From Legal Indeterminacy to Risk-Governed Continuity: Operationalizing Risk Governance for China’s Family Doctor Contract Services

Abstract

China’s family doctor contract services (FDCS) are an important instrument for strengthening Primary Health Care, chronic disease management and continuity of care, but their legal and institutional foundations remain uneven. This Perspective treats legal indeterminacy not simply as a drafting defect but as a governable source of risk. It uses a structured narrative and legal-policy synthesis of Chinese FDCS policy and implementation materials and peer-reviewed studies principally from 2016–2025, relevant current Chinese laws and administrative rules, and limited functional reference points from the United Kingdom and Australia. The analysis distinguishes legal indeterminacy from regulatory fragmentation and implementation failure and develops an event-level qualitative risk-governance framework across five domains: contracting status and liability, rights, obligations and service packages, financing and evaluation, supervision and dispute resolution, and digital and data governance. Specific risk events are prioritized by qualitative likelihood and severity, with exposure and persistence recorded as potential escalation factors, and are then matched to proportionate regulatory responses. A worked example of remote diabetes follow-up illustrates the approach. The framework moves beyond catalogues of legal gaps by providing a transparent method for deciding where legal clarification and regulatory attention are most urgent, while remaining a conceptual tool that requires empirical validation.

Introduction

Family doctor contract services (FDCS) occupy an increasingly visible place in China’s primary health care reform. In policy design, FDCS are expected to create a relatively stable relationship between residents and a family doctor team, usually based in a community health service centre, township health centre or village clinic, and supported where possible by nurses, public health physicians, pharmacists, rehabilitation personnel and specialists from higher-level hospitals. The model is intended to connect prevention, common disease management, chronic disease follow-up, referral support, health counselling and, in some settings, home-based care. In that sense, FDCS are not simply another service item. They are a proposed route through which China can reduce unnecessary hospital bypassing and make primary care a more credible first-contact setting.

The national policy framework has developed quickly. The 2016 Guiding Opinions on Promoting Family Doctor Contract Services set out the basic architecture of voluntary contracting, priority coverage for key populations, service packages, signing fees and team-based incentives.¹ The 2022 guidance on high-quality development shifted policy language from broad expansion toward effective contract performance, enriched service content, referral coordination, home-based services and stronger guarantee mechanisms.² More recent policy documents and official replies have continued to stress service quality, grassroots capacity, medical insurance support and the training of primary care personnel.^3,4 Official statistics also show expanding primary care capacity: by the end of 2024, China reported more than 480,000 family doctor teams serving contracted residents, a key-population contracting rate above 80%, 4.54 general practitioners per 10,000 population and approximately 5.257 million health workers in primary medical and health institutions.^4,5

Administrative coverage figures, however, should not be conflated with survey estimates or with experienced continuity. The National Health Commission’s policy interpretation of the 2022 guidance acknowledged that some localities had placed greater emphasis on signature numbers than service quality and had encountered passive contracting or contracts that were signed but not meaningfully performed.⁶ A national survey reported that 64.73% of respondents were aware of FDCS, while 12.98% of that survey sample reported being contracted and average satisfaction was 3.78 out of 5.⁷ The official rate and the survey rate are not directly comparable: they refer to different populations, denominators and measurement processes, with the former focusing on administratively defined key populations and the latter on respondents in a research sample. The survey should therefore not be read as contradicting official coverage statistics. Its value here is narrower: together with the Commission’s own concern about nominal contracting, it illustrates why administrative coverage and experienced service continuity are analytically distinct. A separate cross-sectional study likewise shows that resident acceptance of family doctors is shaped by practical and perceptual factors rather than formal availability alone.⁸

The next stage of FDCS reform is not mainly about whether China should continue to promote family doctors. It is about how legal and policy architecture can make each contract more meaningful, funded, supervised and trusted. Existing Chinese studies have identified unclear contracting subjects, insufficient specification of rights and obligations, legal fragmentation, weak incentives, uneven service capacity, limited evaluation and digital health risks.^9–17 Yet these problems are usually examined either as policy-tool imbalances,^9–11 implementation barriers,¹³ or discrete legal issues.^12,14,15 The present Perspective adds a different analytical step: it asks when legal indeterminacy becomes a governance risk, how that risk should be prioritized, and what level of regulatory response is proportionate to the likely harm.

A risk-governance approach adds a further analytical step because identifying a legal or implementation problem does not by itself establish its regulatory urgency. Existing approaches can show that contracting rules are fragmented, implementation is uneven, or particular rights and obligations remain unclear, but they do not necessarily distinguish an uncertainty that warrants structural intervention from one that may be addressed through clarification, monitoring or lower-intensity controls. Risk governance therefore asks an additional sequence of questions: through what event could the uncertainty produce harm, how recurrent or broadly distributed is the exposure, how serious could the consequence be, and what intensity of intervention is proportionate? The approach complements rather than replaces implementation analysis or conventional legal interpretation. The United Kingdom and Australia are used later as limited functional reference points rather than comprehensive comparators. They were selected because materially different financing and provider arrangements in the two systems nevertheless illustrate two propositions relevant to the Chinese analysis: service responsibility can be organized above the level of the individual primary-care physician, and residual clinical risk can be pooled or insured. Their role is therefore to examine institutional feasibility, not to determine the Chinese risk ratings or provide a model for transplantation. Legal indeterminacy is used here not simply to mean the absence of legislation. It refers to uncertainty about the identity of the responsible actor, the content or scope of an obligation, the relationship among overlapping rules, or the legal consequence of non-performance. The central contribution is therefore a qualitative risk-governance framework that moves from legal condition to risk event, from risk event to qualitative likelihood and severity assessment, and from assessment to proportionate governance response. Specifically, this Perspective has three objectives: (1) to conceptualize legal indeterminacy in FDCS as an actionable governance risk; (2) to develop an event-level qualitative triage framework across five operational domains; and (3) to demonstrate how the framework connects responsibility, financing, service performance, digital governance and liability through a worked remote chronic-care scenario.

