INTRODUCTION: Cancer patients attending emergency departments (EDs) often present with acute symptoms and are frequently admitted. This study aimed to characterise the profile of oncology patients who were discharged from the ED. METHODS: This was a retrospective audit of patients with cancer-related diagnoses who presented to the ED at the Singapore General Hospital (SGH) over a 6-month period from 1 October 2018 to 31 March 2019 and were directly discharged from the ED. Data was extracted from the hospital's electronic medical record system. RESULTS: Of the 492 participants included in the study, the majority were triaged as Priority 2 (61.4%), while 30.7% were triaged as Priority 3, 6.9% as Priority 1 and 1.0% as Priority 4. There was no statistical difference between the National Early Warning scores across the different triage categories in these patients. The most common complaint was (44.3%), followed by genitourinary symptoms (19.5%) and those related to devices, catheters or stomas (17.3%). More investigations of all types were done for patients being managed in Priority 1 (57.6%) than in the other triage categories (40.1% for Priority 2, 23.2% for Priority 3 and 12.0% for Priority 4). Treatment procedures carried out were mainly symptomatic (analgesics, antiemetics, proton pump inhibitors) for 79.8% of the patients. There were no significant differences in the proportion of patients requiring various treatment modalities among the triage categories. CONCLUSION Selected oncological patients may potentially be managed in an ambulatory setting.
Humans ; Emergency Service, Hospital/statistics & numerical data* ; Retrospective Studies ; Female ; Neoplasms/diagnosis* ; Male ; Singapore ; Patient Discharge/statistics & numerical data* ; Middle Aged ; Aged ; Triage ; Adult ; Emergencies ; Aged, 80 and over

Humans ; Infant, Premature ; Patient Discharge ; Infant, Newborn ; Consensus ; Quality of Life ; Aftercare ; Infant, Premature, Diseases/therapy* ; Follow-Up Studies

OBJECTIVES: To explore the measures to improve the follow-up rate of preterm infants after discharge, and to evaluate the effectiveness of these measures using quality improvement methodology. METHODS: The follow-up status of preterm infants discharged from March to May 2017 was used as the baseline before quality improvement, and a specific quality improvement goal for the follow-up rate was proposed. The Pareto chart was used to analyze the causes of follow-up failure, and a key driver diagram was constructed based on the links involved in improving follow-up rate. The causes of failure were analyzed to determine the key links and intervention measures for quality improvement, and the follow-up rate was monitored weekly using a control chart until the quality improvement goal was achieved. RESULTS: The follow-up rate of preterm infants after discharge was 57.92% (117/202) at baseline before quality improvement, and the quality improvement goal was set to increase the follow-up rate of preterm infants from baseline to more than 80% within 12 months. The Pareto chart analysis showed that the main causes of follow-up failure were deficiencies in follow-up file management and irregular follow-up times (33.70%, 31/92), insufficient follow-up education and poor communication (25.00%, 23/92), and the inability to meet the diverse needs of parents (18.48%, 17/92). Based on the key links for quality improvement and the main causes of follow-up failure, the following intervention measures were adopted: (1) strengthen follow-up publicity and education; (2) build a follow-up team; and (3) establish a follow-up platform and system. The control chart indicated that with the implementation of the above intervention measures, the weekly follow-up rate increased to 74.09% (306/413) in July 2017 and 83.09% (511/615) in December 2017, finally achieving the quality improvement goal. During the COVID-19 pandemic, the follow-up rate of preterm infants fluctuated between 23.54% (460/1 954) and 70.97% (1 931/2 721), and subsequently, it returned to pre-pandemic levels starting in February 2023. CONCLUSIONS The application of quality improvement methodology can help to formulate intervention measures based on the main causes of follow-up failure, thereby improving the follow-up rate of preterm infants after discharge. This quality improvement method is feasible and practical and thus holds promise for clinical application.
Humans ; Quality Improvement ; Infant, Premature ; Infant, Newborn ; Patient Discharge ; Follow-Up Studies ; Female ; Male

