In modern healthcare, the way clinicians document patient encounters significantly influences the quality, safety, and efficiency of care delivery. As the demand for data reuse and interoperability grows, the importance of structured and standardized documentation becomes increasingly clear. This approach not only supports more accurate and comprehensive notes but also facilitates the extraction and sharing of valuable health information across various systems and stakeholders. This article explores the impact of adopting structured documentation practices on the quality of electronic health records (EHRs), based on multicenter retrospective analyses, and discusses the implications for future healthcare data management.
Abstract
The reuse of healthcare data for diverse applications is poised to become even more critical in the future. Achieving effective data reuse hinges on the implementation of structured and standardized documentation, which ensures that clinical notes are both comprehensive and consistent. While the primary goal of clinical documentation remains the delivery of high-quality patient care, optimizing note quality through structured methods offers additional benefits. This study examined how increased use of structured and standardized documentation influences note quality within the Electronic Health Record (EHR). Employing a multicenter, retrospective design, the research compared 144 unstructured notes with 144 structured notes, evaluating their quality using the validated Qnote instrument. Results demonstrated a significant enhancement in note quality with structured documentation, reflected in a mean score increase from 64.35 to 77.2 (p < 0.001). Structured notes were also longer but maintained clarity and conciseness, further affirming the benefits of standardization. These findings support the integration of structured documentation into routine clinical workflows to improve note quality and facilitate data reuse, especially as healthcare organizations increasingly rely on electronic systems for decision support and research. For additional insights into how healthcare data practices evolve, visit resources such as what is the purpose for providing mobile integrated healthcare.
Introduction
Clinical documentation encompasses the process of creating a written record that summarizes patient-provider interactions during clinical encounters [1]. The accuracy, clarity, and completeness of these notes are crucial, as they directly impact patient safety, quality of care, and the reduction of medical errors [2–4]. Beyond immediate clinical use, documentation increasingly serves purposes such as quality measurement, financial reimbursement, and clinical research. As regulatory standards and documentation requirements have intensified, clinicians face mounting administrative burdens, often leading to increased time spent on record-keeping [5–7].
To address these challenges, various tools known as content importing technology (CIT) have been developed. These include copy-paste functions, automated data import from other parts of the EHR, templates, and macros. While CIT can expedite documentation, they also carry risks such as data duplication, note bloat, and reduced note clarity [8]. When used judiciously, these tools can mitigate documentation burdens without compromising quality.
Alongside efficiency, accuracy and standardization are vital. Variability in EHR documentation between providers can hinder effective data utilization, increase the risk of errors, and impair patient safety [9]. The tension between narrative, flexible documentation and rigid, structured formats has been debated. Rosenbloom et al. highlighted that healthcare providers should be able to choose documentation styles based on workflow needs, with structured formats favored when data reuse and interoperability are priorities [1]. Research indicates that structured documentation can improve provider efficiency and reduce documentation time [10], but the impact on note quality remains less explored.
This study aims to fill that gap by evaluating how transitioning from unstructured to structured documentation affects the quality of outpatient clinical notes, considering both the content and the overall utility of the notes.
Methods
Since 2009, the Radboudumc Center for Head and Neck Oncology has implemented a highly structured care pathway, designed to streamline patient management across various stages of treatment. These pathways involve predefined data entry forms embedded within the Epic EHR system (EPIC, Verona, WI), allowing clinicians to record patient information efficiently while capturing structured data elements. These forms support automated functions, such as generating referral letters, triggering standardized orders, and populating quality dashboards, thus enhancing both data integrity and clinical workflow. Similar pathways, with slight variations depending on the EHR vendor (e.g., HiX EHR in Antoni van Leeuwenhoek), have been adopted in other centers, maintaining core structured data elements [see https://medappinsider.blog/what-is-an-erp-system-in-healthcare/].
A multicenter, retrospective review was conducted, comparing notes from two tertiary head and neck cancer centers. In Center A, the transition to structured documentation was gradual, with notes from January–December 2013 (mainly unstructured) contrasted against those from January–December 2019 (more structured). Center B adopted a more immediate shift, with notes from March–July 2020 (less structured) compared to January–April 2021 (more structured). This design minimized potential confounding factors related to temporal changes.
Eligible notes included adult patient consultations, with at least one initial oncological consultation (IOC) or follow-up consultation (FUC). For each period and consultation type, 36 notes were randomly selected, totaling 288 notes, all anonymized to protect patient privacy. To reduce rating bias, clinicians from each center evaluated notes from the other center, ensuring blinding to the origin of the notes.
Note quality was assessed using the validated Qnote instrument, which evaluates individual note elements across seven components (Table 1). The primary outcomes measured were the overall note quality score (0–100 scale), element-specific scores, note length in words, and subjective quality ratings (scale 1–10).
Data analysis involved SPSS version 25, employing two-way ANOVA to compare scores before and after implementation of structured documentation, incorporating variables such as note type, center, and period. Significance was set at p < 0.05.
