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Research Studies is a compilation of published research articles funded by 大象视频or authored by 大象视频researchers.

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1 to 25 of 59 Research Studies Displayed
James TG, Mangus CW, Parker SJ
"Everything is electronic health record-driven": the role of the electronic health record in the emergency department diagnostic process.
The objectives of this study were to identify how electronic health records (EHRs) facilitated or impeded diagnostic process in emergency departments and to identify opportunities to reduce diagnostic errors. The results of semistructured interviews with physicians, nurses, and patients identified four themes related to communication, patient portals, EHR features, and burdensome protocols.
AHRQ-funded; HS026622.
Citation: James TG, Mangus CW, Parker SJ . "Everything is electronic health record-driven": the role of the electronic health record in the emergency department diagnostic process. JAMIA Open 2025 Apr; 8(2):ooaf029. doi: 10.1093/jamiaopen/ooaf029..
Keywords: Electronic Health Records (EHRs), Emergency Department, Health Information Technology (HIT), Diagnostic Safety and Quality
Giardina TD, Vaghani V, Upadhyay DK
Charting diagnostic safety: exploring patient-provider discordance in medical record documentation.
The purpose of this study was to examine concordance between patients' self-reported diagnostic concerns and their clinician鈥檚 documentation in electronic health records. This mixed-methods analysis examined 467 patients who completed a structured tool to identify diagnostic concerns while reviewing their clinicians' notes. Two blinded clinical reviewers independently evaluated the same records using a different structured instrument. Results revealed significant discordance: chart reviews identified 31 diagnostic concerns, with only 11 overlapping with the 51 patient-reported concerns. Content analysis showed several areas of discordant understanding of the diagnostic process between clinicians and patients. Multivariate analysis indicated that clinician-identified diagnostic concerns were associated with patients who self-reported feeling incorrectly diagnosed during their visit.
AHRQ-funded; HS025474.
Citation: Giardina TD, Vaghani V, Upadhyay DK . Charting diagnostic safety: exploring patient-provider discordance in medical record documentation. J Gen Intern Med 2025 Mar; 40(4):773-81. doi: 10.1007/s11606-024-09007-y.
Keywords: Diagnostic Safety and Quality, Patient Safety, Electronic Health Records (EHRs), Health Information Technology (HIT)
Mahajan P, White E, Shaw K
Epidemiology of diagnostic errors in pediatric emergency departments using electronic triggers.
Researchers applied three electronic triggers to study frequency and contributing factors of missed opportunities for improving diagnosis (MOIDs) in pediatric emergency departments. The results revealed that use of electronic triggers with selective record review was an effective process to screen for harmful diagnostic errors.
AHRQ-funded; HS024953.
Citation: Mahajan P, White E, Shaw K . Epidemiology of diagnostic errors in pediatric emergency departments using electronic triggers. Acad Emerg Med 2025 Mar; 32(3):226-45. doi: 10.1111/acem.15087..
Keywords: Diagnostic Safety and Quality, Children/Adolescents, Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT)
Vaghani V, Gupta A, Mir U
Implementation of electronic triggers to identify diagnostic errors in emergency departments.
The objectives of this retrospective study of emergency departments at Veterans Affairs health care sites were to develop and implement a portfolio of electronic triggers (e-triggers) and to examine their performance for missed opportunities in diagnosis (MODs). The findings showed that rules-based e-triggers were useful for post-hoc detection of MODs, but interventions to target work system factors are urgently needed.
AHRQ-funded; HS027363; HS028595; HS029347; HS024459.
Citation: Vaghani V, Gupta A, Mir U . Implementation of electronic triggers to identify diagnostic errors in emergency departments. JAMA Intern Med 2025 Feb; 185(2):143-51. doi: 10.1001/jamainternmed.2024.6214..
Keywords: Emergency Department, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Medical Errors, Adverse Events
Tokede B, Yansane A, Brandon R
The burden of diagnostic error in dentistry: a study on periodontal disease misclassification.
