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AHRQ Research Studies Date
Topics
- Adverse Drug Events (ADE) (1)
- Adverse Events (2)
- Ambulatory Care and Surgery (1)
- Cardiovascular Conditions (3)
- Children/Adolescents (1)
- Clinician-Patient Communication (1)
- Communication (1)
- COVID-19 (1)
- Diabetes (1)
- Diagnostic Safety and Quality (1)
- Elderly (3)
- (-) Electronic Health Records (EHRs) (24)
- Electronic Prescribing (E-Prescribing) (1)
- Emergency Department (4)
- Falls (1)
- Guidelines (1)
- Healthcare-Associated Infections (HAIs) (1)
- Healthcare Utilization (1)
- (-) Health Information Technology (HIT) (24)
- Heart Disease and Health (2)
- Home Healthcare (2)
- Hospitalization (4)
- Hospital Readmissions (2)
- Hospitals (2)
- Injuries and Wounds (3)
- Medication (4)
- Medication: Safety (1)
- Mortality (3)
- Organizational Change (1)
- Osteoporosis (2)
- Outcomes (2)
- Patient-Centered Outcomes Research (1)
- Patient Adherence/Compliance (1)
- Patient Safety (6)
- Prevention (1)
- Primary Care (1)
- Provider (1)
- Provider: Clinician (1)
- (-) Risk (24)
- Sepsis (1)
- Social Determinants of Health (1)
- Surgery (2)
- Transitions of Care (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 24 of 24 Research Studies DisplayedLinfield GH, Patel S, Ko HJ
Evaluating the comparability of patient-level social risk data extracted from electronic health records: a systematic scoping review.
This study’s objective was to evaluate how and from where social risk data are extracted from electronic health records (EHRs) for research purposes, and how observed differences may impact study generalizability. A systematic scoping review was conducted of peer-reviewed literature that used patient-level EHR data to assess 1 ± 6 social risk domains: housing, transportation, food, utilities, safety, social support/isolation. The authors found 111 of 9022 identified articles met inclusion criteria. By domain, martial/partner status was most often included, predominantly defined by marital partner status, and extracted from structured sociodemographic data. Structured housing data was extracted most from billing codes and screening tools. Across domains, data were predominantly sourced from structured fields (n = 89/111) versus unstructured free text (n = 32/111).
AHRQ-funded; HS026383.
Citation: Linfield GH, Patel S, Ko HJ .
Evaluating the comparability of patient-level social risk data extracted from electronic health records: a systematic scoping review.
Health Informatics J 2023 Jul-Sep; 29(3):14604582231200300. doi: 10.1177/14604582231200300..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Social Determinants of Health, Risk
Hobensack M, Ojo M, Barrón Y
Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.
The objectives of this study were to identify risk factors that home healthcare clinicians associate with patient deterioration and to understand clinicians’ response to and documentation of these risk factors. The authors interviewed multidisciplinary home healthcare clinicians and used directed content analysis to identify risk factors for deterioration. A total of 79 risk factors were identified by the clinicians, who responded most often by communicating with the prescribing provider or following up with patients and caregivers. Clinicians also acknowledged that social factors played a role in deterioration risk. The authors noted that, since most risk factors were documented in clinical notes, methods such as natural language processing are needed to extract them. They concluded that by providing a comprehensive list of risk factors grounded in clinician expertise and mapped to standardized terminologies, the results of their study supported the development of an early warning system for patient deterioration.
AHRQ-funded; HS027742.
Citation: Hobensack M, Ojo M, Barrón Y .
Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.
J Am Med Inform Assoc 2022 Apr 13;29(5):805-12. doi: 10.1093/jamia/ocac023..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Home Healthcare, Risk, Hospitalization
Kamran F, Tang S, Otles E
Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.
The authors sought to create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with COVID-19 across institutions, through use of a novel paradigm for model development and code sharing. They determined that a model to predict clinical deterioration was developed rapidly in response to the COVID-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.
AHRQ-funded; HS028038.
Citation: Kamran F, Tang S, Otles E .
Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.
BMJ 2022 Feb 17;376:e068576. doi: 10.1136/bmj-2021-068576..
Keywords: COVID-19, Hospitalization, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Enayati M, Sir M, Zhang X
Monitoring diagnostic safety risks in emergency departments: protocol for a machine learning study.
