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Capturing Structured, Pulmonary Disease-Specific Data Elements in Electronic Health RecordsCapturing Structured Data Elements FREE TO VIEW

Peter E. Gabriel, MD, MSE; Cynthia Gronkiewicz, APN, MS; Edward J. Diamond, MD, MBA, FCCP; Kim D. French, MHSA, CAPPM; John Christodouleas, MD, MPH
Author and Funding Information

From the Perelman School of Medicine at the University of Pennsylvania (Drs Gabriel and Christodouleas) and Suburban Lung Associates (Mss Gronkiewicz and French and Dr Diamond).

CORRESPONDENCE TO: Peter E. Gabriel, MD, MSE, Penn Medicine Department of Radiation Oncology, 3400 Civic Center Blvd, TRC 4 W, Philadelphia, PA 19104; e-mail: peter.gabriel@uphs.upenn.edu


Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2015;147(4):1152-1160. doi:10.1378/chest.14-1471
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Electronic health records (EHRs) have the potential to improve health-care quality by allowing providers to make better decisions at the point of care based on electronically aggregated data and by facilitating clinical research. These goals are easier to achieve when key, disease-specific clinical information is documented as structured data elements (SDEs) that computers can understand and process, rather than as free-text/natural-language narrative. This article reviews the benefits of capturing disease-specific SDEs. It highlights several design and implementation considerations, including the impact on efficiency and expressivity of clinical documentation and the importance of adhering to data standards when available. Pulmonary disease-specific examples of collection instruments are provided from two commonly used commercial EHRs. Future developments that can leverage SDEs to improve clinical quality and research are discussed.

Figures in this Article

The promise of electronic health records (EHRs) to improve health-care quality rests largely on their ability to help physicians and researchers make more effective use of information.1 Simply digitizing clinical records provides some quality benefits through improved legibility and availability of information; however, its impact tends to be self-limited.1,2 A growing body of evidence suggests that a major key to realizing quality improvement is computerized decision support (CDS), a term encompassing a broad variety of tools designed to help providers make better decisions at the point of care.37

Clinical research may also benefit from the ability to mine EHR data to test hypotheses more efficiently than can be done through prospective clinical trials.8,9 Many experts believe that together, advanced CDS and secondary use of EHR data for clinical research have the potential to create a “learning health system” that can truly transform health-care quality.1015

A challenge to this vision is that CDS and secondary use of EHR data are only possible to the extent that computers can interpret and “reason” about the information that EHRs contain. To be “machine interpretable,” information must be structured into sets of facts or assertions to which computer logic can be applied. Availability of such “structured data elements” (SDEs) is, therefore, one of the major keys to using EHRs to improve quality. When we are able to efficiently capture as a byproduct of clinical care and effectively use a wealth of SDEs relevant to the understanding and management of specific diseases, we will begin to unlock the true potential of EHR technology.

Capturing SDEs efficiently and using them effectively are not without challenges.1621 This article reviews important factors to consider in their design and implementation. We present several pulmonary-specific examples that have been developed and incorporated into clinical workflows in two commonly used EHR systems: Epic (Epic Systems, Inc) and Centricity (GE Healthcare). Finally, we review developments in both CDS and the secondary use of EHR data that are likely to have an increasing impact on clinical care and research in the next 3 to 5 years.

Efficiency and Expressivity of Documentation

Its impact on the efficiency of the office visit is widely recognized as an obstacle to the adoption of EHR technology. Evidence suggests that EHRs increase the amount of time physicians spend on documentation.22,23 Many providers feel that SDE capture further decreases efficiency because it may require more clicks or result in double data entry if the same information needs to be recorded separately in structured fields and in a typed or dictated note.

Although development has been somewhat slow, strides have been made to address this problem. As a result of the federal Meaningful Use incentive program, all certified EHRs now have the ability to capture as SDEs key components of the history and physical examination, such as the problem list, allergies, medications, and family and social history.24 Many EHRs also now have the ability, using macros, to pull these SDEs into a consult or progress note automatically.25 A number of EHRs also provide the ability to document narrative aspects of the encounter, such as the history of the current illness, review of systems, physical examination, and assessment and plan, via structured forms that automatically generate prose text for the note.

