Washington: Researchers have used artificial intelligence prophesy admitted to hospital Outcomes of elderly patients with dementia on the first or second day of admission. Early outcome assessment means more timely intervention, better care coordination, more prudent resource allocation, focused care management, and timely treatment for these more vulnerable, high-risk patients.
The study findings were published in the journal Alzheimer’s and Dementia: Translational Research and Clinical Interventions.
Because elderly patients with dementia stay in hospital longer and incur higher health care costs than other patients, the team sought to address this problem by identifying modifiable risk factors and developing an artificial intelligence model that improves patient outcomes, including their quality of life. Increases and decreases their quality of life. Hospital readmission risk, as well as reduced hospitalization costs once the model is implemented.
The study looked at hospital records of 8,407 elderly patients with dementia over 10 years in Houston Methodist’s eight-hospital system, identifying risk factors for poor outcomes in subgroups of patients with different types of dementia arising from diseases such as Alzheimer’s, Parkinson’s, and vascular. Dementia and Huntington’s, among others. From this data, the researchers developed a machine learning model to rapidly identify predictive risk factors early in these patients’ hospital stays and their ranked importance for adverse hospitalization outcomes.
With an accuracy of 95.6%, their model outperformed all other prevailing methods of risk assessment for these multiple types of dementia. The researchers add that no other current methods have applied AI to comprehensively predict hospitalization outcomes of elderly patients with dementia in this way, nor have they been able to identify specific risk factors that could be modified through additional clinical procedures or precautions to reduce risks.
“The study shows that if we can identify older patients with dementia as soon as they are admitted to the hospital and identify significant risk factors, we can immediately implement some appropriate interventions,” said Eugene C. Lai, MD, PhD, said. Stanley H. Robert W. for Parkinson’s Research and Treatment in the Appel Department of Neurology. Hervey Distinguished Endowed Chair. “By reducing and correcting immediately modifiable risk factors for adverse outcomes, we are able to improve outcomes and shorten their hospital stay.”
Lai, a neurologist, has worked with these patients for many years and wants to find ways to better understand how they are managed and behave when hospitalized, so that clinicians can improve care and quality of life for them. He approached Stephen TC Wong, Ph.D., PE, a bioinformatics expert and the TT and WF Chao Center for the Brain at Houston Methodist, with the idea, as he had previously collaborated with Wong and his team knew they had the idea. is Houston Methodist’s vast clinical data warehouse of patients and the ability to use AI to analyze big data.
Risk factors for each type of dementia were identified, including those amenable to intervention. Top identified hospitalization risk factors included encephalopathy, number of medical problems at admission, pressure ulcer, urinary tract infection, falls, source of admission, age, sex, and anemia, with many overlaps in multi-dementia groups.
Ultimately, researchers aim to implement mitigation measures to guide clinical interventions to reduce these negative outcomes. Wong says the emerging strategy of applying powerful AI predictions to trigger the implementation of “smart” clinical pathways in hospitals is novel and will not only improve clinical outcomes and patient experiences, but also reduce hospitalization costs.
“Our next step will be to implement the validated AI model into a mobile app for ICU and key hospital staff to alert elderly patients with dementia who are at high risk of poor hospital admission outcomes and guide them on intervention measures to mitigate such. . risks,” said Wong, the paper’s corresponding author and the John S. Dunn Presidential Distinguished Chair. “We will work with hospital IT to seamlessly integrate this application into EPIC as part of a system-wide implementation for routine clinical use.”
He said this will follow the same Smart Clinical Pathway strategy he is working on to integrate two other novel AI apps his team has developed into the EPIC system for routine clinical use to guide interventions that reduce the risk of patient falls with injuries. and evaluates the breast better. Risk of cancer to reduce unnecessary biopsies and overdiagnosis.
