scholarly journals Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
P. V. Shegai ◽  
P. A. Shatalov ◽  
A. A. Zabolotneva ◽  
N. A. Falaleeva ◽  
S. A. Ivanov ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
Treatment Planning ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Personalized Treatment ◽  
Trial And Error ◽  
Key Characteristics ◽  
First Results ◽  
National Cancer Program

Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients’ cancer treatment and some challenges that this field faces now in Russia. In this report, we consider the key characteristics for planning an optimal clinical treatment regimen and which should be included in the algorithm of clinical decision support systems. We discuss the perspectives of implementing artificial intelligence-based systems in cancer treatment planning in Russia.

The Role of Artificial Intelligence (AI) in Cancer Treatment in Low-Middle Income Countries: The Watson for Oncology (WfO) Experience (Preprint)

2021 ◽  
Author(s):  
Angela Rui ◽  
Srinivas Emani ◽  
Hermano Alexandre Lima Rocha ◽  
Rubina F. Rizvi ◽  
Sergio Ferreira Juaçaba ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cancer Treatment ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Cultural Barriers ◽  
Successful Implementation ◽  
Middle Income ◽  
Middle Income Countries ◽  
Cancer Management ◽  
Low Middle Income Countries

UNSTRUCTURED As technology continues to improve, healthcare systems have the opportunity to utilize a variety of innovative tools for decision making that extend beyond traditional clinical decision support systems (CDSSs). The feasibility and efficacy integrating artificial intelligence (AI) systems into medical practice has shown variable success, especially in resource-poor areas. In this paper, we cover the existing challenges surrounding cancer treatment in low-middle income countries (LMICs). By focusing on the implementation of an AI-based CDSS for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally. Additionally, we summarize current physician perspectives from China, India, Brazil, Thailand, and Mexico in regard to their experiences and recommendations for improving the system. By doing so, we hope to highlight the need for additional research on user experience and unique cultural barriers for the successful implementation of AI in LMICs.

scholarly journals A Personalized Approach to Cancer Treatment: How Biomarkers Can Help

2008 ◽  
Vol 54 (11) ◽  
pp. 1770-1779 ◽  
Author(s):  
Michael J Duffy ◽  
John Crown
Keyword(s):  
Breast Cancer ◽  
Cancer Treatment ◽  
Cancer Patients ◽  
Plasminogen Activator ◽  
Lymphoblastic Leukemia ◽  
Response To Therapy ◽  
Personalized Treatment ◽  
Severe Toxicity ◽  
Trial And Error ◽  
Breast Cancer Patients

Abstract Background: The present approach to cancer treatment is often referred to as “trial and error” or “one size fits all.” This practice is inefficient and frequently results in inappropriate therapy and treatment-related toxicity. In contrast, personalized treatment has the potential to increase efficacy and decrease toxicity. Content: We reviewed the literature relevant to prognostic, predictive, and toxicity-related markers in cancer, with particular attention to systematic reviews, prospective randomized trials, and guidelines issued by expert panels. To achieve personalized treatment for cancer, we need markers for determining prognosis, predicting response to therapy, and predicting severe toxicity related to treatment. Among the best-validated prognostic markers currently available are serum concentrations of α-fetoprotein (AFP), human chorionic gonadotropin (hCG), and lactate dehydrogenase (LDH) for patients with nonseminoma germ cell tumors and tissue concentrations of both urokinase plasminogen activator and plasminogen activator inhibitor 1 (PAI-1) for breast cancer patients. Clinically useful therapy predictive markers are estrogen and progesterone receptors to select patients with breast cancer for treatment with endocrine therapy and human epidermal growth factor receptor 2 (HER-2) to select breast cancer patients for treatment with trastuzumab (Herceptin). Markers available for identifying drug-induced adverse reactions include thiopurine methyltransferase (TPMT) to predict toxicity from thiopurines in the treatment of acute lymphoblastic leukemia and uridine diphosphate glucuronyltransferase to predict toxicity from irinotecan in the treatment of colorectal cancer. Conclusions: Validated prognostic, predictive, and toxicity markers should help cancer treatment move from the current trial-and-error approach to more personalized treatment.

