Education and professional development

Internships in Clinical Data Science: A Brief Report

Authors: Richard Ittenbach orcid logo (Cincinnati Children’s Hospital and University of Cincinnati College of Medicine) , Amy Neubauer (Alexion/AstraZeneca Rare Disease Unit) , Stephen Cameron (ICON plc)

  • Internships in Clinical Data Science: A Brief Report

    Education and professional development

    Internships in Clinical Data Science: A Brief Report

    Authors: , ,

Abstract

Field-based learning opportunities are considered to be an important and increasingly relevant part of professional learning in the biomedical sciences. The purpose of this study was to document internship opportunities in the combined fields of clinical data management and clinical data science among those attending the Society for Clinical Data Management 2022 annual conference. At the time of the survey there were 276 internship positions staffed across 47 organizations representing pharmaceutical, academic, clinical research, software development, and medical device companies, suggesting ample opportunities for those who wished to gain experience in such organizations. The greatest percentage of internships reported were in the United States, but with a wide range of opportunities worldwide.

Keywords: clinical data management, clinical data science, internship, practicum, research education

How to Cite:

Ittenbach, R., Neubauer, A. & Cameron, S., (2024) “Internships in Clinical Data Science: A Brief Report”, Journal of the Society for Clinical Data Management 1. doi: https://doi.org/10.47912/jscdm.316

Funding

  • Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital

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Published on
06 Jun 2024
Peer Reviewed

Field-based learning opportunities are considered to be an important and increasingly relevant part of professional learning in the biomedical sciences today. In a field such as clinical data science, where there are few – if any – formal educational programs worldwide, the importance of rigorous field-based training opportunities is critical not only for a trainee’s learning but also for workforce development.1 Defined simply as learning activities that offer practical, experiential training in a specific area of professional practice, the purpose of field-based educational experiences in clinical data science is generally twofold: to offer learning opportunities that cannot easily be obtained in the classroom while at the same time, developing a pipeline of well-trained talent for employers needing the expertise.

With only a modestly developing literature base regarding field-based learning opportunities in the more established fields of computer and data science,2,3 comparable information in the clinical data sciences is nonexistent. This irony exists despite a rapidly growing industry and a workforce that could easily benefit from the information.4 Such experiences go by many different labels, including practica, internships, apprenticeships, traineeships, or cooperative learning experiences. The advantages of having such information can be tremendous to both the trainees and research organizations. For the aspiring professional, these experiences can introduce trainees to problems they are likely to encounter on the job, as well as exposure to real-life clients who may be as challenging as the problems themselves. Trainees can see first-hand how they will interact with professionals from other specialized areas.5,6 Learning how to navigate new administrative systems is another important benefit worth acquiring early on. Strong training programs with strong highly motivated trainees will serve as good advocates when recruiting others irrespective of the organization (e.g., academe, governmental, industry, and the nonprofit sectors).

Benefits to the hiring organization are many, as well. Hiring organizations are able to employ staff at a considerably lower cost while instilling core knowledge in a potential employee. Internship periods have generally reduced onboarding costs relative to other hires. In the case of clinical data science, that often means training across the areas of computer science, statistics, regulatory affairs, and the medical sciences. Rawlings-Goss (2019) refers to this phenomenon as “learning-oriented employment,” and describes the targeted approach to professional development as particularly well-suited for professionals who enter a field without substantial knowledge and/or fragmented preparation in a specific area of interest.7 As practice patterns in industry have shifted from an emphasis on data quality to data integrity, there has been an equally prominent shift in the skill sets needed, from one of managing the research data to risk-based critical thinking and scientific scrutiny. Yet, today’s data professionals must be proficient at both. For purposes of this paper, we will refer to this collection of practical, experientially driven, field-based learning programs as “internships.”

While many organizations in the biomedical sciences offer internships, many do not. Without a definitive resource for prospective professionals to turn to, it is extremely hard for trainees to know where to obtain the relevant skills, particularly with respect to the core competencies of the profession.8,9,10,11 At a recent educational session devoted to career opportunities in clinical data science, 85 of 141 participants (60%) in India indicated that they would be looking for an internship within the coming year – but needed help finding points of entry into the profession.12 From an organization’s standpoint, trying to find interns in a world without formal training programs can be extremely challenging. Hiring organizations have to do both the vetting and the training, which puts a heavier burden on them. As a result, the Society for Clinical Data Management has begun tracking internship opportunities among participating members and institutions and offers consultative support for both. Hence, the purpose of this report is to document and report on internship opportunities within the combined field of clinical data management and clinical data science today.

