Real‐World Clinical Datasets in Practice: Applications for Learners, Clinician‐Educators, and Health Services Teams

ABSTRACT

Real‐world clinical databases offer students, residents, fellows, clinician‐educators, librarians, and early‐career investigators an accessible entry point into population health, quality improvement, and outcomes research. Leveraging large‐scale, de‐identified datasets enables the investigation of healthcare delivery, cost, treatment effectiveness, and clinical outcomes without the need for patient recruitment or extensive funding sources. This review provides a synopsis of four widely used real‐world clinical datasets: the Healthcare Cost and Utilization Project (HCUP, low‐cost with a Data Use Agreement), the Medical Information Mart for Intensive Care (MIMIC‐IV, openly available), TriNetX (subscription‐based), and Epic Cosmos (institutional access). Highlighting their structure, data types, access requirements, and ideal use cases, the distinguishing features of each database are discussed. Finally, we provide guidance on how learners and research teams can formulate research questions, identify the appropriate dataset, and leverage mentorship resources for database‐focused research, allowing them to meaningfully engage in data‐driven research and contribute to improving healthcare delivery and patient outcomes.

Conceptual workflow for developing a study. The figure illustrates the stepwise process of moving from an initial clinical area of interest to a focused research question, database selection, cohort construction, and final analysis. Key considerations include identifying a knowledge gap, determining which data elements are available, defining inclusion and exclusion criteria, balancing comparison groups, addressing demographic and diagnostic bias, and assessing whether the observed outcomes support the hypothesis.

Abbreviations

CPT

Current Procedural Terminology

DUA

Data Use Agreement

EHR

Electronic Health Record

EMR

Electronic Medical Record

HCUP

Healthcare Cost and Utilization Project

ICD

International Classification of Diseases

ICU

Intensive Care Unit

IRB

Institutional Review Board

KID

Kids' Inpatient Database

LOINC

Logical Observation Identifiers Names and Codes

MIMIC‐IV

Medical Information Mart for Intensive Care IV

MIT

Massachusetts Institute of Technology

NEDS

Nationwide Emergency Department Sample

NIS

Nationwide Inpatient Sample

QI

Quality Improvement

RxNorm

Normalized Nomenclature for Clinical Drugs

SDOH

Social Determinants of Health

SID

State Inpatient Databases

SQL

Structured Query Language

1. Introduction

Real‐world clinical databases are online repositories that provide access to large‐scale, de‐identified data. These resources empower researchers to examine trends in healthcare delivery, costs, outcomes, and treatment effectiveness. These databases typically have a low barrier to entry, making them especially useful for trainees across medicine, nursing, pharmacy, public health, and health services, as well as clinical educators and quality improvement (QI) teams.

A clear understanding of the differences between clinical and administrative databases and evaluating the strengths of each platform can help medical students better align their research questions with the most suitable databases, making meaningful contributions to population health and outcomes research. Previous studies have highlighted the importance of responsibility when utilizing large‐scale clinical datasets, highlighting strategies that create important ethical and practical considerations for translational research [1]. By addressing challenges such as data governance, reproducibility, and maximizing clinical impact, framework studies are able to provide recommendations to ensure that data‐driven translational research promotes both innovation and integrity within the field of medical research [1].

We provide an overview of four widely used real‐world clinical datasets: the Healthcare Cost and Utilization Project (HCUP, low‐cost with a Data Use Agreement), the Medical Information Mart for Intensive Care (MIMIC‐IV, openly available), TriNetX (subscription‐based), and Epic Cosmos (institutional access) [2, 3, 4, 5]. We highlight their structure, utility, and access requirements to help guide medical students, particularly those with research interests in the medical field (Table 1).

TABLE 1.

Database comparison summary.

Database name	Type	Training required	# of Records	Cost
TriNetX	Clinical EMR	Institution specific platform orientation	275,000,000 Encounters (1)	Subscription based
HCUP	Administrative	HCUP Online Training	160,000,000 Patients (2) a	Overall low cost
MIMIC‐IV	Clinical EMR	CITI + PhysioNet DUA	364,000 Patients (3)	Free
Epic Cosmos	Clinical EMR	Institution Specific, Epic internal	277,000,000 Patients (4)	Included with Epic b

^a Annual inpatient and ED records across the entire HCUP family.

^b Free for health care organizations that contribute data.

