The closer the better: does better access to outpatient care prevent hospitalization?

How to best allocate limited public resources across outpatient and inpatient healthcare services to achieve maximum improvement in health outcomes is one of the perennial questions of health policy all over the world.

To inch closer to answering that question, we have to understand, disentangle, and accurately measure the relationships between those two levels of care. Does the provision of more outpatient care, while itself improving health outcomes, also generate more hospitalization episodes and/or make them longer, or, to the contrary, does it help to avoid costlier inpatient care later on? What are the respective and aggregate changes in health care expenditures? In this paper, we use panel data from a quasi-experimental setting provided by an expansion of specialist outpatient care in Hungary between 2010 and 2012, greatly improving access, to contribute to answering those questions. Besides observational or cross-sectional studies, the earlier quasi-experimental literature mainly uses data from the United States, and hence, little is known about the substitution/complementation effects in countries whose health care sector is characterised by different institutional and regulatory frameworks and financing arrangements.

At the highest level of abstraction, nationwide health policy planning is about maximizing health outcomes of the population constrained by limited public and private resources. This is done through financing many functional channels of the health care system, but, in OECD countries, most expenditure goes to curative and rehabilitative care, and, within that, two of the most important functions are outpatient care, upon which 1.2–7.5% of gross domestic product (GDP) is expended; and inpatient care, with 1.5–3.4% of GDP (2015 data from [17]). Given these enormous expenses, the importance of any reliable evidence that can contribute to even a marginal improvement of health outcomes by a better allocation of resources across these two subsectors cannot be overstated. Such evidence can help policy makers to decide whether additional public resources are put to better use by being channelled toward expanding outpatient or inpatient care. In what follows, we first present the possible mechanisms of substitution and complementation and the empirical literature so far, then the Hungarian context, followed by the data, the methods, our results and, finally, our conclusions.

What are the possible theoretical mechanisms of interaction between inpatient and outpatient care? Fortney et al. [8], building on the work of Starfield [21] and others, identify the following mechanisms of substitution (i.e. more outpatient care decreases hospitalization) and complementation (more outpatient care reduces more inpatient care).

Mechanisms of substitution:Mechanisms of complementation:The empirical literature is rather mixed in terms of whether the substitution or the complementation effect dominates. Miller [15] analysing a Massachusetts reform (a health insurance reform was introduced that differentially affected the costs of outpatient and inpatient care) and Rubinstein et al. [20] analysing the effects of a reorganization to increase access to primary care for veterans in Virginia both found a drop in hospitalization in response to more access to primary care. Other papers also found substitution effects in cross-sectional settings [1, 6, 10, 19].

On the other hand, Kaestner and Sasso [11] found that, in the US, an increased outpatient spending was associated with more hospital admissions; the Rand and the Oregon health insurance experiments also showed that improving the availability of medical services through a more generous health insurance coverage was associated with an increase in the use of emergency room services and hospitalization [7, 16].

A third group of studies found neither substitution nor complementation effects. Looking into the same Massachusetts reform as Miller [15], Kolstad and Kowalski [12] found that gaining insurance was associated with a decrease in hospital admissions through emergency department, an increase in hospital admissions through other channels, and no change in total hospitalizations. The instrumental variables analysis by Fortney et al. [8] indicated that an increase in primary care encounters was associated with a decrease in specialty medical encounters, but was not associated with an increase in physical health admissions or outpatient costs.

One promising method to try to sharpen the results in the empirical literature, exemplified by Duscheiko et al. [3], has been to narrow down the focus upon hospitalizations for conditions considered especially sensitive to timely and effective management in primary care; e.g. Kolstad and Kowalski [12], whose inconclusive results put them in the “no effect” camp above, actually find a substitution effect when zooming in on the effects upon preventable hospitalization.

In addition to being, in sum, rather inconclusive, many of these studies are also observational or cross-sectional, making the establishment of causal relationships hard. In the case of papers based on a quasi-experimental or experimental setup, the source of variation that makes identification possible consists in changes in the financing (insurance) mechanism alone and almost all of them examine the US. Our source of variation is different and our evidence comes from a very different, but, by no means, internationally unique institutional setting, shared by most post-communist EU member states (e.g. Poland and the Czech Republic) and the countries emerging from the Soviet Union like Russia and Ukraine [14]. In such countries, our research question has never been addressed before.

