Automating Resident Case Logs: Narrative Review and Challenges Ahead

ABSTRACT

Background

A surgical resident’s logs should represent their operative experience. In practice, manually compiled logs are fraught with inaccuracies and incompleteness. Electronic health record (EHR) data may enable case log automation, potentially improving accuracy and reducing resident administrative burden.

Objective

We examined and summarized the current literature on automated case logging systems to understand the current approaches, outcomes, and ongoing challenges.

Methods

We performed a narrative review using MEDLINE, Scopus, and Embase databases from January 1946 to February 2025 using keywords associated with resident case and procedure logging. English language, peer-reviewed manuscripts evaluating automated or semiautomated case logging systems were included. Articles focusing on case log analysis without addressing automated logging were excluded. Extracted information included automation methods, integration with residency systems, and measured impacts on accuracy, completeness, or efficiency.

Results

A total of 64 deduplicated articles were screened, yielding 8 semiautomated case logging systems used in emergency medicine, anesthesiology, general surgery, and ophthalmology. No fully automated end-to-end systems were identified. These systems typically increased number of cases logged as well as accuracy and completeness. Common methods included EHR data aggregation in dashboards, interfaces with logging applications, and machine learning–assisted decision support. Reported outcomes showed improved logging frequency, accuracy, and reduced variability. Studies consistently demonstrated efficiency gains and reduced resident administrative burdens.

Conclusions

Automating resident case logging by leveraging EHR data can improve log accuracy and decrease administrative workload. Current implementations remain semiautomated and institution specific, highlighting challenges with data integration, coding consistency, and specialty-specific requirements.

Introduction

A surgical resident’s case logs are the core representation of the trainee’s operative experience. These logs, required and housed by the Accreditation Council for Graduate Medical Education (ACGME), are maintained exclusively by the resident and are essential to ascertaining their progression in training, readiness for graduation, and breadth of operative exposure. Program directors rely on these logs to tailor resident education, ensure adequate exposure, and meet accreditation requirements. Yet residents frequently struggle to maintain accurate, complete, and timely logs amid demanding clinical schedules, creating gaps that can affect trainee assessment, program oversight, and institutional workforce planning.^1-3 As expectations for data-driven competency evaluation grow, improving the reliability and efficiency of case logging has become increasingly important for residency programs and graduate medical education (GME) stakeholders. ACGME case logs consistently underreport true operative experience.^1,3-5 The administrative burden is significant, and, in the midst of intensive clinical training, residents simply forget to log cases, choose not to log cases, struggle to identify representative current procedural terminology (CPT) codes, or are unfamiliar with CPT coding altogether.^1,5,6 Furthermore, reminders fail to improve quality of logs.⁷ After reaching minimum graduation requirements, there is little incentive for busy residents to continue logging cases, and therefore many residents stop, contributing to the underreporting of their operative experiences.^6,8

Inaccurate case logs have negative consequences for individual trainees, the residency program, the health system, and national surgical education policy.^9-11 For residents, incomplete or inaccurate logs can obscure true operative experience, limiting opportunities for targeted feedback, impeding timely identification of educational gaps, and contributing to stress or burnout as trainees struggle to keep pace with administrative requirements. Program leadership may make decisions about rotation structure, operative assignments, or readiness for advancement based on flawed data, potentially affecting the quality and equity of training. At the institutional level, inaccurate logs can distort understanding of surgical workload and resource needs, influencing staffing decisions and trainee complement requests. Finally, research and policy efforts that rely on ACGME case log data may reach misleading conclusions about national training trends, operative exposure, or workforce readiness when logs do not reflect actual experience.^12-15

The majority, if not all, of the data necessary to complete a case log is available within an institution’s enterprise data warehouse^16,17; however, accessing and operationalizing this information for resident use is not straightforward. These data are typically stored across multiple clinical, billing, and scheduling systems, requiring technical expertise, institutional support, and deliberate integration to translate them into useful tools for case logging. In attempts to improve accuracy and ease administrative burden on trainees, semiautomated or automated logging systems have been developed in surgical and nonsurgical specialties.^16,18-23 However, durable solutions for automating resident case logging are lacking. Specialty-specific case log requirements and variability in electronic health record (EHR) data access and management across institutions add complexity, and currently, ACGME policies restrict third-party or automated systems from directly entering data into the national case log platform. Here, we present a review of the current state of automated and semiautomated resident case logging in order to understand the approaches taken to date, evaluate their impact on accuracy and efficiency, and identify the persistent challenges that must be addressed to advance this work.

