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Arias JC, Ramos MI, Cubillas JJ. Predicting emergency health care demands due to respiratory diseases. Int J Med Inform 2023; 177:105163. [PMID: 37517299 DOI: 10.1016/j.ijmedinf.2023.105163] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/26/2023] [Accepted: 07/24/2023] [Indexed: 08/01/2023]
Abstract
BACKGROUND Timely care in the health sector is essential for the recovery of patients, and even more so in the case of a health emergency. In these cases, appropriate management of human and technical resources is essential. These are limited and must be mobilised in an optimal and efficient manner. OBJECTIVE This paper analyses the use of the health emergency service in a city, Jaén, in the south of Spain. The study is focused on the most recurrent case in this service, respiratory diseases. METHODS Machine Learning algorithms are used in which the input variables are multisource data and the target attribute is the prediction of the number of health emergency demands that will occur for a selected date. Health, social, economic, environmental, and geospatial data related to each of the emergency demands were integrated and related. Linear and nonlinear regression algorithms were used: support vector machine (SVM) with linear kernel and generated linear model (GLM), and the nonlinear SVM with Gaussian kernel. RESULTS Predictive models of emergency demand due to respiratory disseases were generated with am absolute error better than 35 %. CONCLUSIONS This model helps to make decisions on the efficient sizing of emergency health resources to manage and respond in the shortest possible time to patients with respiratory diseases requiring urgent care in the city of Jaén.
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Affiliation(s)
- J C Arias
- Group TIC-144 of the Andalusian Research Plan, University of Jaen, Spain
| | - M I Ramos
- Department of Cartographic, Geodetic and Photogrammetric Engineering, University of Jaen, Spain.
| | - J J Cubillas
- Department of Information and Communication Technologies applied to Education, International University of La Rioja, Spain
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Machine learning in general practice: scoping review of administrative task support and automation. BMC PRIMARY CARE 2023; 24:14. [PMID: 36641467 PMCID: PMC9840326 DOI: 10.1186/s12875-023-01969-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. METHODS Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. RESULTS 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. CONCLUSION The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done.
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Subrahmanya SVG, Shetty DK, Patil V, Hameed BMZ, Paul R, Smriti K, Naik N, Somani BK. The role of data science in healthcare advancements: applications, benefits, and future prospects. Ir J Med Sci 2021; 191:1473-1483. [PMID: 34398394 PMCID: PMC9308575 DOI: 10.1007/s11845-021-02730-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 07/28/2021] [Indexed: 11/27/2022]
Abstract
Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical examinations, insurance, etc. The data collected from the Internet of Things (IoT) devices attract the attention of data scientists. Data science provides aid to process, manage, analyze, and assimilate the large quantities of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to acquire factual results. The process of data cleansing, data mining, data preparation, and data analysis used in healthcare applications is reviewed and discussed in the article. The article provides an insight into the status and prospects of big data analytics in healthcare, highlights the advantages, describes the frameworks and techniques used, briefs about the challenges faced currently, and discusses viable solutions. Data science and big data analytics can provide practical insights and aid in the decision-making of strategic decisions concerning the health system. It helps build a comprehensive view of patients, consumers, and clinicians. Data-driven decision-making opens up new possibilities to boost healthcare quality.
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Affiliation(s)
- Sri Venkat Gunturi Subrahmanya
- Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Dasharathraj K Shetty
- Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Vathsala Patil
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
| | - B M Zeeshan Hameed
- Department of Urology, Father Muller Medical College, Mangalore, Karnataka, India
| | - Rahul Paul
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Komal Smriti
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Nithesh Naik
- Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bhaskar K Somani
- Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK
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Kamble SS, Gunasekaran A, Goswami M, Manda J. A systematic perspective on the applications of big data analytics in healthcare management. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2018. [DOI: 10.1080/20479700.2018.1531606] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Sachin S. Kamble
- Operations and Supply Chain Management, National Institute of Industrial Engineering, Mumbai, India
| | - Angappa Gunasekaran
- School of Business and Public Administration, California State University, Bakersfield, Bakersfield, CA, USA
| | - Milind Goswami
- National Institute of Industrial Engineering, Mumbai, India
| | - Jaswant Manda
- National Institute of Industrial Engineering, Mumbai, India
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Islam MS, Hasan MM, Wang X, Germack HD, Noor-E-Alam M. A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining. Healthcare (Basel) 2018; 6:E54. [PMID: 29882866 PMCID: PMC6023432 DOI: 10.3390/healthcare6020054] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 05/17/2018] [Accepted: 05/21/2018] [Indexed: 12/17/2022] Open
Abstract
The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.
