Patient preferences for drug delivery- Machine Learning guided human efforts: A Synergistic Approach

Pharmaceutical formulation and treatment process attributes, such as dose frequency and route of administration, can have an impact on quality of life, treatment adherence, and disease outcomes.The aim of this systematic review and meta-analysis is to examine studies on preferences for pharmaceutical treatment process attributes, focusing on research in diabetes, oncology, osteoporosis, autoimmune disorders and other noncommunicable diseases using machine learning software tools in addition to researcher guided efforts.The literature search should be focusing on identifying studies reporting preferences for attributes of the pharmaceutical treatment process.Studies should be required to use formal quantitative preference assessment methods, such as utility valuation, conjoint analysis, or contingent valuation.Searches should be conducted using Medline, EMBASE, Cochrane Library, Health Economic Evaluation Database, and National Health Service Economic Evaluation Database.Data should be extracted by machine learning software tools as well.Quality of the obtained data should be checked by Down and black check list.The statistical analysis should be carried out using the software 'Comprehensive Meta-Analysis software, version 2.0' Despite heterogeneity in study methods and design, some general patterns of preference may clearly emerge.Overall, the results of this review may suggest that treatment process has a quantifiable impact on preference and willingness to pay for treatment, even in many situations where safety and efficacy are the primary concerns.Patient preferences for treatment process attributes can inform drug development decisions to better meet the needs of patients and deliver improved outcomes.Findings from this study may have direct relevance to researchers working in drug development because results can provide insight into the value that patients place on treatment process attributes.Results may also aid clinicians in selecting treatments with attributes that have the potential to enhance treatment adherence.