An Evidence-Based Review of aLentinula edodesMushroom Extract as Complementary Therapy in the Surgical Oncology Patient

2011 ◽  
Vol 35 (4) ◽  
pp. 449-458 ◽  
Author(s):  
Shinil K. Shah ◽  
Peter A. Walker ◽  
Stacey D. Moore-Olufemi ◽  
Alamelu Sundaresan ◽  
Anil D. Kulkarni ◽  
...  
Keyword(s):  
Surgical Oncology ◽  
Complementary Therapy ◽  
Evidence Based ◽  
Oncology Patient

Nutritional Support of the Surgical Oncology Patient

1991 ◽  
Vol 5 (1) ◽  
pp. 125-145 ◽  
Author(s):  
Mike K. Chen ◽  
Wiley W. Souba ◽  
Edward M. Copeland
Keyword(s):  
Nutritional Support ◽  
Surgical Oncology ◽  
Oncology Patient

scholarly journals Musculoskeletal oncology: patient triage and management during the COVID-19 pandemic

2020 ◽  
Vol 27 (5) ◽  
Author(s):  
N. Olshinka ◽  
S. Mottard
Keyword(s):  
Surgical Oncology ◽  
Low Grade ◽  
High Grade ◽  
Oncology Patient ◽  
Exposure Risks ◽  
Grade Malignancy ◽  
Oncology Centre ◽  
Sarcoma Treatment ◽  
Musculoskeletal Oncology ◽  
High Grade Malignancy

Sarcoma treatment during the covid-19 pandemic is a new challenge. This patient population is often immunocom­promised and potentially more susceptible to viral complications.    Government guidelines highlight the need to minimize patient exposure to unnecessary hospital visits. However, those guidelines lack practical recommendations on ways to manage triage and diagnosis expressly for new cancer patients. Furthermore, there are no reports on the efficiency of the guidelines.    One of the main issues in treating musculoskeletal tumours is the complexity and variability of presentation. We offer a triage model, used in a quaternary-referral musculoskeletal oncology centre, that allows us to maintain an open pathway for referral of new patients while minimizing exposure risks. A multidisciplinary approach and analysis of existing investigations allow for a pre-clinic evaluation.    The model identifies 3 groups of patients: Patients with suspected high-grade malignancy, or benign cases with aggressive features, both in need of further evaluation in the clinic and prompt treatment Patients with low-grade malignancy, and benign cases whose treatment is not urgent, that are managed, during the pandemic by telemedicine, with reassurance and information about their illness Patients who can be managed by their local medical professionals    In comparison to a pre-pandemic period, that approach resulted in a higher ratio of malignant-to-benign con­ditions for new patients seen in the clinic (3:4 vs. 1:3 respectively), thus using available resources more efficiently and prioritizing patients with suspected high-grade malignancy.    We believe that this triage system could be applied in other surgical oncology fields during a pandemic.

Surgical oncology outcomes: The role of nurse practice environments

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6105-6105
Author(s):  
C. R. Friese ◽  
L. H. Aiken
Keyword(s):  
Logistic Regression ◽  
Patient Outcomes ◽  
Regression Models ◽  
Surgical Oncology ◽  
Cancer Center ◽  
Practice Environment ◽  
Failure To Rescue ◽  
Oncology Patient ◽  
Logistic Regression Models

6105 Background: Increased attention has focused on the role of hospital characteristics on cancer patient outcomes. We examined two cancer-specific credentials, as well registered nurse practice environments, on outcomes of care. Methods: Through secondary analysis of existing data from hospital claims, the tumor registry, and a statewide of survey of nurses (RNs), we studied 30-day mortality (D) and failure to rescue (death given a complication) (FTR) for surgical oncology patients treated in 164 Pennsylvania hospitals from 1998–1999 (N=24,618). We compared D and FTR rates by a hospital’s NCI cancer center designation, American College of Surgeon’s (ACoS) cancer program approval and categorized scores on the Practice Environment Scale of the Nursing Work Index (PES-NWI). The PES-NWI scales measure RN participation in hospital affairs, RN foundations for quality of care, nurse manager leadership/support, staffing/resource adequacy, and RN-physician relations. Multivariate logistic regression models examined predictors of D and FTR, controlling for 25 patient variables. Standard errors were corrected for patient clustering in hospitals. Results: NCI centers had lower D and FTR rates (p < .01). ACoS hospitals had lower D and FTR rates (ns). Hospitals with low scores on PES-NWI scales had the poorest outcomes (p < .01). In logistic regression models, significant predictors included unfavorable PES-NWI Scores for D (OR=1.32, 95% CI: 1.06–1.65) and FTR (OR=1.39, 95% CI:1.03–1.88), and NCI centers for D (OR=0.64, 95% CI: 0.50–0.83) and FTR (OR=0.67, 95% CI: 0.47–0.96). The NCI effect lost significance when environment was included. ACoS program effects were small (OR= 0.99, p = .90) for both outcomes. Conclusions: Favorable outcomes in NCI centers may be partly explained by practice environments. The practice environment of RNs significantly predicts surgical oncology patient outcomes, and should be a focus of quality improvement activities. No significant financial relationships to disclose.

