Internet Plus Nursing Services emerged as China’s structural answer to a demographic squeeze: one of the world’s fastest-aging populations, a mounting burden of chronic disease, and hospitals that cannot physically absorb every follow-up visit, injection, or dressing change that patients require after discharge. Under the model, professional nursing is extended from the hospital to the home through digital platforms, with public hospitals rather than private intermediaries bearing formal responsibility for clinical quality. Patients place orders online, hospitals verify eligibility and dispatch qualified nurses, care is delivered in the patient’s own residence, and the encounter is documented back into the hospital’s information system. The design aims to preserve continuity of care while extending the reach of scarce nursing capacity. Its appeal is evident for older adults with limited mobility, for patients living far from tertiary centers, and for any health system trying to relieve pressure on physical infrastructure. But the model also strips away the hospital’s layered safety environment. Home nursing transfers clinical responsibility into a setting where readiness must be engineered deliberately rather than assumed.

Previous research on these services has concentrated on the visible surface of the system: service quality measurements, patient experience surveys, nurses’ willingness to participate, and platform functionality. What has been largely missing, the authors argue, is an assessment of whether public hospitals themselves possess the underlying system readiness—the organizational, technical, financial, and governance conditions—required to deliver care that is safe, sustainable, traceable, and accountable. The distinction is far more than academic. A hospital can launch an app and recruit nurses, yet still lack the training infrastructure to standardize procedures performed in private homes, the platform architecture to log every step of an encounter in an auditable record, or the payment structures that make participation worthwhile for patients and staff alike. Without a systematic way to evaluate these preconditions, expansion can outrun capability, and a handful of early adverse events could corrode public trust in an otherwise promising model. The study’s objective was therefore to build a structured set of readiness indicators and determine which of them deserve the highest priority.

The framework’s architecture began with a deliberate theoretical choice. Rather than importing a generic health-system checklist, the team contextualized Porter’s Five Forces model—the classic strategy framework that analyzes competitive rivalry, the threat of new entrants, substitute products or services, the bargaining power of buyers, and the bargaining power of suppliers—as the organizing skeleton for its readiness domains, after weighing it against established health-system and implementation perspectives. The logic fits the problem precisely. A public hospital entering the digital home nursing market confronts exactly these forces: rival service models and platforms, informal family caregiving as a substitute for professional care, patients and payers whose adoption determines viability, and a finite supply of qualified nurses whose availability constrains everything else. That contextualization organized the readiness assessment into five first-level domains, which were then decomposed into fourteen second-level indicators and, ultimately, forty-three third-level indicators. The layered structure matters methodologically: it separates strategic domains from functional components and operational details, so a hospital can be evaluated at the level of philosophy, capability, or concrete practice.

The initial indicator pool was assembled through three converging channels: a structured review of published literature, an analysis of national and local Chinese policy documents governing Internet Plus Nursing Services, and preliminary expert consultation. Policy analysis matters because, in China’s health system, directives issued at national and municipal levels shape how hospital-led digital services may be deployed and scaled. Once the pool was drafted, the team subjected it to two rounds of Delphi consultation—a structured method for distilling group judgment in which experts score and comment on items anonymously, responses are statistically summarized, and items are revised and rescored until opinions stabilize. Fifty-two experts participated in the first round; forty-eight completed the second. Each iteration narrowed, sharpened, and stabilized the indicator set before any weighting began, ensuring that the framework’s final shape reflected disciplined statistical filtering rather than editorial preference.

The Delphi statistics describe a panel converging on consensus. The expert authority coefficient, which combines each expert’s self-assessed basis of judgment and degree of familiarity with the subject, reached 0.74—comfortably above the 0.70 level conventionally treated as credible in Delphi research. Agreement among panelists, quantified with Kendall’s coefficient of concordance, rose between rounds: from 0.150 to 0.196 for the second-level indicators and from 0.144 to 0.205 for the third-level indicators, with both increases statistically significant at P < 0.001. Kendall’s W ranges from zero, indicating no agreement, to one, indicating perfect unanimity; its upward drift confirms that the iterative rounds were doing precisely what they are engineered to do—steering independent experts toward a shared ranking of readiness conditions. By the end of the second round, the panel had effectively negotiated, item by item, what hospital readiness for home-based digital nursing should mean.

