scholarly journals Evaluating the impact of an Acute Care at Home service on acute hospital admissions

2017 ◽  
Vol 17 (5) ◽  
pp. 451 ◽  
Author(s):  
Eamon Farrell ◽  
Patricia McCaffrey ◽  
Roisin Toner ◽  
Catherine Sheeran
Keyword(s):  
Acute Care ◽  
Hospital Admissions ◽  
Acute Hospital ◽  
Home Service ◽  
Care At Home ◽  
The Impact ◽  
At Home

scholarly journals Nurse Led Symptom based Telephonic Triaging in Post Acute Care At Home Programme (PACH): Reduction in unnecessary Acute Hospital admissions

2017 ◽  
Vol 17 (5) ◽  
pp. 523
Author(s):  
Tina Veronique Tng ◽  
Lay Kian Goo ◽  
Yang Ling Lim ◽  
Min Min Loo ◽  
Geraldine Huiling Song ◽  
...  
Keyword(s):  
Acute Care ◽  
Hospital Admissions ◽  
Acute Hospital ◽  
Care At Home ◽  
Post Acute Care ◽  
At Home

scholarly journals 085Southern Trust Acute Care at Home Service for Older People

2017 ◽  
Vol 46 (Suppl_3) ◽  
pp. iii1-iii12
Author(s):  
Ann McCann ◽  
Patricia McCaffrey ◽  
Gail Nicholson ◽  
Eamon Farrell
Keyword(s):  
Older People ◽  
Acute Care ◽  
Home Service ◽  
Care At Home ◽  
At Home

scholarly journals 193A Review of the Acute Care at Home Service in the Southern Trust

2018 ◽  
Vol 47 (suppl_5) ◽  
pp. v13-v60
Author(s):  
Anna Louise Alexander ◽  
Gail Nicholson ◽  
Patricia McCaffrey
Keyword(s):  
Acute Care ◽  
Home Service ◽  
Care At Home ◽  
At Home

scholarly journals Ethical dilemmas in providing acute medical care at home for children: a survey of health professionals

2020 ◽  
Vol 4 (1) ◽  
pp. e000590 ◽  
Author(s):  
Penelope A Bryant
Keyword(s):  
Home Care ◽  
Acute Care ◽  
Healthcare Professionals ◽  
Home Healthcare ◽  
Health Staff ◽  
Annual Conference ◽  
Clinical Scenarios ◽  
Care At Home ◽  
The Impact ◽  
At Home

ObjectiveAcute care at home is increasing. We aimed to determine the views of healthcare professionals on the ethics of providing home care and compare the impact of situational changes on their opinions.DesignAn analysis of opinions of home healthcare professionals.SettingThe Australasian Hospital-in-the-Home Annual Conference, November 2017.ParticipantsEighty physicians, nurses and allied health staff who provide acute care for children and adults at home.MethodsClinical scenarios were presented about a 14 years old receiving intravenous antibiotics at home via an established home care pathway, and participants were asked to vote manually on whether providing home care was ethical.Main outcomesThe proportions of healthcare professionals who believed that provision of home care was ethical in different situations.ResultsFor each question the response rate ranged from 71% to 100%. While the provision of acute home care was deemed ethical by the majority (77/80, 96%), this decreased when other factors were involved such as domestic violence (37/63 (59%) OR 0.06, 95% CI 0.02 to 0.20, p<0.001) and parental reluctance (28/67 (42%) OR 0.02, 95% CI 0.008 to 0.09, p<0.001). The age of consent affected the proportion who considered home care ethical against parental wishes: 16 years (48/58, 83%) versus 14 years (33/53, 52%) OR 4.4, 95% CI 1.9 to 10.1, p<0.001. The lowest proportion to consider home care ethical (16%) was when home care was deemed less than hospital care.ConclusionsHome healthcare providers are supportive of the ethics of providing acute care at home for children, although differ among themselves with situational complexities. Applying the tenets of medical ethics (autonomy, non-maleficence, beneficence and justice) can provide insights into the factors that may influence opinions.

An evaluation of opioid use during acute hospital admissions

2022 ◽  
Vol 4 (1) ◽  
pp. 24-31
Author(s):  
Alison Blackburn
Keyword(s):  
Acute Pain ◽  
Hospital Admissions ◽  
Hospital Setting ◽  
Acute Hospital ◽  
Opioid Use ◽  
Opioid Medication ◽  
Age Range ◽  
The Impact ◽  
Acute Hospital Setting

Long-term opioid use can begin with the treatment of acute pain. However, there is little evidence concerning the impact that better opioid awareness in the acute phase may have on reducing the use of opioids in the long term. This project explored which opioids are routinely prescribed within an acute hospital setting and how these opioids were used over the course of the hospital stay. Codeine and morphine remain the most commonly prescribed opioids. Opioids were prescribed and given to people across the age range, from 16 to 98 years. The project found that 19% of patients were admitted with a pre-existing opioid. Up to 66% of patients were discharged with opioid medication, with almost 20% leaving with more than one opioid. Regular opioid use routinely exposes patients to long-term opioid use and those patients initiated onto opioid medication during admission should have the benefit of planned de-escalation before discharge.

