What are Medical-Legal Partnerships?

Medical-legal partnerships (MLPs) promote health justice by adding lawyers to healthcare teams or hospital referral networks. They are based on the understanding that many health conditions cannot be managed solely in a clinical setting. For example, issues with food insecurity may affect diabetes management, and issues with mold and pests in an apartment complex can impact a child’s chronic asthma. In these examples, medical-legal partnerships could help patients get access to public benefits for food or hold a landlord accountable for environmental allergens. MLPs are designed to help patients navigate these external systems that can affect their health outcomes.

In my work as a liaison librarian for Georgetown’s Health Justice Scholars medical student track, I helped students and faculty with research projects related to social determinants of health and MLPs. Students often came to me for help finding research topics, so I decided to explore major topics, papers, and authors in this interdisciplinary field.

How do you analyze the literature?

I used Jupyter Notebooks to run Metaknowledge, a bibliometrics Python package. The package reads a directory of plain text files containing metadata on publications and citations and writes to a variety of data structures that are suitable for quantitative, network, and text analyses. You will need to set up Jupyter notebooks and download the package before getting started. I use Anaconda to manage Python installations, but you can use whatever setup you prefer. You can find additional Metaknowledge installation instructions here: https://metaknowledge.readthedocs.io/en/latest/install.html.

After installation, I searched Web of Science on the topic of MLPs (TS=(health NEAR/5 justice NEAR/5 partner*)) OR TS=(medical NEAR/5 legal NEAR/5 partner*)). This retrieved 164 citations, which I exported as a CSV file. Once I cleaned the data up, I ingested the CSV file into Jupyter Notebooks and ran the four notebooks/scripts that comprise the Metaknowledge package.

Literature analysis

Gender breakdown

Metaknowledge uses the open-source gender classifier developed for the Open Gender Tracker to estimate the gender of authorships. The MLP field does not follow the general pattern of underrepresentation of women seen in other scientific research areas. 31.9% of the authors were estimated to be male, and 55.1% were female. 

Which papers were cited the most?

MetaKnowlege also provides a basic breakdown of top authors, cited papers, and journals for the body of citations analyzed. For MLPs, it is not surprising that both law and medical journals publish on this topic.

RecordCollection glimpse made at: 2021-08-31 18:03:06
164 Records from MLPsavedrecs
Top Authors
1 Sandel, Megan
2 Beck, Andrew F.
3 Klein, Melissa D.
4 Pettignano, Robert
4 Kahn, Robert S.
4 Martinez, Omar
4 Tsai, Jack
5 Girard, Vicki W.
5 Lawton, Ellen
Top Journals
Top Cited
1 Sandel M, 2010, HEALTH AFFAIR, V29, P1697, DOI 10.1377/hlthaff.2010.0038
2 Weintraub Dana, 2010, J Health Care Poor Underserved, V21, P157, DOI 10.1353/h
2 Zuckerman B, 2004, PEDIATRICS, V114, P224, DOI 10.1542/peds.114.1.224
3 Cohen E, 2010, J GEN INTERN MED, V25, pS136, DOI 10.1007/s11606-009-1239-7
4 Klein MD, 2013, J HEALTH CARE POOR U, V24, P1063, DOI 10.1353/hpu.2013.0147
5 Zuckerman B, 2008, LANCET, V372, P1615, DOI 10.1016/S0140-6736(08)61670-0
6 Ryan AM, 2012, J HEALTH CARE POOR U, V23, P1536, DOI 10.1353/hpu.2012.0179
7 Paul Edward, 2009, J Grad Med Educ, V1, P304, DOI 10.4300/JGME-D-09-00016.1
7 O'Sullivan MM, 2012, J ASTHMA, V49, P911, DOI 10.3109/02770903.2012.724131

We can also see how the number of papes has increased over the years. MLPs seems to be a research area on the rise.

