scholarly journals Towards an App to Estimate Patient-Specific Perioperative Femur Fracture Risk

2020 ◽  
Vol 10 (18) ◽  
pp. 6409 ◽  
Author(s):  
L. Esposito ◽  
V. Minutolo ◽  
P. Gargiulo ◽  
H. Jonsson ◽  
M. K. Gislason ◽  
...  
Keyword(s):  
Decision Making ◽  
Fracture Risk ◽  
Ad Hoc ◽  
Strength Distribution ◽  
Bone Diseases ◽  
Medical Decision ◽  
Patient Specific ◽  
Decision Making Process ◽  
Geometrical Parameters ◽  
Traffic Light

Total Hip Arthroplasty has been one of the most successful surgical procedure in terms of patient outcomes and satisfaction. However, due to increase in life expectancy and the related incidence of age-dependent bone diseases, a growing number of cases of intra-operative fractures lead to revision surgery with high rates of morbidity and mortality. Surgeons choose the type of the implant, either cemented or cementless prosthesis, on the basis of the age, the quality of the bone and the general medical conditions of the patients. Generally, no quantitative measures are available to assess the intra-operative fracture risk. Consequently, the decision-making process is mainly based on surgical operators’ expertise and qualitative information obtained from imaging. Motivated by this scenario, we here propose a mechanical-supported strategy to assist surgeons in their decisions, by giving intelligible maps of the risk fracture which take into account the interplay between the actual mechanical strength distribution inside the bone tissue and its response to the forces exerted by the implant. In the presented study, we produce charts and patient-specific synthetic “traffic-light” indicators of fracture risk, by making use of ad hoc analytical solutions to predict the stress levels in the bone by means of Computed Tomography-based mechanical and geometrical parameters of the patient. We felt that if implemented in a friendly software or proposed as an app, the strategy could constitute a practical tool to help the medical decision-making process, in particular with respect to the choice of adopting cemented or cementless implant.

An Evacuation Decision Making Model for Firefighters in Ad Hoc Robot Network

2013 ◽  
Vol 756-759 ◽  
pp. 504-508
Author(s):  
De Min Li ◽  
Jian Zou ◽  
Kai Kai Yue ◽  
Hong Yun Guan ◽  
Jia Cun Wang
Keyword(s):  
Decision Making ◽  
Markov Decision Process ◽  
Decision Process ◽  
Ad Hoc ◽  
Decision Making Process ◽  
Decision Method ◽  
Markov Decision ◽  
Critical Problems ◽  
Fire Scene ◽  
Decision Making Model

Evacuation for a firefighter in complex fire scene is challenge problem. In this paper, we discuss a firefighters evacuation decision making model in ad hoc robot network on fire scene. Due to the dynamics on fire scene, we know that the sensed information in ad hoc robot network is also dynamically variance. So in this paper, we adapt dynamic decision method, Markov decision process, to model the firefighters decision making process for evacuation from fire scene. In firefighting decision making process, we know that the critical problems are how to define action space and evaluate the transition law in Markov decision process. In this paper, we discuss those problems according to the triangular sensors situation in ad hoc robot network and describe a decision making model for a firefighters evacuation the in the end.

Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

scholarly journals Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 815
Author(s):  
Carlo Ricciardi ◽  
Halldór Jónsson ◽  
Deborah Jacob ◽  
Giovanni Improta ◽  
Marco Recenti ◽  
...  
Keyword(s):  
Decision Making ◽  
Total Hip Arthroplasty ◽  
Hip Arthroplasty ◽  
Quality Data ◽  
National Database ◽  
Patient Specific ◽  
Surgical Approaches ◽  
Decision Making Process ◽  
Mineral Density ◽  
Total Hip

There are two surgical approaches to performing total hip arthroplasty (THA): a cemented or uncemented type of prosthesis. The choice is usually based on the experience of the orthopaedic surgeon and on parameters such as the age and gender of the patient. Using machine learning (ML) techniques on quantitative biomechanical and bone quality data extracted from computed tomography, electromyography and gait analysis, the aim of this paper was, firstly, to help clinicians use patient-specific biomarkers from diagnostic exams in the prosthetic decision-making process. The second aim was to evaluate patient long-term outcomes by predicting the bone mineral density (BMD) of the proximal and distal parts of the femur using advanced image processing analysis techniques and ML. The ML analyses were performed on diagnostic patient data extracted from a national database of 51 THA patients using the Knime analytics platform. The classification analysis achieved 93% accuracy in choosing the type of prosthesis; the regression analysis on the BMD data showed a coefficient of determination of about 0.6. The start and stop of the electromyographic signals were identified as the best predictors. This study shows a patient-specific approach could be helpful in the decision-making process and provide clinicians with information regarding the follow up of patients.

