Introduction of a Comprehensive Modified Early Warning Scoring System in a Large Rural Hospital

PURPOSE: To develop and test a comprehensive modified early warning scoring (MEWS) system for use on two medical-surgical-telemetry units in a large rural hospital in northeastern Kentucky; to educate and train nursing staff in utilization of a new MEWS system and early identification and management of clinical deterioration; and to determine nursing satisfaction regarding education, training, and use of a new MEWS system. BACKGROUND: Adult medical-surgical patients are at risk for clinical deterioration. Rapid response systems and MEWS systems are strategies that have been employed to assist nursing staff in early identification and management of clinical deterioration. Testing of a newly proposed comprehensive MEWS system and an educational intervention is an essential first step in determining interventional effectiveness. STUDY DESIGN: A retrospective, single center, mixed methods observational study. METHODS: In Phase I, retrospective chart reviews (RCRs) were conducted during a 6-month timeframe for patients meeting one of the following severe adverse event (SAE) criteria: in-hospital cardiac arrest, in-hospital death, unexpected transfer to the intensive care unit, and/or rapid response team utilization specifically pertaining to the medical-surgical-telemetry units of interest. Physiologic parameters (i.e., vital signs and level of consciousness) and nursing responses were recorded in the 24-hours leading up to SAEs; MEWS were retrospectively calculated at 24-hours (MEWS24), 16-hours (MEWS16), and 8-hours (MEWS8) to gauge utility of the MEWS tool. In Phase II, a 3-hour education and training workshop designed for nursing staff was developed, implemented, and evaluated. A focus was placed on use of a new MEWS system and early identification and management of clinical deterioration. RESULTS: In Phase I of RCRs, 81 patients met criteria during a study timeframe of September 2016 through February 2017. Demographic data yielded the following: 51.9% male, 76.5% sixty years of age or older, and 98.8% White. MEWS24 (n = 62) had a mean of 3.0, standard deviation (SD) of 1.6, and range of 1.0 – 7.0; MEWS16 (n = 76) had a mean of 3.3, SD of 1.3, and range of 1.0 – 7.0; and MEWS8 (n = 81) had a mean of 5.0, SD of 2.3, and range of 1.0 – 10.0. In Phase II, nine nursing staff participated in one of eight education and training workshops. Participants reported increased confidence in recognizing deterioration, responding to deterioration, and communicating concerns following an educational intervention. Nursing staff consistently reported respiratory effort, level of consciousness, oxygen saturation, respiratory rate, blood pressure, and heart rate as the most influential parameters in a nursing assessment for determining clinical deterioration. Satisfaction was high regarding the education, training, and use of a new MEWS system. CONCLUSION: RCRs indicated that a MEWS system would be feasible in identifying patients at risk for SAEs in this patient population. Introduction of a new comprehensive MEWS system with an educational intervention had a positive effect on nursing staff’s self-reported confidence, knowledge, and skill in recognizing and managing clinical deterioration. RELEVANCE TO CLINICAL PRACTICE: Before full implementation, a prospective study is recommended to test a comprehensive MEWS system for all admissions through discharge over a defined time period and provide a mandatory educational intervention for interdisciplinary staff on the two medical-surgical-telemetry units of interest. Great insight could be learned regarding tool utility, resource utilization, and staff preparedness

Transactions of 2019 International Conference on Health Information Technology Advancement Vol. 4 No. 1

The Fourth International Conference on Health Information Technology Advancement Kalamazoo, Michigan, October 31 - Nov. 1, 2019. Conference Co-Chairs Bernard T. Han and Muhammad Razi, Department of Business Information Systems, Haworth College of Business, Western Michigan University Kalamazoo, MI 49008 Transaction Editor Dr. Huei Lee, Professor, Department of Computer Information Systems, Eastern Michigan University Ypsilanti, MI 48197 Volume 4, No. 1 Hosted by The Center for Health Information Technology Advancement, WM

The design and evaluation of discrete wearable medical devices for vital signs monitoring