2. Overview of Public Healthcare Databases

Clinical databases are structured collections of patient‐level health information that may be used for clinical, observational, or health services research. The two main categories of databases include administrative and clinic‐focused repositories [6].

Administrative databases such as HCUP contain information related to billing and hospital administration, including patient demographics, International Classification of Diseases (ICD) codes, procedure codes, length of stay, and monetary charges [7].

Clinical databases encompass data collected during a patient encounter, including vital signs, laboratory results, medication orders, clinical notes, and diagnoses. Clinical databases provide a comprehensive view of a patient's medical history and are essential for tracking disease progression, evaluating treatment effectiveness, and supporting clinical decision‐making.

Both types of databases contribute to insights in healthcare research. Administrative databases are useful for large‐scale, population‐level studies of costs and broad outcomes. In contrast, clinical electronic health record repositories facilitate the investigation of disease progression, treatment response, and physiological trends [6, 8].

3. Why Use Real‐World Clinical Databases

Real‐world clinical databases are typically large‐scale repositories that offer access to de‐identified patient data. For medical students, approved databases can be leveraged, given the limited funding, dedicated research time, laboratory access, and patient recruitment resources. The ease of use of these datasets often lowers the barrier to entry to clinical data science research, allowing learners to explore meaningful questions relevant to healthcare utilization, epidemiology, costs, and outcomes [3, 9].

With structured training and close mentorship, novice trainees can begin to use these databases for descriptive, exploratory, and hypothesis‐generating work. However, proficiency with a platform interface should not be treated similarly to methodological completeness. Even when graphical user interfaces reduce coding requirements, learners require foundational training in multiple disciplines such as epidemiology, study design, and bias in order not to misclassify and interpret results inappropriately.

For novice learners, reasonable entry level projects include descriptive analysis, cohort comparisons, as well as utilization trends and comparisons monitored under close mentorship. More advanced analysis, including causal inference, target trial emulation, and propensity score designs, requires more formal training in epidemiology and clinical informatics. Descriptive or exploratory analysis may be valuable for education and hypothesis generation, but they might not be appropriate for publications unless they address a clearly defined knowledge gap, use rigorous methods, and include appropriate validation and sensitivity analysis methods.

These databases can also provide free or low‐cost access to patient data in some scenarios. For example, MIMIC‐IV is fully open access once training requirements are completed, while HCUP also provides free tools (Fast Stats) and modestly priced national and state datasets [2].

Data from real‐world clinical databases is also unique in that it represents natural, structured data rather than data collected from a controlled experiment, enabling the identification of real‐world health disparities, formulation of hypotheses for prospective trials, and provision of information on health policy [8].

Importantly, the feasibility of real‐world database research is highly dependent on institutional context. Access to platforms such as TriNetX or Epic Cosmos, availability of informatics support, as well as protected trainee research time and data use agreements all influence whether a project can be completed successfully. Settings without institutional subscriptions, statistical support, or mentors willing to engage in projects may face significant barriers despite the apparent accessibility of graphical user interfaces or public datasets. Therefore, while some platforms reduce the need for programming and coding, they do not eliminate the need for conceptual training in topics like epidemiology, study design, biases, appropriate intervention, and observational data gathering and analysis.

4. Healthcare Cost and Utilization Project (HCUP)

The Healthcare Cost and Utilization Project (HCUP) is a suite of hospital care databases managed by the Agency for Healthcare Research and Quality (AHRQ). The HCUP dataset family provides national and state‐level data, encompassing inpatient, outpatient, and emergency department (ED) visit data. HCUP data are organized at the discharge level, with one record per hospitalization or ED visit and includes demographic variables such as age, sex, race/ethnicity, payer information, admission and discharge dates, up to 30 ICD‐coded diagnoses, procedure codes, length of stay, total charges, and hospital characteristics [2].

HCUP‐associated databases include the Nationwide Inpatient Sample (NIS), State Inpatient Databases (SID), Nationwide Emergency Department Sample (NEDS), and the Kids' Inpatient Database (KIDS). The NIS database contains stratified sample information on US hospital discharges, representing more than 97% of the national population. The SID database stores discharge data for community hospitals in participating states, capturing the most inpatient admissions within each state. The NEDS database holds 20% stratified samples of US. ED visits. The KID database contains information about pediatric inpatient discharges. All users of HCUP‐associated databases must complete a data use agreement (DUA), which includes a short online training session [2, 10].

