Weiguo Zhu

Background: Telemedicine has rapidly expanded; however, standardized, telemedicine-specific patient-reported experience measures tailored to outpatient workflows are limited in many settings. Objective: This study aimed to develop and psychometrically validate the Telemedicine Service Experience Questionnaire (TSEQ) for Chinese outpatients using telemedicine services. Methods: We conducted a web-based survey among outpatients who completed a telemedicine consultation at Peking Union Medical College Hospital between July 1, 2021, to August 31, 2021, and who had used telemedicine services, using an adapted Chinese Patient Experience Questionnaire that encompasses 15 questions across 4 dimensions, to investigate patients’ telemedicine consultation experiences. Item generation was informed by a literature review, workflow mapping, and expert review. We evaluated the factor structure using exploratory factor analysis and confirmatory factor analysis on the full sample with cross-validation. Reliability was assessed using Cronbach α and item-total correlations. Results: In total, 3338 participants completed the survey (mean age 45.3, SD 17.8 y; n=2182, 65.4% female participants; n=1827, 54.8% with college education or above). The exploratory factor analysis of the final 14-item scale resulted in 4 factors. After scrutinizing the content, these factors were labeled “Service Efficiency,” “Post-treatment,” “Information Guidance,” and “Humanistic Care,” and they demonstrated good internal consistency (Cronbach α values of 0.876, 0.840, 0.962, and 0.876, respectively). Moreover, as the average variance extracted values were greater than 0.5 and the composite reliability values were greater than 0.7, the TSEQ scale has high convergent validity. Our findings suggest that the psychometric properties of the 14-item TSEQ are valid and reliable for assessing telemedicine service experience among Chinese outpatients. Conclusions: The TSEQ demonstrates a stable multidomain structure with satisfactory reliability and validity for evaluating outpatient telemedicine service experience in China. The instrument can support routine quality monitoring and guide targeted workflow improvements. Future studies should validate the TSEQ in multisite and postpandemic samples and examine measurement invariance across key subgroups.

Background: Diagnosing rare diseases traditionally requires patients to endure lengthy and challenging journeys to find specialists familiar with their conditions. This study advocates a paradigm shift in rare disease diagnosis, moving from patients seeking physicians to physicians actively identifying patients. Using hereditary angioedema (HAE) as an example, we demonstrate how this approach, supported by electronic medical records (EMRs), enables proactive care for patients with rare diseases. Our EMR system incorporates a free-text search engine to screen for patients with potential HAE based on clinical symptoms and laboratory tests. Search terms include recurrent skin edema, abdominal pain, laryngeal edema, and/or decreased C4 levels. Suspected cases are followed up by telephone calls from trained physicians, inviting patients to undergo confirmatory C1-INH and C4 testing and genetic testing to ensure accurate diagnosis and appropriate treatment. Results: Of 2,689 patients who met the screening criteria, 3,441 records were analyzed. Ninety-five patients had already been diagnosed with HAE. After excluding those with a known etiology for edema or characteristics inconsistent with HAE, three patients with unexplained cutaneous edema, abdominal pain, and/or laryngeal edema were included in the final screening. Laboratory tests confirmed HAE in all three, highlighting the effectiveness of this proactive approach. Conclusions: This study underscores the transformative potential of EMRs in diagnosing rare diseases. By shifting the responsibility of identifying rare diseases from patients to healthcare professionals, we expedite diagnosis and exemplify the spirit of service in medicine, ensuring patients with rare diseases receive timely and effective care.

Background: Primary ciliary dyskinesia (PCD) is a rare, genetically heterogeneous disease. Due to difficulty accessing diagnostic services and a lack of awareness of the syndrome, clinicians often fail to recognize the classic phenotype, leading to missed diagnoses.Methods: Relevant medical records were accessed through The BIG DATA QUERY AND ANALYSIS SYSTEM of Peking Union Medical College Hospital from September 1, 2012 to March 31, 2024. The search strategy included the following key terms: (bronchiectasis OR atelectasis OR recurrent cough OR recurrent expectoration OR hemoptysis) AND (sinusitis OR nasal polyps OR otitis media OR neonatal pneumonia OR neonatal respiratory distress OR ectopic pregnancy OR infertility OR artificial insemination OR assisted reproduction OR hydrocephalus OR congenital heart disease OR organ laterality defect OR right‐sided heart OR semen OR consanguineous marriage). Patients were filtered according to inclusion and exclusion criteria, and those with clinical suspicion of PCD were invited for screening, which included nasal nitric oxide and whole exome sequencing.Results: A total of 874 medical records were retrieved. After filtering based on inclusion and exclusion criteria, 65 patients with clinical suspicion of PCD were identified, 21 of whom accepted our invitation to complete PCD‐related screening. Among them, four were diagnosed with PCD, one was diagnosed with cystic fibrosis, and one was diagnosed with immunodeficiency‐21.Conclusions: This is the first study to use an electronic medical record retrieval system to identify missed diagnoses PCD. We believe that the methods used in this study can be extended to other rare diseases in the future.

