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Topics
- Brain Injury (1)
- Caregiving (3)
- Care Management (1)
- Chronic Conditions (2)
- Clinical Decision Support (CDS) (3)
- COVID-19 (1)
- Dementia (9)
- Diagnostic Safety and Quality (2)
- Elderly (5)
- Electronic Health Records (EHRs) (8)
- Emergency Department (2)
- Healthcare Delivery (2)
- Healthcare Utilization (2)
- (-) Health Information Technology (HIT) (20)
- Long-Term Care (1)
- (-) Neurological Disorders (20)
- Nursing Homes (1)
- Outcomes (1)
- Patient-Centered Outcomes Research (1)
- Patient Experience (1)
- Practice-Based Research Network (PBRN) (1)
- Prevention (1)
- Primary Care (1)
- Quality Improvement (4)
- Quality Measures (1)
- Quality of Care (3)
- Quality of Life (3)
- Racial and Ethnic Minorities (1)
- Screening (1)
- Shared Decision Making (3)
- Surgery (4)
- Telehealth (5)
- Tools & Toolkits (3)
- Workflow (1)
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 20 of 20 Research Studies DisplayedHua Y, Temkin-Greener H, Cai S
Primary care telemedicine use among assisted living residents with dementia during COVID-19: race and dual enrollment status.
The purpose of this study was to explore primary care telemedicine use among Medicare beneficiaries with Alzheimer’s disease and related dementias (ADRD) who resided in Assisted Living Facilities (Als) during the early stage of the COVID-19 pandemic, with a focus on possible racial and socioeconomic differences. The study found that at the start of the pandemic in quarter 2 of 2020, Black residents were less likely to have telemedicine visits than their White counterparts. In the following two quarters, Black residents were more likely to receive primary care via telemedicine than White residents; a similar difference was observed between Hispanic and White residents, but with smaller effect sizes. Compared with nondual residents, dual residents were more likely to receive primary care via telemedicine in Q3. In addition, residents in AL communities with a higher proportion of dual residents, compared with those in low-dual ALs, were less likely to receive primary care via telemedicine throughout the study period. However, the difference in telemedicine use between higher vs lower dual ALs narrowed over time.
AHRQ-funded; HS026893.
Citation: Hua Y, Temkin-Greener H, Cai S .
Primary care telemedicine use among assisted living residents with dementia during COVID-19: race and dual enrollment status.
J Am Med Dir Assoc 2023 Aug; 24(8):1157-58.e3. doi: 10.1016/j.jamda.2023.05.005..
Keywords: COVID-19, Primary Care, Telehealth, Health Information Technology (HIT), Nursing Homes, Long-Term Care, Dementia, Neurological Disorders, Racial and Ethnic Minorities, Elderly
Wissel BD, Greiner HM, Glauser TA
Automated, machine learning-based alerts increase epilepsy surgery referrals: a randomized controlled trial.
Researchers conducted a prospective, randomized controlled trial of a natural language processing-based clinical decision support system in the electronic health record at 14 pediatric neurology outpatient clinics to determine whether automated, electronic alerts increased referrals for epilepsy surgery. Children with epilepsy and at least two prior neurology visits were screened by the system prior to their scheduled visit to identify potential surgical candidates, and the potential candidates randomized 2:1 for their providers to receive an alert or standard of care (no alert). The results showed that patients whose providers received an alert were more likely to be referred for a presurgical evaluation. The researchers concluded that machine learning-based automated alerts may improve the utilization of referrals for epilepsy surgery evaluations.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner HM, Glauser TA .
Automated, machine learning-based alerts increase epilepsy surgery referrals: a randomized controlled trial.
Epilepsia 2023 Jul; 64(7):1791-99. doi: 10.1111/epi.17629..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT)
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.
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)
Gaugler JE, Rosebush CA, Zmora R
Outcomes of remote activity monitoring for persons living with dementia over an 18-month period.
