Is increasing complexity of algorithms the price for higher accuracy? virtual comparison of three algorithms for tertiary level management of chronic cough in people living with HIV in a low-income country

Improving diagnosis of pneumococcal disease by multiparameter testing and micro/nanotechnologies

The diagnosis and management of pneumococcal disease remains challenging, in particular in children who often are asymptomatic carriers, and in low-income countries with a high morbidity and mortality from febrile illnesses where the broad range of bacterial, viral and parasitic cases are in contrast to limited, diagnostic resources. Integration of multiple markers into a single, rapid test is desirable in such situations. Likewise, the development of multiparameter tests for relevant arrays of pathogens is important to avoid overtreatment of febrile syndromes with antibiotics. Miniaturization of tests through use of micro- and nanotechnologies combines several advantages: miniaturization reduces sample requirements, reduces the use of consumables and reagents leading to a reduction in costs, facilitates parallelization, enables point-of-care use of diagnostic equipment and even reduces the amount of potentially infectious disposables, characteristics that are highly desirable in most healthcare settings. This critical review emphasizes our vision on the importance of multiparametric testing for diagnosing pneumococcal infections in patients with fever and examines recent relevant developments in micro/nanotechnologies to achieve this goal.

Creating computable algorithms for symptom management in an outpatient thoracic oncology setting

CONTEXT

Adequate symptom management is essential to ensure quality cancer care, but symptom management is not always evidence based. Adapting and automating national guidelines for use at the point of care may enhance use by clinicians.

OBJECTIVES

This article reports on a process of adapting research evidence for use in a clinical decision support system that provided individualized symptom management recommendations to clinicians at the point of care.

METHODS

Using a modified ADAPTE process, panels of local experts adapted national guidelines and integrated research evidence to create computable algorithms with explicit recommendations for management of the most common symptoms (pain, fatigue, dyspnea, depression, and anxiety) associated with lung cancer.

RESULTS

Small multidisciplinary groups and a consensus panel, using a nominal group technique, modified and subsequently approved computable algorithms for fatigue, dyspnea, moderate pain, severe pain, depression, and anxiety. The approved algorithms represented the consensus of multidisciplinary clinicians on pharmacological and behavioral interventions tailored to the patient's age, comorbidities, laboratory values, current medications, and patient-reported symptom severity. Algorithms also were reconciled with one another to enable simultaneous management of several symptoms.

CONCLUSION

A modified ADAPTE process and nominal group technique enabled the development and approval of locally adapted computable algorithms for individualized symptom management in patients with lung cancer. The process was more complex and required more time and resources than initially anticipated, but it resulted in computable algorithms that represented the consensus of many experts.