ResearchMatch on FHIR: Development and evaluation of a recruitment registry and electronic health record system interface for volunteer profile completion.

Background: Obtaining complete and accurate information in recruitment registries is essential for matching potential participants to research studies for which they qualify. Since electronic health record (EHR) systems are required to make patient data available to external systems, an interface between EHRs and recruitment registries may improve accuracy and completeness of volunteers' profiles. We tested this hypothesis on ResearchMatch (RM), a disease- and institution-neutral recruitment registry with 1357 studies across 255 institutions. Methods: We developed an interface where volunteers signing up for RM can authorize transfer of demographic data, medical conditions, and medications from the EHR into a registration form. We obtained feedback from a panel of community members to determine acceptability of the planned integration. We then developed the EHR interface and performed an evaluation study of 100 patients to determine whether RM profiles generated with EHR-assisted adjudication included more conditions and medications than those without the EHR connection. Results: Community member feedback revealed that members of the public were willing to authenticate into the EHR from RM with proper messaging about choice and privacy. The evaluation study showed that out of 100 participants, 75 included more conditions and 69 included more medications in RM profiles completed with the EHR connection than those without. Participants also completed the EHR-connected profiles in 16 fewer seconds than non-EHR-connected profiles. Conclusions: The EHR to RM integration could lead to more complete profiles, less participant burden, and better study matches for many of the over 148,000 volunteers who participate in ResearchMatch.

Evaluating automated electronic case report form data entry from electronic health records.

Background: Many clinical trials leverage real-world data. Typically, these data are manually abstracted from electronic health records (EHRs) and entered into electronic case report forms (CRFs), a time and labor-intensive process that is also error-prone and may miss information. Automated transfer of data from EHRs to eCRFs has the potential to reduce data abstraction and entry burden as well as improve data quality and safety. Methods: We conducted a test of automated EHR-to-CRF data transfer for 40 participants in a clinical trial of hospitalized COVID-19 patients. We determined which coordinator-entered data could be automated from the EHR (coverage), and the frequency with which the values from the automated EHR feed and values entered by study personnel for the actual study matched exactly (concordance). Results: The automated EHR feed populated 10,081/11,952 (84%) coordinator-completed values. For fields where both the automation and study personnel provided data, the values matched exactly 89% of the time. Highest concordance was for daily lab results (94%), which also required the most personnel resources (30 minutes per participant). In a detailed analysis of 196 instances where personnel and automation entered values differed, both a study coordinator and a data analyst agreed that 152 (78%) instances were a result of data entry error. Conclusions: An automated EHR feed has the potential to significantly decrease study personnel effort while improving the accuracy of CRF data.

Clinical Assessment of Low Calcium In traUMa (CALCIUM).

Major trauma frequently occurs in the deployed, combat setting and is especially applicable in the recent conflicts with explosives dominating the combat wounded. In future near-peer conflicts, we will likely face even more profound weapons including mortars and artillery. As such, the number of severely wounded will likely increase. Hypocalcemia frequently occurs after blood transfusions, secondary to the preservatives in the blood products; however, recent data suggests major trauma in and of itself is a risk factor for hypocalcemia. Calcium is a major ion involved in heart contractility; thus, hypocalcemia can lead to poor contractility. Smaller studies have linked hypocalcemia to worse outcomes, but it remains unclear what causes hypocalcemia and if intervening could potentially save lives. The objective of this study is to determine the incidence of hypocalcemia on hospital arrival and the association with survival. We are seeking to address the following scientific questions, (1) Is hypocalcemia present following traumatic injury prior to transfusion during resuscitation? (2) Does hypocalcemia influence the amount of blood products transfused? (3) To what extent is hypocalcemia further exacerbated by transfusion? (4) What is the relationship between hypocalcemia following traumatic injury and mortality? We will conduct a multicenter, prospective, observational study. We will gather ionized calcium levels at 0, 3, 6, 12, 18, and 24 hours as part of scheduled calcium measurements. This will ensure we have accurate data to assess the early and late effects of hypocalcemia throughout the course of resuscitation and hemorrhage control. These data will be captured by a trained study team at every site. Our findings will inform clinical practice guidelines and optimize the care delivered in the combat and civilian trauma setting. We are seeking 391 patients with complete data to meet our a priori inclusion criteria. Our study will have major immediate short-term findings including risk prediction modeling to assess who is at risk for hypocalcemia, data assessing interventions associated with the incidence of hypocalcemia, and outcome data including mortality and its link to early hypocalcemia.

HL7 FHIR-based tools and initiatives to support clinical research: a scoping review.

