Ensuring quality cancer care through the oncology workforce.
Ontology highlight
ABSTRACT:
SUBMITTER: Levit L
PROVIDER: S-EPMC2805353 | biostudies-literature | 2010 Jan
REPOSITORIES: biostudies-literature
Publications
Ensuring quality cancer care through the oncology workforce.
Levit Laura L Smith Alison P AP Benz Edward J EJ Ferrell Betty B
Journal of oncology practice 20100101 1
A summary of the discussion at the Institute of Medicine National Cancer Policy Forum Workshop to examine oncology workforce shortages and describe current and potential solutions. ...[more]
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Project description:This paper reports the key findings of the Faculty of Radiation Oncology 2014 workforce census and compares the results with earlier surveys.The census was conducted in mid-2014 with distribution to all radiation oncologists, educational affiliates and trainees listed on the college database. There were six email reminders and responses were anonymous. The overall response rate was 76.1%.The age range of fellows was 32-96 (mean?=?49 years, median?=?47 years). The majority of the radiation oncologists were male (n?=?263, 63%). The minority of radiation oncologists were of Asian descent (n?=?43, 13.4%). Radiation oncologists graduated from medical school on average 23 years ago (median?=?22 years). A minority of fellows (n?=?66, 20%) held another postgraduate qualification. Most radiation oncologists worked, on average, at two practices (median?=?2, range 1-7). Practising radiation oncologists worked predominantly in the public sector (n?=?131, 49%), but many worked in both the public and private sectors (n?=?94, 37%), and a minority worked in the private sector only (n?=?38, 14%). The largest proportion of the workforce was from New South Wales accounting for 29% of radiation oncologists. Radiation oncologists worked an average of 43?h/week (median?=?43?h, range 6-80). Radiation oncologists who worked in the private sector worked less hours than their public sector or public/private sector colleagues. (38.3 vs. 42.9 vs. 44.3?h, P?=?0.042). Victorians worked the fewest average hours per week at 38?h and West Australians the most at 46?h/week. Radiation oncologists averaged 48?min for each new case, 17?min per follow up and 11?min for a treatment review. Radiation oncologists averaged 246 new patients per year (median?=?250, range?=?20-600) with men (average?=?268), Western Australians (average?=?354) and those in private practice seeing more (average?=?275). Most radiation oncologists considered themselves as specialists (n?=?151, 60%), but nearly all those from South Australia were generalists (n?=?15, 94%) as were three-quarters of those from private practice. A minority of radiation oncologist respondents (10%) intended to retire within 5 years with a further 16% within 10 years.There was a stabilisation of trainee numbers in Australia and New Zealand with no increase compared with 2010 (142 in 2014 vs. 143 in 2010). The most common age bracket for trainees remained 31-35 years. One-third of trainees were of Asian descent and nearly half held other degrees. The majority of trainees were satisfied with their career, but 30% were not entirely satisfied. Nearly half of trainee respondents would have reconsidered their choice of specialty had they known about the possible oversupply in the workforce with 12.4% undecided about continuing their career in radiation oncology. There were still 16% of trainees with no protected time during the working week, and a further 21% with only 1?h. Only one trainee respondent preferred to work in private practice, and job availability remained a concern for 89% of respondents.The radiation oncologist workforce numbers have increased at a much slower rate, and unemployment remained low. Many parameters remained similar to the 2010 survey. However, there has been a decrease in the average number of new patients seen per year, working hours and also a slight decrease in the time spent per new patient. The trainee numbers have stabilised, but job availability remained a concern. A significant proportion of trainees were not satisfied with their career.
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Project description:The Oncology Care Model (OCM) is a US Centers for Medicare & Medicaid Services (CMS) specialty model implemented in 2016, to provide higher quality, more highly coordinated oncology care at the same or lower costs. Under the OCM, oncology clinics enter into payment arrangements that include financial and performance accountability for patients receiving chemotherapy treatment. In addition, OCM clinics commit to providing enhanced services to Medicare beneficiaries, including care coordination, navigation, and following national treatment guidelines. Nutrition is a component of best-practice cancer care, yet it may not be addressed by OCM providers even though up to 80% of patients with cancer develop malnutrition and poor nutrition has a profound impact on cancer treatment and survivorship. Only about half of US ambulatory oncology settings screen for malnutrition, registered dietitian nutritionists (RDNs) are not routinely employed by oncology clinics, and the medical nutrition therapy they provide is often not reimbursed. Thus, adequate nutrition care in US oncology clinics remains a gap area. Some oncology clinics are addressing this gap through implementation of nutrition-focused quality improvement programs (QIPs) but many are not. What is needed is a change of perspective. This paper outlines how and why quality nutrition care is integral to the OCM and can benefit patient health and provider outcomes.