Materials and Analytical Approach

This Perspective uses a structured narrative and legal-policy synthesis rather than a systematic review or an empirical risk-estimation study. The analytical corpus consists of four groups of materials: national FDCS policy instruments and official replies;^1–6,18 Chinese statutes and administrative rules relevant to health services, professional duties, contracts, medical disputes, personal information and internet diagnosis;^19–26 peer-reviewed empirical, policy and legal studies on FDCS;^{7–17,27–34} and official or professional materials from the United Kingdom and Australia.^35–41 These sources were organized around questions that recur across the FDCS contract lifecycle: who is responsible, what is promised, how the service is funded, how risk is controlled, and how performance is evaluated.

Source identification was purposive and issue-driven rather than systematic or exhaustive. The 2016 national FDCS guidance was used as the starting point for the contemporary national policy architecture, and later national policies and official replies were included where they materially addressed service content, financing, performance, workforce capacity or implementation. Current Chinese statutes and administrative rules were included where they directly governed one or more issues identified in the FDCS lifecycle, including provider responsibility, professional duties, contract performance, medical disputes, personal-information processing and internet diagnosis. Peer-reviewed studies were included where they provided empirical, policy or legal evidence relevant to service implementation, patient or provider experience, financing, evaluation, continuity or identified legal uncertainties. Materials that merely referred to family doctors without bearing on an identified analytical question were not used as evidence anchors. The United Kingdom and Australian materials were selected more narrowly for their relevance to institutional responsibility and risk pooling rather than as a comprehensive comparison.

The cited corpus was assessed against these inclusion principles to ensure that each source used as an evidence anchor had a defined analytical role. Both authors independently reviewed the evidence supporting each event-level assessment using a common analytical template covering the form of legal indeterminacy, the specified risk event, evidence of recurrence and breadth of exposure, plausible severity, and any exposure or persistence modifier. The resulting assessments were compared, and any differences were resolved through discussion with reference to the stated criteria until consensus was reached. No numerical averaging or statistical weighting was used.

The synthesis proceeds in four steps. First, each problem is characterized as a form of legal indeterminacy only where uncertainty concerns the allocation, content, interaction or consequence of legal or quasi-legal rules; workforce shortage or low service capacity by itself is not treated as legal indeterminacy. Second, the analysis identifies the specific risk event through which that uncertainty could affect patients, clinicians, institutions or regulators—for example, non-performance of an underfunded service, delayed clinical escalation, uncertain remedy, or unauthorized data use. The five domains used below are therefore taxonomic categories rather than units of ranking: qualitative priority is assigned to specific risk events within each domain. Third, each event is assessed for qualitative likelihood and severity, with the principal supporting sources identified as evidence anchors.

Throughout this Perspective, “qualitative likelihood” denotes an ordinal, evidence-informed judgment rather than an estimated statistical or actuarial probability. Qualitative likelihood is classified as low, moderate or high according to the documented recurrence of the underlying condition in the policy and implementation evidence and the breadth of potential exposure. High qualitative likelihood is reserved for conditions recurrently documented across multiple policy or implementation sources; where the corpus establishes a legal or institutional exposure but provides little direct evidence about event frequency, qualitative likelihood is kept at moderate. Severity is classified as low, moderate or high according to the plausible magnitude of consequences for patient safety, continuity, legal remedy, sensitive-data rights, workforce sustainability or system trust. Fourth, governance responses are matched to the resulting event-level priority rather than proposed uniformly across an entire domain.

The ratings in Table 1 are analytical judgments rather than prevalence estimates or actuarial probabilities. A high–high combination is treated as very high priority; high–moderate or moderate–high combinations as high priority; moderate–moderate combinations as enhanced priority; and a low rating on either dimension would ordinarily indicate routine or lower-priority monitoring unless a clearly stated exposure or persistence modifier justified escalation. Potential exposure and persistence modifiers include longitudinal recurrence, large-scale digital processing, and concentration of consequences among vulnerable populations. Exposure and persistence were recorded for every event in Supplementary Table S1 but were not treated as a third scoring dimension. They were considered only as a possible basis for escalation where the qualitative likelihood–severity combination would otherwise understate a recurrent, large-scale, or concentrated exposure. No event in the present application required such escalation; accordingly, every priority in Table 1 follows directly from the stated qualitative likelihood–severity mapping. The exposure/persistence column is nevertheless retained to make any future escalation decision visible and reviewable when the framework is applied in other settings. Table 1 identifies the principal evidence anchors for each event, while Supplementary Table S1 provides the full event-level rationale. This limited ordinal approach is deliberate: the available literature does not support precise numerical probabilities, but it does permit a transparent distinction between recurrent, high-consequence risks and problems for which the evidence supports only enhanced monitoring or targeted clarification.

Table 1.

Event-Level Qualitative Risk-Governance Matrix for FDCS

Risk Domain	Specific Risk Event	Qualitative Likelihood	Severity	Priority	Evidence Anchors	Proportionate Response
Contract status and liability	Unclear provider-side contracting entity and external responsibility	Moderate	High	High	[12,14,15]	Anchor external responsibility in the primary medical institution; identify the family doctor team as the responsible service unit.
Contract status and liability	Coordination and public-health duties treated as equivalent to direct clinical obligations	Moderate	Moderate	Enhanced	[12,14,15,19,20,24,26]	Differentiate clinical, coordination and public-health duties and apply function-sensitive liability standards.
Rights, obligations and service packages	Ambiguous boundary between basic, priority and optional paid entitlements	High	Moderate	High	[1,2,14,15]	Use layered service packages and a model contract that clearly separates public entitlements from optional paid services.
Financing and evaluation	Signature-oriented evaluation or vague performance criteria support nominal contracting	High	Moderate	High	[2,6,9–11,13]	Shift evaluation toward effective performance, utilization, continuity, patient experience and workload-sensitive indicators.
Financing and evaluation	Promised services are not traceable to stable funding or workload recognition	High	High	Very high	[1,2,4,13,17,18]	Map each promised service to an identifiable funding source, accountable provider and workload/performance metric.
Supervision and dispute resolution	Cross-agency attribution gap for mixed clinical, payment, data and platform failures	Moderate	Moderate	Enhanced	[12,15,21–26]	Assign lead coordination responsibility, shared indicators and referral rules for cross-cutting regulatory problems.
Supervision and dispute resolution	No differentiated pathway for low-severity contract, communication or service-performance disputes	Moderate	Moderate	Enhanced	[12,15,26]	Create tiered complaint, mediation and escalation channels suited to primary-care and low-severity disputes.
Digital and data governance	Longitudinal sensitive-data use lacks differentiated purpose, access and consent controls	Moderate	High	High	[21–25]	Specify purpose limitation, access control, audit trails, lawful bases, separate consent where required and offline alternatives.
Digital and data governance	Remote follow-up lacks a clear escalation trigger to in-person diagnosis or referral	Moderate	High	High	[2,20,25]	Define clinical red flags, documentation duties and escalation/referral triggers for internet-enabled follow-up.
Digital and data governance	Third-party platform fault or role ambiguity obscures responsibility	Moderate	Moderate	Enhanced	[21–25]	Clarify platform and medical-institution responsibilities for technical defects, data transmission, downtime and complaint handling.