Methods

Procedures

The current survey was a subset of a much larger task analysis survey administered to attendees at the 2022 Society for Clinical Data Management (SCDM) meeting in San Antonio, TX. Surveys were administered by the survey designers and support staff in sessions throughout the conference. When possible, conference attendees were asked to complete and return the surveys while in session, or to complete them on their own and return them to a staff member as soon as possible after completion. A total of 108 surveys were completed and returned (16% of the conference attendees). Response time was approximately 15 minutes for the entire survey, including 2 minutes for the internship portion.

Statistical Analyses

All data were analyzed using traditional descriptive statistics. Frequency counts and percentages were used for binary and categorical data, while measures of central tendency and variability were used for continuous data. A single Spearman-rho correlation coefficient was used to estimate the relationship between organization size and number of interns hired. The criterion for statistical significance was set at the nominal α = 0.05 level. The SAS code and data to corroborate these results are provided in Supplemental Files 1 and 2, respectively.

Results

Forty-seven (43.5%) of 108 participants reported that their organizations hired interns, while 29 (26.8%) reported that their organizations did not (32 [29.6%] did not respond to this question). Of the 47 respondents who reported that their organization routinely hires interns, 35 reported the actual number of internship opportunities staffed or available in their organization at the time of the survey. At the time of the survey, there were 273 staffed internships (7.8 interns/organization (σ = 10.9), ranging from a low of 0 to a high of 50 interns (median = 3) within an organization. A respondent could report zero interns if their organization did not have any interns at the time of the survey. That was the case for one respondent in this survey.

Survey respondents represented small (< 1,000 employees; n = 45 [41.7%]), medium (1,001 to 10,000 employees; n = 18 [16.7%]), and large organizations (> 10,000 employees; n = 45 [41.7%]). The number and pattern was relatively similar for organizations reporting internship opportunities, in favor of the larger organizations: 16 small (34.0%), 9 medium (19.2%), and 22 large (46.8%) organizations (see Table 1). There was no statistically significant relationship between organization size and number of interns (r = –0.31, p = 0.07).

Table 1

Organizational Characteristics.

Factor All Organizations†
n (%)
Organizations with Internships
n (%)
Organizational Size (Number of Employees)
   Small (1,000 or less) 45 (41.7) 16 (34.0)
   Medium (1,001 to 9,999) 18 (16.7) 9 (19.2)
   Large (10,000 or more) 45 (41.7) 22 (46.8)
Organizational Type
   Academic Research Organization 3 (2.8) 1 (2.1)
   Biologics Research & Development 4 (3.7) 3 (6.4)
   College or University 3 (2.8) 1 (2.1)
   Contract Research Organization 21 (19.4) 7 (14.9)
   Medical Device Research & Development 8 (7.4) 5 (10.6)
   Healthcare Facility 1 (0.9)
   Pharmaceutical Research & Development 56 (51.8) 29 (61.7)
   Software Provider 9 (8.3)
   Other 1 (0.9) 1 (2.1)
   No response 2 (1.8)
Geographic Location
   Asia Pacific 3 (2.8) 2 (4.3)
   Canada 3 (2.8) 1 (2.1)
   Europe 2 (1.8)
   India 4 (3.7)
   South America 4 (3.7) 2 (4.3)
   United States 92 (85.2) 42 (89.4)
  • Note. †Sample sizes are as follows: all organizations (n = 108), organizations with internships (n = 47).

A broad range of organizations was represented among the survey participants, with pharmaceutical (n = 56, 51.8%) and contract research organizations (n = 21, 19.4%) having the most interns, followed by software providers (n = 9, 8.3%), medical device (n = 8, 7.4%) and biological research and development companies (n = 4, 3.7%). Other respondents reported having three or fewer internship positions. With respect to organizations offering internship opportunities, the patten was similar: pharmaceuticals (n = 29, 61.7%), contract research organizations (n = 7, 14.9%), and medical device companies (n = 5, 10.6%). See Table 1 for a complete listing of internships by organization type.

