Leveraging Electronic Medical Records for Surveillance of Surgical Site Infection in a Total Joint Replacement Population

2011 ◽  
Vol 32 (4) ◽  
pp. 351-359 ◽  
Author(s):  
Maria C. S. Inacio ◽  
Elizabeth W. Paxton ◽  
Yuexin Chen ◽  
Jessica Harris ◽  
Enid Eck ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Surgical Site Infection ◽  
Joint Replacement ◽  
Chart Review ◽  
Total Joint Replacement ◽  
Health Record ◽  
Site Infection ◽  
Predictive Values ◽  
Electronic Screening ◽  
Screening Algorithm

Objective.TO evaluate whether a hybrid electronic screening algorithm using a total joint replacement (TJR) registry, electronic surgical site infection (SSI) screening, and electronic health record (EHR) review of SSI is sensitive and specific for SSI detection and reduces chart review volume for SSI surveillance.Design.Validation study.Setting.A large health maintenance organization (HMO) with 8.6 million members.Methods.Using codes for infection, wound complications, cellullitis, procedures related to infections, and surgeon-reported complications from the International Classification of Diseases, Ninth Revision, Clinical Modification, we screened each TJR procedure performed in our HMO between January 2006 and December 2008 for possible infections. Flagged charts were reviewed by clinical-content experts to confirm SSIs. SSIs identified by the electronic screening algorithm were compared with SSIs identified by the traditional indirect surveillance methodology currently employed in our HMO. Positive predictive values (PPVs), negative predictive values (NPVs), and specificity and sensitivity values were calculated. Absolute reduction of chart review volume was evaluated.Results.The algorithm identified 4,001 possible SSIs (9.5%) for the 42,173 procedures performed for our TJR patient population. A total of 440 case patients (1.04%) had SSIs (PPV, 11.0%; NPV, 100.0%). The sensitivity and specificity of the overall algorithm were 97.8% and 91.5%, respectively.Conclusion.An electronic screening algorithm combined with an electronic health record review of flagged cases can be used as a valid source for TJR SSI surveillance. The algorithm successfully reduced the volume of chart review for surveillance by 90.5%.

Validation of semiautomated surgical site infection surveillance using electronic screening algorithms in 38 surgery categories

2018 ◽  
Vol 39 (8) ◽  
pp. 931-935 ◽  
Author(s):  
Sun Young Cho ◽  
Doo Ryeon Chung ◽  
Jong Rim Choi ◽  
Doo Mi Kim ◽  
Si-Ho Kim ◽  
...  
Keyword(s):  
Surgical Site Infection ◽  
Surveillance System ◽  
Chart Review ◽  
Medical Center ◽  
Tertiary Care ◽  
Microbial Culture ◽  
Site Infection ◽  
Electronic Screening ◽  
Sensitivity Specificity ◽  
Infection Preventionists

ObjectiveTo verify the validity of a semiautomated surgical site infection (SSI) surveillance system using electronic screening algorithms in 38 categories of surgery.DesignA cohort study for validation of semiautomated SSI surveillance system using screening algorithms.SettingA 1,989-bed tertiary-care referral center in Seoul, Republic of Korea.MethodsA dataset of 40,516 surgical procedures in 38 categories stored in the conventional SSI surveillance registry at the Samsung Medical Center between January 2013 and December 2014 was used as the reference standard. In the semiautomated surveillance system, electronic screening algorithms flagged cases meeting at least 1 of 3 criteria: antibiotic prescription, microbial culture, and infectious disease consultation. Flagged cases were audited by infection preventionists. Analyses of sensitivity, specificity, and positive predictive value (PPV) were conducted for the semiautomated surveillance system, and its effect on reducing the workload for chart review was evaluated.ResultsA total of 575 SSI events (1·42%) were identified by conventional SSI surveillance. The sensitivity of the semiautomated SSI surveillance was 96·7%, and the PPV of the screening algorithms alone was 4·1%. Semiautomated SSI surveillance reduced the chart review workload of the infection preventionists from 1,283 to 482 person hours per year (a 62·4% decrease).ConclusionsCompared to conventional surveillance, semiautomated surveillance using electronic screening algorithms followed by chart review of selected cases can provide high-validity surveillance results and can significantly reduce the workload of infection preventionists.

scholarly journals Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

2021 ◽  
Author(s):  
Jeffrey G Klann ◽  
Griffin M Weber ◽  
Hossein Estiri ◽  
Bertrand Moal ◽  
Paul Avillach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Chart Review ◽  
Learning Approach ◽  
Health Record ◽  
Learning Approaches ◽  
Electronic Health Record Data ◽  
Icu Admission ◽  
Machine Learning Approach ◽  
Electronic Health

Abstract Introduction The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. Objective We sought to develop and validate a computable phenotype for COVID-19 severity. Methods Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. Results The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. Discussion We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. Conclusion We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