Results
Initial notes recorded prior to adopting structured documentation had a mean quality score of 64.35 (95% CI 61.30–67.35). Post-implementation notes showed a marked improvement, with an average score of 77.2 (95% CI 74.18–80.21), representing a 12.8-point increase (p < 0.001). Element-specific analysis revealed significant gains across most components, such as problem list, review of systems, assessment, and follow-up information (Table 2).
Table 2.
Estimated marginal means of Qnote scores and main effects of structured documentation
| Element | Unstructured | Structured | Difference (95% CI) | p-value |
|—|—|—|—|—|
| Chief complaints | 84.0 | 93.3 | +9.3 (4.0 to 14.7) | 0.001 |
| HPI | 71.6 | 87.1 | +15.4 (7.8 to 23.1) | 0.000 |
| Problem list | 23.3 | 39.0 | +15.7 (3.9 to 27.6) | 0.009 |
| Past medical history | 38.8 | 47.0 | +8.2 (0.0 to 16.4) | 0.050 |
| Medications | 29.5 | 42.0 | +12.6 (–3.3 to 28.4) | 0.120 |
| Adverse reactions | 25.6 | 84.7 | +59.1 (47.2 to 71.0) | 0.000 |
| Social and family history | 72.5 | 88.3 | +15.8 (6.3 to 25.5) | 0.001 |
| Physical findings | 82.8 | 85.3 | +2.5 (–2.2 to 7.2) | 0.293 |
| Assessment | 74.5 | 85.9 | +11.4 (5.1 to 17.7) | 0.000 |
| Plan of Care | 74.5 | 80.1 | +5.7 (–2.3 to 13.7) | 0.162 |
| Follow-up info | 72.5 | 86.9 | +14.4 (7.9 to 20.9) | 0.000 |
Overall, notes documented with structured formats were longer but maintained or improved clarity and conciseness, as evidenced by component scores related to information sufficiency, brevity, and understandability (Table 4). Notably, the standard deviation of element scores decreased with structured documentation, indicating reduced variability in quality across notes.
Fig. 1.
Boxplot illustrating the distribution of overall note quality scores by note type
Further analysis confirmed that both IOC and FUC notes benefited from the transition, with IOC scores increasing by approximately 15 points in both centers. The length of notes also increased significantly; IOC notes grew from an average of 442.1 to 639.6 words, and FUC notes from 86.9 to 133.4 words, reflecting more detailed documentation aligned with higher quality standards.
Discussion
The findings demonstrate that structured and standardized documentation positively influences the quality of outpatient clinical notes. The 20% increase in overall note scores signifies improved completeness, clarity, and organization, which are essential for high-quality patient care. The observed longer notes, without evidence of note bloat, suggest that clinicians are including more relevant information in a concise manner, aided by structured templates that prompt for essential data.
Implementing structured documentation reduces variability in note quality, as indicated by decreased element score dispersion, leading to more consistent clinical records. This is particularly important given studies highlighting the risks of variable documentation practices [9]. The improvements across different centers and EHR systems underscore the generalizability and robustness of these benefits.
The increase in note length aligns with the goal of comprehensive documentation but raises concerns about potential note bloat. However, the significant improvements in components related to conciseness and clarity suggest that longer notes in this context reflect more complete yet focused documentation, not unnecessary information.
Previous literature has shown mixed effects of EHR adoption on documentation quality, with some studies reporting declines due to note clutter and copy-paste issues [17]. Our results support the premise that structured templates can mitigate these issues by guiding clinicians through essential elements, resulting in higher-quality notes that are both detailed and accessible.
Strengths of this study include the use of a validated measurement instrument, the inclusion of multiple centers with different EHR systems, and a methodological approach that enhances objectivity. Nonetheless, limitations include its retrospective design, which precludes establishing causality definitively, and the validation of the Qnote instrument primarily in diabetic populations, though its generalizability appears adequate for oncology notes.
The increasing importance of data reuse, enabled by structured documentation, is evident in applications such as automated quality monitoring, referral communication, and research. However, it is crucial to balance the benefits with the potential administrative burden. Efforts to optimize structured data entry should aim to preserve clinician workflow efficiency and acceptability, avoiding additional workload that could hinder adoption [24].
Future research should explore the impact of structured documentation on workflow, clinician satisfaction, and long-term patient outcomes. Studies examining how decision support tools and artificial intelligence can leverage standardized data will be essential in advancing healthcare quality and safety.
Conclusion
Structured and standardized clinical documentation significantly improves EHR note quality, making notes more complete, clear, and concise. These enhancements support better patient care, facilitate data reuse, and enable advanced healthcare analytics. As healthcare providers increasingly rely on electronic systems, integrating structured documentation methods into routine practice is strongly recommended to maximize data utility and uphold high-quality clinical standards.
Supplementary information
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Data availability statement
Data supporting these findings are available upon reasonable request from the authors.
This article is part of the Topical Collection Clinical Systems.
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