A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary

Abstract

There are several challenges in diabetes care management including optimizing the currently used therapies, educating patients on selfmanagement, and improving patient lifestyle and systematic healthcare barriers. The purpose of performing a systems approach to implementation science aided by artificial intelligence techniques in diabetes care is two-fold: 1) to explicate the systems approach to formulate predictive analytics that will simultaneously consider multiple input and output variables to generate an ideal decision-making solution for an optimal outcome; and 2) to incorporate contextual and ecological variations in practicing diabetes care coupled with specific health educational interventions as exogenous variables in prediction. A similar taxonomy of modeling approaches proposed by Brennon et al (2006) is formulated to examining the determinants of diabetes care outcomes in program evaluation. The discipline-free methods used in implementation science research, applied to efficiency and quality-of-care analysis are presented. Finally, we illustrate a logically formulated predictive analytics with efficiency and quality criteria included for evaluation of behavioralchange intervention programs, with the time effect included, in diabetes care and research.

Predictive Analytics with a Transdisciplinary Framework in Promoting Patient-Centric Care of Polychronic Conditions: Trends, Challenges, and Solutions

“…(4) What is the value-based appraisal and evaluation of an innovative chronic care modality? The Patient-Centered Outcomes Research Institute (PCORI) [17] has played a vital role in helping people make informed decisions and improving healthcare delivery and outcomes by producing and promoting high-integrity, evidence-based information that comes from research guided by patients, caregivers, and the broader healthcare community.…”

“…The sensitivity and specificity of each predictive model derived from SVM have not been systematically examined to determine the validity of multidisciplinary patient care since stringent and pre-determined criteria, ranging from the proximal, intermediate, and distal outcomes, and measured at the patient-and population-level, have yet to be formulated in evaluation research. Furthermore, the development of "precision care" or personal care modalities should consider both the quality and efficiency of care plans simultaneously [17].…”

“…Thus, the treatments or interventions could be better monitored and followed up by collaborative research teams. Furthermore, the longitudinal study design will require the collection of multi-waves of patient care data for performing parametric and non-parametric statistical modeling of predictors [17]. Moreover, the ultimate solution for developing disease-specific detection, AI-based diagnostics and prevention strategies, relies on the standardization of measurements and metrics used in the design, implementation, and evaluation of healthcare outcomes [18][19][20].…”

“…waves of patient care data for performing parametric and non-parametric statistical modeling of predictors [17]. Moreover, the ultimate solution for developing disease-specific detection, AI-based diagnostics and prevention strategies, relies on the standardization of measurements and metrics used in the design, implementation, and evaluation of healthcare outcomes [18][19][20].…”