The purpose of this study was to evaluate whether Remote Activity Monitoring (RAM) technology was associated with reductions in negative health transitions and service utilization for persons with Alzheimer's disease or a related dementia over an 18-month period. The researchers enrolled 88 recipients and their caregivers in a clinical trial, with 88 care recipients and their caregivers in the RAM intervention arm and 91 care recipients and their caregivers in the control arm. The treatment group had the RAM system installed in their home. The attention control group did not receive RAM technology. Baseline and follow-up surveys assessed whether the care recipient had fallen or wandered in the past 6 months (yes/no). Caregivers were also asked whether the care recipient had used any of the following healthcare services in the past 6 months: nursing home stays, assisted living stays other residential care stays, hospital stays, or emergency room visits. The study found that in adjusted models, emergency department visits were almost 50% lower in the intervention group compared with the control group. In addition, the odds of experiencing a higher frequency of falls versus a lower frequency of falls was 0.36 for those in the intervention group compared with controls. The RAM technology did not have a statistically significant effect on any other outcome. The researchers concluded that although RAM did not provide direct support for the management of behaviors for persons with AD/ADRD, the findings imply that this technology may prevent some adverse health events for people living with dementia in the community. The ongoing, unobtrusive monitoring and system alerts of RAM may have resulted in caregivers identifying activity or the lack thereof that may
have prevented falls and wandering events. In turn, emergency room use among persons with dementia may have been avoided.
have prevented falls and wandering events. In turn, emergency room use among persons with dementia may have been avoided.
AHRQ-funded; HS022836.
Citation: Gaugler JE, Rosebush CA, Zmora R .
Outcomes of remote activity monitoring for persons living with dementia over an 18-month period.
J Am Geriatr Soc 2022 Aug;70(8):2439-42. doi: 10.1111/jgs.17839..
Keywords: Elderly, Dementia, Neurological Disorders, Telehealth, Health Information Technology (HIT), Outcomes, Caregiving
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
Wissel BD, Greiner HM, Glauser TA
Early identification of epilepsy surgery candidates: a multicenter, machine learning study.
Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. The study objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery. The investigators concluded that site-specific machine learning algorithms could identify candidates for epilepsy surgery early in the disease course in diverse practice settings.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner HM, Glauser TA .
Early identification of epilepsy surgery candidates: a multicenter, machine learning study.
Acta Neurol Scand 2021 Jul;114(1):41-50. doi: 10.1111/ane.13418..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT)
Jacobs M, Briley P, Ellis C
Quantifying experiences with telepractice for aphasia therapy: a text mining analysis of client response data.
The investigators’ goal was to use content analysis (CA) to measure posttreatment satisfaction with a telepractice approach for aphasia treatment. Seventeen persons with aphasia (PWA) received 12 treatment sessions over a 6-week period, after which CA was utilized to explore patient satisfaction with this treatment approach. The investigators concluded that their study demonstrated that CA can be an effective approach for determining satisfaction with aphasia treatment, particularly among PWA with limited verbal abilities.
AHRQ-funded; HS025043.
Citation: Jacobs M, Briley P, Ellis C .
Quantifying experiences with telepractice for aphasia therapy: a text mining analysis of client response data.
Semin Speech Lang 2020 Nov;41(5):414-32. doi: 10.1055/s-0040-1716887..
Keywords: Telehealth, Health Information Technology (HIT), Patient Experience, Neurological Disorders
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), Shared 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
Wissel BD, Greiner TA, Holland-Bouley KD
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective of this study was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epilepsy surgery candidacy scores. The authors suggest that an electronic health record-integrated NLP application can accurately assign surgical candidacy scores to patients in a clinical setting.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner TA, Holland-Bouley KD .
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Epilepsia 2020 Jan;61(1):39-48. doi: 10.1111/epi.16398..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT), Clinical Decision Support (CDS), Shared Decision Making
Dickerson LK, Rouhizadeh M, Korotkaya Y
Language impairment in adults with end-stage liver disease: application of natural language processing towards patient-generated health records.
This study examined the association between language and cognitive impairment in adults with end-stage liver disease (ESLD) and patients post-transplant where the impairments have resolved themselves. Patients showed great improvement after transplant, and the natural language processing (NLP) impairment can be used to detect cognitive impairment in ESLD.
AHRQ-funded; HS023876.
Citation: Dickerson LK, Rouhizadeh M, Korotkaya Y .
Language impairment in adults with end-stage liver disease: application of natural language processing towards patient-generated health records.
NPJ Digit Med 2019 Nov 4;2:106. doi: 10.1038/s41746-019-0179-9..
Keywords: Chronic Conditions, Neurological Disorders, Health Information Technology (HIT)
Possin KL, Merrilees JJ, Dulaney S
Effect of collaborative dementia care via telephone and internet on quality of life, caregiver well-being, and health care use: the Care Ecosystem randomized clinical trial.
