ArticleHealthcare informatics research2012
Data mining application in customer relationship management for hospital inpatients.
Article in Healthcare informatics research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 24 citations in OpenAlex.
- An approach to exploring associations between hospital structural measures and patient satisfaction by distance-based analysis.BMC health services research · 2021Article
- A time-duration measure of continuity of care to optimise utilisation of primary health care: a threshold effects approach among people with diabetes.BMC health services research · 2019Observational
- The Relationship Between the Customer Relationship Management and Patients' Loyalty to Hospitals.Global journal of health science · 2015Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients.
methodsA recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree.
resultsPatients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%.
conclusionsTo discover a hospital's loyal patients and model their medical usage patterns, the application of data-mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with data-mining in CRM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.