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Abstract The Biden 2023 Artificial Intelligence (AI) Executive Order calls for the creation of a patient safety program. Patient safety reports are a natural starting point for identifying issues. We examined the feasibility of this approach by analyzing reports associated with AI/Machine Learning (ML)-enabled medical devices. Of the 429 reports reviewed, 108 (25.2%) were potentially AI/ML related, with 148 (34.5%) containing insufficient information to determine an AI/ML contribution. A more comprehensive approach is needed.

Background:. Operating suites are significant drivers of waste, pollution, and costs. Surgeons can help fight the climate crisis by implementing innovative strategies aimed at mitigating the environmental impact of surgical procedures and decreasing operational costs, and moving toward a more sustainable healthcare system. This study aims to review the literature describing interventions that reduce surgical waste. Methods:. PubMed, Cochrane, and Embase were searched. Studies reporting interventions to reduce operative waste, including emissions, energy, trash, and other, were included. Case reports, opinion-based reports, reviews, and meta-analyses were excluded. Study quality was rated using MINORS and Jadad scales. Data were extracted from each study to calculate waste on a per case basis. Narrative review of studies was performed rather than meta-analysis. Results:. The search yielded 675 unique hits, of which 13 (level of evidence: I–III) met inclusion criteria. Included studies were categorized by intervention type in relation to the operating and procedure room. Three studies evaluated provider education initiatives, three evaluated setup of instruments, two evaluated single-use items, four evaluated technique changes, and one evaluated surgical venue. Seven studies reported significant reductions in disposable surgical waste throughput, and seven reported significant reductions in cost. Conclusions:. The results of this systemic review demonstrated the effectiveness of surgical waste reduction initiatives in reducing waste volume, cost, and carbon emissions. Within plastic surgery, minimal surgical packs resulted in reduced gross waste and cost while promoting patient satisfaction in hand surgery, supporting the continued development and implementation of such initiatives in a surgical context.

# Background When placing orders into the electronic health record (EHR), prescribers often use free-text information to complement the order. However, the use of these free-text fields can result in patient safety issues. The objective of our study was to develop a deeper understanding of the conditions under which free-text information, or special instructions, are used in the EHR and the patient safety issues associated with their use, through an analysis of patient safety event (PSE) reports. # Methods We identified 847 PSE reports submitted to the Pennsylvania Patient Safety Reporting System (PA-PSRS) between January 1, 2021, and December 31, 2022; this dataset was reduced to 677 after controlling for oversampling from particular facilities. After limiting to reports that mentioned the terms "special instructions," "order instructions," "order comments," or "special comments," we analyzed a total of 329 reports. A physician and human factors expert independently reviewed the reports and assigned each a code from the following categories: general care process, medication class, information expressed in the special instruction, special instruction issue, department or staff for which special instruction was intended, and whether the error reached the patient. # Results Almost three quarters of the special instruction reports were related to Medication (n=233 of 329, 70.8%), followed by Laboratory/Blood Bank (n=54, 16.4%), and Radiology (n=23, 7.0%). Medication classes most frequently associated with special instructions included infectious disease medications (n=51 of 230, 22.2%), antithrombotic/antithrombotic reversal agents (n=32, 13.9%), and nutritional/electrolytes/intravenous fluids (n=32, 13.9%). Nearly one quarter each of medication-related special instructions were about timing (n=58 of 233, 24.9%) and dosing (n=54, 23.2%); most about laboratory/blood bank were related to the site of the blood draw (n=33 of 54, 61.1%), and many involving radiology were related to radiology/echocardiography instructions (n=16 of 23, 69.6%). The most frequent issues associated with special instructions were containing information contradictory to the order or other information (n=62 of 329, 18.8%); being confusing, incorrect, or used incorrectly (n=58, 17.6%); and not seen (n=25, 7.6%), not viewable (n=11, 3.3%), or instructions absent (n=11, 3.3%). In more than half of the reports, special instructions were intended for nursing staff (n=184 of 329, 55.9%), followed by pharmacy (n=49, 14.9%), radiology (n=21, 6.4%), and laboratory/blood bank (n=20, 6.1%). The error reached the patient in roughly three quarters (n=243 of 329, 73.9%) of the reports reviewed. # Conclusion Special instructions are frequently used to provide additional context about medication orders and laboratory and radiology procedures and are often intended for nurses and pharmacists. However, these instructions can result in errors and may cause patient harm. Based on our analysis, we provide EHR design strategies and policies and protocols to address patient safety issues associated with free text to enable safer and more resilient care delivery.