Concordance Between Electronic Health Record and Tumor Registry Documentation of Smoking Status Among Patients With Cancer

2021 ◽  
pp. 518-526
Author(s):  
Jennifer H. LeLaurin ◽  
Matthew J. Gurka ◽  
Xiaofei Chi ◽  
Ji-Hyun Lee ◽  
Jaclyn Hall ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Electronic Health Record ◽  
Smoking Status ◽  
Registry Data ◽  
Health Record ◽  
Predictive Values ◽  
Tumor Registry ◽  
Patients With Cancer ◽  
Moderate Agreement ◽  
Status Data

PURPOSE Patients with cancer who use tobacco experience reduced treatment effectiveness, increased risk of recurrence and mortality, and diminished quality of life. Accurate tobacco use documentation for patients with cancer is necessary for appropriate clinical decision making and cancer outcomes research. Our aim was to assess agreement between electronic health record (EHR) smoking status data and cancer registry data. MATERIALS AND METHODS We identified all patients with cancer seen at University of Florida Health from 2015 to 2018. Structured EHR smoking status was compared with the tumor registry smoking status for each patient. Sensitivity, specificity, positive predictive values, negative predictive values, and Kappa statistics were calculated. We used logistic regression to determine if patient characteristics were associated with odds of agreement in smoking status between EHR and registry data. RESULTS We analyzed 11,110 patient records. EHR smoking status was documented for nearly all (98%) patients. Overall kappa (0.78; 95% CI, 0.77 to 0.79) indicated moderate agreement between the registry and EHR. The sensitivity was 0.82 (95% CI, 0.81 to 0.84), and the specificity was 0.97 (95% CI, 0.96 to 0.97). The logistic regression results indicated that agreement was more likely among patients who were older and female and if the EHR documentation occurred closer to the date of cancer diagnosis. CONCLUSION Although documentation of smoking status for patients with cancer is standard practice, we only found moderate agreement between EHR and tumor registry data. Interventions and research using EHR data should prioritize ensuring the validity of smoking status data. Multilevel strategies are needed to achieve consistent and accurate documentation of smoking status in cancer care.

scholarly journals Algorithm to detect pediatric provider attention to high BMI and associated medical risk

2018 ◽  
Vol 26 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Christy B Turer ◽  
Celette S Skinner ◽  
Sarah E Barlow
Keyword(s):  
Body Mass Index ◽  
Electronic Health Record ◽  
Chart Review ◽  
Health Record ◽  
Medical Risk ◽  
Practice Behaviors ◽  
Diagnosis Codes ◽  
Manual Review ◽  
Clinician Practice ◽  
High Bmi

Abstract We developed and validated an algorithm that uses combinations of extractable electronic-health-record (EHR) indicators (diagnosis codes, orders for laboratories, medications, and referrals) that denote widely-recommended clinician practice behaviors: attention to overweight/obesity/body mass index alone (BMI Alone), with attention to hypertension/other comorbidities (BMI/Medical Risk), or neither (No Attention). Data inputs used for each EHR indicator were refined through iterative chart review to identify and resolve modifiable coding errors. Validation was performed through manual review of randomly selected visit encounters (n = 308) coded by the refined algorithm. Of 104 encounters coded as No Attention, 89.4% lacked any evidence (specificity) of attention to BMI/Medical Risk. Corresponding evidence (sensitivity) of attention to BMI Alone was identified in 96.0% (of 101 encounters coded as BMI Alone) and BMI/Medical Risk in 96.1% (of 103 encounters coded as BMI/Medical Risk). Our EHR data algorithm can validly determine provider attention to BMI alone, with Medical Risk, or neither.

scholarly journals 2137. Risk Factors for Surgical Site Infection After Joint Replacement Surgery: Data from the Swiss National Surveillance System

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S629-S629
Author(s):  
Niccolo Buetti ◽  
Andrew Atkinson ◽  
Nicolas Troillet ◽  
Marie-Christine Eisenring ◽  
Marcel Zwahlen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Surgical Site Infection ◽  
Joint Replacement ◽  
Surveillance System ◽  
National Surveillance ◽  
Site Infection ◽  
Joint Replacement Surgery ◽  
Replacement Surgery ◽  
National Surveillance System

scholarly journals Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods

2019 ◽  
Vol 27 (3) ◽  
pp. 480-490 ◽  
Author(s):  
Adam Rule ◽  
Michael F Chiang ◽  
Michelle R Hribar
Keyword(s):  
Systematic Review ◽  
Access Control ◽  
Electronic Health Record ◽  
Chart Review ◽  
Observational Research ◽  
Clinical Activity ◽  
Health Record ◽  
Problem List ◽  
Inclusion Criteria ◽  
Electronic Health

Abstract Objective To systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical activities. Materials and Methods In July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research. Results Eighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy. Discussion While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis. Conclusion EHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.

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