by patients. What seems to be agreed on in the literature is that PS is a prerequisite for patient compliance. Only satisfied patients comply with the recommended treatment and follow physician advice so that clinical outcomes improve. (Junewicz & Youngner, 2015)

1.1.2.5 Limitations & Criticism of PS

Boquiren et al. (2015) point to the contexts of PS as a potential caveat. For the adequate interpretation of PS surveys, it is vital to know what the patient’s evaluation is referring to; whether it concerns overall health care or specific services, one specific encounter or rather multiple encounters during a given period of time, or a healthcare team instead of an individual clinician.

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10 However, the prevailing main critical argument regarding PS is its subjectivity, which causes PS to vary tremendously among individuals. In fact, in investigating variance of PS in primary care, Salisbury et al. (2010) found that the vast majority of 95.4% of variance in PS ratings was caused by individual differences of patients and random error, while only 4.6% resulted from differences between practices. This was seemingly also true for patients who visited and rated the same GP or practice. In satisfaction with waiting time, by contrast, less than 80% of the variance resulted from individual differences between patients (Haggerty, 2010). From that we can conclude that overall satisfaction is highly subjective and varies tremendously among individuals, while waiting time satisfaction shows less variance and is therefore considered a more objective and reliable indicator.

Another aspect is the predominance of positive ratings and its compromising effect of sensitivity, which lowers the precision of the measuring tool. One study has shown, for instance, that positive ratings do not necessarily translate to high quality of care as positive ratings were also given after negative experiences (Schneider & Palmer, 2002; Williams, Coyle, & Healy, 1998). These false positives occurred when patients attributed the negative experience to other aspects that they perceived to be beyond the physician’s control. In that way, insufficient consultation lengths were interpreted by patients as organizational time constraints rather than the doctor’s lack of interest or inability. It has been argued that negative ratings, by contrast, do not include false negatives and therefore carry more weight with regard to reporting incidents such as medical errors or lack of respect (Haggerty, 2010).

Considering its flaws and dangers of misinterpretation, one might question how much sense there is in using PS surveys at all. A practical answer to that question is that in many settings and countries, this is the best and sometimes only available option of assessing quality or how care is perceived by patients or users. Haggerty (2010) and Junewicz & Youngner (2015) suggest in that matter, that PS be used for assessing the interpersonal dimension of healthcare and the subjective perceptions of soft skills that are difficult to measure objectively, while the patient’s subjective view does not qualify for evaluating the technical quality of care. PS instruments are still useful for benchmarking to recognize best practices and to highlight negative assessments. Nonetheless, these need refinement “to maximize precision and minimize bias” (ibid).

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11 It stands to reason that one should be very careful with the interpretation and particularly the generalization of PS ratings. With regard to the concrete interpretation, Haggerty (2010) opines that it would be “better to report the proportion of patients who are less than totally satisfied rather than the average satisfaction” and concludes that “[h]igh satisfaction ratings indicate that care is adequate not that it is of superior quality; low ratings indicate problems and should not be masked by reporting average scores.”

1.2 The Norwegian Primary HC Setting

The Norwegian healthcare system is governed on three levels: the state, the four health regions and the municipalities (an exception form dental services organized on the county level), which organize the provision of sector-bound services. While hospitals are governed by the health regions, primary care lies within the responsibility of the municipalities. GPs are the providers of primary care in single practices, group practices and GP or emergency hospitals (“Legevakt” and

“Sykestue”). In the function of gatekeepers, they regulate the access to specialist care through referrals. (Ringard, Sagan, Sperre Saunes, & Lindahl, 2013)

1.2.1 Reforms in the Norwegian Primary HC System

Since the beginning of the new millennium, the Norwegian healthcare system has undergone several reforms that centered on patient empowerment and structural reorganization of health service provision in order to improve the coordination of health care services between different providers and to increase patient safety as well as quality of care (Ringard et al., 2013). The primary care sector saw the first change in 2001 with the introduction of the patient list system. In order to foster continuity of care, patients were registered with a regular GP and signed onto the GP’s patient list. The length of the list ranges from a minimum of 500 to a maximum of 2500 patients per GP and is agreed upon with municipalities. Since GPs can state their preferred list size, which may deviate from the actual number of enlisted patients, lists can be open and accessible for new patients, or closed, in which case the GP cannot take on any additional patients. Switching the GP is possible but limited to GPs with open lists and restricted to two times per year. (Brekke &