This study provided epidemiological estimates on the rate of diagnostic misclassification in dentistry based on electronic health record-based data. The results revealed that approximately one third of periodontal cases are misclassified. The authors concluded their findings highlighted the potential role of technology in aiding diagnostic decision-making at the point of care.
AHRQ-funded; HS027938.
Citation: Tokede B, Yansane A, Brandon R . The burden of diagnostic error in dentistry: a study on periodontal disease misclassification. J Dent 2024 Sep; 148:105221. doi: 10.1016/j.jdent.2024.105221..
Keywords: Diagnostic Safety and Quality, Dental and Oral Health, Medical Errors, Adverse Events, Patient Safety, Electronic Health Records (EHRs), Health Information Technology (HIT), Clinical Decision Support (CDS)
Bell SK, Harcourt K, Dong J
Patient and family contributions to improve the diagnostic process through the OurDX electronic health record tool: a mixed method analysis.
The authors co-developed an online tool called 'Our Diagnosis (OurDX)'; the purpose of their study was to describe patient/family contributions in OurDX and how these differed between individuals with and without diagnostic concerns. They implemented OurDX in two academic organizations that served patients with chronic conditions. Patients and their families were invited to contribute visit priorities, recent histories, and potential diagnostic concerns. The responses indicated that participants with diagnostic concerns were more likely to contribute information on DxP risks. The authors concluded that partnership with patients/families living with chronic conditions may help clinicians gain a broader perspective of the diagnostic process.
AHRQ-funded; HS027367.
Citation: Bell SK, Harcourt K, Dong J . Patient and family contributions to improve the diagnostic process through the OurDX electronic health record tool: a mixed method analysis. BMJ Qual Saf 2024 Aug 16; 33(9):597-608. doi: 10.1136/bmjqs-2022-015793..
Keywords: Electronic Health Records (EHRs), Diagnostic Safety and Quality, Health Information Technology (HIT), Patient Safety, Quality Improvement, Quality of Care, Patient-Centered Healthcare
Hassoon A, Ng C, Lehmann H
Computable phenotype for diagnostic error: developing the data schema for application of Symptom-Disease Pair Analysis of Diagnostic Error (SPADE).
The objective of this study was to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing a computable phenotype and demonstrating how that schema could be applied in multiple clinical contexts. The authors created an information model for the SPADE processes, then mapped and tested the phenotype in three different sites using four case studies. They concluded that the method of data extraction is potentially universally applicable, and extracted data is available within a network system.
AHRQ-funded; HS027614; HS029350.
Citation: Hassoon A, Ng C, Lehmann H . Computable phenotype for diagnostic error: developing the data schema for application of Symptom-Disease Pair Analysis of Diagnostic Error (SPADE). Diagnosis 2024 Aug 1; 11(3):295-302. doi: 10.1515/dx-2023-0138.
Keywords: Diagnostic Safety and Quality, Medical Errors, Electronic Health Records (EHRs), Health Information Technology (HIT)
Tokede B, Brandon R, Lee CT
Development and validation of a rule-based algorithm to identify periodontal diagnosis using structured electronic health record data.
This article describes the development and validation of an automated electronic health record (EHR) based algorithm to suggest a periodontal diagnosis. It was based on materials from the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions. Findings suggested that a rule-based algorithm using EHR data can be implemented with moderate accuracy in support of chairside clinical diagnostic decision making, and may be particularly useful for inexperienced clinicians. The authors noted that grey-zone cases where clinical judgement will be required still exist and that future applications of similar algorithms will depend upon the quality of EHR data.
AHRQ-funded; HS027938.
Citation: Tokede B, Brandon R, Lee CT . Development and validation of a rule-based algorithm to identify periodontal diagnosis using structured electronic health record data. J Clin Periodontol 2024 May; 51(5):547-57. doi: 10.1111/jcpe.13938..
Keywords: Dental and Oral Health, Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality
Liberman AL, Wang Z, Zhu Y
Optimizing measurement of misdiagnosis-related harms using Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): comparison groups to maximize SPADE validity.
