A quality improvement intervention in geriatric psychiatry care: Results of a pre-post design study

2016 ◽  
Vol 33 (S1) ◽  
pp. S84-S84
Author(s):  
E. Albuquerque ◽  
S. Fernandes ◽  
J. Cerejeira
Keyword(s):  
Emergency Department ◽  
Behavioral Problems ◽  
Hospital Admissions ◽  
Emergency Department Visits ◽  
Quality Improvement Intervention ◽  
Future Research ◽  
Elderly Subjects ◽  
Psychiatric Ward ◽  
Design Study ◽  
Emergency Visits

IntroductionInnovative approaches are needed to respond to the increasing number of elderly subjects with complex psychiatric conditions who require flexible and rapid responses, avoiding unnecessary hospital admissions. A new organizational model was implemented in our psychogeriatric service in September 2011 consisting of:– a comprehensive multidisciplinary geriatric assessment;– a helpline for caregivers for management of acute behavioral problems;– programmed visits to nursing homes.AimsTo evaluate whether the implementation of this program was associated with a reduction in hospital admissions and emergency department visits.MethodsThis is a pre-post test design study, involving 1197 patients who attended the Old Age Psychiatric (OAP) Unit three years before and three years after the implementation of the organizational intervention (1.09.2008 to 1.10.2014). An index of patient × year was calculated considering the period during which the patient was followed in OAP Unit. Data was obtained from the medical files of all eligible patients regarding demographic variables, number and type of hospital admissions and emergency department visits.ResultsDuring the 3 years before the intervention 671.2 patients × years were included (mean age of 75.8 years) while after the intervention this reached 2010.1 patients × years (mean age of 77.8 years). The intervention was associated with a decrease of 44% in psychiatry emergency visits, 48% in general emergency visits, 44% in psychiatric ward admissions and 51% in geriatric ward admissions.ConclusionsThe implementation of this new model was associated with significant reduction of hospital-based service utilization. Future research should determine if this was coupled with increased health outcomes.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm

2018 ◽  
pp. 1-11 ◽  
Author(s):  
Julian C. Hong ◽  
Donna Niedzwiecki ◽  
Manisha Palta ◽  
Jessica D. Tenenbaum
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Supportive Care ◽  
Hospital Admissions ◽  
Learning Algorithm ◽  
Clinical Care ◽  
Prospective Trial ◽  
Emergency Department Visits ◽  
Support Vector ◽  
Emergency Visits

Purpose Patients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs. We developed and evaluated a machine learning (ML) approach to predict these events. Methods A total of 8,134 outpatient courses of RT and CRT from a single institution from 2013 to 2016 were identified. Extensive pretreatment data were programmatically extracted and processed from the electronic health record (EHR). Training and internal validation cohorts were randomly generated (3:1 ratio). Gradient tree boosting (GTB), random forest, support vector machine, and least absolute shrinkage and selection operator logistic regression approaches were trained and internally validated based on area under receiver operating characteristic (AUROC) curve. The most predictive ML approach was also evaluated using only disease- and treatment-related factors to assess predictive gain of extensive EHR data. Results All methods had high predictive accuracy, particularly GTB (validation AUROC, 0.798). Extensive EHR data beyond disease and treatment information improved accuracy (delta AUROC, 0.056). A Youden-based cutoff corresponded to validation sensitivity of 81.0% (175 of 216 courses with events) and specificity of 67.3% (1,218 of 1811 courses without events). Interpretability is an important advantage of GTB. Variable importance identified top predictive factors, including treatment (planned RT and systemic therapy), pretreatment encounters (emergency department visits and admissions in the year before treatment), vital signs (weight loss and pain score in the year before treatment), and laboratory values (albumin level at weeks before treatment). Conclusion ML predicts emergency visits and hospitalization during cancer therapy. Incorporating predictions into clinical care algorithms may help direct personalized supportive care, improve quality of care, and reduce costs. A prospective trial investigating ML-assisted direction of increased clinical assessments during RT is planned.

scholarly journals RATES OF HOSPITALIZATION AND EMERGENCY DEPARTMENT VISITS AND THE QUALITY OF NURSING HOMES IN THE U.S.

