Software projects

 

This section describes ongoing work aimed at developing a series of Apps and other software to assist patients and practitioners in developing integrative care plans based on the best available evidence for both conventional treatments such as medications and psychotherapy, and the range of CAM modalities. I will add regular updates as new software products are released. 

As the book goes to press I am working with software developers to create a series of Apps for planning integrative mental health care. The Apps are being designed to help practitioners and patients develop individualized care plans addressing common mental health problems such as depressed mood, anxiety and many others.

A long-term project aimed at developing sophisticated software using advanced machine learning algorithms and natural language processing is still in the early stages. When finished that software will automate the literature research process, generate individualized evidence tables and algorithms for integrative treatment planning on a case by case basis, and guide the integrative practitioner through all steps involved in planning and refining an integrative care plan addressing the needs, preferences and constraints of each unique patient.

By incorporating the methodology discussed in detail in this book, the software will provide practitioners with a set of user-friendly AI tools for developing individualized care plans addressing the complex needs, preferences and constraints of unique patients. The software will permit integrative practitioners to evaluate the benefits and limitations of disparate treatment choices when addressing the needs and preferences of a unique patient, determine the most effective and cost-effective treatment strategy, and identify treatment combinations most likely to have beneficial synergistic effects on target symptoms.

As envisioned the AI-driven software will:

  • Identify high quality on-line resources and optimize literature research strategies
  • Automate literature research and seamlessly update the relevant evidence tables on an on-going basis
  • Customize the content of evidence tables with respect to the unique history and symptoms of each unique patient
  • Populate the evidence tables with the most relevant and high-value findings from the medical literature, and modify content on an ongoing basis in light of significant new research findings
  • Rate the quality of evidence for different modalities and adjust comparative ratings on an on-going in light of emerging research findings
  • Guide the practitioner in assigning relative priorities to disparate problems being addressed when managing complex patients with high comorbidity
  • Automatically incorporate content from the relevant evidence tables into appropriate steps in the algorithm and make changes in content in the algorithm in light of emerging research findings
  • Identify the most parsimonious care plan that adequately and cost-effectively addresses the symptoms of each unique patient
  • Generate a realistic individualized integrative care plan taking into account each patient’s unique history, preferences and constraints on cost and availability of medical resources

 

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