Methods

We conducted a narrative literature review, searching MEDLINE, Scopus, and Embase. A comprehensive search strategy was developed collaboratively with a medical librarian to capture studies from January 1946 to February 2025. The initial search terms included variations of “case log,” “procedure log,” “resident,” “house officer,” “graduate medical education,” “automation,” “electronic health record,” and “information system.” Detailed search strategies for each database are presented in the online supplementary data. The Scopus search was performed using the keywords and phrases outlined in the MEDLINE search strategy.

During full-text review, peer-reviewed studies published in English were included only if they (1) described development, implementation, or evaluation of an automated or semiautomated case logging process; (2) involved resident or fellow trainees within an ACGME-accredited or equivalent GME program; and (3) reported at least one relevant outcome, including accuracy, completeness, frequency of logging, administrative burden, or system adoption. Studies were excluded if they (1) analyzed resident case logs without assessing an automated logging workflow; (2) described procedure tracking unrelated to resident education; (3) focused exclusively on billing, quality improvement, or workflow analytics without generating resident-facing logging outputs; (4) evaluated educational dashboards without linking data to case logging; or (5) lacked sufficient methodological detail to determine whether automation of logging occurred.

Database searches were conducted by our medical librarian (J.W.). Titles, abstracts, and full-text screenings were performed by 2 authors (A.P.B., H.J.). Extracted data elements included the automation strategy, integration details, and case logging outcomes related to accuracy, completeness, administrative burden, and adoption. Discrepancies in study inclusion decisions were resolved through discussion between the 2 reviewers (A.P.B., H.J.). Disagreements were planned to be adjudicated by a third author when consensus could not be reached, but this did not occur.

We chose a narrative review approach given the limited number of studies, the absence of standardized metrics across implementations, and heterogeneity in study design, specialties, automation strategies, and reported outcomes. These characteristics precluded quantitative synthesis or formal systematic review methodology and instead supported a narrative approach to synthesize themes, contextualize findings, and identify gaps relevant to GME. As this project did not involve human subjects research, institutional review board approval was not required.

Results

Study Characteristics and Specialties Represented

The initial search yielded 64 deduplicated articles, with 8 studies meeting inclusion criteria (Figure 1). Table 1 summarizes key characteristics of the articles reviewed. These articles described systems with significant diversity of automation methods, including EHR data aggregation into dashboards, application programming interface (API) use for data transfer, and machine learning–based predictive algorithms (Table 1). The first efforts in automated logging were published in 2011 in anesthesiology by Simpao et al and in emergency medicine by Seufert et al.^16,23 This review also identified logging systems developed in cardiology, general surgery, and ophthalmology, with the latter 2 representing the only surgical specialties.

Figure 1.

Study Schema

Note: Search criteria included strategy outlined in online supplementary data. Non-English language studies were excluded.

Table 1.

Published Implementations of Automated Case Log Systems

Author	Year	Specialty	Method	Product	Result	Limitation
Thanawala et al18	2022	General surgery	Web-based application that displays aggregated EHR case log information. Machine learning model predicts completed case log form presented to the resident for review.	Decision support tool suggests case information to log. Resident manually transfers this data to ACGME Accreditation Data System.	Increase from 1.44 to 4.77 cases logged per resident per week.	Manual data transfer and entry required at final case log destination.
Evans et al24	2025	Multiple surgical specialties	Leveraged procedure and note data within the EHR to aggregate case-log specific information.	EHR-based dashboard providing granular data necessary for case logging including both operative and nonoperative log requirements.	Frequent use with perceived improvement in case logging time and frequency.	Manual data transfer and entry required at final case log destination.
Xiao et al21	2022	Ophthalmology	Local case logging system developed and integrated into the EHR. Each case resident accesses partially prefilled forms, completes portions such as role and procedure, and submits the log to the local database. Data from the local database is batch uploaded to ACGME via an API.	Integrated case log from that is partially completed automatically. Resident required to manually complete the log prior to local storage and transmission.	System adopted by 100% of residents. Percent of cases logged increased from 85% to 91%.	Multiple data entry steps necessary within the EHR.
Kwan et al19	2024	Emergency medicine	Procedure data compiled via scheduled EHR queries. Data sent to MedHub via API.	Complete automated logging for internal program use.	78% increase in procedures logged with 99.5% accuracy.	Completely automated process but not applicable to ACGME logs.
Douglas et al20	2022	Anesthesiology	Procedure data queried from the EHR enterprise data warehouse, joined to a table of resident data from the local residency management system.	Dashboards for visualization by program leadership and residents to use to assist in ACGME logging.	Increased number of cases captured in the automated system (median of 1226.5 vs 1134.5) with reduced reporting variability.	Manual data transfer and entry required at final case log destination.
Anyanwu et al22	2021	Cardiology	“Clinical-educational report template” consisting of structured data implemented to replace existing unstructured procedure notes. Reports were generated from structured EHR data within the enterprise data warehouse.	Monthly procedure logs provided to program leadership and fellows.	Increase in number of procedures logged weekly. Reduction in self-reported administrative time.	Manual data transfer and entry required at final case log destination.
Seufert et al23	2011	Emergency medicine	Procedure data queried and abstracted by locally developed software which writes directly into local residency management system.	Complete automated logging for internal program use.	Increased daily logs by 168%. Log accuracy and completeness increased significantly.	Completely automated process but not applicable to ACGME logs.
Simpao et al16	2010	Anesthesiology	Parsing and mapping of free-text procedure descriptions and procedure codes to assign correct ACGME categories using Anesthesia Information Management System data. This data is used to generate ACGME-compliant log information.	Procedure reports are generated and entered into the ACGME Accreditation Data System by administrative staff.	Manual logs had considerable over- and underreporting when comparing to automated resident logs (>5% difference).	Manual data transfer and entry required by administrative staff at final case log destination.

