Examining the Distinct Effects of Structural, Financial, and Information-Sharing Integration on Hospital Costs

ABSTRACT

Objective: Hospitals are pursuing integration strategies to contain costs, but evidence remains limited. This study examined the independent associations of 3 forms of integration—structural, financial, and information sharing—with hospital costs.

Study Design: Hospital fixed-effects panel regression using data from multiple sources—CMS Hospital Compare, Let’s Get Healthy California, and the American Hospital Association Annual Surveys and Information Technology Supplements—for years 2014-2016.

Methods: Three integration dimensions were measured: (1) structural integration (health maintenance organization [HMO] participation rate, hospital physician employment/foundation models, and system affiliation); (2) financial integration (% of the hospital’s net patient revenue paid on a shared-risk basis); and (3) information-sharing integration (use of electronic patient data received from outside sources). Hospital fixed-effects regressions with 2-way clustering by hospital and county were estimated to assess within-hospital changes in spending associated with each dimension, controlling for 3 ownership types and size.

Results: Financial integration was associated with lower hospital spending, whereas structural and information-sharing integration showed no independent association. Considered jointly, the 3 integration dimensions were statistically significant as a group. These findings were robust to alternative specifications, including excluding HMO participation from the structural index, orthogonalizing financial integration to the other dimensions, and measuring information-sharing integration by the extent. Additional checks found no evidence that capitation adoption or the capitation share of revenue was related to lower spending.

Conclusions: These findings suggest that carefully designed financial incentives should be prioritized when implementing integration measures in policy and practice.

Am J Manag Care. 2026;32(9):In Press

Takeaway Points

Although hospitals are pursuing integration to reduce costs, evidence on the effectiveness of this strategy remains scarce. This study examined the distinct impacts of 3 forms of integration on hospital costs.

• Distinguishing structural, financial, and information-sharing integration shows that only financial integration is consistently associated with lower hospital costs.
• Cost reductions are driven mainly by shared-risk financial arrangements; capitation-based measures and nonfinancial forms of integration generally do not reduce costs.
• Policy makers should emphasize carefully designed financial integration methods over structural reorganization alone when seeking to control hospital spending.

Hospital integration—defined as the coordination of activities and functions across hospitals, physician groups, and other health care organizations—has been widely studied as a strategy intended to provide benefits such as reduced spending and improved quality.¹ The mechanism operates by linking hospitals and other health care entities to align operations and incentives so they can function as one efficient system rather than as isolated entities.²

Yet empirical evidence on integration does not always support the anticipated benefits of better quality, improved efficiency, or lower spending and prices.³ Hospitals affiliated with multihospital systems or with managed care plans, although they tend to provide better quality of care, have been found to exhibit higher costs per discharge than their independent counterparts.^4,5 In addition, hospital-physician integration often raises prices without consistent quality gains and frequently shows little or no change in spending.^6-11

One possible explanation for failing to support the anticipated benefits lies in how these entities are integrated.¹² For instance, although hospitals may be closely linked to physicians through employment relationships, their incentives may not be aligned if they do not use shared-risk arrangements—that is, payment models in which providers (hospitals, physician groups, clinics) and payers (typically insurance companies or health maintenance organizations [HMOs]) share financial gains when care is delivered efficiently and bear losses when it is not. Another example is hospitals affiliated with the same multihospital system that nonetheless fail to coordinate care delivery because they do not share information across facilities,¹³ a key factor in reducing costs and improving quality.

To make sense of the findings in the existing literature, this study focuses on ways in which these entities are integrated. Specifically, it examines which form of integration contributes most to cost savings by adopting a multidimensional view of hospital integration. This is not to say that previous studies did not attempt to distinguish between different forms of integration. However, there is a gap in prior research: Most work has focused on structural distinctions, such as differences between horizontal (eg, hospital-hospital) and vertical (eg, hospital–physician group) forms.^14,15 Such categorization, however, may capture organizational arrangements but not underlying processes. Other literature that has attempted to focus on process often used overly broad categories, distinguishing only between structural and functional integration,¹⁶ without recognizing that functional integration encompasses both financial management and information systems.¹⁷

To fill the gap in the literature and deepen understanding of the results contradicting the assumed benefits of integration, this study distinguishes 3 dimensions of integration—structural, financial, and information sharing—and examines their distinct associations with hospital outcome. In particular, hospital fixed-effects panel regressions with 2-way clustering by hospital and county were used to examine which form of integration contributes most to cost savings.

METHODS

Data Source

This analysis aggregated data on hospitals in California for the years 2014-2016 from various sources: CMS Hospital Compare, Let’s Get Healthy California (LGHC), and the American Hospital Association (AHA) annual surveys and information technology (IT) supplements. CMS Hospital Compare’s Medicare Spending Per Beneficiary data set provides information on hospital‑level costs, and LGHC provides county-level HMO penetration rates. Hospital-level data on hospital-physician arrangements, system affiliation, financial arrangements, bed size, ownership types, and information-sharing behaviors were obtained from AHA’s annual and IT surveys. After merging these 4 data sets, the final analytic sample consisted of 427 hospitals.

