Jump to ContentJump to Main Navigation
Show Summary Details
More options …


Official Journal of the Society to Improve Diagnosis in Medicine (SIDM)

Editor-in-Chief: Graber, Mark L. / Plebani, Mario

Ed. by Argy, Nicolas / Epner, Paul L. / Lippi, Giuseppe / Singhal, Geeta / McDonald, Kathryn / Singh, Hardeep / Newman-Toker, David

Editorial Board: Basso , Daniela / Crock, Carmel / Croskerry, Pat / Dhaliwal, Gurpreet / Ely, John / Giannitsis, Evangelos / Katus, Hugo A. / Laposata, Michael / Lyratzopoulos, Yoryos / Maude, Jason / Sittig, Dean F. / Sonntag, Oswald / Zwaan, Laura

See all formats and pricing
More options …

The impact of electronic health records on diagnosis

Mark L. Graber
  • Corresponding author
  • 5 Hitching Post, Plymouth, MA, USA
  • President, Society to Improve Diagnosis in Medicine, Senior Fellow, RTI International, NC, USA
  • Professor Emeritus, Stony Brook University, New York, NY, USA, Phone: +919 990-8497
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Colene Byrne / Doug Johnston
Published Online: 2017-09-08 | DOI: https://doi.org/10.1515/dx-2017-0012


Diagnostic error may be the largest unaddressed patient safety concern in the United States, responsible for an estimated 40,000–80,000 deaths annually. With the electronic health record (EHR) now in near universal use, the goal of this narrative review is to synthesize evidence and opinion regarding the impact of the EHR and health care information technology (health IT) on the diagnostic process and its outcomes. We consider the many ways in which the EHR and health IT facilitate diagnosis and improve the diagnostic process, and conversely the major ways in which it is problematic, including the unintended consequences that contribute to diagnostic error and sometimes patient deaths. We conclude with a summary of suggestions for improving the safety and safe use of these resources for diagnosis in the future.

Keywords: decision support; diagnosis; diagnostic error; electronic health records; health care information technology; misdiagnosis


The landmark report Improving Diagnosis in Health Care from the National Academy of Medicine (the NAM report) recently called attention to the unaddressed problem of diagnostic error, pointing out that 12 million Americans are misdiagnosed every year, with an estimated 40,000–80,000 deaths annually [1], [2], [3]. The report specifically emphasized the ever-expanding role that electronic health records (EHRs) play in determining the quality and safety of the diagnostic process, both for better and for worse. With electronic health records now approaching near-universal usage, it is very appropriate to review the ways that the EHR and health care information technology (health IT) support diagnosis, as well as the problems encountered in using these tools that detract from diagnostic safety and contribute to diagnostic error. Improving the safety of diagnosis will require that we identify and optimize the key benefits of the EHR that relate to diagnosis and understand and address its deficiencies going forward. Where appropriate, we will draw comparisons between the EHR and the paper record systems it replaces.

Although our focus is largely on the EHR and its functionality, the ultimate benefits and harms that accrue from using these systems are intimately related to how people use the electronic record and the particulars of the required tasks [4], [5]. A sociotechnical perspective is very helpful in understanding this complex system from a human factors perspective. In particular, these models emphasize that successful application of EHRs depend not only on the software but also on educated and careful users who understand how to get the best out of their particular medical record.

The potential benefits of health IT and electronic health records on diagnosis

The NAM report described diagnosis as a process, beginning with the patient gaining access to care and ending with assignment of a diagnosis (or a decision to defer this) and communicating the diagnosis to the patient. Health IT and the EHR have touched every step of the diagnostic process, from start to finish. We first consider the many ways that health IT and the EHR improves and benefits diagnosis at each of these steps, using the domains and examples listed in Table 1 [6]. Acknowledging that a great deal of future research will be needed to validate the benefits of these effects, each of these are now in active use at some or many health care organizations, and each has excellent potential to improve the safety of diagnosis.

Table 1:

Ways that electronic health records and health IT improves the reliability of diagnosis, with examples of each.

Access to care

One of the most dramatic changes enabled by health IT and the EHR is that the site of care can move out of the physician’s office and into the patient’s home. By taking advantage of electronic portals [7] and asynchronous communication, patients can communicate with their health care team; new problems can be diagnosed as they arise, potentially avoiding hospitalization or readmission. Many envision the day when the scheduled “office visit” is a relic, replaced by just-in-time services provided remotely, using health IT and EHR as the vehicle for providing and documenting care. Telehealth functionality (see discussion below in “Collaboration for Diagnosis”) offers the opportunity to provide real-time consultation with experts in an ever-expanding list of subspecialties, another important demonstration of health IT improving access and quality of care.

Information gathering and access

Paper records are hard to read, easy to lose and seldom complete. Electronic health records, in contrast, potentially provide immediate, reliable and readable access to the patient’s health history. Besides these very welcome improvements, the EHR has enabled new functionalities that could not have been imagined with paper records. EHRs enable new care models, such as team-based care, where different members of a care team can contribute to clinical documentation from different sites and at different times [8]. The EHR allows team members, dispersed in location and time, to see the same information, communicate asynchronously and share in creating care plans, functionality that would have been far more challenging, if not impossible, with paper-based records. As other examples of the novel conveniences the EHR enables, images can be attached to notes to illustrate a finding, and records can be electronically searched to find a specific date or provider or diagnostic test.

Many different applications are emerging to improve data collection for the electronic record. Automated tools that assist in obtaining the key elements of the patient’s history, such as “smart” clinical documentation forms in EHRs, have proven to be useful, and they complement the information gathered through in-person clinician-led interviews [9]. A further advance is to make it easier for patients to provide these data using kiosks and tablets in the waiting room. Practices are also experimenting with ways to interface with various types of patient-generated personal health records [10]. Health information exchange resources will improve access to remote data, expediting diagnosis and reducing unnecessary retesting [11].

The organization and display of information

EHRs have the potential to improve diagnosis by making it easier to organize and display data in more meaningful ways. Note templates can be created that allow a specialty to customize their documentation for their own purposes. A particularly welcome feature that has been shown to improve information processing is the ability to display test data as a graph, instead of as a text result or a text list. Graphic displays facilitate identification of more subtle trends and patterns [12].

