MS

thesisLumpectomy (Breast Conserving Surgery - BCS) followed by radiation therapy provides survival benefit identical to that of mastectomy for early stage breast cancer. Yet BCS is still underutilized and its use varies significantly among hospitals. The purpose of the project was to decrease variation and increase the frequency of BCS across multiple Intermountain Health Care (IHC) facilities, and to sustain this improvement over time. The study methods involved 1) Identification of the outcome measure, data sources and required data elements; 2) Standardization of data collection processes across IHC facilities; 3) Collection and integration of required data elements from local cancer registries into a central database; 4) Development of performance profiling reports from the central database; 5) Implementation of feedback interventions to surgeons and evaluation of the frequency of BCS following these interventions. The results of the study revealed that before the implementation of feedback interventions (1998-2001), the average frequency of BCS was 53% at IHC facilities. After the implementation of feedback (2001), the frequency of BCS increased from 53% to 65% (p<0.001) and has remained elevated over 4 years (2002-2005). The respective frequencies of BCS at each facility also increased after the implementation of feedback. The difference between facility was the lowest frequency and facility with the highest frequency decreased from 35% (74%-39%) to 16% (75%-59%), a decrease of 53% in variation. In summary, integrated data from local cancer registries, the use of central performance reporting and feedback interventions resulted in a decreased variation and a sustained increase in the use of BCS across multiple facilities

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient’s characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient’s ICS response in the next year based on the patient’s characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources

Additional file 1: of A systematic review of predictive models for asthma development in children

Search queries used in the eight databases. (DOCX 87 kb

Utilization of Radiographic Imaging for Infant Hydronephrosis over the First 12 Months of Life

Purpose. The workup and surveillance strategies for infant hydronephrosis (HN) vary, although this could be due to grade-dependent differences in imaging intensity. We aimed to describe the frequency of imaging studies for HN within the first year of life, stratified by initial HN grade, within a large regional healthcare system. Study Design and Data Source. Retrospective cohort using Intermountain Healthcare Data Warehouse. Inclusion criteria: (1) birth between 1/1/2005 and 12/31/2013, (2) CPT code for HN, and (3) ultrasound (U/S) confirmed HN within four months of birth. Data Collection. Grade of HN on initial postnatal U/S; number of HN-associated radiologic studies (renal U/Ss, voiding cystourethrograms (VCUGs), and diuretic renal scans); demographic and medical variables. Primary Outcome. Sum of radiologic studies within the first year of life or prior to pyeloplasty. Statistical Analysis. Multivariate poisson regression to analyze association between the primary outcome and the initial HN grade. Results. Of 1,380 subjects (993 males and 387 females), 990 (72%), 230 (17%), and 160 (12%) had mild, moderate, and severe HN, respectively. Compared with those with mild HN, patients with moderate (RR: 1.57; 95% CI: 1.42–1.73) and severe (RR: 2.09; 95% CI: 1.88–2.32) HN had a significantly higher rate of imaging use over 12 months (or prior to surgery) after controlling for potential confounders. Conclusions. In a large regional healthcare system, imaging use for HN is proportional to its initial grade. This suggests that within our system, clinicians treating this condition are using a risk-stratified approach to imaging