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AHRQ Research Studies Date
Topics
- Chronic Conditions (1)
- Clinical Decision Support (CDS) (1)
- COVID-19 (1)
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- Electronic Health Records (EHRs) (2)
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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 5 of 5 Research Studies DisplayedFritz B, King C, Chen Y
Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study.
This paper describes a protocol for an ongoing study that hypothesizes that anesthesiology clinicians can predict postoperative complications more accurately with machine learning assistance than without machine learning assistance. This investigation is a sub-study nested within the TECTONICS randomized clinical trial. Study team members who are anesthesiology clinicians working in a telemedicine setting are currently reviewing ongoing surgical cases and documenting how likely they feel the patient is to experience 30-day in-hospital death or acute kidney injury. These case reviews will be randomized to be performed with access to a display showing machine learning predictions for the postoperative complications or without access to the display, and the accuracy of the predictions will be compared across these two groups.
AHRQ-funded; HS024581.
Citation: Fritz B, King C, Chen Y .
Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study.
F1000Res 2022; 11:653. doi: 10.12688/f1000research.122286.2..
Keywords: Surgery, Risk, Outcomes, Health Information Technology (HIT)
Grauer A, Duran AT, Liyanage-Don NA
Association between telemedicine use and diabetes risk factor assessment and control in a primary care network.
The purpose of this retrospective cohort study was to explore whether there is a relationship between telemedicine use in primary care and risk factor assessment and control for patients with diabetes mellitus. The study included patients with diabetes mellitus ages 18-75 with a telemedicine visit in a primary care network between February 2020 and December 2020. Researchers evaluated whether low-density lipoprotein cholesterol (LDL-C), blood pressure (BP), and hemoglobin A1c (HbA1c) and were assessed for each patient. The study identified 1,824 patients with diabetes during the study period and found that telemedicine use was associated with a lower proportion of patients with all three risk factors assessed. The researchers concluded that telemedicine use was related with gaps in risk factor assessment for patients with diabetes during the COVID-19 pandemic.
AHRQ-funded; HS026121; HS024262.
Citation: Grauer A, Duran AT, Liyanage-Don NA .
Association between telemedicine use and diabetes risk factor assessment and control in a primary care network.
J Endocrinol Invest 2022 Sep;45(9):1749-56. doi: 10.1007/s40618-022-01814-6..
Keywords: Diabetes, Chronic Conditions, Telehealth, Health Information Technology (HIT), Primary Care, 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
Jacobsohn GC, Leaf M, Liao F
Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.
The authors used a collaborative and iterative approach to design and implement an automated clinical decision support system (CDS) for Emergency Department (ED) providers to identify and refer older adult ED patients at high risk of future falls. The system was developed using collaborative input from an interdisciplinary design team and integrated seamlessly into existing ED workflows. A key feature of development was the unique combination of patient experience strategies, human-centered design, and implementation science, which allowed for the CDS tool and intervention implementation strategies to be designed simultaneously. Challenges included: usability problems, data inaccessibility, time constraints, low appointment availability, high volume of patients, and others. The study concluded that using the collaborative, iterative approach was successful in achieving all project goals, and could be applied to other cases.
AHRQ-funded; HS024558.
Citation: Jacobsohn GC, Leaf M, Liao F .
Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.
Healthc 2022 Mar;10(1):100598. doi: 10.1016/j.hjdsi.2021.100598..
Keywords: Elderly, Clinical Decision Support (CDS), Shared Decision Making, Falls, Risk, Emergency Department, Health Information Technology (HIT)
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)