Figure 1 illustrates an Epic-provided template for documenting the history of present illness for a chief complaint of asthma, along with the progress note text that is autogenerated based on the selections seen. Figure 2 illustrates a similar asthma template custom developed for Centricity by Clinical Content Consultants, LLC. As the design of user interfaces and text generation engines in commercial EHRs become more sophisticated, the usability of such tools should continue to improve.

Figure Jump LinkFigure 1 –  A, Commercial electronic health record-provided template for documenting structured data elements for a chief complaint of asthma. B, Progress note text autogenerated from template.Grahic Jump Location
Figure Jump LinkFigure 2 –  A, Custom-developed Asthma Control Assessment template defaults to age-based content for documenting impairment, lung function, and risk based on National Heart, Lung, and Blood Institute evidence-based guidelines. The level of control is autogenerated. B, Progress note text autogenerated from template.Grahic Jump Location

These technical advances can help increase the efficiency of structured documentation, but workflow is also a key factor. Practices that have made the transition to an EHR understand that processes from the paper chart era need to change to make the most of EHR capabilities.2628 Sharing the documentation burden across members of the care team is critical to optimizing efficiency, as long as the providers are careful to act within the scope of their licenses and federal and state regulations.

Although there may be an upfront cost to entering data discretely the first time, updating it and automatically including or referencing it in subsequent documentation is typically a more efficient process. Some practices have introduced telephone-based EHR “intake” visits for patients new to their practice, so important demographic and clinical SDEs can be entered into the system by a physician-extender before a face-to-face encounter with the primary provider. These kinds of workflow changes allow practices to better leverage SDEs to improve the overall efficiency of the primary provider and the clinic flow.

Expressivity sits in natural tension with increased structure in clinical documentation.16 Patients’ descriptions of their history and providers’ descriptions of their impressions and thought processes tend to include substantial nuance and subtlety. By definition, SDEs force at least some reduction in the flexibility, variability, and expressive richness of a natural language narrative. They can even lead to inaccuracy when precise codes are applied inappropriately for situations that the structured template cannot accommodate adequately.16,21

A balance between structure and expressivity may be achieved by being selective about which information to capture discretely. Although it is possible to cover almost all aspects of a note with SDEs, beyond a certain point, doing so provides diminishing returns for the incremental burden and rigidity that it imposes. For better efficiency and effectiveness, discrete data capture can be limited to the elements that have the greatest impact on clinical decision-making. Often, these are represented by the decision nodes in expert clinical guidelines.

For example, in the American College of Chest Physicians (CHEST) guidelines for the management of indeterminate solitary lung nodules, the key data elements for decision-making include the size of the nodule in millimeters and a quantitative estimate of the patient’s pretest probability for malignancy.29 Ensuring that these key data elements are captured discretely for all patients with solitary lung nodule is a much more purposeful and practical goal than vaguely attempting to structure the entire history and physical examination.

Figure 3A is an example of a relatively simple solitary lung nodule template based on the Fleischner guidelines30 developed for the Centricity EHR by Clinical Content Consultants, LLC. The key pieces of data (nodule size and clinical risk) are captured discretely, with negligible data entry burden. They can then be used to automatically generate documentation (as illustrated in Fig 3B) and trigger appropriate order sets. Any additional descriptive details or comments about the nuances of a particular patient’s case can be added easily as supplemental free text.

