Dr. Grando's main research interest is the development of Clinical Decision Support Systems (CDSS) that support clinical decisions (e.g. what treatment clinical evidence recommends for a patient case?) of clinicians and patients.

She is particularly interested on Information Technology for Patient Empowerment in Healthcare. She is leading projects focused on building portable decision aids that support patient's decision process. For instance, mobile apps for in-home disease management control, and tablet-based electronic systems to educate patients on informed consent processes. 

THE LAB IS RECRUITING POSTDOCS WITH EXPERTISE IN PATIENT-CENTERED TECHNOLOGY. APPLY AT: http://apply.interfolio.com/53102

Research Projects

My Data Choices, Evaluation of Effective Consent Strategies for Patients with Behavioral Health Conditions (R01 MH108992)

The integration of behavioral (mental health and substance abuse) and physical care by health information exchanges (HIE) opens new questions on data privacy, stigma and discrimination. 

The project aims to:

• Elicit patient, surrogate and provider perspectives on granular data control  
• Design educational material to inform patients and providers on the consequences of sharing/not sharing data 
• Deploy and pilot test an informed consent management tool that supports granular patient-driven data control
• Understand ethical, clinical, policy and regulatory implication of implementing granular data control choices.

Wacht presentation: https://www.dropbox.com/sh/poiu64w77tlu91s/AAD-BiyZ-ZAkS6lo9MfI6g6Ra?dl=0

ROOT- Registry of Operations and Tasks.

The aims of this project are to:

• Captue and document high quality snap shots of EHR workflows before the Mayo Clinic undergoes an enterprise-wide converstion to a common EHR
• Study surgical workflows at Ariozna, Florida and Rochester by triangulating outcomes from:
  • Leadership, key personnel and clinical activity interviews
  • Observatoins, shadowing and video ethnography
  • Process mining recordings of system events, such as "At 9AM on 10/10/17 provider C logs into EHR application A to review data on patient P"

​Data-driven behavioral-change education interventions to improve type 1 diabetes treatment adherence  (2018 ASU-Mayo Accelerator grant)

Despite patients self-management diabetes differently, currentt education interventions are based on the "one size fits all" approach.

The project aims to:

• Assess patient-specific barriers to optimal self-management control from data collected by patient's devices: iDECIDE self-management tracking mobile app, insulin pump and glucose monitor
• Conduct individualized education interventions to target patient's adherence barriers and evaluate their effectiveness on blood glucose control