Crowdsourcing the CTSA Innovation Mission

Kahlon, Maninder; Yuan, Leslie; Gologorskaya, Oksana

doi:10.1111/cts.12147

Cited by 2 publications

(3 citation statements)

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“…The first stage is the Ideas Contest, a UCSF Health‐wide call (to learn more about UCSF Health: https://www.ucsf.edu/sites/default/files/052516_About_UCSF.pdf) to identify areas that may be targeted to reduce unnecessary services, inefficiencies, and healthcare costs. We use a crowdsourcing platform—Open Proposals—to solicit the best ideas from frontline clinicians and staff 1 . Open Proposals is a secure, web‐based platform for transparent and collaborative proposal development that displays threads of comments, responses, and revisions, and allows submissions to be “liked.” Open Proposals is managed by the UCSF Clinical and Translational Science Institute, funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health.…”

Section: Plan Do Study Actmentioning

confidence: 99%

“…We use a crowdsourcing platform-Open Proposals-to solicit the best ideas from frontline clinicians and staff. 1 Open Proposals is a secure, web-based platform for transparent and collaborative proposal development that displays threads of comments, responses, and revisions, and allows submissions to be "liked." Open Proposals is managed by the UCSF Clinical and Translational Science Institute, funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health.…”

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confidence: 99%

“…The IV-to-PO switch program and the neonatal sepsis risk prediction project (Table 3) have been successful in reducing unnecessary utilization, but cost and savings estimates are not yet finalized. Three funded projects were equivocal in cost savings but were successful in their primary aims: (1) increasing the appropriateness of CT scan ordering for suspected pulmonary embolus; (2) shortening operating room turnover times; and (3) implementing a postoperative debrief program for the systematic documentation of safety events, waste, and inefficiencies related to surgery.…”

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confidence: 99%

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Caring Wisely: A Program to Support Frontline Clinicians and Staff in Improving Healthcare Delivery and Reducing Costs

Gonzales

Moriates

Lau

et al. 2017

Journal of Hospital Medicine

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We describe a program called "Caring Wisely"®, developed by the University of California, San Francisco's (UCSF), Center for Healthcare Value, to increase the value of services provided at UCSF Health. The overarching goal of the Caring Wisely® program is to catalyze and advance delivery system redesign and innovations that reduce costs, enhance healthcare quality, and improve health outcomes. The program is designed to engage frontline clinicians and staff-aided by experienced implementation scientists-to develop and implement interventions specifically designed to address overuse, underuse, or misuse of services. Financial savings of the program are intended to cover the program costs. The theoretical underpinnings for the design of the Caring Wisely® program emphasize the importance of stakeholder engagement, behavior change theory, market (target audience) segmentation, and process measurement and feedback. The Caring Wisely® program provides an institutional model for using crowdsourcing to identify "hot spot" areas of low-value care, inefficiency and waste, and for implementing robust interventions to address these areas.

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Section: Plan Do Study Actmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Caring Wisely: A Program to Support Frontline Clinicians and Staff in Improving Healthcare Delivery and Reducing Costs

Gonzales

Moriates

Lau

et al. 2017

Journal of Hospital Medicine

Self Cite

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Crowdsourcing Machine Intelligence Solutions to Accelerate Biomedical Science: Lessons learned from a machine intelligence ideation contest to improve the prediction of 3D domain swapping

Shah¹,

Sharma

et al. 2020

Preprint

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Machine intelligence methods, including natural language processing, computer vision, machine vision, artificial intelligence, and deep learning approaches, are rapidly evolving and play an essential role in biomedicine. Machine intelligence methods could help to accelerate image analyses aid in building complex models capable of interpretation beyond cognitive limitations and statistical assumptions in biomedicine. However, irrespective of the democratization via accessible computing and software modules, machine intelligence handiness is scarce in the setting of a traditional biomedical research laboratory. In such a context, collaborations with bioinformatics and computational biologists may help. Further, the biomedical diaspora could also seek help from the expert communities using a crowdsourcing website that hosts machine intelligence competitions. Machine intelligence competitions offer a vast pool of seasoned data scientists and machine intelligence experts to develop solutions through competition portals. An alternate approach to improve the adoption of machine intelligence in biomedicine is to offer machine intelligence competitions as part of scientific meetings. In this paper, we discuss a structured methodology employed to develop the machine intelligence competition as part of an international bioinformatics conference. The competition leads to developing a novel method through crowdsourcing to solve a challenging problem in biomedicine: predicting probabilities of proteins that undergo 3D domain swapping. As a biomedical science conference focused on computational methods, the competition received multiple entries that ultimately helped improve the predictive modeling of 3D domain swapping using sequence information.

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Crowdsourcing the CTSA Innovation Mission

Cited by 2 publications

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Caring Wisely: A Program to Support Frontline Clinicians and Staff in Improving Healthcare Delivery and Reducing Costs

Caring Wisely: A Program to Support Frontline Clinicians and Staff in Improving Healthcare Delivery and Reducing Costs

Crowdsourcing Machine Intelligence Solutions to Accelerate Biomedical Science: Lessons learned from a machine intelligence ideation contest to improve the prediction of 3D domain swapping

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