National Healthcare Quality and Disparities Report
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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 25 of 45 Research Studies DisplayedBradford A, Shofer M, Singh H
AHRQ Author: Shofer M, Singh H
Measure Dx: implementing pathways to discover and learn from diagnostic errors.
This paper discusses Measure Dx, a new AHRQ resource that translates knowledge from diagnostic measurement research into actionable recommendations. This resource guides healthcare organizations to detect, analyze, and learn from diagnostic safety events as part of a continuous learning and feedback cycle. The goal of Measure Dx is to advance new frontiers in reducing preventable diagnostic harm to patients.
AHRQ-authored; AHRQ-funded; 233201500022I; HS027363.
Citation: Bradford A, Shofer M, Singh H .
Measure Dx: implementing pathways to discover and learn from diagnostic errors.
Int J Qual Health Care 2022 Sep 10;34(3). doi: 10.1093/intqhc/mzac068..
Keywords: Diagnostic Safety and Quality, Patient Safety, Quality Improvement, Quality of Care, Electronic Health Records (EHRs), Health Information Technology (HIT), Health Systems, Learning Health Systems
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
Griffey RT, Schneider RM, Todorov AA
The emergency department trigger tool: validation and testing to optimize yield.
Researchers validated the emergency department trigger tool (EDTT) in an independent sample and compared record selection approaches to optimize yield for quality improvement. In this single-site study of the EDTT, they observed high levels of validity in trigger selection, yield, and representativeness of adverse events, with yields that are superior to estimates for traditional approaches to adverse event detection. Record selection using weighted triggers outperformed a trigger count threshold approach and far outperformed random sampling from records with at least one trigger. They concluded that the EDTT is a promising efficient and high-yield approach for detecting all-cause harm to guide quality improvement efforts in the emergency department.
AHRQ-funded; HS025052.
Citation: Griffey RT, Schneider RM, Todorov AA .
The emergency department trigger tool: validation and testing to optimize yield.
Acad Emerg Med 2020 Dec;27(12):1279-90. doi: 10.1111/acem.14101..
Keywords: Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT), Medical Errors, Adverse Events, Patient Safety, Quality Improvement, Quality of Care
Scott HF, Brilli RJ, Paul R
Evaluating pediatric sepsis definitions designed for electronic health record extraction and multicenter quality improvement.
The purpose of this study was to describe the Children's Hospital Association's Improving Pediatric Sepsis Outcomes sepsis definitions and to evaluate the definition using a published framework. The investigators concluded that the Improving Pediatric Sepsis Outcomes Sepsis definitions demonstrated feasibility for large-scale data abstraction. When operationalized, these definitions enabled multicenter identification and data aggregation, indicating practical utility for quality improvement.
AHRQ-funded; HS025696.
Citation: Scott HF, Brilli RJ, Paul R .
Evaluating pediatric sepsis definitions designed for electronic health record extraction and multicenter quality improvement.
Crit Care Med 2020 Oct;48(10):e916-e26. doi: 10.1097/ccm.0000000000004505..
Keywords: Children/Adolescents, Electronic Health Records (EHRs), Health Information Technology (HIT), Sepsis, 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
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
Rudin RS, Friedberg MW, Shekelle P
Getting value from electronic health records: research needed to improve practice.
Electronic health records (EHRs) are now widely adopted in the United States, but health systems have barely begun using them to deliver high-value care. This article describes 4 potential benefits of EHR-based research: improving clinical decisions, supporting triage decisions, enabling collaboration among the care team (including patients), and increasing productivity via automation of tasks.
AHRQ-funded; HS024067.
Citation: Rudin RS, Friedberg MW, Shekelle P .
Getting value from electronic health records: research needed to improve practice.
Ann Intern Med 2020 Jun 2;172(11 Suppl):S130-s36. doi: 10.7326/m19-0878..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care, Healthcare Delivery
Tignanelli CJ, Silverman GM, Lindemann EA
Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.
Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize treatment appropriateness. Natural language processing (NLP) methods present a novel approach to bridge this gap. In this study, the investigators sought to evaluate the efficacy of a novel and automated NLP pipeline to determine treatment appropriateness from a sample of prehospital EMS motor vehicle crash records.
AHRQ-funded; HS026379.
