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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 8 of 8 Research Studies DisplayedColey RY, Smith JJ, Karliner L RY, Smith JJ, Karliner L
External validation of the eRADAR risk score for detecting undiagnosed dementia in two real-world healthcare systems.
Drupal date: Feb, 2023
It is estimated that half of the individuals with dementia remain undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was designed to detect older adults at risk of undiagnosed dementia using routinely gathered clinical information. The purpose of this retrospective cohort study was to externally validate eRADAR in two real-world healthcare systems, examining its performance over time and across race/ethnicity. The study found a total of 7631 dementia diagnoses were observed at KPWA and 216 at UCSF. The area under the curve was 0.84 (95% confidence interval: 0.84-0.85) at KPWA and 0.79 (0.76-0.82) at UCSF. Using the 90th percentile as the cut point for identifying high-risk patients, sensitivity was 54% (53-56%) at KPWA and 44% (38-51%) at UCSF. Performance was consistent over time, including across the transition from International Classification of Diseases, version 9 (ICD-9) to ICD-10 codes, and across racial/ethnic groups (although small samples limited precision in some groups). The study concluded that eRADAR demonstrated strong external validity for identifying undiagnosed dementia in two healthcare systems with diverse patient populations and varying availability of external healthcare data for risk calculations. This study showed that eRADAR is generalizable from a research sample to real-world clinical populations, transportable across health systems, resilient to temporal changes in healthcare, and exhibits similar performance across major racial/ethnic groups.
It is estimated that half of the individuals with dementia remain undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was designed to detect older adults at risk of undiagnosed dementia using routinely gathered clinical information. The purpose of this retrospective cohort study was to externally validate eRADAR in two real-world healthcare systems, examining its performance over time and across race/ethnicity. The study found a total of 7631 dementia diagnoses were observed at KPWA and 216 at UCSF. The area under the curve was 0.84 (95% confidence interval: 0.84-0.85) at KPWA and 0.79 (0.76-0.82) at UCSF. Using the 90th percentile as the cut point for identifying high-risk patients, sensitivity was 54% (53-56%) at KPWA and 44% (38-51%) at UCSF. Performance was consistent over time, including across the transition from International Classification of Diseases, version 9 (ICD-9) to ICD-10 codes, and across racial/ethnic groups (although small samples limited precision in some groups). The study concluded that eRADAR demonstrated strong external validity for identifying undiagnosed dementia in two healthcare systems with diverse patient populations and varying availability of external healthcare data for risk calculations. This study showed that eRADAR is generalizable from a research sample to real-world clinical populations, transportable across health systems, resilient to temporal changes in healthcare, and exhibits similar performance across major racial/ethnic groups.
AHRQ-funded; HS026369.
Citation: Coley RY, Smith JJ, Karliner L RY, Smith JJ, Karliner L .
External validation of the eRADAR risk score for detecting undiagnosed dementia in two real-world healthcare systems.
J Gen Intern Med 2023 Feb; 38(2):351-60. doi: 10.1007/s11606-022-07736-6..
Keywords: Dementia, Neurological Disorders, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT)
Yadgir SR, Engstrom C, Jacobsohn GC
Machine learning-assisted screening for cognitive impairment in the emergency department.
Researchers developed and evaluated an automated screening tool to identify a subset of patients at high risk for cognitive impairment (CI). Using the Blessed Orientation Memory Concentration (BOMC) test, administered in the emergency department, they found that an algorithm based on electronic health record data can define a subset of patients at higher risk for CI. They recommended that incorporating such an algorithm into a screening workflow could allow screening efforts and resources to be focused where they have the most impact.
AHRQ-funded; HS024558.
Citation: Yadgir SR, Engstrom C, Jacobsohn GC .
Machine learning-assisted screening for cognitive impairment in the emergency department.
J Am Geriatr Soc 2022 Mar;70(3):831-37. doi: 10.1111/jgs.17491..
Keywords: Neurological Disorders, Screening, Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT), Elderly
Barnes DE, Zhou J, Walker RL
Development and validation of eRADAR: a tool using EHR Data to detect unrecognized dementia.
The goal of this retrospective cohort study was to develop and validate an electronic health record (EHR)-based tool to help detect patients with unrecognized dementia. The tool was named EHR Risk of Alzheimer’s and Dementia Assessment Rule (eRADAR). This study was conducted at Kaiser Permanente Washington (KPWA) using participants in the Adult Changes in Thought (ACT) study who undergo comprehensive testing every 2 years to detect and diagnose dementia and have linked KPWA EHR data. Overall, 1015 ACT visits resulted in a diagnosis of incident dementia, of which 49% were previously unrecognized in the EHR. The final 31-predictor model included markers of dementia-related symptoms, healthcare utilization patterns, and dementia risk factors. The study showed good discrimination in the development interval and validation samples.
AHRQ-funded; HS022982.
Citation: Barnes DE, Zhou J, Walker RL .
