Data Capture Mechanisms Research Articles

Reliability of Continuous Noninvasive Hemoglobin Monitoring in Healthy Participants During En Route Care Training.

Current standards for hemoglobin monitoring during air transports of U.S. combat wounded are invasive and intermittent. Fielded pulse co-oximeters can noninvasively measure total hemoglobin, but this parameter is not currently utilized. The primary objective of this study was to assess the percentage of vital sign measurements with successful capture of total noninvasive hemoglobin measurement using spectrophotometry-based technology for Hb (SpHb) measurements in healthy participants during training flights. Secondary objectives were to assess the feasibility of a novel electronic data capture mechanism from usual patient movement items and perform a pilot analysis of SpHb changes in healthy participants during transitions from ground to air transport. We conducted a feasibility study enrolling healthy participants who had hemodynamic monitoring during usual U.S. Air Force Critical Care Air Transport (CCAT) flight training exercises from 2022 to 2023. Usual CCAT monitoring equipment and currently used Masimo Rainbow® pulse co-oximeters had the capability to measure SpHb. After each training exercise, the study team wirelessly downloaded case files from patient monitors utilizing the Battlefield Assisted Trauma Distributed Observation Kit (BATDOKTM) Case Downloader application. We then calculated point and precision estimates for the percentage of time for successful SpHb capture during the exercise and compared this to pulse oximetry (SpO2) capture. An a priori precision analysis for percentage of flight-time with successful SpHb data capture and descriptive statistics were performed. This study received Exempt Determination by the 59th Medical Wing IRB. We analyzed 26 records with mean monitoring durations of 94.5 [59.3-119.9] minutes during ground phases and 78.0 [59.9-106.5] minutes during flight phases. SpHb measures were successfully captured for 97.7% (n = 4,620) of possible ground measurements and 97.2% (n = 3,973) of possible in-flight measurements compared to 99.5% ground and 98.2% in-flight capture for SpO2. Mean intervals of missing SpHb data were 2 ± 5 minutes on the ground and 4 ± 6 minutes in-flight. Mean SpHb increased by 0.93 ± 0.96 g/dL during the ground phase, but had minimal changes during ascent, cruising altitude or descent. The BATDOKTM Case downloader completed transfer for all files. Masimo Rainbow® SpHb pulse co-oximeters reliably captured continuous, noninvasive hemoglobin measurements using usual CCAT patient movement items in healthy participants during both ground and flight training. The BATDOKTM Case Downloader successfully imported case files from CCAT patient monitors. Mean SpHb measures had a small increase during the ground phase of monitoring followed by minimal changes when transitioning to flight altitude.

TJ-FlyingFish: An Unmanned Morphable Aerial–Aquatic Vehicle System

In this paper, we present in this work a fairly complete process for developing an unmanned aerial–aquatic vehicle system, TJ-FlyingFish, which includes an innovative design methodology of the aerial–aquatic platform and the cross-medium localization, dynamics modeling, and flight control systems. The development faces the challenge how to manipulate locomotion effectively in both water and air which presents substantial differences in fluid properties. Additionally, there are difficulties in perception and navigation because of the discontinuity of mediums. To cope with these challenges, we designed an innovative unmanned aerial–aquatic vehicle with an optimized dual-speed and tilting propulsion configuration. The rotors/propellers operate in different ranges of rotating speed in the two different mediums, providing sufficient thrust and ensuring output efficiency. Besides, thrust vectoring is achieved by rotating each propulsion unit around its mounted arm, facilitating agile underwater cruising. Another key component of our approach is a sophisticated multi-sensor-based cross-medium localization system that combines SLAM, sensor synchronization, and data capture mechanisms, enabling seamless transitions between aerial and aquatic environments, and supporting autonomous operations. The results are fully validated through actual flight experiments.

Development of prospective hospital-based venous thromboembolism registry across India: a study protocol

IntroductionIndian Council of Medical Research (ICMR), New Delhi has established a nationwide registry ‘Indian Registry for Venous Thromoembolism Disorder (i-RegVeD)’ for real-time analytics of sociodemographic profile of patients, disease patterns,...

Gaining consensus on clinical quality outcomes for eating disorders: Framework for the development of an Australian national minimum dataset

ObjectivesEating disorders (EDs) are complex psychiatric illnesses requiring multidisciplinary care across both mental and medical healthcare settings. Currently, no nationally comprehensive, consistent, agreed on or mandated data set or data...

Strategies to Evaluate and Enhance Accelerated Second-Degree Nursing Pathways.

