Abstract

Background: In January 2022, Longhua District, Shenzhen piloted a digitally enabled generalist and specialist collaborative care model to deliver consistent, continuous services for patients with chronic conditions managed in community health centers. This system-level initiative integrated hospital-based specialists and community-based general practitioners through a vertically aligned care model supported by a shared digital platform. Objective: To evaluate the effect of this digitally enabled Generalist-Specialist collaborative care model on hypertension management capacity at community health centers. Methods: We employed a difference-in-differences approach to examine changes in center-level outcomes before and after the model was implemented during 2021-2024. The treatment group included 84 health centers in Longhua District, and the comparison group included 448 health centers in the rest of districts that were not influenced by the policy. Health centers in the treatment group used the collaborative care model to deliver follow-up services, whereas health centers in comparison groups continued to provide routine services in accordance with the National Basic Public Health Service Standards (Third Edition) protocol. Multivariate linear regression with district and time fixed effects was constructed, controlling for health center characteristics and adjusting for inverse probability of treatment weights, with standard errors clustered at the center level. Robustness checks were conducted to evaluate the reliability and stability of the model. Results: After the implementation of the digitally enabled collaborative care model, compared to centers in comparison groups, on average, quarterly standardized hypertension management rate and hypertension control rate in the treatment group increased by 4.3-percentage-point (DID=0.043, SE=0.011, P<0.001) and 11.5-percentage-point increase (DID=0.115, SE=0.012, P<0.001) per center, respectively. On average, the quarterly number of upward referrals per center decreased by 17.1% (P=0.038), and the quarterly number of total patient visits per center increased by 22.1% in the treatment group (P=0.003), as compared to comparison groups. Conclusion: Our study highlights the significance of the digitally enabled specialist and generalist collaborative care model in enhancing health center capacity in patient management, reducing unnecessary referrals, and optimizing resource utilization. Our study underscores the importance of incorporating this initiative into national health strategies, such as the National Basic Public Health Services Program, to strengthen chronic care management services delivery in more areas of China. Future policies and research should focus on scaling up this approach to a broader range of medical conditions and prioritizing investments in health centers by ensuring stable funding streams and optimizing the implementation strategies for digital integration pathway.

Introduction

Hypertension is one of the most common chronic diseases in China, and its standardized management and control rates are critical indicators for achieving the strategic goals of the Healthy China Initiative. According to the ten-year evaluation report of the National Essential Public Health Services Program, China's standardized hypertension management rate at the primary care level reached 74.4% in 2019, with a blood pressure control rate of 67.7% among managed populations. Further improving the health management of chronic disease patients and the service capacity of primary healthcare institutions represents an important challenge for achieving equitable and high-quality development of national essential public health services. Digitally enabled generalist-specialist collaborative care, which efficiently integrates hospital specialist resources into primary care settings, plays a crucial role in enhancing the diagnostic and treatment capabilities and health management capacity of primary healthcare institutions. The National Guidelines for Hypertension Prevention and Management in Primary Care (2020 Edition) explicitly recommend establishing generalist-specialist management teams and encouraging specialists from higher-level hospitals to join general practitioner teams to provide professional guidance. Currently, various regions including Beijing, Shanghai, and Shenzhen have conducted exploratory practices in generalist-specialist collaboration at different levels.

In alignment with the medical prevention integration work plan and building upon the foundation of national essential public health services, Longhua District in Shenzhen launched a pilot program of "digitalization + health management" generalist-specialist collaborative services across the district in January 2022. This initiative aims to enhance the regional consultation rate and standardized management rate for chronic disease patients through digitally empowered collaboration and integration between primary care generalists and hospital specialists. The digitally enabled generalist-specialist collaborative model supplements the original basic public health services with three key components: (1) establishing a hypertension patient information database that automatically identifies and enrolls patients with unstable blood pressure through big data monitoring, enabling information sharing across different levels of medical institutions and physicians; (2) building generalist-specialist teams within integrated medical consortia, where general practitioners conduct follow-up management according to basic public health service standards, and specialists provide post-consultation services for enrolled patients through the collaborative platform, jointly developing management plans with general practitioners who then execute and oversee subsequent follow-up care; and (3) leveraging the digital platform to facilitate seamless coordination. Over the three years since implementation, big data monitoring has covered 91,524 hypertension patients under primary care management in the district, with a cumulative total of 117,159 consultations for patients with unstable blood pressure enrolled in the system. Both consultation completion rates and post-consultation execution rates have exceeded 97%, demonstrating initial success of the pilot program.

