Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review

Lee, Seungwon; Doktorchik, Chelsea; Martin, Elliot A.; D’Souza, Adam G.; Eastwood, Catherine; Shaheen, Abdel Aziz; Naugler, Christopher; Lee, Joon; Quan, Hude

doi:10.2196/23934

Cited by 20 publications

(23 citation statements)

References 276 publications

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“…Consistent with other literature regarding complex phenotypes, we found that reliance on diagnostic codes can vary in accuracy depending on the jurisdiction [ 14 , 27 ]. System-level and jurisdictional differences in diagnostic coding requirements reduced the sensitivity of case definition 1 in the CPCSSN reference set.…”

Section: Discussionsupporting

confidence: 89%

“…Case definition 4 supplemented specific diagnostic codes with NLP of short diagnostic text fields in the EMR to identify patients with PTSD. Similar to other works, we found that combining structured EMR data and unstructured free text significantly improved diagnostic capture in our pan-Canadian data set yielding higher performance [ 7 , 15 , 20 , 27 ]. However, we did not ascertain additional benefit from using free-text encounter notes when compared to short diagnostic text fields that are more widely available.…”

Section: Discussionsupporting

confidence: 88%

“…Although free-text encounter notes provided additional information regarding risk factors and symptoms, when compared to short diagnostic text fields, their inclusion did not dramatically impact the validation of algorithms intended to identify diagnosed cases. Primary care settings in our sample include regionally or privately operated clinics, different EMR systems, and privacy and confidentiality regulations that can make free-text data difficult to obtain [ 27 ]. We found that when free-text encounter notes are unavailable, short diagnostic text data offer a viable option for identification of a confirmed diagnosis among primary care patients, even when this condition is complex such as PTSD.…”

Section: Discussionmentioning

confidence: 99%

“…However, when we applied the case definitions to the pan-Canadian CPCSSN reference set, provincial differences in diagnostic codes and EMR structure were noticed. Seungwon et al [ 27 ] conducted a scoping review of 274 articles representing 299 algorithms for Charlson conditions reporting that case validation studies frequently focused on a single-center, limiting generalizability of created algorithms. Similarly, we found that our algorithm tested in MaPCReN, which includes only 3 distinct EMR venders, performed better than when tested in a pan-Canadian CPCSSN data set representing 11 different EMR venders across Canada.…”

Section: Discussionmentioning

confidence: 99%

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Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

et al. 2022

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Background The availability of electronic medical record (EMR) free-text data for research varies. However, access to short diagnostic text fields is more widely available. Objective This study assesses agreement between free-text and short diagnostic text data from primary care EMR for identification of posttraumatic stress disorder (PTSD). Methods This retrospective cross-sectional study used EMR data from a pan-Canadian repository representing 1574 primary care providers at 265 clinics using 11 EMR vendors. Medical record review using free text and short diagnostic text fields of the EMR produced reference standards for PTSD. Agreement was assessed with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Results Our reference set contained 327 patients with free text and short diagnostic text. Among these patients, agreement between free text and short diagnostic text had an accuracy of 93.6% (CI 90.4%-96.0%). In a single Canadian province, case definitions 1 and 4 had a sensitivity of 82.6% (CI 74.4%-89.0%) and specificity of 99.5% (CI 97.4%-100%). However, when the reference set was expanded to a pan-Canada reference (n=12,104 patients), case definition 4 had the strongest agreement (sensitivity: 91.1%, CI 90.1%-91.9%; specificity: 99.1%, CI 98.9%-99.3%). Conclusions Inclusion of free-text encounter notes during medical record review did not lead to improved capture of PTSD cases, nor did it lead to significant changes in case definition agreement. Within this pan-Canadian database, jurisdictional differences in diagnostic codes and EMR structure suggested the need to supplement diagnostic codes with natural language processing to capture PTSD. When unavailable, short diagnostic text can supplement free-text data for reference set creation and case validation. Application of the PTSD case definition can inform PTSD prevalence and characteristics.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

et al. 2022

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“…This study has several limitations. First, we identified MCI patients and AD onsets using ICD codes (Supplementary Tables 2) which were provided by physicians and validated in 47, 48 , yet there might be a certain level of inaccuracy due to mis- and under-diagnosis or the lack of clinical details in EHRs or claims 38, 49 . Information contained in clinical notes will be explored in the future through natural language processing to complement the structured codes.…”