Model and Measurement

Hospital-level panel regressions were estimated including hospital fixed effects to account for time-invariant unobserved heterogeneity across hospitals and year fixed effects to control for common shocks over time. SEs were 2-way clustered at the hospital and county levels to account for potential serial and spatial correlation of residuals. The following equation was used to estimate the association between integration dimensions and hospital costs:

Hospital Cost_it = α + β'Xit + μ_i + γ_t + ε_it

For the dependent variable, hospital cost, each hospital’s value was normalized by dividing it by the national episode‑weighted median cost. Because the original data set already adjusted for patient age, health status, and geographic payment differences, the measure indirectly controls for patient characteristics.

For independent variables, 3 dimensions of integration—structural, financial, and information sharing—were used. First, a structural integration index was constructed using 3 indicators: hospital-physician integration, system affiliation, and county-level HMO penetration rates. Hospital-physician integration takes a value of 1 if a hospital adopts an integrated salary or foundation model¹⁸ and 0 otherwise. System affiliation is a dummy variable indicating whether a hospital is part of a multihospital system. An indirect measure of structural integration, county-level HMO, captures the share of insured individuals enrolled in HMOs in a given county, as hospitals located in areas with high HMO penetration are more likely to be coordinated with physicians and other providers in response to managed care’s cost-control demands.^18,19 By standardizing each component and taking their average, a structural integration index was constructed. Second, the level of financial integration was measured by the percentage of net patient revenues paid on a shared-risk basis. This measure captures the degree to which a hospital’s financial performance depends on its ability to manage costs and deliver efficient care, thereby reflecting the alignment of hospital incentives with those of payers. Lastly, for the information-sharing integration index, AHA IT survey data were used to create a dummy variable indicating whether hospitals frequently use patient health information received electronically from outside providers or sources when treating a patient. All 3 integration indices were standardized to enable effective comparison of effect sizes.

Control variables were 2 ownership dummies, for-profit and government (with nonprofit as the reference category to capture the 3 types of ownership), and bed size. Although the set of hospital-level controls in the model is limited, the hospital fixed-effects specification absorbs all time-invariant hospital characteristics.

RESULTS

Table 1 reports descriptive statistics for 427 hospital-year observations in California. To assess potential multicollinearity, pairwise correlations and variance inflation factors (VIFs) were examined among the explanatory variables in a simple ordinary least squares specification. All pairwise correlations were below 0.3, and all VIFs were below 2, indicating that multicollinearity was not a concern in the models.

Table 2 presents the results from the fixed-effects regressions that include hospital and year fixed effects, with SEs 2-way clustered at the hospital and county levels. As mentioned earlier, all integration measures were standardized to have a mean of zero and unit variance to compare the relative magnitudes of different integration dimensions. As Table 2 shows, financial integration was negatively and significantly associated with hospital spending (β = –0.003; P = .008), and structural and information-sharing integration coefficients were also negative but statistically insignificant. Although the estimated coefficient for financial integration appears modest in magnitude, it becomes more interpretable when translated into dollar terms. Because hospital costs are normalized by the national episode-weighted median cost, a coefficient of −0.003 implies that a 1–percentage point increase in financial integration is associated with approximately a 0.3% reduction in hospital-level costs. For a representative $40,000 benchmark cost, this corresponds to an average reduction of approximately $120 in hospital costs per episode.

Control variables, including profit margin, governance, and bed size, showed no meaningful association with spending. An F test of the 3 integration dimensions rejected the null that their coefficients were jointly equal to 0 (F_3,28 = 3.38; P = .028), indicating that, collectively, the 3 forms of integration were significantly associated with spending. It is noteworthy that the within-hospital R² was relatively low (0.02). This is common in 2-way fixed-effects models when hospital and year fixed effects account for most of the variation in spending across hospitals and over time, leaving limited within-hospital variation for the covariates to explain. The remaining within-hospital variation represented meaningful changes over time that identify the effects of integration.

Given that many hospitals used more than 1 form of integration, additional tests were conducted to evaluate whether the negative and statistically significant financial integration effect is truly distinct from the other 2 forms of integration. To do so, the financial integration measure was orthogonalized by regressing it on structural and information-sharing integration (including hospital and year fixed effects), with the resulting residual (“Financial [unique]”) used in the cost model. The coefficient on Financial (unique) remained nearly identical to the baseline estimate, indicating that the financial integration effect was not driven by overlap with other integration dimensions.

Robustness Checks

Several robustness checks were conducted to rule out alternative explanations for the reported results. First, a principal components analysis was used to construct the structural integration index differently by extracting the shared variation among the 3 measures of structural integration, and the results remained the same (Table 3, column 2). Second, to assess whether the results were driven by overlap between county-level HMO participation and other hospital-level integration measures, the models were reestimated after removing HMO participation from the structural index; coefficients on structural, financial, and information-sharing integration were materially unchanged (Table 3, column 3), indicating that the findings did not hinge on the inclusion of HMO participation.

