Impact of Care Coordination on 30-Day Readmission, Mortality, and Costs for Heart Failure

ABSTRACT

Objectives: Although coordination of care has become the main focus of health care reform efforts to improve outcomes and decrease costs, limited information is available concerning the impact of care coordination on 30-day outcomes and costs. We used nationwide, population-based data to examine the influence of care coordination on 30-day readmission, mortality, and costs for heart failure (HF).

Study Design: We analyzed 20,713 patients with HF 18 years or older discharged from hospitals in 2016 using Taiwan’s National Health Insurance Research Database. The coordination of care among a patient’s outpatient physicians was measured with care density.

Methods: Multilevel regression models were used after adjustment for patient and hospital characteristics to explore the impact of care density on 30-day readmission, mortality, and costs.

Results: Patients with high care coordination had lower odds of 30-day readmission (OR, 0.90; 95% CI, 0.82-0.98) and mortality (OR, 0.83; 95% CI, 0.70-0.99) and lower costs (cost ratio [CR], 0.84; 95% CI, 0.79-0.90) compared with those with low care coordination. Patients with medium care coordination had lower costs (CR, 0.92; 95% CI, 0.86-0.98) than those with low care coordination.

Conclusions: High care coordination is associated with decreased 30-day readmission, mortality, and costs for HF. Enhancing coordination of care has the potential to increase the value of care. It is important to monitor coordination of care and develop strategies to maintain high levels of care coordination for HF.

Am J Manag Care. 2024;30(4):e116-e123. https://doi.org/10.37765/ajmc.2024.89533

Takeaway Points

• Although coordination of care has become the main focus of health care reform efforts to improve outcomes and decrease costs, limited information is available concerning the impact of care coordination on 30-day outcomes and costs.
• We show that high care coordination is associated with decreased 30-day readmission, mortality, and costs for heart failure.
• Enhancing coordination of care has the potential to increase the value of heart failure care.

Heart failure (HF) is not only the end stage of all cardiac diseases but also a major cause of morbidity and mortality.¹ Thirty-day readmission rates for patients with HF are high (approximately 25%) in developed countries.² CMS regards 30-day readmission rates for HF, in particular, as an outcome measure because the high readmission rates may be associated with deficiencies in care transitions and care coordination.³ Although clinical risk factors of 30-day readmission for HF are well established,⁴ other determinants of health care outcomes and costs have not been systematically explored. Coordination of care has become the main focus of health care reform efforts to improve outcomes and decrease costs. Discharged patients with HF often receive care from multiple clinicians and facilities; thus, a potential danger of fragmentation of care exists.^5,6 To our knowledge, research evaluating the effect of care coordination on 30-day readmission, mortality, and costs among discharged patients with HF using nationwide population-based data has not been conducted.

Care coordination is defined as the deliberate organization of patient care activities between 2 or more participants involved in a patient’s care to facilitate the appropriate delivery of health care services, which is often managed by the exchange of information among participants within a health services organization or among several health services organizations.^7,8 Continuity of care consists of 3 types of continuity: informational, management, and relational.⁹ Care coordination involves informational continuity and management continuity.^9,10 Informational continuity describes the ongoing availability of information, such as through follow-up, referral, and feedback.^9,11,12 Management continuity is defined as the coherent delivery of care from different physicians, such as through shared management plans and care protocols, and is achieved when services are delivered in a complementary and timely manner.^9,12 Relational continuity, also known as interpersonal continuity or provider continuity, mainly encompasses an ongoing physician-patient relationship.^9,10,12

One study’s findings show that coordination of care measured with care density is associated with fewer hospitalizations and lower costs of care in the subsequent year among patients with chronic HF.¹³ Another study’s findings show that low care coordination is related to higher 1-year postdischarge mortality and costs for discharged patients with HF.¹⁴ Care density measures coordination among physicians, according to the finding that physicians sharing more patients (with sharing measured as claims for a common patient) are more likely to know one another, such as through referrals and advice seeking.¹⁵ To our knowledge, no empirical research has examined the impact of coordination of care on 30-day readmission, mortality, and costs for HF.

In Taiwan, national health insurance is provided to everyone. The sole insurer provides comprehensive benefits, covering inpatient and outpatient care and prescription drugs. Most providers are contracted. Primary care is delivered in clinics, and specialist outpatient care is provided at the outpatient departments of hospitals. Primary care physicians have no compulsory gatekeeping function. Nevertheless, when patients directly visit a specialist in a hospital without a referral, they will pay higher co-payments. Providers are reimbursed for HF care on a fee-for-service basis.

This study used nationwide, population-based data from Taiwan to explore the impact of care coordination on 30-day readmission, mortality, and costs after discharge among patients with HF.