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Project description:ObjectivesTo provide an overview of the evidence base on the effectiveness of workforce interventions for improving the outcomes for older people with cancer, as well as analysing key features of the workforce associated with those improvements.DesignSystematic review.MethodsRelevant databases were searched for primary research, published in English, reporting on older people and cancer and the outcomes of interventions to improve workforce knowledge, attitudes or skills; involving a change in workforce composition and/or skill mix; and/or requiring significant workforce reconfiguration or new roles. Studies were also sought on associations between the composition and characteristics of the cancer care workforce and older people's outcomes. A narrative synthesis was conducted and supported by tabulation of key study data.ResultsStudies (n=24) included 4555 patients aged 60+ from targeted cancer screening to end of life care. Interventions were diverse and two-thirds of the studies were assessed as low quality. Only two studies directly targeted workforce knowledge and skills and only two studies addressed the nature of workforce features related to improved outcomes. Interventions focused on discrete groups of older people with specific needs offering guidance or psychological support were more effective than those broadly targeting survival outcomes. Advanced Practice Nursing roles, voluntary support roles and the involvement of geriatric teams provided some evidence of effectiveness.ConclusionsAn array of workforce interventions focus on improving outcomes for older people with cancer but these are diverse and thinly spread across the cancer journey. Higher quality and larger scale research that focuses on workforce features is now needed to guide developments in this field, and review findings indicate that interventions targeted at specific subgroups of older people with complex needs, and that involve input from advanced practice nurses, geriatric teams and trained volunteers appear most promising.
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Project description:BackgroundParticipants in two recent National Academy of Medicine workshops identified a need for more multi-disciplinary professionals on teams to assist oncology clinicians in precision oncology.MethodsWe developed a graduate school course to prepare biomedical students and pharmacy students to work within a multidisciplinary team of oncology clinicians, pathologists, radiologists, clinical pharmacists, and genetic counselors. Students learned precision oncology skills via case-based learning, hands-on data analyses, and presentations to peers. After the course, a focus group session was conducted to gain an in-depth student perspective on their interprofessional training experience, achievement of the course learning outcomes, ways to improve the course design in future offerings, and how the course could improve future career outcomes. A convenience sampling strategy was used for recruitment into the focus group session. A thematic content analysis was then conducted using the constant comparative method.ResultsMajor themes arising from student feedback were (1) appreciation of a customized patient case-based teaching approach, (2) more emphasis on using data analysis tools, (3) valuing interdisciplinary inclusion, and (4) request for more student discussion with advanced preparation materials.ConclusionsFeedback was generally positive and supports the continuation and expansion of the precision oncology course to include more hands-on instruction on the use of clinical bioinformatic tools.
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Project description:ImportanceArtificial intelligence (AI) large language models (LLMs) demonstrate potential in simulating human-like dialogue. Their efficacy in accurate patient-clinician communication within radiation oncology has yet to be explored.ObjectiveTo determine an LLM's quality of responses to radiation oncology patient care questions using both domain-specific expertise and domain-agnostic metrics.Design, setting, and participantsThis cross-sectional study retrieved questions and answers from websites (accessed February 1 to March 20, 2023) affiliated with the National Cancer Institute and the Radiological Society of North America. These questions were used as queries for an AI LLM, ChatGPT version 3.5 (accessed February 20 to April 20, 2023), to prompt LLM-generated responses. Three radiation oncologists and 3 radiation physicists ranked the LLM-generated responses for relative factual correctness, relative completeness, and relative conciseness compared with online expert answers. Statistical analysis was performed from July to October 2023.Main outcomes and measuresThe LLM's responses were ranked by experts using domain-specific metrics such as relative correctness, conciseness, completeness, and potential harm compared with online expert answers on a 5-point Likert scale. Domain-agnostic metrics encompassing cosine similarity scores, readability scores, word count, lexicon, and syllable counts were computed as independent quality checks for LLM-generated responses.ResultsOf the 115 radiation oncology questions retrieved from 4 professional society websites, the LLM performed the same or better in 108 responses (94%) for relative correctness, 89 responses (77%) for completeness, and 105 responses (91%) for conciseness compared with expert answers. Only 2 LLM responses were ranked as having potential harm. The mean (SD) readability consensus score for expert answers was 10.63 (3.17) vs 13.64 (2.22) for LLM answers (P < .001), indicating 10th grade and college reading levels, respectively. The mean (SD) number of syllables was 327.35 (277.15) for expert vs 376.21 (107.89) for LLM answers (P = .07), the mean (SD) word count was 226.33 (191.92) for expert vs 246.26 (69.36) for LLM answers (P = .27), and the mean (SD) lexicon score was 200.15 (171.28) for expert vs 219.10 (61.59) for LLM answers (P = .24).Conclusions and relevanceIn this cross-sectional study, the LLM generated accurate, comprehensive, and concise responses with minimal risk of harm, using language similar to human experts but at a higher reading level. These findings suggest the LLM's potential, with some retraining, as a valuable resource for patient queries in radiation oncology and other medical fields.
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