# Background Medical oxygen is frequently used in healthcare settings. Challenges with oxygen disruption, such as oxygen tanks running out due to communication issues between staff or tanks not being set up properly, have been noted in the limited existing literature. Challenges and patient safety issues associated with oxygen disruption persist. Utilizing a human factors approach, our study aims to understand the contributing factors and context of oxygen disruption--related patient safety event reports in the inpatient setting and provide person-based and system-based solutions. # Methods Through keyword matching, we identified and then qualitatively analyzed 298 patient safety event reports to understand the factors contributing to oxygen disruption, patient location when the oxygen disruptions occurred, hand-off breakdowns by healthcare team member role, and whether high supplemental oxygen was being administered. # Results The most frequent contributing factor to oxygen disruption was the patient not being transferred to another source of oxygen (n=135 of 298, 45.3%), followed by tank found empty (n=107, 35.9%), patient connected to a functioning oxygen source, no oxygen flowing (n=25, 8.4%), oxygen delivery device malfunction (n=22, 7.4%), and no oxygen available (n=9, 3.0%). Over one-third of all oxygen disruption events occurred on the unit where the patient was admitted (n=109 of 298, 36.6%). Roughly 40% of reports involved a hand-off breakdown (n=123 of 298, 41.3%) and the most frequent breakdowns occurred between a nurse and a patient transporter (n=47 of 123, 38.2%). Almost one quarter of reports involved a patient with high supplemental oxygen requirements (n=74 of 298, 24.8%). # Conclusion Oxygen disruption events can have serious patient safety implications. Many of the oxygen disruption events we reviewed occurred due to lack of situational awareness and hand-off breakdowns. Combining person-based solutions, such as paper-based tools and checklists, with system-based solutions involving central monitoring and supervisory systems may help reduce the risk of oxygen disruption events.

This paper presents a day-ahead forecasting method for photovoltaic (PV) power plants in commercial sectors. The method is based on numerical weather prediction (NWP) models from open weather maps and power plant specifications. The output of the model is the predicted power output from the PV power plant, which is incorporated into an optimal control strategy of the PV plant using battery storage. The use of optimal algorithms assists in the PV power plant curtailment in cases of over-generation and reduces the dependence on conventional power sources such as generators in cases of under-generation by the PV plant. It was found that most forecasting methods do not incorporate PV power and storage systems for proper optimization and demand management. This can be seen as a gap for further research of forecasting models integrated with battery storage systems to improve PV power system performance. Results obtained show a good performance of the developed model. A root means square error (RMSE) of 425.79 W and 595.10 W and a mean absolute error (MAE) of 246.26 W and 238 W were achieved for a summer and winter day, respectively. Furthermore, an excellent positive correlation exists between the predicted output power and the observed results, with R2 values over 90%.

Abstract Patient safety reporting systems give healthcare provider staff the ability to report medication related safety events and errors; however, many of these reports go unanalyzed and safety hazards go undetected. The objective of this study is to examine whether natural language processing can be used to better categorize medication related patient safety event reports. 3,861 medication related patient safety event reports that were previously annotated using a consolidated medication error taxonomy were used to develop three models using the following algorithms: (1) logistic regression, (2) elastic net, and (3) XGBoost. After development, models were tested, and model performance was analyzed. We found the XGBoost model performed best across all medication error categories. ‘Wrong Drug’, ‘Wrong Dosage Form or Technique or Route’, and ‘Improper Dose/Dose Omission’ categories performed best across the three models. In addition, we identified five words most closely associated with each medication error category and which medication error categories were most likely to co-occur. Machine learning techniques offer a semi-automated method for identifying specific medication error types from the free text of patient safety event reports. These algorithms have the potential to improve the categorization of medication related patient safety event reports which may lead to better identification of important medication safety patterns and trends.