scholarly journals Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19

2020 ◽  
Author(s):  
Houssein H. Ayoub ◽  
Hiam Chemaitelly ◽  
Shaheen Seedat ◽  
Monia Makhoul ◽  
Zaina Al Kanaani ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Acute Care ◽  
Hospital Admissions ◽  
Deterministic Model ◽  
Infection Prevalence ◽  
Care Hospital ◽  
Healthcare Needs ◽  
National Response ◽  
The Impact ◽  
Second Wave

AbstractBackgroundMathematical modeling constitutes an important tool for planning robust responses to epidemics. This study was conducted to guide the Qatari national response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. The study investigated the time course of the epidemic, forecasted healthcare needs, predicted the impact of social and physical distancing restrictions, and rationalized and justified easing of restrictions.MethodsAn age-structured deterministic model was constructed to describe SARS-CoV-2 transmission dynamics and disease progression throughout the population.ResultsThe enforced social and physical distancing interventions flattened the epidemic curve, reducing the peaks for incidence, prevalence, acute-care hospitalization, and intensive care unit (ICU) hospitalizations by 87%, 86%, 76%, and 78%, respectively. The daily number of new infections was predicted to peak at 12,750 on May 23, and active-infection prevalence was predicted to peak at 3.2% on May 25. Daily acute-care and ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number R0 had varied between 1.07-2.78, and 50.8% of the population were estimated to have been infected (1.43 million infections). The proportion of actual infections diagnosed was estimated at 11.6%. Applying the concept of Rt tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when Rt declined to 0.7, to buffer the increased interpersonal contact with easing of restrictions and to minimize the risk of a second wave. No second wave has materialized as of October 15, 2020, five months after the epidemic peak.ConclusionsUse of modeling and forecasting to guide the national response proved to be a successful strategy, reducing the toll of the epidemic to a manageable level for the healthcare system.

scholarly journals Effect of Seasonal Variation on Clinical Outcome in Patients with Chronic Conditions: Analysis of the Commonwealth Scientific and Industrial Research Organization (CSIRO) National Telehealth Trial (Preprint)

2017 ◽  
Author(s):  
Ahmadreza Argha ◽  
Andrey Savkin ◽  
Siaw-Teng Liaw ◽  
Branko George Celler
Keyword(s):  
Seasonal Variation ◽  
Length Of Stay ◽  
Clinical Outcome ◽  
Seasonal Variations ◽  
Chronic Conditions ◽  
Hospital Admissions ◽  
Australian Capital Territory ◽  
Home Telemonitoring ◽  
The Impact ◽  
At Home

BACKGROUND Seasonal variation has an impact on the hospitalization rate of patients with a range of cardiovascular diseases, including myocardial infarction and angina. This paper presents findings on the influence of seasonal variation on the results of a recently completed national trial of home telemonitoring of patients with chronic conditions, carried out at five locations along the east coast of Australia. OBJECTIVE The aim is to evaluate the effect of the seasonal timing of hospital admission and length of stay on clinical outcome of a home telemonitoring trial involving patients (age: mean 72.2, SD 9.4 years) with chronic conditions (chronic obstructive pulmonary disease coronary artery disease, hypertensive diseases, congestive heart failure, diabetes, or asthma) and to explore methods of minimizing the influence of seasonal variations in the analysis of the effect of at-home telemonitoring on the number of hospital admissions and length of stay (LOS). METHODS Patients were selected from a hospital list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. A total of 114 test patients and 173 control patients were available in this trial. However, of the 287 patients, we only considered patients who had one or more admissions in the years from 2010 to 2012. Three different groups were analyzed separately because of substantially different climates: (1) Queensland, (2) Australian Capital Territory and Victoria, and (3) Tasmania. Time series data were analyzed using linear regression for a period of 3 years before the intervention to obtain an average seasonal variation pattern. A novel method that can reduce the impact of seasonal variation on the rate of hospitalization and LOS was used in the analysis of the outcome variables of the at-home telemonitoring trial. RESULTS Test patients were monitored for a mean 481 (SD 77) days with 87% (53/61) of patients monitored for more than 12 months. Trends in seasonal variations were obtained from 3 years’ of hospitalization data before intervention for the Queensland, Tasmania, and Australian Capital Territory and Victoria subgroups, respectively. The maximum deviation from baseline trends for LOS was 101.7% (SD 42.2%), 60.6% (SD 36.4%), and 158.3% (SD 68.1%). However, by synchronizing outcomes to the start date of intervention, the impact of seasonal variations was minimized to a maximum of 9.5% (SD 7.7%), thus improving the accuracy of the clinical outcomes reported. CONCLUSIONS Seasonal variations have a significant effect on the rate of hospital admission and LOS in patients with chronic conditions. However, the impact of seasonal variation on clinical outcomes (rate of admissions, number of hospital admissions, and LOS) of at-home telemonitoring can be attenuated by synchronizing the analysis of outcomes to the commencement dates for the telemonitoring of vital signs. CLINICALTRIAL Australian New Zealand Clinical Trial Registry ACTRN12613000635763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364030&isReview=true (Archived by WebCite at http://www.webcitation.org/ 6xLPv9QDb)

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