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Journal articles on the topic 'Patient admission scheduling'
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Zhang, Li Ming, Hui You Chang, and Rui Tian Xu. "The Patient Admission Scheduling of an Ophthalmic Hospital Using Genetic Algorithm." Advanced Materials Research 756-759 (September 2013): 1423–32. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1423.
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Currently, FCFS scheduling method is widely used in hospitals for patient admission scheduling, which ignores the impacts of patient length of stay and surgery arrangement on the usage of hospital resources. This paper proposes a more comprehensive mathematical model and evaluation mechanism for the patient admission scheduling of an ophthalmic hospital. A genetic algorithm (GA) is proposed to optimize the model, which can provide detailed scheduling of patient admission in the hospital for everyday. The result is compared with that of the traditional FCFS method, which indicates that the GA helps to reduce the preoperative waiting time for patients. Besides, GA can provide different kinds of scheduling for the hospital to select by adjusting the relative weights of different objectives in the algorithm.
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Abu Doush, Iyad, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Abdelaziz I. Hammouri, Ra’ed M. Al-Khatib, Saba ElMustafa, and Habes ALkhraisat. "Harmony Search Algorithm for Patient Admission Scheduling Problem." Journal of Intelligent Systems 29, no. 1 (April 28, 2018): 540–53. http://dx.doi.org/10.1515/jisys-2018-0094.
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Abstract The patient admission scheduling (PAS) problem is an optimization problem in which we assign patients automatically to beds for a specific period of time while preserving their medical requirements and their preferences. In this paper, we present a novel solution to the PAS problem using the harmony search (HS) algorithm. We tailor the HS to solve the PAS problem by distributing patients to beds randomly in the harmony memory (HM) while respecting all hard constraints. The proposed algorithm uses five neighborhood strategies in the pitch adjustment stage. This technique helps in increasing the variations of the generated solutions by exploring more solutions in the search space. The PAS standard benchmark datasets are used in the evaluation. Initially, a sensitivity analysis of the HS algorithm is studied to show the effect of its control parameters on the HS performance. The proposed method is also compared with nine methods: non-linear great deluge (NLGD), simulated annealing with hyper-heuristic (HH-SA), improved with equal hyper-heuristic (HH-IE), simulated annealing (SA), tabu search (TS), simple random simulated annealing with dynamic heuristic (DHS-SA), simple random improvement with dynamic heuristic (DHS-OI), simple random great deluge with dynamic heuristic (DHS-GD), and biogeography-based optimization (BBO). The proposed HS algorithm is able to produce comparably competitive results when compared with these methods. This proves that the proposed HS is a very efficient alternative to the PAS problem, which can be efficiently used to solve many scheduling problems of a large-scale data.
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Abdalkareem, Zahraa A., Mohammed Azmi Al-Betar, Amiza Amir, Phaklen Ehkan, Abdelaziz I. Hammouri, and Omar H. Salman. "Discrete flower pollination algorithm for patient admission scheduling problem." Computers in Biology and Medicine 141 (February 2022): 105007. http://dx.doi.org/10.1016/j.compbiomed.2021.105007.
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Barz, Christiane, and Kumar Rajaram. "Elective Patient Admission and Scheduling under Multiple Resource Constraints." Production and Operations Management 24, no. 12 (July 14, 2015): 1907–30. http://dx.doi.org/10.1111/poms.12395.
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Ashraf, Shahzaib, Noor Rehman, Saleem Abdullah, Bushra Batool, Mingwei Lin, and Muhammad Aslam. "Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology." Mathematical Biosciences and Engineering 19, no. 3 (2022): 3147–76. http://dx.doi.org/10.3934/mbe.2022146.
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<abstract><p>Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making issue is to allocate a group of patients to a limited number of resources such as rooms, time slots, and beds based on a set of preset restrictions such as illness severity, waiting time, and disease categories. This is a crucial issue with multi-criteria group decision making (MCGDM). In order to address this issue, we first conduct an assessment of the admission process and gather four (4) aspects that influence patient admission and design a set of criteria. Even while many of these indicators may be accurately captured by the picture fuzzy set, we use an advanced MCGDM approach that incorporates generalized aggregation to analyze patients' hospitalization. Finally, numerical real-world applications of PAS are offered to illustrate the validity of the suggested technique. The advantages of the proposed approaches are also examined by comparing them to various existing decision methods. The proposed technique has been proved to assist hospitals in managing patient admissions in a flexible manner.</p></abstract>
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Chaouch, Rihab, Jihene Tounsi, Issam Nouaouri, and Sabeur Elkosantini. "Mixed Integer Programming For Patient Admission Scheduling in Hospital Network." IFAC-PapersOnLine 58, no. 19 (2024): 259–64. http://dx.doi.org/10.1016/j.ifacol.2024.09.185.
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Ceschia, Sara, and Andrea Schaerf. "Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays." Journal of Scheduling 19, no. 4 (November 27, 2014): 377–89. http://dx.doi.org/10.1007/s10951-014-0407-8.
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Bolaji, Asaju La’aro, Akeem Femi Bamigbola, and Peter Bamidele Shola. "Late acceptance hill climbing algorithm for solving patient admission scheduling problem." Knowledge-Based Systems 145 (April 2018): 197–206. http://dx.doi.org/10.1016/j.knosys.2018.01.017.
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Bastos, Leonardo S. L., Janaina F. Marchesi, Silvio Hamacher, and Julia L. Fleck. "A mixed integer programming approach to the patient admission scheduling problem." European Journal of Operational Research 273, no. 3 (March 2019): 831–40. http://dx.doi.org/10.1016/j.ejor.2018.09.003.
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Range, Troels Martin, Richard Martin Lusby, and Jesper Larsen. "A column generation approach for solving the patient admission scheduling problem." European Journal of Operational Research 235, no. 1 (May 2014): 252–64. http://dx.doi.org/10.1016/j.ejor.2013.10.050.
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Ceschia, Sara, and Andrea Schaerf. "Modeling and solving the dynamic patient admission scheduling problem under uncertainty." Artificial Intelligence in Medicine 56, no. 3 (November 2012): 199–205. http://dx.doi.org/10.1016/j.artmed.2012.09.001.
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Ceschia, Sara, and Andrea Schaerf. "Local search and lower bounds for the patient admission scheduling problem." Computers & Operations Research 38, no. 10 (October 2011): 1452–63. http://dx.doi.org/10.1016/j.cor.2011.01.007.
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Turhan, Aykut Melih, and Bilge Bilgen. "Mixed integer programming based heuristics for the Patient Admission Scheduling problem." Computers & Operations Research 80 (April 2017): 38–49. http://dx.doi.org/10.1016/j.cor.2016.11.016.
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Zhu, Yi-Hang, Túlio A. M. Toffolo, Wim Vancroonenburg, and Greet Vanden Berghe. "Compatibility of short and long term objectives for dynamic patient admission scheduling." Computers & Operations Research 104 (April 2019): 98–112. http://dx.doi.org/10.1016/j.cor.2018.12.001.
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Muklason, Ahmad, Varian Elbert, I. Gusti Agung Premananda, Edwin Riksakomara, Retno Aulia Vinarti, and Arif Djunaidy. "Optimization of Static Patient Admission Scheduling using the Variable Neighborhood Search Method." Procedia Computer Science 234 (2024): 478–85. http://dx.doi.org/10.1016/j.procs.2024.03.030.