Among all survey participants, most respondents were located in the United States (n = 92, 85.2%). Other regions were represented as follows: Asia Pacific (n = 3, 2.8%), Canada (n = 3, 2.8%), Europe (n = 2, 1.8%), India (n = 4, 3.7%), and South America (n = 4, 3.7%). Among those offering internship opportunities, the United States (n = 42, 89.4%), Asia Pacific (n = 2, 4.3%), South America (n = 2, 4.3%), and Canada (n = 1, 2.1%) were the only regions with identified internship opportunities (see Table 1). There were no opportunities reported in Europe or India. Among those with internship opportunities, three sectors predominated: Private or publicly traded companies (n = 43, 91.5%), Academe (n = 2, 4.3%), and Sole Proprietorship companies (n = 1, 2.1%); one respondent failed to report a market sector.

Discussion

Clinical data management and clinical data science represent a critical and emerging component of the biomedical science enterprise. Despite the importance of clinical data management to public health over the past 50 years, formal educational opportunities have been greatly lacking.13 Without the benefit of degree-granting programs in clinical data science at the undergraduate and graduate levels to train the workforce, less formal, employer-driven programs like internships have been used as a principal training mechanism. For those trying to enter the profession, or trying to move from one sector to another, internship programs are considered to be a viable route to continuing education and development. Without a definitive resource in place to document and connect interns with organizations, trainees and organizations must search on their own for the right match. For the trainees, that could mean several years of searching and career progression before achieving the desired role of clinical data manager or clinical data scientist.

The purpose of this report was to document internship opportunities within the combined field of clinical data management and clinical data science. Findings presented here offer initial but convincing evidence of internship opportunities worldwide for those looking to get first-hand experience in pharmaceutical, academic, clinical research, software development, and medical device research and development organizations. While this report is only preliminary, it does suggest that many opportunities exist for those wishing to pursue internships in the newly emerging field of clinical data science. While the survey itself appeared weighted in favor of organizations located within the United States (n = 92), the number of actual internship opportunities was more balanced, with 178 of the 273 (65%) internship opportunities located in the United States, and 95 of 273 (35%) internship opportunities located outside of the United States. Given the comparability of organizations between those with and without internships reported here, it is very likely that the number of internship positions reported in this study is an underestimate. Participants at other professional meetings may report other internship opportunities, as well. Equally promising, the internships described here reflect small, medium, and large research organizations, allowing for variation in preferences for interested applicants.

The major strength of this study is that it formally documents and describes the pattern of internship opportunities available to clinical data management and clinical data science professionals available today—providing a call to action with respect to more formalized training opportunities in the biomedical sciences. Like the professionals currently in the workforce, the internship positions are spread across many types of organizations throughout many regions of the world. There should therefore be opportunities for everyone, it is just a matter of finding the right fit for the intern and for the hiring organization. Recommendations for further research include doing a deeper dive into which organizations have the openings, the number of openings available, and what levels of training and experience are needed for the respective internship positions. Participating organizations should also provide institutional contact information so that potential interns know whom to contact for follow up.

There are a number of limitations to this study. First, it was conducted at a single, albeit international, conference in one location and may not fully represent the pattern of internship opportunities industry-wide. Second, responses were embedded within a longer and more complex survey on practice patterns and competencies with a fairly low participation rate of 16%. No attempt was made to control for multiple responses from the same organization. Future research should have a brief but targeted survey, designed to obtain internship availability information at the organizational level. Finally, it is recommended that professional organizations make obtaining this information a common practice at annual meetings so that the field can track its progress in this important area of professional development.

Conclusion

Findings presented here offer convincing evidence of internship opportunities worldwide for those looking to get first-hand experience in the clinical data sciences, specifically when it relates to pharmaceutical, academic, clinical research, software development, and medical device development organizations.

Additional Files

The additional files for this article can be found as follows:

Funding Information

Portions of this report were made possible by a sabbatical award to RFI by the Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital. The authors would like to extend a heartfelt thank you to Dr. Meredith Zozus and the Clinical Research Informatics team at the University of Texas Health Science Center, San Antonio for their work on the survey, from which the internship data were obtained.

Competing Interests

The authors have no competing interests to declare.

Author Contributions

RFI drafted the initial manuscript. AN and SC provided substantial comment, edits, and contributions to the initial draft. All authors approved the final manuscript for submission and publication.

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