User-Centered Design of a Clinical Tool for Shared Decision-making About Diet in Primary Care.

BACKGROUND: Health information technology is a leading cause of clinician burnout and career dissatisfaction, often because it is poorly designed by nonclinicians who have limited knowledge of clinicians' information needs and health care workflow. OBJECTIVE: Describe how we engaged primary care clinicians and their patients in an iterative design process for a software application to enhance clinician-patient diet discussions. DESIGN: Descriptive study of the steps followed when involving clinicians and their at-risk patients in the design of the content, layout, and flow of an application for collaborative dietary goal setting. This began with individual clinician and patient interviews to detail the desired informational content of the screens displayed followed by iterative reviews of intermediate and final versions of the program and its outputs. PARTICIPANTS: Primary care clinicians practicing in an urban federally qualified health center and two academic primary care clinics, and their patients who were overweight or obese with diet-sensitive conditions. MAIN MEASURES: Descriptions of the content, format, and flow of information from pre-visit dietary history to the display of evidence-based, guideline-driven suggested goals to final display of dietary goals selected, with information on how the patient might reach them and patients' confidence in achieving them. KEY RESULTS: Through three iterations of design and review, there was substantial evolution of the program's content, format, and flow of information. This involved "tuning" of the information desired: from too little, to too much, to the right amount displayed that both clinicians and patients believed would facilitate shared dietary goal setting. CONCLUSIONS: Clinicians' well-founded criticisms of the design of health information technology can be mitigated by involving them and their patients in the design of such tools that clinicians may find useful, and use, in their everyday medical practice.

Response classification based on a minimal model of glioblastoma growth is prognostic for clinical outcomes and distinguishes progression from pseudoprogression.

Glioblastoma multiforme is the most aggressive type of primary brain tumor. Glioblastoma growth dynamics vary widely across patients, making it difficult to accurately gauge their response to treatment. We developed a model-based metric of therapy response called Days Gained that accounts for this heterogeneity. Here, we show in 63 newly diagnosed patients with glioblastoma that Days Gained scores from a simple glioblastoma growth model computed at the time of the first postradiotherapy MRI scan are prognostic for time to tumor recurrence and overall patient survival. After radiation treatment, Days Gained also distinguished patients with pseudoprogression from those with true progression. Because Days Gained scores can be easily computed with routinely available clinical imaging devices, this model offers immediate potential to be used in ongoing prospective studies.