48/6 Model of Care for Senior Clinical Care Management

2016 ◽  
Vol 17 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Yoon-Sook Kim ◽  
Seol-Heui Han ◽  
Jong-Min Lee ◽  
Jae-Kyung Choi ◽  
Jae-Min Park ◽  
...  
Keyword(s):  
Care Management ◽  
Clinical Care ◽  
Model Of Care

Implementing Cancer Exercise Rehabilitation: An Update on Recommendations for Clinical Practice

2019 ◽  
Vol 15 (2) ◽  
pp. 100-109 ◽  
Author(s):  
Kirsten Suderman ◽  
Carolyn McIntyre ◽  
Christopher Sellar ◽  
Margaret L. McNeely
Keyword(s):  
Cancer Survivors ◽  
Research Evidence ◽  
Clinical Care ◽  
Preliminary Evidence ◽  
Model Of Care ◽  
Community Based ◽  
Exercise Rehabilitation ◽  
Barriers To Exercise ◽  
Growing Body ◽  
After Cancer

A growing body of research evidence supports the benefit of exercise for cancer survivors both during and after cancer treatment. The purpose of this paper is to provide an update on our previously published review in 2006 on the state of the evidence supporting exercise for survivors of cancer as well as guidelines for integrating exercise programming in the cancer clinical setting. First, we provide a brief overview on the benefits of exercise as well as preliminary evidence supporting the implementation of community-based exercise programs. Second, we summarize the principles and goals of exercise, and the identified barriers to exercise among cancer survivors. Finally, we propose an interdisciplinary model of care for integrating exercise programming into clinical care including guidelines for medical and pre-exercise screening, exercise testing and programming considerations.

scholarly journals Datasets collected in general practice: an international comparison using the example of obesity

2018 ◽  
Vol 42 (5) ◽  
pp. 563 ◽  
Author(s):  
Elizabeth Sturgiss ◽  
Kees van Boven
Keyword(s):  
Primary Care ◽  
General Practice ◽  
The Netherlands ◽  
Care Management ◽  
Clinical Care ◽  
International Comparisons ◽  
Health Condition ◽  
Practice Management ◽  
Number Of Patients ◽  
Primary Care Management

International datasets from general practice enable the comparison of how conditions are managed within consultations in different primary healthcare settings. The Australian Bettering the Evaluation and Care of Health (BEACH) and TransHIS from the Netherlands collect in-consultation general practice data that have been used extensively to inform local policy and practice. Obesity is a global health issue with different countries applying varying approaches to management. The objective of the present paper is to compare the primary care management of obesity in Australia and the Netherlands using data collected from consultations. Despite the different prevalence in obesity in the two countries, the number of patients per 1000 patient-years seen with obesity is similar. Patients in Australia with obesity are referred to allied health practitioners more often than Dutch patients. Without quality general practice data, primary care researchers will not have data about the management of conditions within consultations. We use obesity to highlight the strengths of these general practice data sources and to compare their differences. What is known about the topic? Australia had one of the longest-running consecutive datasets about general practice activity in the world, but it has recently lost government funding. The Netherlands has a longitudinal general practice dataset of information collected within consultations since 1985. What does this paper add? We discuss the benefits of general practice-collected data in two countries. Using obesity as a case example, we compare management in general practice between Australia and the Netherlands. This type of analysis should start all international collaborations of primary care management of any health condition. Having a national general practice dataset allows international comparisons of the management of conditions with primary care. Without a current, quality general practice dataset, primary care researchers will not be able to partake in these kinds of comparison studies. What are the implications for practitioners? Australian primary care researchers and clinicians will be at a disadvantage in any international collaboration if they are unable to accurately describe current general practice management. The Netherlands has developed an impressive dataset that requires within-consultation data collection. These datasets allow for person-centred, symptom-specific, longitudinal understanding of general practice management. The possibilities for the quasi-experimental questions that can be answered with such a dataset are limitless. It is only with the ability to answer clinically driven questions that are relevant to primary care that the clinical care of patients can be measured, developed and improved.

scholarly journals Impact of Personality Disorder Cluster on Depression Outcomes Within Collaborative Care Management Model of Care