Notes: The five domains are taxonomic categories; specific risk events are the unit of ranking. Ratings are qualitative analytical judgments, not empirical incidence estimates. Qualitative likelihood reflects recurrence in the cited policy and implementation materials and breadth of exposure; where direct evidence on event frequency is limited, qualitative likelihood is kept at moderate. Severity reflects plausible consequences for patient safety, continuity, legal remedy, sensitive-data rights, workforce sustainability and system trust. Priority follows the ordinal rule described in the Materials and Analytical Approach section. Exposure and persistence were recorded for every event in Supplementary Table S1 but were not treated as a third scoring dimension. They were considered only as a possible basis for escalation, and no event required such escalation in the present application. Evidence anchors identify the principal sources supporting each event; full rationales for qualitative likelihood, severity, and exposure/persistence are provided in Supplementary Table S1. The distribution is intentionally non-uniform: underfunded service promises receive the highest priority because both recurrence and plausible consequences are strongly supported in the corpus, whereas several supervisory, dispute-resolution and platform-interface risks remain enhanced priorities because their institutional exposure is identifiable but direct evidence on frequency or severe consequences is limited.

The United Kingdom and Australia were selected purposively as limited functional reference points rather than as representative comparators. The comparative materials do not generate the risk-governance framework or establish the event ratings, both of which are derived from the Chinese legal, policy and implementation analysis. Their narrower purpose is to examine whether two propositions emerging from that analysis are institutionally feasible under materially different primary-care arrangements: first, that external service responsibility can be anchored above the individual clinician; and second, that residual clinical risk can be pooled or insured without requiring a single financing or provider model. England combines a statutory National Health Service (NHS) structure with institutionally organized general practice and pooled clinical-negligence arrangements, whereas Australia combines Medicare financing with predominantly private general practice, professional regulation and indemnity requirements.^35–41 The contrast is therefore used as a functional feasibility check, not as a source of general principles for transplantation.

Why FDCS Requires a Hybrid Legal Framework

FDCS has a hybrid institutional character. This feature gives the reform its policy value, but it also explains why legal uncertainty is difficult to avoid. First, FDCS has a public-service dimension. The Law on Basic Medical and Health Care and the Promotion of Health establishes state responsibility to develop basic medical and health services and strengthen primary medical and health institutions.¹⁹ Family doctor teams are asked to support public health, chronic disease prevention, maternal and child health, care for older adults and continuity for vulnerable groups. These objectives cannot be understood as ordinary consumer transactions.

Second, FDCS has a contractual dimension. Residents usually sign service agreements for a defined term and service package. The contract form matters because it can clarify expectations, provide continuity and give residents a visible service relationship. Yet a contract functions only when the parties, service scope, payment, rights, obligations and remedies are reasonably clear. If the document is vague or residents sign without understanding its content, the contract may become more symbolic than operational.

Third, FDCS has a professional dimension. Diagnosis, treatment, prescription renewal, referral advice and chronic disease management fall within physicians’ legal and professional duties. The Physicians Law, medical-damage rules and internet-diagnosis supervision provide parts of the relevant framework.^20,24–26 A family doctor, however, does not work as an isolated individual. The service is team-based and often depends on cooperation among a primary institution, higher-level hospitals, public health agencies and digital platforms.

Fourth, FDCS has a financing dimension. Basic public health funds, local fiscal subsidies, medical insurance arrangements, paid personalized packages, performance pay and county medical community payment reforms can all be involved. Financing is not merely a budget issue. It determines whether a promised service can realistically be delivered, how team members are motivated and how residents understand the boundary between public entitlements and optional paid services.

Finally, FDCS increasingly has a digital dimension. Online follow-up, electronic health records, remote monitoring, e-prescriptions, appointment booking and referral platforms can make continuity easier, especially for residents with chronic disease or mobility limitations. These tools also process sensitive health data and can blur boundaries among health counselling, internet diagnosis, platform service and public health management.^21–25

Among these dimensions, the public-service and contractual dimensions carry particular weight because they jointly determine whether FDCS is governed mainly as an administrative programme or as an enforceable service relationship. FDCS should therefore not be reduced to a civil contract, an administrative target or a clinical encounter. A civil-contract approach may understate public-service and equity functions; a purely administrative approach may weaken patient autonomy and enforceability; and a narrowly professional-liability approach may fail to capture coordination failures, service-package disputes or data protection concerns. What is needed is a coherent legal ecology in which statutes, administrative rules, contract templates, payment rules, professional standards and digital governance instruments are aligned around the hybrid structure of FDCS.

Evidence on Benefits and Implementation Gaps

Conditional Evidence on FDCS Effects

The empirical literature on FDCS is growing, although its conclusions remain context dependent. Studies have associated contracting with better patient-perceived primary care quality, greater willingness to use primary care, changes in first-contact behaviour and improved access to basic public health services for some groups.^27–33 Discrete choice experiments show that residents pay attention to practical service attributes including physician competence, referral convenience, drug availability, service quality and cost.^28,29 A recent systematic review of chronic disease management suggests potential benefits but also notes heterogeneity in study design and outcomes.³⁴

The most cautious reading is that FDCS can support better primary care when contracts are backed by credible services, competent teams, accessible medicines, referral support and trust. It is less persuasive to infer that signing alone produces health improvement. For risk governance, this distinction matters. A contract can reduce health-system risk when it supports earlier detection, better follow-up and coordinated referral, while the same contract can create legal and reputational risk if it promises services that are not funded, understood or delivered.

