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1. Reenvisioning Patient Education with Smart Hospital Patient Rooms

   https://doi.org/10.1145/3631419  

   Dawson, Joshua; Phanich, K. Jens; Wiese, Jason (December 2023, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Smart hospital patient rooms incorporate various smart devices to allow digital control of the entertainment --- such as TV and soundbar --- and the environment --- including lights, blinds, and thermostat. This technology can benefit patients by providing a more accessible, engaging, and personalized approach to their care. Many patients arrive at a rehabilitation hospital because they suffered a life-changing event such as a spinal cord injury or stroke. It can be challenging for patients to learn to cope with the changed abilities that are the new norm in their lives. This study explores ways smart patient rooms can support rehabilitation education to prepare patients for life outside the hospital's care. We conducted 20 contextual inquiries and four interviews with rehabilitation educators as they performed education sessions with patients and informal caregivers. Using thematic analysis, our findings offer insights into how smart patient rooms could revolutionize patient education by fostering better engagement with educational content, reducing interruptions during sessions, providing more agile education content management, and customizing therapy elements for each patient's unique needs. Lastly, we discuss design opportunities for future smart patient room implementations for a better educational experience in any healthcare context. 

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2. From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

   https://doi.org/10.3390/jpm10010015  

   Capobianco, Enrico; Dominietto, Marco (March 2020, Journal of Personalized Medicine)

   Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management. 

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3. Developing behavior-based diabetic retinopathy screening guidelines

   Lee, T; Dorali, P; Shahmoradi, Z; Weng, C. Y. (June 2021, Investigative ophthalmology visual science)

   null (Ed.)

   Purpose : Personalized screening guidelines can be an effective strategy to prevent diabetic retinopathy (DR)-related vision loss. However, these strategies typically do not capture behavior-based factors such as a patient’s compliance or cost preferences. This study develops a mathematical model to identify screening policies that capture both DR progression and behavioral factors to provide personalized recommendations. Methods : A partially observable Markov decision process model (POMDP) is developed to provide personalized screening recommendations. For each patient, the model estimates the patient’s probability of having a sight-threatening diabetic eye disorder (STDED) yearly via Bayesian inference based on natural history, screening results, and compliance behavior. The model then determines a personalized, threshold-based recommendation for each patient annually--either no action (NA), teleretinal imaging (TRI), or clinical screening (CS)--based on the patient’s current probability of having STDED as well as patient-specific preference between cost saving ($) and QALY gain. The framework is applied to a hypothetical cohort of 40-year-old African American male patients. Results : For the base population with TRI and CS compliance rates of 65% and 55% and equal preference for cost and QALY, NA is identified as an optimal recommendation when the patient’s probability of having STDED is less than 0.72%, TRI when the probability is [0.72%, 2.09%], and CS when the probability is above 2.09%. Simulated against annual clinical screening, the model-based policy finds an average decrease of 7.07% in cost/QALY (95% CI; 6.93-7.23%) and 15.05% in blindness prevalence over a patient’s lifetime (95% CI; 14.88-15.23%). For patients with equal preference for cost and QALY, the model identifies 6 different types of threshold-based policies (See Fig 1). For patients with strong preference for QALY gain, CS-only policies had an increase in prevalence by a factor of 19.2 (see Fig 2). Conclusions : The POMDP model is highly flexible and responsive in incorporating behavioral factors when providing personalized screening recommendations. As a decision support tool, providers can use this modeling framework to provide unique, catered recommendations. 

