Digital Health Work

Publications

  • Malhotra, K., Wiesenfeld, B., Major, VJ., Grover, H., Aphinyanaphongs, Y., Testa, P., Austrian, JS. (2024). Health system-wide access to generative artificial intelligence: the New York University Langone Health experience. Journal of the American Medical Informatics Association. doi: 10.1093/jamia/ocae285. 
  • Jonassen, Z., Lawrence, K., Wiesenfeld, B.M., Feuerriegel, S. and Mann, D. (2024). A qualitative analysis of remote patient monitoring: how a paradox mindset can support balancing emotional tensions in the design of healthcare technologies. arXiv preprint arXiv:2411.14233.
  • Mandal, S., Wiesenfeld, B., Mann, D., Szerencsy, A., Iturrate, E., and Nov, O. (2024). Quantifying the Impact of Telemedicine and Patient Medical Advice Request Messages on Physicians’ Inbasket Work. NPJ Digital Medicine 7,1,35.
  • Small, W., Wiesenfeld, B., Brandfield-Harvey, B., Jonassen, Z., Mandal, S., Stevens, E., Major, V., Lostraglio, E., Szerencsy, A., Jones, S., Aphinyanaphongs, Y., Johnson, S., Nov, O., Mann, D. (2024) Large Language Model-Based Responses to Patients’ In-Basket Messages. JAMA Network Open, v.7.
  • Small, W., Malhotra, K., Major, V.J., Wiesenfeld, B., Lewis, M., Grover, H., Tang, H., Banerjee, A., Jabbour, M.J., Aphinyanaphongs, Y. and Testa, P., (2024). The First Generative AI Prompt-A-Thon in Healthcare: A Novel Approach to Workforce Engagement with a Private Instance of ChatGPT. PLOS Digital Health, 3(7), p.e0000394.
  • Mendel, T., Nov, O. and Wiesenfeld, B. (2024). Advice from a Doctor or AI? Understanding Willingness to Disclose Information Through Remote Patient Monitoring to Receive Health Advice. Proceedings of the ACM on Human-Computer Interaction: CSCW 8(2).
  • Dove, G., Guthmann, M.R., Charvet, L., Nov, O. and Pilloni, G. (2024). “Data Is One Thing, But I Want To Know The Story Behind”: Designing For Self-Tracking and Remote Patient Monitoring In The Context Of Multiple Sclerosis Care. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (pp. 597-618).
  • Lawrence, K. and Levine, D.L. (2024). The Digital Determinants of Health: A Guide for Competency Development in Digital Care Delivery for Health Professions Trainees. JMIR Medical Education, 10, p.e54173.
  • Singh, N., Lawrence, K., Andreadis, K., Twan, C. and Mann, D. (2024). Developing and Scaling Remote Patient Monitoring Capacity in Ambulatory Practice. NEJM Catalyst Innovations in Care Delivery, 5(6), pp.CAT-23.
  • Gan, T., Das, R. and Porfiri, M. (2024). Network modeling of consumers’ selection of providers based on online reviews. IEEE Transactions on Network Science and Engineering.
  • Woo, K.M.C., Simon, G.W., Akindutire, O., Aphinyanaphongs, Y., Austrian, J.S., Kim, J.G., Genes, N., Goldenring, J.A., Major, V.J., Pariente, C.S. and Pineda, E.G. (2024). Evaluation of GPT-4 ability to identify and generate patient instructions for actionable incidental radiology findings. Journal of the American Medical Informatics Association. 31,9, 1983–1993.
  • Lawrence, K. and Mann, D. (2024). Virtual‐first care: Opportunities and challenges for the future of diagnostic reasoning. The Clinical Teacher, p.e13720.
  • Succar, R., Boldini, A. and Porfiri, M. (2024). Detecting hidden states in stochastic dynamical systems. Physical Review Research, 6(1), p.013149.
  • Ventura, R.B., Catalano, A., Succar, R. and Porfiri, M.(2024). Automating the assessment of wrist motion in telerehabilitation with haptic devices. Soft Mechatronics and Wearable Systems (Vol. 12948, pp. 77-84). SPIE.
  • Ventura, R.B., Ruan, L. and Porfiri, M. (2024). Detecting impaired movements of stroke patients in bimanual training from motion sensor data. In Soft Mechatronics and Wearable Systems (Vol. 12948, pp. 69-76). SPIE.
  • Zakreuskaya, A., Buschek, D., Mackay, W.E., Avellino, I., Dove, G. and Eskofier, B.M., (2024). From Text to Treatment: How Medical Discharge Letters Are Used as a Key Artifact for Managing Patient Care. In Proceedings of Mensch und Computer 2024 (pp. 99-110).
  • Feng, J., Beheshti, M., Philipson, M., Ramsaywack, Y., Porfiri, M. and Rizzo, J.R. (2023). Commute Booster: A Mobile Application for First/Last Mile and Middle Mile Navigation Support for People with Blindness and Low Vision. IEEE Journal of Translational Engineering in Health and Medicine.
