scholarly journals A Study on Factors Influencing Medical Treatment Decision-Making for Overactive Bladders in Female Patients over 40 - Data from Clinical Trial Participants -

2013 ◽  
Vol 26 (1) ◽  
pp. 69-81
Author(s):  
In-Suk Ahn ◽  
Dong-Il Kim ◽  
Min-Sun Choi
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Medical Treatment ◽  
Treatment Decision ◽  
Female Patients ◽  
Treatment Decision Making ◽  
Factors Influencing ◽  
Trial Participants

scholarly journals Computer knows best? The need for value-flexibility in medical AI

2018 ◽  
Vol 45 (3) ◽  
pp. 156-160 ◽  
Author(s):  
Rosalind J McDougall
Keyword(s):  
Decision Making ◽  
Medical Treatment ◽  
Shared Decision Making ◽  
Patient Autonomy ◽  
Ethical Issues ◽  
Treatment Decision ◽  
Shared Decision ◽  
Treatment Decision Making ◽  
Value Judgements ◽  
The Relationship

Artificial intelligence (AI) is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system creates both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making.

Medical Treatment Decision-Making for Advanced, Progressive Medullary Thyroid Cancer

2016 ◽  
pp. 365-374
Author(s):  
Ramona Dadu ◽  
Robert F. Gagel ◽  
Mimi I. Hu
Keyword(s):  
Decision Making ◽  
Thyroid Cancer ◽  
Medical Treatment ◽  
Medullary Thyroid Cancer ◽  
Treatment Decision ◽  
Medullary Thyroid ◽  
Treatment Decision Making

Factors influencing treatment decision-making for maintaining or extracting compromised teeth

2013 ◽  
Vol 25 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Bich Hue Lang-Hua ◽  
Colman P. J. McGrath ◽  
Edward C. M. Lo ◽  
Niklaus P. Lang
Keyword(s):  
Decision Making ◽  
Treatment Decision ◽  
Treatment Decision Making ◽  
Factors Influencing

Geriatric and tumour factors influencing cancer treatment decision making: analysis of data from 360 older cancer patients

2013 ◽  
Vol 4 ◽  
pp. S81
Author(s):  
E. Castel-Kremer ◽  
T. Benet ◽  
C. Lombard-Bohas ◽  
P. Mere ◽  
G. Albrand ◽  
...  
Keyword(s):  
Decision Making ◽  
Cancer Treatment ◽  
Cancer Patients ◽  
Treatment Decision ◽  
Treatment Decision Making ◽  
Factors Influencing ◽  
Older Cancer Patients

scholarly journals Decisional Conflict During Major Medical Treatment Decision-making: a Survey Study

2020 ◽  
Author(s):  
Kristen E. Pecanac ◽  
Roger L. Brown ◽  
Hanna B. Kremsreiter
Keyword(s):  
Decision Making ◽  
Medical Treatment ◽  
Treatment Decision ◽  
Decisional Conflict ◽  
Survey Study ◽  
Treatment Decision Making

scholarly journals Integrating Touchscreen-Based Geriatric Assessment and Frailty Screening for Adults with Acute Myelogenous Leukemia to Drive Personalized Treatment Decisions

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4050-4050
Author(s):  
Omer Hassan Jamy ◽  
Stacey Ingram ◽  
D'Ambra Dent ◽  
William Dudley ◽  
Matthew Dudley ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Real Time ◽  
Frailty Index ◽  
Treatment Decision ◽  
Symptom Assessment ◽  
Treatment Decisions ◽  
Frailty Status ◽  
Treatment Decision Making ◽  
Survey Results

Abstract Background: Acute Myeloid Leukemia (AML) is a disease of older adults, with a median age of 68 years at diagnosis. The NCCN guidelines recommend comprehensive geriatric assessments (GA) be included in clinical practice to guide treatment decisions. Utility of GA in older AML patients in a real-world environment is not yet established. We tested the feasibility of using a modified GA (mGA), administered by patient self-report on a touchscreen computer, real-time use and utility by clinicians and the correlation of mGA results on treatment decision-making. Methods: 77 newly diagnosed patients were recruited from three sites to complete a tablet-based mGA screening at a treatment decision visit. The mGA includes four domains: age, activities of daily living (ADLs), instrumental ADLs, and comorbidities. Survey results along with history of falls was used to create the Frailty Index (FI). Providers were asked what they thought the fit/frailty status of the patient was before viewing the results on a dashboard. After viewing the survey results, the clinician discussed the treatment plan with the patient. Patients received intensive or non-intensive therapy. Additional information was captured on clinical trial enrollment. Baseline and 3 month surveys recorded presence and severity of 8 symptoms using the Edmonton Symptom Assessment Scale (ESAS) with 0=no symptom and 10=worst possible symptom and quality of life using the Functional Assessment of Cancer Treatment: Leukemia (FACT-LEU). Results: Participants had a median age of 71 years ( range:61-88y); 50% were female, and 87% white. Frailty Index results for 76 patients were 28 (36.4%) fit, 25 (32.5%) intermediate, and 23 (29.9%) frail. (One patient did not complete the mGA). 52 of 77 (69%) enrolled patients were alive at 3 months; 21(27%) died and 4 (5%) were lost to follow-up. Providers were asked the fit/frailty status prior to seeing the results of the mGA. Of 75 provider responses, results were 27 (36.0%) fit, 29 (38.7%) intermediate, and 19 (25.3%) frail. There was 63% (n=47) provider concordance with the mGA result. There was more agreement with fit (n=22, 81.5%) and frail status (n=11, 57.9%) and less with intermediate (n=14, 48.3%). Of the 25 of 75 (33.%) provider reports that indicated that the mGA result influenced the treatment decision, 6 patients (5 fit, 1 intermediate) received intensive treatment, 15 received non intensive treatment (1 fit, 6 intermediate, 8 frail) and 4 enrolled in a clinical trial (1 fit, 2 intermediate, and 1 frail). Significant symptom improvement at 3 months was seen for drowsiness, lack of appetite, shortness of breath, and anxiety. FACT Leu results did not change over 3 months. Providers reported an average of 4.45 minutes to review the dashboard. Patients were able to complete the surveys unassisted in an average time of 16.24 minutes. Discussion: There was nearly 40% discordance between the provider and mGA, with the most discordance on the intermediate fit status. However, results of the mGA influenced treatment decision making in one third of provider/patient interactions. Further analysis of the mGA domains is warranted to see if additional insights can be gained. With time, some symptoms improve and others don't. This points to the opportunity to direct resources towards symptom assessment during treatment. Feasibility was demonstrated in this study as providers received the aggregated results in real time and reviewed them in less than 5 minutes. In addition, patients were able to complete the survey unassisted without disturbing clinic operations. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

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