Decision support

Decision support, at least in a rudimentary form, was available with paper record systems. A sticker might be taped to the front of each chart as a reminder that the patient needs a flu shot, for example. Well-developed electronic record systems allow for implementing that functionality at a new level of sophistication and reliability, providing reminders for a host of preventive services, or problems that need scheduled follow-up, or providing for prominent display of information relevant to a decision the provider is about to make.

Many of the earliest health IT products focused directly on diagnosis [13], [14], [15], [16], [17], and others focused on treatment and management problems, such as antibiotic selection [18], and ventilator management [19]. The most prominent examples of decision support products that support improved diagnosis are the “symptom checkers” that assist in generating a differential diagnosis. These products provide suggestions on reasonable diagnostic possibilities to consider based on the key findings in a case, and these suggestions are available within seconds. Evaluations of these products have produced mixed results [20], [21], but at least some of these products improve the accuracy of diagnosis [22], [23], [24], and improve patient care [25]. Unfortunately, even when well-designed products are available, they remain underutilized [26], [27].

Another rapidly-expanding field is computer-aided diagnosis, and these software algorithms are being used in a wide range of specialties, including dermatology, and medical imaging for breast, colon and lung cancer [28]. Using “deep learning” approaches, computer-aided diagnosis improves both detection and characterization of visual abnormalities, such as lung or breast nodules, or pigmented skin lesions, and assigning these a diagnosis.

Tools and calculators to assist in clinical decision making

Clinical decisions can involve calculations that embedded tools embedded in the EHR can simplify and facilitate. Does a patient have renal insufficiency? The EHR can immediately calculate an estimated glomerular filtration rate knowing the patient’s age, sex, weight, and measured creatinine [29]. Does a patient have a high risk for atherosclerotic cardiovascular disease? The EHR can provide a calculated risk score using the Framingham formulas for this purpose. Hundreds of other examples can be found, for example, calculation of a risk score based on probability of a specific disease, or a clinical guideline to decide whether a patient should be further evaluated, treated as an outpatient, or requires hospitalization [30].

Intelligent selection of a testing strategy

There are currently over 4000 selectable laboratory tests, and a comparably bewildering number of imaging options. Although clinicians are highly competent in evaluating common complaints and conditions, for unusual conditions or ones where a choice of investigative options exist, electronic record systems can help simplify and guide these selections. EHRs can access and integrate, for example, recommendations on laboratory test selection from reference laboratories [31]. Similarly, preferred imaging strategies are available from online resources provided by the American College of Radiology [32]. Many healthcare organizations now require that clinicians use these electronic utilization management programs to improve appropriate test selection [33].

Reliable follow-up

Diagnosing a patient’s condition can take seconds, hours or months. EHRs facilitate reliable follow-up through reminder systems that track which patients need to be seen again and when. In some advanced EHRs, clinicians can create their own reminder list of patients and tests to follow-up on at a later date. Advanced EHRs also facilitate the creation of internal registries with the same purpose, for example, a list of all the patients who need a follow-up colonoscopy at a later date either for screening or follow-up of a previously abnormal study.

Collaboration for diagnosis

Consultation from specialists is an integral part of many diagnostic evaluations. EHRs and health information exchange make it easier to share information back and forth between the referring and consulting physicians. Besides expediting the request for consultation, a shared EHR allows consultants and collaborators to see beyond the consult request per se and review whatever information may be relevant and necessary. Supplementing the traditional phone call, some organizations provide direct communication in real time using secure messaging systems, or secure directed data transfer using standards such as the direct edge protocols [34].


Telehealth offers another approach to improve collaboration for diagnosis and to improve the quality of health care in general [35]. Timely expert consultation is a requirement for reliable diagnosis. Delays and difficulty accessing specialists have been cited as factors contributing to diagnostic error [36], problems that telehealth and electronic records can help to solve. One of the most successful applications of telehealth is to facilitate access to retinal specialists to diagnose diabetic retinopathy [37]. Teleradiology is another dramatic success story for diagnosis, providing access to appropriate specialists around the clock [38]. For teledermatology, “store and forward” remote consultation has been found to result in more timely diagnosis than the more conventional referral process, although remote consultation was inferior to “in person” examination for accurate diagnosis for pigmented skin lesion [39].

Measuring diagnostic performance and providing feedback

Electronic records make it easier to track populations of patients with the goal of monitoring diagnostic performance and safety. How many patients in our organization received appropriate follow-up of abnormal imaging? How many of my diabetic patients have not had a timely evaluation for retinopathy? Electronic records allow analysis and reporting at the level of a given physician, team, practice site or across an entire organization, functionality that provides the basis for modern organizational quality management. The SureNet program implemented in the Kaiser-Permanente Southern California system is an elegant example of how population-level monitoring of data in the EHR can detect patients at risk for harm for diagnostic error in time to prevent it [40].

Negative effects of health IT and electronic records on diagnosis

At the same time that electronic record systems contribute to successful diagnosis, specific features of EHRs have also created conditions that can degrade diagnostic safety [41]. A growing list of studies have identified issues in which health IT compromises performance, and leading examples are listed in Table 2. These go beyond inconvenience; the EHR is a major contributing factor in patient deaths and harm-related events [5], [42], [43], [44], [45], [46], [47], [48], [49], [50]. Automation by its nature is disruptive, inevitably creates unintended consequences and dramatically changes the nature of work [51]. In this section we highlight several specific examples areas where the quality and safety of diagnosis has been compromised by EHRs.

Table 2:

Ways that electronic health records and health IT degrade the reliability of diagnosis, with examples of each.

Many of the features and functions already discussed as benefits of the EHR might also be classified as problems, depending on the particular EHR product being discussed, or how a given system is configured and used. As an example, EHR and configuration settings vary in terms of where a given test might be filed, making it harder, not easier, to locate critical information. The same diagnostic test may have different names in different organizations, creating similar problems.

Inaccurate, inadequate and missing information

In the days of paper records, the dominant problem was simply finding it – the patients chart was too often “somewhere else”. The EHR has helped solve that problem, but imperfectly. An example is the persisting problem in communicating critical test results: Appropriate follow-up does not reach 100% even in organizations with sophisticated and mature EHRs [52]. In one study, over 10% of critical alerts were never acknowledged within 30 days [53]. Similarly, a systematic review of laboratory tests pending at discharge from inpatient care found that 20%–69% lacked evidence of follow-up [54].

Information exchange to and from physicians’ office can be disrupted by problems with Internet connectivity, system security and firewall restrictions, and granting access to unlicensed personnel to access the data. Ambulatory practice sites may be only loosely associated with the organization (e.g. hospitals) responsible for legislating their practice and system functionality.

