Table 6.
Problem analyzed and data sources in healthcare administration.
| Reference | Focusing Area | Problem Analyzed | Data Source |
|---|---|---|---|
| [89] | Data warehousing and cloud computing | Developing a platform to analyze the causes of readmission | Emory Hospital, US |
| [90] | Development of a clinical data warehouse and analytical tools for traditional Chinese medicine | Traditional Chinese Medicine hospitals/wards | |
| [91] | Cloud and big data analytics based cyber-physical system for patient-centric healthcare applications and services | Not specified | |
| [92] | Repository of radiology reports | Not specified | |
| [93] | Creation of large data repository and knowledge discovery with unsupervised learning | University of Virginia University Health System | |
| [94] | Development of a mobile application to gather, store and provide data for rural healthcare | Not specified | |
| [95] | Healthcare cost, quality and resource utilization | Treatment error prevention to improve quality and reduce cost | National Taiwan University Hospital |
| [96] | Healthcare cost prediction | US health insurance company | |
| [97] | Healthcare resource utilization by lung cancer patients | Medicare beneficiaries for 1999, US | |
| [98] | Length of stay prediction of Coronary Artery Disease (CAD) | Rajaei Cardiovascular Medical and Research Center, Tehran, Iran | |
| [99] | Methodology for structured development of monitoring systems and a primary HC network resource allocation monitoring model | National Institute of Public Health; Health Care Institute, Celje; Slovenian Social Security Database, and Slovenian Medical Chamber | |
| [100] | Assess the ability of regression tree boosting to risk-adjust health care cost predictions | Thomson Medstat’s Commercial Claims and Encounters database. | |
| [101] | Evidence based recommendation in prescribing drugs | Dalhousie University Medical Faculty | |
| [102] | Efficient pathology ordering system | Pathology company in Australia | |
| [103] | Identifying people with or without insurance based on demographic and socio-economic factors | Behavioral Risk Factor Surveillance System 2004 Survey Data | |
| [104] | Predicting care quality from patient experience | English National Health Service website | |
| [105] | Patient management | Scheduling of patients | A south-east rural U.S. clinic |
| [106] | Care plan recommendation system | A community hospital in the Mid-West U.S. | |
| [107] | Examination of risk factors to predict persistent healthcare frequent attendance | Tampere Health Centre, Finland | |
| [108] | Forecasting number of patient visit for administrative task | Health care center in Jaen, Spain | |
| [109] | Critical factors related to fall | 1000 bed hospital in Taiwan | |
| [110] | Verification of structured data, and codes in EMR of fall related injuries from unstructured data | Veterans Health Administration database, US | |
| [111] | Other applications | Relation between medical school training and practice | Center for Medicare and Medicaid Service (CMS) |
| [112] | Analysis of physician reviews from online platform | Good Doctor Online health community | |
| [113] | Evaluation of Key Performance Indicator (KPIs) of hospital | Greek National Health Systems for the year of 2013 | |
| [114] | Post market performance evaluation of medical devices | HCUPNet data (2002–2011) | |
| [115] | Feasibility of measuring drug safety alert response from HC professional’s information seeking behavior | UpToDate, an online medical resource | |
| [116] | Influencing factors of home healthcare service outcome | U.S. home and hospice care survey (2000) | |
| [117] | Compilation of various data types for tracing, and analyzing temporal events and facilitating the use of NoSQL and cloud computing techniques | Taiwan’s National Health Insurance Research Database (NHIRD) |