Journal:Recommendations for achieving interoperable and shareable medical data in the USA

Full article title	Recommendations for achieving interoperable and shareable medical data in the USA
Journal	Communications Medicine
Author(s)	Szarfman, Ana; Levine, Jonathan G.; Tonning, Joseph M.; Weichold, Frank; Bloom, John C.; Soreth, Janice M.; Geanacopoulos, Mark; Callahan, Lawrence; Spotnitz, Matthew; Ryan, Qin; Pease-Fye, Meg; Brownstein, John S. ; Hammond, W. Ed; Reich, Christian; Altman, Russ B.
Author affiliation(s)	U.S. Food and Drug Administration, independent researcher/contractor, Your Health Concierge, Purdue University, Columbia University, Boston Children’s Hospital, Duke Clinical & Translational Science Institute, Stanford University School of Medicine
Primary contact	Email: ana dot szarfman at fda dot hhs dot gov
Year published	2022
Volume and issue	2
Article #	86 (2022)
DOI	10.1038/s43856-022-00148-x
ISSN	2730-664X
Distribution license	Creative Commons Attribution 4.0 International
Website	https://www.nature.com/articles/s43856-022-00148-x
Download	https://www.nature.com/articles/s43856-022-00148-x.pdf (PDF)

Abstract

Easy access to large quantities of accurate health data is required to understand medical and scientific information in real time; evaluate public health measures before, during, and after times of crisis; and prevent medical errors. Introducing a system in the United States of America that allows for efficient access to such health data and ensures auditability of data facts, while avoiding data silos, will require fundamental changes in current practices. Here, we recommend the implementation of standardized data collection and transmission systems, universal identifiers for individual patients and end users, a reference standard infrastructure to support calibration and integration of laboratory results from equivalent tests, and modernized working practices. Requiring comprehensive and binding standards, rather than incentivizing voluntary and often piecemeal efforts for data exchange, will allow us to achieve the analytical information environment that patients need.

Keywords: drug development, public health, interoperability, data exchange, medical informatics

Introduction

Reported worldwide mortality from COVID-19 has surpassed six million people, with over 16% of those deaths found in the United States of America alone. [1] Despite our vaccination efforts against COVID-19, the analytical deficiencies of the country's health information systems uncovered by the pandemic remain largely unresolved. [2] We still cannot answer basic questions that should be answerable by a simple query of the data, such as "what is the mortality rate according to patient variables?" Also, public health systems and practitioners are still forced to rely on outmoded forms of communication (e.g., paper and fax) which do not provide rapid access to needed information.

Although recognized as a leader in advancing cutting-edge biomedical research and medical technology, the USA continues to rely on multiple, independent healthcare systems and versions that cannot seamlessly communicate with each other. This lack of interoperability within and across hospital systems, laboratories, public health programs, physicians’ offices, and regulatory and research data resources hinders rapid improvements in medical treatment, public health, decision-making, and research. The main reason for the failure to achieve interoperability—and for the information loss, inefficient operations, and huge (and frequently hidden) costs that result—is the lack of comprehensive, centrally coordinated, fully validated, traceable, and enforceable medical data collection and transmission standards. [3] Bi- and multi-directional feedback loops that are needed for prompt access to ancillary data, for clarifications, and for quickly reporting and addressing system and data errors are also lacking. Without easy access to this additional information, electronic health records (EHRs) cannot be made portable [4], and the full potential of those records to support research and innovation cannot be realized.

Data that can be easily exchanged is a goal that many parties have long been advocating for. The COVID-19 pandemic has made this issue more urgent than before, as we have “to move faster than the virus” (personal communication from Dr. Mirta Roses). Unfortunately, to date the COVID-19 pandemic has only underscored the consequences of having information systems that rely on non-binding standards for data management and exchange, standards that are themselves based on multiple, unreconciled data models. In principle, a data model should provide universal definitions of data elements (i.e., units of data having a precise meaning and interpretation) for users of heterogeneous data sites that want to share or aggregate data, to allow them to speak a common language. [5]

Emerging technologies that promise to revolutionize healthcare add additional urgency to efforts to achieve interoperability in our health information systems. In a decade, there will be more sources of data [6], for example from wearable devices such as the Apple Watch and Fitbit, that patients will use to record information. By combining these data with artificial intelligence and machine learning, in which machines are able to automatically process the data, diagnosis and prediction of patient outcomes could be improved. However, the successful use of these computational tools strongly depends on accurate collection and exchange of massive amounts of complex data derived from next-generation sequencing, imaging devices, laboratory assays, and many other sources. Unfortunately, the data being collected remain predominantly in local silos, and frequently the data are neither standardized nor of the quality required by these advanced automated approaches. [7,8] The promise of all of these technological and scientific advances will be unrealized without interoperable standards that are fully representative of real-world clinical data (not just based on theoretical examples), and are fit-for-purpose for data collection, exchange, integration, and analysis, and traceable to the original information.

Perhaps the most challenging roadblock for implementing interoperability for data collection is the tolerance for highly customized, proprietary health information systems and their unique versions. The inconsistencies created by unnecessary customization creates a state of confusion that makes it impossible to reliably identify in a timely fashion critical data facts and inconsistencies that must be communicated to those making critical decisions about how these systems should be designed and implemented. These decision makers include those in government organizations, the system vendors, software developers, and other stakeholders, including patients and patient advocates.

By presenting the following description of deficiencies in the US health information system and recommendations for addressing them at their root causes, we hope to stimulate constructive dialog among multiple stakeholders and inform policy changes in the US and other countries where such measures are needed. Recognizing our ethical responsibility to rapidly provide the best information to help patients [9], we propose building an alternative, more transparent system based on interoperability that starts at the data collection stage. This alternative system would be one in which the benefits of new computational technologies can be realized, where patients are able to take control of their data, and where accurate and timely data can be rapidly shared to advance medical research and improve public health.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation and grammar to improve readability. In some cases important information was missing from the references, and that information was added.