Section: Discussionmentioning

confidence: 99%

High-Throughput Clinical Trial Emulation with Real World Data and Machine Learning: A Case Study of Drug Repurposing for Alzheimer’s Disease

Zang

Zhang

et al. 2022

Preprint

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Clinical trial emulation, which is the process of mimicking targeted randomized controlled trials (RCT) with real world data (RWD), has attracted growing attentions and interests in recent years from pharmaceutical industry. Different from RCTs which have stringent eligibility criteria for recruiting participants, RWD are more representative of real world patients whom the drugs will be prescribed to. One technical challenge for trial emulation is how to conduct effective confounding control with complex RWD so that the treatment effects can be objectively derived. Recently many approaches, including deep learning algorithms, have been proposed for this goal, but there is still no systematic evaluation and practical guidance on them. In this paper, we emulate $430,000$ trials from two large-scale RWD warehouses, covering both electronic health records (EHR) and general claims, over 170 million patients spanning more than 10 years, aiming to identify new indications of approved drugs for Alzheimer's disease (AD). We have investigated the behaviors of multiple different approaches including logistic regression and deep learning models, and propose a new model selection strategy that can significantly improve the performance of confounding balance of the participants in different arms of emulated trials. We demonstrate that regularized logistic regression based propensity score (PS) model outperforms deep learning based PS model and others, which contradicts with our intuitions to certain extent. Finally, we identified 8 drugs whose original indications are not AD (pantoprazole, gabapentin, acetaminophen, atorvastatin, albuterol, fluticasone, amoxicillin and omeprazole), hold great potential of being beneficial to AD patients.

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Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France

et al. 2022

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IMPORTANCE Rates of 30-day readmissions following hospitalization for pneumonia are used to publicly report on hospital performance and to set financial penalties for the worst-performing hospitals. However, the rate of avoidable readmission following hospitalization for pneumonia is undefined. OBJECTIVETo assess how often 30-day readmissions following hospitalization for communityacquired pneumonia (CAP) are avoidable. DESIGN, SETTING, AND PARTICIPANTS This cohort study analyzed the results of an independent review of readmissions following hospitalization for CAP within 30 days among patients discharged from 2 large hospitals in France in 2014. Structured clinical records including clinical information (ie, baseline characteristics, physical examination, laboratory findings, x-ray or computed tomography scan findings, discharge plan, and treatments) for both index and readmission stays were independently reviewed by 4 certified board physicians. All consecutive adult patients hospitalized in 2014 with a diagnosis of CAP in our 2 eligible hospitals were eligible. All analyses presented were performed in March 2021. MAIN OUTCOMES AND MEASURESAvoidable readmission within 30 days of discharge from index hospitalization. The likelihood that a readmission was avoidable was quantified using latent class analysis based on the independent reviews. A readmission was considered avoidable if Bayes posterior probability exceeded 50%. RESULTSThe total analytical sample consisted of 1150 index hospital stays with a diagnosis of CAP, which included 651 (56.6%) male patients. The median (IQR) age for all patients was 77.8 (IQR, 62.7-86.4) years. Out of the 1150 index hospital stays, 98 patients (8.5%) died in hospital, and 108 (9.4%) unplanned readmissions were found. Overall, 15 readmissions had a posterior probability of avoidability exceeding 0.50 (13.9% of the 108 unplanned readmissions; 95% CI, 8.0%-21.9%). The median (IQR) delay between the hospital discharge index and readmission was considerably shorter when readmission was deemed avoidable (4 [6-21] days vs 12 [2-18] days; P = .02). CONCLUSIONS AND RELEVANCEOnly a small number of readmissions following hospitalization for CAP were deemed avoidable, comprising less than 10% of all readmissions. Shorter time interval between hospitalization discharge and readmission was associated with avoidability.

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Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review

Cited by 20 publications

References 276 publications

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

High-Throughput Clinical Trial Emulation with Real World Data and Machine Learning: A Case Study of Drug Repurposing for Alzheimer’s Disease

Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France

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