METHODS: A qualitative case study was employed for this research. Semi-structured, in-depth interviews and focus group discussions were conducted on WeChat. Participants were purposively sampled through snowball sampling in Hainan and Dalian, China. A total of 28 older adults aged 60-75 and six adult children were interviewed until data saturation was achieved, followed by a thematic analysis.
RESULTS: The expectations of smart nursing homes include: 1) quality of care supported by governments and societies; 2) smart technology applications; 3) the presence of a skilled healthcare professional team; 4) access to and scope of basic medical services; and 5) integration of medical services. The acceptability of smart nursing homes included factors such as stakeholders' perceived efficaciousness, usability, and collateral damages of using smart technologies, and the coping process of adoption was influenced by factors such as age, economic status, health status, education, and openness to smart technologies among older adults.
CONCLUSIONS: Chinese older adults and their family members have a positive perception of the smart nursing home model. The qualitative evidence regarding their expectations and acceptability of smart nursing homes contributes valuable insights for a wide range of stakeholders involved in the planning and implementation of smart nursing homes.
METHODS: This is a retrospective observational case series of patients under 18 years old who fulfilled the WHO COVID-19 case definition and were referred to our paediatric neurology unit at Hospital Tunku Azizah Kuala Lumpur. Their demographic data, neurological symptoms, laboratory and supporting investigations, neuroimaging, treatment and outcomes were collected and analysed.
RESULTS: There were eleven patients with neurological manifestations who fulfilled the WHO COVID-19 case definition. Nine patients presented with seizures and/or encephalopathy, one patient with eye opsoclonus and another patient with persistent limbs myokymia. Based on the history, clinical, electrophysiological and radiological findings, two of them had febrile infection-related epilepsy syndrome, two had acute disseminated encephalomyelitis, two had acute necrotising encephalopathy of childhood, one each had hemiconvulsion-hemiplegia-epilepsy syndrome, acute encephalopathy with bilateral striatal necrosis, hemi-acute encephalopathy with biphasic seizures and reduced diffusion, infection-associated opsoclonus and myokymia.
CONCLUSIONS: This case series highlighted a wide spectrum of neurological manifestations of COVID-19 infection. Early recognition and prompt investigations are important to provide appropriate interventions. It is essential that these investigations should take place in a timely fashion and COVID-19 quarantine period should not hinder the confirmation of various presenting clinical syndromes.
METHODS: Several methods were implemented. Firstly, a modified Delphi process for the contextualisation of learning outcomes was implemented with a purposefully sampled expert group of Malaysian Family Medicine Specialists. Secondly a small group review for supporting materials was undertaken. Finally, qualitative data in relation to the family medicine specialists' experiences of the processes was collected via online questionnaire and analysed via template analysis. Descriptive statistics were used.
RESULTS: Learning outcomes were reviewed over three rounds; 95.9% (1691/1763) of the learning outcomes were accepted without modification, with the remainder requiring additions, modifications, or deletions. Supporting materials were extensively altered by the expert group. Template analysis showed that Family Medicine Specialists related positively to their involvement in the process, commenting on the amount of similarity in the medical curriculum whilst recognising differences in disease profiles and cultural approaches.
CONCLUSIONS: Learning outcomes and associated material were transferable between "home" and "host" institution. Where differences were discovered this novel approach places "host" practitioners' experiences and knowledge central to the adaptation process, thereby rendering a fit for purpose curriculum. Host satisfaction with the outcome of the processes, as well as ancillary benefits were clearly identified.
METHODS: The urine samples were photographed in a customized photo box, under five simulated lighting conditions, using five smartphones. The images were analyzed using Adobe Photoshop to obtain Red, Green, and Blue (RGB) values. The correlation between RGB values and urine laboratory parameters were determined. The optimal cut-off value to predict dehydration was determined using area under the receiver operating characteristic curve.
RESULTS: A total of 56 patients were included in the data analysis. Images captured using five different smartphones under five lighting conditions produced a dataset of 1400 images. The study found a statistically significant correlation between Blue and Green values with urine osmolality, sodium, urine specific gravity, protein, and ketones. The diagnostic accuracy of the Blue value for predicting dehydration were "good" to "excellent" across all phones under all lighting conditions with sensitivity >90% at cut-off Blue value of 170.
CONCLUSIONS: Smartphone-based urine colorimetry is a highly sensitive tool in predicting dehydration.
METHODS: Data were derived from 360 inpatient medical records from six types C public and private hospitals in an Indonesian rural province. These data were accumulated from inpatient medical records from four major disciplines: medicine, surgery, obstetrics and gynecology, and pediatrics. The dependent variable was provider moral hazards, which included indicators of up-coding, readmission, and unnecessary admission. The independent variables are Physicians' characteristics (age, gender, and specialization), coders' characteristics (age, gender, education level, number of training, and length of service), and patients' characteristics (age, birth weight, length of stay, the discharge status, and the severity of patient's illness). We use logistic regression to investigate the determinants of moral hazard.
RESULTS: We found that the incidences of possible unnecessary admissions, up-coding, and readmissions were 17.8%, 11.9%, and 2.8%, respectively. Senior physicians, medical specialists, coders with shorter lengths of service, and patients with longer lengths of stay had a significant relationship with the incidence of moral hazard.
CONCLUSION: Unnecessary admission is the most common form of a provider's moral hazard. The characteristics of physicians and coders significantly contribute to the incidence of moral hazard. Hospitals should implement reward and punishment systems for doctors and coders in order to control moral hazards among the providers.