The purpose of this paper was to clarify features of the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) approach to accurately measure diagnostic errors to assure that researchers utilize this method to yield valid results, as well as improve the validity of SPADE and related approaches to quantify diagnostic error in medicine. The researchers describe four types of comparators (intra-group and inter-group), detailing the reason for selecting one over the other and conclusions that can be drawn from these comparative analyses.
AHRQ-funded; HS027614.
Citation: Liberman AL, Wang Z, Zhu Y . Optimizing measurement of misdiagnosis-related harms using Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): comparison groups to maximize SPADE validity. Diagnosis 2023 Aug 1; 10(3):225-34. doi: 10.1515/dx-2022-0130..
Keywords: Diagnostic Safety and Quality, Medical Errors, Adverse Events, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Safety
Garber A, Garabedian P, Wu L
Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach.
This study鈥檚 objective was to describe a user-centered approach to develop, pilot test, and refine requirements for 3 electronic health record (EHR)-integrated interventions that target key diagnostic process failures in hospitalized patients. The interventions to be developed were a Diagnostic Safety Column (DSC) within an EHR-integrated dashboard to identify at-risk patients; a Diagnostic Time-Out (DTO) for clinicians to reassess the working diagnosis; and a Patient Diagnosis Questionnaire (PDQ) to gather patient concerns about the diagnostic process. After initial refinement from an analysis, final requirements were created for 10 test cases predicted by the DSC, 18 clinician DTO participants, and 39 PDQ responses including the following: DSC configurable parameters (variables, weights) to adjust baseline risk estimates in real-time based on new clinical data collected during hospitalization; more concise DTO wording and flexibility for clinicians to conduct the DTO with or without the patient present; and integration of PDQ responses into the DSC to ensure closed-looped communication with clinicians. An analysis of focus groups confirmed that tight integration of the interventions with the EHR would be necessary to prompt clinicians to reconsider the working diagnosis in cases with elevated diagnostic error (DE) risk or uncertainty. Potential implementation barriers identified included alert fatigue and distrust of the risk algorithm (DSC); time constraints, redundancies, and concerns about disclosing uncertainty to patients (DTO); and patient disagreement with the care team's diagnosis (PDQ).
AHRQ-funded; HS026613.
Citation: Garber A, Garabedian P, Wu L . Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach. JAMIA Open 2023 Jul; 6(2):ooad031. doi: 10.1093/jamiaopen/ooad031..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality, Patient Safety
Krevat SA, Samuel S, Boxley C
Identifying electronic health record contributions to diagnostic error in ambulatory settings through legal claims analysis.
The purpose of this study was to evaluate legal claims data to assess whether there is a relationship between problems with electronic health records and diagnostic errors. The researchers also explored specific types of errors that took place and at which point in the diagnostic process the errors occurred.
AHRQ-funded; HS027119.
Citation: Krevat SA, Samuel S, Boxley C . Identifying electronic health record contributions to diagnostic error in ambulatory settings through legal claims analysis. JAMA Netw Open 2023 Apr 3; 6(4):e238399. doi: 10.1001/jamanetworkopen.2023.8399..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality
Coley RY, Smith JJ, Karliner L RY, Smith JJ, Karliner L
External validation of the eRADAR risk score for detecting undiagnosed dementia in two real-world healthcare systems.
Drupal date: Feb, 2023
It is estimated that half of the individuals with dementia remain undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was designed to detect older adults at risk of undiagnosed dementia using routinely gathered clinical information. The purpose of this retrospective cohort study was to externally validate eRADAR in two real-world healthcare systems, examining its performance over time and across race/ethnicity. The study found a total of 7631 dementia diagnoses were observed at KPWA and 216 at UCSF. The area under the curve was 0.84 (95% confidence interval: 0.84-0.85) at KPWA and 0.79 (0.76-0.82) at UCSF. Using the 90th percentile as the cut point for identifying high-risk patients, sensitivity was 54% (53-56%) at KPWA and 44% (38-51%) at UCSF. Performance was consistent over time, including across the transition from International Classification of Diseases, version 9 (ICD-9) to ICD-10 codes, and across racial/ethnic groups (although small samples limited precision in some groups). The study concluded that eRADAR demonstrated strong external validity for identifying undiagnosed dementia in two healthcare systems with diverse patient populations and varying availability of external healthcare data for risk calculations. This study showed that eRADAR is generalizable from a research sample to real-world clinical populations, transportable across health systems, resilient to temporal changes in healthcare, and exhibits similar performance across major racial/ethnic groups.