In addition, when measuring the level of financial integration, capitation—one of the defining payment mechanisms of the HMO model, in which providers receive a predetermined amount per patient regardless of the actual services provided—was used to examine whether the financial integration effect changes. Regardless of whether capitation was measured as the percentage of net profits from capitation (Table 3, column 4) or as a dummy variable (Table 3, column 5), the effect of financial integration based on capitation became insignificant. Even when both shared-risk basis and capitation were used to construct the financial integration measure (Table 3, column 6), the effect remained insignificant. In short, only the shared-risk basis mattered.

Lastly, the analysis examined whether the information-sharing effect remained the same when the sharing dummies were replaced with a composite variable that captured both whether information is shared and the extent to which it is shared (Table 3, column 7). The coefficients on information-sharing integration remained insignificant, and the other coefficients were unchanged.

Subsample Analysis

These results suggest that financial integration contributes the most to cost savings. Therefore, a subsample analysis was conducted to examine whether the presence of financial integration—specifically, shared-risk arrangements—strengthens the effects of the other 2 forms of integration. As shown in Table 4, the effect of information-sharing integration became significant when shared-risk arrangements were present, whereas the effect of structural integration remained insignificant regardless of the presence of shared-risk arrangements. Because the orthogonal test already indicated that the effect of financial integration was distinct from the other 2 forms of integration, the subsample analysis further suggests that financial and information-sharing integration, when combined, generate greater cost savings.

DISCUSSION

The results suggest that financial integration—specifically, risk-based contracts—has the most immediate and consistent association with cost containment, independent of the other forms of integration. In contrast, structural and information-sharing integration appear to have less precisely estimated effects or may require more time to influence spending behavior.

Importantly, although the estimated effects of financial integration are modest in magnitude, translating them into dollar terms provides additional context for interpretation. As shown in the Results section, a 1–percentage point increase in financial integration corresponds to approximately a 0.3% reduction in hospital-level costs, or approximately $120 per episode when benchmarked to a $40,000 cost level. Although such effects are unlikely to generate large cost reductions at the individual hospital level, they may still be relevant in aggregate when considered across hospital systems or over time.

In addition, the subsample analysis, which showed that the effect of information-sharing integration—but not structural integration—became significant in the presence of financial integration, highlights the importance of integrating underlying processes, such as incentive alignment and information exchange, rather than relying on structural integration alone. Simply hiring physicians or placing them within the same system does not guarantee that the benefits of integration will be realized.

The results of the robustness checks emphasize the importance of well-structured financial integration. These findings suggest that capitation fails to reduce costs, similar to the other 2 forms of integration. As findings of many prior studies suggest, capitation does not necessarily reduce costs and can create incentives for providers to avoid sicker, more costly patients in favor of healthier ones whose payment is the same.^20,21 In addition, when capitation is combined with fee-for-service (FFS) payments, the cost-control incentives of capitation are diluted because providers can still generate marginal revenue by increasing service volume under the FFS component.^22-24 Overall, these results underscore the importance of carefully selecting appropriate financial mechanisms to achieve the intended benefits of integration.

These findings have important implications for both theory and practice. For theory, this study helps fill a gap in prior literature, which reports mixed findings regarding the effect of integration on health care outcomes, by distinguishing between 3 forms of integration. For practice, this study suggests that the anticipated benefits of integration—here, reduced spending—are more likely to be realized when attention is focused on financial integration. In particular, the finding that not all types of financial integration are successful in reducing costs indicates that understanding the incentive-aligning mechanisms is key to achieving the ultimate goal of reducing costs.

Limitations

This study had several limitations. First, due to data availability, it focused only on hospitals in California rather than on a national sample. In addition, the data cover the 2014-2016 period, but they remain informative for understanding structural relationships between integration and costs. Future research using national or international data sets over a longer period would enrich our understanding and improve the generalizability of the findings. Second, although multiple robustness checks were conducted, there remain various ways to operationalize the 3 forms of integration. Future work could explore alternative structural arrangements and additional types of financial mechanisms. Finally, to better understand the finding that only shared-risk and not capitation-based financial integration is associated with reduced costs, qualitative case studies that interview providers and health care administrators could deepen our understanding of the underlying mechanisms.

CONCLUSIONS

This study extends the prior literature by distinguishing 3 forms of integration and examining their effects on hospital costs. The finding that financial integration is independently and significantly associated with lower hospital costs, whereas structural and informationsharing integration are not, suggests that policy makers should prioritize incentive alignment and coordination through financial integration mechanisms rather than relying solely on structural arrangements. 

Author Affiliation: College of Business, Hongik University, Seoul, South Korea.

Source of Funding: None.

Author Disclosures: The author reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design; acquisition of data; analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content; statistical analysis; provision of patients or study materials; administrative, technical, or logistic support; and supervision.

Address Correspondence to: Na-Eun Cho, PhD, College of Business, Hongik University, 94 Wausan-ro, Mapo-gu, Seoul, 04066, South Korea. Email: ncho@hongik.ac.kr.

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