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opencc-zeroDec 2021View details →

Patient experience with nursing care and patient satisfaction with overall hospital services

Objective: To determine how patient experience with nursing care influence patient satisfaction with overall hospital services. Design: This was a cross-sectional study. Setting: Inpatients were consecutively recruited at the national hospital (with 2000 beds) in Shanghai, China. Participants: The inclusion criteria were as follows: (1) hospitalized for 2 days or more; (2) able to read and understand Chinese; and (3) aged 18 years old or above. Patients with mental health problems were excluded. 756 patient surveys distributed among 36 wards were analyzed. The mean age of participants in the study was 57.7 (SD=14.5) and ranged from 18-80 years. Most participants were male (61.5%) and ever married (94.6%). Primary and secondary outcome measures: Patient experience with nursing care, meaning the sum of all interactions between patients and nurses, was measured using the self-designed questionnaire, which was developed by patient interviews, literature analysis and expert consultation. The overall patient satisfaction question was measured with a ten-point response option ranging from 1-10. Results: A linear relationship between the patient experience with nursing care and overall patient satisfaction was observed. The patient experience with nursing care was significantly associated with overall satisfaction in the crude model and in the adjusted models. Even after adjusting for 6 sociodemographic and 3 disease-related factors, the patient experience with nursing care explained 34.9% of the variation in overall patient satisfaction. Conclusions: This study showed that patient experience with nursing care was an important predictor for overall patient satisfaction.

opencc-by-4.0Mar 2022View details →

Developing an in-depth understanding of the prevalence, risk factors and treatment recommendations for phantom limb pain, and patient-generated care priorities for people who have undergone lower limb amputations.

The file holds data collected for a series of four studies on phantom limb pain.

opencc-zeroJul 2022View details →

Data from: Early prediction of in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational cohort study

Objective: Congestive heart failure (CHF) is a clinical syndrome in which heart disease progresses to a severe stage. Risk assessment and early diagnosis of death in patients with CHF are critical to patient prognosis and treatment. The purpose of this study was to establish a nomogram predicting in-hospital death for CHF patients in the ICU. Design: A retrospective observational cohort study. Setting and participants: The data of study from 30,411 CHF patients in the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). Primary outcome: In-hospital mortality. Results: The inclusion criteria were met by 15983 subjects, whose in-hospital mortality rate was 12.4%. Multivariate analysis determined that the independent risk factors were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, <a>mechanical</a> <a>ventilation</a>, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The C-index of the nomogram (0.767, 95%CI: 0.759–0.779) was <a>superior</a> to that of the traditional SOFA, APSIII and GWTGHF score, indicating its discrimination power. Calibration plots demonstrated that the predicted results are in good agreement with the observed results. The decision curves of the derivation and validation sets both had net benefits. Conclusion: The twenty independent risk factors for in-hospital mortality of CHF patients were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, <a>mechanical</a> <a>ventilation</a>, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The nomogram that included these factors accurately predicted the in-hospital mortality of CHF patients. The novel nomogram has the potential to be a clinical practice aided predictive tool for predicting and assessing mortality in CHF patients in the ICU.

opencc-by-4.0Jul 2022View details →

abnormal CT findings among patients with abdominal pain in the radiology department of a teriary care center

These are Data were collected from the hospital system regarding demographic characteristics, past medical and surgical history, symptom description and physical examination findings, diagnosis (both initial and final), laboratory tests, CT findings, and results from other requested radiology modalities. All information was obtained after obtaining ethical approval.

opencc-by-4.0Aug 2022View details →

Dataset of "Telemedicine and its acceptance by patients with type 2 diabetes mellitus at a single care center during the COVID-19 emergency: A prospective observational study"

Dataset of the article titled "Telemedicine and its acceptance by patients with type 2 diabetes mellitus at a single care center during the COVID-19 emergency: A prospective observational study”.

opencc-by-4.0Apr 2022View details →

Urinary Sodium Excretion is Low Prior to Acute Kidney Injury in Intensive Care Unit Patients

Dataset corresponding to the submitted manuscript "Urinary Sodium Excretion is Low Prior to Acute Kidney Injury in Intensive Care Unit Patients" doi: 10.3389/fneph.2022.929743

opencc-by-4.0Nov 2021View details →

Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)

This video is the fifth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading). Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/egINC9hJifI

opencc-zeroOct 2022View details →

Detection of SARS-CoV-2 in conjunctival secretion and tears in patients with COVID-19 in a tertiary care centre, South India

Aims and objectives: The purpose of this study is to detect the presence of SAR-CoV-2 viral RNA in conjunctival secretions of COVID-19 patients and to compare the RT-PCR positivity rate for SARS-CoV-2 in conjunctival and nasopharyngeal swabs. Materials and method: Eighty hospitalised COVID-19 patients whose nasopharyngeal swab tested positive for SARS-CoV-2 by RT-PCR were included in the study. Conjunctival swab was collected from the eyes of these patients and sent for detection of SARS-CoV-2 by RT-PCR method. Results: Among the eighty patients, 51 (63.7%) were males and 29 (36.3%) were females. The mean age of the patients was 55.93 ± 16.59. Six patients had ocular manifestations. Eleven (13.75%) patients tested positive on conjunctival swab for SARS-CoV-2 viral RNA, and only one of them had ocular manifestations out of the eleven. Conclusion: In our study, the presence of SARS-CoV-2 in conjunctival secretions of COVID-19 patients was detected and this was not dependent on the presence of ocular manifestations or duration of disease. Though the conjunctival positivity is lower compared to the nasopharyngeal swab sampling, ocular surface and secretions can be a potential route of viral transmission.

opencc-by-4.0Oct 2024View details →

Predictive factors of inpatient rehabilitation stay and post-discharge care burden after joint replacement for hip and knee osteoarthritis: a retrospective study on 1,678 patients.