AHRQ-funded; HS026369.
Citation: Coley RY, Smith JJ, Karliner L RY, Smith JJ, Karliner L . External validation of the eRADAR risk score for detecting undiagnosed dementia in two real-world healthcare systems. J Gen Intern Med 2023 Feb; 38(2):351-60. doi: 10.1007/s11606-022-07736-6..
Keywords: Dementia, Neurological Disorders, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT)
Bell SK, Bourgeois F, Dong J
Patient identification of diagnostic safety blindspots and participation in "good catches" through shared visit notes.
The goal of this study was to investigate whether sharing clinical notes with patients supported identification of potential breakdowns in the diagnostic process that might be difficult for clinical staff to observe -- "diagnostic safety blindspots." Researchers analyzed patient-reported ambulatory documentation errors among patients at 3 U.S. healthcare centers. Older, female, unemployed, disabled, or sicker patients, or patients who worked in healthcare, were more likely to identify blindspots; patients who self-identified as Black, Asian, multiple races and those with less formal education as well as those who deferred decision-making to their providers were less likely to report blindspots. The researchers concluded that patients who read notes have unique insight about potential errors in their medical records and that organizations should encourage patient review of notes and create systems to track patient-reported blindspots.
AHRQ-funded; HS027367.
Citation: Bell SK, Bourgeois F, Dong J . Patient identification of diagnostic safety blindspots and participation in "good catches" through shared visit notes. Milbank Q 2022 Dec; 100(4):1121-65. doi: 10.1111/1468-0009.12593..
Keywords: Diagnostic Safety and Quality, Patient Safety, Electronic Health Records (EHRs), Health Information Technology (HIT)
Ganeshan S, Pierce L, Mourad M
Impact of patient portal-based self-scheduling of diagnostic imaging studies on health disparities.
The purpose of this study was to explore the impact of self-scheduling on equitable access to care. The researchers utilized an electronic health record patient portal at the University of California San Francisco which deployed a self-scheduling tool allowing patients to self-schedule diagnostic imaging studies. The study found that among all patient portal users, Latinx, Black/African American, and non-English speaking patients, as well as patients with Medi-Cal, California's Medicaid program, and Medicare insurance were less likely to self-schedule studies. were all less likely to self-schedule when compared with commercially insured patients.
AHRQ-funded; HS026383.
Citation: Ganeshan S, Pierce L, Mourad M . Impact of patient portal-based self-scheduling of diagnostic imaging studies on health disparities. J Am Med Inform Assoc 2022 Nov 14;29(12):2096-100. doi: 10.1093/jamia/ocac152..
Keywords: Disparities, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT)
Malik MA, Motta-Calderon D, Piniella N
A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts.
The purpose of this study was to examine a structured electronic health record (EHR) case review process to identify diagnostic errors (DE) and diagnostic process failures (DPFs) in acute care. The researchers created two test cohorts of all preventable cases (n=28) and an equal number of randomly sampled non-preventable cases (n=28) from 365 adult general medicine patients who expired and were part of the mortality case review process at the research institution. Twenty-seven preventable and 24 non-preventable cases were included in the review process. The study found that the frequency of DE contributing to death was significantly higher for the preventable cohort compared to the non-preventable cohort. The researchers concluded that substantial agreement was observed among final consensus and expert panel reviews using their structured EHR case review process, and DEs contributing to death associated with DPFs were identified in institutionally designated preventable and non-preventable cases.