This study’s objective will be to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. It will use trigger algorithms with electronic health record (EHR) data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on if they meet certain criteria. This study will be conducted by 2 academic medical centers with affiliated community hospitals.
AHRQ-funded; HS027363; HS026622.
Citation: Enayati M, Sir M, Zhang X .
Monitoring diagnostic safety risks in emergency departments: protocol for a machine learning study.
JMIR Res Protoc 2021 Jun 14;10(6):e24642. doi: 10.2196/24642..
Keywords: Emergency Department, Diagnostic Safety and Quality, Patient Safety, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Kandaswamy S, Pruitt Z, Kazi S
Clinician perceptions on the use of free-text communication orders.
The aim of this study was to investigate (1) why ordering clinicians use free-text orders to communicate medication information; (2) what risks physicians and nurses perceive when free-text orders are used for communicating medication information; and (3) how electronic health records (EHRs) could be improved to encourage the safe communication of medication information. The investigators concluded that clinicians' use of free-text orders as a workaround to insufficient structured order entry can create unintended patient safety risks.
AHRQ-funded; HS025136; HS024755.
Citation: Kandaswamy S, Pruitt Z, Kazi S .
Clinician perceptions on the use of free-text communication orders.
Appl Clin Inform 2021 May;12(3):484-94. doi: 10.1055/s-0041-1731002..
Keywords: Electronic Prescribing (E-Prescribing), Health Information Technology (HIT), Electronic Health Records (EHRs), Medication: Safety, Medication, Patient Safety, Communication, Provider: Clinician, Provider, Risk
Topaz M, Woo K, Ryvicker M
Home healthcare clinical notes predict patient hospitalization and emergency department visits.
About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).
AHRQ-funded; HS027742.
Citation: Topaz M, Woo K, Ryvicker M .
Home healthcare clinical notes predict patient hospitalization and emergency department visits.
Nurs Res 2020 Nov/Dec;69(6):448-54. doi: 10.1097/nnr.0000000000000470..
Keywords: Elderly, Home Healthcare, Emergency Department, Hospitalization, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Saleh SN, Makam AN, Halm EA,
Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?
Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). In this study, the investigators assessed how well a previously validated 30-day EHR-based readmission model predicted 7-day readmissions and compared differences in strength of predictors. They suggested that improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.
AHRQ-funded; HS022418.
Citation: Saleh SN, Makam AN, Halm EA, .
Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?
BMC Med Inform Decis Mak 2020 Sep 15;20(1):227. doi: 10.1186/s12911-020-01248-1..
Keywords: Hospital Readmissions, Hospitals, Risk, Transitions of Care, Electronic Health Records (EHRs), Health Information Technology (HIT)
Scott HF, Colborn KL, Sevick CJ
Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival.
The purpose of this observational cohort study was to derive and validate a model of risk of septic shock among children with suspected sepsis, using data known in the electronic health record at hospital arrival. The investigators concluded that their model estimated the risk of septic shock in children at hospital arrival earlier than existing models. They indicate it leveraged the predictive value of routine electronic health record data through a modern predictive algorithm and suggest it has the potential to enhance clinical risk stratification in the critical moments before deterioration.
AHRQ-funded; HS025696.
Citation: Scott HF, Colborn KL, Sevick CJ .
Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival.
J Pediatr 2020 Feb;217:145-51.e6. doi: 10.1016/j.jpeds.2019.09.079..
Keywords: Children/Adolescents, Sepsis, Emergency Department, Hospitals, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Hannan EL, Barrett SC, Samadashvili Z
Retooling of paper-based outcome measures to electronic format: comparison of the NY State public risk model and EHR-derived risk models for CABG mortality.
This study assessed the feasibility of retooling the paper-based New York State coronary artery bypass graft (CABG) surgery statistical model for mortality and readmission into a model for electronic health records (EHRs). Researchers found that only 6 data elements could be extracted from the EHR, and outlier hospitals differed for readmission but was usable for mortality. They concluded that the EHR model was inferior to the NYS model, and that simplifying the EHR risk model couldn’t capture most of the risk factors in the NYS model.
AHRQ-funded; HS022647.
Citation: Hannan EL, Barrett SC, Samadashvili Z .
Retooling of paper-based outcome measures to electronic format: comparison of the NY State public risk model and EHR-derived risk models for CABG mortality.