Policy Imbalances, Implementation Barriers and Equity Concerns

Chinese policy analyses point in the same direction. Xu et al used a PMC-index model to assess representative central policies and found broad policy coverage while recommending more attention to policy duration, multiple incentive tools and professional recognition.⁹ Zhang et al found that policy planning and implementation tools were used far more often than policy evaluation tools.¹⁰ Sun et al reported that environmental tools accounted for the largest share of policy tools, while demand-side tools and policy strength for non-contracting residents remained relatively weak.¹¹ These analyses do not imply that the policy system is empty. They show an imbalance: policy mobilization has been stronger than legal consolidation, feedback and accountability.

Implementation studies add a practical layer. Liu et al, using Smith’s policy implementation model, identified insufficient rule-of-law foundations, limited executor capacity, inadequate incentives, weak professional identity and incomplete trust as constraints.¹³ Studies on home-based medical and nursing services indicate that family doctor teams could play a useful role in ageing-in-place and long-term care but still face shortages of personnel, limited service content and underdeveloped legal support.¹⁶ Research on service capacity points to workload, income, training, resource allocation and professional confidence as important determinants.¹⁷

Taken together, these findings support a reform priority narrower than a general critique and more practical than a call for more signatures. China needs mechanisms that convert signed contracts into service relationships that are legally intelligible, financially supported, clinically realistic, digitally safe and continuously evaluated. The evidence also cautions against collapsing different methods into a single success-or-failure claim: policy text studies show what government instruments emphasize; surveys show awareness, satisfaction and use; choice experiments reveal resident preferences; and outcome studies suggest possible effects under particular conditions. A risk-governance framework should use these strands to identify exposure and plausible harm rather than claim causal effects that the evidence cannot establish.

This point is particularly important for key populations. Older adults, patients with hypertension or diabetes, pregnant women, children, persons with disabilities and residents in rural or remote areas may benefit most from continuity, but they are also more vulnerable to underfunded promises, unclear referral rules and digital exclusion. For them, a contract that is difficult to understand or weakly performed can affect medication continuity, timely referral, home-visit safety and confidence in primary care. The legal framework therefore has an equity function: it helps determine whether FDCS becomes a universalizing institution or a programme that works well only in better-resourced settings.

Legal Indeterminacy as a Risk-Governance Construct

Distinguishing Legal Indeterminacy from Adjacent Concepts

Legal indeterminacy is related to, but distinct from, regulatory fragmentation, implementation gaps and governance failure. Regulatory fragmentation describes the distribution of authority across multiple rules or institutions; fragmentation may be manageable when interfaces and lead responsibility are clear. An implementation gap describes divergence between an otherwise intelligible rule and actual practice. Governance failure is broader still and can result from scarce resources, weak capacity, perverse incentives or poor coordination even where the law is clear. Legal indeterminacy concerns a more specific condition: actors cannot determine with sufficient confidence who is responsible, what must be done, which rule controls, or what consequence follows from non-performance. Fragmentation can generate indeterminacy, and indeterminacy can contribute to implementation failure, but the concepts are not interchangeable.

This distinction matters for reform design. A staffing shortage should not be labelled a legal problem merely because it affects service delivery. Conversely, where an FDCS contract promises home visits but does not identify the responsible institution, financing source, eligibility threshold or liability consequences, the uncertainty is legal-institutional even if additional staff would also help. Treating these problems separately prevents legal reform from being offered as a substitute for capacity investment while still identifying areas where clearer rules can reduce avoidable risk.

Operationalizing Risk: Qualitative Likelihood, Severity and Proportionality

Risk-based regulation directs attention and regulatory resources toward threats according to their likelihood and impact, while responsive approaches emphasize institutional context, feedback and the need to adapt regulatory tools to observed behaviour.^42,43 Applied to FDCS, risk is defined here as the possibility that a legally indeterminate arrangement will generate an identifiable adverse event affecting service continuity, accountability, patient safety, data rights, workforce sustainability or system trust. The framework does not equate uncertainty with harm: an ambiguous rule becomes a priority only when a plausible transmission path connects it to a consequential risk event.

Two dimensions determine qualitative priority. Qualitative likelihood concerns how recurrently the underlying condition appears across the available policy and implementation evidence and how broadly actors are exposed to it. Severity concerns the plausible magnitude of the consequence if the risk event occurs. Exposure and persistence are recorded separately as potential escalation factors rather than as a third scoring dimension. They may justify escalation where the qualitative likelihood–severity combination would otherwise understate a recurrent, large-scale, or concentrated exposure. Proportionality then asks whether clarification, standardized contracting, targeted supervision, insurance, data safeguards or another intervention is commensurate with the assessed risk rather than simply adding regulation.

Figure 1 summarizes this logic. The model is not an empirically validated causal pathway and does not assign numerical probabilities. It is a decision structure for moving from a legal problem to a reasoned regulatory priority: identify the indeterminacy, specify the risk event, assess qualitative likelihood and severity, consider exposure and persistence for possible escalation, and select a proportionate control. This operational step is what distinguishes the framework from a general catalogue of desirable institutional reforms.

Figure 1.

Operationalized risk-governance framework for family doctor contract services. The figure is a conceptual decision structure rather than an empirically validated causal model. Qualitative likelihood and severity are ordinal analytical judgments; exposure and persistence are considered only as potential escalation factors.

How Legal Indeterminacy Can Amplify Risk Across Domains

Legal indeterminacy can also operate as a risk amplifier. When the identity of the contracting subject or the boundary of a promised service is unclear, clinicians and institutions may respond with defensive or merely symbolic service behaviour. Residents who encounter unmet expectations may lose trust and become more likely to withhold relevant information or bypass primary care. Those responses can increase clinical uncertainty and dispute probability, which in turn place more pressure on fragmented supervisory arrangements. Supervisory stress may then encourage additional proceduralism or cautious service design without resolving the underlying allocation problem.

