Data Trending Research Articles

Reducing Inpatient Psychiatric Unit Denials: A Case Management Quality Improvement Project.

The purpose of the inpatient psychiatric unit denials reduction project was to establish a taskforce to analyze denials data for root cause and identify an effective solution to prevent denials through improved provider documentation. The development and implementation of the project occurred at Stanford Health Care, a nonprofit Level 1 trauma hospital, with a 29-bed inpatient psychiatric unit containing both open and secured levels of care. The project was led by the Utilization/Denials Management team, which is subteam within the Case Management Department. Team members are Nurse Case Managers specializing in the management of denials and appeals, data analysis and trending of denials, denial avoidance, and reduction of financial risk. Denials data analysis revealed the Psychiatry Service Line was responsible for over 70% of payer denials within the Length of Stay Write-offs category. In early 2019, the Utilization/Denials Management team initiated a quality improvement project using A3 Lean Approach methodology and evidence-based practice to guide the implementation. The partnership and collaboration included Case Management, Revenue Cycle, Clinical Documentation Improvement, Technology and Digital Solutions, and Psychiatry Service Line leadership that had an interest in reducing payer denials. Outcomes measured for the project were based on denials data for (fiscal year) FY19, the preintervention period, compared to FY20 and FY21, the postintervention period. From FY19 to FY20, revenue loss decreased by 42%. The downward trend continued from FY20 to FY21 with a recorded decrease by 69%. The most important implication of the project was improved physician documentation and medical necessity supporting the severity and intensity of inpatient psychiatric level of care, and medical justification for the inpatient stay not only to prevent medical necessity denials but to accurately reflect the care that was provided. High-quality documentation correlates to the high quality of care being provided. This project is far reaching not only for psychiatric units but also reproducible for other acute hospital service lines. Case Management is a multidisciplinary role, and this work is most meaningful for a Nurse Case Manager engaged in care coordination, including utilization and denials management.

Development of artificial intelligence indicator for elementary students

This research aimed to develop the Artificial Intelligence (AI) indicator for elementary students by synthesizing the components of artificial intelligence learning for elementary students which is designed to be aware of the issue of mental health of children as well. The results were assessed for appropriateness by five experts. Findings demonstrated that the indicator consisted of five aspects ordering by the complex from Grade 1 to 5. Firstly, it was Perception which the student could explain the origin of interaction between the AI and human by designing a reception system to conduct perception and response,and draw the response diagram. Secondly, it was Representation & Reasoning which the student could explain the reasoning algorithm of the AI. Thirdly, it was Machine Learning which the student could explain the machine learning, criticize the input data defect. Fourthly, the Natural interaction, which the student could justify the purpose of data trending and improvement of AI to be more natural by creating a realistic response system. Lastly, it was the Societal Impact which the student could suggest the solution to AI system problems to be more accurate and appropriate with the fewest impact on humans and facilitate human living efficiently.

Issues in sustainable supply chain's futuristic technologies: a bibliometric and research trend analysis.

This paper presents a systematic review and bibliometric analysis in sustainable supply chain’s futuristic technologies. The analysis involves 1596 articles published in the Scopus database from 1990 to 2020. The analysis examines the research outcomes by observing trends in journals, authorship, and keywords. The outcomes are visualized using VOSviewer to show the graphical network of co-authorship and the author’s keywords. The results show that this research area has been a growing trend since 2016 and by 2020. The Journal of Cleaner Production was a leading journal in this area between 2016 and 2020, followed by Sustainability (Switzerland). The content analysis led to a classification of the articles into four main categories and sub-categories. The strategic diagram is used to reveal the emerging research themes analysis during the last 5 years (2016–2020) and to present future research. The data trending or emerging shows that technologies such as the technology by using combustion energy, renewable energy, and electric vehicles have been developing substantially. The research hotspots of the sustainable supply chain include, i.e., life cycle assessment (LCA), green investment, and carbon tax. Finally, the analysis also shows the research gaps that point a direction for future research.Graphical abstract

The Incorporation of Human Performance Improvement into Systems Design

Abstract Although considerable research has been conducted on the human-machine interface, this is a moving target as industry sprints to keep up with technological advances. Conflicts remain between the optimism of technology developers and the real-life operational difficulties that accompany the introduction of these systems. The developers typically claim that the new technology will result in performance improvements. Due to the operational complexities introduced, however, the technology may actually decrease user performance. Unfortunately, the complexities confronting operators are difficult for design teams to predict. Incorporating advances in technology is necessary, but should be properly balanced within the confines of the system. It is easy to forget that humans are a vital part of this system. The human, including the human's inclination for error, should be considered a fundamental aspect of the system, reflected in design and accounted for in the design process. Engaged human involvement is necessary for safe and successful system operation, but like all systems, it has its failure modes. Humans' innate propensity for error in system operation should be addressed from multiple fronts. This article proposes a method to minimize the impact of human error throughout life of a facility via incorporation of a human performance improvement model that institutes human error severity criteria, establishment of a system to capture human error data, and via data trending, a process to predict negative behaviors before potential errors or adverse events can occur.