  • Nov, O., Singh, N., Mann, D.M. (2023). Putting ChatGPT’s Medical Advice to the (Turing) Test. JMIR Medical Education, 9(1), p.e46939.
  • Singh, N., Lawrence, K., Richardson, S., Mann DM (2023). Centering health equity in large language model deployment. PLOS Digit Health 2(10): e0000367. 
  • Lawrence, K., Singh, N., Jonassen, Z., Groom, L., Alfaro-Arias, V., Mandal, S., Schoenthaler, A., Mann, D., Nov, O., Dove, G. (2023). Operational Implementation of Remote Patient Monitoring Within a Large Ambulatory Health System: Multimethod Qualitative Case Study. JMIR Human Factors 10 (1) e45166.
  • Ricci, F.S., Boldini, A., Ma, X., Beheshti, M., Geruschat, D.R., Seiple, W.H., Rizzo, J.R. and Porfiri, M. (2023). Virtual reality as a means to explore assistive technologies for the visually impaired. PLOS Digital Health, 2(6), p.e0000275.
  • Clark, P., Kim, J. and Aphinyanaphongs, Y. (2023). Marketing and US Food and Drug Administration Clearance of Artificial Intelligence and Machine Learning Enabled Software in and as Medical Devices: A Systematic Review. JAMA Network Open, 6(7), pp.e2321792-e2321792.
  • Ricci, F.S., Boldini, A., Beheshti, M., Rizzo, J.R. and Porfiri, M. (2023). A virtual reality platform to simulate orientation and mobility training for the visually impaired. Virtual Reality, 27(2), pp.797-814.
  • Jonassen, Z., Kellogg, K. and Wiesenfeld, B.M. (2023). Socio-emotive practices for team coordination around algorithmic tools. SSRN 4675293.
  • Wiesenfeld, B., Aphinyanaphongs, Y., Nov, O. (2022). Model Transferability in Healthcare: a Sociotechnical Perspective. Nature Machine Intelligence, 4 (10) 807-809. 
  • Lawrence, K., Nov, O., Mann, D., Mandal, S., Iturrate, E., Wiesenfeld, B. (2022). The Impact of Telemedicine on Physicians’ After-hours Electronic Health Record “Work Outside Work” During the COVID-19 Pandemic: Retrospective Cohort Study. JMIR Medical Informatics 10(7).
  • Dove, G., Fernando, A., Hertz, K., Kim, J., Rizzo, JR., Seiple, W., Nov, O. (2022). Digital Technologies In Orientation And Mobility Instruction For People Who Are Blind Or Have Low Vision. Proceedings of the ACM on Human-Computer Interaction (CSCW) 6,2. 
  • Ricci, F.S., Boldini, A., Beheshti, M., Rizzo, J.R. Porfiri, M. (2022). A virtual reality platform to simulate orientation and mobility training for the visually impaired. Virtual Reality, pp.1-18.
  • Mandal S., Belli H., Cruz J., Mann D., Schoenthaler A. (2022). Analyzing user engagement within a patient-reported outcomes texting tool for diabetes management. JMIR Diabetes.
  • Yang, E., Aphinyanaphongs, Y., Punjabi, P., Austrian, J., Wiesenfeld, B. (2022). Quantitative and qualitative evaluation of provider use of a novel machine learning model for favorable outcome prediction. American Medical Informatics Association 2022 Annual Symposium.
  • Seals, A., Pilloni, G., Kim, J., Sanchez, R., Rizzo, JR., Charvet, L., Nov, O., Dove, G. (2022). ‘Are They Doing Better in the Clinic or at Home’: Understanding Clinicians’ Needs When Visualizing Wearable Sensor Data Used in Remote Gait Assessments for People With Multiple Sclerosis. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI).
  • Mandal, S, Wiesenfeld, B., Mann, D., Lawrence, K.,  Chunara, R., Testa, P., Nov, O. (2022). Evidence for telemedicine’s ongoing transformation of healthcare delivery since the onset of COVID-19: A retrospective observational study. JMIR Formative Research.
  • Ng, H.F., Hsu, L.T., Lee, M.J.L., Feng, J., Naeimi, T., Beheshti, M. Rizzo, J.R. (2022). Real-Time Loosely Coupled 3DMA GNSS/Doppler Measurements Integration Using a Graph Optimization and Its Performance Assessments in Urban Canyons of New York. Sensors, 22(17), p.6533.
  • Mann, D.M. and Lawrence, K. (2022). Reimagining connected care in the era of digital medicine. JMIR mHealth and uHealth, 10(4), p.e34483.
  • Ricci, F.S., Boldini, A., Rizzo, J.R. and Porfiri, M. (2022). Learning to use electronic travel aids for visually impaired in virtual reality. In Nano-, Bio-, Info-Tech Sensors, and Wearable Systems 2022 (Vol. 12045, pp. 9-15). SPIE.
