how data and informatics intersect to enable precision ......the resulting information can be...

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thejournalofprecisionmedicine.comJournal of Precision Medicine | Volume 5 | Issue 4 | December 2019 Journal of Precision Medicine | Volume 5 | Issue 4 | December 2019@journprecmed

Introduction and BackgroundThe practice of precision medicine involves the

use and application of patient-specific biomarker

information to diagnose and categorize diseases

that can then guide more effective treatment to

improve clinical outcomes with the least toxicity.1

In cancer, many new treatments approved

by the US Food and Drug Administration

(FDA) target specific genomic defects known

to drive, or significantly contribute, to the

cancer phenotype.2 Precision medicine aims

to accelerate biomarker-driven targeted

therapies to individual patients in a responder

population.3,4 Early examples of biomarker-driven

cancer treatment include hormone-receptor

positive breast cancer and BCR-ABL1 positive

chronic myelogenous leukemia.

Genomic testing has transformed the

precision medicine landscape by bringing

together two medical specialties that are

experts in cancer: pathology and oncology.

Advancing genetics and genomics in the

clinical space necessitates the cooperation and

collaboration of oncologists and pathologists.

Systemic cancer treatment is increasingly

influenced by a shift from histopathologically-

defined disease toward molecularly-defined

disease where targeted therapies are prescribed

to sub-populations of patients expressing

specific genomic variants regardless of tumor

origin. Moreover, the rapid pace of development

and adoption of biomarker testing with next-

generation sequencing (NGS) enables molecular

pathology laboratories to develop multiple-gene

sequencing panels faster and with less expense.

Precision medicine promises to improve

medical care. However, appropriate data

exchange and informatics platforms need

to be in place, to address the many existing

and unforeseen challenges facing care teams

and laboratories providing highly complex

tests. While many factors contribute to the

success of precision medicine programs, we will

focus on the role of how informatics facilitates

communication and collaboration at the

intersection of oncology and pathology.

The Multifactorial Challenges Facing the Precision Oncology Multi‑Disciplinary Care TeamThe concept of precision oncology was

derived from the introduction of precision

medicine by the National Research Council that

established the position that patients could

benefit from targeted genomics. The constant

evolution of the biomarker – “omics” data such

as genomics, transcriptomics, and metabolomics

– has enabled the investigation and treatment

of complex disease and at the same time,

introduced the need for a knowledge-guided

approach that integrates new information at

different levels compared to evidenced-based

practice that relies on statistical significance of

a treatment applied to a group of patients.3

Treatment planning for patients receiving

personalized care such as patients with cancer is

How Data and Informatics Intersect to Enable Precision Medicine to Reach its Full PotentialbyPatricia Goede, PhD, Lori Anderson, BA,

and Emerson Borsato, PhD

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not conducted by a single physician but rather,

by a team of clinical specialists that rely on

each other’s expertise to interpret test results

and assess risk to develop the best treatment

options for the patient. The Commission on

Cancer, a program of the American College

of Surgeons, has developed standards that

focus on improving outcomes through a multi-

disciplinary team (MDT) approach to cancer

care that is applied in the current care setting.5

The same multi-disciplinary team approach is

even more important in the precision medicine

environment as physicians strive to deliver

optimal care, manage risk of cancer-related

comorbidities, and ensure knowledge transfer

across the entire care team. The challenges for

the care team are multifactorial and include: the

complexity of interpreting results and translating

laboratory data into meaningful information

used for treatment planning as illustrated in

Figure 1. The figure was modeled after NCCN

guidelines to the treatment of non-small

cell lung cancer and is a representation of

the stages of a patient journey including the

multi-disciplinary care team (MDT) members,

interactions with various health information

systems, patterns of communication, and flow

of information.

In the near future, multi-disciplinary

tumor boards in precision oncology will still

involve physicians (oncologists, pathologists,

radiologists and other subspecialists), nurses,

and genetic counselors but will also be routinely

extended to include bio-informaticists and

laboratory scientists to help navigate the

complexities of biomarker results that determine

type/subtype and disease stage for the patient.