Background:. Social media and online advertising are increasingly used by plastic surgeons (PSs) to educate patients and obtain referrals, but it remains unclear whether the general public can distinguish the difference in training and accreditation among medical professionals advertising online. Our study elucidates the public’s expectations regarding the distinction between plastic surgery and facial plastic surgery. Methods:. A survey was distributed via MTurk, an Amazon surveying service, to collect information about demographics and assumptions that would be made solely based on the terminology “facial PS” (FPS) and “PS.” Participants were restricted to those residing in the United States. Results:. A total of 253 responses were collected. Based on the term FPS, respondents on average moderately to strongly agreed that they would expect an FPS to have completed a plastic and reconstructive surgery residency program (mean = 1.81; scale from 1= strongly agree to 5= strongly disagree) and would feel moderately to strongly misled if they visited an FPS for a consultation and later learned that the provider did not complete a plastic and reconstructive surgery residency (mean = 3.62; scale from 1 = not misled at all to 5 = extremely misled). Conclusions:. Despite increasing advocacy by professional societies and the member societies of the American Board of Medical Specialties, this study demonstrated that the lay public is unaware of factually different training and certification pathways given similarity in nomenclature. This study was notably not designed to draw conclusions or imply superiority of one specialty, certifying board, or training model over the other.

Background:. The Plastic Surgery Foundation’s Surgeons in Humanitarian Alliance for Reconstruction, Research and Education (SHARE) program seeks to expand surgical capacity worldwide through mentorship and training for local plastic surgeons. This study aims to define the need for microsurgery training among SHARE global fellows and describe results of a pilot course. Methods:. Ten participants of the SHARE Virtual Microsurgical Skills Course were asked to complete an anonymous survey. Pre- and post-course response rates were 100% and 50.0%, respectively. Results:. There was a high incidence of microsurgical problems encountered in the clinical setting. Resource availability was varied, with high access to loupes (100%), yet limited access to microsurgery instruments (50%), medications (40%), operating microscope (20%), skilled nursing (0%) and appropriate peri-operative care settings (0%). Participants identified vessel preparation, instrument selection, and suture handling as priority learning objectives for a microsurgery skills course. Post-course satisfaction with learning objectives was high (60% “very good,” 40% “excellent”). Participants reported high levels of improvement in suture handling (Likert 4.60±0.55), end-to-end anastomosis (4.40±0.55), instrument selection (4.20±0.45), vessel preparation (4.20±0.45), and economy of motion (4.20±0.45). Conclusions:. This study demonstrates a high frequency of reconstructive problems encountered by global fellows yet low access to appropriate resources to perform microsurgical procedures. Initial results from a pilot virtual microsurgery course demonstrate very high satisfaction and high self-rated improvement in key microsurgical skills. The virtual course is an effective and accessible format for training surgeons in basic microsurgery skills and can be augmented by providing longitudinal opportunities for remote feedback.

Background:. To address patient health literacy, the American Medical Association recommends that readability of patient education materials should not exceed a sixth grade reading level; the National Institutes of Health recommend no greater than an eigth-grade reading level. However, patient-facing materials in plastic surgery often remain at an above-recommended average reading level. The purpose of this study was to evaluate ChatGPT 3.5 as a tool for optimizing patient-facing craniofacial education materials. Methods:. Eighteen patient-facing craniofacial education materials were evaluated for readability by a traditional calculator and ChatGPT 3.5. The resulting scores were compared. The original excerpts were then inputted to ChatGPT 3.5 and simplified by the artificial intelligence tool. The simplified excerpts were scored by the calculators. Results:. The difference in scores for the original excerpts between the online calculator and ChatGPT 3.5 were not significant (P = 0.441). Additionally, the simplified excerpts’ scores were significantly lower than the originals (P