In this context, our study employs a quasi-natural experimental design, using longitudinal data from community health service institutions in Shenzhen to evaluate the policy implementation effect of digitally enabled generalist-specialist collaborative service models on improving hypertension management capacity at primary healthcare institutions, with the aim of providing evidence for policy optimization and broader implementation decisions.

Methods

Study Design and Participants

This study utilized community health service institutions operating in Shenzhen from 2021 to 2024 as the research subjects. Exclusion criteria included: (1) institutions that did not operate continuously across the policy implementation time point during the study period; (2) institutions with fewer than 50 registered hypertension patients. Since Bao'an District was also exploring generalist-specialist service models during the study period, which could confound the results, institutions from this district were excluded. A total of 532 community health service institutions were ultimately included in the study sample. As the digitally enabled generalist-specialist collaboration was first piloted in Longhua District in January 2022, all 84 institutions within Longhua District were designated as the treatment group, while the remaining 448 institutions unaffected by the policy intervention served as the control group. The treatment group implemented the digitally enabled generalist-specialist collaborative service model for hypertension patient management after January 2022, whereas the control group continued to provide routine health management services according to the National Basic Public Health Service Standards (Third Edition).

Outcome Measures

This study examined the impact of the digitally enabled generalist-specialist collaborative policy pilot on four key indicators: standardized hypertension management rate, blood pressure control rate among managed populations, number of upward referrals, and total number of patient visits. Following the definitions in the National Basic Public Health Service Standards (Third Edition), standardized hypertension management rate was calculated as (number of hypertension patients managed according to protocol / total number of managed hypertension patients in the quarter) × 100%. Blood pressure control rate among managed populations was calculated as (number of patients with blood pressure at target at the most recent follow-up in the quarter / total number of managed hypertension patients in the quarter) × 100%, with blood pressure control defined as systolic blood pressure <140 mmHg and diastolic blood pressure <90 mmHg (for patients aged 65 and older: systolic <150 mmHg and diastolic <90 mmHg). In addition to analyzing the direct impact on managed hypertension patients, this study also assessed the spillover effect of the policy pilot on the overall patient volume of community health service institutions. To ensure normal distribution of the upward referral count and total patient visit count variables, natural logarithmic transformation (ln) was applied. For interpretability, regression coefficients were transformed using the elasticity formula: relative change rate (%∆y) = (e^β - 1) × 100%.

Statistical Analysis

Inverse Probability of Treatment Weighting (IPTW)

To control for potential systematic differences between the treatment and control groups and reduce confounding bias, this study employed IPTW to balance observed covariates between the two groups, thereby obtaining more accurate estimates of the average treatment effect (ATE) of the policy intervention. The fundamental principle of IPTW is to construct weights based on the inverse of the probability (propensity score) that an institution receives its actual treatment status, creating a weighted sample with balanced covariate distributions that simulates "random assignment." Based on baseline characteristics of community health centers in 2021, we constructed a propensity score model with the following covariates: proportion of patients aged 65 and older, proportion of female patients, years of operation, proportion of licensed physicians, proportion of equipment valued over 100,000 RMB, proportion of fiscal revenue, number of registered hypertension patients, and registration type. Model estimates were used to calculate the probability of each institution being assigned to the treatment group (propensity score). Inverse probability weights were then calculated as follows: for treatment group institutions, IPTW = 1 / [propensity score]; for control group institutions, IPTW = 1 / (1 - propensity score). To improve estimation stability, we applied stabilized weights by adjusting the numerator to the overall proportion of the treatment/control groups, bringing the mean weight closer to 1. Additionally, we trimmed extreme weights exceeding the 99th percentile to reduce the influence of outliers on estimates.

We also conducted propensity score matching (PSM) as an alternative approach, yielding consistent results with IPTW. Given that IPTW maximally preserves original sample information, enhances real-world representativeness, and improves estimation efficiency, we report primarily IPTW-based estimates. Compared to PSM, IPTW offers several advantages: (1) it uses full-sample weighting without discarding observations, improving statistical power and estimation efficiency; (2) it is more suitable for regression modeling and ATE estimation, and can be combined with covariate adjustment to form doubly robust estimators, commonly used in public policy evaluation and external inference; (3) it directly constructs weights from propensity scores, providing a smooth, robust method that reduces reliance on subjective parameters (such as caliper width or number of neighbors) and avoids matching error bias.

