A Review of Factors Influencing Patient Readmission Based on Data Quality Dimension Model
Ensuring the quality of data in healthcare is important as to determine the level of performance of health services offered by them. The key factor that helps boost their performance are the enhancement of timely diagnosis and management of diseases and patient, keeping personnel satisfaction and le...
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Main Authors: | , , , , , |
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Format: | Conference or Workshop Item |
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Institute of Electrical and Electronics Engineers Inc.
2020
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Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097644532&doi=10.1109%2fICIMU49871.2020.9243500&partnerID=40&md5=2baa08fb2c4cb491d2130472ddf4f6e0 http://eprints.utp.edu.my/30032/ |
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Institution: | Universiti Teknologi Petronas |
Summary: | Ensuring the quality of data in healthcare is important as to determine the level of performance of health services offered by them. The key factor that helps boost their performance are the enhancement of timely diagnosis and management of diseases and patient, keeping personnel satisfaction and lessen the hospital costs in running them. Assessing the quality of data in adoption electronic health record (EHR) and preventing the rising of patient readmission rates in Malaysia is the main concern of the project. However, there is lack of study on patient readmission factors in Malaysia and the adoption of electronic health record (EHR) also causing poor quality of patients' data to be analyzed. Lastly, delivering the information to patients is difficult and confusing because of lacking in medical knowledge and terms. Therefore, this paper proposes consistency as additional dimension for data quality dimension to be used for validating patient readmission dataset. Theoretically, this project is seen to improve the quality of information by proposing improvement in used dimension to assess data quality of EHR. On the other hand, practically, this paper will help to visualize the results of medicinal information in the form of dashboard, which can be easily understood by both patients and health practitioners. © 2020 IEEE. |
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