Straume, 2017; Ringard et al., 2013)

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12 The 2012 Coordination Reform (Samhandlingsreformen) was based on the identification of particularly vulnerable patient groups that were prone to suffer from coordination problems, which on a system level resulted in higher healthcare expenses. Consequently, the aim was to facilitate a smoother transfer for the patient and improved coordination of services between the primary and secondary sector in order to reduce unnecessary hospital admissions and pre-discharge waiting times for subsequent services. The easier transition from hospitals to homes, for instance, arguably enhances patient focus and simultaneously curbs healthcare expenditure. In addition, primary care is strengthened through the increased focus on prevention on the municipality level (Ringard et al., 2013)

The reform entered into force with the Municipality Health and Care Act and the Public Health Act of 2011. These provide the legal framework for the coordination of public health work across sectors and actors in healthcare, as well as between authorities at local, regional and national level.

This resulted in two major changes; Firstly, “municipalities were given full responsibility for patients ready to be discharged from hospital treatment”, and secondly, municipalities are now co- financing non-surgical healthcare services that are provided in specialist care (ibid). By means of voluntary agreements between municipalities and hospitals and the strong financial disincentive to treat patients in more costly specialist care facilities, the main goals of improved integration and cost reduction were to be achieved. (ibid)

One original idea put forward as part of the reform was the increase in capacity in the form of municipal GP density in order to strengthen service provision in primary care so that hospital admissions would decline. An analysis by Seim (2010) prior to the introduction of the reform concluded, however, that, contrary to expectations, an increased number of GPs in municipalities would most likely increase hospital admissions. Only municipalities with so-called “GP hospitals”

(Sykestue) correlated with fewer hospital admissions. Nevertheless, a high density of primary care institutions on the municipality level would decrease the elderly’s hospital admission rate. In the light of multi-morbidity and disease complexity of this particular group, such reduced admissions would translate to huge savings in healthcare costs. In the end, the proposal to increase GP density in order to reduce the patient flow to specialist care was discarded, albeit the GP density still increased steadily over the years (Godager & Iversen, 2016).

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13 In 2013, a new GP regulation entered into force (Fastlegeforskrift 2013¹), which aimed particularly at reducing waiting times and increasing access by listing the following demands:

- Patients shall receive appointments as soon as possible; usually within 5 work days.

- GPs are responsible for updating the patient’s “legemiddelliste” as soon as a change in drug prescription has occurred.

- GPs shall offer more preventive measures.

- GPs shall offer home visits if necessary.

- 80% of phone calls to the GP shall be answered within 2 minutes.

- GPs shall be able to receive appointments electronically.

Investigating suitable data sources, what we would expect to observe as a consequence of the successful adoption of the regulation is that 1) waiting time should have decreased from 2013 to 2015, which should coincide with higher waiting time satisfaction; 2) the number of home visits should increase; and 3) access telephonically and electronically should improve, which should translate to increased satisfaction in this domain.

1.2.2 Financial Incentives & GP Payment Scheme

In addition to the legal framework and various reforms addressing quality in primary healthcare, the GP payment scheme provides financial incentives to balance self-motivated interests (income generation) and patient wellbeing. A GPs’ income is subject to a mixed payment system consisting of a lump sum and fees that is designed to balance over- and under-provision of medical services.

The basic practice income stems from the prospective capitation payment for each listed patient on the GP’s list and constitutes one third of the income, which is paid by the municipality. The second third is comprised of fee-for-service payments, which the GP receives from the national insurance scheme as retrospective reimbursement for provided services. The last source of income is composed of patients’ copayments for each visit. Both fee-for-service and copayments are fixed across the country as negotiated on an annual basis, and no other financial arrangements exist.

(Iversen, 2005; Iversen & Lurås, 2000; Iversen & Ma, 2011)

1 http://www.ffo.no/Tema/Helse/Ny-fastlegeforskrift-innfort/

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1.2.3 Competition in the Norwegian Primary Healthcare Market

Formerly, the Norwegian healthcare market was practically non-competitive due to the public provision of services and the tax-based national insurance scheme covering costs. Previous reforms, however, enhanced private provision of care and created financial incentives that introduced some competition among providers in primary and secondary care.

