Acceptability of and symptom findings from an online symptom check-in tool for COVID-19 outpatient follow-up among a predominantly healthcare worker population.

INTRODUCTION: Health systems worldwide have had to prepare for a surge in volume in both the outpatient and inpatient settings since the emergence of COVID-19. Early international healthcare experiences showed approximately 80% of patients with COVID-19 had mild disease and therfore could be managed as outpatients. However, SARS-CoV-2 can cause a biphasic illness with those affected experiencing a clinical deterioration usually seen after day 4 of illness. OBJECTIVE: We created an online tool with the primary objective of allowing for virtual disease triage among the increasing number of outpatients diagnosed with COVID-19 at our hospital. Secondary aims included COVID-19 education and the promotion of official COVID-19 information among these outpatients, and analysis of reported symptomatology. METHODS: Outpatients with acute COVID-19 disease received text messages from the hospital containing a link to an online symptom check-in tool which they were invited to complete. RESULTS: 296 unique participants (72%) from 413 contacted by text completed the online check-in tool at least once, generating 831 responses from 1324 texts sent. 83% of text recipients and 91% of unique participants were healthcare workers. 7% of responses to the tool were from participants who admitted to a slight worsening of their symptoms during follow-up. Fatigue was the most commonly reported symptom overall (79%), followed by headache (72%). Fatigue, headache and myalgia were the most frequently reported symptoms in the first 3 days of illness. 8% of responses generated in the first 7 days of illness did not report any of the cardinal symptoms (fever, cough, dyspnoea, taste/smell disturbance) of COVID-19. Participants found the tool to be useful and easy to use, describing it as 'helpful' and 'reassuring' in a follow-up feedback survey (n=140). 93% said they would use such a tool in the future. 39% reported ongoing fatigue, 16% reported ongoing smell disturbance and 14% reported ongoing dyspnoea after 6 months. CONCLUSION: The online symptom check-in tool was found to be acceptable to participants and saw high levels of engagement and satisfaction. Symptomatology findings highlight the variety and persistence of symptoms experienced by those with confirmed COVID-19 disease.

Usage of unscheduled hospital care by homeless individuals in Dublin, Ireland: a cross-sectional study.

OBJECTIVES: Homeless people lack a secure, stable place to live and experience higher rates of serious illness than the housed population. Studies, mainly from the USA, have reported increased use of unscheduled healthcare by homeless individuals.We sought to compare the use of unscheduled emergency department (ED) and inpatient care between housed and homeless hospital patients in a high-income European setting in Dublin, Ireland. SETTING: A large university teaching hospital serving the south inner city in Dublin, Ireland. Patient data are collected on an electronic patient record within the hospital. PARTICIPANTS: We carried out an observational cross-sectional study using data on all ED visits (n=47 174) and all unscheduled admissions under the general medical take (n=7031) in 2015. PRIMARY AND SECONDARY OUTCOME MEASURES: The address field of the hospital's electronic patient record was used to identify patients living in emergency accommodation or rough sleeping (hereafter referred to as homeless). Data on demographic details, length of stay and diagnoses were extracted. RESULTS: In comparison with housed individuals in the hospital catchment area, homeless individuals had higher rates of ED attendance (0.16 attendances per person/annum vs 3.0 attendances per person/annum, respectively) and inpatient bed days (0.3 vs 4.4 bed days/person/annum). The rate of leaving ED before assessment was higher in homeless individuals (40% of ED attendances vs 15% of ED attendances in housed individuals). The mean age of homeless medical inpatients was 44.19 years (95% CI 42.98 to 45.40), whereas that of housed patients was 61.20 years (95% CI 60.72 to 61.68). Homeless patients were more likely to terminate an inpatient admission against medical advice (15% of admissions vs 2% of admissions in homeless individuals). CONCLUSION: Homeless patients represent a significant proportion of ED attendees and medical inpatients. In contrast to housed patients, the bulk of usage of unscheduled care by homeless people occurs in individuals aged 25-65 years.

Serum potassium levels as an outcome determinant in acute medical admissions.

The relationship between serum potassium levels and mortality in acute medical admissions is uncertain. In particular, the relevance of minor abnormalities in potassium level or variations within the normal range remains to be determined. We performed a retrospective cohort study of all emergency medical admissions to St James's Hospital (Dublin, Ireland) between 2002 and 2012. We used a stepwise logistic regression model to predict in-hospital mortality, adjusting risk estimates for major predictor variables. There were 67,585 admissions in 37,828 patients over 11 years. After removing long-stay patients, 60,864 admissions in 35,168 patients were included in the study. Hypokalaemia was present in 14.5% and hyperkalaemia in 4.9%. In-hospital mortality was 3.9, 5.0, and 18.1% in the normokalaemic, hypokalaemic and hyperkalaemic groups respectively. Hypokalaemic patients had a univariate odds ratio (OR) of 1.29 for in-hospital mortality (95% confidence interval (CI) 1.16-1.43; p<0.001). Hyperkalaemic patients had a univariate OR for in-hospital mortality of 5.2 (95% CI 4.7-5.7; p<0.001). The ORs for an in-hospital death for potassium between 4.3 and 4.7 mmol/l, and 4.7 and 5.2 mmol/l, were 1.73 (95% CI 1.51-1.99) and 2.97 (95% CI 2.53-3.50) respectively. Hyperkalaemia and hypokalaemia are associated with increased mortality.

