Dr. Grando's research can be framed into the field of Artificial Intelligence, and more specifically into knowledge representation and reasoning for deploying Clinical Decision Support Systems (CDSS). Dr. Grando's work focuses on the development of languages, frameworks and applications for CDSS inspired by cognitive theories.  Her research goal is to innovate on the formal foundations of CDSS, but also to create frameworks and develop applications to facilitate the implementation, incorporation, and use of CDSS in health care.

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. 

Research Projects

My Data Choices, Evaluation of Effective Consent Strategies for Patients with Behavioral Health Conditions

Patients differ in their opinions about what categories of information are “sensitive” and how to define the information each category. Furthermore, little is known about data sharing preferences of patients with behavioral health conditions (BHCs).  
For this project funded by the National Institute of Mental Health (NIMH), our hypothesis is that when offered the option, patients with BHCs/surrogates desire a greater degree of choice to determine, at a granular level, which health information to share for care and research. With the support of the NIMH (R01 MH108992-01A1) we will: 

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

iDECIDE - Patient-centered decision support based on device data

With this project funded by the National Library of Medicine (1R21LM011667-01) we aim to empower patients in their treatment choices. Our goal is to propose an ontology-based framework applicable to a wide range of chronic conditions that require patient-centered decision aids to advice patients on treatment options based on the output from monitoring medical devices while concurrently incorporating patients’ treatment preferences and health goals. To demonstrate our approach we will evaluate the framework in the Mayo Clinic, Arizona addressing the problem of pre-meal bolus treatment options for adult type 1 diabetes patients.