2018 ◽  
Vol 9 ◽  
pp. 215013271877687 ◽  
Author(s):  
Merit P. George ◽  
Gregory M. Garrison ◽  
Zachary Merten ◽  
Dagoberto Heredia ◽  
Cesar Gonzales ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Personality Disorder ◽  
Collaborative Care ◽  
Care Management ◽  
Model Of Care ◽  
Clinical Prognosis ◽  
Increased Risk ◽  
Lower Likelihood ◽  
Cluster A

Background: Previous studies have suggested that having a comorbid personality disorder (PD) along with major depression is associated with poorer depression outcomes relative to those without comorbid PD. However, few studies have examined the influence of specific PD cluster types. The purpose of the current study is to compare depression outcomes between cluster A, cluster B, and cluster C PD patients treated within a collaborative care management (CCM), relative to CCM patients without a PD diagnosis. The overarching goal was to identify cluster types that might confer a worse clinical prognosis. Methods: This retrospective chart review study examined 2826 adult patients with depression enrolled in CCM. The cohort was divided into 4 groups based on the presence of a comorbid PD diagnosis (cluster A/nonspecified, cluster B, cluster C, or no PD). Baseline clinical and demographic variables, along with 6-month follow-up Patient Health Questionnaire–9 (PHQ-9) scores were obtained for all groups. Depression remission was defined as a PHQ-9 score <5 at 6 months, and persistent depressive symptoms (PDS) was defined as a PHQ-9 score ≥10 at 6 months. Adjusted odds ratios (AORs) were determined for both remission and PDS using logistic regression modeling for the 6-month PHQ-9 outcome, while retaining all study variables. Results: A total of 59 patients (2.1%) had a cluster A or nonspecified PD diagnosis, 122 patients (4.3%) had a cluster B diagnosis, 35 patients (1.2%) had a cluster C diagnosis, and 2610 patients (92.4%) did not have any PD diagnosis. The presence of a cluster A/nonspecified PD diagnosis was associated with a 62% lower likelihood of remission at 6 months (AOR = 0.38; 95% CI 0.20-0.70). The presence of a cluster B PD diagnosis was associated with a 71% lower likelihood of remission at 6 months (AOR = 0.29; 95% CI 0.18-0.47). Conversely, having a cluster C diagnosis was not associated with a significantly lower likelihood of remission at 6 months (AOR = 0.83; 95% CI 0.42-1.65). Increased odds of having PDS at 6-month follow-up were seen with cluster A/nonspecified PD patients (AOR = 3.35; 95% CI 1.92-5.84) as well as cluster B patients (AOR = 3.66; 95% CI 2.45-5.47). However, cluster C patents did not have significantly increased odds of experiencing persistent depressive symptoms at 6-month follow-up (AOR = 0.95; 95% CI 0.45-2.00). Conclusions: Out of the 3 clusters, the presence of a cluster B PD diagnosis was most significantly associated with poorer depression outcomes at 6-month follow-up, including reduced remission rates and increased risk for PDS. The cluster A/nonspecified PD group also showed poor outcomes; however, the heterogeneity of this subgroup with regard to PD features must be noted. The development of novel targeted interventions for at-risk clusters may be warranted in order to improve outcomes of these patients within the CCM model of care.

scholarly journals A virtual ward model of care for patients with COVID-19 (Preprint)

2020 ◽  
Author(s):  
Olivia R Ferry ◽  
Emma C Moloney ◽  
Owen T Spratt ◽  
Gerald FM Whiting ◽  
Cameron J Bennett
Keyword(s):  
Hospital Care ◽  
Clinical Care ◽  
Supplemental Oxygen ◽  
Nasopharyngeal Swab ◽  
Model Of Care ◽  
Illness Onset ◽  
Sputum Production ◽  
Number Of Patients ◽  
Australian Cohort ◽  
Polymerase Chain