Figure Jump LinkFigure 3 –  A, Custom-developed Lung Nodule Guidelines template based on the Fleischner Guidelines for follow-up and management. B, Recommended treatment from the Fleischner Guidelines is autogenerated in the assessment and plan based on the associated lung nodule size and the risk level of the patient.Grahic Jump Location

A critical challenge to “selective” structured documentation is scalability. As care pathways and guidelines become increasingly complex and personalized based on new evidence, genomics, and other factors, it is difficult for developers to create and maintain documentation tools to cover a wide variety of clinical scenarios. It is also hard for providers to navigate a large selection of tools to choose the right one in the right setting, and they may not be facile with those that are used infrequently. For this reason, wide-ranging SDE capture may be impractical in primary care specialties that manage broadly diverse conditions. Indeed, regardless of specialty, if the goal is to capture structured data for every possible condition or complaint, the scalability problem becomes intractable. However, the baby need not be thrown out with the bath water. There are many areas where a relatively focused effort to capture SDEs could have dramatic benefits. For example, structured assessment of tumor control vs progression scales easily to almost all patients seen in an oncology setting and could deliver tremendous value for research and quality improvement efforts.

Use of Data Standards

In addition to the challenges of collection, a significant barrier to the effective use of SDEs for CDS and research is the use of standards to achieve “semantic interoperability,” or “the ability for systems to exchange data with unambiguous, shared meaning.”31 A simple example can illustrate the importance of this concept: For a CDS tool to understand when and how a patient has been prescribed an inhaled corticosteroid for asthma, a computer must be able to discern that “ADVAIR 100/50,” “fluticasone 100 μg / salmeterol 50 μg,” and “Flovent 100 μg” all contain the same dose of the same drug. It is difficult to achieve this without use of a common standard to represent these drugs. Likewise, it is impossible to conduct research on data that are pooled from multiple practices and EHRs without the ability to normalize the data according to standard concepts with shared meaning.

This typically requires the use of “controlled terminologies,” which are standard sets of terms and codes with explicitly defined meanings. Examples include the familiar International Classification of Diseases, Ninth Revision, Clinical Modification, code set for diagnoses and richer and more powerful standards such as Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT)32 (general medical vocabulary), RxNORM33 (medications), and Logical Observation Identifiers Names and Codes (LOINC)34 (diagnostic tests). The Meaningful Use program has again spurred progress in this area by requiring support for these advanced terminologies in certified EHRs.24 The downside to using controlled terminologies is that their rigidity can negatively impact expressivity, as discussed previously.

An example of the use of data standards in a pulmonary-related setting is illustrated in Figure 4. The National Cancer Institute’s Common Terminology Criteria for Adverse Events is a standard for grading the severity of side effects and adverse events related to cancer therapy.35 The figure shows a structured progress note template based on the Common Terminology Criteria for Adverse Events standard, custom developed in Epic by the authors. It is being used to track toxicities in patients with lung cancer who are undergoing radiation therapy. Data collected via this template can be shared and combined with data from other sources using the same standard. In addition, modules for decision support based on the standard could, in theory, be developed independently from any one EHR technology and shared broadly across institutions and practices.

Figure Jump LinkFigure 4 –  Custom-developed structured progress note template for tracking toxicities of patients with lung cancer who are undergoing radiation therapy.Grahic Jump Location
University of Pennsylvania

The lung cancer toxicity template illustrated in Figure 4 was implemented in April 2011 in the radiation oncology department of the University of Pennsylvania, along with similar templates for other malignancies. We attempted to address efficiency concerns by limiting the templates to a minimum set of “critical” toxicities for each disease site as selected by physician experts. In addition, templates were designed for a team-based workflow. Nurses perform the primary assessment, and physicians can review and agree or selectively update individual toxicity grades as needed. Structured grades can be supplemented with free-text comments for greater expressivity. Finally, a selection list facilitates navigation to the correct template for a particular disease.

Our structured data capture rate was 35% of eligible visits in the first month after implementation. With continued education and reinforcement, the rate had climbed to 70% by 6 months and to > 80% by 1 year, where it has remained. As of June 30, 2014, we have amassed a total of > 1 million individual toxicity observations on > 11,000 unique patients.

