Introduction

With the aging of the global population, healthcare systems worldwide face challenges in sustaining the increasing number of patients with multimorbidity1. Multimorbidity, the presence of two or more chronic conditions, is associated with higher risk of care dependency, hospitalization, and mortality2. Furthermore, multimorbidity is closely linked to rising healthcare and social care costs, placing additional strain on already overburden healthcare systems3,4. Therefore, identifying effective and efficient management strategies for multimorbidity is a critical priority for many countries5.

Managing patients with multimorbidity requires the involvement of several healthcare providers, often leading to care fragmentation6. Care fragmentation refers to the lack of sufficient coordination among healthcare providers, resulting in inefficiencies such as duplicated tests, inappropriate polypharmacy, and ultimately, ineffective and inefficient care. While interdisciplinary collaboration is a key component of effective team-based care, the number of providers involved in a patient’s care contributes to care fragmentation7. The phenomenon of involving many healthcare providers in the care of a single patient is referred to as “polydoctoring,” which is similar to the concept of polypharmacy7,8,9. Although specialists should be involved in managing patients with severe or complex conditions, strictly adhering to disease-specific guidelines may increase the treatment burden and worsen outcomes for patients with multimorbidity10,11. This is because following one guideline may conflict with the management of coexisting conditions, leading to polypharmacy, care fragmentation, and competing treatment priorities, which can reduce overall adherence and negatively affect patient outcomes. Previous studies have reported that an increased number of healthcare providers involved in a patient’s care is associated with higher rates of polypharmacy and rising healthcare costs7. General practitioners (GPs) have also reported challenges in managing the increasing treatment burden from multiple specialist referrals, including difficulties in coordinating care across specialties, ensuring consistent communication, and managing overlapping or conflicting treatment plans12. Therefore, it is crucial to determine whether having multiple physicians involved in the care of patients with multimorbidity improves outcomes or leads to inefficiencies from both clinical and cost perspectives.

The occurrence of polydoctoring in the management of multimorbidity largely depends on the structure of the primary care system in a given country7,8,9. In countries such as the United Kingdom, GP-led gatekeeping systems restrict access to specialists, ensure care continuity, and reduce unnecessary healthcare expenditures13. Conversely, Japan’s healthcare system allows free access to specialists, enabling patients with multimorbidity to consult multiple specialists, such as internists, orthopedic surgeons, and dermatologists14. While free access to specialists promotes access to specialized care, it also creates conditions conducive to polydoctoring7,9,15. The relationship between polydoctoring and health outcomes in Japan’s unique healthcare system presents an opportunity to explore how best to optimize care for patients with multimorbidity. Herein, we aimed to investigate the relationship between the number of regularly visited healthcare facilities and outcomes such as mortality and hospitalization among older population with multimorbidity, using a large-scale claims database in Japan. By exploring this relationship in a healthcare system that differs significantly from gatekeeping models, our findings may inform strategies for optimizing multimorbidity care in other healthcare settings facing similar demographic shifts.

Methods

Study design, setting, data sources, and period

In this retrospective cohort study, we used data from the DeSC database from April 2014 to December 2022. The DeSC database is a large commercial medical claims database provided by DeSC Healthcare Co., Ltd., in Japan. It includes data from multiple insurers covering the North Kanto, Kinki, Tokai, and Shikoku regions, which encompass both urban and rural areas (Figure S1). Japan’s universal healthcare system is primarily based on occupational health insurance schemes16. However, individuals aged ≥ 75 years are enrolled in the regional Long-Life Medical Care System (LLMCS). Using data from the DeSC database, we focused on the LLMCS, which covers nearly the entire elderly population aged ≥ 75 years in the region, excluding only those receiving welfare assistance, due to the universal enrollment of this age group in Japan’s healthcare system.

