National Healthcare Quality and Disparities Report
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Topics
- Adverse Events (3)
- Ambulatory Care and Surgery (2)
- Arthritis (1)
- Cancer (1)
- Cardiovascular Conditions (1)
- Care Management (1)
- Children/Adolescents (2)
- Data (2)
- Diagnostic Safety and Quality (2)
- (-) Electronic Health Records (EHRs) (20)
- Evidence-Based Practice (1)
- Healthcare-Associated Infections (HAIs) (6)
- Health Information Technology (HIT) (16)
- Health Services Research (HSR) (1)
- Health Status (1)
- Hospital Discharge (1)
- Hospital Readmissions (1)
- Hospitals (1)
- Injuries and Wounds (3)
- Inpatient Care (1)
- Medication (1)
- Mortality (1)
- Nursing (1)
- Orthopedics (2)
- Outcomes (3)
- Pain (1)
- Patient-Centered Outcomes Research (1)
- Patient Safety (4)
- Patient Self-Management (1)
- Provider: Nurse (1)
- Quality Improvement (6)
- Quality of Care (5)
- Registries (2)
- Research Methodologies (1)
- Risk (3)
- Shared Decision Making (1)
- (-) Surgery (20)
- Transitions of Care (1)
- Urinary Tract Infection (UTI) (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 DisplayedWu J, Yuan CT, Moyal-Smith R
Electronic health record-supported implementation of an evidence-based pathway for perioperative surgical care.
This study examines the role of electronic health records (EHRs) in implementing enhanced recovery pathways (ERPs) for perioperative surgical care. Interviews with informaticians and clinicians from eight US hospitals revealed three thematic clusters: "EHR difficulties," "EHR enablers," and "EHR barriers." Researchers concluded that high performers and improvers successfully integrated ERPs into EHRs with dedicated multidisciplinary teams, while others faced challenges. Early involvement of informatics expertise benefited ERP implementation and sustainability.
AHRQ-funded; 2332015000201.
Citation: Wu J, Yuan CT, Moyal-Smith R .
Electronic health record-supported implementation of an evidence-based pathway for perioperative surgical care.
J Am Med Inform Assoc 2024 Feb 16; 31(3):591-99. doi: 10.1093/jamia/ocad237.
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery, Evidence-Based Practice, Hospitals
Kutney-Lee A, Brooks Carthon M, Sloane DM
Electronic health record usability: associations with nurse and patient outcomes in hospitals.
Researchers examined associations between electronic health record (EHR) usability and nurse job and surgical patient outcomes. Data from the American Hospital Association, state patient discharges, and nurse surveys were linked in a cross-sectional analysis. The researchers found that employing EHR systems with suboptimal usability was associated with higher odds of adverse nurse job outcomes and surgical patient mortality and readmission.
AHRQ-funded; HS023805.
Citation: Kutney-Lee A, Brooks Carthon M, Sloane DM .
Electronic health record usability: associations with nurse and patient outcomes in hospitals.
Med Care 2021 Jul;59(7):625-31. doi: 10.1097/mlr.0000000000001536..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery, Outcomes, Nursing, Provider: Nurse
Zhu Y, Simon GJ, Wick EC
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. The purpose of the study was to understand the generalizability of a machine learning algorithm between sites; automated surgical site infection (SSI) detection algorithms developed at one center were tested at another distinct center.
AHRQ-funded; HS024532.
Citation: Zhu Y, Simon GJ, Wick EC .
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
J Am Coll Surg 2021 Jun;232(6):963-71.e1. doi: 10.1016/j.jamcollsurg.2021.03.026..
Keywords: Healthcare-Associated Infections (HAIs), Surgery, Adverse Events, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Bucher BT, Shi J, Ferraro JP
Portable automated surveillance of surgical site infections using natural language processing: development and validation.
The authors presented the development and validation of a portable natural language processing (NLP) approach for automated surveillance of surgical site infections (SSIs). Patient clinical text notes from EHRs following surgical procedures from two independent healthcare systems were abstracted. The authors found that automated surveillance of SSIs can be achieved using NLP of clinical notes with high sensitivity and specificity.
