Artificial Intelligence-Generated Draft Replies to Patient Inbox Messages.

Importance: The emergence and promise of generative artificial intelligence (AI) represent a turning point for health care. Rigorous evaluation of generative AI deployment in clinical practice is needed to inform strategic decision-making. Objective: To evaluate the implementation of a large language model used to draft responses to patient messages in the electronic inbox. Design, Setting, and Participants: A 5-week, prospective, single-group quality improvement study was conducted from July 10 through August 13, 2023, at a single academic medical center (Stanford Health Care). All attending physicians, advanced practice practitioners, clinic nurses, and clinical pharmacists from the Divisions of Primary Care and Gastroenterology and Hepatology were enrolled in the pilot. Intervention: Draft replies to patient portal messages generated by a Health Insurance Portability and Accountability Act-compliant electronic health record-integrated large language model. Main Outcomes and Measures: The primary outcome was AI-generated draft reply utilization as a percentage of total patient message replies. Secondary outcomes included changes in time measures and clinician experience as assessed by survey. Results: A total of 197 clinicians were enrolled in the pilot; 35 clinicians who were prepilot beta users, out of office, or not tied to a specific ambulatory clinic were excluded, leaving 162 clinicians included in the analysis. The survey analysis cohort consisted of 73 participants (45.1%) who completed both the presurvey and postsurvey. In gastroenterology and hepatology, there were 58 physicians and APPs and 10 nurses. In primary care, there were 83 physicians and APPs, 4 nurses, and 8 clinical pharmacists. The mean AI-generated draft response utilization rate across clinicians was 20%. There was no change in reply action time, write time, or read time between the prepilot and pilot periods. There were statistically significant reductions in the 4-item physician task load score derivative (mean [SD], 61.31 [17.23] presurvey vs 47.26 [17.11] postsurvey; paired difference, -13.87; 95% CI, -17.38 to -9.50; P < .001) and work exhaustion scores (mean [SD], 1.95 [0.79] presurvey vs 1.62 [0.68] postsurvey; paired difference, -0.33; 95% CI, -0.50 to -0.17; P < .001). Conclusions and Relevance: In this quality improvement study of an early implementation of generative AI, there was notable adoption, usability, and improvement in assessments of burden and burnout. There was no improvement in time. Further code-to-bedside testing is needed to guide future development and organizational strategy.

Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study.

INTRODUCTION: Unnecessary laboratory testing contributes to patient morbidity and healthcare waste. Despite prior attempts at curbing such overutilization, there remains opportunity for improvement using novel data-driven approaches. This study presents the development and early evaluation of a clinical decision support tool that uses a predictive model to help providers reduce low-yield, repetitive laboratory testing in hospitalized patients. METHODS: We developed an EHR-embedded SMART on FHIR application that utilizes a laboratory test result prediction model based on historical laboratory data. A combination of semi-structured physician interviews, usability testing, and quantitative analysis on retrospective laboratory data were used to inform the tool's development and evaluate its acceptability and potential clinical impact. KEY RESULTS: Physicians identified culture and lack of awareness of repeat orders as key drivers for overuse of inpatient blood testing. Users expressed an openness to a lab prediction model and 13/15 physicians believed the tool would alter their ordering practices. The application received a median System Usability Scale score of 75, corresponding to the 75th percentile of software tools. On average, physicians desired a prediction certainty of 85% before discontinuing a routine recurring laboratory order and a higher certainty of 90% before being alerted. Simulation on historical lab data indicates that filtering based on accepted thresholds could have reduced âˆ¼22% of repeat chemistry panels. CONCLUSIONS: The use of a predictive algorithm as a means to calculate the utility of a diagnostic test is a promising paradigm for curbing laboratory test overutilization. An EHR-embedded clinical decision support tool employing such a model is a novel and acceptable intervention with the potential to reduce low-yield, repetitive laboratory testing.

Using Case Mix Index within Diagnosis-Related Groups to Evaluate Variation in Hospitalization Costs at a Large Academic Medical Center.

In analyzing direct hospitalization cost and clinical data from an academic medical center, commonly used metrics such as diagnosis-related group (DRG) weight explain approximately 37% of cost variability, but a substantial amount of variation remains unaccounted for by case mix index (CMI) alone. Using CMI as a benchmark, we isolate and target individual DRGs with higher than expected average costs for specific quality improvement efforts. While DRGs summarize hospitalization care after discharge, a predictive model using only information known before admission explained up to 60% of cost variability for two DRGs with a high excess cost burden. This level of variability likely reflects underlying patient factors that are not modifiable (e.g., age and prior comorbidities) and therefore less useful for health systems to target for intervention. However, the remaining unexplained variation can be inspected in further studies to discover operational factors that health systems can target to improve quality and value for their patients. Since DRG weights represent the expected resource consumption for a specific hospitalization type relative to the average hospitalization, the data-driven approach we demonstrate can be utilized by any health institution to quantify excess costs and potential savings among DRGs.

Bioengineered optogenetic model of human neuromuscular junction.

Functional human tissues engineered from patient-specific induced pluripotent stem cells (hiPSCs) hold great promise for investigating the progression, mechanisms, and treatment of musculoskeletal diseases in a controlled and systematic manner. For example, bioengineered models of innervated human skeletal muscle could be used to identify novel therapeutic targets and treatments for patients with complex central and peripheral nervous system disorders. There is a need to develop standardized and objective quantitative methods for engineering and using these complex tissues, in order increase their robustness, reproducibility, and predictiveness across users. Here we describe a standardized method for engineering an isogenic, patient specific human neuromuscular junction (NMJ) that allows for automated quantification of NMJ function to diagnose disease using a small sample of blood serum and evaluate new therapeutic modalities. By combining tissue engineering, optogenetics, microfabrication, optoelectronics and video processing, we created a novel platform for the precise investigation of the development and degeneration of human NMJ. We demonstrate the utility of this platform for the detection and diagnosis of myasthenia gravis, an antibody-mediated autoimmune disease that disrupts the NMJ function.