Initial Experience with Cancer Guidelines Navigator, a tool to standardize and improve the quality of cancer care in Sub-Saharan Africa, at Ocean Road Cancer Institute in Tanzania.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14106-e14106
Author(s):  
Fernando Jose Suarez Saiz ◽  
Leemor Yuravlivker ◽  
Jerry Ndumbalo ◽  
Julius Mwaiselage ◽  
Sadiq Maalim Siu ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
Treatment Planning ◽  
Reference System ◽  
Point Of Care ◽  
Lessons Learned ◽  
Ease Of Use ◽  
Sub Saharan Africa ◽  
Digital Reference ◽  
Sub Saharan ◽  
Cancer Guidelines

e14106 Background: The IBM Cancer Guidelines Navigator (CGN) is a digital reference system to support treatment planning that allows clinicians to enter a cancer patient’s clinical characteristics and presents the corresponding treatment options in the NCCN Harmonized Guidelines (TM) for Sub-Saharan Africa. In October 2019, Ocean Road Cancer Institute (ORCI) in Tanzania became the first site in Africa to initiate a hospital-wide implementation of the tool to help clinicians reduce cancer treatment variability by increasing adherence to standard evidence-based care. We describe training and lessons learned from system introduction. Methods: Training for clinical staff at ORCI occurred over one week and included daily one-hour lectures, followed by personalized hands-on training. A survey was administered to assess usability and use cases of the tool. Results: Thirty-one ORCI clinical and IT staff members participated in training, and 12 completed the survey. Responses indicated that the most beneficial uses for CGN were at point of care and for self-learning. Participants indicated that the top benefits of the tool were quick access to guidelines and evidence (75%) and ease of use (58%). Expanding cancer coverage (42%), offline access and better integration into the workflow (25%) were identified as areas for improvement. Post-training, ORCI implemented easier access to CGN on each computer and tablet used for consultation and care management. Conclusions: CGN is a digital reference system that is designed to support easy and efficient access to regionalized cancer-treatment guidelines for point-of-care treatment planning and education. Expansion of this program has been planned for other hospitals in Tanzania. Future studies will examine whether CGN usage affects guideline adherence.

EP-1211: A clinical decision support system for breast cancer treatment planning

2014 ◽  
Vol 111 ◽  
pp. S59
Author(s):  
D. Palacios ◽  
J.L. Lopez Guerra ◽  
S. Tortajada ◽  
E. Casitas ◽  
A. Pérez-González ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Cancer Treatment ◽  
Treatment Planning ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Breast Cancer Treatment

scholarly journals AI in Healthcare practice: opportunities and challenges for clinical decision support systems

2019 ◽  
Author(s):  
David R. Millen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Support Systems ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Healthcare Applications

In the past few years there has been great optimism about the potential benefits of incorporating AI (cognitive) capabilities into healthcare products and services. Indeed, progress in Natural Language Processing (NLP) has made electronic health records both more accessible and comprehensible, advances in image processing algorithms has helped to early identify tumors, and large datasets with new discovery services can help with breakthrough insights in life sciences and drug discovery. Importantly, new AI-based solutions are embedded in the sociotechnical systems of clinical care and within complex regulatory environments and globally diverse cultural frameworks. In this talk, I will present several case studies of novel AI – based healthcare applications that have been introduced in recent years and share lessons learned along the way. Particular focus will be on design research challenges for healthcare products, including understanding complex workflows within clinical settings and highly specialized and diverse mental modals, and understanding multiple stakeholders and interdependent participants. Design considerations and emerging opportunities for AI-based clinical decision support systems will also be shared.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

Malignant hyperthermia susceptibility: utilization of genetic results in an electronic medical record to increase safety

2020 ◽  
Vol 21 (17) ◽  
pp. 1207-1215
Author(s):  
Jordan F Baye ◽  
Natasha J Petry ◽  
Shauna L Jacobson ◽  
Michelle M Moore ◽  
Bethany Tucker ◽  
...  
Keyword(s):  
Decision Support ◽  
Malignant Hyperthermia ◽  
Clinical Decision Support ◽  
Perioperative Complications ◽  
Implementation Strategies ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Malignant Hyperthermia Susceptibility ◽  
Alert System ◽  
Genotyping Platform

Aim: This manuscript describes implementation of clinical decision support for providers concerned with perioperative complications of malignant hyperthermia susceptibility. Materials & methods: Clinical decision support for malignant hyperthermia susceptibility was implemented in 2018 based around our pre-emptive genotyping platform. We completed a brief descriptive review of patients who underwent pre-emptive testing, focused particularly on RYR1 and CACNA1S genes. Results: To date, we have completed pre-emptive genetic testing on more than 10,000 patients; 13 patients having been identified as a carrier of a pathogenic or likely pathogenic variant of RYR1 or CACNA1S. Conclusion: An alert system for malignant hyperthermia susceptibility – as an extension of our pre-emptive genomics platform – was implemented successfully. Implementation strategies and lessons learned are discussed herein.

scholarly journals Precision medicine in human heart modeling

2021 ◽  
Author(s):  
M. Peirlinck ◽  
F. Sahli Costabal ◽  
J. Yao ◽  
J. M. Guccione ◽  
S. Tripathy ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Treatment Planning ◽  
Human Heart ◽  
Cardiovascular Health ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Ventricular Assist Devices ◽  
Device Design ◽  
Heart Modeling ◽  
Medical Histories

AbstractPrecision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

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