The GP payment scheme combined with the patient list system introduced a competition element in primary care. There is competition for patients among providers because patients are free to enlist with their preferred GP. With fixed prices and financial incentives in place - capitation as GPs’ base practice income and fee-for-service as an additional compensation for provided health services -, doctors compete for patients due to their income-motivated self-interest (Brekke &

Straume, 2017; Godager et al., 2009).

Nonetheless, competition is limited due to the still largely public provision of services and strong state regulation. Competition on price is very restricted since there are fixed prices for GP services and caps on patients’ copayments, which renders demand highly price inelastic. As a consequence, competition among providers relies heavily on non-price elements such as quality of care and waiting time - the targets for improvement by previous primary care reforms. (Brekke & Straume, 2017)

Economic theory defines competition in terms of consumer choice. Thus, the number of providers or producers in a specified market or geographical region are used to measure competition (Bernstein & Gauthier, 1998). In the case of primary healthcare in Norway, we understand GP services as the ‘supply’ market and define municipalities as the geographic entity in which GPs operate. For this reason, one very intuitive way to measure competition in Norwegian primary healthcare is to utilize municipal GP capacity, which has been used in previous studies (Iversen &

Ma, 2011), even though multiple other competition measures have been proposed and used (Bernstein & Gauthier, 1998; Godager et al., 2015). Municipal capacity comprises of the total number of registered GPs per municipality as well as related measures that are adjusted to population density, such as GP density, i.e. registered GPs per 1000 inhabitants. Competition or patient choice increases, the more GPs per municipality or per 1000 inhabitants are registered.

However, the number of registered GPs in a given municipality does not represent the real choice

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15 patients have among GPs because real choice requires excess capacity (Brekke & Straume, 2017).

Only if there are sufficient accessible GPs, do patients have real opportunities to choose or switch the GP. Therefore, it is much rather the number of GPs with open lists that serves as a proper measure for competition intensity in the Norwegian GP market (Godager et al., 2015). In the present study, we include the following municipality-specific capacity variables as theoretical and real competition measures: free capacity (the number of available GPs with open lists), GP density, available list places per 1000 inhabitants (LPPT), open lists per 1000 inhabitants (OLI), and open list ratio (OLR).

The legal and financial framework (i.e. the Regular GP Scheme and list patient system) in Norwegian PHC is steered from the municipality level. Municipalities regulate GP capacity (the GP supply) by contracting a sufficient number of doctors and are responsible for doctors’ base practice income that is defined by the number of listed patients on the GP’s patient list. In order to secure their base practice income, GPs strive to keep their patients on their lists, thus ensuring not only short-term quality of provided services but also continuity of care in the long run. On the demand side, it was found that the quality of services (as perceived by the patients) is positively associated with demand. The perceived quality of care and patient satisfaction with a GP do influence the choice of GP, thus affecting demand. This renders quality and patient satisfaction a competitive device in Norwegian primary healthcare (Biørn & Godager, 2010).

Another intriguing finding by Godager et al. (2015) is that additional competition in the form of increased GP supply in a given municipality results in higher referral rates to specialized care.

Apparently, GPs also compete for patients by satisfying their requests for referrals in order to gain and retain patients. This behavior, however, weakens the GP’s gatekeeping function and, in turn, causes rising healthcare expenditures. This implies a positive association of patients’ overall satisfaction with the GP and the number of referrals issued by the GP, or the corresponding satisfaction with the GP’s referral policy. Simultaneously, structural indicators and competition measures such as GP supply or available list places need to be balanced in order not to impose counterproductive incentives that weaken other mechanisms such as the GP’s gatekeeping function. (ibid)

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16 Similarly to referrals, prescriptions can also function as a competitive device (Kann, Biorn, &

Luras, 2010; Schaumans, 2015) as indicated by the finding that GPs in Norwegian high- competition municipalities (i.e. municipalities with a high GP density) issued 3% more reimbursed and 2% more addictive drugs per patient. Under patient shortage (defined as more than 50 free list places per GP), the prescription rates were even 5% and 6%, respectively, higher for reimbursed and addictive drugs per patient. It was concluded that high competition can lead to a higher inclination of GPs to achieve PS by following patients’ medically unnecessary demand for prescriptions.