Development and validation of eRADAR: a tool using EHR Data to detect unrecognized dementia.
J Am Geriatr Soc 2020 Jan;68(1):103-11. doi: 10.1111/jgs.16182..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Dementia, Neurological Disorders, Diagnostic Safety and Quality, Clinical Decision Support (CDS), Decision Making
Simon KC, Reams N, Beltran E
Optimizing the electronic medical record to improve patient care and conduct quality improvement initiatives in a concussion specialty clinic.
The purpose of this study was to use the electronic medical record (EMR) to optimize patient care, facilitate documentation, and support quality improvement and practice-based research in a concussion (mild traumatic brain injury; mTBI) clinic. The investigators built a customized structured clinical documentation support (SCDS) toolkit for patients in a concussion specialty clinic. The toolkit collected hundreds of fields of discrete,
AHRQ-funded; HS024057.
Citation: Simon KC, Reams N, Beltran E .
Optimizing the electronic medical record to improve patient care and conduct quality improvement initiatives in a concussion specialty clinic.
Brain Inj 2020;34(1):62-67. doi: 10.1080/02699052.2019.1680867..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Brain Injury, Neurological Disorders
Simon KC, Yucus C, Castle J
Building of EMR tools to support quality and research in a memory disorders clinic.
This article describes the development of a customized EMR toolkit that standardizes patient data collection with hundreds of discrete fields, supports Best Practices for treating patients with memory disorders, and also supports practice-based research. The toolkit was successfully implemented to support Best Practices in the care of patients with memory disorders. Applications are also discussed. Data collection is ongoing, but the authors anticipate that the toolkit will generate data that allows for descriptive and hypothesis-driven research as well as quality improvement among patients seen in memory clinics.
AHRQ-funded; HS024057.
Citation: Simon KC, Yucus C, Castle J .
Building of EMR tools to support quality and research in a memory disorders clinic.
Front Neurol 2019 Mar 7;10:161. doi: 10.3389/fneur.2019.00161..
Keywords: Dementia, Electronic Health Records (EHRs), Health Information Technology (HIT), Neurological Disorders, Quality of Care, Quality Improvement, Tools & Toolkits
Meyers S, Claire Simon K, Bergman-Bock S
Structured clinical documentation to improve quality and support practice-based research in headache.
The authors developed a proprietary toolkit to aid clinicians when creating clinical documentation in electronic medical records (EMRs). This toolkit will help clinicians provide discrete data and not unstructured free text which many clinicians enter into the EMR. The toolkit collects hundreds of fields of data and interprets score tests for a number of difference assessment tools for anxiety disorder, depression, migraine disability, and insomnia. The toolkit was used at 4346 initial patient visits as of April 1, 2018. The toolkit is being shared with other clinics as part of the Neurology Practice-Based Research Network.
AHRQ-funded; HS024057.
Citation: Meyers S, Claire Simon K, Bergman-Bock S .
Structured clinical documentation to improve quality and support practice-based research in headache.
Headache 2018 Sep;58(8):1211-18. doi: 10.1111/head.13348..
Keywords: Quality Improvement, Quality of Life, Tools & Toolkits, Neurological Disorders, Electronic Health Records (EHRs), Health Information Technology (HIT), Practice-Based Research Network (PBRN)
Fosnacht AM, Patel S, Yucus C
From brain disease to brain health: primary prevention of Alzheimer's disease and related disorders in a health system using an electronic medical record-based approach.
This study aimed to primarily prevent Alzheimer's disease and related disorders through electronic medical record (EMR)-based screening, risk assessments, interventions, and surveillance. The investigators are translating research into primary prevention of Alzheimer's disease and related disorders in their health system and aim to shift the paradigm in Neurology from brain disease to brain health.
AHRQ-funded; HS024057.
Citation: Fosnacht AM, Patel S, Yucus C .
From brain disease to brain health: primary prevention of Alzheimer's disease and related disorders in a health system using an electronic medical record-based approach.
J Prev Alzheimers Dis 2017;4(3):157-64. doi: 10.14283/jpad.2017.3..
Keywords: Dementia, Electronic Health Records (EHRs), Health Information Technology (HIT), Neurological Disorders, Prevention
Narayanan J, Dobrin S, Choi J
Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy.
The researchers describe a stepwise process for building structured clinical documentation support tools in the electronic medical record (EMR) that define best practices in epilepsy, and describe how they incorporated these toolkits into their clinical workflow. These tools write notes and capture hundreds of fields of data including several score tests. They also summarize brain imaging, blood laboratory, and electroencephalography results, and document neuromodulation treatments.
AHRQ-funded; HS024057.
Citation: Narayanan J, Dobrin S, Choi J .
Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy.
Epilepsia 2017 Jan;58(1):68-76. doi: 10.1111/epi.13607.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Neurological Disorders, Quality of Care, Quality Improvement, Tools & Toolkits, Workflow