Machine learning builds living evidence maps to tackle primary care inequalities

Health inequalities remain one of the most stubborn problems facing modern medicine, and primary care sits at the front line of the battle. Now, a team of researchers has combined machine learning with a new kind of living evidence map to reveal, in unprecedented detail, what science actually knows about reducing health inequalities in primary care — and, just as importantly, what it does not. The study, published in Public Health in Practice, screened more than 31,000 records and catalogued over a thousand studies and reviews, exposing stark imbalances in the research landscape while demonstrating how artificial intelligence can keep pace with an ever-growing mountain of literature.

The problem the researchers set out to tackle is twofold. First, health systems worldwide struggle to provide fair and equal access to primary care. In the United Kingdom, people living in areas of socioeconomic disadvantage consistently report lower satisfaction with the care they receive, and general practices in deprived areas have fewer doctors, less funding, and are more likely to be rated inadequate, all while serving patients with more complex, long-term health problems at younger ages. This is a textbook illustration of the “Inverse Care Law,” first articulated by Julian Tudor Hart in 1971, which holds that the availability of good medical care tends to vary inversely with the need for it in the population. Second, even where evidence exists, it is becoming nearly impossible to navigate. Primary care publications alone have risen by roughly 380 percent over the past two decades, and the average worldwide growth rate of academic output hovers around four percent per year. A full systematic review takes, on average, sixteen months from design to publication — by which point its findings may already be outdated.

Traditional systematic reviews, the gold standard for synthesising medical evidence, are labour-intensive and slow, and they rapidly fall behind the literature they are meant to summarise. Machine learning offers a way out. Prior work has identified dozens of tools that use machine learning techniques to assist with the systematic reviewing process, supporting everything from study selection to data extraction and gap identification. Yet relatively few studies have systematically combined these methods to support policymakers and practitioners working on health and care inequalities. Until now, no living evidence map existed describing how to address inequalities in and through primary care.

The research team built their Living Evidence Map using EPPI-Reviewer, systematic review management software developed by the EPPI Centre at University College London, together with its integrated suite of machine learning tools. Bibliographic records were drawn from OpenAlex, an open-access database containing more than 250 million scholarly works. At the heart of the workflow was a binary machine learning classifier — a model trained to classify each record as likely relevant or not relevant to the review question. The classifier was developed using 1,006 manually included title and abstract records and 22,426 excluded records, randomly assigned to training, calibration and evaluation sets with stratification by inclusion status. The model learned patterns in titles and abstracts associated with study relevance and assigned each incoming record a relevance score; records falling below a threshold were excluded from the screening pool entirely.

The team’s searches ran approximately monthly using two complementary approaches. Citation-based searches identified records linked to known relevant studies through citation relationships — papers that cited, were cited by, or were otherwise connected to included studies. Automated update searches used a model called ContReview, which combines information from citation links and article text to rank unscreened records by likely relevance. Human reviewers then screened articles in order of predicted relevance, with the screening pool continually re-ranked using an active machine learning approach, meaning the model improved as screening progressed. Screening continued until the rate of inclusion dropped, a standard stopping criterion in automated evidence synthesis.

The classifier’s performance was striking. On the evaluation set of 4,686 records, it achieved a recall of 0.965, meaning it correctly captured nearly 97 percent of relevant articles, while discarding 60.7 percent of records without any manual screening — a workload reduction that translates into months of saved reviewer time. Precision, at 0.105, was deliberately low: the model was tuned to prioritise catching everything relevant over keeping the screened pool small, a sensible trade-off when the cost of missing a key study outweighs the cost of screening a few extra irrelevant ones. Included articles were then manually coded for intervention type, disadvantaged population group, health or care outcome, and study design, with a ten percent sample audited by a second researcher to ensure accuracy.

The resulting map paints a vivid picture of where research attention has flowed — and where it has not. The team included 577 primary studies, 481 systematic reviews and six umbrella reviews, along with 154 minor contributions. Ethnic minority population groups emerged as by far the most frequently studied disadvantaged group, particularly in relation to education interventions, cultural tailoring, and chronic disease management. The single most heavily researched combination was education interventions for ethnic minorities, with 127 systematic reviews and 95 primary studies, followed closely by culturally competent care and advice and counselling interventions for the same groups. Latino and Hispanic populations were the most studied of all, followed by Black African and Caribbean and then Asian populations — a pattern the authors attribute to the predominance of studies originating in the United States.

In sharp contrast, gender and sexual minorities were the most underrepresented of all groups, with the fewest studies identified. The authors suggest this reflects the invisibility of these populations in research and a lack of routine data, since gender expression and sexual orientation are not systematically coded in health care practice, making it harder to target interventions. Notably absent from much of the map, too, were structural interventions — those addressing funding allocation, workforce distribution, and other upstream determinants of health. Such interventions were considerably less common than discrete, individual-level approaches such as education, counselling, and link workers. The researchers argue this is unsurprising but concerning: discrete interventions are easier to evaluate in conventional trial designs over short periods, whereas funding reforms and workforce policies are complex, slow-moving, and require long-term data. Funders, meanwhile, may prefer downstream interventions because they offer more direct, demonstrable benefits to individual patients.

Other patterns emerged in the conditions studied. Research on ethnic minority groups more frequently examined diabetes-related outcomes — with 87 systematic reviews and 94 primary studies on the topic — whereas studies of inclusion health groups, such as people experiencing homelessness or substance dependence, more commonly focused on cancer and substance misuse outcomes. Intriguingly, the team also found that the number of systematic reviews roughly matched the number of primary studies, a potentially unhealthy sign for the research ecosystem. For evidence synthesis to function well, there should always be far more primary research than reviews to draw upon. Recent analyses have found that the number of systematic reviews indexed in PubMed increased more than twenty-fold over two decades, reaching approximately eighty published per day by 2019.

The implications stretch well beyond primary care research. The Living Evidence Map, now publicly available through the Health Equity Evidence Centre, allows policymakers, commissioners and practitioners to explore the evidence interactively, spotting patterns and gaps in real time as new studies are added. The authors acknowledge limitations: the map does not yet capture intersectionality or multiple disadvantage, excludes grey literature and non-English studies, and is limited to high-income, UK-comparable contexts. Some relevant studies that do not mention specific disadvantaged groups in their titles and abstracts may also have been missed. Maintenance funding for living evidence resources remains an open question. Nevertheless, the study demonstrates that machine learning can transform evidence synthesis from a snapshot that ages quickly into a living, continuously updated resource — and it sends a clear message to research funders that the biggest gaps lie not in patient-level education programmes, but in the structural changes that could reshape who gets good care in the first place.