OBJECTIVES: The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research. MATERIALS AND METHODS: We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities. RESULTS: Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself. DISCUSSION: Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities. CONCLUSION: The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.

Making Clinical Data Acquisition Standards Harmonization (CDASH) Electronic Case Report Forms Available on the REDCap Shared Data Instrument Library.

Introduction: A guiding principle behind the development and deployment of the REDCap data management platform has always included attention to workflow design that allows easy implementation of best practices for clinical and translational researchers. CDISC standards such as CDASH have helped the clinical research community improve the efficiency, actionability, and quality of their clinical trials data, but have had limited uptake among the academic institutions. Objective: To create a scalable methodology to convert CDISC CDASH eCRF instrument metadata into REDCap data dictionaries for the purpose of simplifying adoption and use of CDASH instruments by research teams across the REDCap Consortium. Implementation: We have used our replicable methods to translate metadata from 34 CDASH Foundational eCRFs and 20 CDASH Crohn's Disease eCRFs into REDCap eCRF metadata and have made these instruments available in the REDCap Shared Data Instrument Library for widespread sharing and uptake across the REDCap Consortium. Users can import the standardized eCRFs directly into their REDCap projects for immediate use in clinical trial data collection. Conclusion: Disseminating CDISC standards through the REDCap community will increase the accessibility of these standards for academic medical centers. Having academic clinical researchers using CDISC standards may lead to more research datasets that interoperate with pharmaceutical sponsored trials, and more discoveries from secondary use of clinical research data.

MyCap: a flexible and configurable platform for mobilizing the participant voice.

This paper provides a description of the MyCap data collection platform, utilization metrics, and vignettes associated with use from diverse research institutions. MyCap is a participant-facing mobile application for survey data collection and the automated administration of active tasks (activities performed by participants using mobile device sensors under semi-controlled conditions). Launched in 2018, MyCap is a no-code solution for research teams conducting longitudinal studies, integrates tightly with REDCap and is available at no cost to research teams at academic, nonprofit, or government organizations. MyCap has been deployed at multiple research institutions with application usage logged across 135 countries in 2021. Vignettes demonstrate that MyCap empowered research teams to explore and implement novel methods of information collection and use. MyCap's integration with REDCap provides a comprehensive data collection ecosystem and is best suited for longitudinal studies with frequent requests for information from participants.

Follow-up Interactive Long-Term Expert Ranking (FILTER): a crowdsourcing platform to adjudicate risk for survivorship care.

OBJECTIVES: To develop an online crowdsourcing platform where oncologists and other survivorship experts can adjudicate risk for complications in follow-up. MATERIALS AND METHODS: This platform, called Follow-up Interactive Long-Term Expert Ranking (FILTER), prompts participants to adjudicate risk between each of a series of pairs of synthetic cases. The Elo ranking algorithm is used to assign relative risk to each synthetic case. RESULTS: The FILTER application is currently live and implemented as a web application deployed on the cloud. DISCUSSION: While guidelines for following cancer survivors exist, refinement of survivorship care based on risk for complications after active treatment could improve both allocation of resources and individual outcomes in long-term follow-up. CONCLUSION: FILTER provides a means for a large number of experts to adjudicate risk for survivorship complications with a low barrier of entry.

A multicenter cluster randomized, stepped wedge implementation trial for targeted normoxia in critically ill trauma patients: study protocol and statistical analysis plan for the Strategy to Avoid Excessive Oxygen (SAVE-O2) trial.

BACKGROUND: Targeted normoxia (SpO2 90-96% or PaO2 60-100 mmHg) may help to conserve oxygen and improve outcomes in critically ill patients by avoiding potentially harmful hyperoxia. However, the role of normoxia for critically ill trauma patients remains uncertain. The objective of this study is to describe the study protocol and statistical analysis plan for the Strategy to Avoid Excessive Oxygen for Critically Ill Trauma Patients (SAVE-O2) clinical trial. METHODS: Design, setting, and participants: Protocol for a multicenter cluster randomized, stepped wedge implementation trial evaluating the effectiveness of a multimodal intervention to target normoxia in critically ill trauma patients at eight level 1 trauma centers in the USA. Each hospital will contribute pre-implementation (control) and post-implementation (intervention) data. All sites will begin in the control phase with usual care. When sites reach their randomly assigned time to transition, there will be a one-month training period, which does not contribute to data collection. Following the 1-month training period, the site will remain in the intervention phase for the duration of the trial. MAIN OUTCOME MEASURES: The primary outcome will be supplemental oxygen-free days, defined as the number of days alive and not on supplemental oxygen. Secondary outcomes include in-hospital mortality to day 90, hospital-free days to day 90, ventilator-free days (VFD) to day 28, time to room air, Glasgow Outcome Score (GOS), and duration of time receiving supplemental oxygen. DISCUSSION: SAVE-O2 will determine if a multimodal intervention to improve compliance with targeted normoxia will safely reduce the need for concentrated oxygen for critically injured trauma patients. These data will inform military stakeholders regarding oxygen requirements for critically injured warfighters, while reducing logistical burden in prolonged combat casualty care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04534959 . Registered September 1, 2020.