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Hosseini Rad, Reza, Sahba Baniasadi, Parisa Yousefi, Hakimeh Morabbi Heravi, Muzhir Shaban Al-Ani, and Mohsen Asghari Ilani. "Presented a Framework of Computational Modeling to Identify the Patient Admission Scheduling Problem in the Healthcare System." Journal of Healthcare Engineering 2022 (November 29, 2022): 1–15. http://dx.doi.org/10.1155/2022/1938719.
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Operating room scheduling is a prominent study topic due to its complexity and significance. The increasing number of technical operating room scheduling articles produced each year calls for another evaluation of the literature to enable academics to respond to new trends more quickly. The mathematical application of a model for the patient admission scheduling issue with stochastic arrivals and departures is the subject of this study. The approach for applying our model to real-world issues is discussed here. We present a solution technique for efficient computing, a numerical model analysis, and examples to demonstrate the methodology. This study looked at the challenge of assigning procedures to operate rooms in the face of ambiguity regarding surgery length and the arrival of emergency patients based on a flexible policy (capacity reservation). We demonstrate that the proposed methods derived from deterministic models are inadequate compared to the answers produced from our stochastic model using simple numerical examples. We also use heuristics to estimate the objective function to build more complicated numerical examples for large-scale issues, demonstrating that our methodology can be applied quickly to real-world situations that often include big information sets.
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Ab Rashid, Nurul Atiekah, and Suraya Ya'acob. "Predictive Analytics on Scheduled Surgery Cases in Forecasting the Operating Theatre Utilisation." Open International Journal of Informatics 9, no. 1 (June 26, 2021): 45–52. http://dx.doi.org/10.11113/oiji2021.9n1.20.
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The scheduled surgery cases, allocation of clinical provider, machine, equipment, preparation time, surgery performance, and patient recovery give the big impact on OT utilization. Low OT utilization due to no show patient and scheduling bottleneck interrupt patient flow in clinical process. It also decreases the admission to the OT and wastage of resources. In order to improve the capacity of OT, the logical solution need to be carried out is utilization audit. The trend of scheduled surgery cases has identified, and element affect the OT capacity have used to predict the OT optimization for future planning. The purpose of this study is to investigate efficiency of operating theatre (OT) utilization in healthcare institution and the application predictive analytics on its daily operational data. OT contribute to the revenue for the hospital and workload. This project use machine learning in identify which model can use to predict the decision of admission to which facility after the surgery. The model has been going through a few mathematical reasoning to getting the usage efficiency on the dataset. The result shows that Support Vector Machine (SVM) got the highest accuracy in test data rather than Logistic Regression (LR) and Random Forest Classifier. SVM used to predict the admission decision, which contribute to the surgery scheduling in OT. This project can be extended to the admission decision with the factor or severity of patient condition to undergo any intervention in outpatient.
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Lusby, Richard Martin, Martin Schwierz, Troels Martin Range, and Jesper Larsen. "An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem." Artificial Intelligence in Medicine 74 (November 2016): 21–31. http://dx.doi.org/10.1016/j.artmed.2016.10.002.
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Tarr, Joseph T., Cynara L. Coomer, Sara Y. Kim, and Marilyn Ng. "Overnight to Outpatient." Annals of Plastic Surgery 93, no. 1 (July 2024): 43–47. http://dx.doi.org/10.1097/sap.0000000000003922.
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Purpose Minimizing resource use while optimizing patient outcomes has become an ever-growing component in modern healthcare, especially in the era of COVID-19. One essential component of this is deciding whether patients need hospital admission following elective procedures. The aim of this study is to investigate operative factors and patient outcomes when mastectomies with or without reconstruction are performed as ambulatory procedures versus planned inpatient admissions. Methods Patient charts for those undergoing mastectomy with or without reconstruction were retrospectively analyzed ranging from March 2019 until February 2021. Factors such as demographic information, operative type, operating room time, cancer stage, total stay time in the medical environment, and postoperative complications were assessed and compared between the 2 groups. Results A total of 89 patient charts were reviewed, 46 from before the COVID-19 pandemic and 43 from after the start of the pandemic. No differences were observed in demographic factors between the 2 groups. After surgical cases resumed a significant proportion, 79%, of mastectomies with or without reconstruction were performed in the ambulatory center, versus just 2% pre-COVID-19. Similarly, of all of these cases performed, only 19% resulted in hospital admission versus the previous rate of 100% (P < 0.00001). Together, these changes resulted in a significant reduction in length of stay of 39.77 ± 19.2 hours pre-COVID-19 versus 14.81 ± 18.4 hours afterward (P < 0.00001). Unfortunately, a higher number of patients who received surgery after the start of the pandemic elected to forego immediate reconstruction 49% versus 72% (P = 0.032). Most importantly, there were no observable differences found in 7-day readmission, reoperation, or emergency department visit between groups. There was also no difference in 30-day complication rate between groups. Conclusions Mastectomy with or without reconstruction can be safely performed in the ambulatory setting without additional risk of complications or negative patient factors. This divergence from traditional the protocol of inpatient overnight admission may contribute positively toward patient comfort, minimize the use of healthcare costs and resources, and allow for increased scheduling flexibility for patient and provider alike.
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Wong, Andy, Erhan Kozan, Michael Sinnott, Lyndall Spencer, and Robert Eley. "Tracking the patient journey by combining multiple hospital database systems." Australian Health Review 38, no. 3 (2014): 332. http://dx.doi.org/10.1071/ah13070.
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With new national targets for patient flow in public hospitals designed to increase efficiencies in patient care and resource use, better knowledge of events affecting length of stay will support improved bed management and scheduling of procedures. This paper presents a case study involving the integration of material from each of three databases in operation at one tertiary hospital and demonstrates it is possible to follow patient journeys from admission to discharge. What is known about this topic? At present, patient data at one Queensland tertiary hospital are assembled in three information systems: (1) the Hospital Based Corporate Information System (HBCIS), which tracks patients from in-patient admission to discharge; (2) the Emergency Department Information System (EDIS) containing patient data from presentation to departure from the emergency department; and (3) Operation Room Management Information System (ORMIS), which records surgical operations. What does this paper add? This paper describes how a new enquiry tool may be used to link the three hospital information systems for studying the hospital journey through different wards and/or operating theatres for both individual and groups of patients. What are the implications for practitioners? An understanding of the patients’ journeys provides better insight into patient flow and provides the tool for research relating to access block, as well as optimising the use of physical and human resources.
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Neumann, Jan-Oliver, Stephanie Schmidt, Amin Nohman, Paul Naser, Martin Jakobs, and Andreas Unterberg. "Routine ICU Surveillance after Brain Tumor Surgery: Patient Selection Using Machine Learning." Journal of Clinical Medicine 13, no. 19 (September 26, 2024): 5747. http://dx.doi.org/10.3390/jcm13195747.