Efficacy of Nilotinib in Patients With Moderately Advanced Parkinson Disease

There is a critical need for careful and independent validation of reported symptomatic efficacy and dopaminergic biomarker changes induced by nilotinib in Parkinson disease (PD). To assess safety and tolerability of nilotinib in participants with moderately advanced PD. Secondary and exploratory objectives were to assess its affect on PD disability, pharmacokinetics, cerebrospinal fluid (CSF) penetration, and biomarkers. This was a 6-month, multicenter, randomized parallel-group, double-blind, placebo-controlled trial. Recruitment was from November 20, 2017, to December 28, 2018, and follow-up ended on September 9, 2019. The study was conducted at 25 US sites. The study approached 173 patients, of whom 48 declined, 125 were screened, and 76 who received a stable regimen of PD medications were enrolled (39% screen failure). Participants were randomized 1:1:1 to placebo, 150-mg nilotinib, or 300-mg nilotinib once daily orally for 6 months, followed by 2-month off-drug evaluation. The primary outcomes were safety and tolerability. The tolerability end point was defined as the ability to complete the study while receiving the assigned dose. An active arm was considered tolerable if the percentage of participants meeting the tolerability end point for that group was not significantly lower than the percentage observed in the placebo group. Secondary outcomes included change in PD disability (Movement Disorder Society Unified Parkinson's Disease Rating Scale [MDS-UPDRS], Part II OFF/ON). Exploratory outcomes included serum and CSF pharmacokinetic profile, and CSF dopaminergic biomarkers. At baseline, mean (SD) participants' age was 64.6 (7.5) years, 52 were male (68%), mean (SD) disease duration was 9.9 years (4.7), MDS-UPDRS Part 1-3 OFF score was 66.4 (19.3), ON score was 48.4 (16.2), and Montreal Cognitive Assessment score was 27.1 (2.2). The number of participants who completed the study receiving the assigned dose were 21 (84%), 19 (76%), and 20 (77%) in the placebo, 150-mg, and 300-mg arms, respectively. Both active doses had acceptable safety profile. The most common reasons for drug suspension were asymptomatic, dose-dependent elevations of amylase, and/or lipase. Nilotinib, 150 mg and 300 mg, exhibited worse MDS-UPDRS-3 ON scores compared with placebo, achieving significance for nilotinib, 300 mg, at month 1 (P < .01). There was no difference in the change of MDS-UPDRS-3 OFF from baseline to 6 months between groups (P = .17). Cerebrospinal fluid/serum ratio of nilotinib concentration was 0.2% to 0.3%. There was no evidence of treatment-related alteration of dopamine metabolites in the CSF. While we demonstrated acceptable safety and tolerability of nilotinib in our cohort, the low CSF exposure and lack of biomarkers effect combined with the efficacy data trending in the negative direction indicate that nilotinib should not be further tested in PD. ClinicalTrials.gov Identifier: NCT03205488.

Real-Time Diagnostic Method of Gas Turbines Operating Under Transient Conditions in Hybrid Power Plants

Abstract The expansion of renewable power plants has transformed the role and operation of gas turbines to a great extent. From the base load operation era, we are moving into a flexible and dynamic engine operation of gas turbines. Advances in computational intelligence have amplified the importance of condition monitoring, diagnostics, and prognostics capabilities in the face of gas turbine operation. Performing diagnostics in transient conditions is beneficial since the gas turbines are now acting as partners of renewables. This article presents a novel diagnostic approach for determining the health of a gas turbine when it works in conjunction with a wind farm in a hybrid power plant. In this article, we propose a model-based diagnostic method without reconstructing component maps according to their degradation. Once the engine model is adapted to its clean condition, it is tuned in real-time to reflect the changes in both the operation and degradation with respect to a benchmark engine model. Time evolving multiple component degradation scenarios are simulated to test the accuracy and efficiency of the proposed method. From a bank of simulated measurements, data trending is performed which facilitates the detection of degradation and provides useful conclusions about the health state of the engine. This diagnostic method is suitable for gas turbines that spend most of their life time in part-load and transient operation and it can be a useful tool for gas turbine operators in planning their assets maintenance in a computational efficient and accurate manner.

