Value-based modeling for mobile health application development.

Background: In this paper is presented the use of value-based modeling, traditionally a business development tool, for the improvement of mobile health app design. The conceptual foundations for this work are design science, which is the scientific study and creation of artifacts, and convergence, which is a research method that in this case combines engineering with medicine. Relevant previous work done by the research team included the modeling of a case management system using process-based and information-based modeling techniques. Methods: Value-based modeling represents actors who are exchanging with each other things of economic value, including service outcomes. The focus is on how value objects are offered, accepted, and exchanged in a network. Value-based models do not describe how transactions occur, but rather the net value of those transactions. This technique was applied to the design development of a mobile application system for the improvement of access to health services. Results: Significant value-based modeling was performed. These models highlighted the importance in healthcare delivery of effective value exchanges. Conclusions: The results revealed a limitation on the net value of services delivery. These were related to constraints of time, cost, and responsibility. A design improvement was proposed: The development of an automated decision-making subsystem within the machine learning component of the app system. This subsystem would recommend between-visit micro adjustments to the plan of care based upon protocols established by the healthcare provider. Such would provide an agile response to the patient's changing needs as well as an amelioration to the challenges of access to services.

The emerging postural instability phenotype in idiopathic Parkinson disease.

Identification of individuals at high risk for rapid progression of motor and cognitive signs in Parkinson disease (PD) is clinically significant. Postural instability and gait dysfunction (PIGD) are associated with greater motor and cognitive deterioration. We examined the relationship between baseline clinical factors and the development of postural instability using 5-year longitudinal de-novo idiopathic data (n = 301) from the Parkinson's Progressive Markers Initiative (PPMI). Logistic regression analysis revealed baseline features associated with future postural instability, and we designated this cohort the emerging postural instability (ePI) phenotype. We evaluated the resulting ePI phenotype rating scale validity in two held-out populations which showed a significantly higher risk of postural instability. Emerging PI phenotype was identified before onset of postural instability in 289 of 301 paired comparisons, with a median progression time of 972 days. Baseline cognitive performance was similar but declined more rapidly in ePI phenotype. We provide an ePI phenotype rating scale (ePIRS) for evaluation of individual risk at baseline for progression to postural instability.