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Background/Objectives: Routine postoperative ICU admission following brain tumor surgery may not benefit selected patients. The objective of this study was to develop a risk prediction instrument for early (within 24 h) postoperative adverse events using machine learning techniques. Methods: Retrospective cohort of 1000 consecutive adult patients undergoing elective brain tumor resection. Nine events/interventions (CPR, reintubation, return to OR, mechanical ventilation, vasopressors, impaired consciousness, intracranial hypertension, swallowing disorders, and death) were chosen as target variables. Potential prognostic features (n = 27) from five categories were chosen and a gradient boosting algorithm (XGBoost) was trained and cross-validated in a 5 × 5 fashion. Prognostic performance, potential clinical impact, and relative feature importance were analyzed. Results: Adverse events requiring ICU intervention occurred in 9.2% of cases. Other events not requiring ICU treatment were more frequent (35% of cases). The boosted decision trees yielded a cross-validated ROC-AUC of 0.81 ± 0.02 (mean ± CI95) when using pre- and post-op data. Using only pre-op data (scheduling decisions), ROC-AUC was 0.76 ± 0.02. PR-AUC was 0.38 ± 0.04 and 0.27 ± 0.03 for pre- and post-op data, respectively, compared to a baseline value (random classifier) of 0.092. Targeting a NPV of at least 95% would require ICU admission in just 15% (pre- and post-op data) or 30% (only pre-op data) of cases when using the prediction algorithm. Conclusions: Adoption of a risk prediction instrument based on boosted trees can support decision-makers to optimize ICU resource utilization while maintaining adequate patient safety. This may lead to a relevant reduction in ICU admissions for surveillance purposes.
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Barak Corren, Yuval, Joshua Merrill, Ronald Wilkinson, Courtney Cannon, Jonathan Bickel, and Ben Y. Reis. "Predicting surgical department occupancy and patient length of stay in a paediatric hospital setting using machine learning: a pilot study." BMJ Health Care Inform 29, no. 1 (September 2022): e100498. http://dx.doi.org/10.1136/bmjhci-2021-100498.
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ObjectiveEarly and accurate prediction of hospital surgical-unit occupancy is critical for improving scheduling, staffing and resource planning. Previous studies on occupancy prediction have focused primarily on adult healthcare settings, we sought to develop occupancy prediction models specifically tailored to the needs and characteristics of paediatric surgical settings.Materials and methodsWe conducted a single-centre retrospective cohort study at a surgical unit in a tertiary-care paediatric hospital in Boston, Massachusetts, USA. We developed a hierarchical modelling framework for predicting next-day census using multiple types of data—from bottom-up patient-specific orders and procedures to top-down temporal variables and departmental admission statistics.ResultsThe model predicted upcoming admissions and discharges with a median error of 17%–21% (2–3 patients per day), and next-day census with a median error of 7% (n=3). The primary factors driving these predictions included day of week and scheduled surgeries, as well as procedure duration, procedure type and days since admission. We found that paediatric surgical procedure duration was highly predictive of postoperative length of stay.DiscussionOur hierarchical modelling framework provides an overview of the factors driving capacity issues in the paediatric surgical unit, highlighting the importance of both top-down temporal features (eg, day of week) as well as bottom-up electronic health records (EHR)derived features (eg, orders for patient) for predicting next-day census. In the practice, this framework can be implemented stepwise, from top to bottom, making it easier to adopt.ConclusionModelling frameworks combining top-down and bottom-up features can provide accurate predictions of next-day census in a paediatric surgical setting.
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Granja, C., B. Almada-Lobo, F. Janela, J. Seabra, and A. Mendes. "An optimization based on simulation approach to the patient admission scheduling problem using a linear programing algorithm." Journal of Biomedical Informatics 52 (December 2014): 427–37. http://dx.doi.org/10.1016/j.jbi.2014.08.007.
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Granja, Conceição, Bernardo Almada-Lobo, Filipe Janela, João Seabra, and Adélio Mendes. "An Optimization based on Simulation Approach to the Patient Admission Scheduling Problem: Diagnostic Imaging Department Case Study." Journal of Digital Imaging 27, no. 1 (August 6, 2013): 33–40. http://dx.doi.org/10.1007/s10278-013-9626-3.
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Holdsworth, Mark T., and Cathy M. Chavez. "Development of a Home Chemotherapy Program for Pediatric Oncology Patients." Journal of Pharmacy Practice 8, no. 6 (December 1995): 297–302. http://dx.doi.org/10.1177/089719009500800606.
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Provision of home chemotherapy to pediatric oncology patients offers substantial advantages to children and their families, including improved scheduling and continuity of care and decreased disruption of the family unit. These advantages may positively impact upon both parental anxiety and quality of life for these children and their families. Establishing and maintaining a successful home chemotherapy program is a complex task, requiring a detailed orientation program along with an interdisciplinary team approach, a successful communication network, and close patient follow-up. Home chemotherapy delivery offers a unique practice setting with many professional growth opportunities for clinicians. A home chemotherapy program may also result in substantial monetary savings to patients and third-party payors, especially for protocols that require several days of inpatient admission to deliver.
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Boyapati, Raghuram P., Jahnavi Mehta, and Paul Norris. "Same day cancellations of elective operations in a tertiary hospital in south-east England: a review of 11 000 patients in 1 year." British Journal of Healthcare Management 26, no. 1 (January 2, 2020): 27–33. http://dx.doi.org/10.12968/bjhc.2019.0029.
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Cancellations of elective operations have negative consequences, both for patients and the NHS. For the latter, reducing waiting times for surgical procedures remains a pressing concern, as does maintenance of adequate staffing. This study aimed to identify factors contributing to the cancellation of elective operations on the day of the procedure in order to suggest measures that could be taken to reduce these incidents. The retrospective details of just over 11 000 patients awaiting theatre admission for elective operations over a period of 1 year were obtained. The reasons behind last-minute operation cancellations were categorised as either patient factors or hospital factors. Data analysis suggested that the number of cancellations could be reduced by scheduling appointments with a senior doctor closer to the operation date.
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Smith, Thomas, Marleen Radigan, and Franco Mascayano. "T240. DOES SCHEDULING A POST-DISCHARGE OUTPATIENT MENTAL HEALTH APPOINTMENT INCREASE THE LIKELIHOOD OF SUCCESSFUL TRANSITION FROM HOSPITAL TO COMMUNITY-BASED CARE?" Schizophrenia Bulletin 46, Supplement_1 (April 2020): S323—S324. http://dx.doi.org/10.1093/schbul/sbaa029.800.