Experience in SAS, Stata, or the R coding language is recommended for learners to process data in HCUP‐associated datasets. HCUP tutorials assume knowledge of data import, recording, merging hospital identifiers, and basic regression modeling [2]. The HCUP suite of databases is ideal for health policy and epidemiology projects.

A limitation of the HCUP suite is the lack of medication data, such as prescribed drugs, dosages, and adherence information. This lack of drug data can restrict its usefulness in studies that require an understanding of pharmacological intervention.

5. Medical Information Mart for Intensive Care IV (MIMIC‐IV)

The Medical Information Mart for Intensive Care (MIMIC‐IV) is a publicly available clinical database managed by the MIT Laboratory for Computational Physiology in conjunction with PhysioNet. The MIMIC‐IV database comprises real‐world, de‐identified electronic health record (EHR) data from Intensive Care Unit (ICU) admissions at Beth Israel Deaconess Medical Center in Boston, MA. This dataset encompasses a diverse range of clinical variables, including vital signs, laboratory results, imaging reports, clinical notes, and procedure codes [3].

The MIMIC‐IV database is a free‐to‐use repository after completing a short CITI training module and signing a DUA. This database is typically accessible after completing the training within a few days [11]. It is recommended that students using this database have an intermediate knowledge base in Structured Query Language (SQL), Python, or R for data analysis. The MIMIC‐IV database is ideal for students with an interest in clinical informatics, machine learning, and/or critical care research [3, 12]. One limitation of the MIMIC‐IV database is the relatively low number of patient records compared to national‐scale datasets, which may limit the generalizability of findings. Additionally, the MIMIC‐IV database only contains records from the intensive care unit.

6. TriNetX

TriNetX is a health research platform that utilizes an EMR from healthcare organizations to provide access to real‐world clinical data, with over 275 million patient records compiled in one database. These healthcare organizations comprise academic medical centers, specialty networks, and healthcare delivery systems in the United States and internationally [4].

The TriNetX network derives data from EMRs, including population characteristics, treatment patterns, outcomes, comorbidities, diagnoses, medications, procedures, laboratory results, demographics, and visit data, which researchers can query. The data is updated regularly, allowing for longitudinal tracking of patients through parameters such as clinical outcomes, healthcare utilization, and treatment effectiveness [9].

TriNetX can be accessed through subscriptions once institution‐specific training and a DUA is completed. It can be accessed via a drag‐and‐drop interface or through SQL‐based queries and data extraction [4].

We recommend TriNetX for researchers interested in outcomes research, population health, and comparative studies. TriNetX is user‐friendly; however, familiarity with epidemiologic statistics and study designs may be helpful. Limitations include site‐to‐site coding and mapping variability (e.g., ICD‐10, RxNorm) that can affect measurement validity; sensitivity analyses and code‐set review with an appropriate clinical mentor are imperative. Additionally, it.

7. Epic Cosmos

Epic Cosmos is a real‐world data platform that comprises electronic health records (EHRs) from patients using Epic Systems, which healthcare organizations in the United States utilize. This includes 41 thousand clinics and 1809 hospitals, which consist of 17 billion encounters and 300 million patients as of October 2025 (https://cosmos.epic.com/about/). To support research and public health, Cosmos, under the management of Epic, provides de‐identified clinical data from electronic health records (EHRs). De‐identified clinical data consists of demographic variables, diagnoses (ICD codes), procedures, medications, immunizations, laboratory results, vital signs, encounter history, and social determinants of health [5]. Cosmos maintains patient tracking and updates in Epic's EHR system, enabling a longitudinal understanding of patients' care.

Epic Cosmos is designed for those with limited programming experience; however, an understanding of research design and clinical informatics may enhance insights for projects [5]. Epic Cosmos is designed to be accessible for individuals with limited programming experience, making it an attractive tool for medical students and learners who may not have extensive technical backgrounds. Its user‐friendly interface and integration with Epic's EMR system lower the barrier to entry compared to traditional data science platforms that require coding or statistical expertise. However, while advanced programming knowledge is not necessarily essential, a solid grasp of research design, clinical informatics, and biostatistics is critical to ensure that projects are methodologically sound and that data is interpreted appropriately.

Despite these advantages, financial barriers remain. Access to Epic Cosmos often requires institutional subscriptions or licensing agreements, which may not be feasible for all medical schools or individual students. For students without institutional support, the costs associated with data access, platform use, and necessary training may limit opportunities to engage in large‐scale, real‐world EHR‐based research. This cost structure has the potential to exacerbate inequities, particularly for students at schools with fewer resources, and may restrict the diversity of research questions and perspectives represented in Cosmos‐driven projects [5].