Quality control system of Watson for oncology: Artificial intelligence for supporting clinical decisions in oncology.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6616-6616
Author(s):  
Juemin Fang ◽  
Xianling Guo ◽  
Zhongzheng Zhu ◽  
Hui Wang ◽  
Fei Hu ◽  
...  
Keyword(s):  
Quality Control ◽  
Control System ◽  
Treatment Options ◽  
Drug Approval ◽  
Surgical Oncology ◽  
Clinical Decision ◽  
Quality Control System ◽  
Cancer Center ◽  
Evidence Based ◽  
Drug Availability

6616 Background: Watson for Oncology (WFO) is an artificial intelligent clinical decision-support system (AI-CDSS) developed by IBM and trained by Memorial Sloan Kattering Cancer Center to assist in cancer care by providing evidence-based treatment options with priority. However, there are disagreements argue that WFO recommends “unsafe and incorrect” cancer treatments. Also, guidelines and drug availability in China are different from USA. Therefore, a quality control system of WFO is urgently needed to help oncologists better use WFO in China. Methods: Experts from medical oncology, surgical oncology, radiology, intervention, radiology and pathology etc. forming a Multiple Disciplinary Team (MDT) to score Watson recommendations in 6 aspects (shown in the table). Results: With this quality control system, the value of WFO was carefully evaluated by MDT. Recommendations with higher score(especially more than 80) were more standardized, reasonable and evidence-based thus more likely to be chosen. Localization and drug availability problem was solved by taking Chinese guidelines and drug approval into evaluation within this scoring system. Treatment options unsuitable or unavailable for patients by the system will be removed and replaced by the advices of MDT. Conclusions: Reliability and security are the top concerns of applying new technology in healthcare. With the MDT quality control system, AI-CDSS can be used safely and efficiently before it is fully mature. Also, the accuracy and advancement are assessed in this system to help oncologists better use WFO in China in the future. Indicators evaluating the WFO recommendations. [Table: see text]

Implementation of an EMR integrated pathway for the management of malignant bowel obstruction.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 813-813
Author(s):  
Darryl Schuitevoerder ◽  
Charles Christian Vining ◽  
Michael White ◽  
Claire Hoppenot ◽  
Isabel Lazo ◽  
...  
Keyword(s):  
Bowel Obstruction ◽  
Hospital Medicine ◽  
Surgical Oncology ◽  
Evidence Based ◽  
Malignant Bowel Obstruction ◽  
Educational Materials ◽  
Early Suggestion ◽  
Published Evidence ◽  
Integrated Pathway ◽  
Sustainability Framework

813 Background: Despite published evidence based interventions for malignant bowel obstruction (MBO), implementation of a standard pathway is challenging. We hypothesized that using industrial engineering techniques and a modified dynamic sustainability framework for implementation, we can implement an electronic medical record (EMR) based pathway in the management of MBO. Methods: A workflow in the management of MBO was developed using iterative meetings from 8/2018 to 4/2019 including gateway stakeholders (surgical oncology, gynecological oncology and medical oncology), interventional stakeholders (gastroenterology, interventional radiology) and supportive stakeholders (hospital medicine, palliative care, nutrition, nursing). Industrial engineers were utilized to study human factors, and perform a method study. EMR integration was performed using EPIC systems Agile MD pathway and educational materials were created. Interventions such as early placement of gastrostomy tubes, total parenteral nutrition and medications were protocolized. Results: Since implementation, over 6 months the pathway and order set has been activated 56 times. Orders have been employed 21 times through the AgileMD pathway demonstrating a pathway drift of 62.5%. Educational materials have been accessed routinely during this time. Conclusions: Feasibility of implementing an EMR integrated MBO pathway is demonstrated with early suggestion of pathway drift. Utilizing tools of implementation science are necessary to facilitate widespread adoption of evidence based interventions in the management of patients with MBO.

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