BACKGROUND: Lung cancer is the leading malignancy in China in terms of both incidence and mortality. With increased health awareness and the widespread use of low-dose computed tomography (CT), early diagnosis rates have been steadily improving. Surgical intervention remains the primary treatment option for early-stage lung cancer, and video-assisted thoracoscopic surgery (VATS) has become a common approach due to its minimal invasiveness and rapid recovery. However, post-discharge recovery remains incomplete, underscoring the importance of postoperative care. Traditional follow-up methods, lack standardization, consume significant medical resources, and increase the burden of the patients. Artificial intelligence (AI)-driven disease management platforms offer a novel solution to optimize postoperative follow-up. This study followed 463 lung cancer surgery patients using an AI-based platform, aiming to identify common postoperative issues, propose solutions, improve quality of life, reduce recurrence-related costs, and promote AI integration in healthcare. METHODS: Using the AI disease management platform, this study integrated educational videos, collaboration between healthcare teams and AI assistants, daily health logs, health assessment forms, and personalized interventions to monitor postoperative recovery. The postoperative rehabilitation status of the patients was assessed by the Leicester Cough Questionnaire (LCQ-MC). Two independent t-test and one-way ANOVA were used to analyze the causes of postoperative cough in lung cancer. RESULTS: Most issues occurred within 7 d post-discharge, significantly declined on 14 d post-discharge. Factors such as gender, smoking history, and surgical approaches were found to influence cough recovery. The incidence of cough on 7 d post-discharge in females was higher than that in males (P<0.01), while the incidence of cough on 14 d post-discharge in elderly patients was lower than that in young patients (P=0.03). The AI-based platform effectively addressed cough, pain, and sleep disturbances through phased interventions. CONCLUSIONS The AI-based platform significantly enhanced postoperative management efficiency and the self-care capabilities of the patients, particularly in phased cough management. Future integration with wearable devices could enable more precise and personalized postoperative care, further advancing the application of AI technology across multidisciplinary healthcare domains.
Humans ; Lung Neoplasms/rehabilitation* ; Male ; Female ; Middle Aged ; Aged ; Patient Discharge ; Artificial Intelligence ; Adult ; Postoperative Care ; Postoperative Period ; Disease Management ; Quality of Life

OBJECTIVE: To investigate the current status of long-term quality of life in patients with severe acute pancreatitis (SAP) who have been cured and discharged, and to analyze the influencing factors affecting long-term quality of life in SAP cured patients after discharge. METHODS: A retrospective collection was conducted. Patients who were received standardized treatment before being cured and discharged from the hospital admitted to the first department of critical care medcine of the Second Affiliated Hospital of Anhui Medical University from January 2017 to December 2023 were enrolled. According to the 36-item short form health survey scale (SF-36) score, patients were divided into high score group (high quality of life, the top 50% of patients with total SF-36 score) and low score group (low quality of life, the bottom 50% of patients with total SF-36 score). The gender, age, history of hypertension and diabetes, etiology of pancreatitis, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), CT severity index (CTSI), laboratory indicators such as C-reactive protein (CRP), procalcitonin (PCT), blood glucose, and triglycerides upon admission, use of vasoactive drugs, non-invasive/high-flow ventilation, invasive ventilation, retroperitoneal puncture and drainage, open pancreatic surgery treatment and secondary infection during hospitalization were collected, as well as the retention of abdominal drainage tubes at discharge from hospital. Distribute follow-up questionnaires or telephone follow-up surveys through WeChat and Question Star programs to investigate the pancreatic secretion function, chronic abdominal pain, and recurrence of pancreatitis of patients after discharge. Multivariable Logistic regression was used to analyze the relevant factors affecting the long-term quality of life of cured patients with SAP. RESULTS: A total of 86 patients were ultimately enrolled. There were 43 patients in both the high and low score groups. Among 86 patients, 20 experienced acute pancreatitis recurrence, with a recurrence rate of 23.26%. Twenty-two (25.58%) experienced chronic abdominal pain after discharge, and 5 patients (5.81%) needed medication to relieve pain. Thirty-three patients (38.37%) had pancreatic exocrine dysfunction after discharge, characterized by abdominal distension, constipation or diarrhea. Twenty-two patients (25.58%) suffered from pancreatic endocrine dysfunction, and were diagnosed with diabetes. Univariate analysis showed that compared with the high score group, the low score group had more patients with hypertension, initial renal dysfunction, initial severe metabolic acidosis, initial serum calcium < 2.0 mmol/L, blood glucose > 11.1 mmol/L and cultured Gram positive bacteria (from blood/body fluid/pancreatic necrotic tissue) during treatment (48.84% vs. 16.28%, 60.47% vs. 32.56%, 18.60% vs. 4.65%, 88.37% vs. 62.79%, 55.81% vs. 30.23%, 34.88% vs. 13.95%), had higher CTSI score (6.60±1.61 vs. 5.77±1.32), lower hemoglobin level at discharge (g/L: 102.30±18.78 vs. 110.72±16.68), and a lower proportion of etiological interventions after discharge (34.88% vs. 67.44%), the differences were statistically significant (all P < 0.05). Multivariate Logistic regression analysis showed that hypertension [odds ratio (OR) = 4.814, 95% confidence interval (95%CI) was 1.196-19.378], initial serum calcium < 2.0 mmol/L (OR = 6.688, 95%CI was 1.321-33.873) and initial blood glucose > 11.1 mmol/L (OR = 6.473, 95%CI was 1.399-29.950) were risk factors for long-term quality of life in cured SAP patients (all P < 0.05), while post discharge prophylactic intervention was a protective factor for long-term quality of life (OR = 0.092, 95%CI was 0.020-0.425, P < 0.01). CONCLUSIONS Cured SAP patients have varying degrees of impaired secretion function and the possibility of recurrence of acute pancreatitis. Hypertension, initial serum calcium < 2.0 mmol/L and blood glucose > 11.1 mmol/L are independent influencing factors for low long-term quality of life in cured SAP patients. Prevention and intervention targeting the etiology of pancreatitis after discharge can improve the long-term quality of life of cured SAP patients.
Humans ; Quality of Life ; Retrospective Studies ; Pancreatitis/therapy* ; Patient Discharge ; Male ; Female ; Middle Aged ; APACHE ; Adult ; Acute Disease ; Aged

