{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:50:45Z","timestamp":1787028645950,"version":"3.56.0"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cheng Hsin General Hospital","award":["CY11101"],"award-info":[{"award-number":["CY11101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>With the rapid development of information technology, digital health technologies have become increasingly prevalent in the field of healthcare. In this study, business intelligence (BI) techniques were combined with research-based prediction models to increase the efficiency and quality of healthcare practices. A data scenario involving 200 older adults with various measurements, including health beliefs, social support, self-efficacy, and disease duration, was used to establish a medication adherence prediction model in a BI system. A regression model, logistic regression model, tree model, and score-based prediction model were used to predict medication adherence among older adults. The developed BI-based prediction model has visualization, real-time feedback, and data updating functionality. These features enhanced the effectiveness of prediction models in clinical practice. Healthcare professionals can incorporate the proposed system into their care practice for health assessments and management, and patients can use the system to manage themselves. The developed BI-based care system can also be used to achieve effective communication and shared decision-making between care managers and patients. Further empirical studies integrating prediction models into the proposed BI system for assessment, management, and decision-making in healthcare practice are warranted.<\/jats:p>","DOI":"10.3390\/informatics11030065","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T10:58:46Z","timestamp":1725447526000},"page":"65","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adopting Business Intelligence Techniques in Healthcare Practice"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1875-8608","authenticated-orcid":false,"given":"Hui-Chuan","family":"Huang","sequence":"first","affiliation":[{"name":"School of Nursing, College of Nursing, Taipei Medical University, Taipei 110, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui-Kuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hwei-Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"Heart Center, Cheng Hsin General Hospital, Taipei 112, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeng","family":"Wei","sequence":"additional","affiliation":[{"name":"Heart Center, Cheng Hsin General Hospital, Taipei 112, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Hsian","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan"},{"name":"Heart Center, Cheng Hsin General Hospital, Taipei 112, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuan-Chia","family":"Lin","sequence":"additional","affiliation":[{"name":"Heart Center, Cheng Hsin General Hospital, Taipei 112, Taiwan"},{"name":"Community Medicine Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1080\/00325481.2017.1383820","article-title":"Interpretation of correlations in clinical research","volume":"129","author":"Hung","year":"2017","journal-title":"Postgrad. Med."},{"key":"ref_2","first-page":"139","article-title":"Information Technology and Information Management in Healthcare","volume":"274","year":"2020","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_3","first-page":"8","article-title":"Identifying the Knowledge Structure and Trends of Nursing Informatics: A Text Network Analysis","volume":"41","author":"Park","year":"2023","journal-title":"Comput. Inform. Nurs. CIN"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s11912-018-0672-3","article-title":"Using Information Technology in the Assessment and Monitoring of Geriatric Oncology Patients","volume":"20","author":"Loh","year":"2018","journal-title":"Curr. Oncol. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"268","DOI":"10.2174\/1573399816666200719012849","article-title":"Health Information Technology and Diabetes Management: A Review of Motivational and Inhibitory Factors","volume":"17","author":"Dehnavi","year":"2021","journal-title":"Curr. Diabetes Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.ins.2020.06.025","article-title":"An accurate and dynamic predictive model for a smart M-Health system using machine learning","volume":"538","author":"Din","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/13645706.2019.1575882","article-title":"Introduction to artificial intelligence in medicine","volume":"28","author":"Mintz","year":"2019","journal-title":"Minim. Invasive Ther. Allied. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1097\/01.NUMA.0000578988.56622.21","article-title":"How artificial intelligence is changing nursing","volume":"50","author":"Robert","year":"2019","journal-title":"Nurs. Manag."},{"key":"ref_9","first-page":"579","article-title":"Evidence for Busines Intelligence in Health Care: A Literature Review","volume":"235","author":"Loewen","year":"2017","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1186\/s40064-016-2525-6","article-title":"Modeling the prediction of business intelligence system effectiveness","volume":"5","author":"Weng","year":"2016","journal-title":"SpringerPlus"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Andrade, J.R.M., and Blomberg, L.C. (2022). Business intelligence applied to the consumption of iodinated contrast agents in computed tomography scans. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-01814-9"},{"key":"ref_12","first-page":"24","article-title":"Business intelligence for the visualization and data analysis of Telepharmacy activity indicators in a hospital pharmacy service scorecard","volume":"46","author":"Luaces","year":"2022","journal-title":"Farm. Hosp. Organo Expr. Cient. Soc. Esp. Farm. Hosp."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1097\/NNA.0000000000000060","article-title":"Business intelligence and nursing administration","volume":"44","author":"Welton","year":"2014","journal-title":"J. Nurs. Adm."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.gerinurse.2021.08.011","article-title":"A predictive model for identifying low medication adherence among older adults with hypertension: A classification and regression tree model","volume":"42","author":"Chu","year":"2021","journal-title":"Geriatr. Nurs."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1097\/00002820-200304000-00007","article-title":"Reliability and validity of the mammography screening beliefs questionnaire among Chinese American women","volume":"26","author":"Wu","year":"2003","journal-title":"Cancer Nurs."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s10865-008-9170-7","article-title":"Revision and validation of the medication adherence self-efficacy scale (MASES) in hypertensive African Americans","volume":"31","author":"Fernandez","year":"2008","journal-title":"J. Behav. Med."},{"key":"ref_17","first-page":"23","article-title":"Factors Involved in the Medication Adherence of Hypertensive Patients","volume":"11","author":"Ho","year":"2014","journal-title":"J. Nurs. Healthc. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e12488","DOI":"10.1111\/opn.12488","article-title":"Applying classification and regression tree analysis to identify risks of developing sarcopenia in the older population","volume":"17","author":"Nguyen","year":"2022","journal-title":"Int. J. Older People Nurs."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1186\/s13054-022-04274-9","article-title":"Score-based prediction model for severe vitamin D deficiency in patients with critical illness: Development and validation","volume":"26","author":"Kuo","year":"2022","journal-title":"Crit. Care"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e24190","DOI":"10.2196\/24190","article-title":"Mobile Apps to Improve Medication Adherence in Cardiovascular Disease: Systematic Review and Meta-analysis","volume":"23","author":"Mason","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e32557","DOI":"10.2196\/32557","article-title":"Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis","volume":"10","author":"Alhussein","year":"2022","journal-title":"JMIR mHealth uHealth"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1002\/nur.22094","article-title":"Effects of mobile health interventions on improving glycemic stability and quality of life in patients with type 1 diabetes: A meta-analysis","volume":"44","year":"2021","journal-title":"Res. Nurs. Health"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.jad.2023.04.026","article-title":"Predictive models for predicting the risk of maternal postpartum depression: A systematic review and evaluation","volume":"333","author":"Qi","year":"2023","journal-title":"J. Affect. Disord."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-3-030-85292-4_3","article-title":"Foundations of Machine Learning-Based Clinical Prediction Modeling: Part II-Generalization and Overfitting","volume":"134","author":"Kernbach","year":"2022","journal-title":"Acta Neurochir. Suppl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1007\/s10278-019-00184-5","article-title":"ETL Framework for Real-Time Business Intelligence over Medical Imaging Repositories","volume":"32","author":"Godinho","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1111\/1748-8583.12396","article-title":"Building prediction models with grouped data: A case study on the prediction of turnover intention","volume":"34","author":"Yuan","year":"2023","journal-title":"Hum. Resour. Manag. J."},{"key":"ref_27","first-page":"88","article-title":"Exporting Data from a Clinical Data Warehouse","volume":"248","author":"Fette","year":"2018","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.jhin.2015.01.005","article-title":"Role of data warehousing in healthcare epidemiology","volume":"89","author":"Wyllie","year":"2015","journal-title":"J. Hosp. Infect."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109","DOI":"10.3102\/10769986030002109","article-title":"Prediction in Multilevel Models","volume":"30","author":"Afshartous","year":"2005","journal-title":"J. Educ. Behav. Stat."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1111\/joim.12822","article-title":"eDoctor: Machine learning and the future of medicine","volume":"284","author":"Handelman","year":"2018","journal-title":"J. Intern. Med."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/11\/3\/65\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:48:42Z","timestamp":1760111322000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/11\/3\/65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,4]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["informatics11030065"],"URL":"https:\/\/doi.org\/10.3390\/informatics11030065","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,4]]}}}