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Abstract Background Scheduling timely appointments for outpatient follow-up care is a discharge planning practice widely accepted as a standard of care for inpatient treatment. Despite these endorsements, however, rates of hospital providers completing these practices vary widely. Timely scheduling of initial outpatient visits following discharge has been associated with improved rates of attending outpatient psychiatric services, although negative findings have also been reported. Nearly all prior studies were single-site case reports that did not use an experimental design and more rigorous research is needed. In this report, we aimed to examine the association between receiving care transition practices and attending outpatient care after controlling for patient, hospital, and system characteristics in a large cohort of inpatient psychiatric admissions in New York State. We hypothesized that patients for whom hospital providers scheduled a mental health outpatient appointment had a higher likelihood of receiving an initial outpatient psychiatric service following discharge after controlling for the aforementioned covariates. Methods This is a retrospective cohort study that used 2012–2013 New York State Medicaid claims data for psychiatric inpatients, who were under 65 years, admitted to an inpatient psychiatric unit with a principal diagnosis of a mental disorder and discharged to the community. The outcome variable was defined as attending to outpatient psychiatric services within 7 and 30 days following discharge from an inpatient psychiatric unit. Scheduling a mental health outpatient appointment as a discharge planning was the primary independent variable. To address the wide range of potentially confounding covariates, propensity scores for regression models were estimated based on patient, hospital, and service system factors. Results Before matching by propensity scores, those who had an outpatient mental health appointment scheduled were less likely to be homeless at admission, have a co-occurring substance use diagnosis, and live in large central metro areas, and were more likely to be previously engaged in psychiatric outpatient services. After matching, however, most systematic differences between those who had and those who did not have a mental health outpatient appointment scheduled were substantially diminished (standardized differences of <20%). In the adjusted models including propensity scores, patients who had a mental health outpatient appointment scheduled were more likely to be in treatment in aftercare services compared to patients who did not have an outpatient appointment at both 7 and 30 days following discharge. Discussion Scheduling an outpatient mental health appointment increases aftercare attendance following a psychiatric discharge. This effect was noted across all 5 propensity strata, indicating that discharge planning has a positive impact regardless of the presence of other factors highly predictive of failure to attend aftercare appointments.
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Bell, Cathrine, Charlotte Weiling Appel, Anders Prior, Anne Frølich, Asger Roer Pedersen, and Peter Vedsted. "The Effect of Coordinating the Outpatient Treatment across Medical Specialities for Patients With Multimorbidity." International Journal of Integrated Care 24 (April 9, 2024): 4. http://dx.doi.org/10.5334/ijic.7535.
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Introduction: Patients with multimorbidity attend multiple outpatient clinics. We assessed the effects on hospital use of scheduling several outpatient appointments to same-day visits in a multidisciplinary outpatient pathway (MOP). Methods: This study used a quasi-experimental design. Eligible patients had multimorbidity, were aged ≥18 years and attended ≥2 outpatient clinics in five different specialties. Patients were identified through forthcoming appointments from August 2018 to March 2020 and divided into intervention group (alignment of appointments) and comparison group (no alignment). We used patient questionnaires and paired analyses to study care integration and treatment burden. Using negative binomial regression, we estimated healthcare utilisation as incidence rates ratios (IRRs) at one year before and one year after baseline for both groups and compared IRR ratios (IRRRs). Results: Intervention patients had a 19% reduction in hospital visits (IRRR: 0.81, 95% CI: 0.70–0.96) and a 17% reduction in blood samples (IRRR: 0.83, 0.73–0.96) compared to comparison patients. No effects were found for care integration, treatment burden, outpatient contacts, terminated outpatient trajectories, hospital admissions, days of admission or GP contacts. Conclusion: The MOP seemed to reduce the number of hospital visits and blood samples. These results should be further investigated in studies exploring the coordination of outpatient care for multimorbidity. Research question: Can an intervention of coordinating outpatient appointments to same-day visits combined with a multidisciplinary conference influence the utilisation of healthcare services and the patient-assessed integration of healthcare services and treatment burden among patients with multimorbidity?
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Abdel Rahman, Zaid, Joyce Philip, Omar Mohtadi, Pushpinderdeep Singh Kahlon, and Vijaya Donthireddy. "Effect of follow-up appointments and admission unit on readmissions in patient with cancer on chemotherapy: A tertiary center experience." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e18239-e18239. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e18239.
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e18239 Background: Readmissions are a huge burden on patients and organizations, especially in light of the newly emerging bundled payment systems. Many interventions have been proposed to reduce readmissions, including admitting patients to a cancer-specific unit (CSU) and scheduling follow-up appointments after discharge. Methods: We conducted a retrospective cohort study to identify the effect of admission unit and follow-up appointments on readmissions within 30 days among cancer patients receiving outpatient chemotherapy. We included unplanned admissions between July and October 2016 at Henry Ford Hospital. Results: There were 232 inpatient admissions. Of those, 73 (31%) were readmitted. The number of admissions to the CSU was 100 (43%) compared to 132 admissions (57%) to other general practice units (GPUs). Mean length of stay was 5.8 (1-29) days and 3.8 (1-30) days, respectively. Most common malignancies were hematological (27% and 22%) and gastrointestinal (26% and 19%). The most common reasons for admissions were infections (29% and 28%) and pain management (19% and 12%). Of patients who were admitted to the CSU, 24 (24%) were readmitted compared to 49 (37%) for other GPUs, OR 0.53 (95% CI:0.3-0.95, p = 0.033). Readmission rates were also calculated based on the type of appointment scheduled within 30 days of discharge (Primary care (PCP) and oncology, PCP only, Oncology only and neither). Odds ratios of readmission were calculated for the last three categories in comparison to having both appointments. Of admissions that had both appointments, 15 (23%) had a readmission within 30 days. Of admissions that had oncology only appointments, 39 (33%) had a readmission; OR 1.6 (95% CI: 0.8 to 3.3, p = 0.15). Of admissions that had a PCP only appointment, 6 (42%) had a readmission; OR 2.5 (95% CI: 0.7 to 8.3, p = 0.13) and of admissions that had neither appointments, 12 (57%) had a readmission; OR 4.4 (95% CI 1.6 to 12.6, p = 0.0049). Conclusions: Care for cancer patients is challenging as they carry a high risk of readmission, admitting cancer patients to cancer-specific units significantly reduces that risk. Having no follow-up appointments carries a high risk of readmission.
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Mahmed, Ali Nader, and M. N. M. Kahar. "Window-Based Multi-Objective Optimization for Dynamic Patient Scheduling with Problem-Specific Operators." Computers 11, no. 5 (April 25, 2022): 63. http://dx.doi.org/10.3390/computers11050063.
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The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality.
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Mahmed, Ali Nader, and M. N. M. Kahar. "Window-Based Multi-Objective Optimization for Dynamic Patient Scheduling with Problem-Specific Operators." Computers 11, no. 5 (April 25, 2022): 63. http://dx.doi.org/10.3390/computers11050063.
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The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality.
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Mahmed, Ali Nader, and M. N. M. Kahar. "Window-Based Multi-Objective Optimization for Dynamic Patient Scheduling with Problem-Specific Operators." Computers 11, no. 5 (April 25, 2022): 63. http://dx.doi.org/10.3390/computers11050063.
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The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality.
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Skeffington, Patrick, Robert Aisenberg, Janice Dallacosta, Ian Donaghy, Dani Hackner, Kelly Houde, Kathy Moraes, and Annemarie Santos. "896 Fighting the war against COVID-19: administration of bamlanivimab (BAM) or bamlanivimab + etesivimab (BAM + E); a cooperative effort between a community cancer center and an urgent care (UC) facility." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A940. http://dx.doi.org/10.1136/jitc-2021-sitc2021.896.
