National Healthcare Quality and Disparities Report
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Topics
- Adverse Events (2)
- Asthma (1)
- Cardiovascular Conditions (1)
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- Risk (2)
- Surgery (2)
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- Transitions of Care (3)
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 15 of 15 Research Studies DisplayedLopez K, Li H, Lipkin-Moore Z
Deep learning prediction of hospital readmissions for asthma and COPD.
The purpose of this observational study was to identify Electronic Health Record (EHR) features of severe asthma and COPD exacerbations and assess the performance of four machine learning (ML) and one deep learning (DL) model in predicting readmissions using EHR data. The study included 31, 2017 patients hospitalized with asthma and COPD exacerbations. The study found that Black and Hispanic patients had a greater likelihood of readmission for asthma. Patients with COPD readmissions included a high percentage of Blacks and Hispanics. To identify patients at high risk of readmission, index hospitalization data of a subset of 2,682 patients, 777 with asthma and 1,905 with COPD, was analyzed with four ML models, and one DL model. The researchers discovered that multilayer perceptron, the DL method, had the best sensitivity and specificity compared to the four ML methods implemented in the same dataset.
AHRQ-funded; HS027626.
Citation: Lopez K, Li H, Lipkin-Moore Z .
Deep learning prediction of hospital readmissions for asthma and COPD.
Respir Res 2023 Dec 13; 24(1):311. doi: 10.1186/s12931-023-02628-7..
Keywords: Asthma, Respiratory Conditions, Hospital Readmissions, Electronic Health Records (EHRs), Health Information Technology (HIT)
Nguyen OK, Washington C, Clark CR
Man vs. machine: comparing physician vs. electronic health record-based model predictions for 30-day hospital readmissions.
Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. In this study, the investigators sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions.
AHRQ-funded; HS022418.
Citation: Nguyen OK, Washington C, Clark CR .
Man vs. machine: comparing physician vs. electronic health record-based model predictions for 30-day hospital readmissions.
J Gen Intern Med 2021 Sep;36(9):2555-62. doi: 10.1007/s11606-020-06355-3..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Hospital Readmissions
Elysee G, Yu H, Herrin J
Association between 30-day readmission rates and health information technology capabilities in US hospitals.
A study was conducted to determine if there is an association of health information technology (HIT) adoption and a decrease in 30-day hospital readmission rates. Data was used from the 2013 American Hospital Association IT survey which included non-federal U.S. acute care hospitals with self-reported capabilities. A 54-indicator 7-factor structure of hospital health IT capabilities was identified by exploratory factor analysis. A one-point increase in the hospital adoption of patient engagement capability latent scores generally leads to a 0.086% decrease in risk-standardized readmission rates (RSRRs). However, computerized hospital discharge and information exchange among clinicians did not seem as beneficial.
AHRQ-funded; HS022882.
Citation: Elysee G, Yu H, Herrin J .
Association between 30-day readmission rates and health information technology capabilities in US hospitals.
Medicine 2021 Feb 26;100(8):e24755. doi: 10.1097/md.0000000000024755..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Hospital Readmissions, Hospitals, Quality Indicators (QIs), Quality of Care
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)
Smith AB, Mueller D, Garren B
Using qualitative research to reduce readmissions and optimize perioperative cystectomy care.
This study examined the need for qualitative research on meaningful patient-reported outcomes (PROs) to prevent complications and readmissions after cystectomy. The investigators looked at the potential use of mobile communication devices (mHealth) to capture patients’ experiences and to improve outcomes. Interviews were conducted with 15 readmitted patients and 10 of their partners over 45 semi-structured in-depth interviews. The most common perspectives were that patients and their caregivers were overloaded with cystectomy education; they need to know what are normal post-operative symptoms; and that using mHealth would help with patient and caregiver education.
AHRQ-funded; HS024134.
Citation: Smith AB, Mueller D, Garren B .
Using qualitative research to reduce readmissions and optimize perioperative cystectomy care.
Cancer 2019 Oct 15;125(20):3545-53. doi: 10.1002/cncr.32362..
