FEATURED NEWS AND ANNOUNCEMENTS |
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UCSF’s Data Science and Innovation Team received recognition from the San Francisco Chronicle for their novel methods of making data available to aid pandemic response efforts. When faced with the challenge of automating public health data reporting, the team quickly developed a dashboard that extracted data from EHRs and mapped it to the dashboard’s reporting specifications. The result displayed metrics and visualizations for COVID-related data such as hospitalization rates and test positivity in near real-time.
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UCSF’s Center for Digital Health Innovation (CDHI) is collaborating with H2O.ai on the development and training of AI algorithms that automate the processing of the 1.4 million faxed patient referrals and other documents that UCSF Health receives annually. The algorithms identify, classify and prioritize document types, as well as extract key patient data, history and relevant health information that will assist staff and eliminate manual data entry and validation tasks. This technology streamlines operations and produces more affordable, accessible, and efficient healthcare.
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In their series with California Health Care Foundation, SOLVE Health Tech published two papers – How Providers and Plans Can Help Communities Better Adopt Health Tools and How Designers Can Create More Inclusive Digital Health Tools. A plethora of patient-facing digital health tools have been developed in the U.S. over recent years, including mobile phone applications, wearable devices, and patient portals. However, there are large inequities in who is using these digital tools. These two papers focus on how to create equity and inclusion during technology design and implementation to help health care providers, health plans, and developers reach everyone. The papers also provide key stakeholders with action-oriented recommendations to ensure digital health technology meets the needs of diverse patients.
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What happens when a brain MRI lesion segmentation model that was trained on a single institution's data performs worse when applied at a second institution? Investigators from UCSF Radiology and Penn Radiology sought to figure out why as well as assess the effect of multi-institutional training datasets for mitigating performance loss. Overall, the investigators were able to conclude that adding a modest amount of relevant training data from an external institution to a previously trained model can support successful application of the model to this external institution.
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Dr. Julia Adler-Milstein and her team at the Center for Clinical Informatics and Improvement Research were recently awarded a new $4 million R01 from the National Institute on Aging entitled Advancing Coordination of Home and Community based Services for the ADRD Population. This five-year project will leverage newly-available, nationwide Medicaid claims data that capture use of home- and community-based services (HCBS) – such as respite care, home health, and adult day care – which help enable those with Alzheimer’s disease and related dementias (ADRD) and other populations avoid long-term institutional care. CLIIR will undertake the first nationwide effort to identify the specific organizations delivering HCBS to the ADRD population and their respective organizational networks. By measuring and comparing care coordination activities, including electronic information sharing, in networks with better and worse outcomes, we can identify actionable policy and practice-based strategies that improve care for a vulnerable population.
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Chaudhari GR, Chillakuru YR, Chen TL, et al. Clinical language search algorithm from free-text: facilitating appropriate imaging. BMC Med Imaging. 2022;22(1):18. doi:10.1186/s12880-022-00740-6. >>Full Article
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Choi HH, Kotsenas AL, Chen JV, Bronsky C, Roth CJ, Kohli MD. Multi-institutional Experience with Patient Image Access Through Electronic Health Record Patient Portals. J Digit Imaging. 2022;35(2):320-326. doi:10.1007/s10278-021-00565-9 >>Full Article
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Khoong EC, Sharma AE, Gupta K, Adler-Milstein J, Sarkar U. The Abrupt Expansion of Ambulatory Telemedicine: Implications for Patient Safety. J Gen Intern Med. 2022;37(5):1270-1274. doi:10.1007/s11606-021-07329-9 >>Full Article
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Kwon DH, Cadena J, Nguyen S, et al. COVID ‐19 outcomes in patients with cancer: Findings from the University of California health system database. Cancer Medicine. Published online March 9, 2022:cam4.4604. doi:10.1002/cam4.4604 >>Full Article
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Mason AE, Hecht FM, Davis SK, et al. Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study [published correction appears in Sci Rep. 2022 Mar 16;12(1):4568]. Sci Rep. 2022;12(1):3463. Published 2022 Mar 2. doi:10.1038/s41598-022-07314-0. >>Full Article
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Najafi N, Robinson A, Pletcher MJ, Patel S. Effectiveness of an Analytics-Based Intervention for Reducing Sleep Interruption in Hospitalized Patients: A Randomized Clinical Trial. JAMA Intern Med. 2022;182(2):172-177. doi:10.1001/jamainternmed.2021.7387. >>Full Article
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FEATURED VENUES FOR DIGITAL HEALTH |
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Health Informatics Grand Rounds |
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Health Informatics Grand Rounds are hosted by Russ Cucina, the UCSF Health Chief Health Information Officer, and are attended by an array of physicians, nurses, researchers, trainees and other professionals passionate about health informatics. Contact Dr. Raman Khanna to be added to the calendar invite.
4th Thursdays | 3:30pm – 5:00pm
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In honor of the over 300 million people worldwide with a rare disease, the UCSF Catalyst Program and Foundation IPSEN are hosting a Rare Disease Virtual Symposium on Wednesday, April 20, 2022, titled "Rare Disease Discovery: Innovations in Diagnostics and Therapeutics."
>> View the schedule here and register to attend here.
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