Another competitive device is waiting time. GPs can regulate their patients’ waiting time for an appointment and in this way influence the frequency of consultations. Lower waiting time, for instance, was found to translate to a higher number of consultations. Since GPs providing high quality services are faced with excess demand, waiting time for an appointment increases. On the other hand, GPs of lower popularity or patient shortage (due to patients’ perceived lower quality of services) can offer shorter waiting times. This range of perceived quality and popularity of GPs allows for the creation of multiple equilibria through the regulation of waiting time, where the least popular providers offer the lowest waiting time to attract patients and the most popular providers face persistent excess demand resulting in the longest waiting times. (Iversen & Lurås, 2002) On investigating patient satisfaction and patient shortage, Lurås (2007) found that patients visiting a GP with patient shortage (i.e. a considerably lower number of enlisted patients compared to the potential maximum number of patients on a GP’s list) indeed expressed lower satisfaction with the GP’s interpersonal skills, medical skills, referral practices and consultation length while being more satisfied with waiting time. Godager and Iversen (2010) supported the finding that patient shortage or low demand correlates with the GP’s personality, skills and behaviors in the doctor-patient interaction.

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17

1.3 Relevant Research in Norway

1.3.1 Measuring Quality & Patient Satisfaction in Norway

We currently find four independent instruments that measure quality, patient satisfaction or patient experience to varying degrees with regard to primary care in Norway. These include the website Legelisten.no, the Norwegian Survey of Living Conditions (“Levekårsundersøkelse” LKU), the DIFI user report on GPs (Innbyggerundersøkelse – Brukerdel Fastlege), and the patient experience questionnaire by the Norwegian Knowledge Centre for Health Services (Kunnskapssenteret).

The website legelisten.no, introduced in 2012, gives patients the possibility to rate their GPs with a five-star rating system. Authenticated, yet anonymous patients can assign their GP up to five stars in the satisfaction domains Overall satisfaction, satisfaction with Booking, Waiting, Consultation, Listening, Insight and Advice. In addition, patients can formulate comments to give more detailed information on why they rated their GP in a certain way or to state whatever else might seem noteworthy. With its GP-specific content, the website presents an accessible, easy-to-use tool that can guide patients’ decision-making regarding the choice of GP. Nonetheless, the scope of satisfaction is so limited that it is not particularly suitable for evaluating quality of health care services or for comparing geographical areas. Moreover, as the website is based on people’s own initiative and thus self-selection, it does not count among the representative data sources, even though the patterns of satisfaction have been shown to coincide with the ones found in other representative studies (Sivertsen, 2014).

Kunnskapssenteret developed a method to measure patient experience with Norwegian GPs with the specific goals: 1) to provide a standardized questionnaire for national and local surveys; and 2) to test a data collection program that is usable for national surveys and recommendations for conducting local surveys. As the name suggests, the questionnaire focuses on patients’ experience with their GPs (Holmboe, Danielsen, & Iversen, 2015) rather than on patient satisfaction. Based on 26 questions on the six dimensions of Patient Safety, Mastery, Coordination, Employees, GP, and Availability, it combines some objective measures with the subjective patient experience and thus allows for the comparison of patients’ expectations and the evaluation of their experience (e.g.

objective duration of waiting time and experienced length of waiting time). Judging from most

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18 recent literature, this approach could well be considered the most advanced tool to measure the perceived quality of care in Norway. However, it has some drawbacks as well. Due to its design for single GP practices or primary care centers, it is comparatively narrow in the scope of questions and of limited use for larger scale comparisons across regions or the country. In addition, it does not include questions on complaints, referral behavior, and accessibility. As a relatively small survey with only 2377 respondents of a non-randomized sample, it is also non-representative of the Norwegian population. (ibid)

The LKU survey is part of the yearly EU SILC (EU Survey on Income and Living Conditions²).