Abstract Background Acute kidney injury (AKI) following total joint arthroplasty (TJA) is associated with increased morbidity and mortality. Estimated glomerular filtration rate (eGFR) is used as an indicator of renal function. The purpose of this study was (1) to assess each of the five equations that are used in calculating eGFR, and (2) to evaluate which equation may best predict AKI in patients following TJA. Methods The National Surgical Quality Improvement Program (NSQIP) was queried for all 497,261 cases of TJA performed from 2012 to 2019 with complete data. The Modification of Diet in Renal Disease (MDRD) II, re-expressed MDRD II, Cockcroft-Gault, Mayo quadratic, and Chronic Kidney Disease Epidemiology Collaboration equations were used to calculate preoperative eGFR. Two cohorts were created based on the development of postoperative AKI and were compared based on demographic and preoperative factors. Multivariate regression analysis was used to assess for independent associations between preoperative eGFR and postoperative renal failure for each equation. The Akaike information criterion (AIC) was used to evaluate predictive ability of the five equations. Results Seven hundred seventy-seven (0.16%) patients experienced AKI after TJA. The Cockcroft-Gault equation yielded the highest mean eGFR (98.6 ± 32.7), while the Re-expressed MDRD II equation yielded the lowest mean eGFR (75.1 ± 28.8). Multivariate regression analysis demonstrated that a decrease in preoperative eGFR was independently associated with an increased risk of developing postoperative AKI in all five equations. The AIC was the lowest in the Mayo equation. Conclusions Preoperative decrease in eGFR was independently associated with increased risk of postoperative AKI in all five equations. The Mayo equation was most predictive of the development of postoperative AKI following TJA. The mayo equation best identified patients with the highest risk of postoperative AKI, which may help providers make decisions on perioperative management in these patients.

# Background Nurse burnout and distress pose patient safety risks due to impaired nurse attention, increased likelihood of medical error, and increased nurse turnover leading to a reduction in the number of nurses available to deliver care. Some healthcare facilities have launched well-being programs in response to increasing rates of burnout. Many of these programs are based on survey data which may be incomplete, resulting in programs that are not as comprehensive as they should be. We sought to identify nurse concerns related to burnout and well-being through analysis of social media data. We aligned these concerns with well-being program leader perceptions of factors contributing to burnout and well-being program initiatives. # Methods We conducted a qualitative study composed of two parts: social media analysis and semistructured interviews with well-being leaders. The social media analysis focused on 120 nurse comments on Reddit that were retrieved based on a keyword search using the terms "burnout," "stress," and "wellbeing." The interviews were conducted with nine well-being leaders from seven different healthcare systems. Well-being program leaders were asked about factors contributing to burnout and lack of well-being, initiatives to address these factors, and metrics used to evaluate their programs. The social media comments and interview data were reviewed by two experts to identify topics, themes, and subthemes grounded in wellness models. # Results Of the 120 social media comments analyzed, the most frequent topic was Lack of Meaningful Recognition, Compensation, and Influence (n=46 of 120, 38.3%), followed by Work Environment (n=43, 35.8%) and Uninformed or Misinformed Public (n=31, 25.8%). Several themes emerged and the most prevalent was Constrained Professional Agency with the most prevalent subtheme of health system or macrosystem policies or regulations that limit nurses' ability to respond effectively to patient care needs. Of the seven healthcare systems interviewed, the most common topics that emerged from asking about the factors contributing to the lack of nurse well-being were the Work Environment (n=6 of 7, 85.7%), followed by Lack of Meaningful Recognition, Compensation, and Influence (n=4, 57.1%), and Inadequate or Inaccessible Well-Being Resources (n=3, 42.9%). Several novel initiatives were identified, and most healthcare systems relied on surveys as their key metric. # Conclusions The social media analysis revealed nurse concerns that may not be identified as factors contributing to lack of well-being by well-being program leaders. There is an opportunity to optimize our understanding of nurse concerns around well-being through social media, and an opportunity to better align nurse concerns with the focus of well-being programs.