Difference-in-Differences (DID) Method

We employed the DID method, a common approach for policy evaluation, treating the Longhua District pilot as a quasi-natural experiment. All institutions in Longhua District constituted the treatment group, while institutions in other districts unaffected by the policy served as the control group. After controlling for other relevant factors, we compared outcome differences between the treatment and control groups before and after policy implementation to obtain the net policy effect. Based on data quality and availability, we collected hypertension management data from Shenzhen community health service institutions from 2021-2024, structuring the data as institution-quarter panel data. The study period comprised 16 quarters across 532 institutions, totaling 8,258 observations. The first quarter of 2022 served as the policy implementation time point, with Q1-Q4 2021 as the pre-implementation period and Q1 2022-Q4 2024 as the post-implementation period. We constructed the following DID model with quarter and district fixed effects to minimize the impact of temporal trends and regional differences:

$$y_{i,t} = \alpha_0 + \beta_1 \text{treated}i + \beta_2 \text{post}_t + \beta_3 \text{treated}_i \times \text{post}_t + \beta_4 \text{control}$$} + \gamma_t + \mu_i + \varepsilon_{i,t

where $y_{i,t}$ represents the outcome variable for institution $i$ at time $t$; $\text{treated}i$ is the group variable (1 for treatment group, 0 for control group); $\text{post}_t$ is the time dummy variable (1 for Q1 2022-Q4 2024, 0 for Q1 2021-Q4 2021); $\text{treated}_i \times \text{post}_t$ is the interaction term estimating the effect of the digitally enabled generalist-specialist collaborative service model; $\text{control}$ is the error term. $\beta_3$ is the DID coefficient representing the main effect of interest, reflecting the policy impact on hypertension management outcomes. Recognizing that observations from the same institution across quarters may exhibit temporal correlation (clustering of error terms within centers), we clustered standard errors at the institution level to avoid underestimation and obtain robust inference. Based on the propensity scores, we applied inverse probability weights to the regression model to obtain IPTW-adjusted treatment effect estimates.}$ includes control variables such as proportion of patients aged 65+, proportion of female patients, years of operation, proportion of licensed physicians, proportion of high-value equipment, fiscal revenue proportion, number of registered hypertension patients, and registration type; $\gamma_t$ and $\mu_i$ represent quarter and district fixed effects; and $\varepsilon_{i,t

Robustness Checks

We employed event study methodology to test the parallel trends assumption of the DID model, examining whether outcome variable trends were consistent between treatment and control groups before policy implementation. If the parallel trends assumption holds, it strengthens the internal validity of DID estimates. Additionally, event study analysis can characterize the dynamic temporal effects of the intervention, identifying whether hypertension management effects strengthen over time and partially excluding confounding bias from temporal changes or unobserved institutional characteristics. The event study baseline regression model is:

$$y_{i,t} = \alpha_0 + \sum_{s=1}^{D-2} \beta_{pre}^s \text{time}s \times \text{treated}_i + \sum}^{S} \beta_{post}^s \text{times \times \text{treated}_i + \gamma_t + \mu_i + \varepsilon$$

where $\text{time}s$ represents period-specific dummy variables, $D$ is the policy implementation period, $\beta^s$ coefficients should not be significantly different from zero.}^s$ reflects pre-policy differences between groups, and $\beta_{post}^s$ reflects post-policy differences. If the parallel trends assumption holds, $\beta_{pre

To further verify the robustness and credibility of policy effects and test for bias from temporal trends or unobserved institutional characteristics, we conducted placebo tests by randomly selecting 200 "pseudo-treatment" institutions from the 532 samples, constructing pseudo-intervention variables, and repeating the simulation 1,000 times to record the distribution of interaction term coefficient estimates from each simulation, comparing whether the actual estimate falls in the extreme tail of the simulated distribution.

All analyses were performed using Stata 18.0, with statistical significance defined as P<0.05.

Results

Balance Test

To examine covariate balance between treatment and control groups after propensity score weighting, we used standardized mean differences (SMD). Generally, an absolute SMD <0.1 indicates good balance, with values closer to zero representing better balance. As shown in [TABLE:1], several covariates showed notable differences between groups before weighting. After weighting, all covariates achieved SMD <0.1, indicating balanced distributions and successful covariate balance. The covariate balance plot demonstrates that SMDs were substantially reduced after weighting, confirming the effectiveness of IPTW in achieving group balance [FIGURE:1].