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BackgroundGoal of the Massachusetts DPH is to ensure equitable distribution of BAM to the most vulnerable at risk of poor outcomes from COVID-19 and to communities with the highest incidences of COVID-19. Hospitals should allocate available doses in a manner consistent with this guidance:1. Patients who meet the EUA criteria; a lottery system will be used if supply is exceeded 2. Patients with comorbidities (high risk) tend to have worse outcomes when infected with SARS-CoV-2 3. BAM was approved under an EUA for the treatment of mild to moderate COVID-19 for those at high risk of progressing to severe disease (revoked 4/16/21). 4. BAM + E combo was approved under an EUA for the same patients and criteria, Southcoast Health entered into this relationship with DPH to provide this service to the southeastern MA population.MethodsPatients identified based on algorithm using Social Vulnerability Index (SVI) and EUA criteriaRNs screened cases for positive criteria using lottery priority and SVIPulmonologists consented appropriate patients, ordered infusions, routed cases for final scheduling within window of treatmentExperienced nursing staff from various Southcoast departments treated up to 6 patients per dayOncology pharmacies are uniquely experienced to prepare monoclonal antibodies (MABS) such as BAM and BAM + EDue to proximity of the Oncology pharmacy to the UC Center, pharmacy reviewed, prepared and delivered infusions to UC once patient was assessed by RNsResultsFirst 152 cases: 7.2% inpatient admissions within 14 days13.8% ED/UC visits within 14 days2% inpatient admissions in 28 days5.9% ED/UC visits within 28 daysTwo deaths during initial 152 cases.ConclusionsCooperative effort between the Cancer Center and Urgent Care led to positive outcomes for local COVID-19 patients. Southcoast demonstrated a 6% hospital admission rate for COVID-19 patients in the MAB program versus 26% admission rate overall for COVID-19 patients.AcknowledgementsThanks to our colleagues at the University of Rhode Island College of Pharmacy for their support with the poster
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Bagherian, Hossein, Maryam Jahanbakhsh, and Nahid Tavakoli. "A review on the use of operational research techniques in the medical records department." Proceedings of Singapore Healthcare 29, no. 1 (January 22, 2020): 42–49. http://dx.doi.org/10.1177/2010105819899113.
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Introduction: Various operational research (OR) techniques have been used in different areas of healthcare. One of the areas in which OR techniques can be effective is the medical records department (MRD). The aim of this study is to review the applications of OR in the management of MRD and its related processes. Methods: This is a review article. In order to collect data, English-language studies published between 2000 and 2018, related to the use of OR techniques in MRD, in the Medline, Science Direct, ProQuest and Web of science databases were investigated. From 1165 retrieved studies, 19 articles met the inclusion criteria and were included in the final review. Results: The results showed that different OR techniques such as linear programming, integer programming, simulation, hierarchical analysis process, etc. have been used in various aspects of the MRD and its ongoing processes such as improving efficiency, workload management, resource allocation, optimal scheduling of staff work hours, patient scheduling, patient admission and discharge. Conclusion: It can be concluded that if the managers and experts of MRD and health information management become familiar with the principles and techniques of OR and become aware of the importance of these techniques in improving efficiency of MRD, there is a hope that in the future these techniques will find their true place in MRD and ultimately enhance the quality of services provided to patients.
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Amudha, S., and M. Murali. "Implementation of Energy Efficient Fog based Health Monitoring and Emergency Admission Prediction System Using IoT." Webology 18, Special Issue 02 (April 29, 2021): 171–89. http://dx.doi.org/10.14704/web/v18si02/web18065.
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With rapid development in Information Communication Technology (ICT), Wearable Sensor Networks with Internet of Things (WSN-IoT) has produced several improvements in the smart world environment. One of the main research challenges in Wearable Sensor is energy, since all the sensor nodes operation depends on battery power consumption. Hence a new middleware has to be introduced between Wearable Sensor nodes and Cloud to reduce latency and Power Consumption problems. Overcrowding in hospital premise, detecting priority of hospital admission for patients, managing and monitoring health status of the patient constantly are daily problems in any health care system. Even though IoT based wearable sensors monitor health status of patients regularly and provide intent treatment in critical stage, but there is some block hole in that such as latency, energy issues and unawareness of medical execution plans and policies to preserve them from sudden attacks such as Heart attack. The proposed work is to implement energy efficient FoG based IoT network to monitor patients’ health conditions from chronic diseases and highlights utility of Deep Learning model for analyzing the health condition of patients and predicting Emergency readmission cases well in advance. This model is also compare with existing machine learning algorithms such as Gradient boosted, Decision tree, Random forest and Logistic regression to achieve more accuracy. This paper introduces preemptive interval scheduling algorithm with predictive analysis for constant monitoring of status for critical patients. By means of comparative analysis done in this work energy efficiency has been achieved prominently.
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Wanderer, Jonathan P., John Anderson-Dam, Wilton Levine, and Edward A. Bittner. "Development and Validation of an Intraoperative Predictive Model for Unplanned Postoperative Intensive Care." Anesthesiology 119, no. 3 (September 1, 2013): 516–24. http://dx.doi.org/10.1097/aln.0b013e31829ce8fd.
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Abstract Background: The allocation of intensive care unit (ICU) beds for postoperative patients is a challenging daily task that could be assisted by the real-time detection of ICU needs. The goal of this study was to develop and validate an intraoperative predictive model for unplanned postoperative ICU use. Methods: With the use of anesthesia information management system, postanesthesia care unit, and scheduling data, a data set was derived from adult in-patient noncardiac surgeries. Unplanned ICU admissions were identified (4,847 of 71,996; 6.7%), and a logistic regression model was developed for predicting unplanned ICU admission. The model performance was tested using bootstrap validation and compared with the Surgical Apgar Score using area under the curve for the receiver operating characteristic. Results: The logistic regression model included 16 variables: age, American Society of Anesthesiologists physical status, emergency case, surgical service, and 12 intraoperative variables. The area under the curve was 0.905 (95% CI, 0.900–0.909). The bootstrap validation model area under the curves were 0.513 at booking, 0.688 at 3 h before case end, 0.738 at 2 h, 0.791 at 1 h, and 0.809 at case end. The Surgical Apgar Score area under the curve was 0.692. Unplanned ICU admissions had more ICU-free days than planned ICU admissions (5 vs. 4; P < 0.001) and similar mortality (5.6 vs. 6.0%; P = 0.248). Conclusions: The authors have developed and internally validated an intraoperative predictive model for unplanned postoperative ICU use. Incorporation of this model into a real-time data sniffer may improve the process of allocating ICU beds for postoperative patients.
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Abera, Aregawi K., Małgorzata M. O’Reilly, Mark Fackrell, Barbara R. Holland, and Mojtaba Heydar. "On the decision support model for the patient admission scheduling problem with random arrivals and departures: A solution approach." Stochastic Models 36, no. 2 (March 26, 2020): 312–36. http://dx.doi.org/10.1080/15326349.2020.1742161.
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Robertson, SA, RJE Skipworth, DL Clarke, TJ Crofts, A. Lee, AC de Beaux, and S. Paterson-Brown. "Ventilatory and Intensive Care Requirements Following Oesophageal Resection." Annals of The Royal College of Surgeons of England 88, no. 4 (July 2006): 354–57. http://dx.doi.org/10.1308/003588406x98694.