METHODS

Database

We used the national research database provided by the Health and Welfare Data Science Center, which was established by the Ministry of Health and Welfare in Taiwan. The database, which contains deidentified secondary national data, includes the National Health Insurance Research Database (NHIRD) (eg, inpatient, outpatient, and prescription drug claims as well as beneficiaries) and other health-related files (eg, death certificates, medical facilities).¹⁶ The NHIRD contains patient-level demographic, diagnostic, and administrative information across Taiwan.

Study Population

The study population was all patients with HF 18 years or older who were discharged alive from acute care hospitals in Taiwan between January 1 and December 31, 2016, and were identified by International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code I50.17 When a patient had more than 1 HF discharge during the study period, we included only the first event in this analysis. The initial data set consisted of 21,800 patients. We excluded patients who (1) had fewer than 2 outpatient visits within 1 year before admission due to the inability to calculate physician continuity (as defined later; n = 589); (2) had fewer than 2 outpatient physicians within 1 year before admission due to the inability to calculate coordination of care (as defined later; n = 473); and (3) had unknown sex (n = 25). The final data set comprised 20,713 patients.

Study Variables

Independent variables. The coordination of care among a patient’s outpatient physicians was measured with care density as developed by Pollack et al.^13,18 Pollack et al used claims data to develop a measure of care coordination called care density or patient sharing. Several studies have verified the relationship between care density and outcomes and costs of care for HF,^13,14 cancer,^19-21 and diabetes^18,22 in the US and Taiwan. The denominator of care density is the total number of pairs of outpatient physicians that a patient sees within 1 year before admission, and the numerator is the sum of all shared patients in the calendar year before the year of the patient’s discharge among each pair of a patient’s outpatient physicians. The care density (C) was calculated using the following equation:

where P is the total number of physicians, wi is the number of shared patients for each pair of physicians (i), and m is the total number of possible pairs of physicians.

Care density, which is the mean annual number of shared patients per pair of outpatient physicians, indicates the degree of patient sharing among a patient’s outpatient physicians.^13,18 It is based on the premise that certain aspects of coordination may be facilitated by patients seeing physicians whose patients overlap.¹³ A pair of physicians is regarded as having shared a given patient if they both saw the patient.¹⁸ Therefore, higher numbers indicate greater coordination among physicians. The care density was divided into tertiles, as done in previous studies.^13,14,18

Dependent variables. Our primary outcome was 30-day, all-cause, unplanned readmission, which was measured as any unplanned readmission to any acute care hospital within 30 days from the index discharge.²³ Only an unplanned admission to an acute care hospital qualified as a readmission. Planned readmissions are generally not a signal of quality of care.^23,24 We had 2 reasons for assessing readmissions for all causes. First, from a patient perspective, readmission for any cause is an adverse event. Second, inferences about quality issues based solely on the documented cause of readmission are difficult to generate. For instance, a patient with HF who develops a hospital-acquired infection may ultimately be readmitted for sepsis.²⁵ Thirty-day readmission also might be affected by transition care between providers in addition to inpatient care because the 30-day time frame is a clinically meaningful period for hospitals to coordinate with their communities in an effort to reduce readmissions.²⁶ Therefore, hospital readmissions are considered potential indicators of poor care or missed opportunities to better coordinate care.^27,28 In addition, readmissions are expensive for health care systems and commonly represent avoidable adverse events for patients.²⁹ The secondary outcome was 30-day, all-cause mortality, which was defined as death from any cause within 30 days of discharge. Total costs were also measured as the sum of outpatient, inpatient, and pharmacy costs within 30 days after discharge.

Covariates. The covariates included patient and hospital characteristics. The patient covariates were sex, age, low income, rural residence, comorbid conditions, medical history, in-hospital treatment (percutaneous coronary intervention [PCI] use, intensive care unit use, and surgical operation), baseline medications (aspirin, β-blocker, statin, and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker), length of stay, number of hospitalizations during the past year, number of outpatient visits during the past year, and provider continuity.^{4,13,14,17,18,30-36} The hospital covariates included hospital level (academic medical center, regional, and district), teaching status (yes/no), and geographic location (Taipei, northern, central, southern, Kao-Ping region, and eastern).

Rural residence was measured according to patients’ census tracts of residence and the definition of urbanization published by Taiwan’s National Health Research Institutes.³⁷ Comorbid conditions were measured with the Charlson-Deyo Comorbidity Index (CCI).³⁸ This CCI score was the sum of weighted scores based on whether 17 medical conditions existed. However, HF was excluded because it was reflected in the condition being evaluated. A score of 0 indicated that no comorbidity index existed, whereas higher scores indicated a greater burden of comorbidity. Baseline medications were measured based on whether the medications were prescribed during the hospital stay and/or at discharge.

