Dataset Information

Determining the Time of Cancer Recurrence Using Claims or Electronic Medical Record Data.

ABSTRACT: PURPOSE:Data from claims and electronic medical records (EMRs) are frequently used to identify clinical events (eg, cancer diagnosis, stroke). However, accurately determining the time of clinical events can be challenging, and the methods used to generate time estimates are underdeveloped. We sought to develop an approach to determine the time of a clinical event-cancer recurrence-using high-dimensional longitudinal structured data. METHODS:Manual chart abstraction provided information regarding the actual time of cancer recurrence. These data were linked to claims from Medicare or structured EMR data from the Cancer Research Network, which were used to determine time of recurrence for patients with lung or colorectal cancer. We analyzed the longitudinal profile of codes that could help determine the time of recurrence, adjusted for systematic differences between code dates and recurrence dates, and integrated time estimates from different codes to empirically derive an optimal algorithm. RESULTS:We identified twelve code groups that could help determine the time of recurrence. Using claims data for patients with lung cancer, the optimal algorithm consisted of three code groups and provided an average prediction error of 4.8 months. Using EMR data or applying this approach to patients with colorectal cancer yielded similar results. CONCLUSION:Time estimates were improved by selecting codes not necessarily the same as those used to identify recurrence, combining time estimates from multiple code groups, and adjusting for systematic bias between code dates and recurrence dates. Improving the accuracy of time estimates for clinical events can facilitate research, quality measurement, and process improvement.

SUBMITTER: Uno H

PROVIDER: S-EPMC6338474 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Determining the Time of Cancer Recurrence Using Claims or Electronic Medical Record Data.

Uno Hajime H Ritzwoller Debra P DP Cronin Angel M AM Carroll Nikki M NM Hornbrook Mark C MC Hassett Michael J MJ

JCO clinical cancer informatics 20181201

<h4>Purpose</h4>Data from claims and electronic medical records (EMRs) are frequently used to identify clinical events (eg, cancer diagnosis, stroke). However, accurately determining the time of clinical events can be challenging, and the methods used to generate time estimates are underdeveloped. We sought to develop an approach to determine the time of a clinical event-cancer recurrence-using high-dimensional longitudinal structured data.<h4>Methods</h4>Manual chart abstraction provided inform ...[more]

PMID: 30652573

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Project description:BackgroundAsthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.ObjectiveWe propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.MethodsWe performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.ResultsThere were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91-0.94) and 0.94 (0.93-0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms.ConclusionWe were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.

Project description:ObjectivesRecent sepsis studies have defined patients as "infected" using a combination of culture and antibiotic orders rather than billing data. However, the accuracy of these definitions is unclear. We aimed to compare the accuracy of different established criteria for identifying infected patients using detailed chart review.DesignRetrospective observational study.SettingSix hospitals from three health systems in Illinois.PatientsAdult admissions with blood culture or antibiotic orders, or Angus International Classification of Diseases infection codes and death were eligible for study inclusion as potentially infected patients. Nine-hundred to 1,000 of these admissions were randomly selected from each health system for chart review, and a proportional number of patients who did not meet chart review eligibility criteria were also included and deemed not infected.InterventionsNone.Measurements and main resultsThe accuracy of published billing code criteria by Angus et al and electronic health record criteria by Rhee et al and Seymour et al (Sepsis-3) was determined using the manual chart review results as the gold standard. A total of 5,215 patients were included, with 2,874 encounters analyzed via chart review and a proportional 2,341 added who did not meet chart review eligibility criteria. In the study cohort, 27.5% of admissions had at least one infection. This was most similar to the percentage of admissions with blood culture orders (26.8%), Angus infection criteria (28.7%), and the Sepsis-3 criteria (30.4%). Sepsis-3 criteria was the most sensitive (81%), followed by Angus (77%) and Rhee (52%), while Rhee (97%) and Angus (90%) were more specific than the Sepsis-3 criteria (89%). Results were similar for patients with organ dysfunction during their admission.ConclusionsPublished criteria have a wide range of accuracy for identifying infected patients, with the Sepsis-3 criteria being the most sensitive and Rhee criteria being the most specific. These findings have important implications for studies investigating the burden of sepsis on a local and national level.

Project description:BackgroundThe rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs.ObjectiveThe aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes.MethodsData were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality.ResultsThe EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS.ConclusionsIn this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities.

Dataset Information

Determining the Time of Cancer Recurrence Using Claims or Electronic Medical Record Data.

Publications

Determining the Time of Cancer Recurrence Using Claims or Electronic Medical Record Data.

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