Application of a Machine Learning-Based Decision Support Tool to Improve an Injury Surveillance System Workflow.

BACKGROUND: Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data. OBJECTIVE: This study describes the evaluation of a machine learning (ML)-based decision support tool (DST) to assist injury surveillance departments in the validation, coding, and use of their data, comparing outcomes in coding time, and accuracy pre- and postimplementations. METHODS: Manually coded injury surveillance data have been used to develop, train, and iteratively refine a ML-based classifier to enable semiautomated coding of injury narrative data. This paper describes a trial implementation of the ML-based DST in the Queensland Injury Surveillance Unit (QISU) workflow using a major pediatric hospital's ED data comparing outcomes in coding time and pre- and postimplementation accuracies. RESULTS: The study found a 10% reduction in manual coding time after the DST was introduced. The Kappa statistics analysis in both DST-assisted and -unassisted data shows increase in accuracy across three data fields, that is, injury intent (85.4% unassisted vs. 94.5% assisted), external cause (88.8% unassisted vs. 91.8% assisted), and injury factor (89.3% unassisted vs. 92.9% assisted). The classifier was also used to produce a timely report monitoring injury patterns during the novel coronavirus disease 2019 (COVID-19) pandemic. Hence, it has the potential for near real-time surveillance of emerging hazards to inform public health responses. CONCLUSION: The integration of the DST into the injury surveillance workflow shows benefits as it facilitates timely reporting and acts as a DST in the manual coding process.

Utilizing health information technology in the treatment and management of patients during the COVID-19 pandemic: Lessons from international case study sites.

OBJECTIVE: The study sought to develop an in-depth understanding of how hospitals with a long history of health information technology (HIT) use have responded to the COVID-19 (coronavirus disease 2019) pandemic from an HIT perspective. MATERIALS AND METHODS: We undertook interviews with 44 healthcare professionals with a background in informatics from 6 hospitals internationally. Interviews were informed by a topic guide and were conducted via videoconferencing software. Thematic analysis was employed to develop a coding framework and identify emerging themes. RESULTS: Three themes and 6 subthemes were identified. HITs were employed to manage time and resources during a surge in patient numbers through fast-tracked governance procedures, and the creation of real-time bed capacity tracking within electronic health records. Improving the integration of different hospital systems was identified as important across sites. The use of hard-stop alerts and order sets were perceived as being effective at helping to respond to potential medication shortages and selecting available drug treatments. Utilizing information from multiple data sources to develop alerts facilitated treatment. Finally, the upscaling/optimization of telehealth and remote working capabilities was used to reduce the risk of nosocomial infection within hospitals. DISCUSSION: A number of the HIT-related changes implemented at these sites were perceived to have facilitated more effective patient treatment and management of resources. Informaticians generally felt more valued by hospital management as a result. CONCLUSIONS: Improving integration between data systems, utilizing specialized alerts, and expanding telehealth represent strategies that hospitals should consider when using HIT for delivering hospital care in the context of the COVID-19 pandemic.

Experience on how to implement a preanalytical and POCT unit in Madrid's IFEMA field hospital during this unprecedented COVID-19 emergency.

To fight the virus SARS-CoV-2 spread to Europe from China and to give support to the collapsed public health system, the Spanish Health Authorities developed a field hospital located in the facilities of Madrid exhibition centre (IFEMA) to admit and treat patients diagnosed with SARS-CoV-2 infectious disease (COVID-19). The Department of Laboratory Medicine of La Paz University Hospital in Madrid (LMD-HULP) was designated to provide laboratory services. Due to the emergency, the IFEMA field hospital had to be prepared for patient admission in less than 1 week and the laboratory professionals had to collaborate in a multidisciplinary group to assure that resources were available to start on time. The LMD-HULP participated together with the managers in the design of the tests portfolio and the integration of the healthcare information systems (IS) (hospital IS, laboratory IS and POCT management system). Laboratorians developed a strategy to quickly train clinicians and nurses on test requests, sample collection procedures and management/handling of the POCT blood gas analyser both by written materials and training videos. The IFEMA´s preanalytical unit managed 3782 requests, and more than 11,000 samples from March 27th to April 30th. Furthermore, 1151 samples were measured by blood gas analysers. In conclusion, laboratory professionals must be resilient and have to respond timely in emergencies as this pandemic. The lab's personnel selection, design and monitoring indicators to maintain and further improve the quality and value of laboratory services is crucial to support medical decision making and provide better patient care.