A. Delays in Translating Innovative Care Delivery from Research into Practice

When a groundbreaking change in the thinking about disease management appears in the highest impact journals in medicine, it is almost always followed by concerted, investigator-led efforts to publicize the impact of the discovery through editorials, press releases, and various presentations. Sometimes, patients advocate for the adoption of the innovation and extol the value to each other and to their providers. Yet we know that, on average, more than a decade is needed to change practice, despite these efforts.^{Reference Balas, Boren, Bemmel and McCray9} For example, in 2005 it was discovered that patients who smoked cigarettes benefit from even simple counseling using a method known as the 5 A's (ask, advise, assess, assist, arrange follow up).^{Reference Anthonisen, Skeans, Wise, Manfreda, Kanner and Connett10} However, 10 years later, in a national study examining lung cancer screening, less than 25% of people still smoking received this intervention.^{Reference Park11}

There are a number of reasons for the slow uptake of evidence-based medicine into practice.^{Reference Timbie12} Adoption of change must first overcome the fact that clinicians are ethically bound to primum non nocere (first do no harm). Patients in research studies are carefully selected and often differ in subtle ways from the average patient in routine care. Clinicians must be convinced that a pioneering study is replicable and that novel interventions do not have unintended consequences or distract from higher priority tasks. This skepticism is ingrained in health care.^{Reference Ioannidis13} As a consequence, changing the practice of medicine at a local level requires attention to knowledge about the change, attitudes toward the change, and behaviors that facilitate the new processes.^{Reference Donabedian14}

B. Accountability and Wellness Issues Impede Experimentation

As a result of important work summarized by the National Academy of Medicine beginning with To Err is Human,^{Reference Kohn, Corrigan and Donaldson15} there have been two fundamental changes in the U.S. health care system. First, there has been an surge in the adoption of EHRs, in large part motivated by federal regulation and incentives.^{Reference Marcotte, Blumenthal and Tavenner16} Physicians using federally approved (certified) EHRs subjectively report mixed results in terms of their own satisfaction and the impact on the quality of care.^{Reference Williams, Eberts, Capurro and Marmor17} Adoption of EHR technology happened at the same time as the second major change in the system, which Berwick has called the “accountability era” — when regulations and reporting requirements consume more resources and require more non-clinical work from care providers than ever in our history.^{Reference Berwick18} These changes are still very new and need to be incorporated more systematically into the workflow of busy professionals to alert them to deficiencies in the quality of their care. In their present form, the burden of documenting additional information, or copying and pasting information from one encounter to the next,^{Reference Wang, Khanna and Najafi19} compounds the stress on health care personnel.^{Reference Del Carmen and Dyrbye20}

Job-related stress has led to an epidemic of burn-out and its sequelae among health care providers. Suicide rates are now consistently higher among physicians than in the rest of the population, and have been attributed, at least in part, to job stress.^{Reference Gold, Sen and Schwenk21} The EHR has been portrayed as more of a curse than a blessing, with publications citing issues related to system usability^{Reference Khairat and Kroth22} and record usefulness^{Reference Mir23} now commonplace. All of this combines to create a health care workforce more reticent than ever to experiment with innovation.^{Reference Shanafelt and Noseworthy24} This reticence is pervasive and in no way limited to innovation in precision medicine and genomics. Local and national health care leaders must approach any change by first recognizing impact of this stress on the willingness of care providers to change.^{Reference Herzlinger25}

C. Technical Capability Variance Forces less Aggressive Practice Change

The third major change in the national health care environment relates to the ever-expanding chasm between digital natives — people who were born or assimilated into the digital era — and digital immigrants — people who have been asked (or forced) to develop new skills.^{Reference Palfrey and Gasser26} While some industries, such as the airline and ride-share industries, have quickly evolved a “mobile first” business strategy, health care has been obligated to develop a solution for all levels of digital literacy and data privacy concerns,^{Reference Abelson27} which radically slows the pace of change in the field.

