Implementation of Shared Decision-Making Within Internet Hospitals in China Based on Patients’ Needs: Feasibility Study and Content Analysis
Bibliographic record
Abstract
Background Internet hospitals are developing rapidly in China, and their convenient and efficient medical services are being increasingly recognized by patients. Many hospitals have set up their own internet hospitals to provide web-based medical services. Tianjin Medical University General Hospital has established a multidisciplinary and comprehensive internet hospital to provide diversified medical services according to the needs of patients. A way to further improve web-based medical services is by examining how shared decision-making (SDM) can be carried out in internet hospital diagnosis and treatment services, thereby improving patients’ medical experience. Objective The aim of this study was to analyze the feasibility of implementing doctor-patient SDM in internet hospital diagnosis and treatment services based on patients’ needs in China. Methods In this study, the medical data of 10 representative departments in the internet hospital of Tianjin Medical University General Hospital from January 1 to January 31, 2022, were extracted as a whole; 25,266 cases were selected. After excluding 2056 cases with incomplete information, 23,210 cases were finally included in this study. A chi-square test was performed to analyze the characteristics and medical service needs of internet hospital patients in order to identify the strengths of SDM in internet hospitals. Results The internet hospital patients from 10 clinical departments were significantly different in terms of gender (χ29=3425.6; P<.001), age (χ236=27,375.8; P<.001), mode of payment (χ29=3501.1; P<.001), geographic distribution (χ29=347.2; P<.001), and duration of illness (χ236=2863.3; P<.001). Patient medical needs included drug prescriptions, examination prescriptions, medical record explanations, drug use instructions, prehospitalization preparations, further consultations with doctors (unspecified purpose), treatment plan consultations, initial diagnoses based on symptoms, and follow-up consultations after discharge. The medical needs of the patients in different clinical departments were significantly different (χ272=8465.5; P<.001). Conclusions Our study provides a practical and theoretical basis for the feasibility of doctor-patient SDM in internet hospitals and offers some implementation strategies. We focus on the application of SDM in web-based diagnosis and treatment in internet hospitals rather than on a disease or a disease management software. The medical service needs of different patient groups can be effectively obtained from an internet hospital, which provides the practical conditions for the promotion of doctor-patient SDM. Our findings show that the internet hospital platform expands the scope of SDM and is a new way for the large-scale application of doctor-patient SDM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".