Total costs of diabetes in 2017 were $327 billion annually, of which $237 billion were in direct medical costs. Americans with diabetes in 2017 spent approximately $16,700 annually in health care costs, 2.3 times higher than those without diabetes. ĭiabetes imposes significant healthcare utilization and costs. In 2017, 83,564 deaths were attributed to diabetes in the United States, and that year, diabetes was the 7th leading cause of death in the United States (25.7 deaths per 100,000 population). There are approximately 1.5 million new diabetes diagnoses among people 18 years and over every year, and in 2018, approximately 34.2 million persons (10.5%) in the US had diabetes. In this way, this predictive model can be used in moving from prediction to implementation and improved diabetes care management in clinical settings. These findings were translated into a clinical intervention now being piloted at the sponsoring healthcare organization. Post-hoc analysis of the model was used for hypothesis generation, namely that HDL and BP are the strongest contributors to unplanned medical visits among patients with diabetes.
Our machine-learning predictive model more accurately predicted unplanned medical visits among patients with diabetes, relative to conventional models.
These recommendations, along with stakeholder engagement, behavioral economics strategies, and implementation science principles helped to inform the design of a clinical intervention targeting behavioral changes. A sensitivity analysis identified BP and HDL as the strongest predictors, such that disrupting these variables with random noise decreased the model’s overall balanced accuracy by 1.3 and 1.4 percentage points, respectively. This model outperformed a conventional logistic regression by 0.4 percentage points. The best-performing model was a linear-basis support vector machine, which achieved a balanced accuracy (average of sensitivity and specificity) of 65.7%. The best-performing model after cross-validation testing was analyzed to identify strongest predictors. A variety of machine-learning classification models were run using standard EMR variables as predictors (age, body mass index (BMI), systolic blood pressure (BP), diastolic BP, low-density lipoprotein, high-density lipoprotein (HDL), glycohemoglobin (A1C), smoking status, number of diagnoses and number of prescriptions). Methodsĭata were drawn from electronic medical records (EMRs) from a large healthcare organization in the Northern Plains region of the US, from adult (≥ 18 years old) patients with type 1 or type 2 diabetes who received care at least once during the 3-year period.
This study presents a case study of how predictive analytics can inform clinical actions, and describes practical factors that must be incorporated in order to translate research into clinical practice. In this study, a machine learning predictive model was developed to predict unplanned medical visits among patients with diabetes, and findings were used to design a clinical intervention in the sponsoring healthcare organization. Til højre på siden finder du et link til vores Facebook, hvor du kan følge aktuelle diskussioner og debatter.Diabetes is a medical and economic burden in the United States. Se symposiet som web-tv her på siden sammen med interviews med nogle af dem, der var med.
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