The root causes for lags in technical capability stem from both the provider and patient side of health care. From the provider side, there is variation between provider comfort with technology that has been shown to impact ability to use and recommend technology in some settings.^{Reference Thies, Anderson, Cramer and Rigby28} From the patient perspective, there continues to be a wide variation in the level of comfort with technology.^{Reference Bauer29} There is, as Ammenwerth has noted, a compounding challenge when both the capabilities of the people and the capabilities of the technology need to be transformed.

A. Genomic Information and Readiness for EHR Use

Today's EHR is capable of accepting patient information in a variety of forms, ranging from images (photographs, radiographic test results, scanned text) to unstructured text, to highly structured individual data items, such as a code for a test name and a result and reference range in standardized units. As information becomes more structured, it is generally more capable of becoming computable (i.e., understandable by computer software).

In the case of genomic information, multiplexed panels or sequencing tests are able to identify thousands to millions of variants in hundreds of genes — far more than a human health care provider can be expected to manually review and act on. To maximally impact patient care and decision making, patient-specific genomic variation that impacts clinican decision making should exist in the EHR in a structured form that can drive clinical decision support available at the time of medication ordering, diagnostic evaluations, or patient risk assessments.^{Reference Pulley30} However, at this time, most genomic results arrive as a document image, with results that are not encoded and thus not directly computable, analyzable, or sharable except as a copy of the image. Authors have called on reference labs to support delivery of structured data and for EHRs to accept computable results,^{Reference Hoffman and Masys31} but this deficiency persists.

There are a number of technical barriers to closing this gap. First, many health systems outsource genetic testing to external laboratories, most of which do not transmit back machine-readable results. EHRs are able to integrate results fully only when they are transmitted in a standard messaging format that facilitates use by computer algorithms. These standards are still in evolution, and their use is limited.^{Reference Berrios, Beheshti and Costes32} Second, there is more than one standard for identifying gene variants and the reference sequence. Without a universal lexicon of gene variants, any attempt to recognize characters in a PDF file and to extract specific genomic information from these reports is fraught with the potential for error and patient safety risk. In addition, these reports often contain some results based on assays of questionable quality, which is not conveyed on the report itself. Finally, the industry is beset with inertia; most laboratories do not prioritize improving the sharing of discrete, encoded data unless there is health system or regulatory demand. Moreover, vendors of EHRs react to customer demand, rather than proactively anticipating innovation.

Lack of encoded and computable results has many downstream effects. For example, because all variants that are identified are typically reported together in one report, it is virtually impossible to help providers or patients interpret individual findings; such interpretive help is usually accomplished via hyperlinks to external knowledge sources linked to individual findings in more structured reports. This is not a new problem, as noted by Guttmacher in 2007,^{Reference Guttmacher, Porteous and McInerney33} yet it continues to be unsolved and is arguably more significant in the era of consumer-centric care.^{Reference Hurle34} Another challenge related to the result's lack of structure is how to manage updating results. For example, if a variant is initially in the report as actionable, but subsequently found to be clinically insignificant, the report can be reissued, but it may not be clear which results have changed on that report. The University of Michigan has explored ways to address this, but that work is very much in its infancy.³⁵

As we move to a future where genomic test results are delivered in computable form, it will be critical to confront the challenge of accurately interpreting the genomic information in the patient's particular clinical context. It is often the case that genetic variation alone is not the most significant risk factor, and is best evaluated by combining genomic with other clinical data or family history. This combination of information is vital to manage patients with cancer predisposition variants, such as those with positive family histories, and those patients with social/behavioral risk factors (e.g., smoking history). This issue is equally important in non-cancer risk prediction. Warfarin (blood thinner) dosing is one example of non-cancer risk prediction requiring genomic and clinical data. A well-studied algorithm for warfarin dosing incorporates two genes, four demographic parameters, three clinical factors, and four other medications.^{Reference Anderson36} Although this calculator is not integrated with an EHR, similar tools do exist within EHRs, and have achieved acceptance among clinical users.^{Reference Ramirez37}

