How Data and Informatics Intersect to Enable Precision Medicine to Reach its Full Potential

Authors: Patricia Goede, PhD, Lori Anderson, Emerson Borsato, PhD

Introduction and Background

The practice of precision medicine involves the use and application of patientspecific biomarker information to diagnose and categorize diseases that can then guide more effective treatment to improve clinical outcomeswith 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 the -driventoindividual patients in a responder population [3, 4]. Early examples ofbiomarker-driven cancer treatment includehormone-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 patientsexpressing specific genomic variants regardless of tumor origin. Moreover, the rapid pace of development and adoption of biomarker testing with next-generationsequencing (NGS) enables molecular pathology laboratories to develop multiple-gene sequencing panels faster and with less expense.

Precision medicine promises to improve medicalcare, however, appropriate data exchange and informatics platforms need to be in placeto address themany existing and unforeseenchallenges facing care teams and laboratoriesthat provide these highly complextests.While many factorscontribute to the successof precision medicine programs,we willfocus on the role of how informatics facilitates communication and collaboration at the intersection of oncology and pathology.

The Multifactorial Challenges Facing thePrecision Oncology Multi-Disciplinary Care Team

The 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 canceris 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 as illustrated in Figure 1.The challenges for the care team are multifactorial and include the complexity of interpreting results, thus introducing new challenges in translating laboratorydata into meaningful information used for treatment planningas illustrated inFigure 2.The figure was modeled after NCCN guidelines to the treatment of non-small cell lung cancer.

[Pull-out quote] 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 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-yearold male diagnosed with microsatellite stable stage II non-mucinous colonadenocarcinoma 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 wasnot 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 therapythat successfully treated his tumor. [6].

There exists a long-standing disagreementregarding how MDT meetings, such as tumor board, 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 (such as NGS) for cancer.

The Role of the Laboratory and Precision Testing

Laboratory testing is the single highest volume medical activity that drives clinical decision making in precision medicine (PM).  As a result, laboratoriansand 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 right patient at the right time isn’t even possible without that patient first getting the right 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 of a system) 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 withnavigating the increasing number of biomarker testing options in addition to understanding and 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 easily facilitated through the knowledge sharing process within the MDT.

Laboratories have the responsibility to develop informatics solutions that facilitate a clinical consult to assist the ordering clinicians on interpretation and the impact of test results beyond just providing a PDF. While PDFs were well suited during the paper documentation era, the content in the PDF is not easily ingested into the clinical workflow of the oncology module in anelectronic medical record (EMR) or other healthcare IT system prevalent in this digital record era.

The Role of Informatics in the Precision Medicine Landscape

Precision medicine informaticsis 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. [Pull-out quote] Recent survey results conducted with the Journal of Precision Medicine 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, aprecision medicine informatics roadmap designed and architected from a knowledge engineering approach that uses standards and tools willenable interoperability and facilitate turning data into meaningful information. Informatics and a data strategy 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) 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].

The authors have identified four gaps where an informatics approach iscriticalto support the future success of precision medicine.As illustrated in Figure 3

  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 external results from laboratories outside of the health system are not always consumed in an electronic format other than PDF.

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.

There have been several published studies on how EMRs contribute to physician burnout with some studies reporting high job dissatisfaction, stress and burnoutamong physicians [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 notsearchingfor NGS results reported in a PDF document that resides in a media tab somewhere buried in the EMR.

  1. Laboratory information systems: The advanced diagnostics nature of precision medicine requires that laboratories providing advanced NGS assays for use in the clinical setting thus, providing pathways for targeted therapeutics in cancer, become more precision medicine focused [15]. 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.
  2. Interoperability: Interoperability (or the lack thereof) will be a continuous challenge in the precision medicine landscape. 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.
  3. Standards: Health IT standards for data sharing require the adoption of standards that meet the complex needs and requirements of PM. Additionally, data sharing models such as what is defined by long-standing policies of the National Institutes of Health (NIH) and further by the Federal Health Information Technology Standards (FHIT) and the Office of the National Coordinator for Health (ONC) as part of the [16, 17, 18].

The FHIT, in collaboration with Federal entities including CMS, has developed standards for data collection and sharing. Moreover, since patient-level clinical data will need to be tied to biomarker and genomic results not necessarily generated from the same laboratory the NGS tests were generated, successful data collection and sharing can only be facilitated by adopting existing Clinical Document Architecture (CDA) standards and the patient core data set that support interoperability [19].

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. For example,a primary goal of the National Research Council is to uncover tumor sub-classification defined by the distinct molecular mechanisms that underlie variations in disease manifestations and outcomes [3]. To accomplish this, the National Research Council proposes 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.


There 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 around how interpreting, tracking, and communicating test results between multi-disciplinary care team members and to the 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 a given patient who is anticipating treatment options.

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. [Pull-out quote] 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 [20].

The authors have attempted to highlight the importance of communication and collaboration between multi-disciplinary healthcare teams includingthe treating physicians and the laboratories generating the information used to guide therapeutic options in precision medicine.  By taking an informatic 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.