Challenges remain, however. Physicians have proven willing to review and update but less willing to complete entire assessments themselves, resulting in missing data for visits without nursing support. We are currently evaluating newer tools within our EHR to see if the workflow can be streamlined further. Still, current capture rates adequately support our quality improvement and safety monitoring goals. For example, we are able to automatically identify patients experiencing unexpected levels of toxicity and to review those cases in depth for insights, rather than relying on physician memory, voluntary reporting, or labor-intensive chart review processes. Retrospective research is also greatly facilitated because manual chart review can be limited to the approximately 20% of visits with missing data.

Suburban Lung Associates

In 2010, Suburban Lung Associates created the asthma templates illustrated in Figure 2 to promote adherence to evidence-based practice guidelines and facilitate quality improvement initiatives. Despite one-on-one provider training, asthma severity was documented in only 13.9% of patients and asthma control in < 4% over a 2-month period. Educational intervention improved documentation at a 3-month follow-up to 22.8% for asthma severity and 15.5% for asthma control. Although compliance rates met the requirements for some quality reporting programs, they were suboptimal for the practice’s goals. We attributed the low rates of adoption to several factors, including the location of the template outside the main documentation area, the additional clicks required to capture dropdown list items, a perceived lack of usefulness for providers already familiar with asthma guidelines, and the compromised screen readability caused by the inclusion of the comprehensive guideline content.

In contrast, the lung nodule template (Fig 3A) was much more readily accepted because of its simplicity and ease of use. Accessible for medical decision-making directly at the point of care, the bolded “Insert template” button alerts providers to its availability. One click opens a concise management list based on nodule size and smoking status. Another click inserts the guideline-based treatment plan in the assessment and plan and pushes the recommended follow-up to the patient instructions. The provider can free-text additional concerns in either location.

The success of this more simplified template led us to introduce a revised Asthma Severity Assessment CDS prompt. Triggered by relevant International Classification of Diseases, Ninth Revision, codes, it provides a brief list of asthma severity levels and associated therapy recommendations. Initial feedback has been positive and speaks to the importance of not only sharing outcome measure results with providers, but also seeking feedback and modifying templates as needed to improve usefulness. Overall, the importance of incorporating succinct and relevant content and building efficient workflows during template development cannot be overstated.

Clinical Pathways

There is a growing consensus that health-care quality and cost improvement will be achieved through computerized clinical pathways, a type of CDS that details the essential steps in the care of patients with a specific clinical problem.36 A computerized clinical pathway uses patient- and disease-specific SDEs to recommend evidence-based actions or to alert physicians when deviations may be occurring. The recommended actions may be as simple as an automated referral of a former smoker to a lung cancer screening protocol, or a complex order set of evaluations and interventions necessary to manage an acute exacerbation of COPD. Research suggests that when all or portions of a clinical pathway are facilitated through alerts and automated or semiautomated orders, adherence to clinical pathways improves.37 Moreover, the importance of this form of CDS for the practicing pulmonologist is likely to increase as researchers gain a greater understanding of the complex molecular biology underlying pulmonary diseases and their responses to treatments.

In the same way that disease-specific SDEs enable computerized clinical pathways by organizing information into a form that computers can consume, computerized clinical pathways structure treatment decisions into a form that can be tracked by software systems and used to support process management. With disease-specific SDEs and computerized clinical pathways, pulmonary departments can generate a real-time view of which clinical pathways are being used and on which type of patients. In situations in which more than one clinical pathway may be appropriate for the management of a disease, departments may use EHR-based alerts and semiautomation to drive use of the more cost-effective alternative.38 This ability to track and manage care at a departmental or health system level is becoming more important as providers take on greater responsibility for resource use in an accountable care environment. Although there is limited experience with these systems in pulmonary medicine, early experiences integrating oncology clinical pathway systems with EHRs have been promising.38

Registry-Based Research Methods

Disease-specific SDEs will also facilitate novel clinical research methods that may accelerate the building of evidence. For example, several new and pragmatic registry-based randomized trial designs have been proposed.39,40 In these designs, patients are first enrolled in a large observational registry in which disease-specific SDEs and data on treatment outcomes are collected efficiently during routine clinical care. A subset of patients is then randomly selected and offered experimental treatment, and these patients’ outcomes are compared with the cohort of patients who were not offered experimental treatment. By using this alternative randomization approach and relying only on SDEs that are collected during routine clinical care, registry-based randomized trials may greatly improve accrual to phase 3 trials and substantially reduce their overall costs.

