Point of Care Testing is een innovative stroming binnen de geneeskunde. Aan de hand van een voorbeeld, waarbij het doel is flebitis in een vroeg stadium te kunnen onderkennen, wordt in dit artikel het belang van deze methode toegelicht.
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Artikel gepubliceerd in Nurse Academy O&T | nummer 3 | 2024: Steeds vaker meten patiënten hun eigen gezondheid. Deze metingen zijn voorbeelden van ‘point of care’-testen (POCT), een voorbeeld is de COVID-19-test. De ontwikkeling en inzet van POCT dragen bij aan betaalbare en toegankelijke gezondheidszorg. POCT kan een belangrijk instrument zijn voor het monitoren van veranderingen in de gezondheid. Het kan thuiswonende ouderen helpen bij het versterken van hun zelfmanagement LEERDOELEN Na het lezen van dit artikel: •weet u wat de kenmerken van ‘point of care’-testen (POCT) zijn en kent u een aantal voorbeelden van POCT; • heeft u inzicht in de voordelen van het gebruik van POCT in gezondheidsbevordering van oudere cliënten; • kent u een aantal uitdagingen die samenhangen met het gebruik van POCT in de praktijk; • heeft u inzicht in de betekenis van verpleegkundigen voor de inzet van POCT bij de ondersteuning van ouderen
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Steeds vaker meten patiënten hun eigen gezondheid. Deze metingen zijn voorbeelden van ‘point of care’-testen (POCT), een voorbeeld is de COVID-19-test. De ontwikkeling en inzet van POCT dragen bij aan betaalbare en toegankelijke gezondheidszorg. POCT kan een belangrijk instrument zijn voor het monitoren van veranderingen in de gezondheid. Het kan thuiswonende ouderen helpen bij het versterken van hun zelfmanagement.
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Point of Care Testing
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This chapter describes the growing influence of point-of-care diagnostics (POCD) on the daily lives of citizens, their immediate families, and healthcare providers. With a view to the future, the most important contemporary developments in this field are discussed, such as noninvasive sensor technology in the diagnostic process, practical examples of point-of-care diagnostics (POCD), including the quantify-self movement and infrared technology. Cost-effectiveness, adoption of POCD, and the contribution of POCD innovations to self-management and health literacy are also discussed. Developments in which deep learning and artificial intelligence are used to make the diagnostic results more reliable are also conferred, such as the development of point-of-care Internet diagnostics. The discussion of professional advice dilemma’s in POCD, the patient’s appreciation of POCD, and ethical and philosophical considerations conclude this chapter.
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When it comes to hard to solve problems, the significance of situational knowledge construction and network coordination must not be underrated. Professional deliberation is directed toward understanding, acting and analysis. We need smart and flexible ways to direct systems information from practice to network reflection, and to guide results from network consultation to practice. This article presents a case study proposal, as follow-up to a recent dissertation about online simulation gaming for youth care network exchange (Van Haaster, 2014).
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Background: The substitution of healthcare is a way to control rising healthcare costs. The Primary Care Plus (PC+) intervention of the Dutch ‘Blue Care’ pioneer site aims to achieve this feat by facilitating consultations with medical specialists in the primary care setting. One of the specialties involved is dermatology. This study explores referral decisions following dermatology care in PC+ and the influence of predictive patient and consultation characteristics on this decision. Methods: This retrospective study used clinical data of patients who received dermatology care in PC+ between January 2015 and March 2017. The referral decision following PC+, (i.e., referral back to the general practitioner (GP) or referral to outpatient hospital care) was the primary outcome. Stepwise logistic regression modelling was used to describe variations in the referral decisions following PC+, with patient age and gender, number of PC+ consultations, patient diagnosis and treatment specialist as the predicting factors. Results: A total of 2952 patients visited PC+ for dermatology care. Of those patients with a registered referral, 80.2% (N = 2254) were referred back to the GP, and 19.8% (N = 558) were referred to outpatient hospital care. In the multivariable model, only the treating specialist and patient’s diagnosis independently influenced the referral decisions following PC+. Conclusion: The aim of PC+ is to reduce the number of referrals to outpatient hospital care. According to the results, the treating specialist and patient diagnosis influence referral decisions. Therefore, the results of this study can be used to discuss and improve specialist and patient profiles for PC+ to further optimise the effectiveness of the initiative.
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In this post I give an overview of the theory, tools, frameworks and best practices I have found until now around the testing (and debugging) of machine learning applications. I will start by giving an overview of the specificities of testing machine learning applications.
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Standard SARS-CoV-2 testing protocols using nasopharyngeal/throat (NP/T) swabs are invasive and require trained medical staff for reliable sampling. In addition, it has been shown that PCR is more sensitive as compared to antigen-based tests. Here we describe the analytical and clinical evaluation of our in-house RNA extraction-free saliva-based molecular assay for the detection of SARS-CoV-2. Analytical sensitivity of the test was equal to the sensitivity obtained in other Dutch diagnostic laboratories that process NP/T swabs. In this study, 955 individuals participated and provided NP/T swabs for routine molecular analysis (with RNA extraction) and saliva for comparison. Our RT-qPCR resulted in a sensitivity of 82,86% and a specificity of 98,94% compared to the gold standard. A false-negative ratio of 1,9% was found. The SARS-CoV-2 detection workflow described here enables easy, economical, and reliable saliva processing, useful for repeated testing of individuals.
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Through a qualitative examination, the moral evaluations of Dutch care professionals regarding healthcare robots for eldercare in terms of biomedical ethical principles and non-utility are researched. Results showed that care professionals primarily focused on maleficence (potential harm done by the robot), deriving from diminishing human contact. Worries about potential maleficence were more pronounced from intermediate compared to higher educated professionals. However, both groups deemed companion robots more beneficiary than devices that monitor and assist, which were deemed potentially harmful physically and psychologically. The perceived utility was not related to the professionals' moral stances, countering prevailing views. Increasing patient's autonomy by applying robot care was not part of the discussion and justice as a moral evaluation was rarely mentioned. Awareness of the care professionals' point of view is important for policymakers, educational institutes, and for developers of healthcare robots to tailor designs to the wants of older adults along with the needs of the much-undervalued eldercare professionals.
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