OBJECTIVE: To evaluate the impact of critical care warning platform (CWP) on clinical outcomes of patients transferred from internal medical ward to intensive care unit (ICU) based on real-world data. METHODS: A retrospective cohort study was conducted. The patients transferred from internal medical ward to ICU of Zhongda Hospital, Southeast University, between January 2022 and October 2024, were enrolled. They were divided into critical care warning group and conventional treatment group based on whether they were connected to the CWP. The patients in the critical care warning group were connected to the CWP, which collected real-time vital signs and treatment data. The platform automatically calculated severity scores, generated individualized risk assessments, and triggered warning alerts, allowing clinicians to adjust treatment plans accordingly. The patients in the conventional treatment group were not connected to the CWP and relied on conventional clinical judgment and nursing measures for treatment management. Baseline characteristics [gender, age, body mass index (BMI), admission type, severity score of illness, underlying diseases, and disease type at ICU admission], primary clinical outcome (in-hospital mortality), and secondary clinical outcomes [ICU mortality, length of ICU stay, total length of hospital stay, and mechanical ventilation and continuous renal replacement therapy (CRRT) status] were collected. Multivariate Logistic regression was used to analyze the impact of CWP on in-hospital death, and subgroup analyses were performed based on different patient characteristics. RESULTS: A total of 1 281 patients were enrolled, with 768 in the critical care warning group and 513 in the conventional treatment group. Compared with the conventional treatment group, the proportion of patients in the critical care warning group with underlying diseases of diabetes and malignancy and transferred to ICU due to sepsis was lowered, however, there were no statistically significant differences in other baseline characteristics between the two groups. Regarding the primary clinical outcome, the in-hospital mortality in the critical care warning group was significantly lower than that in the conventional treatment group [17.6% (135/768) vs. 25.7% (132/513), P < 0.01]. For secondary clinical outcomes, compared with the conventional treatment group, the patients in the critical care warning group had significantly fewer days of mechanical ventilation within 28 days [days: 2 (1, 6) vs. 2 (1, 8), P < 0.05], significantly shorter length of ICU stay [days: 3 (2, 8) vs. 4 (2, 10), P < 0.01], and significantly lower ICU mortality [15.1% (116/768) vs. 21.4% (110/513), P < 0.01]. Multivariate Logistic regression analysis showed that, after adjusting for age and underlying diseases, the use of CWP was significantly associated with a reduction of in-hospital mortality among patients transferred from internal medical ward to ICU [odds ratio (OR) = 0.670, 95% confidence interval (95%CI) was 0.502-0.894, P = 0.006]. Further subgroup analysis revealed that, among patients transferred to ICU due to sepsis, the use of CWP significantly reduced in-hospital mortality (OR = 0.514, 95%CI was 0.367-0.722, P < 0.001). In patients aged ≥ 70 years old (OR = 0.587, 95%CI was 0.415-0.831, P = 0.003) and those with underlying diseases of malignancy (OR = 0.124, 95%CI was 0.046-0.330, P < 0.001), CWP also showed significant protective effects on in-hospital prognosis. CONCLUSION The use of CWP is significantly associated with a reduction in in-hospital mortality among patients transferred from internal medical ward to ICU, demonstrating its potential in assessing the deterioration of hospitalized patients.
Humans ; Intensive Care Units ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Critical Care ; Male ; Female ; Patient Transfer ; Middle Aged ; Aged ; Cohort Studies