Figure 2 presents this as a feedback cycle rather than a single linear causal chain. The arrows indicate theoretically plausible pathways supported by the cited literature and legal analysis, not empirically established effect sizes. The value of the cycle is diagnostic: it shows why resolving an upstream allocation problem can sometimes interrupt several downstream risks, while isolated interventions may shift rather than reduce the underlying uncertainty.

Figure 2.

Conceptual risk-amplification cycle. Solid arrows show the principal hypothesized pathway; feedback arrows indicate that governance stress, service behaviour and trust can interact recursively. The figure does not represent measured causal effects.

Contract Status, Rights and Liability

The first two domains concern contract status and liability, and rights, obligations and service packages. In local practice, FDCS contracts may be described as agreements with a family doctor, a family doctor team, a community health service centre, a township health centre, a village clinic or a local health authority. Policy documents have not always used these categories consistently. This matters because the identity of the provider-side contracting party determines who organizes services, who bears external responsibility, who handles complaints and who compensates residents when legally recognized harm occurs.^12,14,15

Making the individual family doctor the sole legal contracting party is not the safest solution. Individual clinicians usually lack the organizational authority and compensation capacity to guarantee team-based, institution-dependent services. A more workable design would treat the primary medical and health institution as the provider-side contracting entity, with the family doctor or team identified as the responsible service unit within that institution. This is also more consistent with the Civil Code’s medical-damage structure, under which the medical institution bears compensation responsibility when patients are harmed through fault in diagnosis and treatment, and with medical-dispute rules that place risk-management, complaint and insurance functions at institutional level.^24,26 Specialists from hospitals, private providers, rehabilitation personnel and digital platforms can participate through written cooperative arrangements defining scope, duties and data responsibilities.

Rights and obligations require the same clarity. Wang et al found that medical workers and residents prioritized different rights and obligations in FDCS.¹⁴ Medical workers emphasized personal safety, safe working conditions and truthful patient information, while residents emphasized respectful care and receipt of contracted services. The difference is not necessarily conflict: FDCS depends on reciprocal cooperation. Residents need to provide accurate health information, maintain communication, attend follow-up where feasible and respond to reasonable risk advice; teams need to provide qualified service, privacy protection, continuity, referral support and accessible complaint channels.

Contract templates can reduce this expectation gap. A useful model would distinguish a national basic service package, locally adjusted priority packages for key populations and optional personalized packages. Basic services can cover registration, health assessment, public health services, common disease consultation, chronic disease follow-up, medication guidance and referral advice. Local priority packages can address older adults, pregnant women, children, persons with disabilities, rural residents, migrants and patients with chronic disease. Paid personalized services should be separated from basic entitlements, with transparent pricing and meaningful consent. This distinction makes the contract more operational and reduces the risk that paid services crowd out public-service obligations.

Liability should be differentiated by function rather than treated as a single undivided exposure. Direct diagnosis and treatment are governed by ordinary medical professional standards and the Civil Code’s medical-damage provisions, including institutional liability for fault and duties of disclosure and appropriate diagnosis and treatment.²⁴ Where an enforceable contractual undertaking is not performed but no medical injury occurs, ordinary breach principles, including Article 577 of the Civil Code, may become relevant, while the public-service character of FDCS still constrains a purely commercial reading of the agreement. Coordination duties—such as referral guidance, appointment assistance, record transfer and follow-up reminders—should be assessed by the promised service, reasonableness, available resources and documented communication. Public-health and health-management duties should not be converted into strict civil liability for every adverse outcome. This layered approach protects residents from negligence and meaningful non-performance without implying that family doctors guarantee health outcomes.

Financing, Supervision and Evaluation

A further pair of domains links financing, supervision and evaluation. Legal clarity has limited effect if the contract promises services for which no stable funding is available. The 2016 and 2022 national policies refer to signing fees, public health funds, medical insurance support and performance-based distribution.^1,2 Implementation varies across regions. Some areas rely mainly on basic public health funds; others experiment with medical insurance payment reform, capitation, county medical communities or retained savings. Official replies in 2025 referred to differentiated reimbursement, global-budget payment for close county medical communities and training investment for grassroots personnel.^4,18

The risk-governance implication is specific: every promised service should have an identifiable funding source, an accountable provider and an evaluation metric. The qualitative likelihood of non-performance increases when home visits, chronic disease follow-up or referral support are promised without corresponding funding or workload recognition. Incentives focused mainly on signatures can reward formal contracting over substantive continuity. The strongest response is therefore not simply more funding, but traceability between service entitlement, payment source and accountable provider.

Supervision presents a related institutional problem. FDCS involves health commissions, medical insurance bureaus, finance and human-resources departments, cyberspace and data authorities, primary institutions, public hospitals and sometimes commercial platforms. Fragmentation is not itself failure, but regulatory underlap arises when no actor is responsible for a cross-cutting risk. Telehealth quality, for example, can implicate clinical standards, platform safety, data security, insurance payment and professional responsibility at the same time. Risk-based regulation supports concentrating regulatory attention where likelihood and consequence are greatest rather than treating all providers and service modes identically.^42,43

A practical model would designate lead responsibility for integrated FDCS oversight while retaining sectoral competence. The National Health Commission could coordinate joint standards, shared indicators, regular interdepartmental review and a common information architecture connecting contract registration, service records, patient-reported experience, complaints, quality indicators, payment data and workforce capacity. This does not require a wholly new agency; it requires visible lead responsibility and feedback across existing channels.

Evaluation indicators should likewise move beyond signing rates. Useful measures include effective contract performance, actual service use, fulfilment of service-package items, chronic disease follow-up quality, referral completion, patient-reported experience, complaint handling, data incidents, home-visit safety, staff workload, staff retention and equity for vulnerable groups. Indicators should be adjusted for rurality, ageing, workforce supply and disease burden, and they should avoid excessive documentation that could itself increase frontline workload. Risk-governance measures should therefore be assessed for their administrative burden. New documentation, audit, consent and performance requirements should be integrated into existing workflows where possible and scaled to the underlying risk. A control that consumes substantial clinical time without materially improving continuity, accountability or safety can become a source of implementation risk in its own right, particularly in community health centres and village clinics with limited staffing.