The “copy-paste” problem

The “copy-paste” problem is one of the most ubiquitous and the most troubling concerns in using EHRs [55], [56], [57]. Electronic documentation makes it easy to reuse content from an earlier note. If modified and updated appropriately, this functionality can be a time saver for busy clinicians. Too often though, copy-paste functionality is used carelessly to summarize parts of the existing medical record, or in place of performing one’s own patient history or physical examination. Many organizations and authors have offered advice on minimizing the copy-paste problem, including comprehensive white papers recently sponsored by NIST [58], AHIMA [59] and the ECRI Health IT Partnership [60].

The copy-paste mentality degrades diagnosis in other ways as well. To the extent that documentation is inaccurate or outdated, the credibility of the entire electronic record is called into question, compromising the trust that underlies medical professionalism. “Note bloat” is a related but separate problem that impairs diagnosis by requiring the clinician to find the nuggets of key data buried in pages of unnecessary documentation. Finally, the too-facile recycling of old information tends to inhibit the ongoing questioning and reascertainment process that helps monitor diagnostic accuracy as illnesses evolve over time.

In stark contrast to the problems encountered with paper records, where charts were often incomplete or missing, EHRs create the opposite problem: information and cognitive overload. Note bloat and copy-paste problems contribute to this, along with an avalanche of alerts that providers need to digest and triage. Along with the myriad distractions and interruptions that characterize modern medicine, these factors contribute to cognitive overload, one of the major threats to reliable diagnosis.

Usability problems

Usability problems refer to design choices that hinder efficient and effective work flow, and the current generation of electronic record systems is replete with problems of this type:

Pick errors: represent situations where the wrong selection is made from a long list on a drop-down menu. Similar errors were possible in the era of paper records as well (e.g. choosing the wrong chart) but have proliferated in the electronic era. It is just too easy to pick the wrong medicine from a long list, the wrong test or even the wrong patient [61]. Solutions are starting to emerge that address pick errors, for example, EHR interface terminology with improved search terms for test selection, Tall Man lettering for medication choices or using the patient’s picture as a way to ensure the correct record was selected [62], but these innovations have yet to achieve broad adoption.

Design features that discourage reflection and thoughtful care: One goal of electronic record systems is to facilitate documentation that supports and optimizes billing. This can lead to design choices in the EHR that work at cross purposes with the clinical goal of promoting thoughtful, reflective care [63], [64]. An example is the requirement in many EHR systems to identify a specific diagnosis, or symptom, for each care encounter, and to choose this designation from a discrete list of ICD codes. This approach is ideal for electronic encounter capture for billing purposes but inhibits the clinician’s ability to consider a differential diagnosis, to explain why a certain diagnosis or testing strategy was selected or to document his or her thinking about the case. These thoughts require a narrative; they cannot be captured adequately in structured data. In addition, this too-early designation of a code for diagnosis becomes a diagnostic label that is likely to stick with the patient whether it is correct or not, and that discourages further consideration by subsequent providers the patient may see [65], [66]. We need to disentangle the roles EHRs have for billing and the roles they have for clinical care.

The EHR takes too much time: The time spent attending to the documentation requirements in the EHR adds up [67], to the point that physicians are spending as much time on the computer as with the patient [68], [69]. Replacing the information gaps that were so common with paper charts, there is now too much information, too many documentation requirements and too many alerts [70], all of which require time. A recent survey of 2509 primary care physicians found that providers received an average of 63 alerts each day; 69% of the respondents felt that the number of alerts was excessive, and 70% reported receiving more alerts than they could effectively manage [71]. Many physicians have lamented that if there were one thing that would improve diagnosis the most, it would be having more time. If the EHR is consuming time, instead of saving it, this is not good for diagnosis.

The impact on interpersonal communication and relationships

One of the fundamental concerns with the electronic record is that it can distance clinicians from patients, or from each other, altering the human element that is critical to effective care [64], [72]. This is most obvious in the examination room, as the clinician simultaneously tries to use the EHR while also interacting with the patient. The personal connection to the patient suffers, starting with loss of eye contact. Unless the physician makes special efforts to combat the problem, the patient rightly feels neglected. The problem persists long after the initial encounter, especially for inpatients, where clinicians may spend an inordinate amount of time taking care of the “i-Patient,” at the expense of time spent with the real patient [73], [74]. The electronic record, inadvertently, has effectively and perhaps inexorably compromised the patient-to-physician relationship, the foundation of effective health care.

The EHR has also created walls, electronic silos [75], that distance clinicians from each other. Before electronic records, it was commonplace for clinicians to visit the clinical laboratory, the radiology department or a consultant to discuss results from laboratory tests or imaging, or a referred patient. These rich discussions, and the personal relationships that were established as a result, have essentially disappeared as the EHR has become the de facto vehicle for communicating information [72].

The true impact of this cultural change has yet to be evaluated, but it seems likely that disrupting face-to-face communication will have negative consequences. The result will be episodes like the one illustrated by the first patient misdiagnosed with Ebola in the United States. The ER triage nurse who first interviewed the patient succeeded in obtaining a history of recent travel to an endemic region. This key finding was duly recorded in the EHR, an entry that was missed by the treating physician, who sent the patient home, delaying the diagnosis of his infection [76]. In the pre-EHR era, there is a good chance the nurse would have mentioned this key bit of data to the physician personally, or it would have been front-and-center on the paper ER note.

Ideally, the medical profession would prescribe the ideal culture to optimize the quality and safety of clinical care, and health IT products would be designed to support this. Instead, we too often are required to compromise our cultural norms, or somehow adapt them, to accommodate suboptimal health IT systems. In the Ebola case, for example, the EHR would hopefully support bidirectional nurse-to-physician communication, not inhibit it.

Other issues: Space precludes us from discussing a host of other issues that detract from diagnostic quality, including inconsistent and incomplete vocabularies for structured data elements, and variability and lack of specificity for how diseases are defined and designated. EHRs require constant maintenance. Decision support rules need to constantly updated, as an example, and failures can lead directly to errors and harm [77]. A final problem is the lack of a “gold standard” for evaluating health IT and EHR functionality. Evaluations of decision support products to assist with differential diagnosis, for example, are currently limited to comparing different systems to each other, not to a gold standard [78], [79].