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S688-S688
Author(s):  
Ian Breunig ◽  
Qing Zheng ◽  
Alan White ◽  
Christianna Williams ◽  
Allison Muma
Keyword(s):  
Emergency Department ◽  
Acute Care ◽  
Hospital Admissions ◽  
Emergency Department Visits ◽  
National Database ◽  
Future Research ◽  
Short Stay ◽  
Data Set ◽  
For Profit ◽  
Provider Characteristics

Abstract CMS strives to reduce costs and improve care for nursing home (NH) residents by reducing acute care transfers. We used a national database of Medicare claims and the Minimum Data Set to build NH stays from July 2017 through June 2018 and identify dates of hospital admissions and emergency department visits without hospitalization (ED) among all residents. We calculated rates of 30-day re-hospitalization and ED among short-stay (rehabilitation) residents, and the number of hospitalizations or ED per long-stay resident day (LSRD), then examined associations with NH Five-Star ratings (data.medicare.gov) and other provider characteristics available from Medicare administrative data. We identified 1.79 million short-stays and 898,290 long-stays at 15,576 NHs. Nationally, the 30-day re-hospitalization rate is 22.6%, the short-stay ED rate is 12.0%, there was one hospitalization every 561 LSRD (1.8 per 1000 LSRD), and there was one ED every 617 LSRD (1.6 per 1000 LSRD). Median facility rates were 22.3% (IQR=17.8%, 27.1%) for 30-day re-hospitalizations, 12.0% (IQR=8.7%, 16.1%) for short-stay EDs, 1.6 hospitalizations per 1000 LSRD (IQR=1.1, 2.3), and 1.4 ED per 1000 LSRD (IQR =0.9, 2.2). Higher rates were strongly associated with lower Five-Star ratings, particularly staffing ratings, and larger, for-profit, non-hospital facilities; even after risk-adjustment. NH variation and associations with provider characteristics suggest it is possible to further reduce acute care transfers. CMS incorporated these measures into the Five-Star rating system, providing greater transparency for residents and possibly incentivizing NHs to improve through competition. Future research should monitor success or identify the need for other avenues to improve.

Risk factors of hospital admissions and recurrent emergency department visits among pediatric asthma patients

2020 ◽  
pp. 1011-1015
Author(s):  
Abdullah Aldamigh ◽  
Afaf Alnefisah ◽  
Abdulrahman Almutairi ◽  
Fatima Alturki ◽  
Suhailah Alhtlany ◽  
...  
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Hospital Admissions ◽  
Pediatric Asthma ◽  
Emergency Department Visits ◽  
Asthma Patients

scholarly journals Program of Integrated Care for Patients with Chronic Obstructive Pulmonary Disease and Multiple Comorbidities (PIC COPD+): a randomised controlled trial

2018 ◽  
Vol 51 (1) ◽  
pp. 1701567 ◽  
Author(s):  
Louise Rose ◽  
Laura Istanboulian ◽  
Lise Carriere ◽  
Anna Thomas ◽  
Han-Byul Lee ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Admissions ◽  
Controlled Trial ◽  
Case Manager ◽  
Emergency Department Visits ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Secondary Outcomes ◽  
Randomised Controlled ◽  
Component Case

We sought to evaluate the effectiveness of a multi-component, case manager-led exacerbation prevention/management model for reducing emergency department visits. Secondary outcomes included hospitalisation, mortality, health-related quality of life, chronic obstructive pulmonary disease (COPD) severity, COPD self-efficacy, anxiety and depression.Two-centre randomised controlled trial recruiting patients with ≥2 prognostically important COPD-associated comorbidities. We compared our multi-component intervention including individualised care/action plans and telephone consults (12-weekly then 9-monthly) with usual care (both groups). We used zero-inflated Poisson models to examine emergency department visits and hospitalisation; Cox proportional hazard model for mortality.We randomised 470 participants (236 intervention, 234 control). There were no differences in number of emergency department visits or hospital admissions between groups. We detected difference in emergency department visit risk, for those that visited the emergency department, favouring the intervention (RR 0.74, 95% CI 0.63–0.86). Similarly, risk of hospital admission was lower in the intervention group for those requiring hospital admission (RR 0.69, 95% CI 0.54–0.88). Fewer intervention patients died (21 versus 36) (HR 0.56, 95% CI 0.32–0.95). No differences were detected in other secondary outcomes.Our multi-component, case manager-led exacerbation prevention/management model resulted in no difference in emergency department visits, hospital admissions and other secondary outcomes. Estimated risk of death (intervention) was nearly half that of the control.

