High Risk Infant Followup
Under the auspices of the California Children's Services Program in close collaboration of the California Perinatal Quality Care Collaborative, Research IT has implemented an online data reporting tool designed to capture all details of High Risk Infant Followup (HRIF) program clinic visits, known as the High Risk Infant Follow-up Quality of Care Initiative.
Key features of the system include:
- Comprehensive data capture on all program visits, including the standard visit, which includes an interval medical assessment, neurological, neurosensory and developmental assessment, and a medical and special services review.
- Online reporting for administrative use and program oversight
- Referral workflow support
- A dynamically generated per-patient health audit report for quality improvement on a case by case basis
Launched in 2010, the OncoShare project was a groundbreaking collaboration between Stanford School of Medicine and Palo Alto Medical Foundation to support generation and analysis of sharable, anonymized datasets on breast cancer patients, based on a structured synopsis of their clinical treatment history. The main research goal of this project is to advance our understanding of how the biology of disease interacts with patient and treatment factors to impact outcomes in different clinical settings. It is also hoped that this project, initially funded in part by the community outreach efforts of Stanford's Clinical and Translational Science Award, will pave the way to further collaborative research between PAMF and Stanford.
Initial work on a simple heuristic for matching patients with minimal exchange of PHI was presented at AMIA CRI 2010 and published in JAMIA in January 2012. Since that time the project has had numerous publications - for more information see the Oncoshare Project web site.
Multicenter Perioperative Outcomes Group
The other major collaboration with Anesthesia is in Stanford's involvement with the Multicenter Perioperative Outcomes Group (MPOG). Research IT worked with the Epic team at Stanford Hospitals and Clinics to set up a comprehensive fully de-identified data extract for submission to the national registry.
Joint Replacement Registry
Stanford's Arthritis and Joint Replacement clinic submits extensive data to the American Joint Replacement Registry (AJRR). The data submission machinery was developed and automated by the software developers at Research IT. The department also keeps a local copy of this data set for research purposes, augmented by suitably de-identified DICOM images (X-Rays).