Keywords
Public Health Informatics - Primary Prevention - Secondary Prevention - Precision
Medicine - Digital Health
1. Introduction
Building off the reviews by Pearson et al. [[1]] and Gallego et al. [[2]], the survey paper commissioned for the 2024 Yearbook of Medical Informatics, we
define precision prevention as “the use of biologic, behavioral, socioeconomic, and epidemiologic data to inform the
right intervention at the right intensity in the right community at the right time,
in order to prevent or reduce illness and improve health”. The term grew out of the work started two decades ago in precision medicine, which calls for individualizing patient treatments based on genetic and biomedical
markers, and later gave rise to the notion of precision public health, which emphasizes tailoring of community-level interventions to specific populations
that may benefit most from a given intervention.
An illustration of precision prevention can be found in Lim et al. [[3]], which examined the evidence on interventions for pregnant women with respect to
preventing gestational diabetes mellitus (GDM). The scientists examined more than
10,000 studies to find evidence on the use of pregnant persons' biological, genetic,
and lifestyle characteristics to predict response to a given intervention that aimed
to prevent GDM. The researchers found that combined diet and physical activity worked
best for pregnant persons without polycystic ovary syndrome (PCOS) versus those with
PCOS. Patients with PCOS responded better to metformin interventions. Understanding
which subpopulations respond better to which interventions can help clinicians and
public health programs better prevent onset of disease or support secondary prevention
of morbidity associated with disease.
Despite a decade of work to develop precision prevention techniques and studies on
precision public health approaches, little is known about the role of informatics
in supporting precision prevention. In theory, digital health applications and informatics
approaches are critical to ensure clinical and public health organizations collect,
manage, and analyze the broad landscape of data (e.g., clinical, demographic, behavioral, environmental, social, educational) necessary
to deliver precise interventions to populations. It is precisely for this reason that
the International Medical Informatics Association (IMIA) Yearbook of Medical Informatics
Selection Committee chose “Digital Health for Precision in Prevention” as this year's
theme. The special section of the Yearbook focuses on calling out recent, high-quality
publications that examine and/or advance our understanding of informatics in facilitating
precision prevention.
2. Methods
The primary author (BED) performed a literature search using PubMed in January 2024.
The query was developed to broadly search biomedical journals for articles pertaining
to digital health applications relevant to precision prevention. Both controlled vocabulary
terms (e.g., Medical Subject Headings) and text words were used. Because precision prevention
is relevant to many biomedical domains, we explicitly included terms like “precision
nutrition” and “precision public health” in addition to “precision prevention”. Also,
because many precision medicine approaches involve the use of advanced analytics,
including artificial intelligence in order to predict who might be at risk for a disease
or outcome, we included “artificial intelligence” as an informatics search term. We
employed Boolean logic to identify articles published in English language between
January 1, 2023 and December 31, 2023, that contained at least one information science
term and one precision prevention related term. The full query is included as Appendix A.
Information retrieval yielded 126 candidate articles. Using Covidence systematic review
software (Veritas Health Innovation, Melbourne, Australia[1]), the editors performed initial screening of titles and abstracts. Screening removed
70 studies that did not pertain to the theme. Both editors reviewed the remaining
articles and categorized them into three groups (accept, discuss, and discard) based
on their innovativeness, scientific and/or practical impact, and methodological quality.
Reasons for exclusion included descriptive review articles or commentary/editorial;
published prior to 2023; lacking peer review (published as preprint); or the contribution
of informatics methods or approaches was unclear.
This process yielded 11 articles as candidate best papers [[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]]. In accordance with the IMIA Yearbook selection process, the candidate best papers
were further evaluated by the two section editors, the chief editor of the section,
and by additional external reviewers (at least four reviewers per paper) with expertise
in medical and/or public health informatics.
3. Results
The search yielded a small corpus of papers related to precision prevention involving
information systems. Several of these papers were commentaries or perspective articles
that called for action, encouraging or outlining how digital health could be used
to support precision prevention efforts [[4]
[5]
[6]
[7]]. However, these articles were excluded from consideration as best papers. Instead,
our focus was on those papers that detailed the development, implementation, and/or
evaluation of a digital health solution that aimed to support precision prevention.