Summary:. Vascularized fibular epiphyseal transfer (VFET) offers a functional advantage in pediatric limb salvage due to the preservation of growth potential and an articular surface for remodeling. This review summarizes the available evidence on the clinical characteristics and outcomes of pediatric reconstruction applying VFET at different recipient sites and with varying techniques. VFET was used to reconstruct the proximal humerus, distal radius or ulna, proximal femur, distal fibula, calcaneus, and mandible. Although most often harvested on the anterior tibial artery, VFET has also been performed using the peroneal artery, the inferior lateral genicular artery, and a dual pedicle. Recipient site flap inset most often involved fixation with plates and/or screws as well as soft tissue reconstruction using a retained slip of biceps femoris tendon. Outcomes included limb growth, range of motion, and strength. The most common reported complications were bone flap fracture and peroneal nerve palsy. The anterior tibial artery was the most applied pedicle with reliable limb growth, but with the added risk of postoperative peroneal palsy. Bone flap fracture most often occurred at the proximal humerus and femur recipient sites. Plate fixation and the combined use of allograft had lower instances of bone flap fracture. This review highlights how the anticipated dynamic growth and remodeling this free flap offers in the long term must be weighed against its complexity and potential complications.

Fossil fuels are rapidly running out, and with the demand for environmentally friendly energy sources increasing, power grids are looking for distributed power generation-based renewable resources. The distribution of these energy sources is significantly linked to the development of smart microgrids, which are also extensively connected with the energy internet. This paper explores the energy internet operation, focusing on developing a routing algorithm for an energy router. The energy routing algorithm is further substantiated with the aid of simulations. This algorithm can find all the paths available for energy transmission between two nodes and selects the track with the most negligible losses as the path for transmission. All the possible routes are displayed along with the losses associated with each direction to ensure that the approach with the lowest losses is taken. The algorithm is also tested for 24 h at an hourly interval to observe the change in power transmitted on the system.

Background:. Intraoperative nerve blocks have shown promise in managing pain after nasal surgery. The purpose of this systematic review and meta-analysis was to analyze existing level I and II evidence on intraoperative nerve blocks in nasal surgery to optimize postoperative recovery. Methods:. The primary outcome of this systematic review and meta-analysis was postoperative pain scores; secondary outcomes included perioperative opioid requirements, patient satisfaction scores, and time to first analgesic requirement. PubMed, Embase, and MEDLINE databases were searched, and two independent reviewers conducted article screening. Methodological quality assessment of studies utilized the Jadad instrument, and interrater reliability was assessed using Cohen kappa. An inverse-variance, fixed-effects model was used for meta-analysis with Cohen d used to normalize effect size between studies. I2 and Q statistics were used to assess interstudy variability. Results:. Four studies were included for meta-analysis, totaling 265 randomized patients. The nerve blocks assessed included infraorbital nerve, sphenopalatine ganglion, external nasal nerve, central facial nerve blocks, and total nerve blocks. All demonstrated significantly reduced postoperative pain compared with controls, with a large effect size (P < 0.001). Opioid requirements were lower in the nerve block groups (P < 0.001), and patient satisfaction scores were higher (P < 0.001). Supplemental meta-analyses showed a longer time to first analgesic requirement for patients who received a nerve block (P < 0.001). Conclusions:. These findings support the efficacy of nerve blocks in providing postoperative pain relief and enhancing patient satisfaction with pain management. Perioperative nerve blocks, in combination with general anesthesia, should be considered for postoperative pain control.

This supplementary material has been provided by the authors to give readers additional information about their work.

# Background Despite their prevalence, poorly designed electronic health records (EHRs) are common, and research shows poor design consequences include clinician burnout, diagnostic error, and even patient harm. One of the major difficulties of EHR design is the visual display of information, which aims to present information in an easily digestible form for the user. High-risk industries like aviation, automotive, and nuclear have guidelines for visual displays based on human factors principles for optimized design. # Purpose In this study, we reviewed the visual display guidelines from three high-risk industries—automotive, aviation, nuclear—for their applicability to EHR design and safety. # Methods Human factors experts extracted guidelines related to visual displays from automotive, aviation, and nuclear human factors guideline documents. Human factors experts and a clinical expert excluded guidelines irrelevant to EHR. Human factors experts used a modified reflexive thematic analysis to group guidelines into meaningful topics. Disagreements were discussed until a consensus was reached. # Results A total of 449 guidelines were extracted from the industry documents, and 283 (63.0%) were deemed relevant to EHRs. By industry, 12 of 44 (27.3%) automotive industry guidelines were relevant, 43 of 115 (37.4%) aviation industry guidelines were relevant, and 228 of 290 (78.6%) nuclear industry guidelines were relevant. Guidelines were grouped into six categories: alphanumeric; color, brightness, contrast, and luminance; comprehension; design characteristics; symbols, pictograms, and icons; and tables, figures, charts, and lists. # Conclusion Our analysis identified visual display guidelines organized around six topics from the automotive, aviation, and nuclear industries to inform EHR design. Multiple stakeholders, including EHR vendors, healthcare facilities, and policymakers, can apply these guidelines to design new EHRs and optimize EHRs already in use.