Citation: Tignanelli CJ, Silverman GM, Lindemann EA .
Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.
J Trauma Acute Care Surg 2020 May;88(5):607-14. doi: 10.1097/ta.0000000000002598.
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Keywords: Trauma, Injuries and Wounds, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Hernandez-Boussard T, Blayney DW, Brooks JD
Leveraging digital data to inform and improve quality cancer care.
Efficient capture of routine clinical care and patient outcomes is needed at a population-level, as is evidence on important treatment-related side effects and their effect on well-being and clinical outcomes. The increasing availability of electronic health records (EHR) offers new opportunities to generate population-level patient-centered evidence on oncologic care that can better guide treatment decisions and patient-valued care. This study discussed how to leverage digital data to inform and improve quality cancer care.
AHRQ-funded; R01 HS024096.
Citation: Hernandez-Boussard T, Blayney DW, Brooks JD .
Leveraging digital data to inform and improve quality cancer care.
Cancer Epidemiol Biomarkers Prev 2020 Apr;29(4):816-22. doi: 10.1158/1055-9965.Epi-19-0873..
Keywords: Cancer, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Patient-Centered Healthcare, Patient-Centered Outcomes Research, Evidence-Based Practice
Businger AC, Fuller TE, Schnipper JL
Lessons learned implementing a complex and innovative patient safety learning laboratory project in a large academic medical center.
This paper describes the challenges, recommendations and lessons learned while developing and implementing a Patient Safety Learning Laboratory (PSLL) project, which is comprised of a suite of HIT tools integrated with a newly implemented Electronic Health Record (EHR) vendor system in the acute care setting of a large academic medical center. The PSLL Administrative Core engaged stakeholders and study personnel throughout all phases of the project. Challenges to implementation included stakeholder engagement, project scope and complexity, technology and governance, and team structure. Some changes were implemented during the trial and others were labeled as lessons learned for future iterative interventions. A willingness to think outside of current workflows and processes to change health system culture around adverse event prevention was one of the keys to success.
AHRQ-funded; HS023535.
Citation: Businger AC, Fuller TE, Schnipper JL .
Lessons learned implementing a complex and innovative patient safety learning laboratory project in a large academic medical center.
J Am Med Inform Assoc 2020 Feb;27(2):301-07. doi: 10.1093/jamia/ocz193.
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Keywords: Patient Safety, Implementation, Health Information Technology (HIT), Quality Improvement, Quality of Care, Patient-Centered Healthcare, Electronic Health Records (EHRs), Evidence-Based Practice
Gandrup J, Li J, Izadi Z
Three quality improvement initiatives and performance of rheumatoid arthritis disease activity measures in electronic health records: results from an interrupted time series study.
This study evaluated the effect of 3 HIT initiatives on the performance of rheumatoid arthritis (RA) disease activity measures and outcomes in an academic rheumatology clinic. The three initiatives implemented to facilitate performance of the Clinical Disease Activity Index (CDAI) were: 1) an EHR flowsheet to input scores, 2) peer performance reports, and 3) an EHR Smartform including a CDAI calculator. Data from 995 patients with 8,040 encounters between 2012 and 2017 was included. Electronic capture of CDAI scores increased from 0% to 64%. Peer performance reporting and the SmartForm kept performance stable. Physician satisfaction increased after SmartForm implementation.
AHRQ-funded; HS025638.
Citation: Gandrup J, Li J, Izadi Z .
Three quality improvement initiatives and performance of rheumatoid arthritis disease activity measures in electronic health records: results from an interrupted time series study.
Arthritis Care Res 2020 Feb;72(2):283-91. doi: 10.1002/acr.23848..
Keywords: Arthritis, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Liss DT, Peprah YA, Brown T
Using electronic health records to measure quality improvement efforts: findings from a large practice facilitation initiative.
This study described primary care practices' ability to obtain measures with reporting periods aligning with a large quality improvement initiative. Facilitators reported barriers to data collection such as practices lacking optional EHR features, and EHRs' inability to produce reporting periods across two calendar years. The authors conclude that EHR vendors' compliance with federal reporting requirements is not necessarily sufficient to support real-world quality improvement work. They recommended improvements in the flexibility and usability of EHRs' quality measurement functions, particularly for smaller practices.