Study population

The study participants comprised patients aged 75–89 years at the start of the observation period, who had at least two chronic conditions and had available data on their status (alive or deceased) at the end of the follow-up period. To enhance cohort homogeneity, we excluded individuals aged ≥ 90 years, a group known to have distinct clinical and functional characteristics17. Chronic conditions were defined based on the Fortin list, and conditions were considered chronic if they had been continuously recorded for at least 6 months7,9,18. The full list of conditions is provided in Table S1. We excluded individuals who were hospitalized at the start of the observation period to ensure that all participants began follow-up in the outpatient setting. (Fiugre S2) This allowed us to assess the association between outpatient visit patterns and subsequent hospitalizations. To ensure sufficient observation time for outpatient visit patterns, we excluded individuals with a follow-up period of less than six months after cohort entry19. The index date (cohort entry) was defined as the date of initial inclusion in the database for those who were already aged ≥ 75 years at the start of data collection, and as the date they turned 75 years for those who became eligible during the observation period. (Figure S2)

Outcomes

The primary outcome was all-cause mortality, which was derived from mortality data recorded in the claims database. Secondary outcomes included all-cause hospitalization and hospitalization due to ambulatory care-sensitive conditions (ACSC), which are conditions that may be prevented from progressing to severe stages requiring hospitalization through appropriate primary care interventions. ACSC-related hospitalizations were included as a secondary outcome because they are widely recognized indicators of primary care quality and continuity, and are known to increase with poor care coordination and fragmentation20,21. We hypothesized that patients with either no regular visits or excessively high numbers of visited facilities may have higher risks of ACSC-related hospitalizations due to poor care continuity or fragmentation, while those with a moderate number of regularly visited facilities would experience better coordination and fewer preventable hospitalizations. ACSCs were defined according to established literature, with hospitalizations classified as ACSC if the primary diagnosis during hospitalization matched the ACSC list22,23. Additionally, outpatient medical costs within the first year of observation were calculated to assess the financial burden associated with polydoctoring.　Rather than serving as a direct measure of utilization, outpatient costs were used as a proxy for healthcare resource input in the outpatient setting, reflecting both the frequency and intensity of care. This approach allowed us to examine the association between the level of resource allocation and downstream outcomes.

Study variables

The main exposure variable was the number of regularly visited facilities (RVFs), which served as a proxy indicator for polydoctoring7,8,9. RVFs were defined as the number of healthcare facilities visited at least thrice a year and continuously for 6 months, calculated based on the 1 st year from the start of follow-up. Previous studies have shown that RVFs are associated with fragmented care in patients with multimorbidity and with polypharmacy and outpatient medical costs7. Covariates included age, sex, and the Charlson Comorbidity Index (CCI), which was calculated using ICD-10 codes, according to previous literature24. Given that previous studies have demonstrated regional differences in ACSC hospitalizations, stratification was conducted based on geographic region25,26. The dataset used for the analysis had no missing data for any of the key variables used in the study, including covariates such as age, sex, and geographic location. Therefore, all participants included in the analysis had complete information.

Statistical analysis

As this was an exploratory observational study, a convenience sample was used, and no prior sample size calculation was conducted. Age was treated as a continuous variable, while sex, RVF, CCI, and geographic region were treated as categorical variables. RVF and CCI values above the 95th percentile were grouped into a single category. RVF was categorized into six groups (0, 1, 2, 3, 4, and ≥ 5) based on facility visit frequency, and CCI was categorized similarly (0, 1, 2, 3, 4, and ≥ 5) according to the Charlson comorbidity score distribution.

Kaplan–Meier curves were plotted to illustrate the time to all-cause mortality, first hospitalization, and ACSC hospitalization. Kaplan–Meier curves stratified by CCI were reviewed to assess whether the CCI was appropriately adjusted for the risk of mortality associated with multimorbidity.

Multivariable Cox proportional hazard models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for both primary and secondary outcomes. In each model, the dependent variable was the time to event (either all-cause mortality, all-cause hospitalization, or ACSC-related hospitalization), and the independent variables included RVF, age, sex, CCI, and geographic region. The time to event was measured from the start of the observation period to the occurrence of the specified outcome or the end of follow-up. Proportional hazard assumptions were assessed using log-log plots; this variable was treated as a stratification factor rather than a covariate, given that the assumption did not hold for geographic regions. (Figure S3)

For outpatient medical costs, adjusted cost ratios were calculated using a log-linear regression model after adding a constant of 1 to the actual cost. RVFs, age, sex, and CCI were included as covariates in the adjusted models. Due to the correlation between RVFs and CCI, an interaction term between these two variables was included as a covariate in all multivariable models (Figure S4). All tests were two-sided, and a p-value of < 0.05 was considered statistically significant. All analyses were conducted using R version 4.3.1 in RStudio version 2023.06.1.

