Abstract
Increasing interest in the Quality of Life outcomes in cancer patients led to increase implementation of their use in routine clinical practice. The aim of this systemic review is to review the scientific evidence behind recommending the use of quality of life (QoL) scales routinely in outpatient evaluation.
Systematic review for all published randomized controlled trials in English language between January 1, 1990 till December 31, 2012. Out of 487 articles (476 identified by electronic search+11 articles identified by manual search), six trials satisfied the eligibility criteria: (1) the study was a randomized controlled trial (RCT) with randomization of patients or health care providers; (2) the findings of the administered questionnaire or scale (the intervention) were given to health care provider, and compared to standard care with no questionnaire administered (the control); (3) study was conducted in outpatient oncology clinics; and (4) an outcome was measured that related to (i) QoL improvement, (ii) reduction in morbidity, (iii) reduction in stress for the patients, (iv) improvement in communication between patients and health care provider, or (v) improved patient satisfaction. Assessment for the quality of the study was done using the GRADE methodology.
Serious methodological issues were affecting most of the trials. Overall the evaluation of the quality of the evidence from these identified trials suggests that there is a weak recommendation to use QoL scales in routine oncology practice to improve communication between physicians and patients.
The routine use of such tools in the outpatient settings at improving the patient outcome or satisfaction cannot be recommended based on the available evidence. The potential harm with the excess use of resources needed to implement, collect, store, analyse, and present such data to health care providers should be also considered. Further research and better designed trials is required using recent methodological techniques such as item-response theory based questionnaire and cluster randomization might help to reach an answer to this question.