Provider continuity among a patient’s outpatient physicians within 1 year before admission was measured using the Bice-Boxerman Continuity of Care (COC) Index.^39,40 Bice and Boxerman utilized claims data to establish the calculation of provider continuity, termed the COC Index.^10,39 The COC Index was calculated as follows:

where N is the total number of visits, n_i is the number of visits to a physician i, and P is the total number of physicians.

The COC Index, ranging from 0 to 1, indicates the concentration of visits among a patient’s outpatient physicians.^39,40 If a patient sees only 1 physician, their COC Index would be 1. If a patient sees many physicians with 1 visit each, their COC Index would be 0. In other words, if a patient sees only 1 physician, they will have perfect provider continuity but will not have experienced coordinated care among physicians. Therefore, based on previous studies, provider continuity was used as a covariate.^13,18 The COC Index was divided into tertiles, as done in previous work.¹⁴

Statistical Analysis

We used multilevel regression modeling to examine the impact of care coordination on 30-day outcomes and costs after adjusting for patient- and hospital-level covariates. Multilevel regression modeling is the proper statistical tool for analyzing hierarchically nested data.^41,42 Patients (level 1) were nested within hospitals at level 2.

We applied multilevel logistic regression modeling to explore the relationship of care coordination with 30-day readmission and mortality. We used multilevel linear regression modeling to examine the relationship of care coordination with costs. However, the cost data were skewed, and normality assumptions were better met using the logarithm-transformed scale. The exponent of the regression coefficient, exp (coefficient), of the log-transformed costs was interpreted as the ratio of mean costs for the comparison and reference groups.^14,43 We also performed sensitivity analysis excluding patients who underwent PCI and surgery. SAS 9.4 (SAS Institute Inc) was used for analysis. All statistical tests were 2-tailed, and the significance level was set at a P value of less than .05.

RESULTS

Descriptive statistics are reported in Table 1 [part A and part B]. The 30-day readmission rate in 2016 was 23.9% among 20,713 discharged patients with HF. Approximately half of the patients were male (52.2%). Most of the patients were 65 years or older (75.0%), had a CCI score greater than 1 (68.9%), were not low income (97.6%), and lived in urban areas (64.2%). The mean annual number of all shared patients per pair of outpatient physicians was 164 patients. The 30-day mortality rate was 4.8%. The mean 30-day costs were Taiwanese new dollar (NT$) $49,776.

Pearson χ² test or analysis of variance showed significant associations of care density with patient sex, age, rural residence, comorbidities, medical history, in-hospital treatment, baseline medications, length of stay, previous utilization, provider continuity, hospital level, teaching status, location, 30-day readmission, mortality, and costs (Table 2 [part A and part B]). Among patients with low, medium, and high care coordination, the 30-day readmission rates were 24.2%, 25.3%, and 22.4%, respectively; the 30-day mortality rates were 5.1%, 5.1%, and 4.3%; and the 30-day costs were NT$55,164, NT$49,798, and NT$44,381.

Table 3 [part A and part B] presents the results of the multilevel regression analyses of 30-day readmission, mortality, and costs for HF. After adjustment for patient and hospital covariates, care density was associated with 30-day readmission, mortality, and costs. Patients with high care density had 10% decreased odds of 30-day readmission (OR, 0.90; 95% CI, 0.82-0.98), 17% decreased odds of 30-day mortality (OR, 0.83; 95% CI, 0.70-0.99), and 16% lower 30-day costs (cost ratio [CR], 0.84; 95% CI, 0.79-0.90) compared with those with low care density. Patients with medium care density had 8% lower 30-day costs (CR, 0.92; 95% CI, 0.86-0.98) relative to those with low care density. In a sensitivity analysis excluding patients who underwent PCI and surgery, the results were similar to the main analysis.

DISCUSSION

To our knowledge, this study was the first research using nationwide, population-based data to examine the impact of care coordination on 30-day outcomes and costs among discharged patients with HF. We found that discharged patients with HF receiving care from outpatient physicians with high levels of shared patients (ie, high care coordination) had reduced odds of readmission and mortality and lower costs within 30 days after discharge. These results are similar to those of Pollack et al regarding long-term outcomes and costs among patients with chronic HF¹³ and Chang et al regarding long-term outcomes and costs among discharged patients with HF¹⁴ but different from those of Pollack et al regarding 30-day readmission among discharged patients with HF.¹⁸ Pollack et al verified that patients with chronic HF receiving high care coordination had lower numbers of hospitalizations and lower costs in the subsequent year.¹³ According to Chang et al, low care coordination within 1 year after discharge was associated with higher 1-year postdischarge mortality and costs for discharged patients with HF.¹⁴ However, few studies have examined the impact of care coordination prior to hospital admission on 30-day postdischarge outcomes and costs. Pollack et al used claims data from 3 large commercial insurers to examine the effect of care coordination prior to hospital admission on 30-day readmission but did not identify an effect¹⁸; the major limitation of this earlier study was the lack of generalizability because the data were obtained from only 3 large commercial insurers. Thus, the 30-day readmission rates in 2009 among patients with low, medium, and high care coordination were 7%, 6.5%, and 6.6%, respectively, according to Pollack et al, all of which were lower than those according to Bueno et al and Sundaram et al using US Medicare data and the National Readmissions Database, respectively (20.1% in 2005-2006⁴⁴ and 25.8% in 2012⁴⁵). In addition, the studies did not exclude patients who died during the hospital stay.^18,44,45 Patients who died during the hospital stay had no chance of being readmitted to a hospital.^{3,4,17,31,33,35} The other limitations include the underestimation of care density because of the inability to link providers across insurer and the lack of covariates (concerning patient socioeconomic status, rural/urban residence, and provider characteristics).¹⁸