Med Care 2019 May;57(5):377-84. doi: 10.1097/mlr.0000000000001104..
Keywords: Surgery, Electronic Health Records (EHRs), Health Information Technology (HIT), Mortality, Outcomes, Risk, Cardiovascular Conditions
Grundmeier RW, Xiao R, Ross RK
Grundmeier RW, Xiao R, Ross RK, Ramos MJ, Karavite DJ, Michel JJ, Gerber JS, et al. Identifying surgical site infections in electronic health data using predictive models,.
The objective of this study was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery. The investigators concluded that electronic health record data can facilitate SSI surveillance with adequate sensitivity and positive predictive value.
AHRQ-funded; HS020921.
Citation: Grundmeier RW, Xiao R, Ross RK .
Grundmeier RW, Xiao R, Ross RK, Ramos MJ, Karavite DJ, Michel JJ, Gerber JS, et al. Identifying surgical site infections in electronic health data using predictive models,.
J Am Med Inform Assoc 2018 Sep;25(9):1160-66. doi: 10.1093/jamia/ocy075..
Keywords: Healthcare-Associated Infections (HAIs), Injuries and Wounds, Surgery, Electronic Health Records (EHRs), Health Information Technology (HIT), Risk, Patient Safety, Adverse Events, Ambulatory Care and Surgery
Flory JH, Keating SJ, Siscovick D
Identifying prevalence and risk factors for metformin non-persistence: a retrospective cohort study using an electronic health record.
Non-persistence may be a significant barrier to the use of metformin. The objective of this study was to assess reasons for metformin non-persistence, and whether initial metformin dosing or use of extended release (ER) formulations affect persistence to metformin therapy. The investigators concluded that their data supported the routine prescribing of low starting doses of metformin as a tool to improve persistence.
AHRQ-funded; HS023898.
Citation: Flory JH, Keating SJ, Siscovick D .
Identifying prevalence and risk factors for metformin non-persistence: a retrospective cohort study using an electronic health record.
BMJ Open 2018 Jul 23;8(7):e021505. doi: 10.1136/bmjopen-2018-021505..
Keywords: Diabetes, Electronic Health Records (EHRs), Health Information Technology (HIT), Medication, Patient Adherence/Compliance, Outcomes, Patient-Centered Outcomes Research, Risk
Patterson BW, Repplinger MD, Pulia MS
Using the Hendrich II Inpatient Fall Risk Screen to predict outpatient falls after emergency department visits.
This study examined the utility of using the Hendrich II Inpatient Fall Risk Screen to predict outpatient falls in elderly patients after emergency department (ED) visits. Individuals aged 65 and older seen in the ED from January 2013 to September 30, 2015 participated in the study. The Hendrich II screen was found to correlate with outpatient falls, but it is likely it would have little utility as a stand-alone fall screen. When the screen was combined with other potential confounders or predictors, the screen performed much better.
AHRQ-funded; HS024558.
Citation: Patterson BW, Repplinger MD, Pulia MS .
Using the Hendrich II Inpatient Fall Risk Screen to predict outpatient falls after emergency department visits.
J Am Geriatr Soc 2018 Apr;66(4):760-65. doi: 10.1111/jgs.15299..
Keywords: Elderly, Falls, Risk, Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT), Prevention, Patient Safety, Adverse Events
Rangachari P
Implementing a Social Knowledge Networking (SKN) system to enable meaningful use of an EHR medication reconciliation system.
The study examined user-engagement in the SKN system and associations between "SKN use" and "meaningful use" of electronic health record (EHR). The prospective implementation design is expected to generate context-sensitive strategies for meaningful use and successful implementation of EHR Medication Reconciliation (MedRec) and thereby make substantial contributions to the patient safety and risk management literature.
AHRQ-funded; HS024335.
Citation: Rangachari P .
Implementing a Social Knowledge Networking (SKN) system to enable meaningful use of an EHR medication reconciliation system.
Risk Manag Healthc Policy 2018 Mar 26;11:45-53. doi: 10.2147/rmhp.s152313.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Medication, Patient Safety, Risk
Kan HJ, Kharrazi H, Leff B
Defining and assessing geriatric risk factors and associated health care utilization among older adults using claims and electronic health records.
