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IntroductionA learning health system (LHS) approach is a collaborative model that continuously examines, evaluates, and re‐evaluates data eventually transforming it into knowledge. High quantity of high‐quality data are needed to establish this model. The purpose of this article is to describe the collaborative discovery process used to identify and standardize clinical data documented during daily multidisciplinary inpatient rehabilitation that would then allow access to these data to conduct comparative effectiveness research.MethodsCARE4TBI is a prospective observational research study designed to capture clinical data within the standard inpatient rehabilitation documentation workflow at 15 TBI Model Systems Centers in the US. Three groups of stakeholders guided project development: therapy representative work group (TRWG) consisting of frontline therapists from occupational, physical, speech‐language, and recreational therapies; rehabilitation leader representative group (RLRG); and informatics and information technology team (IIT). Over a 12‐month period, the three work groups and research leadership team identified the therapeutic components captured within daily documentation throughout the duration of inpatient TBI rehabilitation.ResultsData brainstorming among the groups created 98 distinct categories of data with each containing a range of data elements comprising a total of 850 discrete data elements. The free‐form data were sorted into three large categories and through review and discussion, reduced to two categories of prospective data collection—session‐level and therapy activity‐level data. Twelve session data elements were identified, and 54 therapy activities were identified, with each activity containing discrete sub‐categories for activity components, method of delivery, and equipment or supplies. A total of 561 distinct meaningful data elements were identified across the 54 activities.DiscussionThe CARE4TBI data discovery process demonstrated feasibility in identifying and capturing meaningful high quantity and high‐quality treatment data across multiple disciplines and rehabilitation sites, setting the foundation for a LHS coalition for acute traumatic brain injury rehabilitation.
IntroductionA learning health system (LHS) approach is a collaborative model that continuously examines, evaluates, and re‐evaluates data eventually transforming it into knowledge. High quantity of high‐quality data are needed to establish this model. The purpose of this article is to describe the collaborative discovery process used to identify and standardize clinical data documented during daily multidisciplinary inpatient rehabilitation that would then allow access to these data to conduct comparative effectiveness research.MethodsCARE4TBI is a prospective observational research study designed to capture clinical data within the standard inpatient rehabilitation documentation workflow at 15 TBI Model Systems Centers in the US. Three groups of stakeholders guided project development: therapy representative work group (TRWG) consisting of frontline therapists from occupational, physical, speech‐language, and recreational therapies; rehabilitation leader representative group (RLRG); and informatics and information technology team (IIT). Over a 12‐month period, the three work groups and research leadership team identified the therapeutic components captured within daily documentation throughout the duration of inpatient TBI rehabilitation.ResultsData brainstorming among the groups created 98 distinct categories of data with each containing a range of data elements comprising a total of 850 discrete data elements. The free‐form data were sorted into three large categories and through review and discussion, reduced to two categories of prospective data collection—session‐level and therapy activity‐level data. Twelve session data elements were identified, and 54 therapy activities were identified, with each activity containing discrete sub‐categories for activity components, method of delivery, and equipment or supplies. A total of 561 distinct meaningful data elements were identified across the 54 activities.DiscussionThe CARE4TBI data discovery process demonstrated feasibility in identifying and capturing meaningful high quantity and high‐quality treatment data across multiple disciplines and rehabilitation sites, setting the foundation for a LHS coalition for acute traumatic brain injury rehabilitation.
The use of artificial intelligence (AI) is growing across disciplines and becoming increasingly discussed in neurorehabilitation. To capture the latest developments in order to understand which, if any, solutions are sufficiently developed for use in practice, we conducted a very rapid literature review, systematically searching the Embase and MEDLINE databases. The five publications that met the criteria for review point to most recent developments in improving diagnosis and prognostication using AI, with no studies examining AI-based rehabilitation interventions directly. However, there was a theoretical ambition of ingraining this technology in rehabilitation programmes themselves in the future. AI has demonstrated superior predictive power compared to traditional approaches when built on large subsets of patient outcome data and was revealed beneficial in estimating the location and extent of brain damage using brain scans. Nevertheless, the quality of the current evidence is limited by lack of follow-up studies of and lack of variability within the study samples, which reduces generalisation to certain groups, such as those with complex needs.
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