Coming up with a diagnosis is a significant element in the provision of healthcare services to patients in various settings as illustrated by McPherson & Pincus, (2017). Diagnostic decision making is a vital competency and skill that all the providers of healthcare ought to possess and be smart in so that they may not miss out specific vital leads that can assist in the identification and treatment of the patients’ conditions. Pattern recognition of patients’ signs and symptoms offers an essential method that assures of relatively correct and accurate diagnosis as illustrated in this article.
Pattern recognition of signs and symptoms of diseases on a more magnificent extent depend on the experience of the nursing care providers in handling, clerking and determining the needs of various patients (Wolfers et al., 2015). In most instances, patients present with a myriad of signs and symptoms that may point towards a specific disease. This occurs even with the consideration of other external factors affecting health such as the environment as well as the history provided by the patient on diseases or allergies (Haller et al., 2014). In some cases, the presenting symptoms can also provide a guidance to more than one disease or even be an alert concerning other underlying problems that the patient may have. According to Dervilis et al., (2014), the pattern recognition of signs and symptoms can also lead to a diagnosis in the instances where the chronology of events care be related to a previous case thus giving a hand in the identification of the patient’s condition as well as the management.
By determining the symptoms, it gives way for the assignment of the relatively rhyming probabilities to the probable diagnosis since it enables the health practitioner not to be bothered by having differential diagnoses (Chiffi & Zanotti, 2015). The differential diagnosis may make the process too narrow or broad for the determination of the exact condition. In one way or the other, the pattern recognition of symptoms acts as a method of narrowing down to the particular signs and symptoms that the healthcare practitioners are familiar with. The care provider can then conclude on a condition whose symptoms coincide with the determined patterns of signs and symptoms.
The pattern recognition of patients symptoms assist the health care provider to create a balance between various uncertainties and conduct a deductive contemplation for the diagnosis purposes (Zanotti & Chiffi, 2015). For example, in the tropical areas and countries where the malaria condition is endemic, the manifestations of jaundice, headache, fever, shivering, joint pain, nausea, and vomiting can lead to the diagnosis. Such a diagnosis is based on previous occurrences together with the presence of symptoms that are indeed suggestive of malaria.
In the practicum experience, the presentation of Mr. P with a history of increased heart rate, weakness as well as chest pain together with being a known patient with arrhythmias made me to come up with a diagnosis of arrhythmias that have been triggered even before the tests to confirm the same. Furthermore, the existence of hypertension which was diagnosed five years ago provided a link that could lead to such diagnosis. With the determination and relation of the symptoms, the management of the patient could take the form of emergency care while probing on the cause of the trigger and confirmation of the diagnosis undertaken later on. In this scenario, the observation of the pattern of symptoms assisted in the diagnosis of the patient’s condition.
Alongside various assessment criteria and laboratory tests, the pattern recognition of disease symptoms also gives room for determination of a diagnosis. It tends to be reliable when the pattern is similar to a previous scenario as well as the scientifically determined symptoms of the condition.
Chiffi, D., & Zanotti, R. (2015). Medical and nursing diagnoses: a critical comparison. Journal
of evaluation in clinical practice, 21(1), 1-6.
Dervilis, N., Choi, M., Taylor, S. G., Barthorpe, R. J., Park, G., Farrar, C. R., & Worden, K.
(2014). On damage diagnosis for a wind turbine blade using pattern recognition. Journal
of sound and vibration, 333(6), 1833-1850.
Haller, S., Lovblad, K. O., Giannakopoulos, P., & Van De Ville, D. (2014). Multivariate pattern
recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current
challenges and future trends. Brain topography, 27(3), 329-337.
Kahn, P., & Kinsolving, A. (2016). U.S. Patent No. 9,471,751. Washington, DC: U.S. Patent and
Kambeitz, J., Kambeitz-Ilankovic, L., Leucht, S., Wood, S., Davatzikos, C., Malchow, B., … &
Koutsouleris, N. (2015). Detecting neuroimaging biomarkers for schizophrenia: a meta-
analysis of multivariate pattern recognition studies. Neuropsychopharmacology, 40(7),
McPherson, R. A., & Pincus, M. R. (2017). Henry’s Clinical Diagnosis and Management by
Laboratory Methods E-Book. Elsevier Health Sciences.
Wolfers, T., Buitelaar, J. K., Beckmann, C. F., Franke, B., & Marquand, A. F. (2015). From
estimating activation locality to predicting disorder: a review of pattern recognition for
neuroimaging-based psychiatric diagnostics. Neuroscience & Biobehavioral Reviews, 57,
Zanotti, R., & Chiffi, D. (2015). Diagnostic frameworks and nursing diagnoses: A normative
stance. Nursing Ph