OBJECTIVE: To develop a digital intelligent multimodal shift handover system for the intensive care unit (ICU) and evaluate its application effect in ICU shift handovers. METHODS: A research and development team was established, consisting of 1 department director, 1 head nurse, 3 information technology engineers, 3 nurses, and 2 doctors. Team members were assigned responsibilities including overall coordination and planning, platform design and maintenance, pre-application training, collection and organization of clinical feedback, and research investigation respectively. A digital intelligent multimodal shift handover system was developed for ICU based on the Shannon-Weaver linear transmission model. This innovative system integrated automated data collection, intelligent dynamic monitoring, multidimensional condition analysis and visual reporting functions. A cloud platform was used to gather data from multi-parameter vital signs monitors, infusion pumps, ventilators and other devices. Artificial intelligence algorithms were employed to standardize and analyze the data, providing personalized recommendations for healthcare professionals. A self-controlled before-after method was adopted. Before the application of the ICU digital intelligent multimodal shift handover system (from December 2023 to March 2024), the traditional verbal bedside handover was used; from June 2024 to March 2025, the ICU digital intelligent multimodal shift handover system was applied for shift handovers. Questionnaires before the application of the shift handover system were collected in April 2024, and those after the application were collected in April 2025. The shift handover time, handover quality (scored by the nursing handover evaluation scale), satisfaction with doctor-nurse communication (scored by the ICU doctor-nurse scale) before and after the application of the handover system were compared, and nurses' satisfaction with the shift handover system (scored by the clinical nursing information system effectiveness evaluation scale) was investigated. RESULTS: After the application of the ICU digital intelligent multimodal shift handover system, the shift handover time was significantly shorter than that before the application [minutes: 20 (15, 25) vs. 30 (22, 40)], the handover quality was significantly higher than that before the application [score: 84.0 (78.0, 88.5) vs. 71.0 (55.0, 79.0)], and the satisfaction with doctor-nurse communication was also significantly higher than that before the application (score: 84.58±6.79 vs. 74.50±11.30). All differences were statistically significant (all P < 0.05). In addition, the nurses' system effectiveness evaluation scale score was 102.30±10.56, which indicated that nurses had a very high level of satisfaction with the ICU digital intelligent multimodal shift handover system. CONCLUSIONS The application of the ICU digital intelligent multimodal shift handover system can shorten the shift handover time, improve the handover quality, and enhance the satisfaction with doctor-nurse communication. Nurses have a high level of satisfaction with this system.
Intensive Care Units ; Humans ; Patient Handoff ; Artificial Intelligence ; Algorithms

Humans ; Acute Disease ; Pancreatitis/diagnosis* ; Follow-Up Studies ; Patient Discharge ; Disease Progression

INTRODUCTION: Postoperative day 1 (POD 1) mobilisation is a key clinical indicator for the fragility hip fracture surgery population. This study aimed to evaluate the current trends of POD 1 mobilisation at our institution and to review the relationships between early mobilisation and outcomes of early functional recovery, length of stay (LOS) and discharge destination. METHODS: In this preliminary observational study, data pertaining to demographics, premorbid function, health status, injury and surgical factors, POD 1 mobilisation status and clinical outcomes of interest were retrieved from eligible patients. Patients who attained POD 1 ambulation formed the early ambulation (EA) group, while the remaining patients formed the delayed ambulation (DA) group. Data were analysed for any significant difference between the groups. RESULTS: One hundred and fifteen patients were included in the analysis. The rate of patients achieving at least sitting out of bed on POD 1 was 80.0% (n=92), which was comparable to the data available from international hip fracture audit databases. There were 55 (47.8%) patients in the EA group and 60 (52.5%) patients in the DA group. The EA group was approximately nine times more likely to achieve independence in ambulation at discharge compared to the DA group (adjusted odds ratio 9.20, 95% confidence interval 1.50-56.45; P = 0.016). There were observed trends of shorter LOS and more proportion of home discharge in the EA group compared to the DA group (P > 0.05). CONCLUSION This is the first local study to offer benchmark of the POD 1 mobilisation status for this population. Patients who attained POD 1 ambulation had better early functional recovery.
Humans ; Early Ambulation ; Patient Discharge ; Tertiary Care Centers ; Hip Fractures/surgery* ; Physical Therapy Modalities ; Length of Stay

Humans ; Blood Pressure/physiology* ; Cohort Studies ; Patient Discharge ; Ischemic Stroke ; Stroke ; Brain Ischemia ; Diabetes Mellitus ; Hypertension