The future of electronic records and diagnosis

Although clinicians are learning to balance the benefits of the EHR with the new challenges it poses, they are also considering how electronic records can improve diagnosis in the future. Researchers have presented a vision for this evolution that focuses on improved data management and documentation [6], [63], [80], [81]. The recent National Academy report “Improving Diagnosis in Health Care” also lists priorities for improving the medical record in support of diagnosis [1].

An important area for exploration is how electronic records could facilitate the longitudinal aspects of patient care. This could begin with the ability to capture the chief complaint, the symptoms that were the reason for the patient’s visit. The inability to reliably identify and track chief complaints has seriously hampered research efforts to study and improve diagnostic quality. Second, we need tools that eliminate the need to recapture at each encounter data that are relatively static, like the family history and many aspects of the patient’s history that do not change between encounters. Some of these entries might be “red flag” conditions that could be designated and used in decision support, such as a strong family history of early coronary disease or breast cancer [81]. Electronic record systems could support longitudinal displays that better illustrate the patient’s clinical course over time and their response to treatment. Improved capture and documentation of the date and time diagnostic tests were performed, and care was delivered would also promote the goal of achieving failsafe communication and follow-up. Systems in the future could ensure that test results are communicated and acted upon so that nothing falls “between the cracks”. Patients needing periodic follow-up, for example, after successful chemotherapy, could be reliably identified and notified.

Better support of clinical reasoning

Better support of clinical reasoning would be a welcome and valuable feature in future record systems. This should begin with moving away from documentation based on menu-based selections, relying more on the richness of free text notes. Capturing the clinician’s clinical reasoning is an essential element in support of diagnostic quality. “Downstream” clinicians will have a better understanding of these earlier considerations and will have a better concept of which diagnoses are truly established and strongly supported, which are tentative and need further consideration. Clinical documentation should allow the clinician, and others reading a note, to acquire a sense of the certainties and uncertainties that existed at a particular point in time, and how these were considered. Advances in natural language processing (NLP) can bridge the gap between free text notes and the structured data needed for administrative purposes and quality management. Use of NLP could allow EHRs to retain free text narratives and the rich dialogues that capture the clinical reasoning that underlies diagnosis, while still being able to extract out the structured-data elements that are needed in support of billing and quality monitoring.

Decision support should be intelligent and able to provide context-relevant information “on the fly”, decreasing the need for ad hoc searches of the medical record [82]. Ideally, the EHR would know and anticipate the needs of the user and present the right information, organized in the right format, at the right time, to optimize clinical workflow.

Improved problem lists

Improved problem lists would also be welcome [83], [84], [85]. EHR problem lists tend to resemble their paper counterpart in being outdated and inaccurate. Ideally, assuming providers could agree on the key principles, problem lists in the future could be automatically updated, could convey some sense of the support for given diagnosis and its probability of being correct and would be cleansed of any previous diagnoses that are inactive.

Patient engagement

Medical record systems will facilitate patient’s accessing and contributing to their own medical record. There is growing evidence that engaged patients have improved care outcomes [86], and more reliable diagnosis could be one of these. Engaged patients could help populate the background information on their medical history, preventing the documentation errors that now exist with documentation that is “second hand”; it is likely that a patient’s own documentation will be more accurate than what the clinician tries to capture and record. The “open notes” initiative is expanding rapidly, providing patients with “read” functionality, and the ability to now act as their own safety net to make sure documentation is accurate and that important diagnostic test results are not overlooked [87]. Patient-facing applications have enormous potential [88]; “open notes” is just the beginning.

Predictive analytics

The ability to integrate and learn from “big data” would enable several novel applications that could improve diagnosis. One possible application is to “push” reasonable diagnostic possibilities given the key findings of a patient with a new concern, for example, combining data to enhance early detection of sepsis [80]. Conversely, one can envision “error checking”, where proposed diagnoses that are in conflict with data in the EHR could be identified. Second, “big data” applications could enable population-level comparisons, where all of the patients with a given diagnosis could be compared to identify outliers that merit further investigation, or common trends that would never have been obvious in single-patient observations.

Using health IT to prevent diagnostic error or harm

Electronic data create an opportunity to identify patients at elevated risk for diagnostic error and either prevent the error or mitigate harm. The SureNet (formerly the Safety Net) program at Kaiser Permanente Southern California, for example, identifies patients with red flag findings suggestive of colon cancer (positive tests for fecal occult blood, or new iron deficiency anemia) who have not had appropriate imaging or endoscopy to investigate the problem and alerts providers to make sure the findings are followed up [40]. Similarly, “trigger tools” that search out electronic data can follow the track of abnormal laboratory test results sent to providers to ensure the abnormalities are acknowledged and acted upon [89]. The value of trigger tools to improve diagnosis has been confirmed in prospective randomized trials, effectively reducing the time to evaluation for patients with prostate and colon cancer [90], [91].

Promoting expertise and combatting overconfidence through feedback

Developing expertise requires accurate and timely feedback on performance, and diagnostic performance would similarly benefit from improved feedback [92]. Feedback is also the best antidote to clinician overconfidence that develops in the absence of meaningful feedback [93]. In the absence of any feedback, physicians, like everyone, assume that their answers are all correct [94]. Medical record systems in the future could facilitate feedback by informing physicians that their patient’s diagnosis has changed, for example, as the patient moves from an ambulatory clinic to the ER and then the ward or the ICU. This type of automated systematic feedback on diagnostic performance has great potential but is virtually non-existent [6], [92]. El-Kareh et al. [6] found only one study reporting the impact of systematic feedback on clinical diagnostic performance [95].

Solving the “i-Patient” problem

A large challenge for the staff who design and use electronic records is to somehow restore the human interaction essential to the patient-clinician relationship. Thoughtful ways to incorporate the EHR in patient-focused practice may be part of the answer. One simple suggestion is to place the display screen so that both the physician and the patient can see it and interact with it equally [96], [97]. Another promising approach is to use scribes, who can interact with the EHR, freeing the clinician to focus on the patient [98]. These are just first steps, and just in the ambulatory setting. EHR designers should strive to design systems that allow us to spend more meaningful time with patients in all settings, not less.

Summary and conclusions

The electronic health record has had a profound impact on the diagnostic process in clinical practice. Although the benefits the EHR offers are extensive and important, the concerns and negative consequences that are increasingly identified are also very real and very problematic. The challenge moving forward will be to continue building on the power inherent in digital data and machine-aided storage, sorting and management of information to promote the reliability and safety of diagnosis by overcoming issues of usability, quickly addressing unintended consequences as they arise and somehow restoring or even promoting the human element of the clinician-patient relationship that is so critical to successful diagnosis.