Management of Poisoning and Drug Overdose

2021 ◽  
Author(s):  
Timothy J Wiegand ◽  
Manish M Patel ◽  
Kent R. Olson
Keyword(s):  
Emergency Department ◽  
Drug Overdose ◽  
Activated Charcoal ◽  
Hospital Admissions ◽  
Clinical Laboratory ◽  
The United States ◽  
Emergency Department Visits ◽  
The Body ◽  
Smoke Inhalation ◽  
Drug Induced

Drug overdose and poisoning are leading causes of emergency department visits and hospital admissions in the United States, accounting for more than 500,000 emergency department visits and 11,000 deaths each year. This chapter discusses the approach to the patient with poisoning or drug overdose, beginning with the initial stabilization period in which the physician proceeds through the ABCDs (airway, breathing, circulation, dextrose, decontamination) of stabilization. The management of some of the more common complications of poisoning and drug overdose are summarized and include coma, hypotension and cardiac dysrhythmias, hypertension, seizures, hyperthermia, hypothermia, and rhabdomyolysis. The physician should also perform a careful diagnostic evaluation that includes a directed history, physical examination, and the appropriate laboratory tests. The next step is to prevent further absorption of the drug or poison by decontaminating the skin or gastrointestinal tract and, possibly, by administering antidotes and performing other measures that enhance elimination of the drug from the body. The diagnosis and treatment of overdoses of a number of specific drugs and poisons that a physician may encounter, as well as food poisoning and smoke inhalation, are discussed. Tables present the ABCDs of initial stabilization of the poisoned patient; mechanisms of drug-induced hypotension; causes of cardiac disturbances; drug-induced seizures; drug-induced hyperthermia; autonomic syndromes induced by drugs or poison; the use of the clinical laboratory in the initial diagnosis of poisoning; methods of gastrointestinal decontamination; methods of and indications for enhanced drug removal; toxicity of common beta blockers; common stimulant drugs; corrosive agents; dosing of digoxin-specific antibodies; poisoning with ethylene glycol or methanol; manifestations of excessive acetylcholine activity; common tricyclic and other antidepressants; seafood poisonings; drugs or classes that require activated charcoal treatment; and special circumstances for use of activated charcoal. This review contains 3 figures, 22 tables, and 198 references.

An Active In-Home Physician Model of Palliative Care and Its Resulting Performance Indicators Related to Home Deaths, Unplanned Emergency Department Visits and Unplanned Hospital Admissions

2020 ◽  
Vol 36 (1) ◽  
pp. 46-49
Author(s):  
Colleen Webber ◽  
Aurelia Ona Valiulis ◽  
Peter Tanuseputro ◽  
Valerie Schulz ◽  
Tavis Apramian ◽  
...  
Keyword(s):  
Emergency Department ◽  
Palliative Care ◽  
Hospital Admissions ◽  
Case Series ◽  
Palliative Care Team ◽  
Emergency Department Visits ◽  
Care Team ◽  
Retrospective Case ◽  
Palliative Care Teams ◽  
Unplanned Hospital Admissions

Background: Limited research has characterized team-based models of home palliative care and the outcomes of patients supported by these care teams. Case presentation: A retrospective case series describing care and outcomes of patients managed by the London Home Palliative Care Team between May 1, 2017 and April 1, 2019. Case management: The London Home Palliative Care (LHPC) Team care model is based upon 3 pillars: 1) physician visit availability 2) active patient-centered care with strong physician in-home presence and 3) optimal administrative organization. Case outcomes: In the 18 month study period, 354 patients received care from the London Home Palliative Care Team. Most significantly, 88.4% ( n = 313) died in the community or at a designated palliative care unit after prearranged direct transfer; no comparable provincial data is available. 21.2% ( n = 75) patients visited an emergency department and 24.6% ( n = 87) were admitted to hospital at least once in their final 30 days of life. 280 (79.1%) died in the community. These values are better than comparable provincial estimates of 62.7%, 61.7%, and 24.0%, respectively. Conclusion: The London Home Palliative Care (LHPC) Team model appears to favorably impact community death rate, ER visits and unplanned hospital admissions, as compared to accepted provincial data. Studies to determine if this model is reproducible could support palliative care teams achieving similar results.

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