8. Recommendations for Medical Students

For medical students with limited research experience, working with large epidemiological datasets can be intimidating. However, with the appropriate dataset and approach, these tools can become valuable enhancements for learning and scholarly productivity.

We recommend starting with a well‐defined research question and a clear area of interest. Once a question or topic is identified, medical students should leverage online tutorials and practice investigating their question of interest within each respective dataset. Though these results may not be final upon initial analysis, developing familiarity with each dataset will lead to a more developed research question, as the investigator learns the capabilities of each dataset. Medical students can develop skills for their projects and start seeking mentorship from faculty and other content experts. Collaborating with a mentor will help guide project design and statistical analysis, ensuring the appropriate use of data.

A student's choice of project topic should be driven by their interests and will guide which database they will utilize for their project. For example, if you are interested in health policy or hospitalization trends, HCUP may be a good first choice. If you are interested in ICU outcomes, MIMIC‐IV may be most suitable.

Students should also utilize publicly available resources, given that many databases offer community support and free learning materials (Table 2). For example, PhysioNet tutorials and information are available via GitHub repositories for MIMIC‐IV (Table 2). HCUP provides online workshops, documentation, and support forums (Table 2). Additionally, institutional support teams can assist with data access and IRB‐related processes.

TABLE 2.

Database resource hyperlinks.

Database name	Database home page	Overview/About	Tutorials	Access steps/Time to access
TriNetX	https://trinetx.com/	https://trinetx.com/about/	https://trinetx.com/resources/	Online DUA training + signed Data Use Agreement + purchase; ~days
HCUP	https://www.hcup‐us.ahrq.gov/	https://www.hcup‐us.ahrq.gov/overview.jsp	https://www.hcup‐us.ahrq.gov/onlinecourses.jsp	CITI human subjects training + data use agreement quiz/approval; ~days
MIMIC‐IV	https://physionet.org/content/mimiciv/	https://mimic.mit.edu/	https://mimic.mit.edu/docs/	Institutional contract/subscription required; ~weeks–months
Epic Cosmos	https://cosmos.epic.com/	https://www.epic.com/cosmos	https://www.epic.com/software#Cosmos	Institutional onboarding and approval; ~weeks–months

By identifying an interest, seeking guidance, and leveraging publicly available tools, medical students can begin to investigate various epidemiological questions within healthcare, enhancing the field of public health research.

9. Using Multiple Datasets to Strengthen a Single Question

As noted above, each dataset has strengths to answer different aspects of a clinical question. Administrative data (e.g., HCUP) excels at utilization, readmissions, and costs; ICU EHR (e.g., MIMIC‐IV) provides physiology and granular labs; multi‐site EHR networks (e.g., TriNetX) support cohort discovery, labs/meds, and comparative effectiveness; very large EHR repositories (e.g., Cosmos) add scale and Social Determinants of Health (SDOH) breadth. Thus, using two or more in parallel (with consistent definitions) can expose biases, check robustness, and produce more clinically meaningful estimates. When analyzing data across multiple sources, it is important to account for both between‐ and within‐database differences, including variation in the site of care (e.g., ICU, inpatient, outpatient), as groups with similar measured characteristics may differ in unmeasured factors that could bias study conclusions. To illustrate these alignment challenges, consider a cohort of patients with chronic heart failure or type 2 diabetes examined across inpatient and outpatient care settings. Even when ICD‐coded diagnoses are harmonized, fundamental unmeasured differences can bias results. Outpatient cohorts typically include individuals with earlier disease stages, higher functional status, and more longitudinal follow‐up, whereas inpatient cohorts disproportionately capture patients with acute decompensation, higher comorbidity burden, and greater short‐term mortality risk. These differences can lead to misleading conclusions if datasets are compared directly or pooled without adjustment. Strategies to reduce bias include restricting analyses to comparable encounter types, applying consistent look‐back periods to identify baseline disease severity, and performing sensitivity analyses that stratify by site of care. Incorporating these steps ensures that cross‐dataset comparisons, particularly those spanning inpatient and outpatient settings, reflect true clinical differences rather than artifacts of data collection.

