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Eric C Schneider - One of the best experts on this subject based on the ideXlab platform.
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influence of cardiac surgeon report cards on Patient Referral by cardiologists in new york state after 20 years of public reporting
Circulation-cardiovascular Quality and Outcomes, 2013Co-Authors: David L Brown, Arnold M Epstein, Eric C SchneiderAbstract:Background—Report cards of risk-adjusted mortality rates of individual cardiac surgeons have been publicly available in New York State since 1991. A survey of New York cardiologists in 1996 found that these report cards had little effect on their Referral recommendations to cardiac surgeons. It is unknown whether the influence of report cards on Referral behavior has changed over time. Methods and Results—We surveyed cardiologists in New York State in 2011 to determine their awareness of cardiac surgeon report cards, their use of the report card in formulating judgments about the quality of cardiac surgeons and selecting cardiac surgeons for Referral of Patients, and discussion of the report with Patients in need of cardiac surgery. The relation between demographic (age, sex) and professional (teaching, board certification, faculty appointment, general cardiology practice, and hospital employee) characteristics and the influence of report cards on Referral decisions was assessed using χ2 for categorical v...
Ping-shun Chen - One of the best experts on this subject based on the ideXlab platform.
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Solving Patient Referral problems by using bat algorithm.
Technology and health care : official journal of the European Society for Engineering and Medicine, 2020Co-Authors: Huan-chung Yao, Pei-jarn Chen, Yu-ting Kuo, Chun-chin Shih, Xuan-yin Wang, Ping-shun ChenAbstract:BACKGROUND A two-hospital Patient Referral problem intends to calculate an optimal value of Referral Patients between two hospitals and to evaluate whether or not the current number of Referral Patients is too low. OBJECTIVE The goal of this study is to develop a simulation-based optimization algorithm to find the optimal Referral between two hospitals with the unfixed daily Patient Referral policy. METHODS This study applied system simulation and a bat algorithm (BA) to build a simulation model in accordance with the status of the two hospitals case and to calculate an optimal value of daily Referral Patients. RESULTS Based on the 20 test instances, we verified the stability of this algorithm. The results show that the average magnetic resonance imaging (MRI) Patient wait time reduced from 16 days to eight days. The hospital should increase the average total monthly MRI Referral Patients to 370 under the limitation of the daily Referral Patients to 25. CONCLUSIONS This research investigated the two-hospital Patient Referral problems. We conducted and analyzed a simulation model and improved the case hospital's conditions, enhancing the quality of its medical care. The findings of this study can extend to other departments or hospitals.
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Development of simulation optimization methods for solving Patient Referral problems in the hospital-collaboration environment
Journal of biomedical informatics, 2017Co-Authors: Ping-shun Chen, Ming-han LinAbstract:Abstract This research studied a Patient Referral problem among multiple cooperative hospitals for sharing imaging services’ Referrals. The proposed problem consisted of many types of Patients and the uncertainty associated with the number of Patients of each type, Patients’ arrival time, and Patients’ medical operation time, leading to a difficulty in finding solutions due to the uncertain environment. This research used system simulation to construct a model and develop a simulation optimization method, combining the heuristic algorithm (Patient Referral mechanism) with the particle swarm optimization (PSO) method, to determine a better way to refer Patients from one hospital (referring hospital) to another (recipient hospital) to receive certain imaging services. After the simulated model was verified and validated, three Patient Referral mechanisms to dispatch referring Patients to the appropriate recipient hospitals were proposed. Based on the numerical results, the findings showed that Mechanism 2, transferring Patients to the hospital with the shortest waiting time, had good performance in both scenarios: allowing Patient Referrals among all hospitals and limiting the Patients’ waiting time. Finally, this study presents the conclusions and some directions for future research.
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Patient Referral mechanisms by using simulation optimization
Simulation Modelling Practice and Theory, 2016Co-Authors: Ping-shun Chen, Kang-hung Yang, Rex Aurelius C. Robielos, Rozel Aireen C. Cancino, Lea Angelica M. DizonAbstract:Abstract Building Patient Referral mechanisms between two hospitals is the focus of this research, which considers different kinds of magnetic resonance imaging (MRI) services offered by the case hospitals. The simulation optimization approach is the main tool for analysis, along with the formulation of a mathematical model and a simulation framework to conceptualize a collaborative MRI Patient-referring mechanism. The objective of the study is to obtain the best feasible number of Referral outPatients to minimize Patients’ average waiting time or to maximize the revenues of both case hospitals. The results can serve as guidelines for hospital collaboration.
Ming-han Lin - One of the best experts on this subject based on the ideXlab platform.
