Describing current use, barriers, and facilitators of patient portal messaging for research recruitment: Perspectives from study teams and patients at one institution

Abstract Introduction: The electronic health record (EHR) and patient portal are used increasingly for clinical research, including patient portal recruitment messaging (PPRM). Use of PPRM has grown rapidly; however, best practices are still developing. In this study, we examined the use of PPRM at our institution and conducted qualitative interviews among study teams and patients to understand experiences and preferences for PPRM. Methods: We identified study teams that sent PPRMs and patients that received PPRMs in a 60-day period. We characterized these studies and patients, in addition to the patients’ interactions with the PPRMs (e.g., viewed, responded). From these groups, we recruited study team members and patients for semi-structured interviews. A pragmatic qualitative inquiry framework was used by interviewers. Interviews were audio-recorded and analyzed using a rapid qualitative analysis exploratory approach. Results: Across ten studies, 35,037 PPRMs were sent, 33% were viewed, and 17% were responded to. Interaction rates varied across demographic groups. Six study team members completed interviews and described PPRM as an efficient and helpful recruitment method. Twenty-eight patients completed interviews. They were supportive of receiving PPRMs, particularly when the PPRM was relevant to their health. Patients indicated that providing more information in the PPRM would be helpful, in addition to options to set personalized preferences. Conclusions: PPRM is an efficient recruitment method for study teams and is acceptable to patients. Engagement with PPRMs varies across demographic groups, which should be considered during recruitment planning. Additional research is needed to evaluate and implement recommended changes by study teams and patients

DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record

Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant. Healthcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable and reliable models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher created clinical ML models into a widely used electronic medical record (EMR) system. We discuss core functionality and design decisions, including mechanisms to trigger inference based on actions within EMR software, modules that collect real-time data to make inferences, mechanisms that close-the-loop by displaying inferences back to end-users within their workflow, monitoring modules that track performance of deployed models over time, silent deployment capabilities, and mechanisms to prospectively evaluate a deployed model's impact. We demonstrate the use of DEPLOYR by silently deploying and prospectively evaluating twelve ML models triggered by clinician button-clicks in Stanford Health Care's production instance of Epic. Our study highlights the need and feasibility for such silent deployment, because prospectively measured performance varies from retrospective estimates. By describing DEPLOYR, we aim to inform ML deployment best practices and help bridge the model implementation gap

Patient-Reported Outcomes Screening for Improved Patient Wellness: A Cancer Center Initiative

Background: People experiencing serious illness have significant unmet physical, emotional, social and spiritual needs. The Quality Oncology Practice Initiative (QOPI) requires patients to be screened for emotional wellbeing and pain by their second oncology visit. This project details one cancer center’s quality improvement initiative to (a) implement electronic screening of every cancer patient by their second oncology visit, (b) design processes for ongoing assessment and intervention of need(s), and (c) develop measurable and sustainable evaluation metrics to ensure that palliative care needs are met. Methods: In June 2015, we launched electronic collection of patient-reported outcomes (PROs) using the Patient Reported Outcome Measurement Instrument System (PROMIS) global screen. Screening was completed via the health portal or clinic computer prior to the first return visit and at 30-day intervals. Results: The primary measures of interest were the percentage of completed PROMIS questionnaires and the percentage of relevant answers, with a target completion rate of 60%. The highest completion rate was 25.3%. Six weeks of relevant answers were collated from August 18, 2015 through September 30, 2015 with a range of 3.6% to 5.3% of patients having relevant answers. Conclusions: The utilization of a screening tool is only the method by which assessment and evaluation of comprehensive care needs is initiated. Evidence-based practice guidelines and clinical care pathways must also be in place to manage each symptom identified in a standardized way. Support for oncology nurses to lead assessment and connect patients with resources is an opportunity to incorporate primary palliative care into oncology practice

The Duke Hot-Spotting Initiative: Understanding the Role of Medical Students in Improving Patient Outcomes through a Relationship-Based Care Management Program

AbstractBackground: In response to increasing healthcare costs in the United States, the Institute for HealthcareImprovement released the triple aim goals to inspire the creation of programs that would improve quality of care, overall health, and reduce costs simultaneously. Care-management programs are one such initiative that many sites are using to address patient needs. More recently, student-led care management initiatives have been introduced; however, there is limited evidence on the effectiveness of these programs for improving patient outcomes.Objective: The Duke Hot Spotting Initiative (DHSI) is a student-led program started in 2015 aimed at addressing health disparities and high health care costs through a relationship-based care management model. The purpose of this analysis is to examine the effect of the DHSI initiative on specific patient health and utilization outcomes.Methods: This is retrospective review of the past three phases of the DHSI program in Durham, North Carolina. Eligible DHSI patients were those enrolled in preexisting care-management programs at Duke Outpatient Clinic. Mean hemoglobin A1c (HbA1c) and blood pressure readings were compared from the end of the 6-month pre-intervention period to the end of the 6-month intervention period and use as surrogates for type II diabetes mellitus (T2DM) and hypertension respectively. Emergency department (ED) utilization rates and no-show rates were also compared over this time period.Findings: Twenty-nine participants were included in this analysis. Mean hemoglobin A1c values decreased from 9.0 to 8.3 (p = 0.249) and systolic blood pressure (SBP) decreased from 142 to 137 mmHg (p = 0.494) ED utilization rates decreased by 20% (p = 0.970) while no-show rates increased by 20% (p = 0.239).Conclusions: These results demonstrate that student-led navigation under the supervision of a care manager has had a non-statistical yet clinically significant improvement in measures of HbA1c, SBP, and ED utilization rates. The enrollment of patients from previous care-management programs may diminish the full benefit that students have on patient outcomes. This analysis shows that such a model of care- management led by students is a promising strategy. Future studies could measure outcomes in a larger patient sample and also assess qualitative outcomes such as well-being and achievement of patient- specific health goals.Master of Public Healt

Pharmacogenomics

This Special Issue focuses on the current state of pharmacogenomics (PGx) and the extensive translational process, including the identification of functionally important PGx variation; the characterization of PGx haplotypes and metabolizer statuses, their clinical interpretation, clinical decision support, and the incorporation of PGx into clinical care