# Background Medical equipment, supplies, and devices (ESD) serve a critical function in healthcare delivery and how they function can have patient safety consequences. ESD-related safety issues include malfunctions, physically missing ESDs, sterilization, and usability. Describing ESD-related safety issues from a human factors perspective that focuses on user interactions with ESDs can provide additional insights to address these issues. # Methods We manually reviewed ESD patient safety event reports submitted to the Pennsylvania Patient Safety Reporting System to identify ESD-related safety issues using a taxonomy that was informed by the Food and Drug Administration Manufacturer and User Facility Device Experience taxonomy. This taxonomy consisted of the following high-level categories: malfunctions, physically missing, sterilization, and usability. The type of ESD and associated components or ESD subtypes, event classification, and care area group were noted for each report. # Results Of the 450 reports reviewed, the most frequent ESD-related safety issue coded was malfunction (n=365 of 450, 81.1%) followed by sterilization (n=40 of 450, 8.9%), usability (n=36 of 450, 8.0%), and physically missing (n=9 of 450, 2.0%). Among the coded malfunctions, software/output problem (n=122 of 365, 33.4%) was the most frequent, followed by general malfunction (n=103 of 365, 28.2%); material integrity (n=72 of 365, 19.7%); and activation, positioning, or separation (n=68 of 365, 18.6%). The most frequent ESDs noted were infusion pump, instrument set, and intravenous, and the most frequent components/subtypes noted were alarm/alert, tubing, and tray. # Conclusions ESD-related patient safety issues, especially malfunctions, impact patient care despite current policies and practices to address these issues. Healthcare facilities may be able to address some ESD-related patient safety issues during procurement through use of the accompanying procurement assessment tool.

# Background Alarms are signals intended to capture and direct human attention to a potential issue that may require monitoring, assessment, or intervention and play a critical safety role in high-risk industries. Healthcare relies heavily on auditory and visual alarms. While there are some guidelines to inform alarm design and use, alarm fatigue and other alarm issues are challenges in the healthcare setting. Automotive, aviation, and nuclear industries have used the science of human factors to develop alarm design and use guidelines. These guidelines may provide important insights for advancing patient safety in healthcare. # Methods We identified documents containing alarm design and use guidelines from the automotive, aviation, and nuclear industries that have been endorsed by oversight agencies. These guidelines were reviewed by human factors and clinical experts to identify those most relevant to healthcare, qualitatively analyze the relevant guidelines to identify meaningful topics, synthesize the guidelines under each topic to identify key commonalities and differences, and describe how the guidelines might be considered by healthcare stakeholders to improve alarm design and use. # Results A total of 356 guidelines were extracted from industry documents (2012–present) and 327 (91.9%) were deemed relevant to healthcare. A qualitative analysis of relevant guidelines resulted in nine distinct topics: Alarm Reduction, Appropriateness, Context-Dependence, Design Characteristics, Mental Model, Prioritization, Specificity, Urgency, and User Control. There were several commonalities, as well as some differences, across industry guidelines. The guidelines under each topic were found to inform the auditory or visual modality, or both. Certain guidelines have clear considerations for healthcare stakeholders, especially technology developers and healthcare facilities. # Conclusion Numerous guidelines from other high-risk industries can inform alarm design and use in healthcare. Healthcare facilities can use the information presented as a framework for working with their technology developers to appropriately design and modify alarming technologies and can evaluate their clinical environments to see how alarming technologies might be improved.

Abstract The expanded availability of telehealth due to the COVID-19 pandemic presents a concern that telehealth may result in an unnecessary increase in utilization. We analyzed 4,114,651 primary care encounters (939,134 unique patients) from three healthcare systems between 2019 and 2021 and found little change in utilization as telehealth became widely available. Results suggest telehealth availability is not resulting in additional primary care visits and federal policies should support telehealth use.

