The Standard

Winner of the Most Innovative Award for HL7’s 2026 AI Challenge

Non-communicable diseases (NCDs) are no longer only a clinical challenge. They are a health-system, economic, and development challenge.

NCDs account for 75% of non-pandemic-related deaths worldwide. Cardiovascular disease, cancer, chronic respiratory disease and diabetes cause 80% of all premature NCD deaths, and 73% of NCD deaths occur in low- and middle-income countries. [1,4]

Botswana shows why the next phase of NCD prevention must combine clinical care with better data use. The latest national STEPS ( STEPwise approach to NCD risk factor surveillance) survey found that hypertension prevalence rose from 31.1% in 2014 to 39.6% in 2024. Nearly half of adults are now overweight, and more than one in five are obese. [2]

These diseases do not occur independently. Hypertension, diabetes, obesity, cardiovascular disease and chronic kidney disease (CKD) share risk factors, biological pathways and, ultimately, the same patient. Yet our digital systems often still manage them in separate clinical and administrative silos.

That fragmentation is one of the problems we set out to address with the BioESC Health Intelligence Platform (B-HIP). B-HIP received the Most Innovative Award in the 2026 HL7 International AI Challenge, selected from 73 submissions across 25 countries. It was the category's first year and the first time an African-led platform has received the award.

The Idea: Detect Earlier, Connect the Data, Act Sooner

B-HIP began with a simple question. What if the same interoperable data used to estimate one patient's future risk could also help a health system understand its future clinical and economic burden?

The answer is an architecture designed around prevention rather than late-stage disease management. B-HIP brings together a patient-facing mobile app, a clinician portal, a FHIR-based longitudinal health record, disease registries, clinical decision support, e-referral pathways, and population-health analytics.

Rather than building separate digital tools for hypertension, diabetes, kidney disease and cardiovascular risk, we aim to create one common health-intelligence layer in which these conditions are understood together.

One Person, Multiple Risks: The Patient Twin

We call the individual-level view the Patient Twin. It brings together observations such as blood pressure, weight, glucose, kidney function, medications, diagnoses, and laboratory results to build a longitudinal picture of each person’s NCD risk.

Validated clinical models then sit within that common architecture. For kidney disease, B-HIP uses established measures such as the CKD-EPI equation and the Kidney Failure Risk Equation (KFRE), which has been externally validated across international populations. [3] The same architecture can progressively add cardiovascular risk, hypertension control, diabetes risk, and other validated models, without building a new digital system for each disease.

This distinction matters. B-HIP does not rely on a single artificial-intelligence algorithm that claims to predict everything. It is a standards-based environment in which validated clinical equations, machine-learning tools and decision-support pathways coexist, with full transparency about where each prediction comes from.

Since the fall of 2017, electronic Clinical Quality Measures (eCQMs) logic has been written using Clinical Quality Language (CQL). With the introduction of CQL, the eCQMs included in the CMS federal programs had to be translated from Quality Data Model (QDM)-based expression logic to CQL.

Though CQL provides more flexibility in expressing measure criteria within the logic that allows measure developers to include greater specificity and precision within the measure requirements, this did not result in a direct translation process, as new attributes and timings were made available. What this means is that clinical workflow and input are perhaps more important because CQL is able to convey these nuances, whereas the QDM-based logic expressions were limited. CQL can also be used to develop Clinical Decision Support (CDS) rules and can reuse logic from measures in those CDS rules.

When composing measures using the CQL syntax, the QDM is still used in conjunction with CQL as the means to characterize required data elements within the measure logic. This conceptual model defines what type of clinical data is needed to satisfy the measure requirements. Therefore, the QDM is still very much a part of electronically specified measures, which is incorporated into the logic definitions written using CQL.

Health Level Seven (HL7^®) drives the standards upon which electronic Clinical Quality Measures (eCQMs) sit, so participation in these venues is crucial. The HL7 community already has a lot of technical input but lacks input from the clinical side – either from clinicians or clinical informaticists. There is a gap between the clinical intent of quality measures and the technical standard, but clinical informatics experts can help bridge this gap by contributing to the conversation and development of the HL7 standards and resources.