This study used electronic health records (EHRs) to identify patients with factors associated with geriatric risk for hospitalization among older adults. Prevalence was estimated using claims, structured EHRs, and unstructured EHRs. Odds were calculated on the occurrence of hospitalizations for patients with 1 or 2 and greater risk factors.
AHRQ-funded; HS000029.
Citation: Kan HJ, Kharrazi H, Leff B .
Defining and assessing geriatric risk factors and associated health care utilization among older adults using claims and electronic health records.
Med Care 2018 Mar;56(3):233-39. doi: 10.1097/mlr.0000000000000865..
Keywords: Elderly, Hospitalization, Healthcare Utilization, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Sittig DF, Singh H
Toward more proactive approaches to safety in the electronic health record era.
This article discusses a proactive approach to safety in the electronic health record era. It discusses an updated health IT Sentinel Event Alert, released in March 2015 by the Joint Commission which took a broad, sociotechnical approach in exploring the factors involved in the safe use of health IT.
AHRQ-funded; HS023602; HS022087.
Citation: Sittig DF, Singh H .
Toward more proactive approaches to safety in the electronic health record era.
Jt Comm J Qual Patient Saf 2017 Oct;43(10):540-47. doi: 10.1016/j.jcjq.2017.06.005..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Safety, Guidelines, Organizational Change, Risk
Banerji A, Blumenthal KG, Lai KH
Epidemiology of ACE inhibitor angioedema utilizing a large electronic health record.
The objective of this study was to identify the incidence of and risk factors for angioedema caused by angiotensin-converting enzyme inhibitors (ACEIs) using a large integrated electronic health record (EHR). It concluded that the incidence of ACEI angioedema within a large EHR is consistent with large clinical trial data. A history of nonsteroidal anti-inflammatory drug allergy was identified as a risk factor for patients with ACEI angioedema.
AHRQ-funded; HS022728.
Citation: Banerji A, Blumenthal KG, Lai KH .
Epidemiology of ACE inhibitor angioedema utilizing a large electronic health record.
J Allergy Clin Immunol Pract 2017 May - Jun;5(3):744-49. doi: 10.1016/j.jaip.2017.02.018.
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Keywords: Electronic Health Records (EHRs), Medication, Risk, Health Information Technology (HIT), Adverse Drug Events (ADE)
Wang SV, Rogers JR, Jin Y
Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention.
The researchers tested algorithms for identifying atrial fibrillation (AF) patients who also have known risk factors for stroke and major bleeding using electronic healthcare records (EHRs) data. The performance of candidate algorithms in 1000 bootstrap resamples was compared to a gold standard of manual chart review by experienced resident physicians of 480 patient charts. For 11 conditions, the median positive predictive value of the EHR-derived algorithms was greater than 0.90.
AHRQ-funded; HS022193.
Citation: Wang SV, Rogers JR, Jin Y .
Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention.
J Am Med Inform Assoc 2017 Mar 1;24(2):339-44. doi: 10.1093/jamia/ocw082.
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Keywords: Heart Disease and Health, Cardiovascular Conditions, Electronic Health Records (EHRs), Health Information Technology (HIT), Risk
Haas JS, Baer HJ, Eibensteiner K
A cluster randomized trial of a personalized multi-condition risk assessment in primary care.
This study evaluated whether collection of risk factors to generate electronic health record (EHR)-linked health risk appraisal (HRA) for coronary heart disease, diabetes, breast cancer, and colorectal cancer was associated with improved patient-provider communication, risk assessment, and plans for breast cancer screening. It concluded that patient-reported risk factors and EHR-linked multi-condition HRAs in primary care can modestly improve communication and promote accuracy of self-perceived risk.
AHRQ-funded; HS018644.
Citation: Haas JS, Baer HJ, Eibensteiner K .
A cluster randomized trial of a personalized multi-condition risk assessment in primary care.
Am J Prev Med 2017 Jan;52(1):100-05. doi: 10.1016/j.amepre.2016.07.013.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Primary Care, Clinician-Patient Communication, Risk
Taslimitehrani V, Dong G, Pereira NL
Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.
The authors proposed to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5 year survival in heart failure (HF). They found that the new loss function used in the algorithm outperforms other functions used in previous studies and that HF is a highly heterogeneous disease (different subgroups of patients require different types of considerations with their diagnosis and treatment). They concluded that logistic risk models often make systematic prediction errors and that it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases.
