Business analysis for a sustainable, multi-stakeholder ecosystem for leveraging the Electronic Health Records for Clinical Research (EHR4CR) platform in Europe

Using electronic health records to streamline provider recruitment for implementation science studies

Background

Healthcare providers are often targeted as research participants, especially for implementation science studies evaluating provider- or system-level issues. Frequently, provider eligibility is based on both provider and patient factors. Manual chart review and self-report are common provider screening strategies but require substantial time, effort, and resources. The automated use of electronic health record (EHR) data may streamline provider identification for implementation science research. Here, we describe an approach to provider screening for a Veterans Health Administration (VHA)-funded study focused on implementing risk-aligned surveillance for bladder cancer patients.

Methods

Our goal was to identify providers at 6 pre-specified facilities who performed ≥10 surveillance cystoscopy procedures among bladder cancer patients in the 12 months prior to recruitment start on January 16, 2020, and who were currently practicing at 1 of 6 pre-specified facilities. Using VHA EHR data (using CPT, ICD10 procedure, and ICD10 diagnosis codes), we identified cystoscopy procedures performed after an initial bladder cancer diagnosis (i.e., surveillance procedures). Procedures were linked to VHA staff data to determine the provider of record, the number of cystoscopies they performed, and their current location of practice. To validate this approach, we performed a chart review of 105 procedures performed by a random sample of identified providers. The proportion of correctly identified procedures was calculated (Positive Predictive Value (PPV)), along with binomial 95% confidence intervals (CI).

Findings

We identified 1,917,856 cystoscopies performed on 703,324 patients from October 1, 1999—January 16, 2020, across the nationwide VHA. Of those procedures, 40% were done on patients who had a prior record of bladder cancer and were completed by 15,065 distinct providers. Of those, 61 performed ≥ 10 procedures and were currently practicing at 1 of the 6 facilities of interest in the 1 year prior to study recruitment. The random chart review of 7 providers found 101 of 105 procedures (PPV: 96%; 95% CI: 91% to 99%) were surveillance procedures and were performed by the selected provider on the recorded date.

Implications

These results show that EHR data can be used for accurate identification of healthcare providers as research participants when inclusion criteria consist of both patient- (temporal relationship between diagnosis and procedure) and provider-level (frequency of procedure and location of current practice) factors. As administrative codes and provider identifiers are collected in most, if not all, EHRs for billing purposes this approach can be translated from provider recruitment in VHA to other healthcare systems. Implementation studies should consider this method of screening providers.

Improving Accuracy and Timeliness of Nursing Documentation of Pediatric Early Warning Scores

Introduction

Accurate and timely documentation of pediatric early warning scores (PEWS) by the bedside nurse into the electronic health record (EHR) is important to promote early identification of patients in stages of deterioration. Through the implementation of a PEWS calculator embedded in the EHR, we hope to improve the accuracy of the recorded score and reduce the time between vital sign collection and final documentation in the EHR.

Methods

Identification of the highest PEWS value in the 24 hours before all unplanned transfers or rapid response activations without a transfer occurred between the period November 1, 2013, through December 31, 2016. The accuracy of the calculated cardiac or respiratory subscore based on heart rate or the respiratory rate at the time of PEWS calculation was determined. We tracked the calculation of the time to chart via the difference between nursing documentation of PEWS compared to vital sign collection. Before September 3, 2015, PEWS was calculated mentally by the bedside nurse; afterward, the nurse entered the unique PEWS features into the EHR, and the EHR automatically calculated PEWS.

Results

This study evaluated 2,409 PEWS scores, 1,411 before and 998 after initiation of the PEWS calculator. Accuracy before the EHR calculator was 71%, and the median time to document was 55 minutes. Following the implementation of the calculator, no scores were incorrectly calculated too low, and the median time to document was 20 minutes.

Conclusions

Transition to an EHR-based PEWS calculator eliminated inaccurately low PEWS values and reduced time to document.

Clinical Information Systems and Artificial Intelligence: Recent Research Trends

OBJECTIVES

This survey aims at reviewing the literature related to Clinical Information Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) systems, and how collected data can be analyzed by Artificial Intelligence (AI) techniques.

METHODS

We selected the major journals (11 journals) collecting papers (more than 7,000) over the last five years from the top members of the research community, and read and analyzed the papers (more than 200) covering the topics. Then, we completed the analysis using search engines to also include papers from major conferences over the same five years.

