Journal:From months to minutes: Creating Hyperion, a novel data management system expediting data insights for oncology research and patient care

Full article title	From months to minutes: Creating Hyperion, a novel data management system expediting data insights for oncology research and patient care
Journal	PLOS Digital Health
Author(s)	Snyder, Eric; Rivers, Thomas; Smith, Lisa; Paoni, Scott; Cunliffe, Scott; Patel, Arpan; Ramsdale, Erika
Author affiliation(s)	James P. Wilmot Cancer Institute
Primary contact	Erika underscore ramsdale at urmc dot rochester dot edu
Editors	Shah, Rutwik
Year published	2022
Volume and issue	1(11)
Article #	e0000036
DOI	10.1371/journal.pdig.0000036
ISSN	2767-3170
Distribution license	Creative Commons Attribution 4.0 International
Website	https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000036
Download	https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000036&type=printable (PDF)

Abstract

Ensuring timely access to accurate data is critical for the functioning of a cancer center. Despite overlapping data needs, data are often fragmented and sequestered across multiple systems (such as the electronic health record [EHR], state and federal registries, and research databases), creating high barriers to data access for clinicians, researchers, administrators, quality officers, and patients. The creation of integrated data systems also faces technical, leadership, cost, and human resource barriers, among others. The University of Rochester's James P. Wilmot Cancer Institute (WCI) hired a small team of individuals with both technical and clinical expertise to develop a custom data management software platform—Hyperion— addressing five challenges: lowering the skill level required to maintain the system, reducing costs, allowing users to access data autonomously, optimizing data security and utilization, and shifting technological team structure to encourage rapid innovation.

The Hyperion data management platform was designed to meet these challenges in addition to usual considerations of data quality, security, access, stability, and scalability. Implemented between May 2019 and December 2020 at the WCI, Hyperion includes a sophisticated custom validation and interface engine to process data from multiple sources, storing it in a database. Graphical user interfaces (GUIs) and custom wizards permit users to directly interact with data across operational, clinical, research, and administrative contexts. The use of multi-threaded processing, open-source programming languages, and automated system tasks (normally requiring technical expertise) minimizes costs. An integrated ticketing system and active stakeholder committee support data governance and project management. A co-directed, cross-functional team with flattened hierarchy and integration of industry software management practices enhances problem solving and responsiveness to user needs. Access to validated, organized, and current data is critical to the functioning of multiple domains in medicine. Although there are downsides to developing in-house customized software, we describe a successful implementation of custom data management software in an academic cancer center.

Keywords: data management, data security, data visualization, data analysis, data integration, cancer centers

Background and significance

Academic cancer centers, particularly those embracing the learning health systems (LHS) model to dynamically generate and utilize high-quality evidence for patient decision making [1], require integration and maintenance of data systems offering intuitive access and manipulation of valid, ordered, and up-to-date knowledge informing clinical operations, clinical decision support, and research. Electronic health record (EHR) systems do not offer the data curation nor the user experience required to fully meet these needs. EHR systems were primarily designed to improve billing and revenue capture, requiring very different design decisions which often result in clunky, burdensome, and disorganized systems from the perspectives of many end-users. Moreover, useful data are typically not exclusively stored in a single location like the EHR, but across dozens of databases utilizing disparate (and often incompatible) technologies.

Addressing the data needs of an academic cancer center introduces many challenges, including recruitment and retention of the appropriate technical expertise, while adhering to already thin financial budgets. [2] Technical difficulties include seamless integration of multiple data sources, enhancement of user buy-in for the data system (including mitigation of technology burnout), and rapidly changing technical and data landscapes. [2] Leadership challenges implicate the dominant paradigm for vertical, clinician-centric decision-making: current organizational leadership structures may be ill-suited to devising technical data solutions that inherently require systems thinking and rapid adaptation/iteration. Importing organizational processes, systems thinking approaches, and technical domain expertise from other industries could help academic cancer centers around the country surmount many issues impeding data utilization.

Attempts to optimally balance data currency, access, validation, and integrity in the healthcare setting typically involve data or research warehouses. [3,4] Given the disparate nature of the data sets to be merged, as well as the heightened security and privacy concerns involved in storing patient data, barriers include large capital outlays and difficulties accommodating potentially competing design aims such as efficiency, timeliness of reports, user experience, data validity, and accuracy. [5] Different health systems—or even different groups within an individual health system—may prioritize different design aims, such that a one-size-fits-all technical solution is inadvisable and obviates the ability to purchase a ready-made (commercial off-the-shelf) software solution. Even if a ready-made solution is available, the shifting data landscape could quickly make it obsolete; maintenance of data architecture, not only its initial build and deployment, requires significant ongoing technical skill and time. Throughout the processes of data architecture design and implementation, ongoing user feedback is critical, and clinical domain expertise is required at every step to maximize utility, comprehensiveness, and validity.

This paper discusses a novel design and successful implementation of a sophisticated data architecture—Hyperion—to address the data needs of an academic cancer center. Although each center has individualized needs embedded in idiosyncratic circumstances, a few principles may be derived to guide other centers hoping to implement and maintain a customized data architecture that users can employ confidently, productively, and adaptively to facilitate rapid answers to quality and research questions, and ultimately to improve patient outcomes at the point of care.

Supporting information

• S1 Text (.docx): Fig A. Nursing dashboard. Fig B. Example of Provider Dashboard landing page, with interactive features (hover-over pop-up text and ability to click on bar graphs to “drill down” on data). Fig C. Clinical Trial dashboard main page, with interactive features.

Acknowledgements

We acknowledge the continued support of the Wilmot Cancer Institute’s leadership in encouraging innovation, as well as the support of the physicians, nurses, and staff.

Funding

ER is supported by the National Cancer Institute (K08CA248721) and the National Institute on Aging (R03AG067977). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data availability

All data are in the manuscript and/or supporting information files.

Competing interests

The authors have declared that no competing interests exist.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and spelling. In some cases important information was missing from the references, and that information was added. The original includes both an abstract and an author summary, which is confusing since the purpose of the asbtract is to act as a summary of the text; for this version, the two were combined to form an intelligible, coherent abstract.