Incorporating patient preferences and priorities into end-of-life care.

2015 ◽  
Vol 33 (29_suppl) ◽  
pp. 41-41
Author(s):  
Eric Rackow ◽  
Afua Ofori ◽  
Wendy Rodkey ◽  
Roy A. Beveridge
Keyword(s):  
Quality Of Life ◽  
Decision Making ◽  
End Of Life Care ◽  
Medical Decision Making ◽  
Case Managers ◽  
Readiness For Change ◽  
Medical Decision ◽  
Decision Making Process ◽  
Life Care

41 Background: Patients with advanced illness often face painful conversations and difficult decisions. A program was deployed to help patients identify, communicate, and incorporate their personal preferences and priorities into decisions about their care. The program was assessed by measuring movement along the readiness for change continuum. Methods: Patients residing in the home and participating in a chronic care program were referred by their case managers based on clinical conditions and whether the patient appeared to be in their last 12 months of life. Counseling sessions with patients or family caregiver/s were designed to move participants toward the following actions: be fully informed about their medical situation, describe their detailed quality of life priorities, articulate a self-defined medical decision making process, effectively communicate to their family and physicians, and implement and repeat the aforementioned steps. After 5 months (Sept-2014 to Feb-2015), movement along the readiness for change continuum (pre-contemplation, contemplation, preparation, action, maintenance, and advocacy) was reported. Results: Of the 427 patients referred, 33 could not be reached, 116 were ineligible, 50 declined or did not engage. Of the 228 participants, 191 (84%) moved at least one step in readiness for change continuum over the 5-month period. In Nov-2014, 13% of participants were in action, maintenance, or advocacy, which increased to 19% by Feb-2015. The largest observed movement to action, maintenance, or advocacy was in defining quality of life priorities: 2% Nov-2014 to 21% Feb-2015. The least movement to action, maintenance, or advocacy was observed in articulating a self-defined medical decision making process: 3% Nov-2014 to 16% Feb-2015. Case managers reported discomfort in referring members based on their assessment of length of life. Early surveys show high levels of satisfaction. Conclusions: A very high percentage of patients progressed in incorporating their preferences and priorities into end of life care as measured by the readiness to change continuum. This program is currently expanding and the referral process is changing from case manager to algorithm based identification referrals.

scholarly journals Application of a computerized medical decision-making process to the problem of digoxin intoxication

1984 ◽  
Vol 4 (3) ◽  
pp. 571-576 ◽  
Author(s):  
Keith S. White ◽  
Alan Lindsay ◽  
T. Allan Pryor ◽  
Wayne F. Brown ◽  
Kevin Walsh
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Decision Making Process ◽  
Digoxin Intoxication

scholarly journals The role of non-medical factors in physicians’ decision-making process in a pediatric telemedicine service

2019 ◽  
Vol 26 (2) ◽  
pp. 1152-1176 ◽  
Author(s):  
Motti Haimi ◽  
Shuli Brammli-Greenberg ◽  
Yehezkel Waisman ◽  
Nili Stein ◽  
Orna Baron-Epel
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Decision Making Process ◽  
Medical Decisions ◽  
Telemedicine Service ◽  
Medical Factors ◽  
Complex Process ◽  
Clinical Scenarios

The complex process of medical decision-making is prone also to medically extraneous influences or “non-medical” factors. We aimed to investigate the possible role of non-medical factors in doctors’ decision-making process in a telemedicine setting. Interviews with 15 physicians who work in a pediatric telemedicine service were conducted. Those included a qualitative section, in which the physicians were asked about the role of non-medical factors in their decisions. Their responses to three clinical scenarios were also analyzed. In an additional quantitative section, a random sample of 339 parent -physician consultations, held during 2014–2017, was analyzed retrospectively. Various non-medical factors were identified with respect to their possible effect on primary and secondary decisions, the accuracy of diagnosis, and “reasonability” of the decisions. Various non-medical factors were found to influence physicians’ decisions. Those factors were related to the child, the applying parent, the physician, the interaction between the doctor and parents, the shift, and to demographic considerations, and were also found to influence the ability to make an accurate diagnosis and “reasonable” decisions. Our conclusion was that non-medical factors have an impact on doctor’s decisions, even in the setting of telemedicine, and should be considered for improving medical decisions in this milieu.

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