Ethical considerations

The Ethics Committee of Keio University School of Medicine approved this study (ID 20231056). This retrospective study utilized anonymized claims data provided by DeSC Healthcare Co., Ltd., which were collected based on agreements with each insurer. Before being shared with the researchers, DeSC fully anonymized all data, and no personal identifiers were accessible to the investigators. Given this, the Ethics Committee of Keio University School of Medicine waived the requirement for individual informed consent. This study was conducted in accordance with the Declaration of Helsinki.

Results

2,338,965 individuals were included in the final analysis (Figure S1). The median age at index date of the cohort was 78 years (interquartile range [IQR]: 75–82), and 58.0% of the participants were female. The median number of RVFs was 2 (IQR: 1–2), the median number of comorbidities was 5 (IQR: 4–7), and the median CCI was 1 (IQR: 0–2). The median follow-up period was 47 months (IQR: 28–52) (Table 1). Baseline characteristics stratified by the number of RVFs and regions are presented in Table S2 & S3.

Table 1 Participant characteristics at baseline.
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During the study period, 338,249 (14.5%) participants had died, and 1,220,201 (52.2%) participants had been hospitalized, among whom 291,376 (12.5%) had been hospitalized for ACSCs. The most frequent ACSC-related hospitalizations were for congestive heart failure (23.16%), angina (17.61%), hypertension (17.03%), influenza and pneumonia (11.47%), and pyelonephritis (6.86%) (Table S4). The median outpatient medical cost was 371,230 Japanese Yen (JPY) (IQR: 235,740–587,850). The CCI effectively stratified mortality risk (Figure S5).

In the multivariable Cox proportional hazards model for all-cause mortality, we used participants with an RVF of 1 as the reference group. The group with an RVF of 0 had the highest mortality rate; survival rates improved with increasing RVF levels (Fig. 1). Participants with an RVF of 0 had the highest mortality risk (HR: 3.23, 95% CI: 3.14–3.33, p < 0.0001) (Fig. 2). The mortality risk decreased in a dose-dependent manner with increasing RVF. For participants with RVFs of ≥ 5, the HR was 0.67 (95% CI: 0.62–0.73, p < 0.0001) compared to those with an RVF of 1.

Fig. 1
figure 1

Kaplan–Meier plot showing overall survival by RVF categories. RVFs: Regularly visited facilities.

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Fig. 2
figure 2

HRs for all-cause mortality according to multivariate Cox proportional hazard analysis adjusted for age and sex, CCI, and stratified by region. HR: Hazard ratio, CI: Confidence interval, RVFs: Regularly visited facilities, CCI: Charlson Comorbidity Index.

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In the analysis of all-cause and ACSC-related hospitalization, RVF = 1 was again used as the reference group (Figs. 3 and 4). Kaplan—Meier plots for all-cause and ACSC-related hospitalizations were shown in Figure S6 & S7. Participants with an RVF of 0 had the highest HR for hospitalization (HR: 2.08, 95% CI: 2.04–2.12, p < 0.0001). In patients with RVF of > 1, higher RVFs were associated with an increasing HR for all-cause hospitalization, although these were all lower than the HR for an RVF of 0 (Fig. 3). For ACSC-related hospitalizations, participants with an RVF of 0 also had the highest HR (HR: 2.08, 95% CI: 2.00–2.16, p < 0.01). Compared to participants with an RVF of 1, those with RVFs of 2 and 3 had slightly lower HRs (HR: 0.96, 95% CI: 0.94–0.98, p < 0.0001 for RVFs of 2, HR: 0.97, 95% CI: 0.95–0.99, p = 0.0054 for RVFs of 3), while those with RVFs of > 5 had a higher HR for ACSC-related hospitalizations (HR: 1.13, 95% CI: 1.06–1.22, p < 0.01) (Fig. 4). Outpatient medical costs were also compared using RVF = 1 as the reference. An upward trend was observed with increasing RVF levels. Participants with RVFs > 5 had outpatient costs 3.21 times higher than those with an RVF of 1 (95% CI: 3.17–3.26, p < 0.0001) (Fig. 5).

