A decision tree–based classifier for E-visit service provision

ABSTRACT

This study proposes a decision tree-based e-visit classification approach (DTEVCA) to determine clinic visits qualified as e-visits using clinics’ medical records and patients’ demographic data. This study assumes that health care insurance will subsidise e-visit service costs, in which case, identifying patients who benefit most from e-visit service is essential. Using a large data set from Taiwan’s National Health Insurance, this study verifies the efficiency and validity of the DTEVCA. Results indicate that this approach can accurately classify in-office clinic visits that could switch to e-visit services. The straightforward rules of this decision tree also give insurance agencies a clear guideline to understand the circumstances of using e-visits and predict the effects of implementing e-visits in Taiwan. Result of this study can help countries improve the policy formulation process for physicians’ use, or for academic research. The DTEVCA can update classification rules using new data to correct biases and ensure the stability of the e-visit system. In addition, the concept of this approach is feasible not only for e-visit service but also for other ‘new services’ such as new products or new policies.

KEYWORDS:

• E-visit
• healthcare analytics
• data classification
• decision tree
• health care insurance
• data mining

Acknowledgments

This research was sponsored by the Ministry of Science and Technology in Taiwan, under project number MOST 106-2410-H-002-069. This study is based in part on data from the National Health Insurance Research Database provided by the National Health Insurance Administration, Ministry of Health and Welfare and managed by National Health Research Institutes (Registered number NHIRD-103-266).

Disclosure Statement

The authors report no conflicts of interest.

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan [MOST 106-2410-H-002-069].