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INTRODUCTION The aim of this study was to analyse the results of early postoperative extubation following oesophagectomy. PATIENTS AND METHODS All patients who had undergone oesophageal resection between 1994 and 2001 were identified from a prospectively collected database. Their records were then reviewed in order to analyse morbidity and mortality along with intensive care unit (ICU) and ventilatory requirements. All patients were extubated immediately following surgery and monitored on a surgical high dependency unit (HDU). RESULTS A total of 98 resections were undertaken (76 men; mean age, 64.3 years; range, 40–80 years). Surgical procedures were Ivor-Lewis (71), left thoraco-abdominal (15) and transhiatal (12) oesophagectomies. Overall, 8 patients died and 13 patients had anastomotic leaks. Sixteen patients required ventilation and admission to ICU, of whom 5 died. Three patients died on HDU following an elective decision not to transfer to ICU. Reasons for ventilation and ICU admission were anastomotic leaks (6), respiratory problems (6), left ventricular failure (1), cardiac arrest (1), small bowel herniation through the hiatus (1) and ischaemic stomach requiring revision of anastomosis (1). No patient required ventilation and admission to ICU within 48 h of original surgery. CONCLUSIONS Patients undergoing oesophageal resection can be safely managed on a surgical HDU without routine postoperative ventilation. Although ventilation and ICU will be required in a significant number due to postoperative complications, this is unlikely to occur in the first 48 h. The requirement for an ICU bed to be available on the day of surgery should, therefore, no longer be considered necessary. This has important implications for the scheduling of elective oesophageal surgery.
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Mullan, Paul C., and Turaj Vazifedan. "Changing temporal trends in patient volumes in a pediatric emergency department during a COVID-19 pandemic lockdown: A retrospective cohort study." PLOS ONE 17, no. 9 (September 12, 2022): e0271708. http://dx.doi.org/10.1371/journal.pone.0271708.
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Objective Emergency department (ED) teams have had to adjust limited staffing resources to meet the fluctuating levels of patient volume and acuity during the COVID-19 pandemic. Historically, Mondays have had the highest reported ED volumes. We are unaware of any studies reporting on the change of this Monday effect during the COVID-19 pandemic. Methods This retrospective, observational study of a single pediatric ED compared a pandemic lockdown period (3/23/2020-11/1/2020) with a seasonally comparative period (3/25/2019-11/3/2019). We compared the mean number of patients who arrived on Monday versus any other specific weekday (Tuesday, Wednesday, Thursday, or Friday) and the aggregate of other weekdays (Tuesday to Friday) for both study periods. Secondary analyses investigated overall mean volumes, admission rates, and differences in triage acuity levels. Results There were 31,377 and 18,098 patients in the comparative and pandemic periods. The mean number of ED visits on Mondays in the comparative period was significantly more than any other weekday and the aggregate of weekdays (latter p<0.001). In contrast, there were no significant differences in the mean number of ED visits on Mondays in the pandemic period relative to any other weekday and the aggregate of weekdays (all p>0.05). The pandemic period had significantly lower mean volumes, higher admission rates, and more patients with higher acuity levels. Conclusion The previously experienced Monday effect of increased relative ED patient volumes was not seen during the pandemic period. This change has operational implications for scheduling ED staffing resources. Larger database studies are needed to determine the generalizability of these findings.
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Blancas, Isabel, David Martínez-Rodríguez, Fernando Rodríguez-Serrano, Rafael Jacinto Villanueva, and Jose Manuel Garrido. "An optimized mathematical model for cancer patient care planning in the COVID-19 era." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e18663-e18663. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e18663.
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e18663 Background: The COVID-19 pandemic has threatened to collapse hospital and Intensive Care Unit (ICU) services, and it seems to limit the care of oncologic patients. The objective was to develop a mathematical model designed to predict the hospitalization and ICU admission demands due to COVID-19 to forecast the availability of hospital resources for the scheduling of oncological surgery and medical treatment that require hospitalitation or possible use of ICU services. Methods: We have implemented a SEIR model designed to predict the number of patients requiring hospitalization and ICU admissions for COVID-19. We evaluated the model using the number of cases registered in the hospitals of the province of Granada (Spain), that altogether cover 914,678 inhabitants. Calibration was performed using data recorded between March 15 and September 22, 2020. After that, the model was validated by comparing the predictions with data registered between September 23 and November 7, 2020. Besides, we performed a predictive analysis of scenarios regarding different possible sanitary measures. Results: Using patient registered data we developed a mathematical model that reflects the flow among the different sub-groups related to COVID-19 pandemics (Table). The best algorithm that fitted the disease dynamics was Particle Swarm Optimization, that minimized the difference between model output and real data used to calibrate the model. The validation phase showed the accuracy of the predictions, especially concerning trends in hospitalizations and ICU admissions. The different scenarios modelled on November 10, 2020 allowed us to predict the evolution of the pandemic until July 1, 2021, and to detect the peaks and valleys of disease prevalence. Conclusions: The mathematical model presented provides predictions on the evolution of COVID-19, the prevalence and hospital or ICU care demands. The predictions can be used to detect periods of greater availability of hospital resources that make it possible to schedule the oncologic surgery and intensify the care for oncologic patients. Furthermore, our model can be adapted to other population by recalibrating the model according to demographic data, the local evolution of the pandemic and the health policies. [Table: see text]
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Wetzstein, Gene A., Jeffrey E. Lancet, Jennifer E. Kallner, Jeffrey M. Sivik, Viet Q. Ho, Timothy J. George, Sheetal Desai, Shanel Fisher, Michael D. Newton, and Alan F. List. "Safety, Feasibility, and Cost-Effectiveness with Outpatient Administration of High-Dose Cytarabine Consolidation in Acute Myeloid Leukemia." Blood 112, no. 11 (November 16, 2008): 2405. http://dx.doi.org/10.1182/blood.v112.11.2405.2405.
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Abstract Introduction: High-dose cytarabine (HiDAC) consolidation chemotherapy is frequently used in acute myeloid leukemia (AML) patients less than 60 years of age. Traditionally, the potential risk for neurotoxicity, which has been reported to occur in 8–26% of patients, has limited its administration to the inpatient setting with a median length of stay of 5 days. Institutional retrospective data revealed the incidence to be much lower than previously reported with grade III/IV neurotoxicity in only 0.7% of cycles (N=267). As a result, we developed a comprehensive outpatient (OP) approach for the administration of HiDAC utilizing a pre-printed order and monitoring forms, pre-defined eligibility criteria/risk factors, administration/screening checklist, standard ancillary medications, designated scheduling, patient counseling, and written instructions for follow up. The benefits of OP administration may include improved patient satisfaction and decreased institutional costs and constraints on inpatient resources. However, there does not appear to be any published literature to date describing the OP administration of HiDAC. We report herein on the safety, feasibility, and outcomes of 43 patients receiving HiDAC consolidation therapy (n=88 cycles) between September 2006 and July 2008. Methods: Patients < 60 yo received 3 g/m2 over 1 hour every 12 hours on days 1, 3, and 5 and the dose was reduced to 1.5 g/m2 for pts ≥ 60 yo. All cycles of HiDAC were dosed based on age, renal, and hepatic function. There were no empiric dosage adjustments based on renal function alone. Post-treatment, patients received antibiotic prophylaxis and growth-factor support. Patients returned twice weekly for blood work until neutrophil and count recovery. Results: Forty-three patients received 88 cycles of HiDAC as an OP. The median patient age was 49 yrs (23–74) with 16% being over 60. Patient baseline characteristics of the OP group were similar to the inpatient group from our institutional review with respect to age, body surface area, gender, and race. Diagnosis, history of CNS radiation/disease, intrathecal chemotherapy, alcohol abuse, cumulative cytarabine dosage, and concurrent medications were also similar between the two groups. Only 1 patient was ineligible to receive OP therapy which was the result of both renal (estCL < 60mL/min) and hepatic (alk phos > 3X ULN) risk factors. Two patients with moderate renal insufficiency (est CL 30–60 mL/min) received full dose HiDAC without adverse event. All 43 patients successfully completed their full course of therapy. During the 88 cycles, there were no cases of clinically significant neurotoxicity (≥ grade III). There were 3 instances of grade I nystagmus. In all cases, therapy was completed without interruption. Rates of admission to the hospital post HiDAC OP consolidation for neutropenic fever (NF) was comparable to the inpatient group from our institutional review, occurring in 26% and 37% (p=0.07), respectively. All admissions post HiDAC OP was secondary to NF with the exception of one patient who was admitted for mucosal bleeding. Conclusions: In the present analysis, we confirm the safety and feasibility of OP-based HiDAC chemotherapy for the management of AML patients in remission. If patients are dosed and monitored properly, HiDAC can be successfully transitioned to the OP setting. With the implementation of this approach, we have improved patient convenience and satisfaction, decreased pressure on inpatient resources (nearly 450 days of hospital bed use), while maintaining similar rates of hospital re-admission. With proper patient selection, dosing, and education of both providers and patients, HiDAC can be safely administered in the OP setting and has become our standard of care.