Abbreviations: EHR, electronic health record; ACGME, Accreditation Council for Graduate Medical Education; API, application programming interface.

Generally, these efforts leverage structured EHR data. Douglas et al and Evans et al aggregated case data at the resident level, creating local reports and visualizations to reference during the case logging activity.^20,24 Evans et al were the only authors to develop a case log dashboard for multiple specialty groups: general surgery, obstetrics and gynecology, neurological surgery, orthopaedics, and otolaryngology.²⁴ All other systems were oriented toward a single specialty.

Integration With Residency Management and ACGME Systems

Kwan et al, Xiao et al, and Seurfert et al reported on the transfer of data from the EHR to either local or national residency case log systems.^19,21,23 Kwan et al and Seufert et al developed interfaces with their respective local residency management system, writing case logs into a web-based form.^19,23 Xiao et al utilized an API to transfer locally stored case logs to ACGME’s web portal.²¹ However, this system required multiple manual data entry steps to complete a case log in their local database. Thanawala et al developed an “intelligent case logging system” that integrates EHR case data and provides decision support using machine learning to predict case details not represented in the EHR, such as surgeon role and involvement of trauma.^18,25

Impact on Logging Frequency, Accuracy, and Burden

These studies were limited to single-institution, pre-post intervention studies. Each study found increases in case log frequency or number of cases logged. Where accuracy was measured, case log accuracy increased from pre-intervention or was very high post-intervention.^19,21,23 Seufert et al’s system increased accuracy from 87% to 99% (P<.001).²³ Kwan et al did not measure their pre-intervention accuracy but estimated the accuracy of their automated system at 99.5%.¹⁹ When Xiao et al surveyed residents’ perception of accuracy in their logging system, it remained high but not statistically significant from pre-intervention.²¹ When variability in cases logged among trainees was measured, as with Douglas et al and Simpao et al, the interquartile range of case counts decreased, representing decreases in under- or overreporting.^16,20 Even though these systems do not offer full end-to-end process automation, they increased logging efficiency and self-reported decreased administrative time.^22,24 Evans et al found that residents felt increased support in completing administrative tasks.²⁴ Anyanwu et al observed a significant impact of their system, with 73% of fellows reporting a “very positive impact” on their fellowship experience.²²

The overall quality of evidence is low and is limited. Included studies were single-institution implementations using observational or pre-post designs, with small sample sizes and limited follow-up. Outcomes were frequently descriptive and heterogeneous, rarely employing standardized or validated metrics. Few studies included control groups, formal statistical comparisons, or multi-institutional evaluation, and none assessed downstream educational outcomes. As a result, the current evidence base should be interpreted as exploratory and hypothesis-generating, providing early insights into feasibility and potential benefit rather than definitive conclusions regarding effectiveness or generalizability.