Health systems are facing historic staffing crises, and they require efficient pipelines of qualified students into practice. Accelerated second-degree students are helping to address pressing health care market needs. Few publications have assessed the second-degree program landscape or offered comparisons of second-degree pathways. This article discusses the second-degree program landscape, including challenges in second-degree education; compares graduate-level second-degree entry against other pathway options; and presents strategies to optimize and sustain second-degree student pipelines based on these insights. The second-degree program landscape is characterized by variability. Challenges include inconsistent program nomenclature and limited national data collection. Graduate-level second-degree pathways offer financial and career advantages compared with other pathways. Nursing education should standardize second-degree nomenclature, refine national data capture mechanisms, standardize program scope and requirements, and encourage second-degree pathways at the graduate level or above.

What and how will the Risk Based Approach to Monitoring change? Survey of RBM in medical institutions

Paralleling the revision and enforcement of regulatory guidance, the Risk-based Approach to Monitoring (RBM) is becoming increasingly important in process management at medical institutions. Although the incorporation of RBM practices is intended to streamline the monitoring of clinical trials, whether it in fact improves the effectiveness of associated duties at participating organizations remains unclear. Regarding RBM and its benefits, we surveyed 391 clinical research coordinators and data managers at Japanese medical facilities. The results of the survey suggest that RBM does in fact decrease the frequency of on-site visits from clinical research associates. Conversely, rather than reducing staff burden as intended, off-site monitoring (by telephone and e-mail) was reported to increase the workload of monitoring personnel. Our survey findings indicate that sponsors need to optimize the use of on-site, off-site, and central monitoring practices to maximize the efficiency of RBM. Regarding central monitoring duties in particular, data regarding the items of “eligibility/enrollment,” “treatment suspension/dose reduction/discontinuation,” and “initial treatment, related PK and tests, etc.” should be checked carefully for errors and omissions, and information entered through electronic data capture mechanisms should be confirmed cross-sectionally.

Patient-Generated Health Data Quality for Clinical Use: Human and Technology Factors

Background: The proliferation of advanced wearable medical technologies is increasing the production of Patient-Generated Health Data (PGHD). However, there is lack of evidence on whether the quality of the data generated from wearables can be effectively used for patient care. In order for PGHD to be utilized for decision making by health providers, it needs to be of high quality, that is, it must comply with standards defined by health care organizations and be accurate, consistent, complete and unbiased. Although medical wearables record highly accurate data, there are other technology issues as well as human factors that affect PGHD quality when it is collected and shared under patients’ control to ultimately used by health care providers. Objective: This paper explores human factors and technology factors that impact on the quality of PGHD from medical wearables for effective use in clinical care. Methods: We conducted semi-structured interviews with 17 PGHD stakeholders in Australia, the US, and the UK. Participants include ten health care providers working with PGHD from medical wearables in diabetes, sleep disorders, and heart arrhythmia, five health IT managers, and two executives. The participants were interviewed about seven data quality dimensions including accuracy, accessibility, coherence, institutional environment, interpretability, relevancy, and timeliness. Open coding of the interview data identified several technology and human issues related to the data quality dimensions regarding the clinical use of PGHD. Results: The overarching technology issues mentioned by participants include lack of advanced functionalities such as real-time alerts for patients as well as complicated settings which can result in errors. In terms of PGHD coherence, different wearables have different data capture mechanisms for the same health condition that create different formats which result in difficult PGHD interpretation and comparison. Another technology issue that is relevant to the current ICT infrastructure of the health care settings is lack of possibility in real-time PGHD access by health care providers which reduce the value of PGHD use. Besides, health care providers addressed a challenge on where PGHD is stored and who truthfully owns the data that affect the feasibility of PGHD access. The human factors included a lack of digital health literacy among patients which shape both the patients’ motivation and their behaviors toward PGHD collection. For example, the gaps in data recording shown in the results indicate the wearable was not used for a time duration. Participants also identified the cost of devices as a barrier to the long-term engagement and use of wearables. Conclusions: Using PGHD garnered from medical wearables is problematic in clinical contexts due to low-quality data influenced by technology and human factors. At present, no guidelines have been defined to assess PGHD quality. Hence, there is a need for new solutions to overcome the existing technology and human-related barriers to enhance PGHD quality.

Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms.

Objective The objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms. Background Using novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to early detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts. Methods Data was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period. Results Forecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX. Conclusion Though the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness.

