Automated Patient Care Assignment: Accelerating every-day operational efficiency
Optimize patient placement and individual team member assignment
The Duke Institute for Health Innovation (DIHI) is pleased to announce the next emerging ideas and innovation funding cycle. Proposed innovation projects should address actual and important problems encountered by care providers, patients and their loved ones in our clinical enterprise and represent urgent health challenges nationally.
For the upcoming funding cycle, priority will be given to ideas aligned with the thematic area: Automation to enhance healthcare operational efficiency.
Most proposals are expected to request funding in the range of $25,000 to $70,000 over a one-year period. Please share this announcement with your collaborators and others who might be interested in applying. If you have any questions, please email DIHIrfa@duke.edu.
September 19, 2022
October 23, 2022 11:59PM
DIHI RFA open for submission
Application due date
The mission of the Duke Institute for Health Innovation (DIHI) is to catalyze transformative innovations in health and healthcare, and to help cultivate a community of innovation and entrepreneurship across Duke University and the health enterprise. Our annual request for innovation applications is open to all faculty, staff, students, and trainees across Duke University and Duke University Health System.
At Duke, we are uniquely positioned to tap into our exceptional faculty, staff and trainees to look for innovative solutions that address the needs of our patients and communities. But we will not stop there. Our transformative approaches are amenable to scaling such that their benefits can be conferred to large populations, with the potential for global impact.
At DIHI, we look for innovative but practical ways in which we can have measurable impact on the health and wellness of our patients and our people. At the same time, we are committed to fostering an ecosystem that is conducive to the creation, development, integration and scaling of novel ideas through transformative partnerships and strong collaboration. In the end, innovation is very much a team science.
The Duke Institute for Health Innovation (DIHI) announces the next emerging ideas and innovation funding cycle. Applications are open to address actual, important problems encountered by care providers, patients and their loved ones in our clinical enterprise, and to represent urgent national health challenges.
For the 2024 funding cycle, priority will be given to ideas in Generative AI & Large Language Models: AI solutions to improve staff and clinician efficiency, patient journey and outcomes.
Proposals are due by 11:59 PM, November 3, 2023.
DIHI 2024 RFA Timeline | ||
---|---|---|
Sept 25, 2023 | DIHI RFA open for submission | |
Nov 3, 2023 11:59 PM | Application due date | |
Feb 2024 | Finalists – Presentation to leadership | |
Apr 2024 | Funding start | |
Nov 2024 | Midpoint reporting |
The Duke Institute for Health Innovation (DIHI) closed the 2023 emerging ideas and innovation funding cycle. Applications were open to address actual, important problems encountered by care providers, patients and their loved ones in our clinical enterprise, and to represent urgent national health challenges.
For the 2023 funding cycle, priority was given to ideas in Automation to Enhance Healthcare Operational Efficiency.
Optimize patient placement and individual team member assignment
Implement a real-time OPAT patient dashboard to facilitate earlier hospital discharge and automate the process of early post-discharge follow-up.
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.
Optimize the process and enhance the efficiency of quality measure reporting to removes unnecessary burden from clinical staff.
Enable rheumatology providers to identify patients who could be seen by telehealth rather than in-person at their next follow-up appointment.
Automating PFT interpretation
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.
Our commitment to innovation at Duke Health remains strong, as evidenced by the strong slate of projects brought forward by our clinical teams at the forefront of patient care. The winning teams, selected from a highly competitive pool of applications will have significant impact on automating processes, helping us realize greater efficiencies while bringing the highest-quality care to our patients.
Challenges related to hospital access, staffing constraints, and patient-related factors require a refined risk prediction model to enable risk-mitigation strategies earlier in the process.
Developing a eProvider to act as a digital liaison for clinical care.
Optimize, implement, and evaluate the PING (Patient Initiated Note about Goals).
Identifying patients at risk of having behavioral emergencies can help to optimize care delivery.
The Prescriptions for Repair project will help us to understand and learn from the experiences of gun violence victims.
Identify ICI-utilizing patients at risk for a immune related adverse events
Using ML to better predict post-transplant mortality.
The Problem Mitigation of mortality in pediatric sepsis patients require early detection of sepsis warning signs, however vital signs and lab abnormalities are more ambiguous
Identifying PAD patients has historically been difficult because diagnosis codes work very poorly for PAD cohorts.
Developing a perioperative geriatric-specific risk stratification tools to optimize care for older surgical patients.
Let’s improve equity in access to organ transplantation.
Putting a postoperative opioid-use predictive nomogram into clinical practice for those undergoing gynecological surgery.
Problem lists are poorly maintained with no one owner and many contributors. This project takes steps to improve the organization of the PL and will automate cleanup of select clinical entities.
Coordinating care for children with complex health needs.
A machine learning risk stratification model to improve recognition and management of high risk PE while also reducing hospital utilization for low risk patients.
Improving dermatology access, care delivery and cost via machine learning assisted risk stratification.
A population health solution for NAFLD with an ultimate goal to optimize health care resources by improving access for high risk patients and minimizing unnecessary referrals.
Guiding appropriate specialty consultation and delivering tailored patient educational content.
Placing patient-specific 3D images in the Neurosurgeon’s field of view using augmented reality and advanced imaging to increase precision for epilepsy surgery.
Estimates the pre-test probability of bacteremia and post-test probability of blood culture results in hospitalized patients for EHR-based clinical decision support.
A model to aid actionable mild to moderate TBI triage decisions in the ER.
Augment OR case review huddles with a virtual operating room hub to facilitate communication across shifts and within shifts in the OR.
Identify obstetric patients at risk for clinical deterioration by using a variety of patient clinical parameters obtained from multiple data modalities and predictive modeling of pregnancy specific, patient related changes.
A Hospital at Home program in Wake County that would allow patients to be treated for hospital-level conditions in their homes.
Identify high risk mortality inpatients to provide them with a Transition of Care Toolkit to help with advance care planning.