  • Lotan, E., Zhang, B., Dogra, S., Wang, W.D., Carbone, D., Fatterpekar, G., Oermann, E.K. and Lui, Y.W. (2022). Development and Practical Implementation of a Deep Learning–Based Pipeline for Automated Pre-and Postoperative Glioma Segmentation. American Journal of Neuroradiology, 43(1), pp.24-32.
  • Bell, A., Solano-Kamaiko, I., Nov, O., Stoyanovich, J. (2022).  It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT).
  • Barak-Ventura R, Stewart-Hughes, K., Nov, O., Raghavan, P., Marín Ruiz, M.,  Porfiri, M. (2022). Data-driven classification of human movements in virtual reality-based serious games: A pre-clinical rehabilitation study in citizen science. JMIR Serious Games.
  • Balestra, M., Chen, J., Iturrate, E., Aphinyanaphongs, Y., and Nov, O. (2021). Predicting Inpatient Pharmacy Order Interventions Using Provider Action Data. JAMIA Open 4 (3).
  • Nov, O., Aphinyanaphongs, A., Lui Y., Mann D., Porfiri, M., Riedl, M., Rizzo, JR, and Wiesenfeld, B. (2021). The Transformation of Patient-Clinician Relationships with AI-Based Medical Advice: A “Bring Your Own Algorithm” Era in Healthcare. Communications of the ACM 64(3) 46-48.
  • Harish, K., Zhang, B., Stella, P., Hauck, K., Moussa, M.M., Adler, N.M., Horwitz, L.I. and Aphinyanaphongs, Y. (2021). Validation of parsimonious prognostic models for patients infected with COVID-19. BMJ Health & Care Informatics, 28(1).
  • Chunara, R., Zhao, Y., Chen, J., Lawrence, K., Testa, P., Nov, O., and Mann, D. (2021). Telemedicine and Healthcare Disparities: A Cohort Study in a Large Healthcare System in New York City During COVID-19. Journal of the American Medical Informatics Association 28(1) 33-41.
  • Nov, O., Dove, G., Balestra, M., Lawrence, K., Mann, D. and Wiesenfeld, B. (2021). Preferences and patterns of response to public health advice during the COVID-19 pandemic. Scientific Reports.
  • Barak-Ventura, R., Ruiz Marin, M., Nov, O., Raghavan, P., and Porfiri, M. (2021). A Low-Cost Telerehabilitation Paradigm for Bimanual Training. IEEE/ASME Transactions on Mechatronics.
  • Yuan, J., Nov, O., and Bertini, E. (2021). An Exploration and Validation of Visual Factors in Understanding Classification Rule Sets. Proceedings of IEEE InfoVis 2021. 
  • Jethani, N., Sudarshan, M., Aphinyanaphongs, Y. and Ranganath, R., (2021). Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. In International Conference on Artificial Intelligence and Statistics (pp. 1459-1467). PMLR.
  • Boldini, A., Ma, X., Rizzo, J.R. and Porfiri, M. (2021). A virtual reality interface to test wearable electronic travel aids for the visually impaired. In Nano-, Bio-, Info-Tech Sensors and Wearable Systems (11590, p. 115900Q). International Society for Optics and Photonics.
  • Marín, M.R., Martínez, I.V., Bermúdez, G.R. and Porfiri, M. (2021). Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings. Iscience, 24(1), p.101997.
  • Barak Ventura, R., Surano, F.V. and Porfiri, M. (2021) Automated classification of bimanual movements in stroke telerehabilitation: a comparison of dimensionality reduction algorithms. In Proc. of SPIE Vol (11590, pp. 1159005-1).
  • Dove, G., Balestra, M., Mann, D., and Nov, O. (2020). Good for the Many or Best for the Few? A Dilemma in the Design of Algorithmic Advice. Proceedings of the ACM on Human-Computer Interaction. 4,2 (CSCW). 
  • Razavian, N., Major, V.J., Sudarshan, M., Burk-Rafel, J., Stella, P., Randhawa, H., Bilaloglu, S., Chen, J., Nguy, V., Wang, W., Zhang, H., …Aphinyanaphongs, Y. (2020). A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients. NPJ Digital Medicine, 3(1), pp.1-13.
  • Nakayama, S., Richmond, S., Nov, O. and Porfiri, M. (2020). The gold miner’s dilemma: Use of information scent in cooperative and competitive information foraging. Computers in Human Behavior, 109, p.106352.
  • Porfiri, M. (2020). Validity and limitations of the detection matrix to determine hidden units and network size from perceptible dynamics. Physical Review Letters, 124(16), p.168301.
  • Mann, D., Chen, J., Chunara, R., Testa, P., and Nov, O. (2020). COVID-19 Transforms Healthcare Through Telemedicine: Evidence From the Field. Journal of the American Medical Informatics Association 27(7) 1132–1135.