The impact of automating laboratory request forms on the quality of healthcare services

SummaryIn recent decades, healthcare organizations have undergone a significant transformation with the integration of Information and Communication Technologies within healthcare operations to improve healthcare services. Various technologies such as Hospital Information Systems (HIS), Electronic Health Records (EHR) and Laboratory Information Systems (LIS) have been incorporated into healthcare services. The aim of this study is to evaluate the completeness of outpatients' laboratory paper based request forms in comparison with a electronic laboratory request system. This study was carried out in the laboratory department at King Abdulaziz Medical City (KAMC), National Guard Health Affairs, Riyadh, Saudi Arabia. We used a sample size calculator for comparing two proportions. We estimated the sample size to be 228 for each group. Any laboratory requests including paper and electronic forms were included. We categorized the clarity of the forms into understandable, readable, and unclear. A total of 57 incomplete paper forms or 25% were identified as being incomplete. For electronic forms, there were no incomplete fields, as all fields were mandatory, therefore, rendering them complete. The total of understandable paper-based laboratory forms was 11.4%. Additionally, it was found that the total of readable was 33.8% and the total for unclear was 54.8%, while for electronic-based forms, there were no unclear forms. Electronic based laboratory forms provide a more complete, accurate, clear, and understandable format than paper-based laboratory records. Based on these findings, KAMC should move toward the implementation of electronic-based laboratory request forms for the outpatient laboratory department

Taking Note: A Design Solution for Physician Documentation to Balance the Benefits of Handwritten Notes and Electronic Health Records

Master of Design in Integrative DesignUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/136865/1/THo_2017_MDes-Thesis.pd

Assessing context-based learning: Not only rigorous but also relevant

Economic factors are driving significant change in higher education. There is increasing responsiveness to market demand for vocational courses and a growing appreciation of the importance of procedural (tacit) knowledge to service the needs of the Knowledge Economy; the skills in demand are information analysis, collaborative working and 'just-in-time learning'. New pedagogical methods go some way to accommodate these skills, situating learning in context and employing information and communications technology to present realistic simulations and facilitate collaborative exchange. However, what have so far proved resistant to change are the practices of assessment. This paper endorses the case for a scholarship of assessment and proposes the development of technology-supported tools and techniques to assess context-based learning. It also recommends a fundamental rethink of the norm-referenced and summative assessment of propositional knowledge as the principal criterion for student success in universities

Combining Unsupervised, Supervised, and Rule-based Algorithms for Text Mining of Electronic Health Records - A Clinical Decision Support System for Identifying and Classifying Allergies of Concern for Anesthesia During Surgery

Undisclosed allergic reactions of patients are a major risk when undertaking surgeries in hospitals. We present our early experience and preliminary findings for a Clinical Decision Support System (CDSS) being developed in a Norwegian Hospital Trust. The system incorporates unsupervised and supervised machine learning algorithms in combination with rule-based algorithms to identify and classify allergies of concern for anesthesia during surgery. Our approach is novel in that it utilizes unsupervised machine learning to analyze large corpora of narratives to automatically build a clinical language model containing words and phrases of which meanings and relative meanings are also learnt. It further implements a semi-automatic annotation scheme for efficient and interactive machine-learning, which to a large extent eliminates the substantial manual annotation (of clinical narratives) effort necessary for the training of supervised algorithms. Validation of system performance was performed through comparing allergies identified by the CDSS with a manual reference standard

Fat-Fast VG-RAM WNN: A high performance approach

The Virtual Generalizing Random Access Memory Weightless Neural Network (VGRAM WNN) is a type of WNN that only requires storage capacity proportional to the training set. As such, it is an effective machine learning technique that offers simple implementation and fast training – it can be made in one shot. However, the VG-RAM WNN test time for applications that require many training samples can be large, since it increases with the size of the memory of each neuron. In this paper, we present Fat-Fast VG-RAM WNNs. Fat-Fast VG-RAM WNNs employ multi-index chained hashing for fast neuron memory search. Our chained hashing technique increases the VG-RAM memory consumption (fat) but reduces test time substantially (fast), while keeping most of its machine learning performance. To address the memory consumption problem, we employ a data clustering technique to reduce the overall size of the neurons’ memory. This can be achieved by replacing clusters of neurons’ memory by their respective centroid values. With our approach, we were able to reduce VG-RAM WNN test time and memory footprint, while maintaining a high and acceptable machine learning performance. We performed experiments with the Fat-Fast VG-RAM WNN applied to two recognition problems: (i) handwritten digit recognition, and (ii) traffic sign recognition. Our experimental results showed that, in both recognition problems, our new VG-RAM WNN approach was able to run three orders of magnitude faster and consume two orders of magnitude less memory than standard VG-RAM, while experiencing only a small reduction in recognition performance