Hungary is a post-communist EU member state of slightly less than 10 million inhabitants with a single-payer health insurance and de facto universal coverage [5]. In 2015, Hungary spent 7.2% of its GDP on health care, 1.8% of the GDP on outpatient (including government- and household-financed primary and specialist outpatient) and 1.9% of the GDP on inpatient care [17]. The basic benefit package is free of out-of-pocket payments for the patients at the point of care (including outpatient care), although informal gratuity payments are widespread. Primary care by general practitioners is financed by capitation; most outpatient services are financed by the budget based on fee-for-service points, under a system that scores procedures on the basis of their complexity and resource requirements, whereas inpatient services, almost exclusively provided in state-run and -financed hospitals, are reimbursed through a combined payment system based on diagnosis-related groups (acute care) and per diem rates (chronic care).

The relatively high share of outpatient care in provision and financing is due to the heritage of the Semashko-type healthcare system, common in countries once under Soviet dominance. Central to that model was a multi-tiered system of care with a strict referral system and strongly differentiated network of service providers, with outpatient specialist care, provided in dedicated polyclinics and thus separated from primary care, one of the distinct tiers of healthcare provision [9, 13]. Concentrating on the relationship between this type of care and inpatient care can, arguably, provide more precise information on substitution/complementation than what can be obtained in healthcare systems where data on primary and specialised outpatient care are lumped together. Given the institutional arrangement in Hungary, and to avoid confusion, in what follows, we will refer to “specialist outpatient” care, or, for brevity, “outpatient care” as disjoint from “primary care”, whereas we retain the term “ambulatory care” when referring to the general international literature that uses this term subsuming primary care, as well. We will also address another subset of outpatient care, “1-day ambulatory care” later on.

The health status of the Hungarian population is among the poorest in the EU with a life expectancy at birth of 75.7 years, tailing the EU average by 4.9 years, with even worse parameters in rural micro-regions in which the intervention which we use for identification took place.

The intervention which we base our quasi-experimental specification on is the same as used in Elek et al. [5]. Between 2010 and 2012, around 430,000 people gained better access to specialist outpatient care in Hungary when the government created outpatient units in 20 rural micro-regions, which previously lacked capacity. The investments were funded by the Social Infrastructure Operative Programme (SIOP) 2.1.2. of the European Union. Locations for the new units were selected based on the applications of municipalities, making a case for need and demand.¹ Funding accounted for 500–1000 million HUF (2–4 million euros) per unit, generally covering 90–95% of the costs of the establishment of the new units to the municipalities if they complied with a set of administrative requirements (e.g. providing a minimum of services for a minimum of hours/month, keeping the unit in operation for at least 5 years). Competition for scarce funds was not an issue: sufficient funds were allocated to be able to subsidize all likely applicants eligible under those rules. The newly created units (all still in operation as of 2016) provide comprehensive service for the population of the micro-regions with at least 14 separate specialties at each location. As a result, basic specialist outpatient care in the following four specialties: internal medicine, surgery, obstetrics—gynaecology, and pediatrics may now be reached by around 310,000 more people by car in 20 min than before.

At the same time, the other parts of Hungary experienced relatively few changes in the management of outpatient care between 2008 and 2015. Hence, an appropriate control group of micro-regions could be identified, in which the health care indicators may be compared to those in the micro-regions where new outpatient service locations were established (the “treated” micro-regions). The impact of the improvement in accessibility can then be estimated as the difference between the changes in the treated and control groups, with a difference-in-difference-type analysis.

It is the treatment that we use in the paper to identify the sign, the magnitude, and the lag of the effect of more outpatient treatment upon hospitalization at the individual level.