With rotating focus topics each year, health & lifestyle are assessed every third year. The collected data includes health status and lifestyle, diseases, effects of diseases, symptoms and disability of the Norwegian population. It features predominantly objective measures (e.g. the number of visits, or waiting time in days) with some very basic satisfaction questions regarding the use and attitude towards health services. The GP-patient interaction is assessed by means of the following domains:

being taken seriously (“The GP takes me and my problems seriously”), trusting the GP (“I fully trust the treatment I get from the GP”), time to talk (“The GP does not give me enough time”), waiting time for an appointment (“It takes too much time to get an appointment with the GP”), referrals (“The GP refers me to other services if I am in need of it”). These few basic questions on patient satisfaction disregard the important domain of interaction and communication, and do not provide a sufficient base for evaluating the patient’s perceived quality of services. Nonetheless, the LKU survey provides a large, representative dataset, so that it has been used as the main source for studying quality and patient satisfaction in the Norwegian setting.

Lastly, the DIFI survey on GP users³ has the most extensive and detailed questionnaire on patient satisfaction with primary care, as it includes the satisfaction dimensions availability & accessibility of facilities, waiting time, organization, the GP’s and employees’ competence and communication, coordination of services with other providers & referral behavior, and complaints. These domains are all subjective and evaluated by means of satisfaction ratings on a 7-item Likert scale. However, it does not collect any objective data from the users apart from socio-demographic characteristics

2 https://www.ssb.no/innrapportering/personer-og-husholdning/lev

3 https://www.difi.no/rapporter-og-statistikk/undersokelser/innbyggerundersokelsen-2015/hva-mener- brukerne/fastlege

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19 and broadly formulated health issues. One major weakness is therefore the lack of comparability of subjective patient satisfaction to objective measures to establish an expectation baseline that would aid the correct interpretation of data analysis. (Kjøllesdal Eide & Nonseid, 2015)

Legelisten.no Kunnskapssenteret Patient experience with GPs

LKU Norwegian Survey of Living Conditions

DIFI User Report on GPs

Patient Satisfaction Patient Experience Patient Experience Patient Satisfaction No objective data Limited objective data Extensive objective data

(waiting time, disease &

disability, use of services)

Little objective data (socio-demographics) Overall Satisfaction Overall Satisfaction --- Overall Satisfaction Booking

Waiting

Availability

&

Waiting

Availability Accessibility Waiting Consultation

Listening Insight Advice

GP (Competence &

Interaction);

Mastering patients’ health

Competence Communication

Employees Employees

Coordination Coordination

Patient Safety

Self-assessed health Self-assessed health Specifics on physician

(regular GP or other)

Public vs Private GP Switching behavior

Complaints Table 1: Norwegian data sources assessing various dimensions of quality, patient satisfaction & patient experience

1.3.2 Previous Research & Findings in Norway

What determines patient satisfaction with the GP in Norway? Norwegian studies have shown that the GP’s personal characteristics and interpersonal skills are most valued by patients, along with technical competence. More specifically, characteristics such as respect, empathy, listening and understanding the patient, taking time as well as the patient’s impression of being taken seriously by the GP were the most prevalent determinants. (Folmo, 2014; Kilby, 2014)

Studies based on the LKU dataset such as “Brukernes erfaringer med fastlegeordningen 2001- 2015” (Godager & Iversen, 2016) give comparatively little insight into patient satisfaction, particularly overall satisfaction with the GP. However, they provide crucial quantitative information on the development of waiting time, consultation length, contact frequency and

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20 referrals to specialists and allow for a comparison with waiting time satisfaction, satisfaction with referrals to specialist care, and satisfaction with consultation length. The last study showed multiple relevant developments in the context of the last policy change (Fastlegeforskrift 2013). Waiting time decreased steadily from 1999 to 2005 before flattening out in 2008 and 2012, and then dropping from 2012 to 2015. Overall there was a reduction of waiting time by 48% in the period from 1999 to 2015. Median waiting time declined from 7 days in 1999 to 2 days in 2015. As both median and average waiting time fell, waiting time satisfaction increased steadily from 2002 to 2012 (there are no available data on waiting time satisfaction in 2015). Respondents’ satisfaction with referrals increased as well in 2015 compared to 2002 and 2012, and so did the public’s satisfaction levels with consultation length. By contrast, the impression of being taken seriously by the GP dropped in 2015 compared to 2002, although it increased slightly since 2012.