Descriptive Statistics

As presented in [TABLE:2], before implementation of the digitally enabled generalist-specialist collaborative service model, the treatment group had a mean standardized hypertension management rate of 71.8% and a blood pressure control rate of 69.5%. After policy implementation, these rates increased to 71.9% and 81.4%, respectively. The control group showed no improvement in either indicator. Following policy implementation, the treatment group also experienced increases in both average upward referral counts and total patient visits.

DID Model Estimates

As shown in [TABLE:3], after implementing the digitally enabled generalist-specialist collaborative policy and controlling for other factors, the treatment group exhibited a quarterly average increase of 4.3 percentage points in standardized hypertension management rate compared to the control group (DID coefficient=0.043, SE=0.011, P<0.001), representing a 6.9% increase relative to the pre-pilot overall management rate (62.1%). The blood pressure control rate among managed populations in the treatment group increased by an average of 11.5 percentage points quarterly (DID coefficient=0.115, SE=0.012, P<0.001), representing a 15.5% increase relative to the pre-pilot overall control rate (74.1%).

Additionally, compared to the control group, the treatment group showed an average quarterly decrease of 18.7 percentage points in the natural logarithm of upward referral counts (DID coefficient=-0.187, SE=0.090, P=0.038) and an average quarterly increase of 20 percentage points in the natural logarithm of total patient visits (DID coefficient=0.200, SE=0.067, P=0.003). Using the elasticity formula to interpret these coefficients, the treatment group experienced an average 17.1% reduction in quarterly upward referrals and a 22.1% increase in quarterly total patient visits.

Parallel Trends Test Results

To avoid multicollinearity, we designated the quarter immediately preceding policy implementation as the baseline period and excluded its dummy variable from the model. "0" represents the policy implementation quarter, while "-t" and "+t" represent t quarters before and after implementation, respectively. As shown in [FIGURE:2], the four outcome indicators exhibited similar average trends between treatment and control groups before policy implementation, with most pre-policy period dummy variable coefficients being non-significant, supporting the parallel trends assumption. After policy implementation, blood pressure control rates showed a significant upward trend starting from the fourth post-implementation quarter, while upward referral counts demonstrated a clear downward trend from the second quarter onward. These findings indicate that the positive health effects of the digitally enabled generalist-specialist collaborative service model emerged gradually within one year after implementation, with lagged but sustained intervention effects.

Placebo Test Results

As shown in [FIGURE:3], estimated coefficients under pseudo-treatment conditions were concentrated around zero, showing no significant difference from baseline regression results. This indicates that the true policy intervention effects were not attributable to random error or unobserved factors.

Discussion

Impact on Standardized Hypertension Management at the Primary Care Level

The National Basic Public Health Service Standards (Third Edition) require providing at least four face-to-face follow-up visits and health examinations annually for patients with primary hypertension. Our findings demonstrate that digitally enabled generalist-specialist collaboration significantly improved standardized hypertension management at community health institutions. By establishing a digital health information platform enabling full-process tracking from diagnosis through treatment to follow-up, the system facilitates supervisory oversight of protocol implementation and ensures compliance with standardized management requirements. Automated reminders for general practitioners to complete patient follow-up tasks enhance the proactivity and timeliness of patient management, helping general practitioners more efficiently fulfill national essential public health service standards.

Impact on Health Outcomes Among Managed Hypertension Populations

The National Basic Public Health Service Standards (Third Edition) specify that patients with unsatisfactory blood pressure control or severe adverse drug reactions/complications should be referred promptly, with follow-up on referral status within two weeks. Our results show that digitally enabled generalist-specialist collaboration significantly improved health outcomes among managed hypertension populations, with better blood pressure control and substantially reduced upward referrals, representing important medical resource savings. On one hand, the digital platform enabled interactive collaboration between primary care generalists and hospital specialists, allowing senior physicians to provide guidance on complex cases and assist generalists in managing patients with unstable conditions, thereby improving primary care diagnostic and treatment capabilities. On the other hand, real-time big data monitoring of blood pressure and medication adherence enabled timely intervention for high-risk patients, while seamless information flow of patient conditions, medication records, and treatment information ensured service continuity and improved therapeutic effectiveness, playing a crucial role in enhancing blood pressure control among managed populations.