A vision of facility siting possibilities

AbstractFacility siting methods to optimize the layout of industrial facilities for risk reduction have been evolving for decades from subjective views, standards, and guidelines to quantitative numerical analysis. The authors of this paper have tossed out the past, moved beyond the present, and taken out their crystal balls to provide a discussion around the future of facility siting by focusing on technology driven enhancements associated with three main themes: mainstreaming of current advanced analysis techniques into the base case methodology, incorporating company and/or site specific data trending and analytics to operationalize the studies, and the potential transformational change to machine learning‐based predictive risk management. With technological advancements touching nearly every area of business, it is no surprise that it is also changing the landscape of consequence and risk‐based facility siting approaches. As with all markets, the customer will be a key driver for the advancements of technical safety studies to suit their adapting needs. However, as this article will show, personnel conducting facility siting studies are also using technological advancements to challenge the status quo by improving data fidelity, increasing the robustness and depth of analysis, and providing improved insights to aid decision making.

Improved X-Ray Safety, Quality Control, and Resource Management in Medical Imaging Using QATrack+

Introduction/BackgroundManagement of a quality assurance program in diagnostic imaging involves a variety of machine types, multiple vendors, and a large number of frontline staff who have different specializations. Standardizing tests across multiple platforms in the face of vendor recommendations, regulatory requirements, and professional practice protocols can present challenges to maintain a robust and coherent quality assurance program. The current work presents a unique application of an existing tool that can be used to manage a comprehensive quality assurance program in a diagnostic imaging department. Materials and MethodsQATrack+ is an open source, quality assurance platform originally developed for and currently widely used in radiation therapy departments. The use of QATrack+ for quality assurance program management within a large diagnostic imaging department is a novel use of this tool. Results and DiscussionQATrack+ was successfully implemented in a large, multisite diagnostic imaging department. The progression toward a single platform for the quality assurance program has addressed issues of end of life with previous software packages and has improved the standardization of testing across the institution. The configuration of the software has enabled frontline staff to be directly engaged in the quality control (QC) program, improving the efficiency of resource allocation for QC and promoting a strong safety culture and commitment to quality. Trending tools within QATrack+ allow for simplified review of tests and enable the early identification of potential failures. ConclusionOriginally developed for radiation therapy programs, QATrack+ is well suited to applications within diagnostic imaging. It is versatile and is easily adapted to the individual needs of a department for activities ranging from quality control testing, scheduling, test review, and data trending. It simplifies the standardization of quality control practices across platforms, thereby facilitating training and promoting involvement in the quality assurance program by all staff.

Abstract TP487: Data Dashboard: Making the Electronic Medical Record Work for You

Background: Stroke care is becoming increasingly complex in the 21st century. In order to assure optimal patient outcomes, our health network developed a stroke care pathway based on evidence-based practices and expert consensus. Overall goals for the project include increased adherence to practice standards and defined process metrics. This project is part of a larger institutional initiative for specific disease states. An integral aspect of the project was the development and implementation of a dashboard allowing for a global view of the stroke population as well as the ability to drill down to the patient or provider level. Purpose: The purpose of the dashboard is to use data to evaluate trends and develop data driven interventions. This tool is available to key stakeholders including administration, quality, program leadership and care providers. Methods: The pathway initiative is a collaboration between quality, clinical, and data analytic teams. It used a multidisciplinary approach and included input from services throughout the care continuum. Clinical pathways for stroke alert, reperfusion, neurointerventional care, and nursing care were developed and recently launched. The dashboard auto-populates from discreet fields within the electronic medical record. It is an informational tool that allows for data transparency through the use of interactive charts and graphs which provides temporal trending. Results: Utilization of this approach supports system level data trending and analysis. Preliminary results demonstrate improved provider engagement, reduction in variability in several care processes, and data transparency through accessible real-time reporting. Conclusions: Disease specific dashboards using automatic data feeds from the electronic medical record allow for evaluation of program efficiencies, population trends, and provider level data without additional manual abstraction or analysis. Evaluating data in real time allows for identification of variations in practice, real-time interventions targeting identified variances, as well as determining the effectiveness of interventions on quality and cost of care.

Leading the Charge: Achievement of National Accreditation for a Nurse Residency Program.

