Projects

Many patients miss their appointments or give late cancellation notice, leading to unused booking slots and missed opportunities to receive quality healthcare. In this study, we found the characteristics and factors that drove patient No Shows for Radiology exams. We then created a predictive algorithm for those at-risk patients that allowed for tailored interventions.

Many patients are lost to follow up as they transition through the healthcare system. Often the patient or primary provider might overlook a recommendation until the underlying pathology has progressed. In this project, we examined Radiology patients over a decade to understand which patients were lost to follow up. An automated system was then constructed to recognize Radiologists’ recommendations, and to automate flagging at risk patient populations.

Standardization of workflow has shown huge benefit in non-healthcare industries in reducing waste and in introducing predictability for planning purposes. In medicine, the historical challenge has been how to standardize without losing sight of the complexity of patients’ co-morbidities and social environment.

In this study, Radiology MRI exams were analyzed to identify unwanted variability. We identified multiple protocols that would benefit from standardization. After changes were introduced, the image capture time was reduced, and variability decreased.

Quality in healthcare is more than providing the best medical advice to patients, it’s about ensuring consistent clinical outcomes with predictable business results. Poor quality leaves patients, providers, and administrators frustrated and dissatisfied.

Unfortunately, quality is often hard to measure without incurring costs or impediments in the workflow that make it impractical. In this project, we examined a method to automatically extract common medical imaging quality issues so that automated, feedback systems could be created.