AHRQ-funded; HS023921.
Citation: Liss DT, Peprah YA, Brown T .
Using electronic health records to measure quality improvement efforts: findings from a large practice facilitation initiative.
Jt Comm J Qual Patient Saf 2020 Jan;46(1):11-17. doi: 10.1016/j.jcjq.2019.09.006..
Keywords: Patient-Centered Outcomes Research, Evidence-Based Practice, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Kang SK, Garry K, Chung R
Natural language processing for identification of incidental pulmonary nodules in radiology reports.
The authors developed natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations using the electronic health records for patients who underwent chest CT before and after implementation of a department-wide dictation macro of the Fleischner Society recommendations. They concluded that NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management.
AHRQ-funded; HS024376.
Citation: Kang SK, Garry K, Chung R .
Natural language processing for identification of incidental pulmonary nodules in radiology reports.
J Am Coll Radiol 2019 Nov;16(11):1587-94. doi: 10.1016/j.jacr.2019.04.026..
Keywords: Imaging, Diagnostic Safety and Quality, Health Information Technology (HIT), Electronic Health Records (EHRs), Quality Improvement, Quality of Care
Knierim KE, Hall TL, Dickinson LM
Primary care practices' ability to report electronic clinical quality measures in the EvidenceNOW Southwest Initiative to Improve Heart Health.
The objective of this study was to determine how quickly primary care practices can report electronic clinical quality measures (eCQMs) and to identify the practice characteristics associated with faster reporting. Examining the EvidenceNOW Southwest initiative, the researchers’ results showed that the time to report eCQMs varied by measure and practice type, with very few practices reporting quickly. Additional support for practices to succeed in new programs that require eCQM reporting was recommended.
AHRQ-funded; HS023904.
Citation: Knierim KE, Hall TL, Dickinson LM .
Primary care practices' ability to report electronic clinical quality measures in the EvidenceNOW Southwest Initiative to Improve Heart Health.
JAMA Netw Open 2019 Aug 2;2(8):e198569. doi: 10.1001/jamanetworkopen.2019.8569..
Keywords: Primary Care, Quality Indicators (QIs), Quality Measures, Quality Improvement, Quality of Care, Heart Disease and Health, Cardiovascular Conditions, Patient-Centered Outcomes Research, Evidence-Based Practice, Electronic Health Records (EHRs), Health Information Technology (HIT)
Dalal AK, Fuller T, Garabedian P
Systems engineering and human factors support of a system of novel EHR-integrated tools to prevent harm in the hospital.
This study examined systems engineering and human factors support of a system of novel electronic health record (EHR)-integrated tools for patient safety in the hospital. The authors established a Patient Safety Learning Laboratory of 2 core and 3 individual project teams to introduce a suite of digital health tools integrated with their EHR to identify, assess, and mitigate threats to patient safety. They identified 7 themes regarding use of 12 systems engineering and human factors over the 4-year project.
AHRQ-funded; HS023535.
Citation: Dalal AK, Fuller T, Garabedian P .
Systems engineering and human factors support of a system of novel EHR-integrated tools to prevent harm in the hospital.
J Am Med Inform Assoc 2019 Jun;26(6):553-60. doi: 10.1093/jamia/ocz002..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Safety, Hospitals, Quality Improvement, Quality of Care
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)
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
Liu LH, Choden S, Yazdany J
Quality improvement initiatives in rheumatology: an integrative review of the last 5 years.
Researchers reviewed recent quality improvement initiatives in the field of rheumatology to identify common strategies and themes leading to measurable change. Their review demonstrated common solutions, particularly involving leveraging health IT and workflow redesign. Electronic health record (EHR)-based interventions were most successful when reinforced with provider education, reminders, and performance feedback. Structured models such as the learning collaborative may help disseminate successful initiatives across practices.
AHRQ-funded; HS025638.
Citation: Liu LH, Choden S, Yazdany J .
Quality improvement initiatives in rheumatology: an integrative review of the last 5 years.
Curr Opin Rheumatol 2019 Mar;31(2):98-108. doi: 10.1097/bor.0000000000000586..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Quality of Care, Quality Improvement
Cykert S, DeWalt DA, Weiner BJ
A population approach using cholesterol imputation to identify adults with high cardiovascular risk: a report from AHRQ's EvidenceNow initiative.
