on improving health care through knowledge translation, clinical epidemiology, application of the validated health care knowledge, development and testing of evidence based information products related to clinical care, randomized trials of medical and surgical procedures. He has also worked on patient compliance with therapeutic regimens. Haynes published an article in 1976 about improving patient compliance in hypertension. He assigned 38 hypertensive and medically non-compliant steel workers to either control or experimental groups and taught them to measure their blood pressures and create charts about their blood pressures. He also assigned the experimental group weekly visits from a non-professional high school graduate who reinforced regular pill taking behavior. As a result of this experiment, compliance of the control group decreased while it increased in the experimental group. In an article in 1979 about clinical measurements and detection of patient noncompliance, he stressed the importance of measuring patient compliance accurately in order to manage low patient compliance. He devised and evaluated methods of assessing a patient's medical compliance and suggested against using a patient's health beliefs, traits and perceptions as criteria for measurement of compliance. Haynes then shifted his research focus on documenting the problems of evidence handling by practitioners and proposing solutions. He wrote an article explaining the constraints faced by medical practitioners in handling research and clinical evidence. Haynes suggests the use of measurement principles and information tools to reduce the inaccuracy in the data gathering, interpretation, communication and application of the research evidence for patient care. Haynes worked on evidence retrieval from bibliographic databases and published over 50 articles on the theme of ‘Developing optimal search strategies for detecting clinically sound studies in MEDLINE’ as well as EMBASE, CINAHL, and PsycLIT. He aimed to design efficient search strategies and conducted a survey about the characteristics of search strategies for studies that would provide higher quality evidence for use by clinical practitioners (medicine, nursing, rehabilitation). He pioneered the use of machine learning to empirically determine the optimal search filters for various types of studies (e.g., prevention, treatment, diagnosis, prognosis, causation, quality improvement and cost-effectiveness). His research validates the empirical search strategies for enhancing the retrieval of articles. After the documentation and retrieval of evidence, Haynes worked on synthesizing the evidence retrieved. He authored a review with his colleagues about the interventions to encourage people to follow medical prescriptions. He summarized the results collected from randomized controlled trials of interventions. The