Predictive analytics in chronic disease management uses historical and real-time patient data, such as health records, lab results, and readings from connected devices, to estimate what is likely to happen next: who will develop a condition, whose condition will worsen, and when to intervene. Diabetes is the clearest example. A continuous glucose monitor (CGM) can warn of a low before it happens, and research models now predict glucose 30 to 60 minutes ahead.
The need is large. According to the CDC, three in four American adults have at least one chronic condition, and more than half have two or more. Ninety percent of the nation’s $5.3 trillion in annual health care spending goes to people with chronic and mental health conditions, CDC data show.
This guide explains what predictive analytics is, how it is used across chronic disease care, what glucose prediction looks like in diabetes devices today, and how FDA regulates predictive software.
What is predictive analytics?
Predictive analytics is the practice of using historical data, statistical methods, and machine learning to estimate the likelihood of future events. In healthcare, it estimates the likelihood of outcomes such as a diagnosis, a hospital admission, or a dangerous change in a patient’s condition.
It differs from descriptive analytics, which reports what has already happened, such as last month’s average glucose. Predictive analytics looks forward, and that is what makes it useful for chronic disease: most of the value comes from acting before a problem becomes an emergency.
Use | What it predicts | Typical data |
|---|---|---|
Early detection | Who is likely to develop a condition | Health records, claims, lab results |
Progression and deterioration | Whose condition is likely to worsen, and when | Longitudinal records, remote monitoring |
Treatment response | Which therapy is likely to work for a patient | Imaging, clinical and genomic data |
Real-time alerts | An event in the next minutes or hours | Continuous sensor data, such as CGM |
How predictive analytics is used in chronic disease care
Early detection
Models trained on routine data can flag people at high risk before symptoms appear. Health records are a rich source: a landmark study in npj Digital Medicine applied deep learning to the records of 216,221 hospitalized adults and predicted in-hospital mortality with an area under the curve (AUROC) of 0.93 to 0.94, and 30-day readmission with an AUROC of 0.75 to 0.76.
Early detection matters most when it leads to earlier care. For people with diabetes, that can mean identifying those most at risk of complications or severe low glucose while there is still time to change treatment.
Predicting progression and deterioration
Predictive models can track how a patient’s data changes over time and flag deterioration that is easy to miss.
- Severe hypoglycemia in older adults. A 2024 study in PLOS Medicine used health record data from 364,863 older adults with diabetes to predict hospitalization for severe hypoglycemia within one year. The best model reached an AUROC of 0.978, and 0.856 in external validation.
- Heart failure. Implanted device algorithms can warn of fluid buildup before it leads to a hospital stay. In a 2022 real-world study of 107 patients, a heart failure alert care path detected impending fluid retention with 79% sensitivity and 0.23 unexplained alerts per patient-year.
Unexplained alerts are the practical limit. A model that warns too often trains clinicians and patients to ignore it, so false alert rates matter as much as accuracy.
Personalizing treatment
Predictive models can compare a patient with others who share similar characteristics to estimate which treatment is most likely to work. In lung cancer, a 2020 study in Cancer Immunology Research found that changes in CT scan features, analyzed by machine learning, predicted response to immunotherapy in 139 patients with an AUC of 0.81 to 0.88 across three groups.
In diabetes, personalization is already built into devices. Several automated insulin delivery algorithms adapt to each user’s insulin needs over time. See our guide to AI in diabetes devices for how those algorithms work and how FDA reviews them.
Glucose prediction in diabetes devices
Glucose prediction is where predictive analytics has moved furthest into everyday care, because a CGM produces a reading every few minutes.
In cleared devices today:
- Predictive low alerts. Dexcom’s Urgent Low Soon alert notifies the user when glucose is predicted to reach 55 mg/dL within 20 minutes.
- Predictive high and low alerts. Medtronic’s Guardian Connect CGM, approved by FDA in March 2018 (P160007), can alert users to potential high or low glucose “up to 60 minutes in advance,” according to Medtronic.
- Prediction inside automated insulin delivery. AID algorithms predict where glucose is heading and adjust insulin to match. That prediction is part of the algorithm’s FDA clearance.