AHRQ-funded; HS025776.
Citation: Bucher BT, Shi J, Ferraro JP .
Portable automated surveillance of surgical site infections using natural language processing: development and validation.
Ann Surg 2020 Oct;272(4):629-36. doi: 10.1097/sla.0000000000004133..
Keywords: Surgery, Healthcare-Associated Infections (HAIs), Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Giardina JC, Cha T, Atlas SJ
Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.
The purpose of this study was to develop and validate an algorithm to identify patients receiving four elective orthopedic surgeries to promote shared decision-making. The surgeries included were: 1) knee arthroplasty to treat knee osteoarthritis (KOA); 2) hip arthroplasty to treat hip osteoarthritis (HOA); 3) spinal surgery to treat lumbar spinal stenosis (SpS); and 4) spinal surgery to treat lumber herniated disc (HD). Electronic medical records were reviewed to ascertain a “gold standard” determination of the procedure and primary indication status. Each case had electronic algorithms consisting of ICD-10 and CPT codes for each combination and indication applied to their record. A total of 790 procedures were included in the study. The sensitivity of the algorithms ranged from 0.70 (HD) to 0.92 (KOA). Specificity ranged from 0.94 (SpS) to 0.99 (HOA, KOA).
AHRQ-funded; HS000055.
Citation: Giardina JC, Cha T, Atlas SJ .
Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.
BMC Med Inform Decis Mak 2020 Aug 12;20(1):187. doi: 10.1186/s12911-020-01175-1.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Orthopedics, Surgery, Arthritis, Shared Decision Making
Bronsert M, Singh AB, Henderson WG
Identification of postoperative complications using electronic health record data and machine learning.
Investigators developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR). They concluded that using machine learning on EHR postoperative data linked to American College of Surgeons National Surgical Quality Improvement Program outcomes data, a model with 163 predictors from the EHR identified complications well at their institution.
AHRQ-funded; HS026019.
Citation: Bronsert M, Singh AB, Henderson WG .
Identification of postoperative complications using electronic health record data and machine learning.
Am J Surg 2020 Jul;220(1):114-19. doi: 10.1016/j.amjsurg.2019.10.009..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery, Quality Improvement, Quality of Care, Diagnostic Safety and Quality
Weng Y, Tian L, Tedesco D
Trajectory analysis for postoperative pain using electronic health records: a nonparametric method with robust linear regression and K-medians cluster analysis.
Postoperative pain scores are widely monitored and collected in the electronic health record, yet current methods fail to fully leverage the data with fast implementation. This article describes a trajectory analysis for postoperative pain using electronic health records. A robust linear regression was fitted to describe the association between the log-scaled pain score and time from discharge after total knee replacement.
AHRQ-funded; HS024096.
Citation: Weng Y, Tian L, Tedesco D .
Trajectory analysis for postoperative pain using electronic health records: a nonparametric method with robust linear regression and K-medians cluster analysis.
Health Informatics J 2020 Jun;26(2):1404-18. doi: 10.1177/1460458219881339..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Pain, Surgery, Orthopedics, Research Methodologies, Health Services Research (HSR)
Liu L, Ni Y, Zhang N
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
The objectives of this study were: 1) to develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation. The study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The author’s approach offers the promise of targeted interventions to significantly decrease both healthcare costs and families' negative experiences.
AHRQ-funded; HS024983.
Citation: Liu L, Ni Y, Zhang N .
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
Int J Med Inform 2019 Sep;129:234-41. doi: 10.1016/j.ijmedinf.2019.06.007..
Keywords: Children/Adolescents, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery
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
Colborn KL, Bronsert M, Hammermeister K
Identification of urinary tract infections using electronic health record data.
Using the American College of Surgeons National Surgical Quality Improvement Program UTI status of patients who underwent an operation at the University of Colorado Hospital, the investigators sought to develop an algorithm for identifying UTIs using data from the electronic health record. The investigators concluded that a model with 14 predictors from the electronic health record identifies UTIs well, and it could be used to scale up UTI surveillance or to estimate the impact of large-scale interventions on UTI rates.
