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Search All Research Studies
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- Adverse Events (2)
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- (-) Diagnostic Safety and Quality (6)
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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 6 of 6 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
Cifra CL, Sittig DF, Singh H
Bridging the feedback gap: a sociotechnical approach to informing clinicians of patients' subsequent clinical course and outcomes.
This paper discusses challenges to the development of systems for effective patient outcome feedback to improve diagnosis and proposes the application of a sociotechnical approach using health information technology (HIT) to support the implementation of such systems. It discusses current barriers to effective clinician feedback, reasons for them, and features of potential IT solutions. Evaluation and implementation of the feedback process within a sociotechnical health system are then discussed. The authors use an eight-dimension sociotechnical model for studying health IT by authors Sittig and Singh. The eight dimensions are hardware and software; clinical content; human–computer interface; people; workflow and communication; organisational policies and procedures; external rules, regulations and pressures; and system measurement and monitoring. A table is included that shows the potential considerations for each dimension.
AHRQ-funded; 33201500022I; HS027363.
Citation: Cifra CL, Sittig DF, Singh H .
Bridging the feedback gap: a sociotechnical approach to informing clinicians of patients' subsequent clinical course and outcomes.
BMJ Qual Saf 2021 Jul;30(7):591-97. doi: 10.1136/bmjqs-2020-012464..
Keywords: Health Information Technology (HIT), Diagnostic Safety and Quality, Patient Safety, Quality Improvement, Quality of Care
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
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
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
Muldoon MF, Kronish IM, Shimbo D
Of signal and noise: overcoming challenges in blood pressure measurement to optimize hypertension care.
This paper reviews the manifestations and consequences of BP mismeasurement and misinterpretation in clinical practice and draw on recent research to propose a set of solutions that leverage available technologies to optimize hypertension care.
AHRQ-funded; HS024262.
Citation: Muldoon MF, Kronish IM, Shimbo D .
Of signal and noise: overcoming challenges in blood pressure measurement to optimize hypertension care.
Circ Cardiovasc Qual Outcomes 2018 May;11(5):e004543. doi: 10.1161/circoutcomes.117.004543..
Keywords: Blood Pressure, Diagnostic Safety and Quality, Adverse Events, Medical Errors, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality of Care