Differential Diagnosis Generators: an Evaluation of Currently Available Computer Programs

Bond, William F.; Schwartz, Linda; Weaver, Kevin; Levick, Donald; Giuliano, Michael; Graber, Mark L.

doi:10.1007/s11606-011-1804-8

Cited by 106 publications

(93 citation statements)

References 14 publications

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“…Physicians will have greater success using this generalized search tool, because they will be able to quickly rule out the great majority of the less-relevant suggestions that are made, but physicians would be much better served by using diagnosis generators that have been specifically designed for the task. These outperform Google appreciably in both sensitivity (including the correct diagnosis) and specificity (not including so many irrelevant suggestions) [16].…”

Section: How Can Patients Help With Their Diagnosis?mentioning

confidence: 99%

Let patients help with diagnosis

Graedon

2014

Diagnosis

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Diagnostic errors are common and have seemed intractable for decades. Although time pressure is often cited as a contributor to this problem, overconfidence on the part of the diagnostician may also play a role. Successful strategies for reducing error in the diagnostic process should include patients and families in innovative ways. Changing the existing paradigm of patients responding passively to one of patients participating actively has the potential to assist in achieving greater diagnostic accuracy. Providers should welcome patients' online research into their symptoms, succinct summaries of their course of illness and questions about the differential diagnoses that might be applicable. Systematic methods for following up after the initial diagnosis are essential for verifying accuracy as well as providing excellent patient care.

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Section: How Can Patients Help With Their Diagnosis?mentioning

confidence: 99%

Let patients help with diagnosis

Graedon

2014

Diagnosis

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“…By providing access to a broad differential, DDX generators can potentially help users overcome cognitive biases (such as availability bias) [2,3] and fund of knowledge deficiencies by directing them to diagnoses they might not have considered otherwise. In fact, simulations suggest that DDX generators have the potential to improve diagnostic accuracy [4][5][6][7]. However, there is a paucity of research examining the use of such systems in real-world settings [8], and even less is known about the impact of DDX generator use on patient care and resource utilization [9,10].…”

Section: Introductionmentioning

confidence: 99%

Uptake and impact of a clinical diagnostic decision support tool at an academic medical center

2015

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“…A number of algorithms have been created for addressing diagnostic procedures; examples include Iliad 16 and DXplain 17 among many others 18 . These algorithms are mostly based on limited datasets and as such, accuracy had been low when deployed in larger datasets or a different disease 5 .…”

Section: Benefits and Challenges Of Technology-supported Medical Diagmentioning

confidence: 99%

Robots to the Rescue: A Review of Studies on Differential Medical Diagnosis Employing Ontology-Based Chat Bot Technology

Edwards¹,

Muniru²,

Cheok³

2016

Preprint

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Access to medical care is a global issue. Technology-aided approaches have been applied in addressing this. Interventions have however not focused on medical diagnosis as a fully automated procedure and available applications employ mainly text-based inputs rather than conversation in natural language. We explored the utility of ontology-based chatbot technology for the design of intelligent agents for medical diagnosis through a systematic review of the most recent related literature. English articles published in 2011-2016 returned 233 hits which yielded 11 relevant articles after a 3-stage screening. Findings showed that the creation of expert systems had been the focus of many the studies which utilize the physician-system-patient framework with system training based mostly on expert knowledge for designing web-or mobile phone-based applications that serve assistive purposes. Findings further indicated gaps in the design and evaluation of more effective systems deployable as standalone applications, for example, on an embodied robotic system. The need for technology supporting the physical examination part of diagnosis, connection to data sources on patients' vitals and medical history are also indicated in addition to the need for more qualitative work on natural language-based interaction. The system should be one that is continuously learning. Future works should also be directed towards the building of more robust knowledge base as well as evaluation of theory-based diagnostic methodological options

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Differential Diagnosis Generators: an Evaluation of Currently Available Computer Programs

Abstract: Consensus criteria for DDX generator evaluation were developed. Application of these criteria as well as performance testing supports the use of DxPlain® and Isabel© over the other currently available DDX generators.

Cited by 106 publications

References 14 publications

Let patients help with diagnosis

Let patients help with diagnosis

Uptake and impact of a clinical diagnostic decision support tool at an academic medical center

Robots to the Rescue: A Review of Studies on Differential Medical Diagnosis Employing Ontology-Based Chat Bot Technology

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