The DIFI GP User Report 2015 (Kjøllesdal Eide & Nonseid, 2015) points out that overall satisfaction with the GP largely depends on the GP’s competence and communication with the patient; two areas, with which the users are very satisfied. Of mediocre importance for overall GP satisfaction is customization and user-focus, with which respondents expressed satisfaction. The elements of minor influence include service (with high satisfaction), availability (satisfaction) and providing important information (not significant). However, there is little information on the determinants of waiting time satisfaction on the one hand, and other influencing factors of overall GP and waiting time satisfaction such as socio-demographic or municipality-specific competition indicators, which will be the main focus in the present analysis.

1.4 Study Setting

The present study is based on merged data from the DIFI Citizen Survey and GP User Survey (“Innbyggerundersøkelse” and “Brukerdel Fastlege”) from the years 2010, 2013 and 2015. The results of the DIFI surveys are published online and provide an overview of the current state and development of user satisfaction with all available public services in Norway. All user reports feature the same 7 key domains and ratings scales, which allows for a comparison and ranking of all public services in Norway according to user satisfaction.

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21 1) Total Satisfaction & Trust

2) Availability & Physical Conditions 3) Employee skills & User Customization 4) Employee Services

5) Digital Services

6) Information & Communication 7) Proceedings & Complaints

In comparing the various public services, the last user report (Kjøllesdal Eide & Nonseid, 2015) showed that health and care services are located at the midpoint between public libraries with the highest and government agencies with the lowest user satisfaction rates. Health services improved continuously from 2010 to 2015, and, among these, particularly GPs received very good results in terms of overall satisfaction. However, there is little change in waiting time satisfaction from 2010 to 2015. What determines overall satisfaction with any given public service are service provision and user customization. Also overall GP satisfaction is largely determined by these two factors.

Taking a look at the results of the GP User Report 2015, we obtain an overview of the current state of user satisfaction with GPs. Overall GP Satisfaction increased slightly, while overall dissatisfaction remained stable compared to 2013 and 2010. Waiting time satisfaction (waiting time for an appointment) improved lightly since more users expressed satisfaction and increased their satisfaction rating, while simultaneously fewer respondents were dissatisfied. Only three percent of users reported complaints in 2015, which corroborates the generally high satisfaction levels.

Further it was found that women generally rate more positively than men. More than two thirds of users (67%) report having no health issues; 30% state physical conditions and 8% psychological issues. This development corresponds to an increase in users with regular health issues over the past five years. The most frequent contact reason in 2015 was follow-ups and prescription renewals.

The majority of users (58%) stated a contact frequency of two to five times per year, including actual consultations as well as phone calls or accessing digital information. According to respondents’ answers, 43% of users visited a public GP and 49% a private primary care physician.

This seems to be an unrealistic distribution given the fact that 95% of Norwegian GPs are self- employed and only 5% count as public GPs (Brekke & Straume, 2017). Therefore, this variable is treated cautiously in the analysis.

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22

1.5 Aim & Outline

Previously, the fusions of LKU (Helse) with the GP database have been used as the main data source to provide an overview over the development of various quality indicators and patient satisfaction with primary and specialist care. The respective studies (Godager & Iversen, 2010, 2014, 2016) analyze quality and patient satisfaction both on the GP and municipality level. This approach and the respective findings provide the main base of the present analysis. In this way, this study serves as an additional source of information testing similar assumption based on DIFI data and general municipality-specific GP data. Thus, it adds to the ongoing validation process of scientific findings, which provides an opportunity to compare outcomes to draw wider conclusions.

By contrast to the by and large objective, quantitative data in the LKU studies, the present analysis is limited to subjective satisfaction variables on various dimensions regarding GP services, which were combined with quantitative municipality-specific capacity and competition measures. Thus, patient satisfaction data was merged with indicators such as municipal GP density and free capacity. In order to adjust for the difference in measurement level, the quantitative data was transformed into categorical data of three groups, dividing the values into 33% percentiles.

