Electronic Detection of Delayed Test Result Follow-Up in Patients with Hypothyroidism

Meyer, Ashley N. D.; Murphy, Daniel R.; Al-Mutairi, Aymer; Sittig, Dean F.; Li, Wei; Russo, Elise; Singh, Hardeep

doi:10.1007/s11606-017-3988-z

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…Therefore, we combined conventional (telephone follow-up) and electronic surveillance methods to ensure timeliness and a higher participation rate. Although electronic monitoring is widely used, there are obstacles to its use in real-world applications [ 38 ], such as potential delays [ 39 ] and low response rates; thus traditional manual follow-up still plays an important role in management. The key to resolving these limitations is to consider how to change the passive follow-up pattern to one in which patients actively report symptoms, which means patients actively report to the electronic monitoring system when menstruating.…”

Section: Discussionmentioning

confidence: 99%

WeChat assisted electronic symptom measurement for patients with adenomyosis

Xu,

Zhang,

et al. 2024

BMC Med Inform Decis Mak

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Purpose Symptom assessment is central to appropriate adenomyosis management. Using a WeChat mini-program-based portal, we aimed to establish a valid symptom assessment scale of adenomyosis (AM-SAS) to precisely and timely identify needs of symptom management and ultimately, to alert disease recurrence. Methods A combination of intensive interviews of patients with adenomyosis and natural language processing on WeChat clinician-patient group communication was used to generate a pool of symptom items-related to adenomyosis. An expert panel shortened the list to form the provisional AM-SAS. The AM-SAS was built in a Wechat mini-programmer and sent to patients to exam the psychotically validity and clinical applicability through classic test theory and item response theory. Results Total 338 patients with adenomyosis (29 for interview, 179 for development, and 130 for external validation) and 86 gynecologists were included. The over 90% compliance to the WeChat-based symptom evaluate. The AM-SAS demonstrated the uni-dimensionality through Rasch analysis, good internal consistency (all Cronbach’s alphas above 0.8), and test-retest reliability (intraclass correlation coefficients ranging from 0.65 to 0.84). Differences symptom severity score between patients in the anemic and normal hemoglobin groups (3.04 ± 3.17 vs. 5.68 ± 3.41, P < 0.001). In external validation, AM-SAS successfully detected differences in symptom burden and physical status between those with or without relapse. Conclusion Electronic PRO-based AM-SAS is a valuable instrument for monitoring AM-related symptoms. As an outcome measure of multiple symptoms in clinical trials, the AM-SAS may identify patients who need extensive care after discharge and capture significant beneficial changes of patients may have been overlooked. Trial registration This trial was approved by the institutional review board of the Chongqing Medical University and three participating hospitals (Medical Ethics Committee of Nanchong Central Hospital, Medical Ethics Committee of Affiliated Hospital of Southwest Medical University, and Medical Ethics Committee of Haifu Hospital) and registered in the Chinese Clinical Trial Registry (registration number ChiCTR2000038590), date of registration was 26/10/2020.

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Section: Discussionmentioning

confidence: 99%

WeChat assisted electronic symptom measurement for patients with adenomyosis

Xu,

Zhang,

et al. 2024

BMC Med Inform Decis Mak

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“…In fact, e-triggers have recently shown effectiveness in proactively detecting delays in care after an abnormal test result suspicious for cancer, [18][19][20]22,[80][81][82] kidney failure, 83,84 infection, 83 and thyroid dysfunction. 85 Similar, more prospective approaches could be applied to reduce diagnostic error risk for patients presenting to the ED. There are several limitations of our study.…”

Section: Discussionmentioning

confidence: 99%

Validation of an electronic trigger to measure missed diagnosis of stroke in emergency departments