There is intense interest at the present time to improve the interoperability, usability and other generic aspects of electronic health records. Progress in these areas would go towards improving diagnosis, but we are asking the vendor community, as well as the clinical users, to think more specifically about functionality and usage that would improve the diagnostic process. Major recommendations in this regard, based on recent diagnostic process and outcome frameworks [1], [99] are summarized in Table 3. The list includes several functionalities that are not typically offered in the current generation of EHRs, including better ways to follow clinical care over time, data aggregators to be used at acute points of care, smarter decision support that is well integrated in clinical workflow and detects the risk of harm, ways to make diagnosis easier, timely feedback on diagnostic performance and functionality to make patients a partner in the diagnostic process.

Table 3:

EHR and health IT functionality that would improve diagnostic quality and safety.

Although the EHR is fundamental to achieving these high levels of reliability, it goes without question that culture, system redesign and full provider participation will also be critical elements in efforts to realize the highest levels of performance. Ensuring that the EHR performs optimally in clinical settings requires not just optimizing the software but also consideration of all the sociotechnical factors that determine performance in using health IT applications [4], [100], [101]. The user is critical in determining optimal performance, which can obviously be compromised if clinicians and patients do not take advantage of features that are available. Patients are slow to use communication portals, for example, and physicians have a reputation for underutilizing and disregarding electronic help and suggestions [26], [27], [102]. Overconfidence in their abilities may underlie the observation that clinicians do not take advantage of resources that could help them make better decisions [93].

EHR designers need to continue focusing on the how the EHR can meet the needs of clinicians, instead of providing products and functionality that clinicians need to adapt to. Similarly, clinical users need to use these tools appropriately. Ideally, at that point, the EHR will become “a life raft for improving care, not an anchor” [72].


  • 1.

    Institute of Medicine. Improving diagnosis in health care. Washington, DC: National Academies Press, 2015. Google Scholar

  • 2.

    Leape L, Berwick D, Bates D. Counting deaths from medical errors. J Am Med Assoc 2002;288:2405. CrossrefGoogle Scholar

  • 3.

    Graber M. The incidence of diagnostic error. BMJ Qual Saf 2013;22(Part 2):ii21–7. PubMedCrossrefGoogle Scholar

  • 4.

    Sittig D, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010;19:i68–74. CrossrefPubMedGoogle Scholar

  • 5.

    Magrabi F, Ong M-S, Runciman W, Coiera E. Using FDA reports to inform a classification for health information technology safety problems. J Am Med Inform Assoc 2012;19:45–53. PubMedCrossrefGoogle Scholar

  • 6.

    El-Kareh R, Hasan O, Schiff G. Use of health information technology to reduce diagnostic error. BMJ Qual Saf 2013;22ii:40–4. Google Scholar

  • 7.

    Office of the National Coordinator for Health Information Technology. What is a patient portal? 2015. https://www.healthit.gov/providers-professionals/faqs/what-patient-portal. Accessed: 22 Aug 2017. 

  • 8.

    Claflin N. Computerized interdisciplinary assessment. J Healthc Qual 2000;22:25–33. CrossrefPubMedGoogle Scholar

  • 9.

    Zakim D, Braun N, Fritz P, Alscher M. Underutilization of information and knowledge in everyday practice: evaluation of a computer-based solution. BioMed Central 2008;8:50. Google Scholar

  • 10.

    Johansen M, Henriksen E. The evolution of personal health records and their role for self-management: a literature review. Stud Health Technol Inform 2014;205:458–62. PubMedGoogle Scholar

  • 11.

    Yaraghi N. An empirical analysis of the financial benefits of health information exchange in emergency departments. J Am Med Inform Assoc 2015:22:1169–72. PubMedGoogle Scholar

  • 12.

    Sittig D, Murphy D, Smith M, Russo E, Wright A, Singh H. Graphical display of diagnostic test results in electronic health records: a comparison of 8 systems. J Am Med Inform Assoc 2015;22:900–4. PubMedCrossrefGoogle Scholar

  • 13.

    Miller R, Pople HJ, Myers J. INTERNIST-1, an experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med 1982;307:468–76. PubMedCrossrefGoogle Scholar

  • 14.

    Barnett G, Cimino J, Hupp J, Hoffer E. DXplain – an evolving diagnostic decision-support system. J Am Med Assoc 1987;258:67–74. CrossrefGoogle Scholar

  • 15.

    Warner H, Toronto A, Veasey L, Stephenson R. A mathematical approach to medical diagnosis. Application to congenital heart disease. J Am Med Assoc 1961;177:177–83. CrossrefGoogle Scholar

  • 16.

    Kligfield P, Gettes L, Bailey J, Childers R, Deal B, Hancock E, et al. Recommendations for the standardization and interpretation lf the electrocardiogram. Part 1: the electrocardiogram and its technology. Heart Rhythm 2007;4:394–412. CrossrefGoogle Scholar

  • 17.

    Open Clinical. Decision Support Systems. Available at: www.openclinical.org. Accessed: 22 Aug 2017. 

  • 18.

    Evans R, Pestotkik S, Classen D, Clemmer T, Weaver L, Orne JJ, et al. A computer-assisted management program for antibiotics and other antiinfective agents. N Engl J Med 1998;338:232–8. PubMedCrossrefGoogle Scholar

  • 19.

    Morris AH. Developing and implementing computerized protocols for standardization of clinical decisions. Ann Int Med 2000;132:373–83. CrossrefGoogle Scholar

  • 20.

    Riches N, Panagioti M, Alam R, Cheraghi-Sohl S, Campbell S, Esmail A, et al. The effectiveness of electronis differential diagnosis (DDX) generators: a systematic review and meta-analysis. PloS one 2016;11:e0148991. CrossrefGoogle Scholar

  • 21.

    Semigran H, Levine D, Nundy S, Mehrotra A. Comparison of physician and computer diagnostic accuracy. JAMA Internal Med 2016;176:1860–1. CrossrefGoogle Scholar

  • 22.

    Ramnarayan P, Roberts GC, Coren M, Nanduri V, Tomlinson A, Taylor PM, et al. Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study. BMC Med Inform Decis Mak 2006;6:22. CrossrefPubMedGoogle Scholar

  • 23.