Few health systems have adopted effective dementia care management programs. The Care Ecosystem is a model for delivering care from centralized hubs across broad geographic areas to caregivers and persons with dementia (PWDs) independently of their health system affiliations. The purpose of this study was to determine whether the Care Ecosystem was effective in improving outcomes important to PWDs, their caregivers, and payers beyond those achieved with usual care.
AHRQ-funded; HS022241.
Citation: Possin KL, Merrilees JJ, Dulaney S .
Effect of collaborative dementia care via telephone and internet on quality of life, caregiver well-being, and health care use: the Care Ecosystem randomized clinical trial.
JAMA Intern Med 2019 Sep 30;179(12):1658-67. doi: 10.1001/jamainternmed.2019.4101..
Keywords: Dementia, Neurological Disorders, Telehealth, Health Information Technology (HIT), Care Management, Healthcare Delivery, Caregiving, Quality of Life
Wissel BD, Greiner HM, Glauser TA
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluations. To assess this, an NLP algorithm was trained to identify potential surgical candidates using 1097 notes from 175 epilepsy patients with a history of resective epilepsy surgery and 268 patients who achieved seizure freedom without surgery (total N = 443 patients).
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner HM, Glauser TA .
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.
Epilepsia 2019 Sep;60(9):e93-e98. doi: 10.1111/epi.16320..
Keywords: Neurological Disorders, Surgery, Clinical Decision Support (CDS), Healthcare Utilization, Health Information Technology (HIT), Shared Decision Making
Gillespie SM, Wasserman EB, Wood NE
High-intensity telemedicine reduces emergency department use by older adults with dementia in senior living communities.
Individuals with dementia have high rates of emergency department (ED) use for acute illnesses. In this study, the investigators evaluated the effect of a high-intensity telemedicine program that delivered care for acute illnesses on ED use rates for individuals with dementia residing in senior living communities (SLCs; independent and assisted living).
AHRQ-funded; HS018047.
Citation: Gillespie SM, Wasserman EB, Wood NE .
High-intensity telemedicine reduces emergency department use by older adults with dementia in senior living communities.
J Am Med Dir Assoc 2019 Aug;20(8):942-46. doi: 10.1016/j.jamda.2019.03.024..
Keywords: Elderly, Telehealth, Health Information Technology (HIT), Dementia, Neurological Disorders, Healthcare Delivery, Chronic Conditions, Emergency Department, Healthcare Utilization
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
Gaugler JE, Zmora R, Mitchell LL
Six-month effectiveness of remote activity monitoring for persons living with dementia and their family caregivers: an experimental mixed methods study.
This paper describes a pilot study conducted to evaluate the effectiveness of remote activity monitoring (RAM) for persons living with Alzheimer’s disease or a related dementia (ADRD) and their family caregivers. An experimental mixed methods study of 132 persons living with ADRD was conducted for six months. There were mixed results as the early months spent calibrating and modifying the RAM system was challenging for families. For families who care for ADRD patients with less severe cognitive impairment and difficulty navigating around the most there was a statistically significant increase in competence and self-sufficiency. However, it may not be as effective for patients with more severe cognitive impairment.
AHRQ-funded; HS022836.
Citation: Gaugler JE, Zmora R, Mitchell LL .
Six-month effectiveness of remote activity monitoring for persons living with dementia and their family caregivers: an experimental mixed methods study.
Gerontologist 2019 Jan 9;59(1):78-89. doi: 10.1093/geront/gny078..
Keywords: Caregiving, Dementia, Elderly, Health Information Technology (HIT), Neurological Disorders, Quality of Care, Quality Improvement
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
Carlozzi NE, Schilling SG, Lai JS
HDQLIFE: the development of two new computer adaptive tests for use in Huntington disease, speech difficulties, and swallowing difficulties.
The authors developed a patient-reported outcome measure for use in the Huntington Disease Health-Related Quality of Life (HDQLIFE) Measurement System that focused on the impact that difficulties with speech and swallowing have on health-related quality of life in Huntington disease. These measures may have clinical utility in other populations where speech and swallowing difficulties are prevalent.
AHRQ-funded; HS023313.
Citation: Carlozzi NE, Schilling SG, Lai JS .
HDQLIFE: the development of two new computer adaptive tests for use in Huntington disease, speech difficulties, and swallowing difficulties.
Qual Life Res 2016 Oct;25(10):2417-27. doi: 10.1007/s11136-016-1273-y.
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Keywords: Neurological Disorders, Quality of Life, Patient-Centered Outcomes Research, Health Information Technology (HIT), Quality Measures