A recent publication in the Journal of Clinical

Oncology – Precision Oncology describes the

importance of the molecular tumor board in a

case study of a 54-year old male diagnosed with

microsatellite stable stage II non-mucinous colon

adenocarcinoma with no evidence of RAS or

BRAF mutations. After six months of treatment

with capecitabine, multiple peritoneal and live

metastases were identified at which time he

was referred for additional molecular testing.

The NGS results identified a new ALK-fusion

in his colon cancer through comprehensive

genomic analysis. The interpretation of

the results was not obvious and required

extensive research into the prevalence of

such a mutation and the possibility of a false

positive. Alignment of the MDT to include the

bioinformaticist to evaluate the results, notably

the sequences, rendered a decision to place the

patient on a targeted therapy that successfully

treated his tumor.6

There exists a long-standing disagreement

regarding how MDT meetings, such as tumor

boards, may, or may not, improve patient

outcomes. However, the knowledge sharing and

transfer across care team members is invaluable,

especially in the current environment of high

complexity biomarker testing for cancer.

“In the near future, multi-disciplinary tumor boards in precision oncology will still involve physicians (oncologists, pathologists, radiologists and other

subspecialists), nurses and genetic counselors but will also be routinely extended to include bio-informaticists

and laboratory scientists to help navigate the complexities of biomarker results that determine type/subtype and

disease stage for the patient.”

Figure 1: Care process model based on National Comprehensive Cancer Network (NCCN) for non-small cell lung cancer (NSCLC).

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The Role of the Laboratory and Precision TestingLaboratory testing is the single highest volume

medical activity that drives clinical decision

making in PM. As a result, laboratorians and

clinicians have the responsibility to match

appropriate testing with appropriate therapy.7

Unfortunately, the laboratory’s contribution

to the success of PM has historically been

under-recognized. By definition, PM is driven

by biomarker data generated in the laboratory,

yet stakeholders have done little to improve

the delivery, utilization, and access to the

wealth of knowledge residing within the

laboratory system.

Getting the right treatment to the specific

patient at the right time isn’t even possible

without that patient first getting the correct

laboratory test, at the right time, with the results

being received by the care team in a timely

fashion in an easily assessable and interpretable

format. The current system (or lack thereof)

is fraught with inefficiencies that contribute

to the uncertainty of the value of laboratory

testing that in turn, limits the potential success

of precision medicine programs. These

inefficiencies have a real and significant impact

on patient outcomes often resulting in restricted

access to potentially beneficial therapies.

Clinicians are faced with navigating the

increasing number of biomarker testing

options in addition to understanding how and

when to order the right test at the right time.

The Institute of Medicine identified the need to

optimize how genomic diagnostic testing and

the resulting information can be integrated into

the clinical pathway to maximize patient benefit

and minimize harm.8,9 The integration of this

type of information can be facilitated through

the knowledge sharing process within the MDT

but at the same time, requires the thoughtful

engagement pathologists with ordering

physicians to ensure best outcomes for patients.

Laboratories have the responsibility to

develop informatics solutions that facilitate

clinical consultation to assist ordering clinicians

on the interpretation and the impact of test

results beyond just providing a PDF lab report.

While PDFs were well suited during the paper

documentation era, the content in a PDF is

not easily ingested into the clinical workflow of

an oncology module in an electronic medical

record (EMR) or other healthcare IT system and

provides little to no means of computable data.

The Role of Informatics in the Precision Medicine LandscapePrecision medicine informatics is the science

of linking computer science, healthcare

information, and biology of disease to the

individual patient. The application of informatics

to precision medicine requires a different

approach as technologies are introduced

and the data generated in precision medicine

initiatives evolves from evidence-based practice

into treating the mechanisms of the disease.

