Technology adoption of electronic medical records in developing economies: A systematic review on physicians' perspective.

Electronic Medical Records (EMRs) are a tool that could potentially improve the outcomes of patient care by providing physicians with access to up-to-date and accurate vital patient information. Despite this potential, EMR adoption in developing economies has been dilatory. This systematic review aims to synthesize the related literature on the adoption of EMRs in developing economies, with a focus on the perspective of physicians. With the aim to discern the key factors that impact EMR adoption as perceived by physicians and to offer guidance for future research on filling any gaps identified in the existing literature, this study utilized a systematic literature review by following the PRISMA guidelines. Out of 1160 initial articles, 21 were selected for analysis after eliminating duplicates and non-qualifying articles. Results show that common enablers of EMR adoption from physicians' perspective were identified to be computer literacy, education, voluntariness, and the system functionality including its features and user interface, implying that the provision of proper interventions focusing on the aspects of the health information system has an impact in maximizing the utilization and capabilities of EMRs among healthcare providers. The most prevalent barriers include the lack of training and IT usage experience along with resistance to changes associated with respondents' age and gender, the lack of time for learning complex EMR systems, and costs of the new technology. This indicates that a thorough planning and proper budget allocation is necessary prior to implementing and integrating EMR systems in healthcare institutions. From this synthesis of the common research conclusions, limitations, and recommendations from physicians' perspective, the result of this systematic review is expected to shed light on the optimal technology adoption of EMRs and its contribution to the health care systems of developing economies.

Evaluation of polyethylene terephthalate glycol (PETG), Simubone™, and photopolymer resin as 3D printed temporal bone models for surgical simulation.

OBJECTIVES: Among types of 3D printing, fused deposition modeling (FDM) and digital light processing (DLP) are the most accessible, making them attractive, low-cost options for simulating surgical procedures. This study characterized and compared inexpensive, synthetic temporal bone models printed using Resin, PETG, and Simubone™. MATERIALS AND METHODS: This study compared models made of polyethylene terephthalate glycol (PETG), Simubone™ produced from a FDM printer, and photopolymer resin from a DLP printer. These temporal bone models were processed by: (1) DICOM files from a patient's CT scan were segmented to define critical parts expected in a temporal bone surgery. (2) The model was appended with a base that articulates with a 3D-printed temporal bone holder. (3) The refined, patient-specific model was manufactured using FDM and DLP printing technologies. (4) The models were sent to evaluators, who assessed the models based on anatomic accuracy, dissection experience, and its applicability as a surgical simulation tool for temporal bone dissection. RESULTS: The photopolymer resin outperformed PETG and Simubone™ in terms of anatomical accuracy and dissection experience. Additionally, resin and PETG were evaluated to be appropriate for simple mastoidectomy and canal wall down mastoidectomy while Simubone™ was only suitable for simple mastoidectomy. All models were unsuitable for posterior tympanotomy and labyrinthectomy. CONCLUSIONS: Photopolymer resin and PETG have shown to be suitable materials for dissection models with 3D-printed resin models showing more accuracy in replicating anatomical structures and dissection experience. Hence, the use of 3D-printed temporal bones may be a suitable low-cost alternative to cadaveric dissection.

Virtual surgical planning in craniomaxillofacial surgery: a structured review.

Craniomaxillofacial (CMF) surgery is a challenging and very demanding field that involves the treatment of congenital and acquired conditions of the face and head. Due to the complexity of the head and facial region, various tools and techniques were developed and utilized to aid surgical procedures and optimize results. Virtual Surgical Planning (VSP) has revolutionized the way craniomaxillofacial surgeries are planned and executed. It uses 3D imaging computer software to visualize and simulate a surgical procedure. Numerous studies were published on the usage of VSP in craniomaxillofacial surgery. However, the researchers found inconsistency in the previous literature which prompted the development of this review. This paper aims to provide a comprehensive review of the findings of the studies by conducting an integrated approach to synthesize the literature related to the use of VSP in craniomaxillofacial surgery. Twenty-nine related articles were selected as a sample and synthesized thoroughly. These papers were grouped assigning to the four subdisciplines of craniomaxillofacial surgery: orthognathic surgery, reconstructive surgery, trauma surgery and implant surgery. The following variables - treatment time, the accuracy of VSP, clinical outcome, cost, and cost-effectiveness - were also examined. Results revealed that VSP offers advantages in craniomaxillofacial surgery over the traditional method in terms of duration, predictability and clinical outcomes. However, the cost aspect was not discussed in most papers. This structured literature review will thus provide current findings and trends and recommendations for future research on the usage of VSP in craniomaxillofacial surgery.

Days-ahead water level forecasting using artificial neural networks for watersheds.

Watersheds of tropical countries having only dry and wet seasons exhibit contrasting water level behaviour compared to countries having four seasons. With the changing climate, the ability to forecast the water level in watersheds enables decision-makers to come up with sound resource management interventions. This study presents a strategy for days-ahead water level forecasting models using an Artificial Neural Network (ANN) for watersheds by conducting data preparation of water level data captured from a Water Level Monitoring Station (WLMS) and two Automatic Rain Gauge (ARG) sensors divided into the two major seasons in the Philippines being implemented into multiple ANN models with different combinations of training algorithms, activation functions, and a number of hidden neurons. The implemented ANN model for the rainy season which is RPROP-Leaky ReLU produced a MAPE and RMSE of 6.731 and 0.00918, respectively, while the implemented ANN model for the dry season which is SCG-Leaky ReLU produced a MAPE and RMSE of 7.871 and 0.01045, respectively. By conducting appropriate water level data correction, data transformation, and ANN model implementation, the results of error computation and assessment shows the promising performance of ANN in days-ahead water level forecasting of watersheds among tropical countries.