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4. Rule-Based Cohort Definitions for Acute Respiratory Failure: Electronic Phenotyping Algorithm

   https://doi.org/10.2196/18402  

   Essay, Patrick; Mosier, Jarrod; Subbian, Vignesh (January 2020, JMIR Medical Informatics)

   Background Acute respiratory failure is generally treated with invasive mechanical ventilation or noninvasive respiratory support strategies. The efficacies of the various strategies are not fully understood. There is a need for accurate therapy-based phenotyping for secondary analyses of electronic health record data to answer research questions regarding respiratory management and outcomes with each strategy. Objective The objective of this study was to address knowledge gaps related to ventilation therapy strategies across diverse patient populations by developing an algorithm for accurate identification of patients with acute respiratory failure. To accomplish this objective, our goal was to develop rule-based computable phenotypes for patients with acute respiratory failure using remotely monitored intensive care unit (tele-ICU) data. This approach permits analyses by ventilation strategy across broad patient populations of interest with the ability to sub-phenotype as research questions require. Methods Tele-ICU data from ≥200 hospitals were used to create a rule-based algorithm for phenotyping patients with acute respiratory failure, defined as an adult patient requiring invasive mechanical ventilation or a noninvasive strategy. The dataset spans a wide range of hospitals and ICU types across all US regions. Structured clinical data, including ventilation therapy start and stop times, medication records, and nurse and respiratory therapy charts, were used to define clinical phenotypes. All adult patients of any diagnoses with record of ventilation therapy were included. Patients were categorized by ventilation type, and analysis of event sequences using record timestamps defined each phenotype. Manual validation was performed on 5% of patients in each phenotype. Results We developed 7 phenotypes: (0) invasive mechanical ventilation, (1) noninvasive positive-pressure ventilation, (2) high-flow nasal insufflation, (3) noninvasive positive-pressure ventilation subsequently requiring intubation, (4) high-flow nasal insufflation subsequently requiring intubation, (5) invasive mechanical ventilation with extubation to noninvasive positive-pressure ventilation, and (6) invasive mechanical ventilation with extubation to high-flow nasal insufflation. A total of 27,734 patients met our phenotype criteria and were categorized into these ventilation subgroups. Manual validation of a random selection of 5% of records from each phenotype resulted in a total accuracy of 88% and a precision and recall of 0.8789 and 0.8785, respectively, across all phenotypes. Individual phenotype validation showed that the algorithm categorizes patients particularly well but has challenges with patients that require ≥2 management strategies. Conclusions Our proposed computable phenotyping algorithm for patients with acute respiratory failure effectively identifies patients for therapy-focused research regardless of admission diagnosis or comorbidities and allows for management strategy comparisons across populations of interest. 

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5. An In Silico Glioblastoma Microenvironment Model Dissects the Immunological Mechanisms of Resistance to PD‐1 Checkpoint Blockade Immunotherapy

   https://doi.org/10.1002/smtd.202100197  

   Zhang, Zhuoyu; Liu, Lunan; Ma, Chao; Cui, Xin; Lam, Raymond H. W.; Chen, Weiqiang (April 2021, Small Methods)

   Abstract The PD‐1 immune checkpoint‐based therapy has emerged as a promising therapy strategy for treating the malignant brain tumor glioblastoma (GBM). However, patient response varies in clinical trials, mainly due to the tumor heterogeneity and immunological resistance in the tumor microenvironment. To further understand how mechanistically the niche interplay and competition drive anti‐PD‐1 resistance, an in silico model is established to quantitatively describe the biological rationale of critical GBM‐immune interactions, such as tumor growth and apoptosis, T cell activation and cytotoxicity, and tumor‐associated macrophage (TAM) mediated immunosuppression. Such an in silico experimentation and predictive model, based on the in vitro microfluidic chip‐measured end‐point data and patient‐specific immunological characteristics, allows for a comprehensive and dynamic analysis of multiple TAM‐associated immunosuppression mechanisms against the anti‐PD‐1 immunotherapy. The computational model demonstrates that the TAM‐associated immunosuppression varies in severity across different GBM subtypes, which results in distinct tumor responses. The prediction results indicate that a combination therapy by co‐targeting of PD‐1 checkpoint and TAM‐associated CSF‐1R signaling can enhance the immune responses of GBM patients, especially those patients with mesenchymal GBM who are irresponsive to the single anti‐PD‐1 therapy. The development of a patient‐specific in silico–in vitro GBM model will help navigate and personalize immunotherapies for GBM patients. 

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