Health informatics Article

Perspective

Meeting the challenge: Health information technology’s

essential role in achieving precision medicine

Teresa Zayas-Cab�an, 1 Kevin J. Chaney, 1 Courtney C. Rogers, 2 Joshua C. Denny3, and

P. Jon White4,5

1Office of the National Coordinator for Health Information Technology, U.S. Department of Health and Human Services, Washing-

ton, DC, USA, 2Department of Engineering Systems and Environment, University of Virginia, Charlottesville, Virginia, USA, 3All of

Us Research Program, National Institutes of Health, Bethesda, Maryland, USA, 4Veterans Affairs Salt Lake City Health Care Sys-

tem, Salt Lake City, Utah, USA, and 5Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA

*Corresponding Author: Teresa Zayas-Cab�an, PhD, National Library of Medicine, National Institutes of Health, Bg 38A Rm

4S415 8600 Rockville Pike, Bethesda, MD 20894, USA; [email protected]

Received 12 November 2020; Editorial Decision 4 February 2021; Accepted 9 February 2021

ABSTRACT

Precision medicine can revolutionize health care by tailoring treatments to individual patient needs. Advancing

precision medicine requires evidence development through research that combines needed data, including clin-

ical data, at an unprecedented scale. Widespread adoption of health information technology (IT) has made digi-

tal clinical data broadly available. These data and information systems must evolve to support precision medi-

cine research and delivery. Specifically, relevant health IT data, infrastructure, clinical integration, and policy

needs must be addressed. This article outlines those needs and describes work the Office of the National Coor-

dinator for Health Information Technology is leading to improve health IT through pilot projects and standards

and policy development. The Office of the National Coordinator for Health Information Technology will build on

these efforts and continue to coordinate with other key stakeholders to achieve the vision of precision medicine.

Advancement of precision medicine will require ongoing, collaborative health IT policy and technical initiatives

that advance discovery and transform healthcare delivery.

Key words: data, health information technology, policy, precision medicine, standards

THE PROMISE OF PRECISION MEDICINE

A profound revolution in health is underway. The sequencing of the

human genome has changed our understanding of human biology

and approach to improving health. In 2004, Francis Collins pub-

lished a description of a large-scale research cohort whose partici-

pants’ genetic information would be correlated with clinical

information and outcomes.1 Subsequently, the National Academies

of Science called for broad use of all biological data, including ge-

netic, clinical, microbiomic, proteomic, and others, integrated

through a computational knowledge network to develop a new and

deep understanding of disease mechanisms applied for the benefit of

individuals.2 This vision was prophetic; our understanding of biol-

ogy and human physiology is being transformed by large-scale re-

search initiatives and enabled by increased adoption of electronic

health record (EHR) systems,3,4 computing advances,5,6 reduction

in costs of genetic sequencing,7,8 and acceptance of these technolo-

gies, as shown in Figure 1.

In 2015, the Precision Medicine Initiative (PMI)9,10 was

launched with the goal of advancing treatment and prevention strat-

egies, collectively referred to as precision medicine, that are tailored

not only to individuals’ biology, but also to other unique character-

istics such as their environment and lifestyle.10–13 The PMI recog-

Published by Oxford University Press on behalf of the American Medical Informatics Association 2021.

This work is written by US Government employees and is in the public domain in the US.

1345

Journal of the American Medical Informatics Association, 28(6), 2021, 1345–1352

doi: 10.1093/jamia/ocab032

Advance Access Publication Date: 22 March 2021

Perspective

nized the need to advance data science and solve data infrastructure

issues to reach its goals. Relevant and related programs, such as the

Million Veterans Program,14 eMERGE (Electronic Medical Records

and Genomics) Network,15 UK Biobank,16 and the All of Us Re-

search Program (All of Us),17 are advancing precision medicine, but

their success depends on modern computing capabilities18 and

health information technology (IT).