Advancements in informatics over the

past few decades allowed the development

of computer systems to assist with storage,

processing, and sharing of healthcare

information as electronic health and/or medical

record systems (EHR, EMR), laboratory

information systems (LIS), and radiology

information systems (RIS) were developed and

standardized to replace paper charts, and a

combination of separate computer programs,

spreadsheets, and paper documentation

for tracking administrative, pharmacy, and

billing records.

The evolution of the EMR allowed for storing

data for a patient across time with a focus on

care management, improvement of outcomes,

and population health. Unfortunately, those

very same monolithic healthcare systems

were not designed, and cannot scale, to meet

the requirements of a learning healthcare

system model that integrates biology into a

knowledge base to support just-in-time use of

clinical-genomic information. Recent survey

results conducted with the Journal of Precision

Medicine and XIFIN, Inc reported that

approximately 59% of respondents indicated

that their enterprise EMR or EHR did not or only

partially met their needs as an end-user.10

The challenges that have contributed to a

lack of solutions to meet the needs of precision

oncology are partially attributed to the

constantly evolving nature of precision medicine.

Other contributors include: the complexity and

heterogeneous nature of data generated for

a variety of tests, lack of true interoperability

and therefore, inaccessibility to the patient

record data (internally and externally), and

continued emphasis on quality (performance-

based) requirements in the value-based

healthcare landscape.

In the value-based care era, a precision

medicine informatics roadmap should be

designed and architected from a knowledge

engineering approach, by leveraging

standards and tools to enable interoperability,

thus facilitate turning data into meaningful

information. Informatics and data strategies

are critical to linking access to information,

both internally and externally, through EMRs to

physicians, laboratory scientists, pathologists,

and bioinformaticists. All stakeholders can

benefit from this approach.

Professional societies including, but not

limited to the American Medical Informatics

Association (AMIA) and American Health

Information Management Association (AHIMA)

“Recent survey results conducted with the Journal of Precision Medicine and XIFIN, Inc reported that

approximately 59% of respondents indicated that their enterprise EMR or EHR did not or only partially met

their needs as an end-user.”

Figure 2: Informatics and knowledge engineering approach to data integration and sharing.

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have identified and published many peer-

reviewed manuscripts and opinions on the gaps

resulting from the lack of interoperability and

the failure of EMRs to meet the new challenges

of how PM will be integrated into value-based

care models.11,12 Communication of clinically

relevant genomic findings requires an elevated

level of interoperability by leveraging standards

associated with a knowledge engineering

approach. A knowledge-based approach

focuses on seamless integration of systems,

people, and processes, and is fundamental to

achieving interoperability that significantly affect

patient care. Figure 2 illustrates a knowledge

engineering approach necessary to achieve

interoperability in PM.

Although there are many factors that will

contribute to the success of precision medicine

programs, we have identified four areas where

an informatics approach is critical for success.

1. Electronic medical records: Electronic

medical records will be required to extend

and scale to meet the growing requirements

of precision medicine. To become fully

integrated, EMR vendors will have to expand

beyond manual upload of documents or

PDFs and fully adopt standards to enable

content to be produced and consumed

from heterogeneous systems and networks.

Healthcare systems can already consume

pathology/histology through an HL7

message but results from laboratories

outside of the health system are not always

consumed in an electronic format other

than a PDF document.

Traditional EMRs are designed to

address some common needs of providers

(scheduling, billing, ordering). It is valid

to assert that these “traditional systems”

provide very important tasks for the

day-to-day operations of healthcare

organizations, but they fall short when

addressing the rapid pace of the current

precision medicine needs, because they

are not designed to keep up with the pace

of new datasets that are being generated

by the ever-increasing number of high

complexity genomic laboratory tests.

Studies have reported on how EMRs

contribute to physician burnout, high

job dissatisfaction, and increased stress.