Fig. 3
figure 3

HRs for all-cause hospitalization according to multivariate Cox proportional hazard analysis adjusted for age, sex, and CCI, and stratified by region. HR: Hazard ratio, CI: Confidence interval, RVFs: Regularly visited facilities, CCI: Charlson Comorbidity Index.

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Fig. 4
figure 4

HRs for ACSC-related hospitalization according to multivariate Cox proportional hazard analysis adjusted for age, sex, and CCI, and stratified by region. ACSC: Ambulatory care-sensitive conditions, HR: Hazard ratio, CI: Confidence interval, RVFs: Regularly visited facilities, CCI: Charlson Comorbidity Index.

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Fig. 5
figure 5

Cost ratio for outpatient medical cost adjusted for age, sex, CCI, and region. HR, hazard ratio; CI, confidence interval; RVFs, regularly visited facilities; CCI: Charlson comorbidity index.

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Discussion

In this large-scale study, polydoctoring was associated with a lower all-cause mortality rate. However, polydoctoring was also associated with an increased hospitalization rate and higher outpatient medical costs. Thus, while polydoctoring may contribute to improved survival, it comes at the cost of increased healthcare utilization and expenses, raising concerns about sustainability from a healthcare system perspective. A U-shaped relationship was observed with ACSC-related hospitalizations, indicating that RVFs of 2–3 may strike an optimal balance between minimizing care fragmentation and maximizing benefits, while RVFs of ≥ 5 warrant caution due to increased risks. Our findings also suggest that regular medical care is crucial for older adults with chronic conditions, as those with no RVFs exhibited the highest rates of mortality, hospitalizations, and ACSC-related admissions, underscoring the importance of usual care for this population. While previous studies have described the concept of polydoctoring or assessed care fragmentation using provider-level continuity indices, this study is novel in evaluating polydoctoring based on a facility-level measure (RVF) and linking it to multiple patient outcomes—including mortality, hospitalization, and outpatient costs—using large-scale claims data from an elderly population with multimorbidity in Japan7,9,23. By using a standardized metric applicable across diverse clinical settings, our findings offer empirical evidence for understanding the system-level implications of care fragmentation in Japan’s specialist-driven healthcare model.

The observed trend of lower mortality with increased RVFs could be attributed to the benefits of specialized care. Previous studies have demonstrated that specialist care can lead to better outcomes in patients with certain conditions, such as diabetes and heart failure, compared to generalist care10,27,28,29. The improved survival rate in our study may be attributed to the expertise and targeted treatment offered by specialists managing individual chronic conditions. However, not all studies have demonstrated the superiority of specialist care for every condition, with some reporting equivalent outcomes between specialist and generalist care10. This suggests that the benefits of polydoctoring may vary depending on the combination of conditions a patient has. Therefore, further research is needed to investigate how different disease patterns influence the impact of polydoctoring. Additionally, it may be necessary to examine the combination of conditions and optimal number of healthcare providers, considering patient and physician capacity when determining the appropriate RVF. Machine learning approaches may be useful in identifying combinations of conditions that are associated with better outcomes depending on the level of RVF.

Regarding ACSC-related hospitalizations, we observed a slight decrease in admissions for patients with RVFs of 2 or 3 but an increase in those with RVFs of ≥ 5. This suggests that RVFs of 2 or 3 might lead to better care coordination and a reduction in ACSC-related hospitalizations. This could be because care coordination is more manageable at this level, whereas having many providers (RVFs ≥ 5) may result in care fragmentation and increased hospitalization. Previous studies have demonstrated that maintaining higher continuity of care leads to fewer ACSC hospitalizations and emergency visits30. Our findings support this, suggesting that involving too many healthcare providers may interrupt this continuity, reducing its overall benefits.

