• DOI:10.1089/tmj.2013.0261
• Corpus ID: 21575914

Development of a telehealthcare decision support system for patients discharged from the hospital.

@article{Lin2014DevelopmentOA,
  title={Development of a telehealthcare decision support system for patients discharged from the hospital.},
  author={Hanjun Lin and Yeh-Liang Hsu and Ming-Shinn Hsu and Chih-Ming Cheng},
  journal={Telemedicine journal and e-health : the official journal of the American Telemedicine Association},
  year={2014},
  volume={20 8},
  pages={
          748-56
        }
}

• Hanjun Lin, Yeh-Liang Hsu, +1 author Chih-Ming Cheng
• Published 24 July 2014
• Medicine
• Telemedicine journal and e-health : the official journal of the American Telemedicine Association

OBJECTIVE
This article presents the development of a telehealthcare decision support system (TDSS) for patients discharged from the hospital, where symptom data are important indications of the recovery progress for patients. Symptom data are difficult to quantify in a telehealthcare application scenario because the observations and perceptions on symptoms by the patient themselves are subjective. In the TDSS, both symptom data from patients and clinical histories from the hospital information system are collected. Machine learning algorithms are used to build a predictive model for classifying patients according to their symptom data and clinical histories, to provide a degree of urgency for the patient to return to the hospital.

MATERIALS AND METHODS
During a 1-year period, 1,467 patient cases were collected. Symptom data and clinical histories were preprocessed into 49 parameters for machine learning. The training data of patients were validated manually with their actual clinical histories of returning to the hospital. The performances of predictive models trained by five different machine learning algorithms were evaluated and compared.

RESULTS
The Bayesian network algorithm had the best performance among the machine learning algorithms tested in this application scenario and was selected to be implemented in the TDSS. On the 1,467 patient cases collected, its precision in 10-fold cross-validation was 79.3%. The most important six parameters were also selected from the 49 parameters by feature selection. The performance of correct prediction by the TDSS is comparable to that by the nursing team at the call center.

CONCLUSIONS
The TDSS provides a degree of urgency for patients to return to the hospital and thereby assists the telehealthcare nursing team in making such decisions. The performance of the TDSS is expected to improve as more cases of patient data are collected and input into the TDSS. The TDSS has been implemented in one of the largest commercialized telehealthcare practices in Taiwan administered by Min-Sheng General Hospital.