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Development of simulation optimization methods for solving Patient Referral problems in the hospital-collaboration environment
Journal of biomedical informatics, 2017Co-Authors: Ping-shun Chen, Ming-han LinAbstract:Abstract This research studied a Patient Referral problem among multiple cooperative hospitals for sharing imaging services’ Referrals. The proposed problem consisted of many types of Patients and the uncertainty associated with the number of Patients of each type, Patients’ arrival time, and Patients’ medical operation time, leading to a difficulty in finding solutions due to the uncertain environment. This research used system simulation to construct a model and develop a simulation optimization method, combining the heuristic algorithm (Patient Referral mechanism) with the particle swarm optimization (PSO) method, to determine a better way to refer Patients from one hospital (referring hospital) to another (recipient hospital) to receive certain imaging services. After the simulated model was verified and validated, three Patient Referral mechanisms to dispatch referring Patients to the appropriate recipient hospitals were proposed. Based on the numerical results, the findings showed that Mechanism 2, transferring Patients to the hospital with the shortest waiting time, had good performance in both scenarios: allowing Patient Referrals among all hospitals and limiting the Patients’ waiting time. Finally, this study presents the conclusions and some directions for future research.
Zubya Mumtaz - One of the best experts on this subject based on the ideXlab platform.
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The effectiveness of Patient Referral in Pakistan
Health policy and planning, 2001Co-Authors: S Siddiqi, Aa Kielmann, Khan, Nabeela Ali, Abdul Ghaffar, Unaiza Sheikh, Zubya MumtazAbstract:In Pakistan, despite an elaborate network of over 5000 basic health units and rural health centres, supported by higher-level facilities, primary health care activities have not brought about expected improvements in health status, especially of rural population groups. A poorly functioning Referral system may be partly to blame. System analysis of Patient Referral was conducted in a district of Punjab province (Attock) for the purpose of identifying major shortcomings, if any, in this domain. Respondents from 225 households were interviewed. Of the households experiencing serious illnesses less than half were taken to a nearest first-level care facility (FLCF). Major reasons included dissatisfaction with quality of care offered, non-availability of physician, and Patients being too ill to be taken to the FLCF. The FLCF utilization rate was less than 0.6 Patient visits/person/year. The mean number of Patients referred per FLCF during the previous 3 months was 6.5 +/- 5.0. Only 15% of Patients were referred on the prescribed Referral form. None of the higher-level facilities provided feedback to FLCFS: Records of higher-level facilities revealed lack of information on either Patient Referrals or feedback. There were no surgical or emergency obstetric services available at any of the first-level Referral facilities. Seventy-five percent of the Patients attending the first-level Referral facilities and 44% of the Patients attending higher-level facilities had a problem of a primary nature that could well have been managed at the FLCF. As a result of the study findings, eight principal criteria were identified that need to be satisfied before a Referral system may be considered functional.
Monica Morrow - One of the best experts on this subject based on the ideXlab platform.
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patterns and correlates of Patient Referral to surgeons for treatment of breast cancer
Journal of Clinical Oncology, 2007Co-Authors: Steven J Katz, Timothy P Hofer, Sarah T Hawley, Paula M Lantz, Nancy K Janz, Kendra Schwartz, Lihua Liu, Dennis Deapen, Monica MorrowAbstract:Purpose Characteristics of surgeons and their hospitals have been associated with cancer treatments and outcomes. However, little is known about factors that are associated with Referral pathways. Methods We analyzed tumor registry and survey data from women with breast cancer diagnosed in 2002 and reported to the Detroit and Los Angeles Surveillance, Epidemiology, and End Results registries (n 1,844; response rate, 77.4%) and their attending surgeons (n 365; response rate 80.0%). Results About half of the Patients (54.3%) reported that they were referred to the surgeon by another provider or health plan; 20.3% reported that they selected the surgeon; and 21.9% reported that they both were referred and were involved in selecting the surgeon. Patients who selected the surgeon based on reputation were more likely to have received treatment from a high-volume surgeon (adjusted odds ratio [OR], 2.2; 95% CI, 1.5 to 3.4) and more likely to have been treated in an American College of Surgeons–approved cancer program or a National Cancer Institute (NCI) – designated cancer center (adjusted OR, 2.0; 95% CI, 1.3 to 3.1; adjusted OR, 3.4; 95% CI, 1.9 to 6.2, respectively). Patients who were referred to the surgeon were less likely to be treated in an NCI-designated cancer center (adjusted OR, 0.5; 95% CI, 0.3 to 0.9). Conclusion Women with breast cancer who actively participate in the surgeon selection process are more likely to be treated by more experienced surgeons and in hospitals with cancer programs. Patients should be aware that provider or health plan– based Referral may not connect them with the most experienced surgeon or comprehensive practice setting in their community. J Clin Oncol 25:271-276. © 2007 by American Society of Clinical Oncology