Consultant duration of clinical practice as a cost determinant of an emergency medical admission.

BACKGROUND: Little data exists relating years of hospital consultant work experience, from time of consultant certification, and costs incurred for emergency medical patients under their care. We examined the total cost of emergency medical episodes in relation to certified consultant years experience using a database of emergency admissions. METHODS: All emergency admissions (19,295 patients) from January 2008 to December 2012 were studied. Consultants were categorized by total years of certified experience according to four experience categories (< 15, 15-20, > 20 to ≤ 25, and > 25 years). Costs per case calculations included all pay, non-pay, and diagnostic/support infra-structural costs. We used quantile regression analysis to examine the impact of predictor variables on total costs over the predictor distribution and logistic regression on outcomes and costs, adjusting for other major predictors of cost. RESULTS: Major predictors of costs were identified. Quantile regression cost parameter estimates of hospital episode costs decreased with experience; the unit change at the Q25 point of the years experience distribution was - 62 (95 % CI - 87, - 37), - 162 (95 % CI - 203, - 120) at the median, but decreased at the Q75 point to - 340 (95 % CI - 416, - 264). The odds ratio of a hospital episode cost being below the median for each category of consultant experience >15 years qualified were 0.75 (95 % CI 0.68, 0.83), 0.77 (95 % CI 0.70, 0.86), and 0.70 (95 % CI 0.64, 0.78): p < 0.001 for each experience category vs. <15 years qualified. CONCLUSIONS: There appear to be cost advantages to care delivered by certified consultants of >20 years in clinical practice.

MRI in acutely ill medical patients in an Irish hospital: influence on outcomes and length of hospital stay.

PURPOSE: MRI is an important diagnostic tool for acute medical admissions. Its relevance to in-hospital mortality and length of stay (LOS) has been examined at St James's Hospital in Dublin, Ireland. METHODS: All patients admitted for medical emergencies from 2010 through 2012 were studied (18,534 episodes); any relationship between an MRI request, underlying diagnosis on any in-hospital death, and LOS was examined. Logistic regression with generalized estimating equations, adjusted for correlated observations (readmissions), odds ratio estimates, and zero-truncated Poisson regression for LOS were used. RESULTS: MRI procedures were requested in 8.6% of episodes. The in-hospital mortality rate was significantly higher when MRI was performed (7.8% vs 4.6%, P < .001). The unadjusted odds ratio for in-hospital death during that episode was 1.74 (95% confidence interval, 1.26-2.37; P < .001) compared with episodes without MRI. The hospital stay for those MRI episodes was longer (median, 9.1 days; interquartile range, 4.0-26.8 days) than for non-MRI episodes (median, 5.8 days; interquartile range, 2.2-12.2; P < .001). Each unit increase in MRI waiting time (cutoffs set at 0, 1, 3, 7, and 14 days) gave an estimated increase of 1.12 days in hospital LOS, adjusted for illness severity and comorbidities. CONCLUSIONS: MR imaging identified in a subgroup of emergency patients at higher risk of an in-hospital death. These patients have longer LOS attributable in part to procedure wait times, not merely to illness severity or comorbidities.

Factors predicting the hospital episode costs of emergency medical admissions.

BACKGROUND: Important outcome predictor variables for emergency medical admissions are the Manchester Triage Category, Acute Illness Severity, Chronic Disabling Disease and Sepsis Status. We have examined whether these are also predictors of hospital episode costs. METHODS: All patients admitted as medical emergencies between January 2008 and December 2012 were studied. Costs per case were adjusted by reference to the relative cost weight of each diagnosis related group (DRG) but included all pay costs, non-pay costs and infra-structural costs. We used a multi-variate logistic regression with generalized estimating equations (GEE), adjusted for correlated observations, to model the prediction of outcome (30-day in-hospital mortality) and hospital costs above or below the median. We used quantile regression to model total episode cost prediction over the predictor distribution (quantiles 0.25, 0.5 and 0.75). RESULTS: The multivariate model, using the above predictor variables, was highly predictive of an in-hospital death-AUROC of 0.91 (95% CI: 0.90, 0.92). Variables predicting outcome similarly predicted hospital episode cost; however predicting costs above or below the median yielded a lower AUROC of 0.73 (95% CI: 0.73, 0.74). Quantile regression analysis showed that hospital episode costs increased disproportionately over the predictor distribution; ordinary regression estimates of hospital episode costs over estimated the costs for low risk and under estimated those for high-risk patients. CONCLUSION: Predictors of outcome also predict costs for emergency medical admissions; however, due to costing data heteroskedasticity and the non-linear relationship between dependant and predictor variables, the hospital episode costs are not as easy to predict based on presentation status.