Measures of Treatment Workload for Patients With Breast Cancer.

PURPOSE: Patients with breast cancer spend a large amount of time and effort receiving treatment. When the number of health care tasks exceeds a patient's ability to manage that workload, they could become overburdened, leading to decreased plan adherence. We used electronic health record data to retrospectively assess dimensions of treatment workload related to outpatient encounters, commuting, and admissions. METHODS: Using tumor registry and scheduling data, we evaluated the sensitivity of treatment workload measures to detect expected differences in breast cancer treatment burden by stage. We evaluated the impact of the on-body pegfilgrastim injector on the treatment workload of patients undergoing a specific chemotherapy protocol. RESULTS: As hypothesized, patients with higher stage cancer experienced higher treatment workload. Over the first 18 months after diagnosis, patients with stage III disease spent a median of 81 hours (interquartile range [IQR], 39 to 113 hours) in outpatient clinics, commuted 61 hours (IQR, 32 to 86 hours), and spent $1,432 (IQR, $690 to $2,552) in commuting costs. In contrast, patients with stage I disease spent a median of 29 hours (IQR, 18 to 46 hours in clinic), commuted for 34 hours (IQR, 19 to 55 hours), and spent $834 (IQR, $389 to $1,649) in commuting costs. In addition, we substantiated claims that the pegfilgrastim on-body injector was effective in reducing some dimensions of workload such as unique appointment days. CONCLUSION: Treatment workload measures capture an important dimension in the experience of patients with cancer. Patients and health care organizations can use workload measures to plan and allocate resources, leading to higher quality and better coordinated care.

Using Wayfinding Data to Understand Patient Travel Within a Medical Center.

Navigating through parking lots, public areas, and hallways is a stressful task for patients visiting large medical centers. Little is known about the patient experience from when they arrive at a medical center to when they check-in at their clinic. In a pilot study, we used requests for wayfinding directions from a mobile application to form a network of patient movement through the Vanderbilt University Medical Center (VUMC). From September 2016 to September 2017, patients using the wayfinding application made 3493 requests using the VUMC WalkWays application. Results show that patients frequently request directions from parking garages, on-site eateries, and the emergency room. We calculated the approximate distance patients walked to determine the extent to which associated clinical areas were co-located. Applied more generally, medical centers could use similar technologies to inform clinic placement, signage design, and resource allocation to improve the patient experience and operational efficiency.

Determining Burden of Commuting for Treatment Using Online Mapping Services - A Study of Breast Cancer Patients.

For patients with breast cancer who must frequent medical centers for care, commuting is a significant burden. This burden could affect their decisions during treatment. We developed a method to use census tracts and zip codes to determine commuting burden for patients with breast cancer with online mapping services, while protecting patient addresses from third parties. We found that patients who lived farther from Vanderbilt had fewer unique appointment days and more appointments scheduled per day. Total burden decreased over time after diagnosis, but advanced stage patients had sustained high levels of commute time until ten months after diagnosis. Additionally, we found that patients who lived far from Vanderbilt were less likely to receive radiotherapy from Vanderbilt. With the amount of work patients put into traveling for care, understanding commuting burden could help healthcare organizations form strategies to improve access to care and compliance with care plans.

Data Driven Approach to Burden of Treatment Measurement: A Study of Patients with Breast Cancer.

Chronic disease affects patient quality of life through symptoms of the disease and the work of receiving treatment. While the effects of illness are well investigated, the burden of treatment is not commonly studied or monitored. We developed a method to quantify one dimension of the burden of treatment based on patient encounters with the healthcare system. We applied this method to a population of stage I-III breast cancer patients. As hypothesized and observed, stage IIIpatients had more appointments, spent more time in clinic, and spent more time admitted to the hospital in the first 18 months after diagnosis compared to stage I and II patients. Future work will evaluate the reproducibility and generalizability of this method for quantifying burden of treatment across other clinical settings and chronic diseases. This approach could enable identification of high-risk groups that could benefit from interventions to decrease patient work and improve outcomes.