The concept of a living lab for innovation is not novel to Duke, however DIHI’s approach may be. A living lab is a space (either physical or virtual) to aid in ideation, exploration, experimentation and evaluation of innovative ideas and concepts in real-life use cases. Ensuring that all ideas are integrated and incorporated into the clinical workflow of the provider and effectively and sustainably engages patients. The approach of a living lab allows for the concurrent holistic view of the impact of an innovation on the system while also the responsiveness of users of the innovation.
We are implementing this concept with existing clinics within the Duke University Health System. These clinics are open to testing new models of care and DIHI is providing a set of core capabilities to aid in the success of the new models of care or the technologies that are integrated into the clinical workflow of the provider and/or patient.
It is understood in DIHI that physicians and administrators are extremely busy so we set out to leverage and or create capabilities that can assist clinics in trying new things that can have a positive impact on patient care, access, and lowering the total cost of care. Aside from ensuring issues like governance are well established, these capabilities include access to data, analytics and visualization, project management, measurement and evaluation (both clinically and economically), and the ability to disseminate the learnings from the innovation.
The Duke Medicine living lab for innovation is something that can be utilized by industry to drive new technologies into clinics or aid in Duke University Health System’s focus on innovating in the care delivery environment. If you are industry and looking to partner in the living lab click here.
Project Highlights
Automating Behavioral Health Evaluation in Primary Care
Automating follow up to a positive BH screen in 6-11 year old patients within primary care by distributing, collecting and scoring ADHD, Depression and Anxiety questionnaires directly within the EHR.
Automated Post-Hospitalization Track for Patients Discharged on Outpatient Parenteral Antimicrobial Therapy (OPAT)
Implement a real-time OPAT patient dashboard to facilitate earlier hospital discharge and automate the process of early post-discharge follow-up.
Using Machine Learning to Appropriately Triage Patients with Low Back Pain (LBP)
Develop and validate a machine learning algorithm capable of predicting patient need for spinal surgery or non-operative management utilizing elements from the electronic health record.
Automating CMS Quality Measure Curation
Optimize the process and enhance the efficiency of quality measure reporting to removes unnecessary burden from clinical staff.
Automated Patient Care Assignment: Accelerating every-day operational efficiency
Optimize patient placement and individual team member assignment
Patient Initiated Note about Goals (PING)
Optimize, implement, and evaluate the PING (Patient Initiated Note about Goals).