AHRQ-funded; HS026613.
Citation: Malik MA, Motta-Calderon D, Piniella N . A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts. Diagnosis 2022 Nov;9(4):446-57. doi: 10.1515/dx-2022-0032..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality, Medical Errors
Shafer GJ, Singh H, Thomas EJ
Frequency of diagnostic errors in the neonatal intensive care unit: a retrospective cohort study.
The objective of this study was to determine the frequency and etiology of diagnostic errors during the first 7 days of admission for inborn neonatal intensive care unit (NICU) patients. The "Safer Dx NICU Instrument" was used to review electronic health records. The reviewers discovered that the frequency of diagnostic error in inborn NICU patients during the first 7 days of admission was 6.2%.
AHRQ-funded; HS027363.
Citation: Shafer GJ, Singh H, Thomas EJ . Frequency of diagnostic errors in the neonatal intensive care unit: a retrospective cohort study. J Perinatol 2022 Oct;42(10):1312-18. doi: 10.1038/s41372-022-01359-9..
Keywords: Newborns/Infants, Intensive Care Unit (ICU), Critical Care, Diagnostic Safety and Quality, Medical Errors, Adverse Events, Patient Safety, Electronic Health Records (EHRs), Health Information Technology (HIT)
Bradford A, Shofer M, Singh H
大象视频Author: Shofer M, Singh H
Measure Dx: implementing pathways to discover and learn from diagnostic errors.
This paper discusses Measure Dx, a new 大象视频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
Giardina TD, Choi DT, Upadhyay DK
Inviting patients to identify diagnostic concerns through structured evaluation of their online visit notes.
This study鈥檚 objective was to test if patients can identify concerns about their diagnosis through structured evaluation of their online visit notes in an electronic health record (EHR) system. Patients aged 18-85 years in a large integrated health system who actively used the patient portal were invited to respond to an online questionnaire if an EHR algorithm detected any recent visit following an initial primary care consultation. The authors developed and tested an instrument (Safer Dx Patient Instrument) to help patients identify concerns related to the diagnostic process based on notes review and recall of recent 鈥渁t-risk鈥 visits. The algorithm identified 1282 eligible patients, of whom 486 responded. Of the 418 patients included in the analysis, 51 patients (12.2%) identified a diagnostic concern. Patients were more likely to report a concern if they disagreed with statements "The care plan the provider developed for me addressed all my medical concerns", "I trust the provider that I saw during my visit" and agreed with the statement "I did not have a good feeling about my visit".
AHRQ-funded; HS027363; HS025474.
Citation: Giardina TD, Choi DT, Upadhyay DK . Inviting patients to identify diagnostic concerns through structured evaluation of their online visit notes. J Am Med Inform Assoc 2022 May 11;29(6):1091-100. doi: 10.1093/jamia/ocac036..
Keywords: Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Experience, Patient Safety
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
Enayati M, Sir M, Zhang X
Monitoring diagnostic safety risks in emergency departments: protocol for a machine learning study.
This study鈥檚 objective will be to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. It will use trigger algorithms with electronic health record (EHR) data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on if they meet certain criteria. This study will be conducted by 2 academic medical centers with affiliated community hospitals.
AHRQ-funded; HS027363; HS026622.
Citation: Enayati M, Sir M, Zhang X . Monitoring diagnostic safety risks in emergency departments: protocol for a machine learning study. JMIR Res Protoc 2021 Jun 14;10(6):e24642. doi: 10.2196/24642..
Keywords: Emergency Department, Diagnostic Safety and Quality, Patient Safety, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Jones OT, Calanzani N, Saji S
Artificial intelligence techniques that may be applied to primary care data to facilitate earlier diagnosis of cancer: systematic review.
This study鈥檚 objective was a systematic review of artificial intelligence (AI) techniques that might facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. Findings showed that AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.
AHRQ-funded; HS027363.
