Importance of Race/Ethnicity and Genetics in Biomedical Research and Clinical Practice: Lessons Learned from the Genetics of Asthma in Latino Americans (Gala) Study

Race and ethnicity are commonly reported variables in biomedical research, but how they were initially determined is often not described and the rationale for analyzing them is often not provided. JAMA improved the reporting of these factors by implementing a policy and procedure for doing so. However, still lacking are careful consideration of what is actually being measured when race/ethnicity is described, consistent terminology, hypothesis-driven justification for analyzing race/ethnicity, and a consistent and generalizable measurement of socioeconomic status. Furthermore, some studies continue to use race/ethnicity as a proxy for genetics. Research into appropriate measures of race/ethnicity and socioeconomic factors, as well as education of researchers regarding issues of race/ethnicity, is necessary to clarify the meaning of race/ethnicity in the biomedical literature.

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Lessons learned from the delivery of virtual integrative oncology interventions in clinical practice and research during the COVID-19 pandemic

Supportive Care in Cancer ◽

10.1007/s00520-021-06174-0 ◽

2021 ◽

Author(s):

Robert Knoerl ◽

Carolyn S. Phillips ◽

Juliana Berfield ◽

Heather Woods ◽

Meghan Acosta ◽

...

Keyword(s):

Clinical Practice ◽

Lessons Learned ◽

Integrative Oncology

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Correction: Lessons Learned from Development of De-identification System for Biomedical Research in a Korean Tertiary Hospital

Healthcare Informatics Research ◽

10.4258/hir.2013.19.3.232 ◽

2013 ◽

Vol 19 (3) ◽

pp. 232

Author(s):

Soo-Yong Shin ◽

Yongman Lyu ◽

Yongdon Shin ◽

Hyo Joung Choi ◽

Jihyun Park ◽

...

Keyword(s):

Biomedical Research ◽

Tertiary Hospital ◽

Lessons Learned ◽

Identification System

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Physicians’ requirements and expectations of future Artificial Intelligence applications for healthcare – a web-based survey in German university hospitals (Preprint)

10.2196/preprints.26646 ◽

2020 ◽

Author(s):

Oliver Maassen ◽

Sebastian Fritsch ◽

Julia Gantner ◽

Saskia Deffge ◽

Julian Kunze ◽

...

Keyword(s):

Artificial Intelligence ◽

Clinical Practice ◽

Patient Care ◽

Biomedical Research ◽

Positive Attitude ◽

Patient Data ◽

University Hospitals ◽

Web Based ◽

Wide Range ◽

Medical Disciplines

BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of healthcare. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research has not been investigated widely in German university hospitals. OBJECTIVE Evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research e.g. for the development of machine learning (ML) algorithms in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given on Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS 121 (39.9%) female and 173 (57.1%) male physicians (N=303) from a wide range of medical disciplines and work experience levels completed the online survey. The majority of respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). 82.5% of respondents (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism, there come several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (e.g., imaging procedures in radiology and pathology) or data is collected continuously (e.g. cardiology and intensive care medicine), physicians’ expectations to substantially improve future patient care are high. However, for the practical usage of AI in healthcare regulatory and organizational challenges still have to be mastered.

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Wiki-Based Clinical Practice Guidelines for the Management of Adult Onset Sarcoma: A New Paradigm in Sarcoma Evidence

Sarcoma ◽

10.1155/2015/614179 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 6

Author(s):

S. J. Neuhaus ◽

D. Thomas ◽

J. Desai ◽

C. Vuletich ◽

J. von Dincklage ◽

...

Keyword(s):

Clinical Practice ◽

Clinical Practice Guidelines ◽

Practice Guidelines ◽

Guideline Development ◽

Lessons Learned ◽

Evidence Based ◽

Adult Onset ◽

New Paradigm ◽

Software Application ◽

Evidence Based Guidelines

In 2013 Australia introduced Wiki-based Clinical Practice Guidelines for the Management of Adult Onset Sarcoma. These guidelines utilized a customized MediaWiki software application for guideline development and are the first evidence-based guidelines for clinical management of sarcoma. This paper presents our experience with developing and implementing web-based interactive guidelines and reviews some of the challenges and lessons from adopting an evidence-based (rather than consensus-based) approach to clinical sarcoma guidelines. Digital guidelines can be easily updated with new evidence, continuously reviewed and widely disseminated. They provide an accessible method of enabling clinicians and consumers to access evidence-based clinical practice recommendations and, as evidenced by over 2000 views in the first four months after release, with 49% of those visits being from countries outside of Australia. The lessons learned have relevance to other rare cancers in addition to the international sarcoma community.

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Quality Care in Survivorship: Lessons Learned From the ASCO Quality Oncology Practice Initiative

JCO Oncology Practice ◽

10.1200/op.21.00290 ◽

2021 ◽

pp. OP.21.00290

Author(s):

Charles L. Shapiro ◽

Nicole Zubizarreta ◽

Erin Moshier ◽

Julia P. Brockway ◽

John Mandeli ◽

...

Keyword(s):

Smoking Cessation ◽

Clinical Practice ◽

Negative Binomial ◽

Scientific Evidence ◽

Lessons Learned ◽

Negative Binomial Regression Model ◽

Smoking Cessation Counseling ◽

Curative Intent ◽

Oncology Practice ◽

Over Time

PURPOSE: The ASCO Quality Oncology Practice Initiative (QOPI) project was established to evaluate the influence of guideline recommendations on routine clinical practice. METHODS: QOPI provided summary data from 839 unique practices in which data were collected every six months from the Fall of 2015 to the Spring of 2019. From these data, six items were chosen based on their relationship to domains of survivorship. A zero-inflated negative binomial regression model was used to test for trends in QOPI measures adherence rates over time. The models were adjusted for the time period, region, practice-ownership, multispecialty site, fellowship program, and hospital type. RESULTS: Smoking cessation counseling recommended and smoking cessation counseling administered or referred both increased over time, 50%-61% (adjusted incidence rate ratios (IRR), 1.028; 95% CI, 1.016 to 1.040; P < .001) and 34%-49% (adjusted IRR, 1.052; 95% CI, 1.035 to 1.070; P < .001), respectively. Infertility risks discussed before chemotherapy increased from 36% to 53% (adjusted IRR, 1.056; 95% CI, 1.035 to 1.078; P < .001) and fertility options discussed or referred to specialists increased from 23% to 38% (adjusted IRR, 1.074; 95% CI, 1.046 to 1.102; P < .001). Twenty-nine percent documented a positron emission tomography, computed tomography, or bone scan within the first 12 months for women diagnosed with early breast cancer treated for curative intent (adjusted IRR, 1.000; 95% CI, 0.977 to 1.024; P = .971). Tumor marker surveillance within 12 months increased from 78% to 87% (adjusted IRR, 1.018; 95% CI, 1.002 to 1.033; P = .023). CONCLUSION: As scientific evidence to guide cancer survivorship care grows, the role of guideline recommendations permeating clinical practice using quality metrics will become increasingly important.

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