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Affiliation(s)
- Md Saiful Islam
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA.
| | - Md Mahmudul Hasan
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA.
| | - Xiaoyi Wang
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA.
| | - Hayley D Germack
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA.
- National Clinician Scholars Program, Yale University School of Medicine, New Haven, CT 06511, USA.
- Bouvé College of Health Sciences, Northeastern University, Boston, MA 02115, USA.
| | - Md Noor-E-Alam
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA.
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Chang DS, Leu JD, Wang WS, Chen YC. Improving waiting time for surgical rooms using workflow and the six-sigma method. TOTAL QUALITY MANAGEMENT & BUSINESS EXCELLENCE 2018. [DOI: 10.1080/14783363.2018.1456329] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Dong-Shang Chang
- Department of Business Administration, National Central University, Taoyuan City, Taiwan
| | - Jun-Der Leu
- Department of Business Administration, National Central University, Taoyuan City, Taiwan
| | - Wen-Sheng Wang
- Department of Business Administration, National Central University, Taoyuan City, Taiwan
| | - Yi-Chun Chen
- Department of Business Administration, National Central University, Taoyuan City, Taiwan
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Ramos MI, Cubillas JJ, Feito FR. Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools. J Med Syst 2015; 40:6. [PMID: 26573643 DOI: 10.1007/s10916-015-0379-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2015] [Accepted: 10/09/2015] [Indexed: 11/29/2022]
Abstract
The continued availability of products at any store is the major issue in order to provide good customer service. If the store is a drugstore this matter reaches a greater importance, as out of stock of a drug when there is high demand causes problems and tensions in the healthcare system. There are numerous studies of the impact this issue has on patients. The lack of any drug in a pharmacy in certain seasons is very common, especially when some external factors proliferate favoring the occurrence of certain diseases. This study focuses on a particular drug consumed in the city of Jaen, southern Andalucia, Spain. Our goal is to determine in advance the Salbutamol demand. Advanced data mining techniques have been used with spatial variables. These last have a key role to generate an effective model. In this research we have used the attributes that are associated with Salbutamol demand and it has been generated a very accurate prediction model of 5.78% of mean absolute error. This is a very encouraging data considering that the consumption of this drug in Jaen varies 500% from one period to another.
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Affiliation(s)
- M Isabel Ramos
- Department of Cartography, Geodesy and Photogrammetry Engineering, University of Jaen, Jaen, Spain
| | - Juan José Cubillas
- Department of Computer Science, TIC-144 Andalusian Research Plan (PAI), University of Jaen, Jaen, Spain.
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Vidal C, Garcia M, Benito L, Milà N, Binefa G, Moreno V. Use of text-message reminders to improve participation in a population-based breast cancer screening program. J Med Syst 2014; 38:118. [PMID: 25073694 DOI: 10.1007/s10916-014-0118-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Accepted: 07/16/2014] [Indexed: 11/28/2022]
Abstract
To analyze the effect of a cell text message reminder service on participation in a mammogram screening program in Catalonia, Spain. A quasi-experimental design was used with women aged 50 to 69 years who had been scheduled mammogram appointments in June or July 2011. Women were personally invited by letter to attend to the breast cancer screening program (n = 12,786). Prior to the invitation, 3,719 (29.1 %) of them had provided their cell telephone number to the National Health Service. These women received a text message reminder 3 days before their scheduled appointment. Logistic regression models were used to analyze whether the text message reminder was associated with participation in screening. Cost-effectiveness of adding a text message reminder to the invitation letter was also analyzed. The overall rate of participation in breast cancer screening was 68.4 %. The participation rate was significantly higher in the text messaging group, with an age-adjusted OR of 1.56 (95 %CI: 1.43-1.70). A detailed analysis showed that the increase in participation related to the text message reminder was higher among women without previous screening who lived in areas where access to postal mail was limited (OR=2.85; 95 %CI: 2.31-3.53) compared to those who lived in areas of easier postal mail access (OR=1.66; 95 %CI: 1.36-2.02). The invitation letter+text message reminder was a cost-effective strategy. Text message reminders are an efficient cost-effective approach to improve participation in difficult-to-reach populations, such as rural areas and newly developed suburbs.
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Affiliation(s)
- C Vidal
- Cancer Prevention and Control Program, Catalan Institute of Oncology, IDIBELL, Av. Gran Via 199-203, 08908, L'Hospitalet de Llobregat, Barcelona, Spain
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