One study reported that EMRs and EHRs

contribute to burnout from the burden of

searching for critical information residing

somewhere in a tab within the EMR and

EHR, and the clerical burden of charting

and cumbersome, and time-consuming

data entry at home during evenings or

weekends.13 Additionally, The Doctors

Company survey reported that 61% of

physicians reported EMRs have disrupted

the physician-patient relationship, reduced

clinical efficiency and lowered productivity.14

Physicians in the era of PM need to focus

on their patients and not searching for NGS

results reported in a PDF document that

resides in a media tab somewhere buried

within the EMR.

2. Laboratory information systems:

The nature of precision medicine requires

that laboratories become more precision

medicine focused.15 Laboratories that

provide advanced NGS assays for use in

the clinical setting will need to take a more

involved role with the MDT and patient

care. The laboratory of today and in the

future will need to develop new diagnostic

strategies as testing becomes increasingly

complex and pricing considerations impact

the adoption of new tests. Laboratories

generate large volumes of digital data

from complex biomarker testing, new test

development, and the adoption of digital

pathology. Access, retention, and reuse

of the data is critical for laboratories to

remain competitive. A laboratory focused

informatics approach to facilitate data reuse

for education, publication, collaboration, and

quality assurance is essential.

3. Interoperability: The industry has

made significant improvements in order

to enhance interoperability among

heterogenous healthcare systems in

the past few decades. As a result, the

HL7 group was established as a not-for-

profit organization that has, since 1987,

provided standards for healthcare data

interoperability and is probably the most

well-known name in this area. The most

common and widely adopted standards

are HL7 version 2 (HL7v2), and HL7 Fast

Healthcare Interoperability Resources

(FHIR).16 The availability and continuous

development of existing standards will not

address the interoperability challenges,

due to the level of data granularity PM

requires. The complexity and granularity of

the data coupled with the complete lack

of communication across vendor systems,

the exponential increase and complexity of

data that is generated from a single patient,

and different sources will magnify the

problems of patient/provider data matching,

information sharing and flow, and data

quality across care settings.

On February 11, 2019, US Health and

Human Services (HHS), Centers for Medicare

and Medicaid Services (CMS) and the Office

of the National Coordinator for Health

Information Technology (ONC) proposed

new rules to support seamless exchange

of electronic health information with the

intent to promote access by clinicians and

patients.17 One outcome of the Proposed

Rule is to focus on improved interoperability

to help patients and physicians make better

choices about care and treatment that

focus on patient centered health. The added

complexity of diagnostics, where the

results are generated by an external entity

as a static PDF document or worse, in a

facsimile will not meet future interoperability

requirements or standards. Additionally,

clinical care in the precision oncology setting

will require further discussion on definition,

specification, and implementation of data

exchange methods around data sharing

in precision medicine programs that can

integrate with the EMR/EHR use in precision

medicine programs.

4. Standards: Health IT data sharing requires

the adoption of standards to meet the

complex needs of PM and will overlap

with new and long-standing standards

defined by the National Institutes of Health

(NIH) described above and further by the

Federal Health Information Technology

Standards (FHIT) and the Office of the

National Coordinator for Health (ONC).18,19,20

Data generated in PM is more complex than

data from episodic care and consists of

structured and unstructured text, imaging

and is subspecialized and multi-modal in

nature. Moreover, since patient-level clinical

data will need to be tied to biomarker and

genomic results not necessarily generated

from the same laboratory where the

NGS tests were performed, successful

data collection and sharing can only be

facilitated by adoption of existing standards

“With over 75,000 genetic tests on the market

in 2017 with 10 new tests being added daily,

it is little wonder that uncertainty exists.”

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(e.g. HL7v2 and FHIR) along with patient

core datasets that support interoperability.16

HL7v2 and FHIR are adopted industry

wide and all the major EHR and EMR

vendors utilize them for common tasks

like transferring of demographics, orders,

and common laboratory results. The vast

diversity of NGS tests coupled with the

granularity of data generated in the PM

field, makes the interoperability among

heterogenous systems nontrivial. Integration

challenges will be further complicated by

the fact that neither HL7v2 nor FHIR dictate

what ontologies, dictionaries, and coding

standards should be used. Thus, providers

will still face significant data integration

challenges in PM.

