Usability and Acceptability of Clinical Decision Support Based on the KIIDS-TBI Tool for Children with Mild Traumatic Brain Injuries and Intracranial Injuries

Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review

Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.

The burden of geriatric traumatic brain injury on trauma systems: Analysis of 348,800 Medicare inpatient claims

BACKGROUND

Traumatic brain injury (TBI) is a leading cause of death and disability in older adults. The aim of this study was to characterize the burden of TBI in older adults by describing demographics, care location, diagnoses, outcomes, and payments in this high-risk group.

METHODS

Using 2016-2019 Centers for Medicare & Medicaid Services (CMS) Inpatient Standard Analytical Files (IPSAF), patients >65 years with TBI (>1 injury ICD-10 starting with "S06") were selected. Trauma center levels were linked to the IPSAF file via American Hospital Association Hospital Provider ID and fuzzy-string matching. Patient variables were compared across trauma center levels.

RESULTS

Three hundred forty-eight thousand eight hundred inpatients (50.4% female; 87.1% white) from 2963 US hospitals were included. Level I/II trauma centers treated 66.9% of patients; non-trauma centers treated 21.5%. Overall inter-facility transfer rate was 19.2%; in Level I/II trauma centers transfers-in represented 23.3% of admissions. Significant TBI (Head AIS ≥3) was present in 70.0%. Most frequent diagnoses were subdural hemorrhage (56.6%) and subarachnoid hemorrhage (30.6%). Neurosurgical operations were performed in 10.9% of patients and operative rates were similar regardless of center level. Total unadjusted mortality for the sample was 13.9%, with a mortality of 8.1% for those who expired in-hospital, and an additional 5.8% for those discharged to hospice. Medicare payments totaled $4.91B, with the majority (73.4%) going to Level I/II trauma centers.

CONCLUSIONS

This study fills a gap in TBI research by demonstrating that although the majority of older adult TBI patients in the United States receive care at Level I/II trauma centers, a substantial percentage are managed at other facilities, despite 1 in 10 requiring neurosurgical operation regardless of level of trauma center. This analysis provides preliminary data on the function of regionalized trauma care for older adult TBI care. Future studies assessing the efficacy of early care guidelines in this population are warranted.