In research and newer products:
Study | What it found |
|---|---|
Liu et al., JMIR Medical Informatics, 2023: review of 46 studies | Average prediction error (RMSE) rose from about 19 mg/dL at 15 minutes to about 30 mg/dL at 60 minutes. Neural networks performed best |
van Doorn et al., PLOS ONE, 2021: 851 people with CGM | More than 98% of 60-minute predictions were clinically safe |
Herrero et al., Journal of Diabetes Science and Technology, 2024: a predictive CGM app | 30-minute low glucose prediction had 95.2% sensitivity and 98.9% specificity, with an average warning of 16 minutes |
A model developed in 192 patients and validated in 427 others predicted mild hypoglycemia 30 minutes ahead with AUC above 97% |
Two patterns stand out. Accuracy falls as the prediction horizon grows, so a 60-minute forecast is much less precise than a 15-minute one. And nighttime lows are harder to predict than daytime ones: in the Herrero study, sensitivity for nighttime low prediction was 55.3%.
Connected devices and predictive analytics
Connected devices make predictive analytics practical outside the hospital. CGMs, connected blood pressure cuffs, smart scales, inhalers, and insulin pens collect data continuously and send it to apps and care teams.
That data does three things a model needs:
- Frequency. Readings every few minutes reveal trends that monthly clinic visits miss.
- Context. Meals, insulin doses, activity, and sleep help explain changes in a reading.
- Reach. Remote monitoring brings predictions to patients at home, where most chronic disease management happens. See our guide to remote patient monitoring for diabetes.
How FDA regulates predictive software
Whether predictive software is regulated as a medical device depends on what it analyzes, who uses it, and how urgent the decision is. FDA’s Clinical Decision Support Software guidance, revised in January 2026, draws three lines that matter for predictive analytics:
- Continuous sensor data makes it a device. FDA treats CGM readings as a “pattern/signal.” Software that analyzes them, such as a hypoglycemia alert, is a device.
- Time-critical predictions are devices. Software that predicts a cardiovascular event “in the next 24 hours” is a device, because clinicians cannot independently review an urgent recommendation.
- Some longer-term risk tools for clinicians are lower risk. A tool that predicts future cardiovascular risk for a clinician to consider can fall under FDA enforcement discretion when it meets the guidance’s other conditions.
Software that gives predictions directly to patients does not qualify as non-device clinical decision support. For the full criteria and examples, see our guide to clinical decision support software. If a predictive model will be retrained after clearance, a predetermined change control plan can let planned updates proceed without a new submission.
Challenges to solve
- Data quality. Connected devices produce gaps, noise, and sensor errors. A model is only as good as the data it learns from and the data it sees in use.
- Interoperability. Data from different devices and record systems often arrives in different formats. Standards such as HL7 FHIR help. See our guide to diabetes device interoperability.
- Alert fatigue. Every false alarm reduces trust. Design for an acceptable false alert rate, not just high sensitivity.
- Bias and generalization. Models trained in one population can perform worse in another. External validation, as in several of the studies above, is the test that matters.
- Privacy and security. Continuous health data needs strong protection. See our guides to HIPAA in MedTech development and GDPR for MedTech and SaMD.
- Architecture. Separate data collection, models, and user interfaces so you can update a model without rebuilding the product. See component architecture for SaMD development.
Frequently asked questions
What is predictive analytics in chronic disease management?
It is the use of patient data, such as health records, lab results, and connected device readings, to estimate future outcomes like disease onset, deterioration, or hospitalization. It helps care teams intervene earlier and personalize treatment for conditions such as diabetes, heart failure, and kidney disease.
How is predictive analytics used in diabetes?
The most common use is glucose prediction. CGMs can alert users before glucose is predicted to go too low or too high, and automated insulin delivery algorithms predict glucose trends to adjust insulin. Research models also predict longer-term risks, such as hospitalization for severe hypoglycemia.
How accurate is glucose prediction?
Accuracy depends on the time horizon. A 2023 review of 46 studies found an average prediction error of about 19 mg/dL at 15 minutes and about 30 mg/dL at 60 minutes. Short-term predictions of low glucose can be highly sensitive, but nighttime lows remain harder to predict.
Is predictive analytics software regulated by the FDA?
It depends on the function. Under FDA’s 2026 clinical decision support guidance, software that analyzes continuous sensor data such as CGM readings, supports time-critical decisions, or gives predictions directly to patients is a medical device. Some longer-term risk tools for clinicians can fall under FDA enforcement discretion.
What data does predictive analytics need?
It needs enough accurate, consistent data to learn from and to make predictions in use. For chronic disease, that usually means health records and lab results, plus continuous data from connected devices such as CGMs, blood pressure monitors, and scales.
Build predictive features into connected devices with Sequenex
Predictive analytics depends on the software around the model: reliable device data, clean integration, clinician and patient interfaces that make predictions useful, and a regulatory path planned from the start.
Sequenex builds that software for connected device and SaMD companies, from device connectivity and data platforms to the apps that deliver insights. See our SaMD development services, or contact us to discuss your project.