Converting structured opinion into ranked weights demanded a second quantitative layer: a score-informed adaptation of the analytic hierarchy process, or AHP. In classical AHP, experts compare indicators pairwise on Saaty’s nine-point importance scale, and the resulting judgment matrices are decomposed to yield relative weights. Here, the team instead transformed the second-round Delphi importance scores into Saaty-scale pairwise judgments, reducing respondent burden while preserving the mathematical machinery. Each judgment matrix was then tested for internal consistency using the consistency ratio, or CR, derived from the matrix’s principal eigenvalue: the closer the CR is to zero, the more logically coherent the comparisons. Every applicable matrix passed the conventional 0.10 acceptability threshold, with CR values ranging from 0.0088 to 0.0516—evidence that the final weights emerged from internally consistent judgments rather than contradictory scoring. The team also performed sensitivity analyses, deliberately shifting the boundaries used to convert importance scores into Saaty values; small changes did not materially alter the overall priority structure, meaning the framework’s hierarchy is robust to reasonable variation in the score-to-weight pipeline.

The resulting hierarchy is itself a finding. Workforce and Digital Platform Infrastructure received the highest first-level weight at 32.29 percent—nearly a third of total readiness—signaling that the two most decisive preconditions are the people who deliver home care and the digital system that connects, tracks, and supports them. Patient Adoption, Payment, and Value Recognition followed at 24.47 percent, a reminder that demand-side economics are not an afterthought: services stall when patients do not adopt them, reimbursement mechanisms do not sustain them, and nurses are neither financially compensated nor professionally recognized for the extra responsibility of working inside private homes. Operational Quality, Safety, and Accountability ranked third at 18.54 percent, anchoring the clinical-governance dimension of the model. The remaining two domains together accounted for roughly a quarter of the total weight, completing a balanced structure in which no single dimension monopolizes readiness, but workforce and technology set the ceiling for what every other domain can achieve.

That ordering carries practical weight for anyone running or regulating these services. A hospital with superb clinicians but a fragile platform cannot guarantee traceability: every medication administered, vital sign recorded, and complication managed in a living room must be captured in an auditable digital record for accountability to function at all. Conversely, a polished app cannot compensate for nurses who lack home-specific training, standardized dispatch protocols, or protection from occupational risk when entering private residences. The framework effectively argues that readiness is systemic—capability emerges where workforce, technology, financing, and safety governance reinforce one another, and it collapses where any single link is missing. For hospital administrators, the forty-three third-level indicators function as a diagnostic checklist, pinpointing which element of the chain is weakest before any attempt to scale. For regulators, the weighted structure supplies a defensible, quantitative basis for pilot approvals, allowing authorities to prioritize hospitals that meet the highest-weighted conditions before authorizing broader rollout.

The authors are appropriately measured about what the framework can and cannot yet claim. Its weights rest on structured expert judgment rather than measured service outcomes, and the team—drawn from Tianjin Medical University General Hospital, Tianjin University of Traditional Chinese Medicine, Tianjin Medical University, and Hebei University of Technology—explicitly calls for validation with multicenter and real-world service data before the priorities are treated as definitive. The study was approved by the Ethics Committee of Tianjin Medical University General Hospital and supported by the Key Project of the National Social Science Fund of China together with a Tianjin Municipality Education Commission major research project in the social sciences. Even so, its significance reaches well beyond China. Health systems from Japan to the United Kingdom to the United States are experimenting with hospital-at-home programs, and all confront the same governance gap this team identified: enthusiasm for delivering care at a distance has outrun systematic methods for deciding whether an institution is prepared to do it safely. By fusing competitive-strategy theory, iterative expert consensus, and eigenvalue-based weighting into a single instrument, the study offers a template that any health system—wherever nurses are heading toward the front door—could adapt before the knock sounds.