The observation, recording and appraisal of an individual’s vital signs, namely temperature, heart rate, blood pressure, respiratory rate and blood oxygen saturation (SpO2), are key components in the assessment of their health and wellbeing. Measurements provide valuable diagnostic data, facilitating clinical diagnosis, management and monitoring. Respiratory rate sensing is perhaps the most under-utilised of all the vital signs, being routinely assessed by observation or estimated algorithmically from respiratory-induced beat-to-beat variation in heart rate. Moreover there is an unmet need for wearable devices that can measure all or most of the vital signs. This project therefore aims to a) develop a device that can measure respiratory rate and b) develop a wearable device that can measure all or most of the vital signs. An accelerometer-based clavicular respiratory motion sensor was developed and compared with a similar thoracic motion sensor and reference using exhalatory flow. Pilot study results established that the clavicle sensor accurately tracked the reference in monitoring respiratory rate and outperformed the thoracic device. An Ear-worn Patient Monitoring System (EPMS) was also developed, providing a discrete telemonitoring device capable of rapidly measuring tympanic temperature, heart rate, SpO2 and activity level. The results of a comparative pilot study against reference instruments revealed that heart rate matched the reference for accuracy, while temperature under read (< 1°C) and SpO2 was inconsistent with poor correlation. In conclusion, both of the prototype devices require further development. The respiratory sensor would benefit from product engineering and larger scale testing to fully exploit the technology, but could find use in both hospital and community-based The design and evaluation of discrete wearable medical devices for vital signs monitoring DG Pitts ii Cranfield University monitoring. The EPMS has potential for clinical and community use, having demonstrated its capability of rapidly capturing and wirelessly transmitting vital signs readings. Further development is nevertheless required to improve the thermometer probe and resolve outstanding issues with SpO2 readings

Development of machine learning schemes for use in non-invasive and continuous patient health monitoring

Stephanie Baker developed machine learning schemes for the non-invasive and continuous measurement of blood pressure and respiratory rate from heart activity waveforms. She also constructed machine learning models for mortality risk assessment from vital sign variations. This research contributes several tools that offer significant advancements in patient monitoring and wearable healthcare

Program analysis for android security and reliability

The recent, widespread growth and adoption of mobile devices have revolutionized the way users interact with technology. As mobile apps have become increasingly prevalent, concerns regarding their security and reliability have gained significant attention. The ever-expanding mobile app ecosystem presents unique challenges in ensuring the protection of user data and maintaining app robustness. This dissertation expands the field of program analysis with techniques and abstractions tailored explicitly to enhancing Android security and reliability. This research introduces approaches for addressing critical issues related to sensitive information leakage, device and user fingerprinting, mobile medical score calculators, as well as termination-induced data loss. Through a series of comprehensive studies and employing novel approaches that combine static and dynamic analysis, this work provides valuable insights and practical solutions to the aforementioned challenges. In summary, this dissertation makes the following contributions: (1) precise identifier leak tracking via a novel algebraic representation of leak signatures, (2) identifier processing graphs (IPGs), an abstraction for extracting and subverting user-based and device-based fingerprinting schemes, (3) interval-based verification of medical score calculator correctness, and (4) identifying potential data losses caused by app termination

Practical and Robust Power Management for Wireless Sensor Networks

Wireless Sensor Networks: WSNs) consist of tens or hundreds of small, inexpensive computers equipped with sensors and wireless communication capabilities. Because WSNs can be deployed without fixed infrastructure, they promise to enable sensing applications in environments where installing such infrastructure is not feasible. However, the lack of fixed infrastructure also presents a key challenge for application developers: sensor nodes must often operate for months or years at a time from fixed or limited energy sources. The focus of this dissertation is on reusable power management techniques designed to facilitate sensor network developers in achieving their systems\u27 required lifetimes. Broadly speaking, power management techniques fall into two categories. Many power management protocols developed within the WSN community target specific hardware subsystems in isolation, such as sensor or radio hardware. The first part of this dissertation describes the Adaptive and Robust Topology control protocol: ART), a representative hardware-specific technique for conserving energy used by packet transmissions. In addition to these single-subsystem approaches, many applications can benefit greatly from holistic power management techniques that jointly consider the sensing, computation, and communication costs of potential application configurations. The second part of this dissertation extends this holistic power management approach to two families of structural health monitoring applications. By applying a partially-decentralized architecture, the cost of collecting vibration data for analysis at a centralized base station is greatly reduced. Finally, the last part of this dissertation discusses work toward a system for clinical early warning and intervention. The feasibility of this approach is demonstrated through preliminary study of an early warning component based on historical clinical data. An ongoing clinical trial of a real-time monitoring component also provides important guidelines for future clinical deployments based on WSNs