Keywords: Hospital Readmissions, Surgery, Health Information Technology (HIT), Quality Improvement, Quality of Care, Hospitals, Patient-Centered Healthcare
Vest JR, Unruh MA, Freedman S
Health systems' use of enterprise health information exchange vs single electronic health record vendor environments and unplanned readmissions.
Enterprise health information exchange (HIE) and a single electronic health record (EHR) vendor solution are 2 information exchange approaches to improve performance and increase the quality of care. This study sought to determine the association between adoption of enterprise HIE vs a single vendor environment and changes in unplanned readmissions. The investigators concluded that reductions in the probability of an unplanned readmission after a hospital adopts a single vendor environment suggested that HIE technologies can better support the aim of higher quality care.
AHRQ-funded; HS024717.
Citation: Vest JR, Unruh MA, Freedman S .
Health systems' use of enterprise health information exchange vs single electronic health record vendor environments and unplanned readmissions.
J Am Med Inform Assoc 2019 Oct;26(10):989-98. doi: 10.1093/jamia/ocz116..
Keywords: Health Systems, Health Information Exchange (HIE), Electronic Health Records (EHRs), Health Information Technology (HIT), Hospital Readmissions, Hospitals
Kimmel HJ, Brice YN, Trikalinos TA
Real-time emergency department electronic notifications regarding high-risk patients: a systematic review.
In this study, the authors systematically reviewed evidence on the feasibility and efficacy of real-time electronic notifications about patients at high risk of emergency department (ED) recidivism. They concluded that real-time electronic notifications of ED providers regarding patients at high risk of ED recidivism are feasible and may help reduce resource utilization and costs. The authors indicate that large knowledge gaps remain regarding patient- and provider-centered outcomes.
AHRQ-funded; HS022998.
Citation: Kimmel HJ, Brice YN, Trikalinos TA .
Real-time emergency department electronic notifications regarding high-risk patients: a systematic review.
Telemed J E Health 2019 Jul;25(7):604-18. doi: 10.1089/tmj.2018.0117..
Keywords: Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT), Hospital Readmissions
Ganapathy D, Acharya C, Lachar J
The patient buddy app can potentially prevent hepatic encephalopathy-related readmissions.
The researchers aimed to define the feasibility of using the Patient Buddy App and its impact on 30-day readmissions by engaging and educating cirrhotic inpatients and caregivers in a pilot study. In their proof-of-concept trial, the use of Patient Buddy is feasible in recently discharged patients with cirrhosis and their caregivers. Eight hepatic encephalopathy-related readmissions were potentially avoided after the use of the App.
AHRQ-funded; HS024004.
Citation: Ganapathy D, Acharya C, Lachar J .
The patient buddy app can potentially prevent hepatic encephalopathy-related readmissions.
Liver Int 2017 Dec;37(12):1843-51. doi: 10.1111/liv.13494.
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Keywords: Caregiving, Chronic Conditions, Health Information Technology (HIT), Patient and Family Engagement, Hospital Readmissions
Symer MM, Abelson JS, Milsom J
A mobile health application to track patients after gastrointestinal surgery: results from a pilot study.
Many surgical readmissions are preventable. Mobile health technology can identify nascent complications and potentially prevent readmission. The researchers performed a pilot study of a new mobile health application in adults undergoing major abdominal surgery and determined the app can track patient recovery from major abdominal surgery, is easy to use, and has potential to improve outcomes.
AHRQ-funded; HS000066.
Citation: Symer MM, Abelson JS, Milsom J .
A mobile health application to track patients after gastrointestinal surgery: results from a pilot study.
J Gastrointest Surg 2017 Sep;21(9):1500-05. doi: 10.1007/s11605-017-3482-2..
Keywords: Telehealth, Health Information Technology (HIT), Hospital Readmissions, Surgery, Adverse Events, Patient Safety, Digestive Disease and Health, Prevention
Makam AN, Nguyen OK, Clark C
Predicting 30-day pneumonia readmissions using electronic health record data.
The objective of this study was to develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay"). The investigators concluded that EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. They suggest that this approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores.
AHRQ-funded; HS022418.