This article describes best practice recommendations in program development for a nurse residency program. This registered nurse residency program is the first in New England to acquire the American Nurses Credentialing Center's Practice Transition Accreditation. Best practices identified in this effort include (a) use of a quality improvement analyst for data trending, (b) inclusion of an off-shift nurse educator, (c) use of evidence-based practice, (d) a standardized preceptor program, and (e) appropriate evaluation instruments. New graduate nurse satisfaction increased and turnover decreased after program implementation.

Automated retinal imaging and trend analysis &amp;ndash; a tool for health monitoring

Most current diagnostic devices are expensive, require trained specialists to operate and gather static images with sparse data points. This leads to preventable diseases going undetected until late stage, resulting in greatly narrowed treatment options. This is especially true for retinal imaging. Future solutions are low cost, portable, self-administered by the patient, and capable of providing multiple data points, population analysis, and trending. This enables preventative interventions through mass accessibility, constant monitoring, and predictive modeling.

85. EEG automated analysis compared to direct vision in ION setting: An overall impression

The automated analysis is an alternate representation of the raw EEG traces to deliver a more compacted form of information to clinicians, in order to quickly understand a large amount of data for the diagnostic purposes. Also ION instruments have the possibility to perform similar automatic analysis to provide visual or audible warning messages during surgical procedures. Anyway clinician’s primary concern is whether false alarms or true positives might create a useful and reliable tool for detection and diagnosis. We compared automatic EEG analysis to expert physician visual direct examination in some different condition (carotid endo-arterectomy; MAV surgery; exclusion of intracranial aneurism; carotid-media artery by-pass). The automated analysis was performed through an algorithm based on Fourier spectral transform. Compared to early and accurate expert physician surveillance, this framework showed a good but delayed sensitivity in detecting significant neural activity modifications. Our overall impression is that direct visual EEG evaluation still represents the most consistent monitoring system. Although creating an algorithm that produces correct results in all instances is nearly impossible, the EEG automated analysis pitfall is yet a lack of a prompt processing of data trending, so as to fit the aim of ION procedure about timely warning messages.

Série histórica secular de brilho solar e suas relações com os modos de variabilidade ENOS e ODP em Santa Maria-RS

Os objetivos neste trabalho foram testar a homogeneidade, recalcular a média climatológica e determinar possíveis tendências e variabilidade interanual e interdecadal na série histórica secular mensal e sazonal de brilho solar, em Santa Maria - RS. Os dados de brilho solar foram obtidos na Estação Climatológica Principal (ECP) do Instituto Nacional de Meteorologia (8o DISME), no período de 1912 a 2011. Foram usados os testes de Sequência, de Mann-Kendall e Pettitt para análise de homogeneidade e tendência, respectivamente. As séries históricas na escala mensal e sazonal são estacionárias e homogêneas, exceto para o mês de dezembro e para a estação de outono. Há variabilidade interanual e interdecadal nas séries, devido aos fenômenos El Niño-Oscilação Sul (ENOS) e da Oscilação Decadal do Pacífico (ODP), com sinal marcante da fase fria de ambos os fenômenos, que aumentam o brilho solar, principalmente nos meses de outubro e novembro (primavera).

SU-E-T-103: Development and Implementation of Web Based Quality Control Software

Purpose: Historically many radiation medicine programs have maintained their Quality Control (QC) test results in paper records or Microsoft Excel worksheets. Both these approaches represent significant logistical challenges, and are not predisposed to data review and approval. It has been our group's aim to develop and implement web based software designed not just to record and store QC data in a centralized database, but to provide scheduling and data review tools to help manage a radiation therapy clinics Equipment Quality control program. Methods: The software was written in the Python programming language using the Django web framework. In order to promote collaboration and validation from other centres the code was made open source and is freely available to the public via an online source code repository. The code was written to provide a common user interface for data entry, formalize the review and approval process, and offer automated data trending and process control analysis of test results. Results: As of February 2014, our installation of QAtrack+ has 180 tests defined in its database and has collected ∼22 000 test results, all of which have been reviewed and approved by a physicist via QATrack+'s review tools. These results include records for quality control of Elekta accelerators, CT simulators, our brachytherapy programme, TomoTherapy and Cyberknife units. Currently at least 5 other centres are known to be running QAtrack+ clinically, forming the start of an international user community. Conclusion: QAtrack+ has proven to be an effective tool for collecting radiation therapy QC data, allowing for rapid review and trending of data for a wide variety of treatment units. As free and open source software, all source code, documentation and a bug tracker are available to the public at https://bitbucket.org/tohccmedphys/qatrackplus/.