Another important and novel research design that leverages SDE collection is the high-throughput clinical phenotype-genotype study. Studies attempting to identify genetic predictors of common and complex diseases (clinical phenotypes) may require thousands, even tens of thousands, of cases and control subjects.41 Prospectively identifying and accruing such a large number of patients for individual disease-specific genome-wide association or other types of genetic studies would be prohibitively expensive. To address this problem, researchers are linking data from EHRs and biorepositories across multiple institutions to support a wide range of ad hoc high-throughput clinical phenotype-genotype studies.42 For this approach to be successful, however, researchers must be able to accurately identify populations with and without the phenotype of interest using only information available in the EHR. The sensitivity and specificity of automated phenotype identification algorithms are greatly enhanced by the availability of disease-specific SDEs.43

The routine capture of disease-specific, computer-interpretable data elements has the potential to transform medical care. The basic building blocks for designing and implementing effective SDE collection tools are already in place in many commercial EHRs, but barriers to adoption exist. When creating templates and strategies for SDE collection, providers should take into consideration the efficiency and expressivity of clinical documentation, as well as the use of data standards. SDE capture may be less practical in primary care settings because of the diversity of conditions encountered, and it is not scalable to cover all possible clinical scenarios in any practice. However, as more critical pieces of information are captured in well-designed SDEs, we can expect exciting advances in computerized decision support and the use of EHR data for clinical research.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr Christodouleas is employed by Elekta/IMPAC Medical Systems Inc, Sunnyvale, California, which develops and sells an electronic health record system. Drs Gabriel and Diamond and Mss Gronkiewicz and French have reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

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Figures

Figure Jump LinkFigure 1 –  A, Commercial electronic health record-provided template for documenting structured data elements for a chief complaint of asthma. B, Progress note text autogenerated from template.Grahic Jump Location
Figure Jump LinkFigure 2 –  A, Custom-developed Asthma Control Assessment template defaults to age-based content for documenting impairment, lung function, and risk based on National Heart, Lung, and Blood Institute evidence-based guidelines. The level of control is autogenerated. B, Progress note text autogenerated from template.Grahic Jump Location
Figure Jump LinkFigure 3 –  A, Custom-developed Lung Nodule Guidelines template based on the Fleischner Guidelines for follow-up and management. B, Recommended treatment from the Fleischner Guidelines is autogenerated in the assessment and plan based on the associated lung nodule size and the risk level of the patient.Grahic Jump Location
Figure Jump LinkFigure 4 –  Custom-developed structured progress note template for tracking toxicities of patients with lung cancer who are undergoing radiation therapy.Grahic Jump Location

Tables

References

Blumenthal D, Glaser JP. Information technology comes to medicine. N Engl J Med. 2007;356(24):2527-2534. [CrossRef] [PubMed]
 
Baron RJ. Quality improvement with an electronic health record: achievable, but not automatic. Ann Intern Med. 2007;147(8):549-552. [CrossRef] [PubMed]
 
Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10):742-752. [CrossRef] [PubMed]
 
Goldzweig CL, Towfigh A, Maglione M, Shekelle PG. Costs and benefits of health information technology: new trends from the literature. Health Aff (Millwood). 2009;28(2):w282-w293. [CrossRef] [PubMed]
 
Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011;30(3):464-471. [CrossRef] [PubMed]
 
Jones SS, Rudin RS, Perry T, Shekelle PG. Health information technology: an updated systematic review with a focus on meaningful use. Ann Intern Med. 2014;160(1):48-54. [CrossRef] [PubMed]
 
Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med. 2012;157(1):29-43. [CrossRef] [PubMed]
 
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