Technological interventions in functional capacity evaluations: An insight into current applications.

BACKGROUND: Functional Capacity Evaluation (FCE) is a crucial component within return-to-work decision making. However, clinician-based physical FCE interpretation may introduce variability and biases. The rise of technological applications such as machine learning and artificial intelligence, could ensure consistent and precise results. OBJECTIVE: This review investigates the application of information and communication technologies (ICT) in physical FCEs specific for return-to-work assessments. METHODS: Adhering to the PRISMA guidelines, a search was conducted across five databases, extracting study specifics, populations, and technological tools employed, through dual independent reviews. RESULTS: Nine studies were identified that used ICT in FCEs. These technologies included electromyography, heart rate monitors, cameras, motion detectors, and specific software. Notably, although some devices are commercially available, these technologies were at a technology readiness level of 5-6 within the field of FCE. A prevailing trend was the combined use of diverse technologies rather than a single, unified solution. Moreover, the primary emphasis was on the application of technology within study protocols, rather than a direct evaluation of the technology usability and feasibility. CONCLUSION: The literature underscores limited ICT integration in FCEs. The current landscape of FCEs, marked by a high dependence on clinician observations, presents challenges regarding consistency and cost-effectiveness. There is an evident need for a standardized technological approach that introduces objective metrics to streamline the FCE process and potentially enhance its outcomes.

Omission responses in local field potentials in rat auditory cortex.

BACKGROUND: Non-invasive recordings of gross neural activity in humans often show responses to omitted stimuli in steady trains of identical stimuli. This has been taken as evidence for the neural coding of prediction or prediction error. However, evidence for such omission responses from invasive recordings of cellular-scale responses in animal models is scarce. Here, we sought to characterise omission responses using extracellular recordings in the auditory cortex of anaesthetised rats. We profiled omission responses across local field potentials (LFP), analogue multiunit activity (AMUA), and single/multi-unit spiking activity, using stimuli that were fixed-rate trains of acoustic noise bursts where 5% of bursts were randomly omitted. RESULTS: Significant omission responses were observed in LFP and AMUA signals, but not in spiking activity. These omission responses had a lower amplitude and longer latency than burst-evoked sensory responses, and omission response amplitude increased as a function of the number of preceding bursts. CONCLUSIONS: Together, our findings show that omission responses are most robustly observed in LFP and AMUA signals (relative to spiking activity). This has implications for models of cortical processing that require many neurons to encode prediction errors in their spike output.

Assessing patient risk of central line-associated bacteremia via machine learning.

BACKGROUND: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLABSIs and, in real time, prevent them from occurring. METHODS: A predictive model was developed using retrospective data from a large academic healthcare system. Models were developed with machine learning via construction of random forests using validated input variables. RESULTS: Fifteen variables accounted for the most significant effect on CLABSI prediction based on a retrospective study of 70,218 unique patient encounters between January 1, 2013, and May 31, 2016. The area under the receiver operating characteristic curve for the best-performing model was 0.82 in production. DISCUSSION: This model has multiple applications for resource allocation for CLABSI prevention, including serving as a tool to target patients at highest risk for potentially cost-effective but otherwise time-limited interventions. CONCLUSIONS: Machine learning can be used to develop accurate models to predict the risk of CLABSI in real time prior to the development of infection.

Using electronic medical records to increase the efficiency of catheter-associated urinary tract infection surveillance for National Health and Safety Network reporting.

BACKGROUND: Streamlining health care-associated infection surveillance is essential for health care facilities owing to the continuing increases in reporting requirements. METHODS: Stanford Hospital, a 583-bed adult tertiary care center, used their electronic medical record (EMR) to develop an electronic algorithm to reduce the time required to conduct catheter-associated urinary tract infection (CAUTI) surveillance in adults. The algorithm provides inclusion and exclusion criteria, using the National Healthcare Safety Network definitions, for patients with a CAUTI. The algorithm was validated by trained infection preventionists through complete chart review for a random sample of cultures collected during the study period, September 1, 2012, to February 28, 2013. RESULTS: During the study period, a total of 6,379 positive urine cultures were identified. The Stanford Hospital electronic CAUTI algorithm identified 6,101 of these positive cultures (95.64%) as not a CAUTI, 191 (2.99%) as a possible CAUTI requiring further validation, and 87 (1.36%) as a definite CAUTI. Overall, use of the algorithm reduced CAUTI surveillance requirements at Stanford Hospital by 97.01%. CONCLUSIONS: The electronic algorithm proved effective in increasing the efficiency of CAUTI surveillance. The data suggest that CAUTI surveillance using the National Healthcare Safety Network definitions can be fully automated.