Investigators estimated cholesterol scores for patients between the ages of 40 and 79 in large practice electronic health networks who did not have that in their electronic health record (EHR). This data was used to calculate 10-year Assessment of Cardiovascular Disease Risk (ASCVD) risk scores for patients in 219 practices. They estimated the scores using both “good value’ estimation methodology and formal imputation. The “good value” estimation methodology resulted in less patients with risk scores than imputation but it had higher specificity and a lower false positive rate.
AHRQ-funded; HS023912.
Citation: Cykert S, DeWalt DA, Weiner BJ .
A population approach using cholesterol imputation to identify adults with high cardiovascular risk: a report from AHRQ's EvidenceNow initiative.
J Am Med Inform Assoc 2019 Feb;26(2):155-58. doi: 10.1093/jamia/ocy151..
Keywords: Cardiovascular Conditions, Electronic Health Records (EHRs), Heart Disease and Health, Evidence-Based Practice, Health Information Technology (HIT), Patient-Centered Outcomes Research, Quality Improvement
Shah T, Patel-Teague S, Kroupa L
Impact of a national QI programme on reducing electronic health record notifications to clinicians.
In this study, the investigators evaluated the impact of a national, multicomponent, quality improvement (QI) programme designed to reduce low-value EHR notifications. The investigators found that, based on prior estimates on time to process notifications, this national QI programme potentially saved 1.5 hours per week per PCP to enable higher value work. The investigators also found that the number of daily notifications remained high, suggesting the need for additional multifaceted interventions and protected clinical time to help manage them.
AHRQ-funded; HS022087.
Citation: Shah T, Patel-Teague S, Kroupa L .
Impact of a national QI programme on reducing electronic health record notifications to clinicians.
BMJ Qual Saf 2019 Jan;28(1):10-14. doi: 10.1136/bmjqs-2017-007447..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Indicators (QIs), Quality Improvement, Quality of Care, Primary Care, Primary Care: Models of Care
Dowding D, Merrill J, Russell D
Using feedback intervention theory to guide clinical dashboard design.
The provision of feedback to clinicians and organizations on the quality of care they provide is thought to influence clinician and organizational behavior leading to care improvements. Clinical Dashboards use data visualization techniques to provide feedback to individuals on their performance compared to quality metrics. In this paper the authors outline a theoretical approach to the design of a clinical dashboard; Feedback Intervention Theory (FIT).
AHRQ-funded; HS023855.
Citation: Dowding D, Merrill J, Russell D .
Using feedback intervention theory to guide clinical dashboard design.
AMIA Annu Symp Proc 2018 Dec 5;2018:395-403..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Provider Performance, 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)
Colin NV, Cholan RA, Sachdeva B
Understanding the impact of variations in measurement period reporting for electronic clinical quality measures.
The purpose of the study was to understand the impact of varying measurement period on the calculation of electronic Clinical Quality Measures (eCQMs). Variations in measurement periods were associated with variation in performance between clinics for 3 of the 4 eCQMs, but did not have significant differences when calculated within clinics. Variations from standard measurement periods may reflect poor data quality and accuracy.
AHRQ-funded; HS023908.
Citation: Colin NV, Cholan RA, Sachdeva B .
Understanding the impact of variations in measurement period reporting for electronic clinical quality measures.
eGEMS 2018 Jul 19;6(1):17. doi: 10.5334/egems.235..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Measures, Quality Improvement, Quality of Care
Dowding D, Merrill JA
The development of heuristics for evaluation of dashboard visualizations.
Heuristic evaluation is used in human-computer interaction studies to assess the usability of information systems. This article develops a heuristic evaluation checklist that can be used to evaluate systems that produce information visualizations. The authors suggest that a checklist of usability heuristics for evaluating information visualization systems can contribute to assuring high quality in electronic data systems developed for health care.
AHRQ-funded; HS023855.
Citation: Dowding D, Merrill JA .
The development of heuristics for evaluation of dashboard visualizations.
Appl Clin Inform 2018 Jul;9(3):511-18. doi: 10.1055/s-0038-1666842..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Provider Performance, Quality of Care, Quality Improvement
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)