In addition, precision medicine research and delivery will require

unprecedented access to standardized data and novel approaches to

handle large datasets and derive reliable observations. As the pace of

scientific discovery quickens, the healthcare system must adapt to

deliver relevant evidence. While current health information systems

have been shown to be effective at improving outcomes,19,20 they

may be inadequate for the scope and scale of future needs.21 It will

be important to generate evidence regarding the effectiveness of pre-

cision medicine, and data from clinical care and other sources will

be needed to make that determination. Health information systems

will need to operate at a truly vast scale.

As people gain real-time access to their health data, lines will

blur between personal and professional use. Incorporation of indi-

vidual preferences into interventions will become a necessity, rather

than an afterthought. Technology and relevant policies and pro-

grams must anticipate and prepare for foreseeable uses of this infor-

mation as well as monitor for and adapt to the unexpected.

As the lead U.S. agency for health IT policy responsible for coor-

dinating nationwide implementation of health IT and advancing

health information exchange,22 the Office of the National Coordi-

nator for Health Information Technology (ONC) plays an impor-

tant role in this endeavor. This article articulates the role of health

IT in achieving the promise of precision medicine, illustrates how

ONC’s efforts and authorities address precision medicine health IT

needs, and discusses future work that remains to be done.

HEALTH IT NEEDS FOR PRECISION MEDICINE

In order to realize precision medicine’s potential, an enormous array

of data is needed to fully understand the interactions of one’s ge-

nome with the environment and various lifestyle factors. New path-

ways for storing, accessing, and analyzing these data will be

necessary to create portable digital clinical data that are easily ex-

changed among healthcare providers, researchers, and individuals

for precision medicine care and research. Infrastructure to connect

systems will need to be built to support data sharing for activities

such as integrating data into the EHR, supporting clinical care and

research, and delivering results to individuals. Specifically for re-

search, tools and methods will need to be developed to assemble

cohorts, consent and enroll participants, track and collect partici-

pant data, and analyze large datasets. As precision medicine is still

nascent, the time is now to tackle multiple complex challenges and

needs for health IT development. These needs, summarized in Fig-

ure 2, include determining requirements for data, building robust in-

frastructure, integrating precision medicine into clinical care, and

developing relevant policies.

To generate precision medicine approaches, methodologies need

to be created to collect diverse types of data such as biological; social

determinants of health; patient-generated; patient-reported, and

patient-centered outcome; and environmental data. To ensure the

usability of these data types, methods must be employed to verify

the accuracy, completeness, consistency, and reliability of the

data,23–27 and proof of validity will be required to certify the sensi-

tivity, specificity, precision, and performance of data collection

tools.28 Moreover, standardization of these data types is critical for

efficient data exchange and comparisons.23,28–38 Industry standards

may need to be developed to ensure that data can be synchronized

across organizations, making it more widely available for use.24

Without agreed-upon data models and shared protocols for data

sharing, it is unlikely that a foundation for collaborative data sys-

tems could be built.2

To harness and share precision medicine data, infrastructure will

require certain functionalities, and resource considerations. Needed

functionality includes tools and technologies designed to capture di-

verse types of data. These data then will need to be accessed and ac-

quired from multiple sources, processed into a usable format, shared

efficiently and effectively, aggregated, and analyzed; all activities

that have associated monetary costs.26,36,39 Functionality that sup-

ports interoperability, such as common exchange models, will be

critical for the transfer and compilation of these data.40 Resources

are also needed to access, process, and store data, including but not

limited to cloud-based data warehouses, hardware and software,

elastic computing power, and encryption capabilities.25,35,41 The

Figure 1. Precision medicine history. ONC: Office of the National Coordinator for Health Information Technology.

1346 Journal of the American Medical Informatics Association, 2021, Vol. 28, No. 6

systems that house the data must also be configurable and coordi-

nated to adapt to user needs and support transferability.37 Establish-

ing these infrastructure requirements will make large-scale analytics

possible, allowing for the identification of disease mechanisms and

treatment strategies.