Digital FDCS and Data Governance

Digital Opportunities and Regulatory Boundaries

Digitalization is likely to remain a major part of FDCS. Internet-enabled services can help teams communicate with residents, conduct chronic disease follow-up, support long prescriptions, coordinate appointments and manage referrals. For rural residents, older adults with mobility limitations and people with chronic disease, digital tools may improve convenience and continuity. Yet digitalization also moves FDCS into a more complex legal environment.

China’s internet diagnosis framework and the 2022 Internet Diagnosis and Treatment Supervision Rules require online diagnosis and treatment to remain anchored in qualified medical institutions, impose identity and record requirements, and require termination of internet diagnosis where the patient is not suitable for online management.²⁵ FDCS, however, is broader than a discrete internet consultation: it may include repeated data collection, health management, reminders, remote monitoring, referral coordination, health education and platform-mediated communication. The key governance question is therefore when a longitudinal digital contact becomes diagnosis and treatment, when escalation to in-person care is required, and which institution remains responsible across the transition.

Health Data Protection, Consent and Platform Responsibility

Health data protection is central. The Personal Information Protection Law classifies medical and health information as sensitive personal information and requires a specific purpose, sufficient necessity and strict protection; where processing relies on consent, sensitive information generally requires separate consent.²¹ At the same time, consent is not the only lawful basis for every data flow: processing necessary to perform a contract, fulfil statutory duties, respond to emergencies or other legally specified grounds may apply in particular contexts. FDCS therefore needs a clearer map of which data flows are necessary for ordinary continuity, which involve joint or entrusted processing, and which require additional consent or information duties.

Electronic consent should reflect the longitudinal nature of FDCS. A simple one-click agreement is unlikely to provide adequate transparency where residents authorize repeated sharing among primary institutions, hospitals, platforms and public health agencies. A layered process can present essential information first and then explain what data are shared, with whom, for what purpose and for how long. Higher-risk or optional uses should receive separate attention. The objective is not repeated formal consent for every clinically necessary exchange, but a data environment that is predictable, proportionate and reviewable.

Platform governance also requires clear allocation. Residents may experience a digital platform as part of the family doctor system even when a third party operates it. Appointment failure, missing remote-monitoring data or poorly designed escalation can therefore create ambiguity between technical fault and medical institutional responsibility. Consistent with personal-information rules on joint and entrusted processing and internet-diagnosis supervision, contracts with platforms should define security duties, service availability, audit cooperation, data interfaces, complaint handling and responsibility for technical defects.^21,25 Private agreements cannot displace statutory responsibility, but they can reduce operational grey zones.

Digital Inclusion and Public-Health Functions

Digital inclusion is itself a risk-governance concern. If FDCS becomes too dependent on mobile platforms, older adults, rural residents, persons with disabilities, migrants and residents with low digital literacy may receive weaker service. A digital-plus-human model is therefore safer: online tools can support reminders, records, communication and referral, while paper contracts, telephone contact, offline visits, family-member assistance and community support remain available where needed.

Electronic consent should not become the only practical route for older adults in rural areas or other residents with limited digital literacy. Primary-care institutions should provide assisted explanation, accessible interfaces and paper or other offline alternatives. Where consent is legally required and the resident is able to provide it, assistance by family members or community personnel should support rather than replace the resident’s own decision. Digital convenience should expand access rather than make access conditional on digital competence.

Data governance must also accommodate lawful public-health functions. FDCS may support vaccination reminders, chronic disease follow-up, infectious disease monitoring, maternal and child health and services for older adults. Overly restrictive or unclear rules can hinder continuity, while overly broad sharing can damage trust. A workable model would define legitimate care-continuity and public-health purposes in advance, record access and disclosure, and provide residents with understandable information about routine data flows.

Comparative Reference Points: United Kingdom and Australia

The comparative materials have a deliberately limited role. They do not supply the analytical framework and are not presented as models for institutional transplantation. Instead, they provide a functional check on whether responsibility and liability can be organized above the level of the individual primary-care physician under different financing and provider structures.

In England, general practice operates within the National Health Service statutory framework, GP practices provide an organizational locus for service responsibility, professional standards are regulated through the General Medical Council, and the Clinical Negligence Scheme for General Practice pools specified negligence liabilities.^35–38 Australia presents a materially different configuration: Medicare financing coexists with predominantly private general practice, professional registration through Australian Health Practitioner Regulation Agency (AHPRA) and the Medical Board of Australia, professional standards, and indemnity requirements.^39–41 The relevant contrast is therefore not between a “better” and a “worse” system, but between two different institutional arrangements in which clinical and organizational risk is not left exclusively with the personal capacity or assets of an individual general practitioner.

What the comparison adds to the Chinese analysis is a limited but relevant institutional proposition: organizational anchoring and risk pooling are not dependent on a single primary-care financing model. China therefore need not reproduce either the NHS structure or Australian private general practice in order to place external FDCS responsibility more clearly at the institutional level and develop pooled or insurable arrangements for residual risk. Other elements of the proposed framework—including service-package clarification, funding–service alignment, data safeguards and integrated supervision—rest primarily on the Chinese legal and policy analysis; the comparative materials are corroborative rather than constitutive.

Local Variation and a Qualitative Risk-Governance Matrix

FDCS has never developed through a single uniform pathway. Large cities such as Shanghai and Beijing began earlier community-based family doctor experiments, usually relying on relatively strong community health service centres and specialist support from public hospitals. County and rural areas increasingly link FDCS with county medical communities, township health centres, village clinics and basic public health programmes. Some localities use grid-based community governance; others rely more heavily on internet hospitals or regional digital platforms. Such variation is valuable because it adapts service design to different populations, fiscal capacities and provider networks.

Variation also creates a legal problem when minimum rights, liability allocation, data protection or dispute channels depend entirely on local discretion. A national framework should therefore operate as a floor rather than a ceiling: it should define minimum safeguards while allowing local governments to add service packages, payment innovations and digital tools according to capacity. Table 1 translates the framework into event-level qualitative triage. The five domains identify where legal indeterminacy arises, while the individual risk events within those domains constitute the unit of ranking.