    Ramnarayan P, Cronje N, Brown R, Negus R, Coode B, Moss P, et al. Validation of a diagnostic reminder system in emergency medicine: a multi-centre study. Emerg Med J 2007;24:619–24. CrossrefPubMedGoogle Scholar

  • 24.

    Porat T, Delaney B, Kostopoulou O. The impact of a diagnostic decision support system on the consultation: perceptions of GPs and patients. BMC Med Inform Decis Mak 2017;17:79. CrossrefPubMedGoogle Scholar

  • 25.

    Bright T, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux R, et al. Effect of clinical decision support systems: a systematic review. Ann Int Med 2012;157:29–43. CrossrefGoogle Scholar

  • 26.

    Sittig DF, Krall MA, Dykstra RH, Russell A, Chin HL. A survey of factors affecting clinician acceptance of clinical decision support. BMC Med Inform Decis Mak 2006;6:6. PubMedCrossrefGoogle Scholar

  • 27.

    Miller RA. Computer-assisted diagnostic decision support: history, challenges, and possible paths forward. Adv Health Sci Educ Theory Pract 2009;14(Suppl 1):89–106. CrossrefPubMedGoogle Scholar

  • 28.

    Takahashi R, Kajikawa Y. Computer-aided diagnosis: a survey with bibliometric analysis. Int J Med Inform 2017;101:58–67. PubMedCrossrefGoogle Scholar

  • 29.

    Levey AS, Inker LA, Coresh J. GFR estimation: from physiology to public health. Am J Kidney Dis 2014;63:820–34. CrossrefPubMedGoogle Scholar

  • 30.

    Svirbely J, Sriram M. Medal, a compendium of medical algorithms for access over the internet. Proc AMIA Symp. 19991;1172. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232813/pdf/procamiasymp00004-1209.pdf. Accessed: 22 Aug 2017. 

  • 31.

    ARUP. ARUP Consult – The Physician’s Guide to Laboratory Test Selection and Interpretation. http://wwwarupconsultcom/. 2015. 

  • 32.

    American College of Radiology. ACR Appropriateness Criteria. http://wwwacrorg/Quality-Safety/Appropriateness-Criteria. 2015. 

  • 33.

    Duszak R, Berlin J. Utilization management in radiology, Part 1: rationale, history, and current usage. J Am Coll Radiol 2012;9:694–9. CrossrefGoogle Scholar

  • 34.

    Office of the National Coordinator for Health Information Technology. The Direct Project. Available at: www.healthitgov/policy-researchers-implementers/direct-project

  • 35.

    Hersh W, Hickman D, Severance S, Dana T, Krages K, Helfand M. Diagnosis, access, and outcomes: update of a systematic review of telemedicine services. J Telemed Telecare 2006;12:3–31. CrossrefGoogle Scholar

  • 36.

    Graber ML, Franklin N, Gordon R. Diagnostic error in internal medicine. Arch Intern Med 2005;165:1493–9. CrossrefPubMedGoogle Scholar

  • 37.

    Cavallerano J, Lawrence MG, Zimmer-Galler I, Bauman W, Bursell S, Gardner WK, et al. Telehealth practice recommendations for diabetic retinopathy. Telemed J E Health 2004;10:469–82. PubMedCrossrefGoogle Scholar

  • 38.

    Binkhuysen B, Ranschaert E. Teleradiology: evolution and concepts. Eur J Radiol 2011;78:205–9. PubMedCrossrefGoogle Scholar

  • 39.

    Warshaw EM, Hillman YJ, Greer NL, Hagel EM, MacDonald R, Rutks IR, et al. Teledermatology for diagnosis and management of skin conditions: a systematic review. J Am Acad Dermatol 2011;64:759–72. CrossrefPubMedGoogle Scholar

  • 40.

    Graber M, Trowbridge R, Myers J, Umscheid C, Strull W, Kanter M. The next organizational challenge: finding and addressing diagnostic error. Jt Comm J Qual Patient Saf 2014;40:102–10. PubMedCrossrefGoogle Scholar

  • 41.

    Koppel R. Great promises of healthcare information technology deliver less. In: Weaver C, Ball M, Kim G, Kiel J, editors. Healthcare information management systems: cases, strategies, and solutions. Switzerland: Springer International Publishing, 2016:101–25. Google Scholar

  • 42.

    Sparnon E, Marella W. The role of the electronic health record in patient safety events. Pennsylvania Patient Safety Advisory 2012;9:113–21. Google Scholar

  • 43.

    Meeks D, Smith M, Taylor L, Sittig D, Scott J, Singh H. An analysis of electronic health record-related patient safety concerns. J Am Med Inform Assoc 2014;21:1053–9. CrossrefPubMedGoogle Scholar

  • 44.

    Castro G. Investigations of health IT-related deaths, serious injuries, or unsafe conditions. Available at: https://www.healthit.gov/playbook/pdf/safety-resources-health-it-related-deaths.pdf. Accessed: 22 Aug 2017. 

  • 45.

    The Joint Commission. Investigations of health-IT-related deaths, serious Injuries, or unsafe conditions. Office of the National coordinator for health information technology. 2015. 

  • 46.

    ECRI Institute. ECRI Institute PSO Deep Dive: Health Information Technology. 2012. 

  • 47.

    Graber M, Siegal D, Riah H, Johnston D. EHR-related events in medical malpractice claims. Office of the National Coordinator for Health Information Technology. 2015. Google Scholar

  • 48.

    Schiff G, Amato M, Equale T, Boehne J, Wright A, Koppel R, et al. Computerized physicianorder entry-related medication errors: analysis of reported errors and vulnerability testing of current systems. BMJ Qual Saf 2015;24:264–71. CrossrefPubMedGoogle Scholar

  • 49.

    Mardon R, Olinger L, Szekendi M, Williams T, Sparnon E, Zimmer K. Health information technology adverse event reporting: analysis of two databases. Office of the national coordinator for health IT. 2014. Available at: https://www.healthit.gov/sites/default/files/Health_IT_PSO_Analysis_Final_Report_11-25-14.pdf

  • 50.

    The Joint Commission. Sentinel Event Alert #54: Safe Use of Health Information Technology. wwwjointcommissionorg. 2015. 

  • 51.

    Vicente K. Less is (sometimes) more in cognitive engineering: the role of automation technology in improving patient safety. Qual Saf Health Care 2003;12:291–4. PubMedCrossrefGoogle Scholar

  • 52.