Once precision medicine research is ready for translation, the ev-

idence will have to be integrated into clinical care. Organizations

must invest in the infrastructure needed to support precision medi-

cine, which will necessitate buy-in from those with decision-making

power.42 For clinicians to use this evidence, it must be presented in a

manner that promotes the trustworthiness and usability of relevant

data.42–44 The interfaces that communicate this evidence must incor-

porate principles of user-centered design45,46 and training will be re-

quired to understand how to use these platforms and extract

meaningful precision medicine evidence for patient care.37,43,47–49

Moreover, the delivery of this evidence must be seamlessly inte-

grated into existing or improved workflows to facilitate uptake of

the evidence by providers.43,46,50–52 Although the EHR may seem

like a natural platform to integrate precision medicine data, current

EHR systems may not be designed to handle the volume and variety

of such data, nor computationally powerful enough to enable

individual-level data analysis and predictions.36,51,53–56 Successful

delivery of precision medicine will likely require translation of vast

complexity into simple recommendations and interpretations. Other

platforms may need to be developed to deliver precision medicine

evidence or to serve as an intermediary to process and analyze infor-

mation for EHR display. Careful consideration of health IT design

and integration into clinical care are needed for precision medicine

research to have an impact on patient outcomes.

As more and new types of data are collected from individuals,

there are additional policy considerations for privacy and security,

ethics, and data sharing. Highly identifiable patient information

may be shared with many different clinical entities, creating privacy

risks.28,29,41,55,57–59 Dense molecular information such as the germ-

line genome is highly unique, presenting risks of reidentification if

connected to a naming source, and revealing insights into future dis-

ease risk not known to the patient. Health IT security vulnerabilities

must be addressed because a large amount of individual data will be

stored, most likely on cloud-based servers. With regard to research

ethics, informed consent will become an increasingly rigorous pro-

cess because new uses for precision medicine are rapidly evolving

and digital consent tools are emerging.36,48,59–61 There are also ethi-

cal issues surrounding data ownership, stewardship, and identifica-

tion, as research participants may desire updates on how their data

are being used.36,37,48,59 Last, there will be challenges with data

sharing across institutions if links to patient identifiers need to be

maintained and if electronic health data are shared across healthcare

providers and academic, private, and government research institu-

tions.36,60,62 Data sharing regulations will also need to be adapted

to support exchanging this information across multiple types of enti-

ties (eg, health systems, insurance companies, the pharmameuctical

industry), both domestic and global.2,43,62 Because individual partic-

ipation is critical to advancing precision medicine, it is imperative to

develop policies and procedures to protect these data, and also to en-

sure that individuals understand who will have access to their data

and how they will be used.

ONC ADDRESSES PRECISION MEDICINE HEALTH IT NEEDS

Precision medicine requires data beyond those from health IT sys-

tems, but digital clinical data from those systems are of primary im-

portance. Therefore, ONC’s mission and authority are critical to

achieve precision medicine. Under its authorities established by the

Health Information Technology for Economic and Clinical Health

(HITECH) Act of 200963 and 21st Century Cures Act of 2016,64

ONC is the primary federal regulator of health IT, and its certifica-

tion program applies the force of law to require data standards and

functionality with a focus on interoperability that underpin a na-

tional knowledge network supporting research and advanced care

delivery. In addition, ONC’s coordination efforts focus and amplify

the considerable authorities of federal stakeholders such as the Na-

tional Institutes of Health, Food and Drug Administration, Centers

for Disease Control and Prevention, and others to specifically ad-

dress health IT challenges. Finally, ONC has led development efforts

resulting in technical achievements that advance the cause of preci-

sion medicine.

Figure 2. Precision medicine health information technology (IT) needs.

Journal of the American Medical Informatics Association, 2021, Vol. 28, No. 6 1347

As part of the PMI,10 ONC works closely with participating

partners by (1) supporting pilot projects and standards to improve

health IT interoperability for research, (2) adopting policies and

standards to support privacy and security of participant data, and

(3) advancing standards that support participants’ decisions to share

their data with researchers. More specifically, ONC is leading col-

laborative projects, listed in Table 1, to advance the use of health IT

and digital clinical data for precision medicine.65 Collectively, these

projects address health IT data, infrastructure, clinical integration,

and policy precision medicine needs.

Table 1. Office of the National Coordinator for Health Information Technology Precision Medicine Initiative projects

Project Name Project Goals and Outcomes Precision Medicine Health IT Needs Addressed

• Advancing Standards for Precision Medi-

cine

Goal: Further standards development and test-

ing for new and diverse data types to make

health data easier to collect, share, curate,

aggregate, and synthesize.

