Do you believe in the power of clinical examination? The answer must be yes!

Abstract

Dinosaurs were declared extinct many centuries ago. Mankind cannot now do anything to undo this for itself. In an article in 1996, Schechter et al. say in their first line: "Skilled historytaking is in danger of becoming a lost art". Mankind, and doctors especially, can and must do their best to prevent this extinction. 1 Hampton et al., in 1975, stated that history-taking was responsible for 82.5% of all diagnoses in their group of 80 patients. 2 Physical examination was responsible for 8.75% and laboratory investigation for a further 8.75%. Sandler, in 1979, analyzed the importance of history-taking, examination and routine and special investigation among 630 patients. 3 He concluded that history-taking was the most important factor in both diagnosis and management in cardiovascular, neurological, respiratory, urinary and other miscellaneous problems, and was decisive in 56% of all diagnoses. Hampton's and Sandler's data were obtained from British general practitioners.Peterson et al. attempted to quantify the relative contributions of history-taking, physical examination and laboratory investigation in making medical diagnoses in an outpatient unit in the United States in 1992. 4 They concluded that history-taking was responsible for the final diagnosis in 76% of all patients. Physical examination led to the diagnosis in another 12%, and laboratory investigation in a further 11% of all patients. They also concluded that, although physical examination and laboratory investigation led to fewer diagnoses, they were instrumental in excluding certain diagnostic possibilities and increasing physicians' confidence in their diagnoses.More recently, in India in 2000, Roshan and Rao attempted to quantify the relative contributions of history-taking, physical examination and laboratory investigations in making medical diagnoses. 5 History-taking was responsible for the diagnoses of 78.6% of all patients. Physical examination was responsible for another 8.2% and laboratory investigation a further 13.2% of all diagnoses. Now, recent research performed in Brazil has shown that, after excluding dermatological diagnoses and diagnoses from screening, history-taking is responsible for 77.8% of all diagnoses, physical examination for 10% and laboratory investigation for a further 10% (Benseñor IM, unpublished data). If we

Large scale mining and evidence combination to support medical diagnosis

“…Yet the facts and figures presented earlier rather suggest misdiagnoses to be more of a systemic problem. Part of the problem stems from the accessibility of patient data, in particular patient history that is credited for being the key factor leading to diagnosis in 56% to 82.5% of the cases according to a review several studies on factors contributing to a diagnosis ( [7]). Patient data is currently scattered across various locations often using different platforms and data storage standards and is sometimes not accessible because it is not digitized or discarded after real-time use.…”

“…Een van de oorzaken van het probleem is de toegankelijkheid van patiëntdata. Toegang tot de medische geschiedenis van de patiënt wordt in het specifiek aangewezen als belangrijk voor de diagnose in 56% tot 82.5% van de gevallen, volgens een review van verscheidene studies over factoren die bijdragen aan een diagnose ( [7]). Patiëntdata is momenteel verdeeld over verschillende locaties, platformen en dataformaten, en is soms niet beschikbaar omdat het niet gedigitaliseerd is of weggegooid xvi wordt na direct gebruik.…”

“…Rare diseases or conditions (also called zebras) are very likely to be misdiagnosed since clinicians are trained to look for the most common diagnoses 1 i.e resulting in death, permanent disability, or near life-threatening event 2 i.e resulting in short-term morbidity, increased length of stay, higher level of care or invasive procedure 2 1 Introduction first but even common conditions such as pneumonia, asthma or breast cancer are routinely misdiagnosed especially if the symptoms presentation is atypical ( [5,6]). Part of the problem stems from the accessibility of patient data, in particular patient history that is credited for being the key factor leading to diagnosis in 56% to 82.5% of the cases according to a review several studies on factors contributing to a diagnosis ( [7]). Patient data is currently scattered across various locations often using different platforms and data storage standards and is sometimes not accessible because it is not digitized or discarded after realtime use.…”

“…The EEGs in both datasets predominantly represent standard EEGs (15 to 40 minutes' EEGs) i.e the most common EEGs in clinical practice (91.6% of the EEGs recorded per year at the local hospital). 7 and about a third of the annual Dutch data in filesize …”

“…In particular, the execution times for files of durations between 15 and 30 minutes are between 8.8 seconds and 131.7 seconds, that is execution times between 6.8 to 204.5 times lower than the duration of the original EEGs. Furthermore, the method doesn't impose any condition on the signals to be compared as it handles the cases where EEGs to be compared 7 autoregressive model 8 We use an AR model instead of an ARIMA model since it is argued in [101] that "for every ARIMA model there exists an equivalent AR model, that can be obtained from the ARIMA model by polynomial division" and the LPC cepstrum of the time series is, subsequently (in [101] ), computed based on that equivalent AR model 2.12063087537 )) using the Levenberg-Marquardt algorithm Moreover, fractal interpolation doesn't require model selection as AR modeling does, which considerably speeds up EEG interpolation. Moreover, with our dataset, the computation of the Euclidean distance between the cepstrum coefficients calculated based on the EEGs AR models leads to a matrix of NaN (i.e not a number) 9 : the AR modeling method is therefore less stable than the fractal interpolation-based method.…”