Enterprise Scheduling for Large‐Scale Integrators (LSIs)

AbstractLarge scale integrators (LSIs) are concerned with the assembly of multiple systems into a “system of systems.” Architecting an Enterprise schedule solution that supports LSI systems engineers requires examining tool commonality with the individual systems, total solution cost, schedule visualization, and cross‐schedule integration techniques. Examined are concepts for Enterprise schedule management unique to this level for configuration management, adherence to systems engineering control gate policy, and data trending.

All Systems Go: How Systems Engineering Can Improve Healthcare Technology

All Systems Go: How Systems Engineering Can Improve Healthcare Technology

G-RCA: A Generic Root Cause Analysis Platform for Service Quality Management in Large IP Networks

An increasingly diverse set of applications, such as Internet games, streaming videos, e-commerce, online banking, and even mission-critical emergency call services, all relies on IP networks. In such an environment, best-effort service is no longer acceptable. This requires a transformation in network management from detecting and replacing individual faulty network elements to managing the end-to-end service quality as a whole. In this paper, we describe the design and development of a Generic Root Cause Analysis platform (G-RCA) for service quality management (SQM) in large IP networks. G-RCA contains a comprehensive service dependency model that incorporates topological and cross-layer relationships, protocol interactions, and control plane dependencies. G-RCA abstracts the root cause analysis process into signature identification for symptom and diagnostic events, temporal and spatial event correlation, and reasoning and inference logic. G-RCA provides a flexible rule specification language that allows operators to quickly customize G-RCA and provide different root cause analysis tools as new problems need to be investigated. G-RCA is also integrated with data trending, manual data exploration, and statistical correlation mining capabilities. G-RCA has proven to be a highly effective SQM platform in several different applications, and we present results regarding BGP flaps, PIM flaps in Multicast VPN service, and end-to-end throughput degradation in content delivery network (CDN) service.

Tackling Today's Data Center Energy Efficiency Challenges—A Software-oriented Approach

Energy consumption is a growing concern for data centers. Advances in server equipment technologies and increased demand for computing power have increased load densities in the computer room, which in turn has caused corresponding increases in data center power consumption. Energy efficiency measures are thus of high importance for data center designers, operators, and owners. This article outlines a software-based approach to the data gathering, trending, and analysis that are necessary to apply successful energy efficiency measures in data center environments.

International Space Station power reinitialization

The photovoltaic (PV) module on the International Space Station (ISS) has been operating since November 2000 and supporting electric power demands of the ISS and its crew of three. The PV module contains photovoltaic arrays that convert solar energy to electrical power, and an integrated equipment assembly (IEA) that houses electrical hardware and batteries for electric power regulation and storage. Each PV module contains two independent power channels for fault tolerance. Each power channel contains three batteries in parallel to meet its performance requirements and for fault tolerance. Each battery consists of 76 Nickel-Hydrogen (Ni-H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2_{) cells in series. These 76 cells are contained in two orbital replaceable units (ORU) connected in series. On-orbit data are monitored and trended to ensure that all hardware is operating normally. Review of on-orbit data showed that while five batteries are operating very well, one is showing signs of mismatched ORUs. The cell pressure in the two ORUs differs by an amount that exceeds the recommended range. The reason for this abnormal behavior may be that the two ORUs have a different use history. An assessment was performed and it was determined that capacity of this battery would be limited by the lower pressure ORU. Steps are being taken to reduce this pressure differential before battery capacity drops to the point of affecting its ability to meet performance requirements. As a first step, a battery reinitialization procedure was developed to reduce this pressure differential. The procedure was successfully carried out on-orbit and the pressure differential was reduced to the recommended range. This paper describes the battery performance and the consequences of mismatched ORUs that make a battery. The paper also describes the re-initialization procedure, how it was performed on-orbit, and battery performance after the re-initialization. On-orbit data monitoring and trending is an ongoing activity, and will continue as ISS assembly progresses.}</sub>

Using the data you already have.

Managing a group practice requires constant attention to financial and operational data to achieve a sustainable business concern. By identifying key performance areas, a group practice can gather, report, and analyze data that are readily available but often overlooked. Although performance indicators will not solve all management problems, the insight gleaned from historical and projected data trending, in addition to external comparison and benchmarking, can result in proactive analysis and problem identification, mitigating the troubling impacts of ignorance and surprise.