We use anonymized individual-level administrative data on inpatient stays and specialist outpatient visits, exclusively provided to us for this research project by the Hungarian National Healthcare Services Centre (ÁEEK). Data cover years 2008–2015 for the population of 20 treated and 20 control micro-regions with approximately 1,060,000 people in Hungary (around 10% of the population of the country). The control micro-regions were chosen with propensity score matching to approximate the pre-treatment demographic, socio-economic, and health characteristics of the treated micro-regions. Elek et al. [5] provide the details on the matching procedure as well as on the treated control balance in terms of the observed pre-treatment characteristics.² The balance is satisfactory in most variables, although there remains a slight—statistically not significant—difference in pre-treatment outpatient care provision. The number of weekly specialist outpatient consultation hours per 1000 residents averaged to 0.6 in the treated and 1.2 in the control micro-regions in 2008, but the latter was still very small compared to the average value of all non-treated micro-regions in Hungary (3.8). We will control for this pre-treatment difference by the fixed-effects models.

The annual panel data set used in our analysis contains for each person-year the number of inpatient stays (and of its certain subgroups, see below), the number of specialist outpatient visits (and of its certain subgroups), the estimated inpatient and outpatient care expenditures, as well as demographic information such as gender, year of birth, and settlement of residence.³ Year of death is also recorded for those who died during the period. We omit newborns from the sample, and, hence, restrict the analysis to those at least 2 years of age.

Annually, around 13% of the population of the control micro-regions was hospitalized. We also define potentially avoidable hospitalization (PAH), i.e. hospitalization due to ambulatory care sensitive conditions (ACSCs), based on the ICD-10 category of the primary diagnosis of the inpatient episode. Our main definition for PAH follows Purdy et al. [18] as described in detail by Eggli et al. [4].⁴ According to this definition, around 2.4% of the population was hospitalized due to an ACSC in a given year. We classify this category into the following subgroups (see "Appendix 1" for details):Figure 1 shows that hospitalization case number, hospitalization probability, as well as PAH probability decreased more in the population of the treated group than of the control group after 2010–2012, when the new outpatient units started to operate in the treated micro-regions.⁵ Most new units were established in 2011. The difference between the treated and control values was slightly positive or roughly zero before 2011, but became negative afterwards. We will also examine certain other subgroups of hospitalization such as acute and chronic episodes.

Meanwhile, according to Fig. 1, the number of outpatient visits jumped high in the treated compared to the control group after 2010–2012. The levels and trends are consistent with outpatient capacities: due to some existing outpatient units in the control micro-regions, outpatient care use was slightly higher in the control than in the treated group before 2011, but this difference quickly reversed when the new outpatient units emerged in the treated micro-regions. We also note that, before 2011, outpatient case numbers in the treated group were well below the national average and also below (by 20–25%) those rural micro-regions that already had substantial outpatient capacities [5], so the sudden increase was just a catch-up from a low initial level in an underserved population.

We will specifically examine outpatient visits associated with certain ACSCs such as those in cardiology, pulmonology, or diabetes, defined by the ICD-10 code of the outpatient event. We hypothesize that a growing ratio of patients treated in outpatient care with such conditions may have caused the decreased prevalence of PAH.

Finally, the lower two graphs in Fig. 1 show that, while outpatient expenditures increased, the estimated inpatient expenditures decreased in the treated compared to the control micro-regions.⁶

Obviously, outpatient care use and inpatient care use are strongly correlated on the individual level. In the control micro-regions, patients hospitalized in a given year visited (non-laboratory) outpatient care 2.9 times more often in the previous year than non-hospitalized patients, and the difference persists when age and gender are controlled for. However, these cross-sectional correlations are non-causal. Estimation of a causal relationship between outpatient and inpatient care requires a quasi-experiment such as the establishment of the new outpatient locations in our case. Therefore we apply a difference-in-difference-type analysis to measure the treatment effect (and check the pre-treatment parallel trends in the treated and control group with a placebo test). In support of our identification approach, it is important to also add that, although hospital capacities had been curtailed to save costs in 2007, before the time period under scrutiny, there was virtually no policy-driven change in the supply of inpatient care during the time-span which we analyse. Still, we use three explanatory variables to control for possible exogenous changes in health care supply in the examined period: the number of wider regional (county-) level number of hospital beds; the ratio of unfilled GP practices in the settlement of the individual; and the availability of special 1-day ambulatory services (aimed at providing certain treatments in internal care, neurology, and physiotherapy) that started to operate in some treated and control micro-regions in the examined period (see "Appendix 3" for details, and Table 10 in "Appendix 4" for descriptive statistics of these variables in the treated and control groups).

