Cumulus: A federated EHR-based learning system powered by FHIR and AI.

Objective: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app 'listener' that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). Methods: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and AI for processing unstructured text. Results: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across five healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Discussion and Conclusion: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs (2), increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence.

OBJECTIVE: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). METHODS: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. RESULTS: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. DISCUSSION AND CONCLUSION: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

Predicting venous thromboembolism (VTE) risk in cancer patients using machine learning.

Background: The association between cancer and venous thromboembolism (VTE) is well-established with cancer patients accounting for approximately 20% of all VTE incidents. In this paper, we have performed a comparison of machine learning (ML) methods to traditional clinical scoring models for predicting the occurrence of VTE in a cancer patient population, identified important features (clinical biomarkers) for ML model predictions, and examined how different approaches to reducing the number of features used in the model impact model performance. Methods: We have developed an ML pipeline including three separate feature selection processes and applied it to routine patient care data from the electronic health records of 1910 cancer patients at the University of California Davis Medical Center. Results: Our ML-based prediction model achieved an area under the receiver operating characteristic curve of 0.778 ± 0.006 (mean ± SD) when trained on a set of 15 features. This result is comparable with the model performance when trained on all features in our feature pool [0.779 ± 0.006 (mean ± SD) with 29 features]. Our result surpasses the most validated clinical scoring system for VTE risk assessment in cancer patients by 16.1%. We additionally found cancer stage information to be a useful predictor after all performed feature selection processes despite not being used in existing score-based approaches. Conclusion: From these findings, we observe that ML can offer new insights and a significant improvement over the most validated clinical VTE risk scoring systems in cancer patients. The results of this study also allowed us to draw insight into our feature pool and identify the features that could have the most utility in the context of developing an efficient ML classifier. While a model trained on our entire feature pool of 29 features significantly outperformed the traditionally used clinical scoring system, we were able to achieve an equivalent performance using a subset of only 15 features through strategic feature selection methods. These results are encouraging for potential applications of ML to predicting cancer-associated VTE in clinical settings such as in bedside decision support systems where feature availability may be limited.

Risk factors for quantity not sufficient sweat collection in infants 3 months or younger.

OBJECTIVES: The purpose is to identify demographic characteristics associated with a quantity not sufficient (QNS) sweat collection in infants 3 months or younger. METHODS: History of premature birth, infant race and sex, gestational age at delivery, and weight of the infant were compared with QNS collection. RESULTS: Of 221 sweat collections from 197 infants, 25 were QNS. Infant weight less than 3 kg and history of prematurity were associated with QNS collection (P < .001). Thirteen (30.2%) of 43 infants weighing less than 3 kg had QNS collections compared with 12 (7.9%) of 151 infants 3 kg or more. Twelve (46.2%) premature infants had QNS collections compared with 13 (7.6%) term infants. Lower birth gestational age and corrected gestational age were associated with QNS collections. Six (86%) of seven infants who weighed less than 3 kg, had a history of prematurity, and were more than 54 days old at testing had a QNS result. Sex and race did not correlate with QNS collections. CONCLUSIONS: Weight less than 3 kg and history of prematurity are associated with an increased chance of QNS sweat collections.