B. Genomic Information Availability

There are two major factors associated with the availability of genomic information. The first of these factors relates to the cost associated with this new data source and its integration into health care. A detailed discussion about the costs of implementing and managing genomic information is beyond the scope of this paper, but is reviewed elsewhere.^{Reference Christensen, Dukhovny, Siebert and Green38} In brief, however, organizations involved with utilizing information must confront costs associated with identifying labs that provide high quality genetic results; performing sequencing, storing results, and sharing results through electronic interfaces that must be built and maintained as source and destination systems change; and increasing institutional insurance to cover any liability associated with breaches that compromise the privacy of genomic data. These costs can easily mount to millions of dollars to initiate such a program.

The second major factor impacting availability is the need for technology standards and guidance for decisions about how best to manage genomic information. The impact of this factor cannot be overstated. Although most EHRs are capable of accepting health data in many formats, most EHR systems currently support neither the volume nor the complexity of genomic data that is needed to optimally impact patient care.^{Reference Ammenwerth39}

Genomic data come in many forms, ranging from individual variant names to raw data from various types of tests. The largest datasets come from whole genome sequencing and can require as much as 90 gigabytes per report, which equates to 80 terabytes of data for every 1000 patients. Data files of this size or larger are commonplace in radiology, where the response has been to store those raw data in a separate departmental computer system called a Picture Archiving and Communication System (PACS). These systems are designed to support browsing of information; expert interpretations are sent to the EHR as reports that can be used to generate alerts and reminders for providers, while the volumes of raw data remain sequestered. In the case of genomic data, the science of interpretation is very new. In some cases, findings with uncertain significance become actionable overnight. This discovery makes what should or should not be stored in the EHR less certain, especially since patients increasingly are relying on 2014 regulatory changes to demand access to “raw” or uninterpreted genomic data, which limits the role of sequestration.^{Reference Evans40} The complexity and volume of the data and changing interpretation have contributed to the slow response to requests by experts to incorporate genomic data into the EHR. New strategies are being proposed to address this, such as the omics ancillary system by Starren,^{Reference Starren, Williams and Bottinger41} but as of now, this challenge remains unresolved.

C. Patient and Provider Interaction

For centuries, patients have relied on clinicians as an authoritative source to explain medical facts and disease probabilities.^{Reference Starr42} This influence has increasingly been questioned with the emergence of the Internet; surveys demonstrate the challenges doctors face as patients use the Internet to seek answers tor medical questions.^{Reference Potts and Wyatt43} With the explosion of information about the importance of genomics and the provision of direct-to-consumer testing, this problem has escalated. Questions related to privacy risks,^{Reference Clayton, Evans, Hazel and Rothstein44} direct-to-consumer testing value,^{Reference Roberts45} and appropriate indications for provider-initiated testing may be posed to physicians and advanced practice nurses, with the unfounded assumption that these professionals are experts in this area.^{Reference Pet and Haga46} In addition to requiring signifi-cant foundational knowledge to which many providers have not been exposed,⁴⁷ the knowledge itself is evolving too quickly to be easily summarized, making the time investment to stay current virtually impossible in general practice.^{Reference Overby48}

Decision making in the genomic era is fraught with its own misconceptions. For example, patients incorrectly believe that a lab report identifying a variant and offering the interpretation that this variant makes a particular drug “less likely to be effective” means that that drug should never be prescribed to them. In fact, this interpretation itself is subject to debate.^{Reference Katsanis, Katsanis, Korngiebel, Fullerton and Burke49} Labs vary in their quality and lack consistency in their ability to identify a variant. The variants themselves may confer minimal risk of inappropriate drug response, but many people (including physicians) have deficiencies in their understanding of probability and risk.^{Reference Crowson, Lipkus, Samsa and Rimer50}

Finally, other patient-specific factors, including an allergy to other more effective medications, may make the “less effective” medication the best choice.^{Reference Schumock51} In short, medical decisions made on the basis of the specific test that was ordered and these patient factors may differ substantially from decisions made without that information.^{Reference Bhatti and Redelmeier52}