Beyond an impact assessment of the establishment of the new outpatient units, we use this quasi-experiment to estimate the structural effect of bringing outpatient care 1 min closer to the residence of the individual on hospitalization. Therefore, we define the travel time (in minutes) needed to reach the nearest outpatient unit by car from the settlement of each individual. This distance measure decreased in the treated micro-regions from 24 min in 2008 to 10 min in 2012, while it was essentially unchanged (21 min on average) in the control micro-regions.

Table 1 presents the estimated treatment effects on the use of inpatient and outpatient care. Descriptive statistics of the variables used in the models are shown in Tables 8, 9 and 10 of "Appendix 4". The left panel displays the annual baseline probabilities of receiving a certain type of care in the control group, along with the effects of the treatment on these probabilities. Odds ratios (i.e. \(\exp (\gamma _d )\)) are shown, which roughly correspond in the case of inpatient care to multiplicative changes in probabilities, because hospitalization is relatively rare in the population. The right panel gives the baseline case numbers (per 100 inhabitants), the multiplicative effects of the treatment according to the FE Poisson models, i.e. \(\exp (\beta _d^{\text {poi}} ),\) and the additive effects \(\beta _d^{\text {lin}}\) according to the FE linear models. The lower panel of the Table contains the baseline health expenditure values in the control group and how the treatment affects them.

Our quasi-experimental estimates indicate that bringing outpatient care closer to a previously underserved population may yield considerable effects and not just short-term ones.

As already shown in Elek et al. [5] on a shorter period, indicators of outpatient care use (expenditures and number of visits) increased right after the new outpatient centres were established.

But what is the effect of more access to specialist outpatient services upon inpatient care? Controlling for health care supply variables, fixed effects, and patient age, we find marked substitution effects between outpatient care and hospitalization. As theory predicts, there is smaller effect upon inpatient care when leaving out potentially avoidable hospitalization due to ambulatory care sensitive conditions, but it is larger when concentrating upon potentially avoidable hospitalization. It is especially strong in the two specialisations of diabetes and cardiology. In the case of these two fields, we find corresponding sizeable increases in outpatient laboratory case numbers, strengthening the case that, out of the different theoretical mechanisms, here, the substitution channel of management of chronic health conditions in outpatient care is of great importance. In these specialisations, substitution clearly dominates potential complementation mechanisms.

The dynamics of the effects is also noteworthy: as can be expected the substitution effects are stronger if we allow for a lag of several years for the additional outpatient care to take effect. The substitution effect upon potentially avoidable hospitalization (PAH) is exerted more rapidly than upon hospitalization for the other diagnostic groups, indicating that, in those specialisations, direct substitution mechanisms are present. We interpret the fact that, with a lag of several years, the substitution effect upon non-PAH also becomes significant as a sign that, in addition to prevention due to the early detection, other, slower mechanisms of substitution, notably, better management of chronic conditions are also present. This suggests the presence of medium-term health benefits, although we cannot measure them directly using the available data. Thus, in terms of health policy implications, while we have no conclusive evidence of bringing outpatient care closer upon health outcomes, those effects are likely to be positive.

Finally, the effects concerning expenditures are also significant and sizable. Even though the official Hungarian reimbursement fees may not exactly reflect variable social costs of the treatment (and fix costs are not addressed at all), we consider it remarkable that, according to our estimates, the extra (variable) cost of additional outpatient care (HUF 1300) is partially cancelled out by savings in financing the hospitalization of the patients in question (HUF 800).

What is the external validity of our results vis-a-vis other countries? We can only speculate, but it stands to reason that the more similar the institutional and incentive framework of a healthcare provision to that of Hungary, the more likely that our findings carry over. Thus, we expect that post-communist countries with Semashko-type healthcare setup are the most likely to exhibit a similar substitution relationship between outpatient and inpatient care, but the relationship that we find could also be true for the other countries with single-payer healthcare systems. In as much as our findings for potentially avoidable hospitalization reflect the medical reality of managing chronic health conditions in outpatient vs. inpatient care, they should hold in all the developed countries.