For example, a study of type 2 diabetes complications may appear straightforward across HCUP, MIMIC‐IV, TriNetX and Epic Cosmos because diabetes can be identified using ICD‐coded diagnoses in each dataset. However, the populations represented by these datasets may differ substantially based on the criteria for the dataset. HCUP inpatient records may capture patients hospitalized for acute complications or co‐morbid illness; MIMIC‐IV captures a high‐acuity ICU population. Even if sex, age, and ICD codes are matched, unmeasured differences in duration of disease, glycemic history, and medication adherence could be vastly different. Dataset Alignment strategies should include restricting to comparable care settings, applying consistent lookback and follow‐up windows, and propensity matching for medications, disease states, and the interpretation of discordant signatures as potential evidence of setting‐specific selection rather than true biological or treatment differences. We have included a summary table of this for reference (Table 3).

TABLE 3.

Database cross‐setting troubleshooting.

Cross‐setting issue	Example of potential bias	Alignment strategy
Care setting differences	ICU cohorts may have higher acuity than outpatient EHR cohorts even with the same diagnosis code	Restrict by encounter type or stratify analyses by inpatient, ICU, ED, and outpatient setting
Disease severity	Type 2 diabetes patients in MIMIC‐IV may have more acute illness than patients in outpatient EHR networks	Use consistent look‐back windows; include severity proxies such as labs, complications, medication intensity, or prior utilization
Coding variation	ICD codes may be used differently across hospitals, billing systems, or EHR networks	Use validated code sets when available; conduct sensitivity analyses with narrow and broad definitions
Medication capture	Inpatient medication orders may not reflect outpatient adherence or long‐term exposure	Define medication exposure windows clearly; separate inpatient administration from outpatient prescriptions
Laboratory availability	HbA1c, creatinine, or inflammatory markers may be missing more often in some settings	Report missingness; avoid assuming missing labs indicate normal values; perform complete‐case and missingness sensitivity analyses
Follow‐up structure	Administrative discharge data may not capture longitudinal outpatient outcomes	Align follow‐up windows; avoid comparing short‐term inpatient outcomes directly with longitudinal outpatient outcomes
Socioeconomic and access factors	Patients in different systems may differ in insurance, referral patterns, or access to specialty care	Include available SDOH or payer variables; discuss residual confounding when variables are unavailable
Outcome ascertainment	Mortality, readmission, or complications may be captured differently across datasets	Prefer datasets with direct outcome measurement; use validation datasets cautiously and report limitations

For example, for a vascular medicine question, you might (a) use TriNetX to compare short‐term complications after covered stents in aorto‐iliac disease (with lab and med adjustment); (b) validate national readmissions and costs with HCUP; and (c) explore ICU trajectories for severe cases in MIMIC‐IV. Concordant signals across datasets raise confidence, while discrepancies prompt sensitivity analyses and better reporting of uncertainty.

When answering your research question with multiple datasets, it is important to describe what each dataset does, how you made them comparable, and how you judged agreement. We suggest the following framework as a helpful summary:

1. State dataset roles (primary vs. validation)

Specify which dataset drives inference and which provides corroboration. Give a one‐line rationale (e.g., richer covariates, national coverage). For example: “The primary analysis used [Dataset A] (reason: e.g., labs/meds available). Validation analyses used [Dataset B] and [Dataset C] to assess reproducibility across different settings/time periods.”

• 2Harmonize definitions and note differences

Report how you aligned code lists (ICD/CPT/RxNorm/LOINC), time windows (index date, look‐back, follow‐up), and outcome definitions. Disclose known differences that may affect estimates (e.g., deduplication rules, date handling/shifting, refresh rate, missingness, etc.) and point to your supplemental code lists. For example: “We applied a shared specification across datasets: identical inclusion/exclusion codes, [X‐month] look‐back, and [Y‐day] follow‐up. [Dataset B] uses encounter‐level records with [deduplication rule]; [Dataset C] applies per‐patient ±[range] day date shifting. Full code sets and timing rules are in Supplement Table X.” To address the challenges of multi‐dataset integration, including differences in inclusion criteria, coding practices, and coverage periods, it is standard practice to consult a data scientist or informatics expert to ensure accurate harmonization.

