Meagan Moyer

All Publications

• A sociotechnical approach to defining clinical responsibilities for patient-generated health data. NPJ digital medicine Griffin, A. C., Moyer, M. F., Anoshiravani, A., Hornsey, S., Sharp, C. D. 2025; 8 (1): 270

  View details for DOI 10.1038/s41746-025-01680-5

  View details for PubMedID 40355597

  View details for PubMedCentralID 6550281

• Telemedicine and the environment: life cycle environmental emissions from in-person and virtual clinic visits. NPJ digital medicine Thiel, C. L., Mehta, N., Sejo, C. S., Qureshi, L., Moyer, M., Valentino, V., Saleh, J. 2023; 6 (1): 87

  Abstract

  Concern over climate change is growing in the healthcare space, and telemedicine has been rapidly expanding since the start of the COVID19 pandemic. Understanding the various sources of environmental emissions from clinic visits-both virtual and in-person-will help create a more sustainable healthcare system. This study uses a Life Cycle Assessment with retrospective clinical data from Stanford Health Care (SHC) in 2019-2021 to determine the environmental emissions associated with in-person and virtual clinic visits. SHC saw 13% increase in clinic visits, but due to the rise in telemedicine services, the Greenhouse Gas emissions (GHGs) from these visits decreased 36% between 2019 and 2021. Telemedicine (phone and video appointments) helped SHC avoid approximately 17,000 metric tons of GHGs in 2021. Some departments, such as psychiatry and cancer achieved greater GHG reductions, as they were able to perform more virtual visits. Telemedicine is an important component for the reduction of GHGs in healthcare systems; however, telemedicine cannot replace every clinic visit and proper triaging and tracking systems should be in place to avoid duplicative care.

  View details for DOI 10.1038/s41746-023-00818-7

  View details for PubMedID 37160996

• Predicting malnutrition from longitudinal patient trajectories with deep learning. PloS one Jin, B. T., Choi, M. H., Moyer, M. F., Kim, D. A. 2022; 17 (7): e0271487

  Abstract

  Malnutrition is common, morbid, and often correctable, but subject to missed and delayed diagnosis. Better screening and prediction could improve clinical, functional, and economic outcomes. This study aimed to assess the predictability of malnutrition from longitudinal patient records, and the external generalizability of a predictive model. Predictive models were developed and validated on statewide emergency department (ED) and hospital admission databases for California, Florida and New York, including visits from October 1, 2015 to December 31, 2018. Visit features included patient demographics, diagnosis codes, and procedure categories. Models included long short-term memory (LSTM) recurrent neural networks trained on longitudinal trajectories, and gradient-boosted tree and logistic regression models trained on cross-sectional patient data. The dataset used for model training and internal validation (California and Florida) included 62,811 patient trajectories (266,951 visits). Test sets included 63,997 (California), 63,112 (Florida), and 62,472 (New York) trajectories, such that each cohort's composition was proportional to the prevalence of malnutrition in that state. Trajectories contained seven patient characteristics and up to 2,008 diagnosis categories. Area under the receiver-operating characteristic (AUROC) and precision-recall curves (AUPRC) were used to characterize prediction of first malnutrition diagnoses in the test sets. Data analysis was performed from September 2020 to May 2021. Between 4.0% (New York) and 6.2% (California) of patients received malnutrition diagnoses. The longitudinal LSTM model produced the most accurate predictions of malnutrition, with comparable predictive performance in California (AUROC 0.854, AUPRC 0.258), Florida (AUROC 0.869, AUPRC 0.234), and New York (AUROC 0.869, AUPRC 0.190). Deep learning models can reliably predict malnutrition from existing longitudinal patient records, with better predictive performance and lower data-collection requirements than existing instruments. This approach may facilitate early nutritional intervention via automated screening at the point of care.

  View details for DOI 10.1371/journal.pone.0271487

  View details for PubMedID 35901027

• The Evolution and Utilization of Telehealth in Ambulatory Nutrition Practice. Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition Shah, N. D., Krupinski, E. A., Bernard, J., Moyer, M. F. 2021

  Abstract

  The term telehealth is often used interchangeably with telemedicine. Telemedicine involves the electronic exchange of medical information between two remote sites for the optimization of patient care, whereas telehealth is the application of all electronic communication and delivery systems in the provision of healthcare. Telehealth gives patients an opportunity to communicate with their healthcare providers and, overall, access ambulatory care that otherwise is not available in their area of residence. Several telehealth delivery systems are available for electronic communication. Telehealth and other communications technologies used in the delivery of healthcare services are regulated at both the federal and state levels. Coverage and payment policies vary among the different insurers (e.g., Medicare, Medicaid, and private plans), and policies may further be defined by state telehealth parity laws. Telenutrition involves the use of digital technology to provide nutrition care to patients and caregivers and shows potential to optimize nutrition care and outcomes. The coronavirus disease 2019 pandemic has contributed to sweeping legislative and regulatory changes that allowed the temporary expansion of telehealth delivery and reimbursement to maintain continuity of care for patients who were not able to come in for an in-person office visit with their healthcare provider. The purpose of this review is to introduce key concepts of telehealth delivery systems including policy, legal, and regulatory considerations for ambulatory care as well as the role of telenutrition in nutrition care, and highlight the evolving role of telehealth in optimizing patient and nutrition care during a pandemic and beyond.

  View details for DOI 10.1002/ncp.10641

  View details for PubMedID 33734469