Nursing

Visual analytics

Big data

World Wide Web

Philosophy

Health care

Analytics

Social psychology

Visualization

Computer security

Social media analytics

Economics

Transparency (behavior)

Data mining

Computer science

Risk analysis (engineering)

Business

Medical prescription

Psychology

Social media

Epistemology

Data science

Artificial intelligence

Information retrieval

Medicine

Deep learning

Economic growth

Recommender system

Operationalization

Consistency (knowledge bases)

Popularity

Machine learning

User-generated content

MIS Quarterly

This paper presents and synthesizes results from three studies (two controlled experiments and one interview) on using recommender systems to reduce healthcare costs at prescription time, while taking time pressure into account. All of our subjects were real practicing physicians, nurse practitioners, or physician assistants. Across these studies, a total of 160 medical practitioners used a system that provides recommendations for medications along with associated cost information. The main finding was a general tendency among practitioners to reduce healthcare costs by prescribing lower cost medications when cost information is provided by a recommender system. The time pressure faced daily by prescribers, however, appears to impact the use of recommendations by nurse practitioners and physician assistants more than it does physicians. These results have significant implications for cost reduction in healthcare and for the design of effective real-time healthcare recommender systems.

Can Recommender Systems Reduce Healthcare Costs? The Role of Time Pressure and Cost Transparency in Prescription Choice