Outcome: Advanced and harmonized standards

for mobile health, sensor, and wearable; and

SDOH data through demonstrations result-

ing in development of 2 implementation

guides.

• Data

� Collecting diverse data types

� Standardization

� Quality

• Infrastructure

� Functionality

� Resource needs

• Clinical integration

� Integrating data and evidence

• API Privacy and Security Considerations Goal: Test and assess the privacy and security

of Sync for Science API implementations that

enable digital clinical data sharing.

Outcome: Outlined key considerations for

implementing and managing APIs in health

care with respect to the privacy and security

of health information.

• Policy

� Data privacy and security

� Data sharing across institutions

• PMI Data Security Principles Implementa-

tion Guide

Goal: Assist organizations participating in pre-

cision medicine research in implementing the

“Precision Medicine Initiative: Data Security

Policy Principles and Framework”66 and pri-

oritizing participants’ trust in organizations’

ability to protect their data.

Outcome: Outlined best practices in security

and data management for precision medicine

through an example use case.

• Policy

� Data privacy and security

� Data sharing across institutions

• Sync for Genes Goal: Standardize the sharing of genomic data

and information among laboratories, pro-

viders, patients, and researchers and advance

the development and use of industry-sup-

ported standards for genomic data.

Outcome: Advanced the development of the

FHIR Clinical Genomics specification and

demonstrated exchange of genomic data

across settings.

• Data

� Collecting diverse data types

� Standardization

� Quality

• Infrastructure

� Functionality

� Resource needs

• Clinical integration

� Integrating data and evidence

• Policy

� Data privacy and security

• Sync for Science Goal: Develop and demonstrate a simplified,

scalable, and secure way for individuals to

access and share their digital clinical data

with researchers using open standards.

Outcome: Successfully pilot-tested the use of a

FHIR API across multiple EHR systems and

provider sites to allow patients to share their

digital clinical data with the All of Us Re-

search Program via a consumer application.

• Data

� Collecting diverse data types

� Standardization

• Infrastructure

� Functionality

� Resource needs

API: application programming interface; EHR: electronic health record; FHIR: Fast Healthcare Interoperability Resources; IT: information technology; SDOH:

social determinants of health.

1348 Journal of the American Medical Informatics Association, 2021, Vol. 28, No. 6

Precision medicine requires rich datasets to inform research and

integrate evidence into practice. These projects have developed

methodologies to collect and exchange diverse data types through

standardized application programming interfaces (APIs) that allow

for the extraction of data from health IT systems and devices.67–72

These projects also identified data quality issues such as accuracy,

consistency, completeness, and usefulness. In particular, they found

a trade-off between richness of data and simplicity of standards.

