Diagnosis Bias and its Revelance During the Diagnosis Process

Esteban‐Zubero, Eduardo; Valdivia-Grandez, Mario Antonio; Alatorre-Jiménez, Moisés Alejandro; Torre, Lourdes del Carmen Rizo-De La; Marín-Medina, Alejandro; Alonso-Barragan, Sara Anabel; López‐García, Carlos

doi:10.26502/acmcr.9655005

Cited by 4 publications

(3 citation statements)

References 22 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Availability bias might occur as a result of having experience with cases or studies that come more easily to mind, which may yield assumptions of the same scenario being repeated ( 29 ). Further, satisfaction bias occurs when a clinician concludes a diagnosis when identifying a single disease as the root cause although more causes may be possible ( 30 ).…”

Section: Challenges With the Three Components Of Ebpmentioning

confidence: 99%

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare

Nilsen,

Sundemo,

Heintz

et al. 2024

Front. Health Serv.

View full text Add to dashboard Cite

BackgroundEvidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this.Challenges with the three components of EBPClinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources.AI assistance for the three components of EBPAI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue.ConclusionThis review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.

show abstract

Section: Challenges With the Three Components Of Ebpmentioning

confidence: 99%

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare

Nilsen,

Sundemo,

Heintz

et al. 2024

Front. Health Serv.

View full text Add to dashboard Cite

show abstract

“…In this instance, because the diagnosis of IE was promptly made using many different sources of information, likely, the causative origin of IE was not carefully considered during the initial consultation. Such an oversight can be attributed to satisfaction bias, a common occurrence in clinical settings [15]. In addition, the patient thought that the process of making the diagnosis was complete when she was diagnosed with IE and did not consider the possibility of additional diagnoses (premature closure).…”

Section: The Diagnostic Process and The Significance Of Patient Persp...mentioning

confidence: 99%

A Case of Group A Streptococcus Bacteremia and Infective Endocarditis Caused by Right Ovarian Tube Endometriosis Where the Patient’s Perspective Was Key to the Diagnostic Process

Ito,

Miyagami,

Suzuki

et al. 2023

Cureus

View full text Add to dashboard Cite

Group A streptococcus pyogenes (GAS) is a common organism that can cause upper respiratory infections. We encountered a case where GAS caused infective endocarditis (IE) due to an entry from the vagina. In this case, although echocardiography was negative, we were able to make a diagnosis of IE based on the 2023 Duke International Society for Cardiovascular Infectious Diseases Criteria, and we started antimicrobial therapy for IE. However, the patient subsequently developed persistent abdominal pain, which was atypical; hence, we reviewed the differential diagnosis. It is difficult to locate the primary site of infection because GAS rarely causes vaginal infections, and vaginal infections rarely cause IE. This case highlights the significance of revisiting medical history and the value of using a system 3 approach to refine diagnostic directions.

show abstract

“…Cognitive errors (mithyadrushtam) are the most frequent in the field of 'Emergency medicine'. 97 'Type 1' thinking is a fast, intuitive, pattern recognition driven method of problem solving, which places a low cognitive burden on the user, and allows one to make fast and accurate decisions rapidly (pragnaaparadhajam). However, this type 1, rapid decision was poor at predicting diagnosis or aiding further prognostication (pragnaaparadhajam).…”

Section: Psychological Concepts In Charaka Indriya Sthana 'Sattvam Bhakti ---Jignaasamaanena Bhishajaa' (Verse 3)mentioning

confidence: 99%

Dementia, delirium & neuropsychiatric conditions in Charaka indriya sthana

Gupta¹,

Mamidi²

2020

PPIJ

View full text Add to dashboard Cite

Charaka Samhita is the oldest and most authentic treatise on Ayurveda (an ancient Indian system of medicine). Indriya sthana (prognostic medicine) is one among the eight sections of Charaka samhita and it deals with prognostic aspects. Arishta lakshanas are the signs and symptoms which indicates imminent death. Various psychiatric and neuropsychiatric conditions are mentioned throughout 'Charaka Indriya sthana' in a scattered form. Dementia and delirium are commonly seen at terminal stages or at the end-of-life. As indriya sthana deals with terminal illnesses or end-of-life stages, there is a hypothesis that description of conditions like dementia and delirium may be traceable in 'Charaka indriya sthana'. The present study attempts to screen various references pertaining to psychiatric and neuropsychiatric conditions of 'Charaka Indriya sthana' and explore their rationality, clinical and prognostic significance in present era. Dementia, Delirium and neuropsychiatric conditions of 'Charaka Indriya sthana' have been explored in the present study. 'Dementia' and 'Delirium' are the two most common conditions found through out 'Charaka indriya sthana'. Various references related to other psychiatric and neuropsychiatric conditions like, 'Hallucinations', 'Trichotillomania', 'Bruxism', 'Nail biting', 'Impulse control disorders', 'Major depressive disorder', 'Catatonia' and 'Negative symptoms of Schizophrenia'. The psychiatric/neuropsychiatric conditions mentioned in 'Charaka Indriya sthana' are characterized by poor prognosis, having irreversible underlying pathology, chronic, progressive and debilitating in nature and commonly found in dying patients or at the endof-life stages. It seems that psychiatric conditions mentioned in 'Charaka Indriya sthana' have clinical applicability and prognostic significance in present era also. Further studies are required to substantiate the clinical findings described in 'Charaka Indriya sthana'.

show abstract

Diagnosis Bias and its Revelance During the Diagnosis Process

Cited by 4 publications

References 22 publications

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare

A Case of Group A Streptococcus Bacteremia and Infective Endocarditis Caused by Right Ovarian Tube Endometriosis Where the Patient’s Perspective Was Key to the Diagnostic Process

Dementia, delirium & neuropsychiatric conditions in Charaka indriya sthana

Contact Info

Product

Resources

About