1
|
Salami A, Afshar-Nadjafi B, Amiri M. A Two-Stage Optimization Approach for Healthcare Facility Location- Allocation Problems With Service Delivering Based on Genetic Algorithm. Int J Public Health 2023; 68:1605015. [PMID: 36926284 PMCID: PMC10011119 DOI: 10.3389/ijph.2023.1605015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 01/23/2023] [Indexed: 03/06/2023] Open
Abstract
Objective: This study assesses a multi-period capacitated maximal-covering location-allocation model for healthcare services, taking interservice referral as well as equity access into account. Methods: A two-stage optimization strategy is used to formulate the model. In the first stage, facilities are located to maximize covered demand, and in the second stage, patients are allocated to capacitated facilities based on their radius of coverage over multiple time periods. The problem, which belongs to the NP-hard class of optimization problems, is solved using a linear mixed-integer programming (MILP) model. Results: A numerical example is presented to evaluate the efficiency of the proposed model. In addition, to identify near-optimal solutions for large instances, a hybrid genetic-sequential quadratic programming approach (GA-SQP) is developed. To examine the performance and efficiency of the GA-SQP, we employed several randomly generated test instances of various sizes and compared them to those obtained using the exact method. Conclusion: The proposed model has demonstrated an excellent ability in locating healthcare facilities and allocating health services while taking shortage and equity into account during each time period.
Collapse
Affiliation(s)
- Azadeh Salami
- Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Behrouz Afshar-Nadjafi
- Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Maghsoud Amiri
- Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran
| |
Collapse
|
3
|
Mondal H, Parvanov ED, Singla RK, Rayan RA, Nawaz FA, Ritschl V, Eibensteiner F, Siva Sai C, Cenanovic M, Devkota HP, Hribersek M, De R, Klager E, Kletecka-Pulker M, Völkl-Kernstock S, Khalid GM, Lordan R, Găman MA, Shen B, Stamm T, Willschke H, Atanasov AG. Twitter-based crowdsourcing: What kind of measures can help to end the COVID-19 pandemic faster? Front Med (Lausanne) 2022; 9:961360. [PMID: 36186802 PMCID: PMC9523003 DOI: 10.3389/fmed.2022.961360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Background Crowdsourcing is a low-cost, adaptable, and innovative method to collect ideas from numerous contributors with diverse backgrounds. Crowdsourcing from social media like Twitter can be used for generating ideas in a noticeably brief time based on contributions from globally distributed users. The world has been challenged by the COVID-19 pandemic in the last several years. Measures to combat the pandemic continue to evolve worldwide, and ideas and opinions on optimal counteraction strategies are of high interest. Objective This study aimed to validate the use of Twitter as a crowdsourcing platform in order to gain an understanding of public opinion on what measures can help to end the COVID-19 pandemic faster. Methods This cross-sectional study was conducted during the period from December 22, 2021, to February 4, 2022. Tweets were posted by accounts operated by the authors, asking “How to faster end the COVID-19 pandemic?” and encouraging the viewers to comment on measures that they perceive would be effective to achieve this goal. The ideas from the users' comments were collected and categorized into two major themes – personal and institutional measures. In the final stage of the campaign, a Twitter poll was conducted to get additional comments and to estimate which of the two groups of measures were perceived to be important amongst Twitter users. Results The crowdsourcing campaign generated seventeen suggested measures categorized into two major themes (personal and institutional) that received a total of 1,727 endorsements (supporting comments, retweets, and likes). The poll received a total of 325 votes with 58% of votes underscoring the importance of both personal and institutional measures, 20% favoring personal measures, 11% favoring institutional measures, and 11% of the votes given just out of curiosity to see the vote results. Conclusions Twitter was utilized successfully for crowdsourcing ideas on strategies how to end the COVID-19 pandemic faster. The results indicate that the Twitter community highly values the significance of both personal responsibility and institutional measures to counteract the pandemic. This study validates the use of Twitter as a primary tool that could be used for crowdsourcing ideas with healthcare significance.
Collapse
Affiliation(s)
- Himel Mondal
- Saheed Laxman Nayak Medical College and Hospital, Koraput, Odisha, India
| | - Emil D. Parvanov
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Translational Stem Cell Biology, Research Institute of the Medical University of Varna, Varna, Bulgaria
| | - Rajeev K. Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, India
- Rajeev K. Singla ;
| | - Rehab A. Rayan
- Department of Epidemiology, High Institute of Public Health, Alexandria University, Alexandria, Egypt
| | - Faisal A. Nawaz
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Valentin Ritschl
- Section for Outcomes Research, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Vienna, Austria
| | - Fabian Eibensteiner
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Chandragiri Siva Sai
- Amity Institute of Pharmacy, Amity University, Lucknow Campus, Lucknow, Uttar Pradesh, India
| | | | - Hari Prasad Devkota
- Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto, Japan
- Headquarters for Admissions and Education, Kumamoto University, Kumamoto, Japan
| | - Mojca Hribersek
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Ronita De
- ICMR-National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India
| | - Elisabeth Klager
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Maria Kletecka-Pulker
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Institute for Ethics and Law in Medicine, University of Vienna, Vienna, Austria
| | - Sabine Völkl-Kernstock
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Child and Adolescent Psychiatry, Medical University Vienna, Vienna, Austria
| | - Garba M. Khalid
- Pharmaceutical Engineering Group, School of Pharmacy, Queen's University, Belfast, United Kingdom
| | - Ronan Lordan
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Mihnea-Alexandru Găman
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, Bucharest, Romania
- Department of Hematology, Center of Hematology and Bone Marrow Transplantation, Fundeni Clinical Institute, Bucharest, Romania
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Tanja Stamm
- Section for Outcomes Research, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Vienna, Austria
| | - Harald Willschke
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
| | - Atanas G. Atanasov
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzẹbiec, Poland
- *Correspondence: Atanas G. Atanasov
| |
Collapse
|