AHRQ-funded; HS023077.
Citation: Taslimitehrani V, Dong G, Pereira NL .
Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.
J Biomed Inform 2016 Apr;60:260-9. doi: 10.1016/j.jbi.2016.01.009.
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Keywords: Electronic Health Records (EHRs), Heart Disease and Health, Health Information Technology (HIT), Risk
Wolfson J, Bandyopadhyay S, Elidrisi M
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.
This paper proposed an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. It compared the predictive performance of that method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrated its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system.
AHRQ-funded; HS017622.
Citation: Wolfson J, Bandyopadhyay S, Elidrisi M .
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.
Stat Med 2015 Sep 20;34(21):2941-57. doi: 10.1002/sim.6526..
Keywords: Risk, Electronic Health Records (EHRs), Health Information Technology (HIT), Cardiovascular Conditions
Amarasingham R, Velasco F, Xie B
Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models.
The purpose of this study was to evaluate the degree to which electronic medical record-based risk models for 30-day readmission or mortality accurately identify high risk patients and to compare these models with published claims-based models. The researchers found that a new electronic multicondition model based on information derived from the electronic medical record predicted mortality and readmission at 30 days, and was superior to previously published claims-based models
AHRQ-funded; HS022418.
Citation: Amarasingham R, Velasco F, Xie B .
Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models.
BMC Med Inform Decis Mak 2015 May 20;15:39. doi: 10.1186/s12911-015-0162-6.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Mortality, Hospital Readmissions, Risk
LaFleur J, Steenhoek CL, Horne J
Comparing fracture absolute risk assessment (FARA) tools: an osteoporosis clinical informatics tool to improve identification and care of men at high risk of first fracture.
The researchers compared 2 fracture absolute risk assessment (FARA) tools for use with electronic health records (EHRs) to determine which would more accurately identify patients known to be high risk for fracture. They found that absolute fracture risk estimation with the VA-FARA is more predictive of a first fracture than the WHO’s eFRAX in male veterans when used in an EHR-based population screening tool.
AHRQ-funded; HS018582.
Citation: LaFleur J, Steenhoek CL, Horne J .
Comparing fracture absolute risk assessment (FARA) tools: an osteoporosis clinical informatics tool to improve identification and care of men at high risk of first fracture.
Ann Pharmacother 2015 May;49(5):506-14. doi: 10.1177/1060028015572819..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Injuries and Wounds, Osteoporosis, Risk
Unni S, Yao Y, Milne N
An evaluation of clinical risk factors for estimating fracture risk in postmenopausal osteoporosis using an electronic medical record database.
The researchers sought to identify variables in an EMR database for calculating fracture risk Assessment (FRAX) score in a cohort of postmenopausal women, to estimate absolute fracture risk. They found that mean 10-year risk for any major fracture was 11.1 percent when bone mineral density (BMD) was used and 11.2 percent when BMI was used.
AHRQ-funded; HS0018582.
Citation: Unni S, Yao Y, Milne N .
An evaluation of clinical risk factors for estimating fracture risk in postmenopausal osteoporosis using an electronic medical record database.
Osteoporos Int 2015 Feb;26(2):581-7. doi: 10.1007/s00198-014-2899-7..
Keywords: Electronic Health Records (EHRs), Injuries and Wounds, Risk, Osteoporosis, Health Information Technology (HIT)
Faerber AE, Horvath R, Stillman C
Development and pilot feasibility study of a health information technology tool to calculate mortality risk for patients with asymptomatic carotid stenosis: the Carotid Risk Assessment Tool (CARAT).
The researchers describe the development of the CArotid Risk Assessment Tool (CARAT) into a 2-year mortality risk calculator within the electronic medical record. They integrated the tool into the clinical workflow, trained the clinical team to use the tool, and assessed the feasibility and acceptability of the tool in one clinic setting.
AHRQ-funded; HS021581.
Citation: Faerber AE, Horvath R, Stillman C .
Development and pilot feasibility study of a health information technology tool to calculate mortality risk for patients with asymptomatic carotid stenosis: the Carotid Risk Assessment Tool (CARAT).
BMC Med Inform Decis Mak 2015;15:20. doi: 10.1186/s12911-015-0141-y..
Keywords: Health Information Technology (HIT), Electronic Health Records (EHRs), Mortality, Risk