Worked Application: Remote Diabetes Follow-up

A worked service-level example illustrates what the matrix adds beyond a general reform heuristic. Remote follow-up for a contracted patient with diabetes is useful because it crosses several domains at once: clinical assessment, longitudinal health management, possible prescription renewal, personal-information processing, insurance payment and contract performance. The framework requires each element to be made explicit before the service is expanded.

Table 2 carries the same service through the five analytical questions and then adds liability allocation. The example does not prescribe one national payment rule or clinical protocol; those details vary by locality and patient condition. Its purpose is to show that a risk-governance design can identify the points at which an otherwise convenient digital service becomes legally or operationally fragile.

Table 2.

Worked Application of the Framework to Remote Diabetes Follow-up

Analytical Question	Remote Follow-Up Design	Legal/Regulatory Anchor	Risk-Control Implication
Who is responsible?	The primary medical and health institution is the provider-side contracting entity; the assigned family doctor team performs routine follow-up. A third-party platform performs only defined technical functions unless separately authorized to provide medical services.	Basic health law and Physicians Law;19,20 Civil Code medical-damage rules;24 internet-diagnosis supervision.25	Prevents a patient from having to determine whether the doctor, team, institution or platform is the legal counterpart after a failure.
What is promised?	Specify follow-up frequency or trigger, review of self-reported/monitoring information, medication guidance within professional scope, referral or in-person escalation criteria, and communication channels. Do not promise a guaranteed glycaemic outcome.	National FDCS policies;1,2 professional and medical-dispute duties.20,24,26	Converts a general promise of “continuous management” into observable service obligations while preserving clinical judgment.
How is it funded?	Identify whether each element is supported by basic public health funding, medical insurance, local subsidy or an optional paid package; avoid using one undifferentiated fee for services with different legal bases.	FDCS financing policies and official replies.1,2,4,18	Reduces the likelihood that a signed service is not performed because workload and payment are disconnected.
How is risk controlled?	Verify patient identity and suitability for online follow-up; document key information; define abnormal-value or symptom triggers for termination of online management and in-person referral; apply access controls and purpose-limited data flows; obtain separate consent where legally required.	Personal Information Protection Law (PIPL);21 Data Security and Cybersecurity laws;22,23 internet-diagnosis supervision.25	Addresses the transition point where routine remote management can become unsafe, legally unsupported or privacy intrusive.
How is performance evaluated?	Track completion of scheduled follow-up, documented escalation when indicated, referral completion, continuity of medication support, patient-reported experience, complaints, data incidents and staff workload.	High-quality FDCS policy and oversight logic.2,6,18	Measures substantive continuity rather than signature numbers alone and identifies recurrent failure patterns.
How is liability allocated?	Fault in diagnosis/treatment is assessed under medical-damage rules and ordinarily attaches externally to the medical institution. Legally enforceable non-performance without medical injury may engage contract remedies; coordination failure is assessed against the promised service, reasonableness and documentation. Data or platform faults follow the applicable information-processing and fault allocation rules.	Civil Code Article 577 and Arts 1218–1226;24 Medical Disputes Regulation, including institutional risk management, complaint and insurance mechanisms;26 PIPL.21	Separates clinical negligence, service non-performance, coordination failure and data/technical fault instead of treating all adverse outcomes as “family doctor liability”.

Reform Priorities: From Coverage to Accountable Continuity

The risk matrix changes the order of reform priorities. The aim is not to regulate every FDCS activity more intensely. It is to stabilize the legal and financial conditions whose failure is most likely to undermine continuity and then apply more targeted controls where severity is high.

Legal and Contractual Clarification

The first priority is to establish a clearer legal foundation for FDCS within the existing statutory framework. A stand-alone Family Doctor Law is not necessarily the most realistic near-term route. A more feasible path is to integrate FDCS explicitly with the Law on Basic Medical and Health Care and the Promotion of Health, the Physicians Law, the Civil Code, medical-insurance rules, medical-dispute rules and personal-information law.^19–26 An implementing regulation or national normative framework could define FDCS as a public-interest primary-care service delivered through standardized agreements between residents and primary medical and health institutions, with assigned family doctor teams responsible for day-to-day performance.

A national model contract can provide a minimum legal floor while allowing local adaptation. Mandatory clauses should cover contracting parties, team members, replacement procedures, service package content, frequency or trigger conditions, online and offline channels, referral support, resident cooperation, privacy and data use, fees, duration, renewal, termination, complaint channels and liability. Optional clauses can allow local governments to add services according to capacity and health needs. Readability matters: short clauses, service tables and plain-language explanations are particularly important for older adults and residents with limited health literacy.

Funding, Liability and Dispute Management

Financing reform should follow the very-high-priority risk identified in Table 1. Basic services for priority populations can be supported by public health funds, local fiscal subsidies and medical-insurance arrangements; medical insurance can support eligible primary-care visits, chronic disease management, long prescriptions and referral pathways; and local subsidies may be needed for home visits and vulnerable groups. Personalized paid services can be permitted where basic entitlements are protected, prices are transparent and consent is meaningful. The essential rule is traceability: a service should not appear in the contract unless its funding and provider responsibility can be identified.

Liability reform should preserve ordinary medical-damage rules while clarifying non-clinical functions. The Medical Disputes Regulation already requires institutions to strengthen medical-risk identification and prevention, and establish complaint mechanisms, and it supports insurance as a socialized risk-sharing mechanism.²⁶ FDCS can build on this existing architecture rather than create a parallel liability regime. Pooled or publicly supported indemnity could initially focus on socially valuable but difficult-to-price risks such as home visits, remote follow-up and coordination across institutions. Low-severity disputes concerning communication, missed follow-up, privacy, contract performance or paid services should have accessible institutional review and mediation pathways without limiting judicial remedies.

