Evaluating and Using Medical Evidence in Integrative Mental Health Care: Literature Review, Evidence Tables, Algorithms, and the Promise of Artificial Intelligence

  • James H. Lake


The problem of evidence in medicine is discussed. Criteria are introduced for assigning levels of evidence to CAM modalities. In Western medicine findings from laboratory studies comprise the highest level of evidence for a putative mechanism of action and the relationship between “treatment” and “outcomes.” In contrast, in non-Western systems of medicine “evidence” reflects the values and beliefs of the parent culture. Important differences between quantitative and qualitative evidence are described. Special problems pertaining to literature research on CAM are discussed including how to formulate a question, identifying resources most likely to yield pertinent information on a particular subject, and using methods for optimizing and streamlining literature research. A clearly phrased question is the basis for any literature search. If the question is ambiguous or unfocused important resources will be overlooked, and relevant information will be missed. Valuable web-based resources are identified and practical tips are provided for obtaining current reliable information. Techniques for using prefiltered databases and evidence mapping are reviewed. The concepts of the evidence table and the algorithm are introduced. A methodology is proposed for using these tools when planning integrative mental health care. The accuracy and quality of information put into an algorithm will determine the effectiveness and relevance of clinical solutions generated by it for each unique patient. The optimal integrative care plan for a patient depends on history, symptoms, circumstances, preferences, and financial constraints in the context of locally available health care resources, and the professional judgment and clinical experience of the practitioner. The chapter concludes with a discussion of advances in artificial intelligence (AI) software and AI’s implications for the future of mental health care.


Evaluating medical evidence Quantitative medical evidence Optimizing and streamlining literature search Algorithms in Western medicine Algorithms in mental health care Artificial intelligence in mental health care Big data in mental health care Machine learning software to automate integrative mental health care 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • James H. Lake
    • 1
  1. 1.Center for Integrative MedicineUniversity of Arizona College of MedicineTucsonUSA

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