Dataset of the study entitled "Predictive factors of inpatient rehabilitation stay and post-discharge care burden after joint replacement for hip and knee osteoarthritis: a retrospective study on 1,678 patients"

opencc-zeroJun 2019View details →

Data from: Performance of primary care in different health care facilities: a cross sectional study of patients' experiences in Southern Malawi

Objective: In most African countries, primary care is delivered through a district health system. Many factors, including staffing levels, staff experience, availability of equipment and facility management, affect the quality of primary care between and within countries. The purpose of this study was to assess the quality of primary care in different types of public health facilities in Southern Malawi. Study design: This was a cross sectional quantitative study. Setting: The study was conducted in 12 public primary care facilities in Neno, Blantyre and Thyolo districts in July 2018. Participants: Patients aged 18 years and above, excluding the severely ill, were selected to participate in the study. Primary outcomes: We used the Malawian primary care assessment tool to conduct face-to-face interviews. ANOVA at 0.05 significance level was performed to compare primary care dimension means and total primary care scores. Linear regression models at 95% CI were used to assess associations between primary care dimension scores, patients' characteristics and healthcare setting. Results: The final number of respondents was 962 representing 96.1% response rate. Patients in Neno hospitals scored 3.77 points higher than those in Thyolo health centers, and 2.87 higher than those in Blantyre health centers in total primary care performance. Primary care performance in health centers and in hospital clinics was similar in Neno (20.9 vs 19.0, p= 0.608) while in Thyolo, it was higher at the hospital than at the health centers (19.9 vs 15.2, p<0.001). Urban and rural facilities showed a similar pattern of performance. Conclusion: These results showed considerable variation in experiences among primary care users in the public health facilities in Malawi. Factors such as funding, policy and clinic level interventions influence patients' reports of primary care performance. These factors should be further examined in longitudinal and experimental settings.

opencc-zeroJun 2021View details →

Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database

Objective: The predictors of in-hospital mortality for intensive care units (ICU)-admitted HF patients remain poorly characterized.We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients. Design: A retrospective cohort study. Setting and Participants: Data were extracted from the MIMIC-III database. Data on 1,177 heart failure patients were analysed. Methods: Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation and validation groups. Independent risk factors for in-hospital mortality were screened using XGBoost and LASSO regression models in the derivation sample. Multivariable logistic regression analysis was used to build prediction models. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients. Results: Among the 1,177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression, and GWTG-HF risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (P<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram. Conclusions: Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.

opencc-by-4.0Aug 2021View details →

Modeling Quality of Care in Home Health Nurse-to-Patient Assignment Problem

This file is for the multi-period home health care nurse assignment problem. The parameters are explained in the comment line in the data file. The users can refer to the "readme.txt" to get to know how to use these files.

opencc-by-4.0Jul 2023View details →

GERDAT020-Dataset for- Challenges of caring for older patients with multimorbidity including cancer

This dataset shows the data underlying the publication Challenges of caring for older patients with multimorbidity including cancer, published in JGO on 14 July 2023, which can be found at <a href="https://doi.org/10.1016/j.jgo.2023.101588">https://doi.org/10.1016/j.jgo.2023.101588</a>

opencc-by-4.0Oct 2023View details →

Prepare for your next consultation with your health care provider - patient questions prompt list

A list of questions patients with cancer and multimorbidity can use to be better prepared for their next consultation with their health care provider.

Symptom Burden and Unmet Supportive Care Needs in Lung Cancer Patients Undergoing First or Second Line Immunotherapy

ClinicalTrials.gov study NCT03741868. IPD Sharing: NO. Countries: 1. Publications: 2.

Three Care Models for Elderly Patients With Hip Fracture

ClinicalTrials.gov study NCT01350557. IPD Sharing: NO. Countries: 1. Publications: 3.

Role of Some Biochemical Indices for Prediction of Acute Kidney Injury in Intensive Care Unit Patients in Upper Egypt

ClinicalTrials.gov study NCT06791200. IPD Sharing: NO. Countries: 1. Publications: 1.

Music Use and Perceived Psycho-social Benefits of Music of Caregivers of Patients in an Intensive Care Unit

ClinicalTrials.gov study NCT03156192. IPD Sharing: NO. Countries: 1. Publications: 4.

Correlation Between Noninvasive Blood Vessel Functionality Parameters and Cerebral Hemodynamics in Neurocritical Care Patients

ClinicalTrials.gov study NCT06511804. IPD Sharing: NO. Countries: 1. Publications: 1.