Discussion

Given the limited number and heterogeneous nature of available studies, this narrative review serves as an initial assessment of whether automated case logging represents a meaningful problem space and opportunity for innovation. Across surgical and other procedural specialties, the reviewed studies suggest that leveraging structured EHR data may offer early preliminary evidence of improved accuracy, completeness, and logging efficiency, while reducing administrative burden. Each system addressed distinct components of the logging workflow, including data ingestion, aggregation, visualization, and partial automation of case entry. Collectively, these findings suggest that even semiautomated solutions can outperform traditional resident-entered logs and are often well received by trainees and program leadership. Across the limited number of studies reviewed, several implementations reported favorable uptake among trainees and program leadership, reflected by high rates of voluntary use, sustained engagement over time, and positive qualitative feedback regarding perceived reductions in logging effort and improved visibility of operative experience.^18,21,24

At the same time, these studies highlight persistent limitations that constrain scalability and generalizability. Most implementations were single-institution efforts requiring local informatics expertise and custom integration, limiting portability across training environments. Variation in specialty-specific requirements, inconsistencies in procedure coding, and incomplete capture of resident role or case complexity further complicate automation. Together, these challenges underscore why existing systems remain semiautomated and why broader adoption will require coordination among health systems, residency programs, and accrediting bodies.

Data Access and Management

Across the reviewed studies, access to relevant EHR data and the ability to integrate those data into resident-facing tools required close collaboration with institutional informatics teams and locally developed infrastructure. The consistent reliance on single-institution solutions suggests that variability in data organization, documentation practices, and technical resources may complicate efforts to generalize or scale automated case logging systems. Residents may care for patients at neighboring health systems whose EHR configuration may limit generalizable solutions, even when systems have the same EHR vendor. Implementation of a partial solution, like automating case logging at a single hospital, may interrupt a resident’s typical case logging strategy and inhibit adoption, particularly when a resident may operate at multiple hospitals within the same week or even the same day. An optimal automation solution is compatible with the totality of EHRs in operation at a training program. Ideally, the solution would be truly EHR agnostic, allowing for adoption across the country.

Several studies noted that automated case logging remains dependent on the accuracy and completeness of underlying clinical documentation, including procedure records and billing data, which may be delayed or incompletely reflect procedures actually performed, and currently lacks consistent safeguards for log accuracy within routine clinical workflows.^18,21,24 These more systemic issues require a multi-systems approach coordinating local IT, billing and coding, trainee behavior, and GME.

Inconsistencies and Subjectivity in Procedure Coding Terminology

Beyond technical and data quality considerations, inconsistencies and subjectivity in procedure coding represent a foundational challenge to case log automation, as accurate translation of operative work into educational records depends on alignment between clinical documentation, billing practices, and accreditation-defined logging requirements. Across specialties, discrepancies between EHR procedure data, surgeon-entered CPT codes, finalized billing codes, and ACGME logging terminology frequently necessitate manual interpretation by residents, particularly for complex cases. For example, searching the CPT code 1400 results in 3 options, 2 of which appear analogous (Figure 2). Automating which code to pick is much more challenging than simply matching CPT codes. Delays in code finalization and the need to translate non-CPT or payer-modified codes into ACGME-compatible entries further limit the feasibility of real-time automation and contribute to ongoing subjectivity in case logging.

Figure 2.

Example of Multiple Ways to Log a Case With a Single CPT Code

List of abbreviations: ACGME, Accreditation Council for Graduate Medical Education; CPT, current procedural terminology; EHR, electronic health record.

Variation in Requirements Among Specialties

There is significant variation in required information for each surgical specialty (Table 2), which complicates the development of unified or scalable automation solutions by introducing specialty-specific nuances in what constitutes a complete and valid case log. This heterogeneity limits the feasibility of a standardized case logging workflow within a health system or at the ACGME level, leading to specialty-specific tools as identified in this review. Certain data elements are consistent across each specialty log including case ID, case date, case year (resident’s postgraduate year), site (for systems with multiple training sites), attending name, and procedure. Other required data points in the case logs often vary significantly.

Table 2.

Case Log Required Fields by Specialty

	Case ID	Case Date	Case Year (PGY status)	Role	Site	Attending	CPT Codes	Patient Type	Patient Gender	Rotation Primary Service for the Case	Patient Age	Special Equipment	Use of Microscope	CPT Code Check Box for Robotic
General surgery	✓	✓	✓	✓	✓	✓	✓	✓		✓				✓
Neurological surgery	✓	✓	✓	✓	✓	✓	✓	✓	✓
OB/GYN	✓	✓	✓	✓	✓	✓	✓	✓
Ophthalmology	✓	✓	✓	✓	✓	✓	✓
Orthopedic surgery	✓	✓	✓	✓	✓	✓	✓	✓					✓
Otolaryngology	✓	✓	✓	✓	✓	✓	✓				✓	✓
Plastic surgery	✓	✓	✓	✓	✓	✓	✓	✓
Urology	✓	✓	✓	✓	✓	✓	✓	✓						✓
Vascular surgery	✓	✓	✓	✓	✓	✓	✓			✓

Abbreviations: PGY, postgraduate year; CPT, current procedural terminology; OB/GYN, obstetrics and gynecology.

