Informatics tools to implement late cardiovascular risk prediction modeling for population management of high-risk childhood cancer survivors.

BACKGROUND: Clinical informatics tools to integrate data from multiple sources have the potential to catalyze population health management of childhood cancer survivors at high risk for late heart failure through the implementation of previously validated risk calculators. METHODS: The Oklahoma cohort (n = 365) harnessed data elements from Passport for Care (PFC), and the Duke cohort (n = 274) employed informatics methods to automatically extract chemotherapy exposures from electronic health record (EHR) data for survivors 18 years old and younger at diagnosis. The Childhood Cancer Survivor Study (CCSS) late cardiovascular risk calculator was implemented, and risk groups for heart failure were compared to the Children's Oncology Group (COG) and the International Guidelines Harmonization Group (IGHG) recommendations. Analysis within the Oklahoma cohort assessed disparities in guideline-adherent care. RESULTS: The Oklahoma and Duke cohorts both observed good overall concordance between the CCSS and COG risk groups for late heart failure, with weighted kappa statistics of .70 and .75, respectively. Low-risk groups showed excellent concordance (kappa > .9). Moderate and high-risk groups showed moderate concordance (kappa .44-.60). In the Oklahoma cohort, adolescents at diagnosis were significantly less likely to receive guideline-adherent echocardiogram surveillance compared with survivors younger than 13 years old at diagnosis (odds ratio [OD] 0.22; 95% confidence interval [CI]: 0.10-0.49). CONCLUSIONS: Clinical informatics tools represent a feasible approach to leverage discrete treatment-related data elements from PFC or the EHR to successfully implement previously validated late cardiovascular risk prediction models on a population health level. Concordance of CCSS, COG, and IGHG risk groups using real-world data informs current guidelines and identifies inequities in guideline-adherent care.

Rural, Large Town, and Urban Differences in Optimal Subspecialty Follow-up and Survivorship Care Plan Documentation among Childhood Cancer Survivors.

BACKGROUND: Children with cancer from rural and nonurban areas face unique challenges. Health equity for this population requires attention to geographic disparities in optimal survivorship-focused care. METHODS: The Oklahoma Childhood Cancer Survivor Cohort was based on all patients reported to the institutional cancer registry and ≤ 18 years old at diagnosis between January 1, 2005, and September 24, 2014. Suboptimal follow-up was defined as no completed oncology-related clinic visit five to 7 years after their initial diagnosis (survivors were 7-25 years old at end of the follow-up period). The primary predictor of interest was rurality. RESULTS: Ninety-four (21%) of the 449 eligible survivors received suboptimal follow-up. There were significant differences (P = 0.01) as 36% of survivors from large towns (n = 28/78) compared with 21% (n = 20/95) and 17% (n = 46/276) of survivors from small town/isolated rural and urban areas received suboptimal follow-up, respectively. Forty-five percent of adolescents at diagnosis were not seen in the clinic compared with 17% of non-adolescents (P < 0.01). An adjusted risk ratio of 2.2 (95% confidence interval, 1.5, 3.2) was observed for suboptimal follow-up among survivors from large towns, compared with survivors from urban areas. Seventy-three percent of survivors (n = 271/369) had a documented survivorship care plan with similar trends by rurality. CONCLUSIONS: Survivors from large towns and those who were adolescents at the time of diagnosis were more likely to receive suboptimal follow-up care compared with survivors from urban areas and those diagnosed younger than thirteen. IMPACT: Observed geographic disparities in survivorship care will inform interventions to promote equitable care for survivors from nonurban areas.

An Analysis of Medication Adherence of Sooner Health Access Network SoonerCare Choice Patients.

Medication adherence is a desirable but rarely available metric in patient care, providing key insights into patient behavior that has a direct effect on a patient's health. In this research, we determine the medication adherence characteristics of over 46,000 patients enrolled in the Sooner Health Access Network (HAN), based on Medicaid claims data from the Oklahoma Health Care Authority. We introduce a new measure called Specific Medication PDC (smPDC), based on the popular Proportion of Days Covered (PDC) method, using the last fill date for the end date of the measurement duration. The smPDC method is demonstrated by calculating medication adherence across the eligible patient population, for relevant subpopulations over a two-year period spanning 2012 - 2013. We leverage a clinical analytics platform to disseminate adherence measurements to providers. Aggregate results demonstrate that the smPDC method is relevant and indicates potential opportunities for health improvement for certain population segments.

Crossing the evidence chasm: building evidence bridges from process changes to clinical outcomes.

OBJECTIVE: Although demand for information about the effectiveness and efficiency of health care information technology grows, large-scale resource-intensive randomized controlled trials of health care information technology remain impractical. New methods are needed to translate more commonly available clinical process measures into potential impact on clinical outcomes. DESIGN: The authors propose a method for building mathematical models based on published evidence that provides an evidence bridge between process changes and resulting clinical outcomes. This method combines tools from systematic review, influence diagramming, and health care simulations. MEASUREMENTS: The authors apply this method to create an evidence bridge between retinopathy screening rates and incidence of blindness in diabetic patients. RESULTS: The resulting model uses changes in eye examination rates and other evidence-based population parameters to generate clinical outcomes and costs in a Markov model. CONCLUSION: This method may serve as an alternative to more expensive study designs and provide useful estimates of the impact of health care information technology on clinical outcomes through changes in clinical process measures.