Digital Governance and Workforce Sustainability

For digital FDCS, the priority is to make the boundary between health management and internet diagnosis operational. Rules should identify when online contact requires qualified clinical assessment, when online management must end, how records are kept, how identity is verified and how platforms interact with medical institutions.²⁵ Data governance should map purposes, roles, access, retention, audit and breach response rather than relying on generic consent language.^21–23

Workforce sustainability remains essential but should not be misclassified as a purely legal problem. General practitioner training, continuing education, career promotion, occupational safety, remuneration and professional recognition affect whether contractual duties can be performed.¹⁷ The legal contribution is to match obligations with realistic capacity and to avoid creating unfunded or unlimited duties. Higher-level specialists participating in FDCS require defined responsibilities and incentives, while rural and under-resourced areas need stable subsidies, training and appropriate digital support.

Policy and Risk-Management Implications and Limitations

The practical value of the framework lies in regulatory triage rather than in treating every legal uncertainty as requiring the same response. Very-high-priority events call for correction of the underlying structural arrangement. In the present application, the only event at this level is the mismatch between promised services and stable funding or workload recognition. High-priority events warrant standardized safeguards and targeted supervision, whereas enhanced-priority events may justify clarification, monitoring, complaint pathways or other lower-intensity controls. Risks supported by weaker evidence can remain under review rather than automatically generating additional regulation. This graduated approach can help health authorities direct limited regulatory and administrative capacity toward risks with the strongest combination of recurrence, exposure and plausible consequence while avoiding indiscriminate legal formalization.

The framework’s distinctive contribution is narrower than a general theory of primary-care governance. Existing policy-tool studies identify what instruments government uses,^9–11 implementation models identify actor, resource and environmental barriers,¹³ and legal studies identify specific uncertainties in contract status or rights.^12,14,15 The present approach connects these levels by asking a further question: which legal uncertainty creates which risk event, how urgent is that risk relative to others, and what regulatory intensity is proportionate? The framework therefore separates taxonomy from triage: domains locate the source of legal indeterminacy, whereas event-level ratings determine regulatory priority. In that sense, the framework complements rather than replaces implementation science or broader health-system governance.

For risk management, FDCS can be understood as preventive legal infrastructure. A well-governed system may reduce health-system risk by supporting earlier detection, medication continuity, coordinated referral, home-based care and public-health functions. A poorly governed system can generate symbolic contracts, underfunded promises, data leakage, uncertain telehealth responsibility, professional burnout and disputes over paid services. Legal design therefore belongs inside policy design: contract templates, payment arrangements, professional standards, data governance and complaint channels shape whether residents trust FDCS and whether clinicians can provide services safely.

The Chinese case also has comparative relevance. Many health systems face ageing, multimorbidity, hospital congestion, weak primary-care gatekeeping and digital expansion. The transferable proposition is not that China provides a model for other systems or vice versa. It is that reforms promising continuity, integration, digital convenience and home-based support also require supporting institutional arrangements: durable provider responsibility, funding for promised services, rules for sharing information, escalation pathways, and mechanisms for allocating liability.

This proposition may be particularly relevant to health systems expanding universal health coverage through stronger primary-care enrolment, empanelment or capitation. Empanelment can strengthen population responsibility and continuity in primary care, but formal enrolment or service coverage does not by itself establish effective coverage, which also depends on actual service use and quality.^44,45 Strategic purchasing experience in middle-income countries likewise shows that progress toward UHC depends not only on extending coverage but also on defining benefit packages, aligning provider payment with expected services, monitoring performance and maintaining effective purchaser–provider relationships.⁴⁶ Capitation illustrates the importance of this institutional alignment: it can promote efficiency and strengthen primary-care financing, but poorly designed arrangements may also create incentives for underservice or reduced quality.⁴⁷ The value of the present risk-oriented framework is therefore not to export the Chinese ratings, but to provide a sequence for asking which locally evidenced institutional uncertainties create consequential risks and what level of intervention is proportionate. This may be especially useful where regulatory and administrative capacity is constrained, provided that risk controls themselves remain proportionate to frontline capacity.

The approach has important limitations. It is a Perspective rather than a systematic review, field study or quantitative evaluation. The qualitative likelihood and severity ratings synthesize the cited materials and legal structure; they are not measured probabilities and should be empirically tested. Local FDCS arrangements vary widely, and some jurisdictions may already have stronger contract templates, payment mechanisms or digital safeguards than the national picture suggests. The worked diabetes example is illustrative and should not be treated as a clinical protocol. Future research should empirically test rather than assume the validity of the proposed priority structure. A mixed-methods design could combine coding of provincial contract templates and financing rules with administrative data on complaints, contract non-performance, referral completion, service continuity, data incidents and workforce burden, supplemented by interviews or surveys of residents, family doctors and primary-care managers. Comparative provincial research would be particularly useful because fiscal capacity, workforce supply and digital infrastructure differ substantially across China. Studies comparing relatively well-resourced eastern coastal provinces with western inland settings could examine whether similar legal uncertainties generate different exposure or consequences under different implementation conditions. A central validation question would be whether prospectively assigned qualitative risk categories correspond to subsequently observed patterns of service failure, complaints, continuity indicators or professional workload.

Conclusion

China’s family doctor contract services have moved from policy experimentation to a central component of primary health care reform. The next stage is less about formal contracting numbers alone than whether each contract creates a service relationship that residents understand, clinicians can perform, institutions can finance, and regulators can evaluate.

Legal indeterminacy is not the only challenge facing FDCS, but it is a governable source of risk when uncertainty about responsibility, service content, funding, supervision or data use can be linked to identifiable adverse events. Operationalizing that relationship through qualitative likelihood and severity, with exposure and persistence considered for possible escalation and proportionality guiding the regulatory response, makes the risk-governance claim more than a call for better institutional design. The resulting priorities are concrete: anchor provider responsibility in primary medical institutions, standardize minimum contract content, link promised services to funding, differentiate liability, integrate cross-cutting supervision, govern digital data and escalation, and evaluate continuity rather than signatures alone. These priorities should be read as reasoned regulatory judgments rather than empirically validated estimates of event frequency or harm. The framework requires empirical validation, but it provides a transparent basis for moving FDCS from coverage-oriented expansion toward accountable and risk-governed continuity.

Funding Statement

This study was funded by Hainan Provincial Leading Group for Educational Science Planning, grant number QJY202412210.

Disclosure

The authors report no conflicts of interest in this work.