Background: Duplicate medication orders are a prominent type of medication error that in some circumstances has increased after implementation of health information technology. Duplicate medication orders are commonly defined as two or more active orders for the same medication or medications within the same therapeutic class. While there have been several studies that have identified contributing factors and described potential solutions, duplicate medication order errors continue to impact patient safety. Methods: We analyzed 377 reports from 95 healthcare facilities to more granularly define the types of duplicate medication order errors and the context under which these errors occurred, as well as potential contributing factors. Results: Of the 377 reports reviewed, 304 (80.6%) met the criteria to be defined as a duplicate medication order error. The most frequent duplicate medication order error type was same order (n=131, 43.1%), followed by same therapeutic class (n=98, 32.2%) and same medication (n=70, 23.0%). Errors were identified during different medication process tasks and most commonly during medication reconciliation during the patient’s stay in the hospital (n=72, 23.7%) and during pharmacy verification (n=36, 11.8%). Factors contributing to these errors included health information technology issues (n=63, 20.7%), gaps in care coordination (n=44, 14.5%), and a prior dose or medication order not being discontinued (n=52, 17.1%). Conclusion: Our results highlight specific areas for practice improvement, and we make recommendations for how healthcare facilities can better address duplicate medication order errors.

Background:. Artificial intelligence (AI) leverages today’s exceptional computational powers and algorithmic abilities to learn from large data sets and solve complex problems. The aim of this study was to construct an AI model that can intelligently and reliably recognize the anatomy of cleft lip and nasal deformity and automate placement of nasolabial markings that can guide surgical design. Methods:. We adopted the high-resolution net architecture, a recent family of convolutional neural networks–based deep learning architecture specialized in computer-vision tasks to train an AI model, which can detect and place the 21 cleft anthropometric points on cleft lip photographs and videos. The model was tested by calculating the Euclidean distance between hand-marked anthropometric points placed by an expert cleft surgeon to ones generated by our cleft AI model. A normalized mean error (NME) was calculated for each point. Results:. All NME values were between 0.029 and 0.055. The largest NME was for cleft-side cphi. The smallest NME value was for cleft-side alare. These errors were well within standard AI benchmarks. Conclusions:. We successfully developed an AI algorithm that can identify the 21 surgically important anatomic landmarks of the unilateral cleft lip. This model can be used alone or integrated with surface projection to guide various cleft lip/nose repairs. Having demonstrated the feasibility of creating such a model on the complex three-dimensional surface of the lip and nose, it is easy to envision expanding the use of AI models to understand all of human surface anatomy—the full territory and playground of plastic surgeons.

Multi-modality imaging constitutes a foundation of precision medicine, especially in oncology where reliable and rapid imaging techniques are needed in order to insure adequate diagnosis and treatment. In cervical cancer, precision oncology requires the acquisition of 18F-labelled 2-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET), magnetic resonance (MR), and computed tomography (CT) images. Thereafter, images are co-registered to derive electron density attributes required for FDG-PET attenuation correction and radiation therapy planning. Nevertheless, this traditional approach is subject to MR-CT registration defects, expands treatment expenses, and increases the patient's radiation exposure. To overcome these disadvantages, we propose a new framework for cross-modality image synthesis which we apply on MR-CT image translation for cervical cancer diagnosis and treatment. The framework is based on a conditional generative adversarial network (cGAN) and illustrates a novel tactic that addresses, simplistically but efficiently, the paradigm of vanishing gradient vs. feature extraction in deep learning. Its contributions are summarized as follows: 1) The approach-termed sU-cGAN- uses, for the first time, a shallow U-Net (sU-Net) with an encoder/decoder depth of 2 as generator; 2) sU-cGAN's input is the same MR sequence that is used for radiological diagnosis, i.e. T2-weighted, Turbo Spin Echo Single Shot (TSE-SSH) MR images; 3) Despite limited training data and a single input channel approach, sU-cGAN outperforms other state of the art deep learning methods and enables accurate synthetic CT (sCT) generation. In conclusion, the suggested framework should be studied further in the clinical settings. Moreover, the sU-Net model is worth exploring in other computer vision tasks.