Aim: To investigate the cost homogeneity within the Diagnosis-Related Group (DRG) “major operation of pancreas and liver, with general complications or comorbidities” (HB13), its cost-influencing factors, and to propose suggestions for better grouping efficacy.Methods: Medical and insurance settlement data of inpatients covered by the DRG payment system at the author’s institution were collected from March 15, 2022 to December 31, 2023. The cost homogeneity of group HB13 was assessed using the coefficient of variation (CV). Clinical factors that may have an impact on hospitalization cost for patients undergoing pancreatic surgery were identified through a semi-structured interview administered to the pancreatic surgeons in author’s department, their significance was analyzed using multiple linear regression, along with their impact on the cost of different service categories. A proposal to subdivide HB13 was made and evaluated by CV and t-test.Results: The CV of the HB13 group was 0.4. Robotic-assisted surgery and pancreaticoduodenectomy were two independent factors that significantly affected the total cost. Patients undergoing robotic-assisted surgery have an average increase of 41,873 CNY in total cost, primarily derived from operation fee. Patients undergoing pancreaticoduodenectomy have an average increase of 37,487 CNY in total cost, with significant increases across all service categories. HB13 was subdivided based on whether pancreaticoduodenectomy was performed. The newly formed groups exhibited lower CVs than the original HB13.Conclusion: The cost homogeneity of HB13 was lower than that of other DRG groups in author’s department. It is recommended to introduce a supplementary payment for patients requiring robotic-assisted surgery, to guarantee their access to this advanced technology. It is recommended to establish a new group with higher payment standard for patients undergoing pancreaticoduodenectomy. A tiered CV criterion for the evaluation of grouping efficacy is recommended to increase intra-group homogeneity, facilitating a better allocation of health insurance funds, and the prevention of unintended negative outcomes such as service cuts and cherry-picking.

Introduction: Fluid resuscitation of patients with sepsis is crucial. This study explored the role of fluid balance in the early resuscitation of sepsis patients in the intensive care unit (ICU). Material and methods: A retrospective study of patients with sepsis using the Peking Union...

BACKGROUND Telemedicine has been widely used for the treatment and management of patients with chronic diseases. However, there is both favorable and controversial evidence on the application of telemedicine to this field. We conducted an overview of all the systematic reviews available to provide the benefits and challenges of applying telemedicine to patients with diabetes, lung diseases, cancer and cardiovascular diseases. OBJECTIVE Given that the concept of telemedicine is complicated, it is essential to clarify and evaluate the effective interventions of telemedicine on chronic disease patients. The study aims to evaluate telemedicine's impact on chronic disease patients from three dimensions (Improving the patient experience of care, improving populations' health, and reducing the per capita cost of health care). We focus on the four major chronic diseases that WHO has highlighted [16] , namely, cardiovascular diseases (e.g., heart attacks and stroke), cancers, chronic lung diseases (such as chronic obstructed pulmonary disease and asthma), and Diabetes. Our findings, with solid evidence and insights, will help policymakers harness the power of telemedicine to improve the care of those with chronic diseases. METHODS Medline, EMBASE and Web of Science databases were explored for systematic reviews of telemedicine applications for the chronic diseases from 1 January 2019 to 31 December 2022. Two patient engagement frameworks were synthesized to provide a three-level classification of interventions and solutions to patient care. GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) was used to evaluate the quality of evidence. The IHI Triple Aim framework was adopted to evaluate the effectiveness of telemedicine on patients with diabetes, lung diseases, cancer and cardiovascular diseases. RESULTS Of the 90 included reviews, 30 focused on diabetes, 13 focused on lung diseases, 20 examined telemedicine in cancer and 27 in diabetes. Interventions at the consultation level were included in 23 reviews. 59 reviews examined interventions at the involvement level, eight at the level of partnership and shared leadership and 23 investigated various interventions across the three levels. With respect to the outcomes, 18 reviews reported the effects of telemedicine on patient experience of care, such as good receptiveness and empowerment. 26 reviews examined the effects of telemedicine on the improvement of population health, such as a significant reduction in HbA1c. Nine reviews focused on the use of telemedicine in reducing the per capita cost of healthcare. CONCLUSIONS Telemedicine has the potential to improve patient experience of care and population health, and may reduce healthcare costs per capita. However, future research is needed to examine telemedicine interventions at different levels in order to confirm their feasibility in chronic diseases.