The projects advanced the standardization of relevant data,

which improved data quality and consistency reducing the effort re-

quired for data curation. However, project participants also found

that use of data standards does not always result in data standardi-

zation. Not all health IT developers have implemented the latest ver-

sion of some standards, causing compatibility issues; and standards

are not always consistently implemented across systems or organiza-

tions, leading to challenges with participant matching and data ag-

gregation.68,69,71,72 Furthermore, use of competing or proprietary

standards across data partners created the need for ongoing harmo-

nization and mappings, which may require continued updating as

standards evolve.70 The projects also found that metadata, such as

data provenance or device identification, are needed to enhance

data, making it easier to work with and integrate across diverse data

types.69,70 However, metadata and use of metadata standards are of-

ten lacking. The projects further uncovered that researchers need ac-

cess to standardized data that were not required under

regulation73,74 including clinical notes, raw genomic data, and

images.68,71,72

In addition, the projects addressed infrastructure requirements

regarding data capture, data access, and interoperability functional-

ity needs. The projects advanced functionality designed to capture

data from outside clinical care,70 access data from other information

systems,68–72 and advance interoperability through use of standard-

ized functions.67–72 Projects revealed testing and validation activities

are important for successful implementation of new functionality,

but are often resource intensive. Employing testing tools72,75,76 or

participating in joint testing exercises (eg, connect-a-thons)67,68 fa-

cilitated testing and validation across project sites and lowered im-

plementation barriers. The projects also revealed resource

considerations and challenges. For example, firewalls impeded

needed access to outside data sources such as devices,70 and some

sites did not have access to adequate data storage and management

resources.68,71

By design, several projects tested the ability to integrate data be-

ing shared for precision medicine into clinical systems. Considera-

tions uncovered when integrating these data into clinical care

included the need to not only consider clinical workflows, but to

also augment data with provider and patient education materials to

understand and communicate genomic information.68,71 In addition,

genetic test results were shared in a PDF form not easily integrated

into an EHR, creating barriers to use. Furthermore the ability of

clinical and laboratory information systems to accommodate the

complexities of genomic use cases required development of custom-

ized implementation guides,69 which may limit the guides’ utility

across provider organizations. The projects also revealed the impor-

tance of using standards that are device or wearable agnostic, which

facilitates integrating data from such devices into clinical care.70

Last, these efforts have provided key insights and resources to

address policy needs specifically related to privacy and security, and

data sharing related to precision medicine. An important concern

across projects was ensuring privacy and security of data being

shared.77 While existing standards were used to authenticate users

and APIs were tested against industry privacy and security stand-

ards,78 the ability to authenticate users and their devices across mul-

tiple settings remains a major challenge. Furthermore, the

complexity of aligning security protocols and permission require-

ments across systems often resulted in connection problems requir-

ing intensive analysis to find and correct the issue. Protocols were

established across projects regarding the period within which data

can be shared, but those protocols varied across sites and their infor-

mation systems.70 Decisions regarding the frequency of data updates

may impact the ability of clinicians and researchers alike to obtain

needed data in a timely way. Projects also identified some concerns

regarding the state of industry-wide use of access control and secu-

rity standards, as well as the need for clarity regarding how genomic

data in particular are protected under current policies and legisla-

tion.68,71

From data to infrastructure to clinical integration to policy,

ONC’s efforts are specifically addressing some of the key health IT

challenges to successfully advancing precision medicine. Progress

has been made on many fronts, while new areas for additional work

have also been uncovered.

THE ROAD AHEAD

ONC projects have addressed some of the health IT requirements

needed to realize the promise of precision medicine. However, sev-

eral needs persist. The work to date leaves multiple opportunities

for future progress in the 4 areas identified in Figure 2. While data

collection was advanced for some novel data types, these data are

not yet routinely integrated into clinical care, and constant vigilance

is required across stakeholders to anticipate additional data types.

Data quality and validity are better understood, but specific meas-

ures to improve these attributes need to be identified and developed

by the research and clinical communities. In addition, the projects

advanced standardization of needed data, but tested standards are

not yet widely adopted and require further development. The ability

to assess, collect, and represent existing and novel data will rely on

existing data standards and infrastructure, but future progress

depends on identifying and dealing with gaps in our data ecosystem

and anticipating future needs.

Substantive resources are required to enhance the existing infra-

structure, such as implementing updated data standard versions. If

precision medicine is to work for everyone it must reflect needs from

across the population, which will require participation of small or

under-resourced organizations in precision medicine research and

delivery. These organizations may lack resources and additional

training and technical assistance may need to be provided by the fe-

deral government to ensure broad participation in precision medi-

cine. Additional resources such as conversion tools for standards

versioning or to conduct implementation testing and validation are

also clearly still needed. A shift to cloud-based infrastructure, along

with careful and balanced consideration of security and access

needs, can be facilitated by federal resources, incentives, and

requirements. Privacy preserving data linkage processes will likely

be an increasingly important process. Consideration of and support

for organization-specific workflows is needed and starts with human

factors and ergonomics-based understanding of stakeholder work.79

Journal of the American Medical Informatics Association, 2021, Vol. 28, No. 6 1349

Future policies and programs will be needed for delivery of knowl-

edge to informaticians, researchers, patients, and providers.