Reference Publication Year Spectroscopy

Reference Publication Year Spectroscopy (RPYS) is a way to inspect the top publications in years where there is a substantial deviation from the median. Pronounced peaks represent years where citations to published books or articles deviate from a 5-year median. In the standard RPYS plot for MLP, we can see the highest peak in 1999. By examing the dataset, the citation for this paper is identfied as

Lerner, Alan M. (1999) “Law & Lawyering in the Work Place: Building Better Lawyers by Teaching Students to Exercise Critical Judgment as Creative Problem Solver,” Akron Law Review: Vol. 32 : Iss. 1 , Article 2.
Available at: https://ideaexchange.uakron.edu/akronlawreview/vol32/iss1/2

Although this publication is from a law journal, the later peaks come from health journals. So the RPYS shows an interesting shift in research impact in this field from law reviews in the late 90s to primarily health journals in 2020. 


The Multi-RPYS is another way to inspect publication impact over the years. It is useful for differentiating between historical publications that have a lasting impact versus those that are influential only within a short time frame. It rank transforms the data and enables researchers to compare different time slices within the same field. The transformed data is then visualized as a heatmap, revealing a dynamic picture of changes in citations to historical publications over time. Darker bands across both time periods indicate lasting influence. For example, we can see a darker band in 1999 from the Lerner citation.

Coauthor relationships

In addition to identifying high-impact citations, Metaknowledge can be used to measure author connections within a field. Degree centrality identifies authors with the most connections. Which author can quickly connect us to the wider network of researchers in this field? Authors like Andrew Beck and Megan Tschudy may be good people to contact. 

The package also measures betweenness centrality, which calculates the number of bridges in a citation network.

Coauthor Network

Coauthor networks are used to identify researchers who collaborate or work in similar research clusters. The coauthor network for MLPs does not have many connections, suggesting many researchers are working in isolation or small clusters on this topic.

When in on the central cluster, we see a higher level of connection. This group would perhaps be good to target as influencers for research collaboration.

Topic Analysis

Metaknowledge also uses natural language processing to identify topic areas represented by a body of literature. Although it can be messy to gain insights from this data, we can see that issues related to veterans’ health, women’s health, the criminal justice system, and pediatric health outreach all fall under the MLP umbrella. Seeing this topic list might spark some research ideas for students exploring this interdisciplinary field.

Topic #0:
health legal mental care veterans public approach medicallegal mlps affect
Topic #1:
treatment psychiatric criminal obligations measures review police mental justice health
Topic #2:
publicprivate legal partnerships partnership health services consultation justice public national
Topic #3:
screening health law intervention mlp medical legal students community families
Topic #4:
health care legal social address medicallegal patients needs partnerships mlps
Topic #5:
violence forensic sleep times women issues activity violent arising felt
Topic #6:
social health policy encourage single women schoolbased research leaders important
Topic #7:
grantee performance sshs initiative outcomes operations characteristics outcome schoolshealthy grant
Topic #8:
active prison los ont prisoners age culture previously prevalence sd
Topic #9:
reentry parents healthcare youth providers care access perspectives juvenile health
Topic #10:
health acp residents care direct observed social performance developed improving
Topic #11:
health care partnerships publicprivate partnership justice public social hiv studies
Topic #12:
measures ipv assessment existing criteria practitioners systematic review health type
Topic #13:
implementation juvenile core study science seven health involving partner design
Topic #14:
health legal care social services needs patients medical partnerships partnership

Recommendations for MLP researchers

  • Connect isolated researcher nodes. Address the lack of connectivity among HJS scholars by hosting a research conference or special journal issue. Use social media to identify additional thought leaders in this field. 
  • Expand research subtopics. Use topics list and keywords to generate ideas for student lit reviews and/or optimize abstracts for database ingestion and SEO keyword targeting.


MLPs are multidisciplinary. I only searched Web of Science, so this dataset may not adequately cover law literature. In the future, I would expand the search to additional databases.


Citations: Reid McIlroy-Young, John McLevey, and Jillian Anderson. 2015. metaknowledge: open source software for social networks, bibliometrics, and sociology of knowledge research. URL: http://www.networkslab.org/metaknowledge.