RESULTS

We defined a taxonomy of major features and research areas of CIS, HIS, EHR systems. We also defined a taxonomy for the use of Artificial Intelligence (AI) techniques on healthcare data. In the light of these taxonomies, we report on the most relevant papers from the literature.

CONCLUSIONS

We highlighted some major research directions and issues which seem to be promising and to need further investigations over a medium- or long-term period.

[Value of hospital electronic health records for clinical research: contribution of the European project EHR4CR]

Electronic health records in hospitals contribute to improving the quality of care by enabling better management of clinical information. The databases thus constituted facilitate the exchange of health information with healthcare providers and optimize multidisciplinary coordination for better therapeutic results. The EHR4CR (Electronic Health Records for Clinical Research) European project has developed an innovative pilot platform enabling the reuse of this digital information for clinical research. By enhancing and speeding up clinical research procedures, this innovative approach makes it possible to conduct clinical trials more efficiently, faster, and more economically.

An initiative using informatics to facilitate clinical research planning and recruitment in the VA health care system

Background

Randomized clinical trials are the gold standard for evaluating healthcare interventions and, more generally, add to the medical knowledge related to the treatment, diagnosis and prevention of diseases and conditions. Recent literature continues to identify health informatics methods that can help improve study efficiency throughout the life cycle of a clinical trial. Electronic medical record (EMR) data provides a mechanism to facilitate clinical trial research during the study planning and execution phases, and ultimately, can be utilized to enhance recruitment. The Department of Veterans Affairs (VA) has a strong history of clinical and epidemiological research with over four decades of data collected from Veterans it has served nationwide. The VA Informatics and Computing Infrastructure (VINCI) provides VA research investigators with a nationwide view of high-value VA patient data. Within VA, the Cooperative Studies Program (CSP) Network of Dedicated Enrollment Sites (NODES) is a consortium of nine sites that are part of an embedded clinical research infrastructure intended to provide systematic site-level solutions to issues that arise during the conduct of VA CSP clinical research. This paper describes the collaboration initiated by the Salt Lake City (SLC) node site to bring informatics and clinical trials together to enhance study planning and recruitment within the VA.

Methods

The SLC VA Medical Center physically houses both VINCI and a node site and the co-location of these two groups prompted a natural collaboration on both a local and national level. One of the functions of the SLC NODES is to enhance recruitment and promote the success of CSP projects. VINCI supports these efforts by providing VA researchers access to potential population pools. VINCI can provide 1) feasibility data during study planning, and 2) active patient lists during recruitment. The process for CSP study teams to utilize these services involves regulatory documentation, development of queries, revisions to the initial data request, and ongoing communications with several key study personnel including the requesting research team, study statisticians, and VINCI data managers.

Results

The early efforts of SLC NODES and VINCI aimed to provide patient lists exclusively to the SLC CSP study teams for the following purposes: 1) increasing recruitment for trials that were struggling to meet their respective enrollment goals, and 2) decreasing the time required by study coordinators to complete chart review activities. This effort was expanded to include multiple CSP sites and studies. To date, SLC NODES has facilitated the delivery of these VINCI services to nine active CSP studies.

Conclusion

The ability of clinical trial study teams to successfully plan and execute their respective trials is contingent upon their proficiency in obtaining data that will help them efficiently and effectively recruit and enroll eligible participants. This collaboration demonstrates that the utilization of a model that partners two distinct entities, with similar objectives, was effective in the provision of feasibility and patient lists to clinical trial study teams and facilitation of clinical trial research within a large, integrated healthcare system.

The positive impacts of Real-World Data on the challenges facing the evolution of biopharma

Demand for healthcare services is unprecedented. Society is struggling to afford the cost. Pricing of biopharmaceutical products is under scrutiny, especially by payers and Health Technology Assessment agencies. As we discuss here, rapidly advancing technologies, such as Real-World Data (RWD), are being utilized to increase understanding of disease. RWD, when captured and analyzed, produces the Real-World Evidence (RWE) that underpins the economic case for innovative medicines. Furthermore, RWD can inform the understanding of disease, help identify new therapeutic intervention points, and improve the efficiency of research and development (R&D), especially clinical trials. Pursuing precompetitive collaborations to define shared requirements for the use of RWD would equip service-providers with the specifications needed to implement cloud-based solutions for RWD acquisition, management and analysis. Only this approach would deliver cost-effective solutions to an industry-wide problem.