Search results

Electronic health records, or EHRs, have information about a patient's health such as test results, diagnoses, and treatments. EHRs also have clinical notes that doctors and patients can use to track goals and decisions.

Clinical notes may be useful for research or to help improve care. But it's hard to get information from these notes across large groups of patients. The notes may use different ways to describe the same thing. For example, high blood pressure may be called hypertension. Also, the notes may use abbreviations or have spelling mistakes.

In this project, the research team designed and built a search engine to make EHR notes easier to search and use for patient care and research.

Based on the state of the scientific evidence, the Sequenced Treatment Effectiveness for Posttraumatic Stress (STEPS) Trial was designed to address three specific aims. The first aim was to quantitatively compare engagement, self-reported PTSD symptom severity (primary outcome), quality of life, and recovery outcomes of primary care patients randomized to initially receive brief psychotherapy (Written Exposure Therapy - WET) or their choice of the three selective serotonin reuptake inhibitors (SSRIs). The second aim was, among patients not responding to initial treatment, to quantitatively compare outcomes of primary care patients randomized to: 1) augment the SSRI with WET, 2) switch from the SSRI to another class of antidepressants (serotonin-norepinephrine reuptake inhibitors - SNRI), or 3) switch from WET to the choice of the three SSRIs. The third specific aim was to quantitatively examine treatment heterogeneity among subgroups of primary care patients receiving pharmacotherapy and psychotherapy, including veterans, women, and those using cannabis.

Patients may respond differently to the same treatment due to individual traits such as age or gender. Knowing how different traits can affect a patient's response to treatment can help doctors and patients make better treatment decisions. For example, this information can help doctors know what types of cancer medicines work better for certain patients. This project focuses on improving the methods that researchers use to compare how treatments work for different patients.

In this project, the research team developed and tested a statistical method called random forests, or RF. RF is a way to analyze data using a technique called machine learning. In machine learning, computers use data to learn how to perform different tasks with little or no human input. Many types of RF methods exist. The team compared multiple RF methods to learn how well the methods would work to find out how patients with different traits respond to the same treatment.

To access the R package, please visit the randomForestSRC CRAN webpage.

According to the World Health Organization, palliative care is "an approach that improves the quality of life of patients and their families facing the problems associated with life-threatening illness, through the prevention and relief of suffering by means of early identification and impeccable assessment and treatment of pain and other problems, physical, psycho-social and spiritual." The goal of the study was to generate comparative effectiveness research evidence to support the delivery of coordinated, community-based palliative care that effectively implements care plans consistent with the goals and preferences of older adults with advanced illness and their caregivers.

This study included a pragmatic, two-arm, multi-site randomized controlled trial of older adults (50+ years) with either poor prognosis cancer or end-stage organ failure who were recruited during an emergency department (ED) visit, along with their informal caregivers, to compare nurse-led telephonic case management to facilitated, outpatient specialty palliative care on: 1) quality of life in patients, 2) loneliness, 3) healthcare use in the 12 months following enrollment, 4) symptom burden, 5) caregiver strain, 6) caregiver quality of life, and 7) bereavement.