The aim of this analysis is to investigate whether patient satisfaction ratings obtained from the DIFI Citizen and GP User Surveys combined with data on municipality-specific capacity measures correspond to existing knowledge gained from previous research, particularly the “User Experience with the GP Scheme” (Godager & Iversen, 2016), by utilizing different data sources on quality and patient satisfaction with Norwegian general practitioners (LKU and Legelisten). Moreover, the analysis aims to trace the development of patient satisfaction before and after the introduction of the coordination reform in 2012. The specific objective is to explore potential influencing factors such as socio-demographic characteristics, health status, health care encounter variables and municipal capacity in relation to Overall GP Satisfaction and waiting time satisfaction. While previous studies investigated actual waiting times extensively and waiting time satisfaction to some degree, Overall GP Satisfaction has hardly been studied in Norway since it is not part of LKU or Legelisten data, the prime sources of satisfaction surveys. The relation of Waiting Time Satisfaction and Overall GP Satisfaction with one another as with municipality-specific

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23 competition indicators such as GP density and free capacity (GP’s with open lists) will also be examined.

In light of undertaken healthcare reforms and previous study findings (particularly (Godager &

Iversen, 2010, 2014, 2016), the analysis shall answer the following research questions:

1) How did the perceived quality of GP services and patient satisfaction develop over time, particularly in light of the 2012 Coordination Reform and the 2013 GP Regulation?

2) More specifically, does municipality-specific capacity influence respondents’ ratings of Overall GP Satisfaction and Waiting Time Satisfaction?

3) In how far do potential predictors such as socio-demographic variables, self-assessed health status and other satisfaction ratings influence Overall GP Satisfaction and Waiting Time Satisfaction?

In order to answer these questions, we will investigate exogenous and endogenous factors in relation to Overall GP Satisfaction and Waiting Time Satisfaction, respectively. To do so, we include in our analysis socio-demographic characteristics of the patient such as age, income and level of education as exogenous factors, the GP type (private or public) as an endogenous factor, self-assessed health status (exogenous), structural municipality-specific competition indicators such as GP density and free capacity (exogenous) and the subjective determinants Waiting Time Satisfaction, Life Satisfaction or overall happiness and Municipal GP Supply Satisfaction. Based on the results of previous satisfaction studies relating to these predictors, the following hypotheses were formulated:

H1: Overall GP Satisfaction is associated with the socio-demographic variables age, income, and level of education. Numerous studies show internationally that higher age correlates with higher satisfaction levels. Based on the findings Russel et al. (2015) and Zhang (2012), we expect a positive association between overall GP satisfaction and age as well as a negative correlation with income. In Norway, Zhang (2012) discovered more switching as an expression of dissatisfaction with the GP among younger people and those with below-median income. We further assume a negative correlation between overall GP satisfaction and education level based on studies of Norwegian GP switching behavior due to overall dissatisfaction (Zhang, 2012).

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24 H2: Both Overall GP Satisfaction and Waiting Time Satisfaction correlate with users’ self- assessed health status. In the US, patients who assessed themselves as healthy were generally more satisfied with their GP or the GP consultation compared to those who described their health as bad (Badri, Attia, & Ustadi, 2009). On the other hand, patients with multiple chronic illnesses reported higher overall satisfaction as well (Carlin et al., 2012). In Norway, Zhang (2012) showed that worse health is associated with more frequent switching and thus lower satisfaction, while a better health status correlates with higher satisfaction ratings (Jackson, Chamberlin, & Kroenke, 2001).

An analysis of LKU data showed that good health was also positively associated with waiting time satisfaction (Grytten, Carlsen, & Skau, 2009).

H3: Waiting Time Satisfaction correlates with the socio-demographic variables age, income, and level of education. Grytten et al. (2009) found that waiting time satisfaction is positively associated with age in Norway. Based on the findings that Overall GP Satisfaction is negatively correlated with income and education, a similar effect of these predictors is assumed for waiting time satisfaction.

H4: We assume no correlation between Overall GP Satisfaction and type of GP (public or private), while we expect a positive association of Waiting Time Satisfaction with private GPs.

Due to a lack of evidence, we can only assume to find higher waiting time satisfaction (due to its inverse relation with actual waiting time) with private GPs since public GPs are likely to face higher demand and therefore longer waiting times. Since the values of the variable do not correspond to the Norwegian composition of public vas self-employed GPs, it will be regarded with caution and interpreted in terms of users’ perception.