    Singh H, Arora HS, Vij MS, Rao R, Khan MM, Petersen LA. Communication outcomes of critical imaging results in a computerized notification system. J Am Med Inform Assoc 2007;14:459–66. CrossrefGoogle Scholar

  • 53.

    Singh H, Thomas EJ, Sittig DF, Wilson L, Espadas D, Khan MM, et al. Notification of abnormal lab test results in an electronic medical record: do any safety concerns remain? Am J Med 2010;123:238–44. CrossrefGoogle Scholar

  • 54.

    Callen JL, Westbrook JL, Georgious A, Li J. Failure to follow-up test results for ambulatory patients: a systematic review. J Gen Int Med 2012;27:1334–48. CrossrefGoogle Scholar

  • 55.

    Siegler E, Adelman R. Copy and paste: a remediable hazard of elctronic health records. Am J Med 2009;122:495–6. CrossrefGoogle Scholar

  • 56.

    Sheehy A, Weissburg D, Dean S. The role of copy-and-paste in the hospital electronic health record. JAMA Intern Med 2014;174:1217–18. PubMedCrossrefGoogle Scholar

  • 57.

    American Health Information Management Association. Managing copy functionality and information integrity in the EHR. 2012. Available at: http://library.ahima.org/doc?oid=105240. Accessed: 22 Aug 2017. 

  • 58.

    Lowry S, Ramaiah M, Prettyman S, Simmons D, Brick D, Deutch E, et al. NISTIR 8166: Examining the ‘Copy and Paste’ function in the use of electronic health records. 2017. Google Scholar

  • 59.

    American Health Information Management Association. Appropriate use of the copy and paste functionality in electronic health records. 2014. Available at: www.ahima.org. Accessed: 22 Aug 2017. 

  • 60.

    Partnership for Health IT Patient Safety. Health IT safe practices: toolkit for the safe use of copy and paste. ECRI Institute, 2016. Available at: https://www.ecri.org/Resources/HIT/CP_Toolkit/Toolkit_CopyPaste_final.pdf. Accessed: 22 Aug 2017. 

  • 61.

    Green R, Hripcsak G, Salmasian H, Lazar EJ, Bostwick SB, Bakken SR, et al. Intercepting wrong-patient orders in a computerized provider order entry system. Ann Emerg Med 2015;65:679–86.e1. CrossrefGoogle Scholar

  • 62.

    Tridandapani S, Olsen K, Bhatti P. Improvement in detection of wrong-patient errors when radiologistgs include patient photographs in their interpretation of portable chest radiographs. J Digit Imaging 2015;28:664–70. PubMedCrossrefGoogle Scholar

  • 63.

    Schiff G, Bates DW. Can electronic clinical documentation help prevent diagnostic errors? N Engl J Med 2010;362:1066–9. CrossrefPubMedGoogle Scholar

  • 64.

    Hartzband P, Groopman J. Off the record – avoiding the pitfalls of going electronic. N Engl J Med 2008;358:1656–8. PubMedCrossrefGoogle Scholar

  • 65.

    Croskerry P. From mindliess to mindful practice – cognitive bias and clinical decision making. N Engl J Med 2013;368:2445–7. PubMedCrossrefGoogle Scholar

  • 66.

    Rosenbaum L. Living unlabeled – diagnosis and disorder. N Engl J Med 2008;359:1650–3. CrossrefPubMedGoogle Scholar

  • 67.

    Wears R. “Just a few seconds of your time…” at least 130 million times a year. Ann Emerg Med 2015;65:687–9. CrossrefPubMedGoogle Scholar

  • 68.

    Sinsky C, Colligan L, Li L, Prgomet M, Reynolds S, Goeders L, et al. Allocation of physician time in ambulatory practices: a time and motion study in 4 specialties. Ann Int Med 2016;165:753–60. CrossrefGoogle Scholar

  • 69.

    Tai-Seale M, Olson C, Li J, Chan A, Morikanza C, Durbin M, et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Affairs 2017;36:655–62. CrossrefGoogle Scholar

  • 70.

    Coleman JJ, van der Sijs H, Haefeli WE, Slight SP, McDowell SE, Seidling HM. On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop. BMC Med Inform Decis Mak 2013;13:111. PubMedCrossrefGoogle Scholar

  • 71.

    Singh H, Spitzmueller C, Peterson N, Sawhney M, Sittig DF. Information overload and missed test results in electronic health record-based settings. JAMA Internal Med 2013;173:702–4. CrossrefGoogle Scholar

  • 72.

    Wachter R. The digital doctor. Hope, hype, and harm at the dawn of medicine’s computer age. New York, NY: McGraw Hill Education, 2015. Google Scholar

  • 73.

    Verghese A. Culture shock – patient as icon, icon as patient. N Engl J Med 2008;359:2748–51. CrossrefPubMedGoogle Scholar

  • 74.

    Mamykina L, Vawdrey D, Hripcsak G. How do residents spend their shift time? A time and motion study with a particular focus on the use of computers. Acad Med 2016;91:827–32. CrossrefPubMedGoogle Scholar

  • 75.

    Stoller J. Electronic siloing: an unintended consdequence of the electronic health record. Cleve Clin J Med 2013;80:406–9. CrossrefPubMedGoogle Scholar

  • 76.

    Upadhyay D, Sittig D, Singh H. Ebola US patient zero: lessons on misdiagnosis and effective use of electronic health records. Diagnosis 2014;1:283–6. Google Scholar

  • 77.

    Wright A, Hickman T-T, McEvoy D, Aaron S, Ai A, Andersen JM, et al. Clinical decision support failures. J Am Med Inform Assoc 2016;23:1068–76. Google Scholar

  • 78.

    Bond W, Schwartz L, Weaver K, Levick D, Giuliano M, Graber M. Differential diagnosis generators: an evaluation of currently available computer programs. J Gen Int Med 2011;27:213–9. Google Scholar

  • 79.

    Berner E, Webster G, Shugerman A, Jackson J, Algina J, Baker A, et al. Performance of four computer-based diagnostic systems. N Engl J Med 1994;330:1792–6. PubMedCrossrefGoogle Scholar

  • 80.

    Liebovitz D. Next steps for electronic health records to improve the diagnostic process. Diagnosis 2015;2:111–26. Google Scholar

  • 81.