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Hunis, Brian, Alvaro Jose Alencar, Aurelio Bartolome Castrellon, Luis E. Raez, and Vedner Guerrier. "Making steps to decrease emergency room visits in patients with cancer: Our experience after participating in the ASCO Quality Training Program." Journal of Clinical Oncology 34, no. 7_suppl (March 1, 2016): 51. http://dx.doi.org/10.1200/jco.2016.34.7_suppl.51.
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51 Background: Overutilization of emergency room services by oncology patients is a known problem associated with increased admission rates and health care expenditure. A review of our oncology patients’ emergency room (ER) visits from January to May 2015 demonstrated that 48% of ER visits happened during office hours. Consequently a rapid cycle quality improvement project was developed with an aim to decrease ER visits by 30% by September 2015. Methods: A multidisciplinary team completed an action plan, starting with a project charter and definition of aim statement. A process map for patient scheduling/triage was created. A cause and effect diagram helped identify potential causes patient utilization of the ER. Diagnostic data were obtained querying our EMR (EPIC) for ER visits from January to May 2015. A Pareto chart identified Breast, Hematology and GI malignancies as main diagnosis utilizing the ER. Plan-do-study-act (PDSA) #1 began with development of a protocol to guide the handling of patients' calls that could previously resulted in an ER visit. Staff from the patient access center (PAC), a telephone operator service, and physicians’ offices were trained on its application. PDSA #2 focused on patient education to the importance of contacting the PAC for any concern or symptom related to active chemotherapy treatment. Results: The implementation of a triage system at our PAC resulted in a 60% decrease in the number of patients utilizing the ER, which met our goal. Patients’ calls to the PAC have increased. Two new materials were developed: a telephone triage form categorizing the patient’s complaint and the resulting action by our PAC center staff, and a patient Clinical Intervention Triage Tracking Log which allowed for the tracking of all patients triaged, their data, and the responsible team member. Conclusions: This study suggests that the development of a tool to properly identify and address emergent chemotherapy symptoms without utilizing the ER during working hours resulted in an intervention that positively affected the pre-specified endpoint.
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Young, Karen, Devesh Hargunani, and Vitaliy Poylin. "EFFECTS OF DELAYED ENDOSCOPIES IN INFLAMMATORY BOWEL DISEASE PATIENTS DURING THE COVID-19 PANDEMIC." Inflammatory Bowel Diseases 28, Supplement_1 (January 22, 2022): S81. http://dx.doi.org/10.1093/ibd/izac015.130.
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Abstract OBJECTIVES Care for many chronic conditions was altered during the COVID-19 pandemic. For patients with Inflammatory Bowel Disease (IBD), routine maintenance including endoscopies were postponed. The effects of delaying endoscopies on IBD outcomes are currently unknown. This study aimed to evaluate effects of delays of maintenance endoscopies on patients with IBD during the COVID-19 pandemic. METHODS This was a retrospective review of all IBD patients scheduled for routine endoscopy at Northwestern Memorial Hospital March 13, 2020 through May 31, 2020. All endoscopic examinations were canceled in this period due to COVID-19. Patients were divided between those whose endoscopies were rescheduled promptly (on-time) or postponed (delayed) after August 31, 2020. Patient outcomes were examined one year after cancellation. Primary outcomes included hospital and emergency room admissions. Secondary outcomes included need for surgery and medication changes. RESULTS 100 patients were included in the delayed group and 150 in the on-time group, with a mean age of 47.5 and 42.8 years respectively. 59.2% had Crohn’s disease (CD), 39.2% had Ulcerative Colitis (UC) and 1.2% had indeterminate colitis. Both groups had similar initial severity scores as measured by the Harvey-Bradshaw Index in CD and the Simple Clinical Colitis Activity Index in UC. On average, the on-time group endoscopy was re-scheduled 2.8 months after closure compared to 9.1 months for the delayed group. There was no difference in the number of emergency room visits or hospital admissions during the delay. At one-year post-endoscopy, there was no difference in the number of emergency room visits between the on-time group (n=10, 6.7%) and the delayed group (n=3, 3%), p= 0.17. One-year post-endoscopy there were significantly more hospitalizations in the on-time group (n=14, 9.3%) compared to the delayed group (n=3, 3%), p=0.03. There was one malignancy in the on-time group and two in the delayed group which did not reach statistical significance. Although clinical severity scores were similar at 1 year, there were more IBD related surgeries in the on-time group (16) compared to the delayed group (4), p=0.03. DISCUSSION Patients with delayed endoscopies due to COVID-19 did not experience worse outcomes compared to patients whose endoscopies remained on-time. There was a trend towards increased malignancies in the delayed group, but higher number of admissions and operations in the on-time group despite similar degree of inflammation on endoscopy. Retrospective nature of this review did not allow us to evaluate all factors that may have influenced the decision for admission and surgery. CONCLUSIONS Controlled delay in endoscopies in patients with IBD with closely monitored re-scheduling efforts is safe and can be utilized in times of emergencies without compromising patient outcomes.
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Choudhury, Avishek, and Estefania Urena. "Forecasting hourly emergency department arrival using time series analysis." British Journal of Healthcare Management 26, no. 1 (January 2, 2020): 34–43. http://dx.doi.org/10.12968/bjhc.2019.0067.
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Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.
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Brandwein, J. M., J. Callum, M. Rubinger, J. G. Scott, and A. Keating. "An evaluation of outpatient bone marrow harvesting." Journal of Clinical Oncology 7, no. 5 (May 1989): 648–50. http://dx.doi.org/10.1200/jco.1989.7.5.648.
