Potential Return on Subscription to a Smartphone Based Biometric Health Risk Assessment Tool

OBJECTIVES: Assess budget impact of smartphone-based risk assessment to identify individuals at risk of obesity, overweight, depression, general anxiety disorder, metabolic syndrome and type 2 diabetes and sequelae, after early intervention in the USA and Singapore.

METHODS: A decision tree model assessed potential reduction in cases and relevant complications through early risk assessment and subsequent intervention for those at risk. A budget impact model was used to determine potential cost savings from early intervention.

Treatment costs and expected reduction in number of cases and complications over two (Singapore) and five years (USA) through early intervention was obtained via a PubMed search covering Obesity, Overweight, Metabolic Syndrome, Prediabetes, Diabetes Complications, Depression, and Anxiety.

Treatment costs were assessed from a health care perspective and included service costs, overall costs, and costs of treating complications.

Percentages of potential remission of conditions and costs of interventions per remission were also researched.

RESULTS: Conservative estimates were based on prevention of up to 15% of diabetes type II cases over the years mentioned. Depression and Anxiety screening may increase cases by 20%, for early treatment.

Per person, the estimate shows a reduction of up to 15% in costs due to the avoidance of diabetes type II, and an increase of up to 20% in costs for early treatment of depression and anxiety, which is included in the overall estimate.

In Singapore, the potential cost savings per 100,000 people over two years is SGD76.52 per-person, totaling SGD7,652,000. In the USA, the potential cost savings per 100,000 people over five-years is USD344.01 per person, totaling USD34,401,000.

CONCLUSIONS: Early intervention is established as cost-saving in the longer term. The availability of remote this risk assessment technology and its advanced ability to predict risk using AI, may be very cost-effective. The technology makes greater pro-activity in assessing large populations more cost-effective and less prohibitive.