Although candidate papers did not have to propose a novel digital health application,
they had to be relevant to biomedical informatics. Some excluded papers, for example,
involved clinical interventions for precision prevention that did not pertain to digital
health or involve information systems.
Candidate papers relevant to informatics could be categorized into one of three groups:
1) precision
nutrition applications, such as the use of machine learning to predict response to a dietary fiber intervention
based on metabolism and/or gut microbiome [[15]]; 2) precision medicine, in which the use of machine learning or artificial intelligence (AI) is used to
predict therapeutic response to treatments; or 3) precision public health, applications in which analytics are utilized to predict response to social or behavioral
interventions in order to prevent disease onset or result in harm reduction (e.g., smoke cessation).
Most candidate papers could be categorized as precision nutrition applications. While
many used machine learning or AI approaches to predict response to interventions or
outcomes given genetic risk profiles, several papers pertained to infrastructure needed
to advance precision nutrition approaches. One paper, for example, described the development
of a database of food-drug interactions that could be used to power a clinical decision
support system designed to detect and alert patients to potential interactions that
might prevent effectiveness of therapeutics prescribed by their clinicians [[8]]. Another paper described a database designed for use with digital health applications
(e.g., wearables) that could provide customized nutrition and lifestyle guidelines to patients
[[9]]. These applications make us excited about the power of AI and machine learning
to help patients make healthy choices when eating and create customized nutrition
plans to optimize health, including weight loss and muscle development, in the future.
The other set of papers that excited us were those focused on precision public health
applications. Some of these papers were clinically oriented, such as the risk communications
trial from Guan et al. [[10]]. In this trial of women at average risk for breast cancer, some women received
customized risk communications that provided alternatives to annual mammograms. These
alternatives included delaying screening or reducing the frequency of screening in
alignment with recommendations from the United States Preventative Services Task Force.
Armed with genetic, behavioral, and mammography risk information, women in the intervention
arm were more willing to delay or reduce frequency of screening. The trial demonstrated
that customized risk information can be useful in helping patients choose appropriate
screenings in addition to treatments and lifestyle interventions. More evidence on
precision public health applications using dashboards, smart devices, SMS, patient
kiosks, etc. would be useful to help populations increase awareness of their risks
and the benefits of more tailored interventions designed to keep them healthy.
The papers selected by the section editors in consultation with the editorial board
as best papers are summarized in [Table 1] [[11]
[12]
[13]
[14]]. Final selection was based on these criteria: 1) reviewer ratings and comments;
2) equity across authors' nation and world regions; and 3) content balance across
disciplines in medicine and public health. Some disciplines had more candidate papers
than others, and the editors felt that all disciplines should be represented in the
special section. A content summary of the selected best papers can be found in Appendix
B of this synopsis.
Table 1.
Number of papers collected in the PubMed database from 2013 to 2023 for each country
based on the first author's affiliation country.
|
Notable Papers Published in 2023 focused on Digital Health for Precision Prevention
|
|
Andrade AQ, Calabretto JP, Pratt NL, Kalisch-Ellett LM, Le Blanc VT, Roughead EE.
Precision public-health intervention for care coordination: a real-world study. Br
J Gen Pract. 2023;73(728):e220-e30.
|
|
Hwang U, Kim SW, Jung D, Kim S, Lee H, Seo SW, et al. Real-world prediction of preclinical
Alzheimer's disease with a deep generative model. Artif Intell Med. 2023;144:102654.
|
|
Lee S, Kim S, Yoon DS, Park JS, Woo H, Lee D, et al. Sample-to-answer platform for
the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based
assay. Nat Commun. 2023;14(1):2361.
|
|
Wang B, Liu F, Deveaux L, Ash A, Gerber B, Allison J, et al. Predicting Adolescent
Intervention Non-responsiveness for Precision HIV Prevention Using Machine Learning.
AIDS and behavior. 2023;27(5):1392-402.
|
4. Conclusions
The best papers on digital health for precision prevention in 2023 represent a fraction
of the strong scientific articles relevant to this topic published before and after
this synopsis. Given rising rates of noncommunicable morbidity and mortality following
the COVID-19 pandemic, precision prevention is in the forefront of the work we must
do in digital health. Continued research and development is necessary to implement
digital health tools that support precision prevention for all persons across the
globe.