    Singh H. Improving diagnostic safety in primary care by unlocking digital data. Jt Comm J Qual Patient Saf 2017;43:29–31. CrossrefPubMedGoogle Scholar

  • 82.

    Horsky J, Ramelson H. Development of a cognitive framework of patient-record summary review in the formative phase os user-centered design. J Biomed Inform 2016;64:147–57. CrossrefGoogle Scholar

  • 83.

    Wright A, McCoy AB, Hickman TT, Hilaire DS, Borbolla D, Bowes WA, et al. Problem list completeness in electronic health records: a multi-site study and assessment of success factors. Int J Med Inform 2015;84:784–90. PubMedCrossrefGoogle Scholar

  • 84.

    Wright A, Pang J, Feblowitz J, Maloney F, Wilcox A, Ramelson H, et al. A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. J Am Med Inform Assoc 2011;18:859–67. CrossrefGoogle Scholar

  • 85.

    Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, McLoughlin KS, et al. Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial. J Am Med Inform Assoc 2012;19:555–61. CrossrefPubMedGoogle Scholar

  • 86.

    Hibbard J, Greene J. What the evidence shows about patient activation: better health outcomes and care expectations; Fewer data on costs. Health Affairs 2013;32:207–14. CrossrefGoogle Scholar

  • 87.

    Bell S, Folcarelli P, Anselmo M, Crotty B, Flier L, Walker J. Connecting patients and clinicians: the anticipated effects of open notes on patient safety and quality of care. Jt Comm J Qual Patient Saf 2015;41:378–84. CrossrefPubMedGoogle Scholar

  • 88.

    Yang Y, Asan O. Designing patient-facing health information technologies for the outpatient settings: a literature review. J Innov Health Inform 2016;2016:1. Google Scholar

  • 89.

    Singh H, Giardina T, Forjuoh S, Reis M, Kosmach S, Khan M, et al. Electronic health record-based surveillance of diagnostic errors in primary care. BMJ Qual Saf 2012;21:93–100. PubMedCrossrefGoogle Scholar

  • 90.

    Murphy D, Laxmisan A, Reis B, Thomas E, Esquivel A, Furjouh S, et al. Electronic health record-based triggers to detect potential delays in cancer diagnosis. BMJ Qual Saf 2014;22:8–16. Google Scholar

  • 91.

    Murphy D, Wu L, Thomas E, Forjuob S, Meyer A, Singh H. Electronic trigger-based intervention to reduce delays in diagnostic evaluation for cancer: a cluster randomized controlled trial. J Clin Oncology 2015;33:3560–7. CrossrefGoogle Scholar

  • 92.

    Schiff GD. Minimizing diagnostic error: the importance of follow-up and feedback. Am J Med 2008;121(Suppl 5):S38–42. PubMedCrossrefGoogle Scholar

  • 93.

    Berner E, Graber M. Overconfidence as a cause of diagnostic error in medicine. Am J Med 2008;121(Suppl 5):S2–23. PubMedCrossrefGoogle Scholar

  • 94.

    Schulz C. On Being Wrong. TED Talk. Available at: https://www.ted.com/talks/kathryn_schulz_on_being_wrong?language=en. Accessed: 22 Aug 2017. 

  • 95.

    Berner E, Sciff G. Closing the feedback loop to improve diagnostic quality. 2014. Available at: https://healthit.ahrq.gov/ahrq-funded-projects/closing-feedback-loop-improve-diagnostic-quality/final-report. Accessed: 22 Aug 2017. 

  • 96.

    Frankel R, Altschuler A, George S, Kinsman J, Jimison H, Robertson N, et al. Effects of exam-room computing on clinician-patient communication. J Gen Int Med 2005;20:677–82. CrossrefGoogle Scholar

  • 97.

    White K. Engaging patients to improve the healthcare experience. Healthc Financ Manage 2012;66:84–8. PubMedGoogle Scholar

  • 98.

    Carr S. Medical scribes improve physician documentation. Can they improve diagnosis, too? ImproveDx Newsletter. 2014; http://www.improvediagnosis.org/resource/resmgr/1_ImproveDX.March.final.pdf. Accessed: 22 Aug 2017. 

  • 99.

    Singh H, Sittig D. Advancing the science of measurement of diagnostic errors in healthcare: the safer DX framework. BMJ Qual Saf 2015;24:103–10. CrossrefPubMedGoogle Scholar

  • 100.

    Carayon P, Schoofs Hunt A, Karsh BT, Gurses A, Alvarado C, Smith M, et al. Work system design for patient safety: the SEIPS model. Qual Saf Health Care 2006;15(Suppl 1):i50–8. CrossrefPubMedGoogle Scholar

  • 101.

    Holden R, Carayon P, Gurses A, Hoonakker P, Hundt A, Ozok A, et al. SEIPS 2.0: a human factors frameowrk for studying and improving the work of healthcare professionals and patients. Ergonomics 2013;56:1669–86. CrossrefGoogle Scholar

  • 102.

    Miller R, Masarie F, Jr. The demise of the “Greek Oracle” model for medical diagnostic systems. Methods Inf Med 1990;29:1–2. PubMedGoogle Scholar

About the article

Corresponding author: Mark L. Graber, MD FACP, 5 Hitching Post, Plymouth, MA, USA; President, Society to Improve Diagnosis in Medicine, Senior Fellow, RTI International, NC, USA; and Professor Emeritus, Stony Brook University, New York, NY, USA, Phone: +919 990-8497

Received: 2017-03-13

Accepted: 2017-07-12

Published Online: 2017-09-08

Published in Print: 2017-11-27

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: This review was sponsored in part by the Office of the National Coordinator; Contract Number HHSP23320095651WC; Order Number HHS P23337047T ONC Health IT Safety Center Road Map, to RTI International.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

Citation Information: Diagnosis, Volume 4, Issue 4, Pages 211–223, ISSN (Online) 2194-802X, ISSN (Print) 2194-8011, DOI: https://doi.org/10.1515/dx-2017-0012.

Export Citation

©2017 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Roland Kammergruber and Jürgen Durner
LaboratoriumsMedizin, 2018, Volume 0, Number 0
Adrian Israel Martinez-Franco, Melchor Sanchez-Mendiola, Juan Jose Mazon-Ramirez, Isaias Hernandez-Torres, Carlos Rivero-Lopez, Troy Spicer, and Adrian Martinez-Gonzalez
Diagnosis, 2018, Volume 0, Number 0

Comments (0)

Please log in or register to comment.
Log in