Objective: Fluid therapy for sepsis patients has always been a problem that puzzles clinicians, that is, knowing when patients need fluid infusion and when they need negative fluid balance. Different clinicians may have different judgment criteria and make different decisions. Recently, studies have suggested that different fluid treatment strategies can cause different clinical outcomes. This study is intended to establish and verify a model for judging the direction of fluid therapy based on machine learning.Method: This study included 2705 sepsis patients from the Peking Union Medical College Hospital Intensive Care Medical Information System and Database (PICMISD) from January 2016 to April 2020. The training set and test set (January 2016 to June 2019) were randomly divided. Twenty-seven features were extracted for modeling, including 25 state features (bloc, vital sign, laboratory examination, blood gas assay and demographics), 1 action feature (fluid balance) and 1 outcome feature (ICU survival or death). SARSA was used to learn the data rules of the training set. Deep Q-learning (DQN) was used to learn the relationship between states and actions of the training set and predict the next balance. A double-robust estimator was used to evaluate the average expected reward of the test set in the deep Q-learning model. Lastly, we verified the difference between the predicted fluid therapy model and the actual treatment for the patient's prognoses, with sepsis patient data from July 2019 to April 2020 as the validation set.Results: The training set and test set were extracted from the same database, and the distribution of liquid balance was similar. Actions were divided into five intervals corresponding to 0–20, 20–40, 40–60, 60–80, and 80–100% percentiles of fluid balance. The higher the reward of Q(s, a) calculated by SARSA from the training set, the lower the mortality rate. Deep Q-learning indicates that both fluid balance differences that are too high and too low show an increase in mortality. The more consistent the fluid balance prediction with the real result, the lower the mortality rate. The smaller the difference between the prediction and the reality, the lower the mortality rate. The double-robust estimator shows that the model has satisfactory stability. The validation set indicates that the mortality rate of patients in the “predicted negative fluid balance and actual negative fluid balance” subgroup was the lowest, which was statistically significant, indicating that the model can be used for clinical verification.Conclusion: We used reinforcement learning to propose a possible prediction model for guiding the direction of fluid therapy for sepsis patients in the ICU. This model may accurately predict the best direction for fluid therapy, thereby improving patient prognosis.

Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation. To derive the MLS, an existing dataset of 22,272 patient encounters from 2012 to 2019 from our institution’s electronic emergency triage system (EETS) was used for algorithm training and validation. The area under the receiver operating characteristic curve (AUC) was 0.875 ± 0.006 (CI:95%) in retrospective dataset using fivefold cross validation, higher than that of reference model (0.843 ± 0.005 (CI:95%)). In the prospective cohort study, compared to the traditional triage system’s 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9%. This MLS method with a real-time explanation for triage officers was able to lower the mis-triage rate of critically ill ED patients.

Background: Regional citrate anticoagulation (RCA) is an important local anticoagulation method during bedside continuous renal replacement therapy. To improve patient safety and achieve computer assisted dose monitoring and control, we took intensive care units patients into cohort and aiming at developing a data-driven machine learning model to give early warning of citric acid overdose and provide adjustment suggestions on citrate pumping rate and 10% calcium gluconate input rate for RCA treatment.Methods: Patient age, gender, pumped citric acid dose value, 5% NaHCO₃solvent, replacement fluid solvent, body temperature value, and replacement fluid PH value as clinical features, models attempted to classify patients who received regional citrate anticoagulation into correct outcome category. Four models, Adaboost, XGBoost, support vector machine (SVM) and shallow neural network, were compared on the performance of predicting outcomes. Prediction results were evaluated using accuracy, precision, recall and F1-score.Results: For classifying patients at the early stages of citric acid treatment, the accuracy of neutral networks model is higher than Adaboost, XGBoost and SVM, the F1-score of shallow neutral networks (90.77%) is overall outperformed than other models (88.40%, 82.17% and 88.96% for Adaboost, XGBoost and SVM). Extended experiment and validation were further conducted using the MIMIC-III database, the F1-scores for shallow neutral networks, Adaboost, XGBoost and SVM are 80.00%, 80.46%, 80.37% and 78.90%, the AUCs are 0.8638, 0.8086, 0.8466 and 0.7919 respectively.Conclusion: The results of this study demonstrated the feasibility and performance of machine learning methods for monitoring and adjusting local regional citrate anticoagulation, and further provide decision-making recommendations to clinicians point-of-care.