Algorithm to Identify Systemic Cancer Therapy Treatment Using Structured Electronic Data.

PURPOSE: With the shift in the majority of oncology clinical care in the United States from paper records to electronic health records, researchers need efficient and validated processes to obtain accurate data about the entire treatment history of patients diagnosed with cancer. The objective of this study was to develop and validate an algorithm that is agnostic to the source of data but that can identify specific regimens in the entire course of systemic therapy treatment for patients diagnosed with breast, colorectal, or lung cancer. METHODS: A cohort of patients with incident breast, colorectal, and lung cancer were randomly distributed into six groups. The algorithm was iteratively modified, and the performance was assessed until no additional modifications could be identified in the first three groups. The performance of the algorithm was confirmed in the three groups that remained. RESULTS: The final model produced ranges of sensitivity between 97.2% and 100% for first-course systemic therapy across all cancers, with a false-positive rate of 0%. The algorithm matched the exact number of courses and the exact regimens of systemic therapy agents as captured by infusion, pharmacy, and procedure electronic medical record data for all courses of therapy 88% to 100% of the time. CONCLUSION: Use of our validated algorithm that characterizes entire courses of systemic therapy treatment in patients diagnosed with breast, colorectal, and lung cancer will allow researchers in a variety of settings to conduct comparative effectiveness studies related to the uptake, safety, outcomes, and costs associated with the use of both novel and standard regimens.

Characteristics of Newly Enrolled Members of an Integrated Delivery System after the Affordable Care Act.

Of 89,289 newly enrolled non-Medicare members, 25.3% completed the Brief Health Questionnaire between 1/1/2014, and 8/31/2014. Of these, 3593 respondents were insured through Medicaid, 9434 through the individual health exchange, and 9521 through primarily commercial plans. Of Medicaid, exchange, and commercial members, 19.5%, 7.1%, and 5.3%, respectively, self-reported fair or poor health; 12.9%, 2.0%, and 3.3% of each group self-reported 2 or more Emergency Department visits during the previous year; and 8.1%, 4.3%, and 4.4% self-reported an inpatient admission during the previous year.

Assessment of morbidity over time in predicting health outcomes.

BACKGROUND: Administratively derived morbidity measures are often used in observational studies as predictors of outcomes. These typically reflect a limited time period before an index event; some outcomes may be affected by rate of morbidity change over longer preindex periods. OBJECTIVES: The aim of the study was to develop statistical models representing the trajectory of individual morbidity over time and to evaluate the performance of trajectory versus other summary morbidity measures in predicting a range of health outcomes. METHODS: From a retrospective cohort study of integrated health system members aged 65 years or older with 3 or more common chronic medical conditions, we used available diagnoses for up to 10 years to examine associations between variations of the Charlson Comorbidity Index (CCI, Quan adaptation) and health outcomes. A linear mixed effects model was used to estimate the trajectory of individual CCI over time; estimated parameters describing individual trajectories were used as predictors for health outcomes. Other variations of CCI were: a "snapshot" measure, a cumulative measure, and actual baseline and rate of change. Models were developed in an initial cohort for whom we had survey data, and verified in a larger cohort. RESULTS: Among 961 surveyed members and 13,163 members of a secondary cohort, cumulative and snapshot measures provided best fit and predictive ability for utilization outcomes. Incorporating trajectory resulted in a slightly better model for self-reported health status. CONCLUSIONS: Modeling longitudinal morbidity trajectories did not add substantially to the association between morbidity and utilization or mortality. Standard snapshot morbidity measures likely sufficiently capture multimorbidity in assessing these outcomes.