BACKGROUND The COVID-19 pandemic has necessitated the implementation of innovative healthcare models in preparation for an influx of patients. A virtual ward model delivers clinical care remotely to patients in isolation. We report an Australian cohort of patients with COVID-19 treated in a virtual ward. OBJECTIVE The aim of this study was to describe and evaluate the safety and efficacy of a virtual ward model of care for an Australian cohort of patients with COVID-19. METHODS A retrospective clinical audit was undertaken of 223 patients with confirmed COVID-19 treated in a virtual ward in Brisbane, Australia, from March 25 to May 15, 2020. Statistical analyses of variables associated with length of stay and hospitalisation were undertaken. RESULTS Of 223 patients, 205 (92%) recovered without need for escalation to hospital care. The median virtual ward admission length was eight days (range, 1 to 44). Eighteen (8%) patients were referred to hospital with one-third discharged after emergency department assessment. Twelve patients were admitted to hospital, four required supplemental oxygen and two required mechanical ventilation. No deaths were recorded. Factors associated with escalation to hospital care were: hypertension (OR 3.6, CI 1.28-9.87, P=.01), sputum production (OR 5.2, CI 1.74-15.49, P=.001) or arthralgia (OR 3.8, CI 1.21-11.71, P=.02) at illness onset and polymerase chain reaction cycle threshold of 20 or less on diagnostic nasopharyngeal swab (OR 5.0, CI 1.25-19.63, P=.02). CONCLUSIONS Our results suggest a virtual ward model of care to treat patients with COVID-19 is safe and efficacious. A small number of patients required escalation to hospital care. Further studies are recommended to validate this model of care. CLINICALTRIAL

Ketamine treatment for depression: A model of care

2021 ◽  
pp. 000486742110391
Author(s):  
Adam Bayes ◽  
Vanessa Dong ◽  
Donel Martin ◽  
Angelo Alonzo ◽  
Michael Kabourakis ◽  
...  
Keyword(s):  
Clinical Care ◽  
Initial Assessment ◽  
Dose Titration ◽  
Treatment Resistant Depression ◽  
Model Of Care ◽  
Treatment Resistant ◽  
Detailed Model ◽  
Policies And Procedures ◽  
Resistant Depression ◽  
Related Compounds

Objective: Ketamine and related compounds are emerging as rapidly acting therapies for treatment-resistant depression. Ketamine differs from standard antidepressants in its speed of action, specific acute and cumulative side effects, risk of dependence and regulatory requirements. However, there is currently little guidance offering translation from research studies into clinical practice. We therefore detail a comprehensive model of care for ketamine treatment of depression. Method: We formulated a set of policies and procedures for a ‘compassionate use’ ketamine programme that developed out of our clinical research in ketamine. These policies and procedures were formulated into a detailed model of care. Results: The current Australian and New Zealand regulatory frameworks and professional bodies’ recommendations regarding ketamine are detailed along with clinical governance and infrastructure considerations. We next describe a four-step model comprising initial assessment, pre-treatment, treatment and post-treatment phases. The model comprises thorough psychiatric and medical assessments examining patient suitability, a rigorous consenting process and structured safety monitoring across an acute treatment course or maintenance therapy. Our ketamine dose-titration method is detailed allowing flexible dosing of patients across a treatment course enabling individualised treatment. Conclusion: The model of care aims to bridge the gap between efficacy studies and clinical care outside of research settings as ketamine and related compounds become increasingly important therapies for treatment-resistant depression.

scholarly journals Pharmacogenomics and Pharmacogenetics in Osteosarcoma: Translational Studies and Clinical Impact

2020 ◽  
Vol 21 (13) ◽  
pp. 4659 ◽  
Author(s):  
Claudia Maria Hattinger ◽  
Maria Pia Patrizio ◽  
Silvia Luppi ◽  
Massimo Serra
Keyword(s):  
Care Management ◽  
Scientific Evidence ◽  
Clinical Care ◽  
Treatment Strategies ◽  
Clinical Settings ◽  
Genetic Biomarkers ◽  
Recent Developments ◽  
High Grade Osteosarcoma ◽  
Genetic Events ◽  
Pharmacogenomic Studies

High-grade osteosarcoma (HGOS) is a very aggressive bone tumor which primarily affects adolescents and young adults. Although not advanced as is the case for other cancers, pharmacogenetic and pharmacogenomic studies applied to HGOS have been providing hope for an improved understanding of the biology and the identification of genetic biomarkers, which may impact on clinical care management. Recent developments of pharmacogenetics and pharmacogenomics in HGOS are expected to: i) highlight genetic events that trigger oncogenesis or which may act as drivers of disease; ii) validate research models that best predict clinical behavior; and iii) indicate genetic biomarkers associated with clinical outcome (in terms of treatment response, survival probability and susceptibility to chemotherapy-related toxicities). The generated body of information may be translated to clinical settings, in order to improve both effectiveness and safety of conventional chemotherapy trials as well as to indicate new tailored treatment strategies. Here, we review and summarize the current scientific evidence for each of the aforementioned issues in view of possible clinical applications.

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