2,230 research outputs found
Achieving Efficiency: Lessons From Four Top-Performing Hospitals
Synthesizes lessons from case studies of how four hospitals achieved greater efficiency, including pursuing quality and access, customizing technology, emphasizing communications, standardizing processes, and integrating care, systems, and providers
HITECH Revisited
Assesses the 2009 Health Information Technology for Economic and Clinical Health Act, which offers incentives to adopt and meaningfully use electronic health records. Recommendations include revised criteria, incremental approaches, and targeted policies
Access Update, October 2010
Monthly newsletter for the Iowa Department of Public Healt
Quick Reads, August 26, 2009
IDPH Quick Reads is an electronic newsletter produced by the Director’s Office at the Iowa Department of Public Health. IDPH Quick Reads are published every three to four weeks
Harnessing Openness to Transform American Health Care
The Digital Connections Council (DCC) of the Committee for Economic Development (CED) has been developing the concept of openness in a series of reports. It has analyzed information and processes to determine their openness based on qualities of "accessibility" and "responsiveness." If information is not available or available only under restrictive conditions it is less accessible and therefore less "open." If information can be modified, repurposed, and redistributed freely it is more responsive, and therefore more "open." This report looks at how "openness" is being or might usefully be employed in the healthcare arena. This area, which now constitutes approximately 16-17 percent of GDP, has long frustrated policymakers, practitioners, and patients. Bringing greater openness to different parts of the healthcare production chain can lead to substantial benefits by stimulating innovation, lowering costs, reducing errors, and closing the gap between discovery and treatment delivery. The report is not exhaustive; it focuses on biomedical research and the disclosure of research findings, processes of evaluating drugs and devices, the emergence of electronic health records, the development and implementation of treatment regimes by caregivers and patients, and the interdependence of the global public health system and data sharing and worldwide collaboration
Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence
Background: Manual data collection is still the gold standard for disease-specific patient registries. However, CAPRI-3 uses text mining (an artificial intelligence (AI) technology) for patient identification and data collection. The aim of this study is to demonstrate the reliability and efficiency of this AI-driven approach. Methods: CAPRI-3 is an observational retrospective multicenter cohort registry on metastatic prostate cancer. We tested the patient-identification algorithm and automated data extraction through manual validation of the same patients in two pilots in 2019 and 2022. Results: Pilot one identified 2030 patients and pilot two 9464 patients. The negative predictive value of the algorithm was maximized to prevent false exclusions and reached 94.8%. The completeness and accuracy of the automated data extraction were 92.3% or higher, except for date fields and inaccessible data (images/pdf) (10–88.9%). Additional manual quality control took over 3 h less time per patient than the original fully manual CAPRI registry (105 vs. 300 min). Conclusions: The CAPRI-3 patient-identification algorithm is a sound replacement for excluding ineligible candidates. The AI-driven data extraction is largely accurate and complete, but manual quality control is needed for less reliable and inaccessible data. Overall, the AI-driven approach of the CAPRI-3 registry is reliable and timesaving.</p
Variational Knowledge Distillation for Disease Classification in Chest X-Rays
Disease classification relying solely on imaging data attracts great interest
in medical image analysis. Current models could be further improved, however,
by also employing Electronic Health Records (EHRs), which contain rich
information on patients and findings from clinicians. It is challenging to
incorporate this information into disease classification due to the high
reliance on clinician input in EHRs, limiting the possibility for automated
diagnosis. In this paper, we propose \textit{variational knowledge
distillation} (VKD), which is a new probabilistic inference framework for
disease classification based on X-rays that leverages knowledge from EHRs.
Specifically, we introduce a conditional latent variable model, where we infer
the latent representation of the X-ray image with the variational posterior
conditioning on the associated EHR text. By doing so, the model acquires the
ability to extract the visual features relevant to the disease during learning
and can therefore perform more accurate classification for unseen patients at
inference based solely on their X-ray scans. We demonstrate the effectiveness
of our method on three public benchmark datasets with paired X-ray images and
EHRs. The results show that the proposed variational knowledge distillation can
consistently improve the performance of medical image classification and
significantly surpasses current methods
- …