• 3Pre‐specify agreement checks and conflict resolution

Define a priori how you will judge concordance (e.g., same effect direction, effect sizes within ±20% or overlapping 95% CIs, similar subgroup patterns). Explain what you'll do if results differ (e.g., sensitivity analyses, re‐harmonization, privileging the dataset with the most direct outcome measurement). For example: “We considered results concordant if effects were in the same direction and either (a) the 95% CIs overlapped or (b) point estimates differed by ≤ 20%. Discrepancies triggered sensitivity analyses (alternative code sets, windows, covariates) and adjudication favoring the dataset with the directly measured [outcome]. All divergences and their probable causes are reported in Supplement Table X.” Specific database search terms along with a date and time stamp should be included to ensure reproducibility and validity when using the large clinical datasets, including widely used biobanks (e.g., the UK Biobank), offer substantial analytic flexibility. However, this flexibility can increase researcher degrees of freedom, enabling unintentional selective cohort construction, multiple hypothesis testing, and analytic variation that may reduce reproducibility. Predefined protocols, transparent cohort selection criteria, and clear documentation of code sets and analytic windows are essential safeguards. Tools embedded within platforms such as HCUP, MIMIC‐IV, TriNetX, and Cosmos, along with institutional review processes, further support best practices for reproducible research. Adhering to these principles reduces the risk of spurious associations and strengthens the reliability of findings generated from large‐scale real‐world datasets.

10. Conclusions

Public healthcare databases offer important resources for medical research, providing large‐scale clinical and administrative data that can be leveraged to address diverse questions in epidemiology, outcomes research, and clinical informatics. Administrative databases, such as HCUP, facilitate the analysis of healthcare utilization, costs, and population outcomes (Table 4). In contrast, clinical databases, such as MIMIC‐IV, offer detailed patient‐level data that are ideal for studying disease progression. Databases like TriNetX and Epic Cosmos expand access to electronic medical record (EMR) data, enabling population‐level biomedical research (Table 4).

TABLE 4.

Database selection guide.

Question type	Database	Reasoning	Examples of clinical questions	Limitations
Utilization, costs, LOS, readmissions (policy/epi)	HCUP (NIS/SID/NEDS)2	Large, nationally representative administrative records; inexpensive; great for rates & trends	“National 30‐day readmission after COPD admission, 2019–2023; age/sex stratified.”	No meds; limited longitudinal linkage; requires DUA & HCUP training.
ICU physiology, notes, time‐series ML	MIMIC‐IV3	Rich ICU EHR (vitals, labs, notes); fully open after CITI/DUA; great for methods and phenotype work	“Sepsis phenotypes from vitals/labs; 48‐h mortality prediction benchmarking.”	Single‐center ICU; smaller N than national datasets; intermediate coding skills needed.
Comparative effectiveness, treatment patterns, labs	TriNetX4	Multi‐site real‐world EHR; GUI + SQL; good for cohort discovery, PSM, external controls	“SGLT2i vs. ARNI in HFrEF: 6‐mo admissions in adults with eGFR ≥ 30.”	Access via institutional subscription; watch code mapping quality.
Large‐scale multi‐system EHR with SDOH	Epic Cosmos4	Massive Epic‐contributed EHR network; SDOH variables; good for broad prevalence & risk patterns	“National prevalence of long‐COVID symptoms by rurality & payer, 2020–2024.”	License/subscription via Epic; access varies by institution; equity of access concern.
Not sure where to start? (students)	Use the question flow	Start with field/interest → data type (admin vs. clinical) → feasibility → outcomes	Follow the prompts: desired field → needed data type → inclusion/exclusion → outcomes → stats.	This is a framing tool, not a dataset; pair it with Tables 1 and 2 links.

These databases have distinct strengths and limitations. Our decision analysis helps match the research question to the most appropriate dataset. Equally important, many projects benefit from a complementary, multi‐dataset approach: answering the same question from administrative, ICU, and multi‐site EHR perspectives increases robustness and clinical relevance. When reported transparently with de‐identification safeguards, cell suppression, and date‐stamped analyses, this triangulation produces findings that are more reproducible and actionable for healthcare delivery and policy. To help solve some logistical issues that a learner may come across, we have provided a “Common Pitfalls and Practical Solutions” guide for database usage (Table S1).

In summary, repositories offer medical students an accessible, de‐identified dataset without the need for costly resources. Using these tools, researchers can generate hypotheses, investigate health disparities, and contribute to the development of clinical practices. By utilizing these databases, current and future medical students can contribute to advancing healthcare research and improving patient outcomes.

Funding

The authors have nothing to report.

Disclosure

The authors have nothing to report.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Common pitfalls and practical fixes.