Though this study focuses on Japan, the findings have broader implications for countries facing aging populations, especially given Japan’s status as one of the countries with the highest life expectancy. Japan’s healthcare system, with its free access to specialists and the lack of a GP gatekeeper model, provides a unique environment for studying the effects of polydoctoring. In contrast to countries such as the UK, where GPs act as gatekeepers, Japan’s system allows for more frequent and direct access to specialists, leading to higher rates of polydoctoring. Gatekeeping may help reduce healthcare costs by restricting access to specialists, but it also carries the risk of worsening patient outcomes, and evidence on its overall effectiveness remains inconsistent13. This specialist-driven primary care model in Japan may have contributed to the reduction in mortality observed herein, particularly among older adults.

However, an important finding from this study is the strong association between higher RVFs and increased outpatient medical costs7. This is particularly relevant in countries like Japan, where a rapidly aging population is expected to place significant strain on healthcare resources. As multimorbidity becomes increasingly common among older adults in high-income countries, financial sustainability of healthcare systems is becoming a pressing concern31. Previous research has shown that when specialists are the usual source of care, continuity tends to decrease, whereas continuity remains higher and healthcare costs lower when generalists provide primary care32. Recognizing these challenges, Japan has begun to focus on developing GPs capable of delivering comprehensive care, starting from undergraduate medical education33. Historically, Japan’s primary care system has been largely provided by specialists. Therefore, many conditions associated with polydoctoring, such as osteoarthritis, osteoporosis, benign prostatic hyperplasia, and allergic conjunctivitis, fall within the scope of family medicine in other countries8. Furthermore, the development of GPs as specialists in Japan has only recently gained momentum34. Investigating whether an increased presence of GPs can help reduce healthcare costs without compromising mortality rates is essential. Additionally, the optimal management approach for multimorbidity in older adults may vary significantly depending on the prioritized outcomes. As highlighted by the Quadruple Aim framework proposed in the United States, assessing cost, patient experience, equity, and the well-being of care teams is crucial35. A comprehensive, multidimensional analysis, including patient-reported outcomes, is necessary to ensure that healthcare systems effectively meet the diverse needs of both patients and healthcare providers. Furthermore, future studies should investigate regional disparities in polydoctoring and their potential impact on patient outcomes.

This study has some limitations. First, the RVF measure used in this study only captures the number of healthcare facilities visited without assessing the quality or coordination of care among providers. Care coordination, which is difficult to measure, may have influenced outcomes among patients with polydoctoring. Second, our findings were not adjusted for patients’ socioeconomic backgrounds, such as education level and income, which could impact healthcare access and utilization and cofound our results. Future research should adjust for these variables to clarify the relationship between polydoctoring and patient outcomes. Third, as this study only examined the outpatient medical costs associated with polydoctoring, a more detailed cost-effectiveness analysis, including lifetime healthcare costs, is necessary to evaluate the sustainability of the healthcare system. Fourth, the study cohort was limited to individuals aged 75 to 89 years with multimorbidity in specific geographic regions in Japan, which may limit the generalizability of our findings to other populations or settings. Fifth, our definition of polydoctoring was based on the number of RVFs. However, due to data limitations, we could not determine whether the same physician or care team provided care at multiple facilities. Moreover, we were unable to account for situations in which patients were treated by multiple physicians within the same facility. As a result, our measure may not fully capture the actual number of distinct providers or the complexity of care fragmentation.

Conclusion

In Japan, polydoctoring is associated with reduced all-cause mortality but is also associated with an increase in ACSC-related and all-cause admissions and higher outpatient medical costs. As countries face the challenges of aging populations and constrained healthcare resources, exploring cost-effective strategies for managing multimorbidity is essential. Developing a sustainable healthcare system requires careful consideration of the balance between specialized care, continuity, and healthcare costs. Future efforts should focus on identifying optimal care models that improve patient outcomes while ensuring the financial sustainability of healthcare systems in aging societies.