Identifying the appropriate data for each specialty’s requirements is a complex task. For general surgery residents, only one CPT code counts toward the minimum case requirements. For complex cases with multiple procedures, the resident must decide which procedure will “count,” often leaving residents to not list additional procedures beyond the primary, worsening to log incompleteness.³ To address this issue, Thanawala et al have employed advanced machine learning to predict the primary procedure based on a resident’s prior logging behaviors, learning how a resident has logged prior cases, and based on knowledge of the resident’s remaining case requirements. To add to the complexity, additional procedure information outside of CPT codes is often necessary. For the otolaryngology trainee, the use of certain special equipment, such as ultrasounds and lasers, must be captured. In gynecology, whether the case involved invasive cancer or not is required, which may not be determined until weeks after the case is performed. These nuances complicate scalable automation solutions.

The resident’s “case role” is defined per specialty at the ACGME level, with significant differences in role and definitions among subspecialties (Table 3). A resident’s determination of their own role within a case is heavily influenced by their past logging behavior.^8,20 Some “roles” do not count toward graduation requirements, likely discouraging accurate logging by junior residents.⁴ The necessary information to determine an accurate role, like whether a resident performed the critical portion of the case, may not be available in the EHR,²⁶ increasing the challenge of accurate automation. Emerging techniques, like machine learning, can assist in predicting roles based on logging behavior across an institution,^18,27 and novel intraoperative data monitoring may offer insights into the actions of trainees and one day be able to assess the level of involvement in a case.²⁸

Table 3.

Available Resident Roles by Specialty

	General Surgery	Neurological Surgery	OB/GYN	Ophthalmology	Orthopedic Surgery	Otolaryngology	Plastic Surgery	Urology	Vascular Surgery
Assists and/or engages with appropriate portions of the case	First assistant	Assistant resident surgeon	Assistant	Assistant	Level 2: Assisting resident surgeon	Resident assistant surgeon		Assistant	First assistant
Participates between opening and closing of case, not necessarily at critical portions		Senior resident surgeon
Has significant role in management and critical portions of case	Surgeon junior or surgeon chief depending on if performed during the chief experience	Lead resident surgeon	Surgeon	Surgeon	Level 1: Primary or supervising resident surgeon	Resident surgeon	Resident surgeon	Surgeon	Surgeon junior or surgeon chief depending on if performed during the chief experience
Oversees and assists junior resident through procedure	Teaching assistant		Teaching assistant			Resident supervisor		Teaching assistant	Teaching assistant

Abbreviation: OB/GYN, obstetrics and gynecology.

Future Directions

Findings from this narrative review suggest several near- and long-term directions for advancing resident case log automation. In the near term, efforts should focus on developing standardized, EHR-derived reports that aggregate resident-level case data in formats that facilitate accurate and efficient manual entry into the ACGME case log portal. Longer-term progress will likely require coordinated, multi-institutional research to develop and validate generalizable solutions, as well as advocacy and collaboration with accrediting bodies to reconsider technical and policy constraints that currently limit direct integration. Addressing these challenges will be essential to realizing scalable, high-fidelity solutions that reduce administrative burden while preserving the educational intent of case logging.

Limitations

There are several limitations to this study. Relevant literature is difficult to identify and there is a scarcity of high-quality, multicenter studies. The heterogeneous study settings and nature of reported outcomes precluded meta-analysis. The systems evaluated are frequently proprietary, limiting replication and adding to confirmation bias. Failed attempts at case log automation may not have been published. Health system preferences and local, state, and federal regulations restrict the extent of EHR data available and also limit generalizability of published works. Despite these limitations, this review identifies critical gaps and opportunities for future innovation and research.

Conclusions

This narrative review identified a small body of primarily single-institution studies providing early evidence that semiautomated resident case logging systems can improve logging completeness, accuracy, and efficiency compared with traditional resident-entered logs. Across surgical and procedural specialties, existing implementations demonstrate feasibility and favorable user uptake but remain constrained by institution-specific infrastructure and specialty-specific requirements. Large gaps remain in the literature, including the absence of multi-institutional evaluations, standardized outcome measures, and assessment of downstream educational impact.

Author Notes

Funding: The authors report no external funding source for this study.

Conflict of interest: The authors declare they have no competing interests.

Editor’s Note

The online supplementary data contains detailed search strategies for each database.