AHRQ-funded; HS026019.
Citation: Colborn KL, Bronsert M, Hammermeister K .
Identification of urinary tract infections using electronic health record data.
Am J Infect Control 2019 Apr;47(4):371-75. doi: 10.1016/j.ajic.2018.10.009..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Quality of Care, Quality Improvement, Surgery, Urinary Tract Infection (UTI)
Colborn KL, Bronsert M, Amioka E
Identification of surgical site infections using electronic health record data.
The objective of this study was to develop an algorithm for identifying surgical site infections (SSIs) using independent variables from electronic health record data and outcomes from the American College of Surgeons National Surgical Quality Improvement Program to supplement manual chart review. The investigators concluded that they identified a model that accurately identified SSIs. They indicated that the framework presented can be easily implemented by other American College of Surgeons National Surgical Quality Improvement Program-participating hospitals to develop models for enhancing surveillance of SSIs.
AHRQ-funded; HS026019.
Citation: Colborn KL, Bronsert M, Amioka E .
Identification of surgical site infections using electronic health record data.
Am J Infect Control 2018 Nov;46(11):1230-35. doi: 10.1016/j.ajic.2018.05.011..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Healthcare-Associated Infections (HAIs), Injuries and Wounds, Patient Safety, Surgery
Karavite DJ, Miller MW, Ramos MJ
User testing an information foraging tool for ambulatory surgical site infection surveillance.
Surveillance for surgical site infections (SSIs) after ambulatory surgery in children requires a detailed manual chart review to assess criteria defined by the National Health and Safety Network. Electronic health records (EHRs) impose an inefficient search process. Using text mining and business intelligence software, the authors developed an information foraging application, the SSI Workbench, to visually present which postsurgical encounters included SSI-related terms and synonyms, antibiotic, and culture orders. This study compares the Workbench and EHR.
AHRQ-funded; HS020921.
Citation: Karavite DJ, Miller MW, Ramos MJ .
User testing an information foraging tool for ambulatory surgical site infection surveillance.
Appl Clin Inform 2018 Oct;9(4):791-802. doi: 10.1055/s-0038-1675179..
Keywords: Surgery, Ambulatory Care and Surgery, Children/Adolescents, Electronic Health Records (EHRs), Health Information Technology (HIT), Healthcare-Associated Infections (HAIs), Patient Safety
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
Devine EB, Van Eaton E, Zadworny ME
Automating electronic clinical data capture for quality improvement and research: The CERTAIN Validation Project of Real World Evidence.
Washington State's Surgical Care Outcomes and Assessment Program (SCOAP) is a network of hospitals participating in quality improvement (QI) registries wherein data are manually abstracted from EHRs. To create the Comparative Effectiveness Research and Translation Network (CERTAIN), researchers semi-automated SCOAP data abstraction using a centralized federated data model, created a central data repository (CDR), and assessed whether these data could be used as real world evidence for QI and research. They concluded that semi-automated data abstraction may be useful, although raw data collected as a byproduct of health care delivery is not immediately available for use as real world evidence. New approaches to gathering and analyzing extant data are required.
AHRQ-funded; HS020025.
Citation: Devine EB, Van Eaton E, Zadworny ME .
Automating electronic clinical data capture for quality improvement and research: The CERTAIN Validation Project of Real World Evidence.
eGEMS 2018 May 22;6(1):8. doi: 10.5334/egems.211..
Keywords: Patient-Centered Outcomes Research, Quality Improvement, Registries, Surgery, Electronic Health Records (EHRs)
Skube SJ, Lindemann EA, Arsoniadis EG
Characterizing functional health status of surgical patients in clinical notes.
The researchers of this study hypothesize that important functional status data is contained in clinical notes. They found that several categories of phrases related to functional status including diagnoses, activity and care assessments, physical exam, functional scores, assistive equipment, symptoms, and surgical history were important factors. They conducted a chart review and compared functional health status level terms from the chart review to National Surgical Quality Improvement Program determinations.
AHRQ-funded; HS024532.
Citation: Skube SJ, Lindemann EA, Arsoniadis EG .