Health IT precision medicine needs, in particular research needs,

include a subset of the broader research needs that need to be

addressed to more effectively leverage health IT systems and digital

health information for research. These needs closely align with the

priorities and strategies identified in the ONC National Health IT

Priorities for Research Policy and Development Agenda (hereafter

referred to as the Agenda), which describes a health information

ecosystem that supports effective research and integration of knowl-

edge at the point of care.80–82 The Agenda’s goals and priorities ar-

ticulate data and infrastructure needs relevant to precision medicine.

Specifically, the Agenda calls for improving data quality at the point

of care, increasing data harmonization for research, improving ac-

cess to interoperable health data, improving data storage, aggrega-

tion, and analysis, and integrating emerging novel data sources. All

of the priorities and associated actions in the Agenda will need to be

addressed to achieve the vision for precision medicine.

ONC is already addressing some of these priorities including ad-

vancing high-priority health data and metadata standards83 for care

and research and better understanding the current API and app eco-

system for patients, providers, developers, and research.84 In 2020,

ONC published its Cures Act Final Rule, which requires adoption of

the U.S. Core Data for Interoperability, including clinical notes.73

ONC published a draft of the second version of the U.S. Core Data

for Interoperability in early 2021,85 which will be continually

updated on an annual basis, offering precision medicine stakehold-

ers (eg, researchers, clinicians, developers, patients, provider organi-

zations) the opportunity to propose inclusion of data classes and

elements and relevant standards needed for precision medicine. Re-

cent Leading Edge Acceleration Projects in Health IT awards86 will

develop and evaluate needed research functionality through open,

FHIR (Fast Healthcare Interoperability Resources)-based health IT

tools and platforms. Tools to be developed and tested will support

data acquisition, data transformation, and advanced analytics,

allowing users to annotate FHIR data for analytics, extract meaning

from text notes, de-identify data, and query cohorts. ONC will con-

tinue to work in close collaboration with key stakeholders and pro-

grams, such as All of Us, to pursue additional priorities that are as

yet unaddressed.82

Over the past decade major infrastructure and standards invest-

ments have been made to transform our care and delivery infrastruc-

ture through certified health IT systems. These same information

systems will be required to make precision medicine a possibility

and at the same time, if done right, will create the data infrastructure

for the healthcare system required for scientific discovery at the

omic scale. Ongoing coordination with federal partners in a unified

push to advance data standards and necessary technical infrastruc-

ture will be critical for impact and success. With foresight and plan-

ning of specific health IT policy and technical initiatives, done with

clinical integration in mind, successful development that supports

precision medicine will transform our healthcare delivery system

and improve the health of all.

FUNDING

This work received no specific grant from any funding agency in the public,

commercial or not-for-profit sectors. The findings and conclusions in this pa-

per are those of the authors and do not necessarily represent the views of the

National Institutes of Health or the U.S. Department of Veterans Affairs.

AUTHOR CONTRIBUTIONS

TZ-C, KJC, CCR, and PJW led the conception of the article. JCD provided

critical input into the article. All authors revised the article critically and pro-

vided intellectual content; and approved the final version for submission. The

order of authors listed in the manuscript has been approved by all authors.

ACKNOWLEDGMENTS

The authors would like to thank Carrie Edlund for copy editing support. We

would also like to thank Stephanie Garcia and Tracy Okubo from the ONC

for leadership and contributions on several ONC projects supporting the

PMI. Last, the authors thank all the collaborators across these projects for

their invaluable contributions: Sync for Science: Joshua Mandel and David

Kreda (Harvard Medical School), Anita Samarth and the Clinovations Gov-

ernmentþHealth team, and participating health IT developers and provider

organizations; Sync for Genes: Gil Alterovitz (U.S. Department of Veterans

Affairs), Robert R. Freimuth (Mayo Clinic), demonstration site participants,

and HL7 collaborators; and Advancing Standards for Precision Medicine: Ida

Sim (University of California San Francisco), Keith Boone (Audacious In-

quiry), Lawrence Garber (Reliant Medical Group), Chris Grasso (Fenway

Health), demonstration site participants, and IHE collaborators.

DATA AVAILABILITY STATEMENT

No new data were generated or analyzed in support of this work.

CONFLICT OF INTEREST STATEMENT

The authors have no competing interests to declare.

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