Vaghani

Mushtaq

et al. 2021

Journal of the American Medical Informatics Association

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Objective Diagnostic errors are major contributors to preventable patient harm. We validated the use of an electronic health record (EHR)-based trigger (e-trigger) to measure missed opportunities in stroke diagnosis in emergency departments (EDs). Methods Using two frameworks, the Safer Dx Trigger Tools Framework and the Symptom-disease Pair Analysis of Diagnostic Error Framework, we applied a symptom–disease pair-based e-trigger to identify patients hospitalized for stroke who, in the preceding 30 days, were discharged from the ED with benign headache or dizziness diagnoses. The algorithm was applied to Veteran Affairs National Corporate Data Warehouse on patients seen between 1/1/2016 and 12/31/2017. Trained reviewers evaluated medical records for presence/absence of missed opportunities in stroke diagnosis and stroke-related red-flags, risk factors, neurological examination, and clinical interventions. Reviewers also estimated quality of clinical documentation at the index ED visit. Results We applied the e-trigger to 7,752,326 unique patients and identified 46,931 stroke-related admissions, of which 398 records were flagged as trigger-positive and reviewed. Of these, 124 had missed opportunities (positive predictive value for “missed” = 31.2%), 93 (23.4%) had no missed opportunity (non-missed), 162 (40.7%) were miscoded, and 19 (4.7%) were inconclusive. Reviewer agreement was high (87.3%, Cohen’s kappa = 0.81). Compared to the non-missed group, the missed group had more stroke risk factors (mean 3.2 vs 2.6), red flags (mean 0.5 vs 0.2), and a higher rate of inadequate documentation (66.9% vs 28.0%). Conclusion In a large national EHR repository, a symptom–disease pair-based e-trigger identified missed diagnoses of stroke with a modest positive predictive value, underscoring the need for chart review validation procedures to identify diagnostic errors in large data sets.

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“…Certain e-trigger tools can additionally monitor for high-risk situations prospectively, such as when risk of harm is high, even if no harm has yet occurred. Several studies have shown that e-trigger tools offer promise in detecting errors of omission, such as detecting delays in care after an abnormal test result suspicious for cancer,26–30 35 kidney failure,29 36 infection29 and thyroid conditions,37 as well as patients at risk of delayed action on pregnancy complications 38 39. Such triggers can identify situations where earlier intervention can potentially improve patient outcomes.…”

Section: Conceptualising Diagnostic Safety E-triggersmentioning

confidence: 99%

Application of electronic trigger tools to identify targets for improving diagnostic safety

et al. 2018

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Progress in reducing diagnostic errors remains slow partly due to poorly defined methods to identify errors, high-risk situations, and adverse events. Electronic trigger (e-trigger) tools, which mine vast amounts of patient data to identify signals indicative of a likely error or adverse event, offer a promising method to efficiently identify errors. The increasing amounts of longitudinal electronic data and maturing data warehousing techniques and infrastructure offer an unprecedented opportunity to implement new types of e-trigger tools that use algorithms to identify risks and events related to the diagnostic process. We present a knowledge discovery framework, the Safer Dx Trigger Tools Framework, that enables health systems to develop and implement e-trigger tools to identify and measure diagnostic errors using comprehensive electronic health record (EHR) data. Safer Dx e-trigger tools detect potential diagnostic events, allowing health systems to monitor event rates, study contributory factors and identify targets for improving diagnostic safety. In addition to promoting organisational learning, some e-triggers can monitor data prospectively and help identify patients at high-risk for a future adverse event, enabling clinicians, patients or safety personnel to take preventive actions proactively. Successful application of electronic algorithms requires health systems to invest in clinical informaticists, information technology professionals, patient safety professionals and clinicians, all of who work closely together to overcome development and implementation challenges. We outline key future research, including advances in natural language processing and machine learning, needed to improve effectiveness of e-triggers. Integrating diagnostic safety e-triggers in institutional patient safety strategies can accelerate progress in reducing preventable harm from diagnostic errors.

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Electronic Detection of Delayed Test Result Follow-Up in Patients with Hypothyroidism

Cited by 8 publications

References 34 publications

WeChat assisted electronic symptom measurement for patients with adenomyosis

WeChat assisted electronic symptom measurement for patients with adenomyosis

Validation of an electronic trigger to measure missed diagnosis of stroke in emergency departments

Application of electronic trigger tools to identify targets for improving diagnostic safety

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