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The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap.

Richard P Mann¹, Faisal Mushtaq², Alan D White³

¹School of Mathematics, University of Leeds , Leeds , UK.

Frontiers in Public Health

|December 20, 2016

Summary

This summary is machine-generated.

Leveraging existing small datasets can create predictive models for public health, demonstrating tangible benefits. This approach bridges the gap between big data

Keywords:

big data health economics length of stay small data surgery

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Area of Science:

Public Health Informatics
Health Services Research
Data Science in Healthcare

Background:

Big data initiatives in public health face a public perception gap despite scientific optimism.
A disconnect exists between the potential of big data and public understanding of its benefits.

Purpose of the Study:

To demonstrate the immediate value of small data approaches in public health.
To illustrate how existing data can yield practical healthcare improvements.
To propose a balanced strategy for data utilization in public health.

Main Methods:

Developed a proof-of-concept predictive model using existing smaller datasets.
Focused on predicting hospital length of stay as a tangible healthcare metric.
Emphasized the utilization of current information resources.

Main Results:

The small data model generated reasonable predictions for hospital length of stay.
Demonstrated that existing small datasets are sufficient for creating valuable predictive models.
Highlighted the feasibility of using smaller datasets for immediate healthcare applications.

Conclusions:

Small data approaches can yield immediate, tangible benefits in public health.
Integrating small data strategies alongside big data initiatives is crucial.
Increased attention and funding for utilizing existing data resources are recommended.