Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing

Murff, Harvey J.; FitzHenry, Fern; Matheny, Michael E.; Gentry, Nancy; Kotter, Kristen; Crimin, Kimberly; Dittus, Robert S.; Rosen, Amy K.; Elkin, Peter L.; Brown, Steven H.; Speroff, Theodore

doi:10.1001/jama.2011.1204

Cited by 377 publications

(297 citation statements)

References 21 publications

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“…50 Associations between endoscopy non-adherence, male gender, psychiatric illness, and appointment wait time have all been reported previously. 17,22,26 This study has several limitations. We did not investigate certain variables that may be associated with non-attendance, including socio-economic status, ethnicity, indication for colonoscopy, colonoscopy referral source, or patient-specific barriers to attendance.…”

Section: Discussionmentioning

confidence: 96%

“…NLP has been used to facilitate numerous assessments of health care quality and safety. [18][19][20][21][22] This project had two major aims: 1) to develop an automated method utilizing NLP that evaluates patients' prior non-adherence with health care services; 2) to use this adherence metric and readily available clinical, demographic, and scheduling data to develop and validate a prediction model that accurately identifies individuals at high risk for non-adherence with scheduled colonoscopy.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

et al. 2015

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BACKGROUND: Accurately predicting the risk of no-show for a scheduled colonoscopy can help target interventions to improve compliance with colonoscopy, and thereby reduce the disease burden of colorectal cancer and enhance the utilization of resources within endoscopy units. OBJECTIVES: We aimed to utilize information available in an electronic medical record (EMR) and endoscopy scheduling system to create a predictive model for no-show risk, and to simultaneously evaluate the role for natural language processing (NLP) in developing such a model. DESIGN: This was a retrospective observational study using discovery and validation phases to design a colonoscopy non-adherence prediction model. An NLPderived variable called the Non-Adherence Ratio ("NAR") was developed, validated, and included in the model. PARTICIPANTS: Patients scheduled for outpatient colonoscopy at an Academic Medical Center (AMC) that is part of a multi-hospital health system, 2009 to 2011, were included in the study. MAIN MEASURES: Odds ratios for non-adherence were calculated for all variables in the discovery cohort, and an Area Under the Receiver Operating Curve (AUC) was calculated for the final non-adherence prediction model. KEY RESULTS: The non-adherence model included six variables: 1) gender; 2) history of psychiatric illness, 3) NAR; 4) wait time in months; 5) number of prior missed endoscopies; and 6) education level. The model achieved discrimination in the validation cohort (AUC= =70.2 %). At a threshold non-adherence score of 0.46, the model's sensitivity and specificity were 33 % and 92 %, respectively. Removing the NAR from the model significantly reduced its predictive power (AUC = 64.3 %, difference = 5.9 %, p<0.001). CONCLUSIONS: A six-variable model using readily available clinical and demographic information demonstrated accuracy for predicting colonoscopy non-adherence. The NAR, a novel variable developed using NLP technology, significantly strengthened this model's predictive power.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

et al. 2015

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“…[16][17][18] 3) Meticulous interpretation of results. Our algorithm performed very well in the RPDR; however, upon application to a substantially larger database, we discovered a limitation of the algorithm that was produced by a change in the ICD-9 coding method.…”

Section: Discussionmentioning

confidence: 99%

Quantifying a Rare Disease in Administrative Data: The Example of Calciphylaxis

et al. 2014

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“…The use of the EMR has considerable potential to improve institutional performance, including decreasing the readmission rate for prevalent conditions such as AMI. [11][12][13] Because EMRs are widely adopted, predictive models that exploit these resources could be seamlessly integrated into clinical pathways, offering an inexpensive tool to assist clinicians in assessing risk. However, the dataset in a comprehensive EMR is complex and unless methods are developed to interpret and present data, the full benefit of the EMR may not be realised.…”

Section: Discussionmentioning

confidence: 99%

“…[11][12][13] In the present study we examined all the available administrative hospital factors associated with readmission in a cohort of patients admitted with AMI to a large regional hospital in Geelong (Vic., Australia). We used data from the EMR to derive and internally validate a model to predict unplanned ischaemic heart disease (IHD) readmissions over a 30-day period and all-cause readmission over 12 months after an admission with AMI.…”

Section: Introductionmentioning

confidence: 99%

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Rana¹,

Tran²,

Luo³

et al. 2014

Aust. Health Review

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Objective. Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations.Methods. The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation.Results. The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78; 95% confidence interval (CI) 0.71-0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72; 95% CI 0.66-0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission.Conclusions. Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions.What is known about the topic? Many clinical and demographic risk factors are known for hospital readmissions following acute myocardial infarction, including multivessel disease, high baseline heart rate, hypertension, diabetes, obesity, chronic obstructive pulmonary disease and psychiatric morbidity. However, combining these risk factors into indices for predicting readmission had limited success. A recent study reported a C-statistic of 0.73 for predicting 30-day readmissions. In a recent American study, a simple seven-factor score was shown to predict hospital readmissions among medical patients. What does this paper add? This paper presents a way to predict readmissions following myocardial infarction using routinely collected administrative data. The model performed better than the recently described HOSPITAL score and a model derived from Elixhauser comorbidities. Moreover, the model uses only data generally available in most hospitals. What are the implications for practitioners? Routine hospital data available at discharges can be used to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.

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Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing

Cited by 377 publications

References 21 publications

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

Quantifying a Rare Disease in Administrative Data: The Example of Calciphylaxis

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

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