Harnessing ICT potential

PurposeThis paper aims to examine how effective higher education institutions have been in harnessing the data capture mechanisms from their student information systems, learning management systems and communication tools for improving the student learning experience and informing practitioners of the achievement of specific learning outcomes. The paper seeks to argue that the future of analytics in higher education lies in the development of more comprehensive and integrated systems to value add to the student learning experience.Design/methodology/approachLiterature regarding the trend for greater accountability in higher education is reviewed in terms of its implications for greater “user driven” direction. In addition, IT usage within higher education and contemporary usage of data captured from various higher education systems is examined and compared to common commercial applications to suggest how higher education management and teachers can gain greater understanding of the student cohort and personalise and enhance the learning experience much as commercial entities have done for their client base. A way forward for higher education is proposed.FindingsIf the multiple means that students engage with university systems are considered, it is possible to track individual activity throughout the entire student life cycle – from initial admission, through course progression and finally graduation and employment transitions. The combined data captured by various systems builds a detailed picture of the activities students, instructors, service areas and the institution as a whole undertake and can be used to improve relevance, efficiency and effectiveness in a higher education institution.Originality/valueThe paper outlines how academic analytics can be used to better inform institutions about their students learning support needs. The paper provides examples of IT automation that may allow for student user‐information to be translated into a personalised and semi‐automated support system for students.

A multi-sectoral approach to capture information on road traffic injuries

Background:Regularly available data is shown to be inadequate for developing, implementing, and evaluating injury prevention and control programs in India. The present study was undertaken in the hospitals of Bangalore and Pune, to examine the feasibility of gathering information on injuries using multiple sources.Materials and Methods:Stakeholders meeting and training programs were held for the hospital staff, police personnel, and traffic and transport staff, to identify their roles and responsibilities. Prospective data on morbidity and mortality due to injuries were collected by trained staff from Emergency Departments on a pre-tested questionnaire. The information gathered was cross-checked with the hospital and police records.Results:The stakeholders meeting and training programs were able to motivate the departments to provide the correct data. Data on 32188 patients could be extracted from hospital and police records during the study period. Injuries accounted for 16% of the emergency cases. Unintentional injuries were 64%, and 32% were intentional. Road traffic injuries accounted for 44% of all the injuries. One-third of the injured were children and young adults below 25 years. Among the injured, two wheeler riders were 29% and pedestrians were 23%.Conclusion:It was possible to improve the data on injuries by adequate training and a data linking mechanism between the Police, Hospital, and Transport Departments. The problem of road traffic injuries could be highlighted and addressed by a good data capture mechanism.

Computer-Assisted Operations — A Practical Approach To Networking Field Computers

Abstract Computer Assisted Operations (CAO) is a computer network using new techniques and available technology to transfer information electronically between computing systems located in the field. Field computing systems have historically been data capture mechanisms developed to funnel data to a central computing facility. CAO is unique in that the network is designed for multi-directional data flow, enhancing computerized decision support to field personnel. Field staffs are given PCS at their field facilities for local decision support. A central PC, called a CAO Host, provides a repository of area data and local CAD network control at the operating centre. The Host recognizes each piece of data and knows which PCS on the network require a copy. For example, gas charts are integrated in a central office, and then passed via the network to the PC at the facility where the chart originated. The PC then uses the information in its facility, modeling programs. A pilot CAD installation was successfully tested at Amoco's Pembina Cardium operation. The pilot field locations do not have telephone communication. This does not present all obstacles because CAO user hand-held computers as the medium through which the network transfers data. Introduction Successful exploration and production companies consistently make profitable decisions in all areas of their business activities. The importance of effective decision making is not new, but the pace at which decisions must be made and executed is accelerating. Effective computing tools for decision support stimulate and sustain the competitive health of these companies. Systems professionals have done an admirable job in identifying and developing new computing opportunities in the office environment, and are now beginning to supply field operations with practical decision support systems. Reasons for the lack of computer use in field locations are many and varied, but a factor common to many operations is the absence of a computer infrastructure in remote field locations. Communications, processing power, and user support that are taken for granted in office environments are not readily available in many field requirements, field computer applications will become increasingly prevalent. Measured by new systems installed, field operations will be the largest growth sector of petroleum computing in the next decade. Field computing systems have historically been intended as monitoring systems. They are designed to capture data and then feed it to central systems. Little emphasis has been placed on providing information access back to field staff, particularly at locations remote from an area office. For example, Supervisory Control And Data Acquisition (SCADA) systems have been used for many years to gather data. SCADA systems are designed to funnel electronically monitored field data to central computing systems, however, field access to this information is generally limited to printed reports or occasional remote terminal access. Very few SCADA systems are designed to automatically communicate information to and from other computing systems in a network. New applications for on-site decision support have begun to proliferate, but have been limited by an inability to share information with other systems.