Real-time prediction of glucose via the proposed NNM may provide a means of intelligent therapeutic guidance and direction.

Abbreviations: (AD%) absolute difference percent, (ANN) artificial neural network, (CGM) continuous glucose monitoring, (CGMS) continuous clucose monitoring system, (GUI) graphical user interface, (MAD%) mean absolute difference percent Keywords: neural network, diabetes, glycemic predictions, CGM

Modern medical management of comorbid conditions has resulted in escalating use of multiple medications and the emergence of the twin phenomena of multimorbidity and polypharmacy. Current understanding of how the polypharmacy in conjunction with multimorbidity influences trauma outcomes is limited, although it is known that trauma patients are at increased risk for medication-related adverse events. The comorbidity-polypharmacy score (CPS) is a simple clinical tool that quantifies the overall severity of comorbidities using the polypharmacy as a surrogate for the “intensity” of treatment necessary to adequately control chronic medical conditions. Easy to calculate, CPS is derived by counting all known pre-injury comorbid conditions and medications. CPS has been independently associated with mortality, increased risk for complications, lower functional outcomes, readmissions, and longer hospital stays. In addition, CPS may help identify older trauma patients at risk of post-emergency department undertriage. The goal of this article was to review and refine the rationale for CPS and to provide an evidence-based outline of its potential clinical applications.

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We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to “train” the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies.

Hospitals have struggled for years regarding the handoff process of communicating patient information from one health care professional to another. Ineffective handoff communication is recognized as a serious patient safety risk within the health care community. It is essential to take communication into consideration when examining the safety of neonates who require immediate medical attention after birth; effective communication is vital for positive patient outcomes, especially with neonates in a delivery room setting. Teamwork and effective communication across the health care continuum are essential for providing efficient, quality care that leads to favorable patient outcomes. Interprofessional simulation and team training can benefit health care professionals by improving interprofessional competence, defined as one’s knowledge of other professionals including an understanding of their training and skillsets, and role clarity. Interprofessional teams that include members with specialization in obstetrics, gynecology, and neonatology have the potential to considerably benefit from training effective handoff and communication practices that would ensure the safety of the neonate upon birth. We must strive to provide the most comprehensive systematic, standardized, interprofessional handoff communication training sessions for such teams, through Graduate Medical Education and Continuing Medical Education that will meet the needs across the educational continuum.

Machine learning and artificial intelligence (AI) have arrived in medicine and the healthcare community is experiencing significant growth in their adoption across numerous patient care settings. There are countless applications for machine learning and AI in medicine ranging from patient outcome prediction, to clinical decision support, to predicting future patient therapeutic setpoints. This commentary discusses a recent application leveraging machine learning to predict one‐year patient survival following orthotopic heart transplantation. This modeling approach has significant implications in terms of improving clinical decision‐making, patient counseling, and ultimately organ allocation and has been shown to significantly outperform pre‐existing algorithms. This commentary also discusses how adoption and advancement of this modeling approach in the future can provide increased personalization of patient care. The continued expansion of information systems and growth of electronic patient data sources in health care will continue to pave the way for increased use and adoption of data science in medicine. Personalized medicine has been a long‐standing goal of the healthcare community and with machine learning and AI now being continually incorporated into clinical settings and practice, this technology is well on the pathway to make a considerable impact to greatly improve patient care in the near future.

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