Citation: Makam AN, Nguyen OK, Clark C .
Predicting 30-day pneumonia readmissions using electronic health record data.
J Hosp Med 2017 Apr;12(4):209-16. doi: 10.12788/jhm.2711..
Keywords: Pneumonia, Hospital Readmissions, Hospitalization, Electronic Health Records (EHRs), Health Information Technology (HIT)
Driessen J, Bonhomme A, Chang W
Nursing home provider perceptions of telemedicine for reducing potentially avoidable hospitalizations.
The goal of this study was to survey a nationally representative sample of nursing home physicians and advanced practice providers to quantify provider perceptions and desired functionality of telemedicine in nursing homes to reduce potentially avoidable hospitalizations. The authors found that there is a high degree of confidence in the potential for a telemedicine solution and concrete views about its features, concluding that further research is needed to study the impact of successful implementations.
AHRQ-funded; HS018721; HS022989; HS022465; HS023779.
Citation: Driessen J, Bonhomme A, Chang W .
Nursing home provider perceptions of telemedicine for reducing potentially avoidable hospitalizations.
J Am Med Dir Assoc 2016 Jun;17(6):519-24. doi: 10.1016/j.jamda.2016.02.004.
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Keywords: Health Information Technology (HIT), Hospital Readmissions, Hospitalization, Nursing Homes, Telehealth
Donovan JL, Kanaan AO, Gurwitz JH
A pilot health information technology-based effort to increase the quality of transitions from skilled nursing facility to home: compelling evidence of high rate of adverse outcomes.
The authors investigated whether or not patients transferred from skilled nursing facilities to home may be at risk for adverse outcomes. They tracked rehospitalization within 30 days after discharge and adverse drug events within 45 days. They concluded that older adults discharged from skilled nursing facilities are at high risk of adverse outcomes immediately following discharge.
AHRQ-funded; HS017817.
Citation: Donovan JL, Kanaan AO, Gurwitz JH .
A pilot health information technology-based effort to increase the quality of transitions from skilled nursing facility to home: compelling evidence of high rate of adverse outcomes.
J Am Med Dir Assoc 2016 Apr;17(4):312-7. doi: 10.1016/j.jamda.2015.11.008.
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Keywords: Health Information Technology (HIT), Transitions of Care, Adverse Events, Elderly, Hospital Readmissions
Swain MJ, Kharrazi H
Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data.
The researchers conducted a semi-systematic review of readmission predictive factors published prior to March 2013. They found that mapping of these variables with common HL7 segments resulted in an 89.2 percent total coverage, with the DG1 (diagnosis) segment having the highest coverage of 39.4 percent. The PID (patient identification) and OBX (observation results) segments cover 13.9 percent and 9.1 percent of the variables.
AHRQ-funded; HS022578.
Citation: Swain MJ, Kharrazi H .
Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data.
Int J Med Inform 2015 Dec;84(12):1048-56. doi: 10.1016/j.ijmedinf.2015.09.003.
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Keywords: Health Information Exchange (HIE), Hospital Readmissions, Health Information Technology (HIT), Data
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
Black JT, Romano PS, Sadeghi B
A remote monitoring and telephone nurse coaching intervention to reduce readmissions among patients with heart failure: study protocol for the Better
The objective of this randomized controlled comparative effectiveness study was to evaluate the effectiveness of a care transition intervention that included pre-discharge education about heart failure and post-discharge telephone nurse coaching combined with home telemonitoring of weight, blood pressure, heart rate, and symptoms in reducing all-cause 180-day hospital readmissions for older adults hospitalized with heart failure.
AHRQ-funded; HS019311.
Citation: Black JT, Romano PS, Sadeghi B .
A remote monitoring and telephone nurse coaching intervention to reduce readmissions among patients with heart failure: study protocol for the Better
Trials 2014 Apr 13;15:124. doi: 10.1186/1745-6215-15-124..
Keywords: Cardiovascular Conditions, Comparative Effectiveness, Health Information Technology (HIT), Heart Disease and Health, Hospital Readmissions, Telehealth, Transitions of Care