The coronavirus disease 2019 (COVID-19) pandemic has prompted rapid restructuring of the health-care system in an effort to stop the spread of the virus and to treat patients who are acutely ill with COVID-19, while continuing to provide outpatient care for the remainder of patients. To help control spread of this pandemic, many centers, including total joint arthroplasty clinics, have boosted telemedicine capability to care for patients who would typically be seen in person in outpatient settings. We review key components relevant to the establishment and effective use of telemedicine, focused on patient education, practice logistics, technological considerations, and sensitive patient health information–associated compliance factors, which are necessary to provide care remotely for total joint arthroplasty patients.

Background: Dose calculation errors are one of the most common types of medication errors impacting children and they can result in significant harm. Technology-based solutions, such as computerized provider order entry, can effectively reduce dose calculation issues; however, these technologies are not always optimized, resulting in potential benefits not being fully realized. Methods: We analyzed pediatric dose-related patient safety event reports submitted to the Pennsylvania Patient Safety Reporting System using a task-analytic approach that focused on information being used in the dose calculation, calculation errors during ordering, and errors during dose preparation or administration. From these reports, we identified whether the patient was impacted by the error, the type of medication involved, and whether a technology optimization could have mitigated the issue. Results: Of the 356 reports reviewed, 326 (91.6%) met the criteria for a dose calculation issue. The 326 reports meeting criteria had the following dose calculation issue types: wrong information used in the calculation (49 of 326, 15.0%), incorrect calculation during ordering (97 of 326, 29.8%), and calculated dose was not properly used or incorrect calculation during preparation/administration (180 of 326, 55.2%). Most of these dose calculation issues impacted the patient (219 of 326, 67.2%). Analysis of these issues by patient age group and drug class also revealed interesting patterns. Technology optimizations potentially could have addressed 81.6% of the dose calculation issues identified. Conclusion: While many healthcare facilities have adopted health information technology and other devices to support the medication process, these technologies are not always optimized to address dose calculation issues. Human factors–informed recommendations, a safety checklist, and test cases for optimizing technology are provided in the context of these findings.

Background:. Although polysomnography is paramount when evaluating neonatal airway obstruction, “normal” published references do not exist. We present normative polysomnography data for newborns age 0–1 month. We compare this reference to pre and postoperative sleep data from infants undergoing mandibular distraction osteogenesis (MDO) at this same age. Methods:. Following IRB approval, normative subjects were recruited from our neonatal intensive care unit to undergo nap polysomnography. One blinded sleep physician read all studies. From 2016 to 2019, we prospectively collected sleep data for newborns undergoing MDO. Results:. In total, 22 neonates without airway obstruction provided normative sleep data. Median total apnea-hypopnea index (AHI), obstructive apnea-hypopnea index (OAHI), and central apnea index (CAI) were 7.3, 4.9, and 0.7 events/hour. Median O2 nadir was 91%. Polysomnography for 13 neonates before MDO and during consolidation showed median preoperative AHI was 38.3, OAHI was 37.0, CAI was 1.9, and median O2 nadir was 83%. Following MDO, median AHI was 6.1, OAHI was 4.0, CAI was 1.3, and median O2 nadir was 92.5%. Paired t-tests confirmed significant improvements in all indices; when comparing the postoperative group with the normative group, there was no difference in oxygenation nor any respiratory index. Conclusions:. “Normal” neonates have more obstructive events and lower oxygenation nadirs than previously appreciated. We provide normative nap polysomnography values for this age group and encourage centers with multidisciplinary MDO teams to utilize this data to calibrate patient selection algorithms, inform treatment discussions, and better understand surgical outcomes. Limitations include a small sample size and single institution study.

Businesses can use price optimization to discover the most profitable price point by using customer and market data to drive their decisions. The optimal price points will result in the company making the most money possible, but they may also be created to help the company expand into untapped markets or increase its market share, for example Businesses can use machine learning to price products and services to maximise sales or profitability by using data instead of educated guess-work. When utilised for price optimization, ML-based algorithms can be used to forecast demand for a particular product as well as the ideal price and how buyers will respond to specific pricing. Pricing decisions can be made more accurately using machine learning, which will boost a company's revenue.