ABSTRACT
There is a high percentage of error in the approach of patients with joint pain by primary care physicians. An algorithm can help improve this misdiagnosis problem. Our study seeks to determine the effectiveness of an algorithm when used by primary care physicians for the diagnosis of cases of joint pain patients. A randomized clinical experiment was carried out. Primary care physicians from five cities in Colombia developed a series of clinical cases, which were presented to them through a website on their personal cell phones. Half of the doctors developed the cases using the diagnostic algorithm, and the other half developed the cases without the use of the algorithm. Main measures were proportion of correct diagnosis, number, type of laboratory and diagnostic images requested for the diagnostic approach of clinical cases. Two hundred and twenty-four primary care physicians participated. The overall proportion of cases correctly diagnosed was 37.3% higher in the intervention group; we found a greater difference in cases of spondyloarthritis (60.8%), followed by systemic lupus erythematosus with joint involvement (32.2%), rheumatoid arthritis (30.3%) and osteoarthritis (25.9%). The average number of tests requested to develop clinical cases was lower in the intervention group than in the control group, both globally and for each of the four diseases, with statistically significant differences for each of the comparisons. The diagnostic algorithm proved to be an effective tool when used by primary care physicians; the proportion of correct diagnoses increased, and the number of tests requested in the development of the cases decreased.
Subject(s)
Algorithms , Arthralgia/diagnosis , Arthritis, Rheumatoid/diagnosis , Diagnostic Errors/prevention & control , Lupus Erythematosus, Systemic/diagnosis , Osteoarthritis/diagnosis , Physicians, Primary Care , Spondylarthropathies/diagnosis , Adult , Arthralgia/etiology , Arthritis, Rheumatoid/complications , Colombia , Female , Humans , Lupus Erythematosus, Systemic/complications , Male , Middle Aged , Osteoarthritis/complications , Random Allocation , Spondylarthropathies/complicationsABSTRACT
INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Methodologies, Models and Algorithms for Patients Rehabilitation". BACKGROUND: ICT is an emerging alternative to paper-and-pencil tests for the assessment of cognitive functions, allowing for the monitoring of participants' behavior while they perform simulations of instrumental activities of daily life (IADLs) in a virtual reality (VR) scenario. This requires normative values for each VR task, so that deviations to normality can be identified as indicators of cognitive impairment. OBJECTIVES: To identify normative data for several IADL tasks that are available on the Systemic Lisbon Battery (SLB). METHODS: Fifty-nine participants performed a series of VR tasks that require the use of the memory and executive function dimensions of cognitive functionality. RESULTS: Normative data for the different subsets and total score of the SLB, based on proportion of correct hits per execution time, were identified. Age and experience with video games affect (respectively, negatively and positively) performance on the SLB and should be taken into account in assessment; on the other hand, gender and education do not. CONCLUSIONS: Overall results suggest that the SLB may be useful to assess cognitive functioning during the execution of activities of daily living, but larger studies and with clinical samples are needed.
