SBIR/STTR Research Award

STTR Phase I: A Social and Data-Driven Platform for Searching Healthcare Providers

Award identifier 6eabd865fb2bba4d77fc3d67b85e307e55b71d7aef0e27b4416e449c2d7d082fAwarding agency National Science Foundation

Award amount

$225,000

Program and phase

STTR · Phase I

Award year

2015

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
SmartBot360
Proposal award date
2015-01-01
Contract end date
2015-12-31
UEI
JVQUMBYZ28W3
Solicitation number
Not reported
Topic code
IC

Research abstract

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project results from it providing a novel way to more effectively match patients to healthcare providers. Patients will be able to use the developed system to discover and compare the most suitable and experienced healthcare providers for their unique health needs, instead of just viewing a directory of providers in their area. For example, the proposed system will recommend to a patient searching for "knee replacement surgery" orthopedic surgeons with experience in this or similar procedures, positive health outcomes and high past patients satisfaction levels. The patient may then compare these surgeons by expertise, relative cost, location and several other attributes. By helping patients select the best healthcare providers, the proposed system not only has the potential to improve health outcomes, because providers will see the patients more relevant to their expertise, but may also reduce the number of "shopping-around" visits which may lead to a reduction in overall healthcare costs. This Small Business Technology Transfer (STTR) Phase I project will study methods to exploit Big Data, ranging from government-published health metrics and surveys to public social media data, to better match patients to healthcare providers. A key challenge is that many of these sources include free text, which must be analyzed to extract medically significant information. Further, different sources refer to the same medical terms in different ways; for example "myocardial infarction" vs. "heart attack", or "doctor knows her stuff" vs. "doctor is knowledgeable". Data mining and artificial intelligence techniques will be leveraged to identify which provider properties - medical school, years of experience, affiliated hospitals, and so on - should be used when searching for providers and how these properties should be best weighted and combined, given the healthcare needs of a patient.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

3 SBIR/STTR records share this UEI (2015–2020). Reported award amounts total $706,000. Reported amounts are not federal obligations.

Phases: Phase I

Agencies: National Science Foundation

Agency intelligence

National Science Foundation

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