ConclusionsThere is no question that the rapid development

of next-generation sequencing (NGS) testing

has impacted cancer treatment with the

promise of improving cancer care and outcomes

through comprehensive NGS diagnostic tests.

Likewise, advancements and adoption of NGS

in the clinical setting have also spawned new

developments in biomarker-driven cancer

therapies as well as uncertainty regarding how

best to interpret, track, and communicate test

results to multi-disciplinary care team members

and patients. Discussion in the molecular tumor

board generates shared information that is used

for subtyping and staging patients as well as

documentation of the patient’s disease that is

critical and time-sensitive for any given patient

who is anticipating treatment.

Clinicians are already overburdened with the

volume and the complexity of the data that

they must consider when developing optimal

treatment plans for their patients. This is

particularly cumbersome in oncology, where

cancers are increasingly molecularly defined as

are the targeted therapies. With over 75,000

genetic tests on the market in 2017 with 10 new

tests being added daily, it is little wonder that

uncertainty exists.21

Federal entities and stakeholders have

established methodologies and approaches

to facilitate the capture and linking of clinical

data from treating physicians, with genomic

information generated from labs to track

outcomes and uncover tumor sub-classification

defined by the distinct molecular mechanisms

that underlie variations in disease manifestations.

To accomplish this, the National Research

Council and other standards bodies have

proposed that a taxonomy of agreed

phenotype definitions and ontologies will be

required to make sense of the large amounts

of heterogeneous data to facilitate the

classification of disease on an individual basis.

The standards and method of single-gene

analysis will no longer be acceptable.

The authors have attempted to highlight the

importance of communication and collaboration

between multi-disciplinary healthcare teams

including the treating physicians and the

laboratories generating the information used to

guide therapeutic options in precision medicine.

By taking an informatics approach that includes

the standards and best practices of data science

and knowledge engineering, we suggest a

technology-based solution that improves

the process and bridges the gaps for all

stakeholders including care teams, laboratories

and most importantly, patients. JOPM

Patricia Goede is VP Clinical Informatics at XIFIN, Inc., where she brings 22 years’ experience developing biomedical imaging informatics solutions and technology to facilitate multi-modality and multispecialty image-based exchange, collaboration and management in distributed environments. Goede founded VisualShare and served as CEO until its acquisition by XIFIN in 2015.

Previously, Goede was at the University of Utah where she pioneered a number of image, visualization and collaboration tools. She is was the co-founder of the Electronic Medical Education Resource Group (EMERG), and as its director, established the Utah Center of Excellence for Electronic Medical Education. Goede holds an MS in Computational Visualization and a PhD in Biomedical Imaging Informatics.

Lori Anderson has over 25 years of experience in the diagnostic and life sciences laboratory industries. As a laboratory scientist and business professional, Lori has developed numerous clinical and translational research assays predominantly in the cardiology and oncology space. She has coauthored 30 peer reviewed publications and book chapters in addition to

over 45 abstracts and poster presentations. Recently, Lori became an expert in the data and evidentiary requirements necessary to gain market access for diagnostic tests. She comes to XIFIN from Quest Diagnostics where she was a Director of Health Economics and Outcomes Research (HEOR) where she defi ned the role HEOR within Medical Aff airs and developed a comprehensive coverage strategy framework for new assays. Lori graduated as a medical laboratory technologist from Fanshawe College in London, Canada and became an ART (Advanced Registered Technologist) in Immunohematology. She also has a BA in Administrative and Commercial Studies from Western University in London, Canada.