Findings from this study may support that high care coordination exerts an effect on decreasing 30-day readmission, mortality, and costs among discharged patients with HF. Because Taiwan’s health care system is a single-payer system with unique deidentified patient and physician codes, we can accurately calculate care density using all national patient and physician data. Care density is calculated based on the number of shared patients for each pair of physicians rather than the number of outpatient visits. Increased visitation may reflect patient propensity for adherence, but increased shared patients may measure relationships that exist between a patient’s physicians that serve to facilitate key domains of coordination, including appropriate follow-up and communication among practitioners.¹³ The possible explanation for the relationship of care density to 30-day readmission, mortality, and costs is that patients who see outpatient physicians with high numbers of shared patients are more likely to receive better coordinated transitional and postacute care (including informational continuity and management continuity),^9,10 which may prevent their conditions from worsening; therefore, they are less likely to be readmitted and die and will incur lower costs. Barnett et al found that outpatient physicians with high numbers of shared patients are more likely to obtain consults from one another, have referral relationships, or work in the same practice.¹⁵ For discharged patients with HF, the transition from inpatient to outpatient care is a particularly vulnerable period because of multiple clinician involvement (including primary care physicians and specialists), the progressive nature of the disease, complicated medical regimens, and the presence of comorbid conditions.⁵ Moreover, multiple outpatient physicians must collaborate to reduce readmissions and mortality, especially within 30 days after discharge.²⁶

Limitations

This study has several limitations. First, similar to prior studies using administrative databases, we do not have information on HF severity for HF risk adjustment. Nevertheless, we controlled for patient age, low income, rural residence, comorbid conditions, medical history, in-hospital treatment, baseline medications, length of stay, and previous utilization, which are also important factors for the adjustment of HF complexity.^{4,13,17,18,30,33,34} Second, due to the lack of information on processes of care, we were unable to identify the mechanisms through which care coordination influences HF outcomes and costs. Other unavailable variables may also explain the differences in 30-day outcomes and costs. In addition, there may be particular aspects of Taiwan’s health care system that cannot be extrapolated to other health care systems.

CONCLUSIONS

Findings from our national, population-based study showed the relationship of care coordination with 30-day readmission, mortality, and costs of care for discharged patients with HF. As health care payers and policy makers in many countries have gradually advocated for enhancing care coordination to improve care outcomes and lower care costs, empirical research on whether care coordination influences outcomes and costs of care has become very important. Our analysis provides a first insight into the impact of care coordination on 30-day readmission, mortality, and costs and finds that high care coordination leads to lower 30-day readmission, mortality, and costs for HF. Enhancing coordination of care has the potential to increase the value of care. Therefore, the coordination of care must be monitored and strategies must be developed to maintain high levels of care coordination for HF. For instance, national care protocols or value-based payments might be implemented to assist all providers in achieving better coordinated care for patients with HF.

Author Affiliations: Department of Family Medicine, Chang Gung Memorial Hospital, Linkou Branch (GMC), Taoyuan, Taiwan; Institute of Health Policy and Management, College of Public Health, National Taiwan University (YCT), Taipei, Taiwan.

Source of Funding: This study was supported by grants from the Ministry of Science and Technology in Taiwan (grant number MOST 110-2410-H-002-116-MY3) and the Population Health Research Center from Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan (grant number NTU-112L9004).

Author Disclosures: The authors report 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 (GMC, YCT); acquisition of data (YCT); analysis and interpretation of data (GMC, YCT); drafting of the manuscript (GMC, YCT); critical revision of the manuscript for important intellectual content (GMC, YCT); statistical analysis (YCT); provision of patients or study materials (YCT); obtaining funding (YCT); administrative, technical, or logistic support (YCT); and supervision (YCT).

Address Correspondence to: Yu-Chi Tung, PhD, Institute of Health Policy and Management, College of Public Health, National Taiwan University, No. 17, Xu-Zhou Road, Taipei 100, Taiwan. Email: yuchitung@ntu.edu.tw.

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