Impact on Tiered Diagnosis and Treatment System Improvement

Our findings also reveal significant spillover effects of the digitally enabled generalist-specialist collaborative policy. The intervention not only effectively reduced upward referrals for managed hypertension patients, helping retain more patients at the primary care level, but also promoted overall growth in patient visits to community health service institutions, playing an important role in improving the tiered diagnosis and treatment system. Through information integration, patients could meet most of their healthcare needs at the primary level, reducing the time and economic burden of traveling to larger hospitals, improving patient compliance and service satisfaction, and facilitating smoother implementation of primary care first contact and two-way referral systems. Following resource optimization, patients completed blood pressure monitoring and follow-up management at the primary level, while information on patients with unstable conditions was promptly fed back to higher-level hospital specialists through the platform, promoting efficient collaboration with vertical integration and separation of acute and chronic care.

Study Limitations

This study has several limitations. First, we used institution-level longitudinal administrative data. Although employing a quasi-experimental design, the data remain observational rather than experimental. We enhanced estimation accuracy and strengthened causal inference between policy implementation and outcomes by constructing a difference-in-differences fixed-effects model with inverse probability weighting. Second, while the DID method can control for time-invariant factors affecting outcomes, estimates may still be influenced by omitted variables such as patient case-mix or other relevant policies (e.g., medical prevention integration) and external environmental changes. We validated the stability and reliability of our findings through parallel trends tests and multiple robustness checks, which confirmed consistent results across different model specifications and external conditions without substantial bias.

Policy Implications

Leveraging the National Essential Public Health Services Platform to Provide Policy Support for Sustainable Generalist-Specialist Collaboration

To promote sustainable development of generalist-specialist collaborative management, policy guidance and support are crucial. As a systematic national institutional arrangement, the high-quality development needs of essential public health services provide policy opportunities and important support for generalist-specialist collaboration. Collaborative management should continue to rely on essential public health services as its foundation while introducing higher-level specialist resources to enhance primary care capacity. This approach not only advances primary healthcare development but also provides clear policy justification and direction for implementing generalist-specialist collaborative services. In addition to service content coordination, management mechanism integration is essential. Project subsidy funds for essential public health services already constitute a major source of primary care fiscal subsidies. Payment reform and optimized performance evaluation systems should be implemented to stimulate the enthusiasm and initiative of primary care generalists and higher-level specialists, establishing stable and effective operational mechanisms that promote sustainable development of the generalist-specialist collaborative model.

Fully Utilizing Digital Empowerment to Provide Technical Support for Chronic Disease Health Management at the Primary Care Level

Unlocking the potential of digital technology is key to improving generalist-specialist collaborative service quality. First, we should strengthen the development of digitally enabled generalist-specialist collaborative service pathways and promote innovation in health management service models. By enhancing collaborative efficiency among service entities, diversifying service content, and driving intelligent transformation of health decision-making, we can comprehensively reshape the collaboration model between generalists and specialists. Simultaneously, digital elements should be continuously optimized based on practical needs to ensure flexibility and adaptability of service pathways and management models. This requires focusing on critical aspects such as information platform construction, user interaction, and work feedback to create user-friendly, convenient information management platforms that comprehensively promote innovative development of generalist-specialist collaborative management.

Optimizing Policy Mechanisms to Provide Institutional Guarantees for Sustainable Digitally Enabled Generalist-Specialist Collaboration

Efficient operation of generalist-specialist collaboration depends on robust institutional mechanisms and safeguards. Close integration with medical consortium construction is essential to build a clear organizational system with well-defined rights, responsibilities, and aligned objectives, promoting collaborative integration between primary care generalists and higher-level specialist institutions to form synergistic workforces. Regarding incentive mechanism design, diversified measures such as performance rewards and career advancement opportunities should be adopted, linking management outcomes to performance to motivate participation among both general practitioners and specialists. Additionally, flexible feedback and adjustment mechanisms should be established to address potential implementation issues through regular monitoring and dynamic optimization, continuously improving institutional design and implementation pathways to promote continuous improvement and broader application of the digitally enabled generalist-specialist collaborative model.

Conclusion

Based on a quasi-experimental design of the policy pilot, this study used longitudinal hypertension management data from community health service institutions to establish econometric regression models, evaluating the policy effects of digitally enabled generalist-specialist collaboration on improving hypertension management capacity. The findings demonstrate that implementing digitally enabled generalist-specialist collaboration significantly enhanced standardized management effectiveness at primary care institutions, improved health outcomes among managed populations, effectively reduced upward referrals for managed patients, and retained more patients at the primary care level through policy spillover effects, playing an important role in improving tiered diagnosis and treatment. Future efforts should focus on refining policy mechanisms and establishing standardized implementation pathways to provide references for comprehensive promotion of digitally enabled generalist-specialist collaborative services, thereby advancing equitable and high-quality development of essential public health services.

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