Author Correction: Advanced maturation of human cardiac tissue grown from pluripotent stem cells.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Quantification of human neuromuscular function through optogenetics.

The study of human neuromuscular diseases has traditionally been performed in animal models, due to the difficulty of performing studies in human subjects. Despite the unquestioned value of animal models, inter-species differences hamper the translation of these findings to clinical trials. Tissue-engineered models of the neuromuscular junction (NMJ) allow for the recapitulation of the human physiology in tightly controlled in vitro settings. Methods: Here we report the first human patient-specific tissue-engineered model of the neuromuscular junction (NMJ) that combines stem cell technology with tissue engineering, optogenetics, microfabrication and image processing. The combination of custom-made hardware and software allows for repeated, quantitative measurements of NMJ function in a user-independent manner. Results: We demonstrate the utility of this model for basic and translational research by characterizing in real time the functional changes during physiological and pathological processes. Principal Conclusions: This system holds great potential for the study of neuromuscular diseases and drug screening, allowing for the extraction of quantitative functional data from a human, patient-specific system.

Dual IFN-γ/hypoxia priming enhances immunosuppression of mesenchymal stromal cells through regulatory proteins and metabolic mechanisms.

The immunosuppressive capacity of human mesenchymal stromal cells (MSCs) renders them promising candidates for treating diverse immune disorders. However, after hundreds of clinical trials, there are still no MSC therapies approved in the United States. MSCs require specific cues to adopt their immunosuppressive phenotype, and yet most clinical trials use cells expanded in basic culture medium and growth conditions. We propose that priming MSCs prior to administration will improve their therapeutic efficacy. Interferon-gamma (IFN-γ) priming are cues common to situations of immune escape that have individually shown promise as MSC priming cues but have not been systematically compared. Using mixed lymphocyte reactions, we show that priming MSCs with either cue alone improves T-cell inhibition. However, combining the two cues results in additive effects and markedly enhances the immunosuppressive phenotype of MSCs. We demonstrate that IFN-γ induces expression of numerous immunosuppressive proteins (IDO, PD-L1, HLA-E, HLA-G), whereas hypoxia switches MSCs to glycolysis, causing rapid glucose consumption and production of T-cell inhibitory lactate levels. Dual IFN-γ/hypoxia primed MSCs display both attributes and have even higher induction of immunosuppressive proteins over IFN-γ priming alone (IDO and HLA-G), which may reflect another benefit of metabolic reconfiguration.

Advanced maturation of human cardiac tissue grown from pluripotent stem cells.

Cardiac tissues generated from human induced pluripotent stem cells (iPSCs) can serve as platforms for patient-specific studies of physiology and disease1-6. However, the predictive power of these models is presently limited by the immature state of the cells1, 2, 5, 6. Here we show that this fundamental limitation can be overcome if cardiac tissues are formed from early-stage iPSC-derived cardiomyocytes soon after the initiation of spontaneous contractions and are subjected to physical conditioning with increasing intensity over time. After only four weeks of culture, for all iPSC lines studied, such tissues displayed adult-like gene expression profiles, remarkably organized ultrastructure, physiological sarcomere length (2.2 µm) and density of mitochondria (30%), the presence of transverse tubules, oxidative metabolism, a positive force-frequency relationship and functional calcium handling. Electromechanical properties developed more slowly and did not achieve the stage of maturity seen in adult human myocardium. Tissue maturity was necessary for achieving physiological responses to isoproterenol and recapitulating pathological hypertrophy, supporting the utility of this tissue model for studies of cardiac development and disease.

Real-Time Bioluminescence Imaging of Cell Distribution, Growth, and Differentiation in a Three-Dimensional Scaffold Under Interstitial Perfusion for Tissue Engineering.

Bioreactor systems allow safe and reproducible production of tissue constructs and functional analysis of cell behavior in biomaterials. However, current procedures for the analysis of tissue generated in biomaterials are destructive. We describe a transparent perfusion system that allows real-time bioluminescence imaging of luciferase expressing cells seeded in scaffolds for the study of cell-biomaterial interactions and bioreactor performance. A prototype provided with a poly(lactic) acid scaffold was used for "proof of principle" studies to monitor cell survival in the scaffold (up to 22 days). Moreover, using cells expressing a luciferase reporter under the control of inducible tissue-specific promoters, it was possible to monitor changes in gene expression resulting from hypoxic state and endothelial cell differentiation. This system should be useful in numerous tissue engineering applications, the optimization of bioreactor operation conditions, and the analysis of cell behavior in three-dimensional scaffolds.

Tissue-Engineering for the Study of Cardiac Biomechanics.

The notion that both adaptive and maladaptive cardiac remodeling occurs in response to mechanical loading has informed recent progress in cardiac tissue engineering. Today, human cardiac tissues engineered in vitro offer complementary knowledge to that currently provided by animal models, with profound implications to personalized medicine. We review here recent advances in the understanding of the roles of mechanical signals in normal and pathological cardiac function, and their application in clinical translation of tissue engineering strategies to regenerative medicine and in vitro study of disease.