Citation: Jones OT, Calanzani N, Saji S . Artificial intelligence techniques that may be applied to primary care data to facilitate earlier diagnosis of cancer: systematic review. J Med Internet Res 2021 Mar 3;23(3):e23483. doi: 10.2196/23483..
Keywords: Cancer, Diagnostic Safety and Quality, Primary Care, Electronic Health Records (EHRs), Health Information Technology (HIT)
Lacson R, Cochon L, Ching PR
Integrity of clinical information in radiology reports documenting pulmonary nodules.
Researchers sought to quantify the integrity, measured as completeness and concordance with a thoracic radiologist, of documenting pulmonary nodule characteristics in CT reports, and to assess impact on making follow-up recommendations. Their retrospective cohort study was performed at an academic medical center and natural language processing was used on radiology reports of CT scans of chest, abdomen, or spine to assess presence of pulmonary nodules. They found that essential pulmonary nodule characteristics were under-reported, potentially impacting recommendations for pulmonary nodule follow-up. They concluded that the lack of documentation of pulmonary nodule characteristics in radiology reports was common, with the potential for compromising patient care and clinical decision support tools.
AHRQ-funded; HS024722.
Citation: Lacson R, Cochon L, Ching PR . Integrity of clinical information in radiology reports documenting pulmonary nodules. J Am Med Inform Assoc 2021 Jan 15;28(1):80-85. doi: 10.1093/jamia/ocaa209..
Keywords: Imaging, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Safety
Salmasian H, Blanchfield BB, Joyce K
Association of display of patient photographs in the electronic health record with wrong-patient order entry errors.
Wrong-patient order entry (WPOE) errors have a high potential for harm; these errors are particularly frequent wherever workflows are complex and multitasking and interruptions are common, such as in the emergency department (ED). The purpose of this study was to evaluate whether the use of noninterruptive display of patient photographs in the banner of the electronic health record (EHR) is associated with a decreased rate of WPOE errors.
AHRQ-funded; HS024713.
Citation: Salmasian H, Blanchfield BB, Joyce K . Association of display of patient photographs in the electronic health record with wrong-patient order entry errors. AMA Netw Open 2020 Nov 2;3(11):e2019652. doi: 10.1001/jamanetworkopen.2020.19652..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Medical Errors, Adverse Drug Events (ADE), Adverse Events, Medication, Medication: Safety, Patient Safety, Diagnostic Safety and Quality
Rogith D, Satterly T, Singh H
Application of human factors methods to understand missed follow-up of abnormal test results.
This study demonstrated application of human factors methods for understanding causes for lack of timely follow-up of abnormal test results ("missed results") in outpatient settings. The investigators identified 30 cases of missed test results by querying electronic health record data, developed a critical decision method based interview guide to understand decision-making processes, and interviewed physicians who ordered these tests. They analyzed transcribed responses, developed a CI-based flow model, and conducted a fault tree analysis to identify hierarchical relationships between factors that delayed action.
AHRQ-funded; HS022087; HS022901.
Citation: Rogith D, Satterly T, Singh H . Application of human factors methods to understand missed follow-up of abnormal test results. Appl Clin Inform 2020 Oct;11(5):692-98. doi: 10.1055/s-0040-1716537..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Shared Decision Making, Diagnostic Safety and Quality, Communication, Clinician-Patient Communication
Shah RU, Mutharasan RK, Ahmad FS
Development of a portable tool to identify patients with atrial fibrillation using clinical notes from the electronic medical record.
The electronic medical record contains a wealth of information buried in free text. In this study, the investigators created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone. The authors concluded that this approach allowed better use of the clinical narrative and created an opportunity for precise, high-throughput cohort identification.
AHRQ-funded; HS026385.
Citation: Shah RU, Mutharasan RK, Ahmad FS . Development of a portable tool to identify patients with atrial fibrillation using clinical notes from the electronic medical record. Circ Cardiovasc Qual Outcomes 2020 Oct;13(10):e006516. doi: 10.1161/circoutcomes.120.006516..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality

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