H5: There is a positive relation between Overall GP Satisfaction and Waiting Time Satisfaction. While there are no Norwegian studies investigating the relation of overall GP satisfaction and waiting time satisfaction so far, US studies found that both actual and perceived waiting time influence patient satisfaction and perceived quality. (George & Sanda, 2007; Michael, Schaffer, Egan, Little, & Pritchard, 2013; Russell et al., 2015; Vogus & McClelland, 2016).

H6: Life satisfaction is positively associated with both Overall GP Satisfaction and Waiting Time Satisfaction. In the US context, George & Sanda (2007) found that life satisfaction and general happiness or quality of life predict patients’ overall satisfaction with their GP. Based on

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25 this finding, we assume to find the same relation in the Norwegian primary care setting and expect a similar effect on patients’ ratings of waiting time satisfaction.

H7: Municipal GP Supply Satisfaction influences Waiting Time Satisfaction. We assume that GP supply satisfaction presents a subjective measure of the available choice of GPs in a municipality, reflecting users’ perceived GP density or free capacity in their municipality. Based on the assumption that the actual municipal GP supply is positively associated with waiting time satisfaction, we also suspect municipal GP supply satisfaction as ‘perceived GP supply’ to follow the same positive relation. We expect that the more satisfied people are with the GP supply in their municipality, the more satisfied they will be with waiting time. (However, a positive correlation btw WTS and GPS could also be of a reversed direction; GP supply satisfaction could result from waiting time satisfaction since people who perceive waiting time as satisfactory would probably consider the municipal GP supply satisfactory as well, while dissatisfaction with waiting time could correlate with decreased municipal GP supply satisfaction). This association has not been investigated so far as it results from the merged DIFI dataset.

H8: Some municipality-specific capacity measures influence Overall GP Satisfaction and Waiting Time Satisfaction. Previous studies found no relation between GP density and patient satisfaction levels (Godager & Iversen, 2014, 2016; Wensing, Baker, Szecsenyi, Grol, & Group, 2004). Nonetheless, it is conceivable that a higher GP density would increase competition among GPs and therefore raise quality, which, in turn, could result in higher overall satisfaction with the GP. Waiting time satisfaction is presumably also positively related to GP density based on the finding that higher GP capacity correlated with lower waiting time (Godager & Iversen, 2014).

Free capacity (the amount of GPs with open lists per municipality) correlates positively with the switching behavior of Norwegian patients (Iversen & Lurås, 2008; Zhang, 2012) because more choice appears to reduce patients’ satisfaction levels. For this reason, we assume that municipalities with a high count of free capacity will coincide with lower levels of overall GP satisfaction but higher levels of waiting time satisfaction based on good capacity correlating with shorter waiting time (Godager & Iversen, 2010). Further, we do not expect any relation between overall GP satisfaction and the competition measures open list ratio, open lists per 1000 inhabitants, and available list places per 1000 inhabitants as they rather affect accessibility positively and therefore influence waiting time satisfaction. Consequently, we expect to find positive relations between

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26 waiting time satisfaction and GP density, free capacity, open lists per 1000 inhabitants based on the finding of increased waiting time satisfaction in municipalities with patient-shortage (Grytten et al., 2009) and available list places per 1000 inhabitants based on the outcome that more choice increases competition and consequently reduces waiting time. Previous studies concluded that in 2008, GP capacity was associated with lower waiting time, while no such relation was found in 2012 (Godager & Iversen, 2010, 2014). Similarly, it was shown that more than 50 free list places per 1000 inhabitants resulted in significantly shorter waiting time (Godager & Iversen, 2010). We assume no relation between municipality size and Overall GP Satisfaction or Waiting Time Satisfaction. Lastly, municipalities with patient shortage yielded significantly more waiting time satisfaction (Grytten et al., 2009). Though patient shortage is a GP-specific indicator (reflecting the desired GP list size compared to the actual number of listed patients), it is related to the number of available GPs as well as the number of open lists per 1000 inhabitant.

Quality Ratings and Patient Satisfaction with Norwegian GPs