Dr. Emerson Borsato has 20 years of experience in Health Care it with focus on medical knowledge engineering, collaborative tools, standards, and semantically linked records. He is responsible for the core design and architecture of the latest Visual Strata. Prior to joining Xifi n, Dr. Borsato worked at Intermountain Healthcare (2007-2017) where

was the technical lead and architect for the knowledge repository where he led a number of knowledge integration activities with clinical decision support systems, and also participated as an integration architect of the Cerner Millennium solutions for iCentra. Dr. Borsato also worked with University of Utah (2011-2012) where he was the lead of implementation of State Wide Master patient index. Dr. Borsato completed a PhD in Medical Informatics from the Federal University of Parana, Brazil (2005). Dr. Borsato received his MS (2000) and his Bachelor Computer Science (1997) from the Pontifi cal Catholic University of Parana, Brazil. His areas of interest include knowledge management and processes to biomedicine; modeling and representation of ontologies, data, and knowledge; and development and implementation of interoperability standards. Dr. Emerson is also a certifi ed commercial pilot that do charity fl ights when schedule permits.

References1. National Research Council (US) Committ ee on a framework for developing a

new taxonomy of disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington, DC, 2011.

2. Lih CJ, Harrington RD, et al. Analytical Validation of the Next Generation Sequencing Assay for a Nationwide Signal-Finding Clinical Trial: Molecular Analysis for Therapy CHoice Clinical Trial (NCI-MATCH, EAY131) J Mol Diagn. 2017

3. National Research Council. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: The National Academies Press; 2011.

4. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793–795.

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6. Hosono et at Evaluation of ALK Fusion Newly Identifi ed in Colon Cancer by a Comprehensive Genomic Analysis. JCO Precision Oncology htt ps://ascopubs.org/doi/fi gure/10.1200/PO.19.00268

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9. Plotkin, E, Allen, T.C et al Integration of pathology within the multidisciplinary cancer care team htt ps://ascopubs.org/doi/abs/10.1200/JCO.2019.37.27_suppl.49

10. Greefkes, S. The Value of Precision Medicine Informatics Initiatives: Adoption, Uses, Challenges, and Approaches. XIFIN Inc.; 2019. www.visualstrata.com

11. Tenenbaum JD, Avillach P, Benham-Hutchins M, et al. An informatics research agenda to support precision medicine: seven key areas. J Am Med Inform Assoc. 2016;23(4):791–795. doi:10.1093/jamia/ocv213

12. Butler, M. White Paper: EHRs Not Pulling Their Weight in Precision Medicine Research htt ps://journal.ahima.org/2016/08/17/white-paper-ehrs-not-pulling-their-weight-in-precision-medicine-research/

13. Collier R. Electronic health records contributing to physician burnout. CMAJ. 2017;189(45):E1405–E1406. doi:10.1503/cmaj.109-5522

14. The Doctors Company. The Future of Healthcare National Survey of Physicians. htt ps://www.thedoctors.com/contentassets/23c0cee958364c6582d4ba95afa47fcc/tdc_the-future-of-healthcare-survey-2018.pdf

15. Rajeevan MS, Li T, Unger ER. Precision Medicine Requires Precision Laboratories. J Mol Diagn. 2017;19(2):226–229. doi:10.1016/j.jmoldx.2017.01.001

16. Health Level Seven (HL7) htt p://www.hl7.org/index.cfm

17. National Institutes of Health Data Sharing Policy and Implementation Guide htt ps://grants.nih.gov/grants/policy/data_sharing/data_sharing_guidance.htm

18. U.S. Department of Health and Human Services Laws and Regulations htt ps://www.hhs.gov/regulations/index.html

19. Off ice of the National Coordinator for Health Information Technology: Interoperability Roadmap htt ps://www.healthit.gov/sites/default/fi les/hie-interoperability/nationwide-interoperability-roadmap-fi nal-version-1.0.pdf

20. Off ice of the National Coordinator for Health Information Technology: Clinical Document Architecture and the Role in Meaningful Use. htt ps://www.healthit.gov/sites/default/fi les/cda_c-cda_theirrole_in_mu.pdf

21. Phillips KA, Deverka PA, Hooker, GW, Douglas MP. Genetic Test Availability And Spending: Where Are We Now? Where Are We Going? Health Aff (Millwood). 2018 May; 37(5): 710–716.

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