Characterizing functional health status of surgical patients in clinical notes.
AMIA Jt Summits Transl Sci Proc 2018 May 18;2017:379-88..
Keywords: Health Status, Patient Safety, Risk, Surgery, Electronic Health Records (EHRs)
Smith AB, Basch E
Role of patient-reported outcomes in postsurgical monitoring in oncology.
This article describes the benefits of electronic patient-reported outcomes (ePROs) in postsurgical symptom monitoring for surgical oncology patients; ePROs can identify at-risk patients, provide closer monitoring, and provide a mechanism to identify and treat complications before they worsen. The article also summarizes the literature of ePRO use in surgical oncology.
AHRQ-funded; HS024134.
Citation: Smith AB, Basch E .
Role of patient-reported outcomes in postsurgical monitoring in oncology.
J Oncol Pract 2017 Aug;13(8):535-38. doi: 10.1200/jop.2017.023838..
Keywords: Cancer, Care Management, Health Information Technology (HIT), Electronic Health Records (EHRs), Surgery, Outcomes
Hu Z, Melton GB, Arsoniadis EG
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for postoperative complication detection is unclear. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values.
AHRQ-funded; HS024532.
Citation: Hu Z, Melton GB, Arsoniadis EG .
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
J Biomed Inform 2017 Apr;68:112-20. doi: 10.1016/j.jbi.2017.03.009.
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Keywords: Data, Electronic Health Records (EHRs), Healthcare-Associated Infections (HAIs), Registries, Surgery, Injuries and Wounds, Health Information Technology (HIT), Quality Improvement, Quality of Care, Adverse Events
Wilcox L, Woollen J, Prey J
Interactive tools for inpatient medication tracking: a multi-phase study with cardiothoracic surgery patients.
This study explored the design and usefulness of patient-facing tools supporting inpatient medication management and tracking. Patients reported that the medication-tracking tools were useful. Patients' interview responses and audit logs revealed that they made frequent use of the hospital medications feature and found electronic reporting of questions and comments useful.
AHRQ-funded; HS021816; HS021393.
Citation: Wilcox L, Woollen J, Prey J .
Interactive tools for inpatient medication tracking: a multi-phase study with cardiothoracic surgery patients.
J Am Med Inform Assoc 2016 Jan;23(1):144-58. doi: 10.1093/jamia/ocv160..
Keywords: Electronic Health Records (EHRs), Inpatient Care, Medication, Patient Self-Management, Surgery
Acher AW, LeCaire TJ, Hundt AS
Using human factors and systems engineering to evaluate readmission after complex surgery.
The study objective was to use a human factors and systems engineering approach to understand contributors to surgical readmissions from a patient and provider perspective. Patients and clinician providers identified a number of factors during the transition of care that may have contributed to readmission, including poor patient and caregiver understanding; inadequate discharge preparation for home care; insufficient educational process and materials.
AHRQ-funded; HS022446.
Citation: Acher AW, LeCaire TJ, Hundt AS .
Using human factors and systems engineering to evaluate readmission after complex surgery.
J Am Coll Surg 2015 Oct;221(4):810-20. doi: 10.1016/j.jamcollsurg.2015.06.014..
Keywords: Surgery, Hospital Readmissions, Hospital Discharge, Transitions of Care, Electronic Health Records (EHRs)
Melton GB, Wang Y, Arsoniadis E
Analyzing operative note structure in development of a section header resource.
Using their experience with clinical standards evaluation, the researchers sought to use the HL7 Implementation Guide for Clinical Document Architecture Release 2.0 Operative Note Draft Standard for Trial Use (HL7-ON DSTU) Release 1 (HL7-ON DSTU) and Logical Observation Identifiers Names and Codes (LOINC®) section codes to represent operative note section headers and to develop a resource for operative note section headers.
AHRQ-funded; HS022085.
Citation: Melton GB, Wang Y, Arsoniadis E .
Analyzing operative note structure in development of a section header resource.
Stud Health Technol Inform 2015;216:821-6..
Keywords: Health Information Technology (HIT), Surgery, Electronic Health Records (EHRs)