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In order to avoid the difficulties in scheduling and cost involved in admitting patients to hospital to undergo bone marrow harvests, we assessed outpatient marrow harvesting for autologous bone marrow transplant (BMT) candidates. Over a 13-month period, 39 consecutive patients with hematologic malignancies underwent bone marrow harvests as outpatients. For comparison we also evaluated 20 consecutive patients with similar disease status who had undergone bone marrow harvests as inpatients over the preceding 6 months. The mean hemoglobin value prior to harvest the mean volume of marrow harvested, and the mean nucleated cell count in the outpatient group were not significantly different from those of the inpatient group. There were no intraoperative complications. Of these 39 patients, 36 were discharged later the same day on oral iron supplements, with no adverse sequelae. Local pain was well controlled at home with mild oral analgesics. Two patients required admission due to postoperative hypotension--both responded promptly to intravenous (IV) fluids and blood and were discharged the following day. One patient was admitted postoperatively due to fever. There was a trend for the outpatients to receive less intra- and postoperative blood transfusions, but this did not reach statistical significance. The results suggest that most candidates for autologous BMT can safely undergo bone marrow harvesting as outpatients, thereby relieving pressure for hospital beds, potentially reducing costs and improving bed utilization. The study also raises the possibility of safely performing outpatient harvests on allogeneic BMT donors.
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Tafazal, H., P. Spreadborough, D. Zakai, N. Shastri-Hurst, S. Ayaani, and M. Hanif. "Laparoscopic cholecystectomy: a prospective cohort study assessing the impact of grade of operating surgeon on operative time and 30-day morbidity." Annals of The Royal College of Surgeons of England 100, no. 3 (March 2018): 178–84. http://dx.doi.org/10.1308/rcsann.2017.0171.
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Introduction There is an increasing trend towards day case surgery for uncomplicated gallstone disease. The challenges of maximising training opportunities are well recognised by surgical trainees and the need to demonstrate timely progression of competencies is essential. Laparoscopic cholecystectomy provides the potential for excellent trainee learning opportunities. Our study builds upon previous work by assessing whether measures of outcome are still affected when cases are stratified based on procedural difficulty. Material and methods A prospective cohort study of all laparoscopic cholecystectomies conducted at a district general hospital between 2009 and 2014, performed under the care of a single consultant. The operative difficulty was determined using the Cuschieri classification. The primary endpoint was duration of operation. Secondary endpoints included length of hospital stay, delayed discharge rate and 30-day morbidity. Results A total of 266 laparoscopic cholecystectomies were performed during the study period. Mean operative time for all consultant-led cases was 52.5 minutes compared with 51.4 minutes for trainees (P = 0.67 unpaired t-test). When cases were stratified for difficulty, consultant-led cases were on average 5 minutes faster. Median duration of hospital stay was equivalent in both groups and there was no statistical difference in re-attendance (12.9% vs. 15.3% P = 0.59) or re-admission rates (3.2% vs. 8.1% P = 0.10) at 30 days. Conclusions Our study provides evidence that laparoscopic cholecystectomy provides a good training opportunity for surgical trainees without being detrimental to patient outcome. We recommend that, in selected patients, under consultant supervision, laparoscopic cholecystectomy can be performed primarily by the surgical trainee without impacting on patient outcome or theatre scheduling.
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Shah, Deepa, Nicole Dalal, Lauren Janchenko, Evan Neighey, Sarah K. Garrigues, Katerina Kafkas, Lauren Shizue Maeda, and Mohana Roy. "Closing the loop: Utilizing advanced practice providers for a 7-day post hospital discharge follow-up of GI oncology patients at an academic cancer center." JCO Oncology Practice 20, no. 10_suppl (October 2024): 304. http://dx.doi.org/10.1200/op.2024.20.10_suppl.304.
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304 Background: Acute care visits for oncology patients may be preventable, especially for high-risk patients with a recent hospitalization. During our 2023 Fiscal Year, we observed an approximate 22% rate for 30 day readmissions for oncology patients, with our gastrointestinal (GI) oncology patients having the most readmissions, at Stanford Cancer Center. Readmission review through clinician surveys suggested that about 28% of unplanned admissions may have been preventable, with root cause analysis identifying timely access to ambulatory symptom management as a major driver. Methods: We established a cancer transitional care (CTC) clinic to provide consistent and timely post-discharge follow-up for oncology patients. We initiated a pilot program for GI oncology patients (chosen due to high acuity and readmission rate) who had an unplanned admission to Stanford Hospital. We aimed to schedule a visit with an advanced practice provider (APPs) within 7 days of discharge. The A3 problem solving process was implemented weekly by a multidisciplinary team. We created a referral in our electronic medical record. Inpatient oncology teams were instructed to place this referral within 3 days before discharge, allowing for adequate patient and family education regarding the goals of CTC visits and ample time for the post-discharge CTC visit to be scheduled prior to patient discharge. Prior to CTC visits, pharmacists performed outreach to reconcile medications to reduce medication errors. Eight oncology APPs were trained regarding the focused nature of a CTC visit, including symptom management, medication reconciliation, and goals of care discussions. Results: At baseline, from June 1 and November 30th, 2023, 61% of patients had follow-up within 7 days of discharge and 12% of patients with none. Between December 13, 2023 and April 19, 2024, CTC implementation resulted in a total of 220 referrals for GI oncology discharges, yielding 144 CTC clinic visits. Resulting in 66.1% of patients having follow up within 7 days of discharge and 9% of patients had no follow up scheduled. 79% of visits were within 7 days of discharge, representing a 29% increase in our goal scheduling timeline. The average time between discharge date and CTC visit was 6.2 days. Sixty six patients also received detailed pharmacy reviews with medication reconciliation and addressing medication access issues. Conclusions: Through our CTC clinic, we standardized post-discharge follow-up access for GI oncology patients, reducing variability in follow up. We plan to disseminate this model across medical oncology subspecialty clinics. Barriers included standardization of follow up with clinical team and expectations for the visit for both patients and clinicians. Future directions include integrating same day symptom management and urgent care access.
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Nguyen, Nam P., Vincent Vinh-Hung, Brigitta G. Baumert, Alice Zamagni, Meritxell Arenas, Micaela Motta, Pedro Carlos Lara, et al. "Older Cancer Patients during the COVID-19 Epidemic: Practice Proposal of the International Geriatric Radiotherapy Group." Cancers 12, no. 5 (May 19, 2020): 1287. http://dx.doi.org/10.3390/cancers12051287.
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The coronavirus disease 19 (COVID-19) pandemic is unprecedented as it reached all countries in the world within a record short period of time. Even though COVID-19 infection may be just severe in any adults, older adults (65-year-old or older) may experience a higher mortality rate. Among those affected, cancer patients may have a worse outcome compared to the general population because of their depressed immune status. As the health resources of most countries are limited, clinicians may face painful decisions about which patients to save if they require artificial ventilation. Cancer patients, especially the older ones, may be denied supportive care because of their shorter life expectancy. Thus, special considerations should be taken to prevent infection of older cancer patients and to provide them with adequate social support during their cancer treatment. The following proposal was reached: (1) Education of health care providers about the special needs of older cancer patients and their risks of infection. (2) Special consideration such as surgical masks and separate scheduling should be made to protect them from being infected. (3) Social services such as patient navigators should be provided to ensure adequate medical supply, food, and daily transportation to cancer centers. (4) Close monitoring through phone calls, telecommunication to ensure social distancing and psychological support from patient family to prevent anxiety and depression. (5) Shorter course of radiotherapy by use of hypofractionation where possible to decrease the needs for daily transportation and exposure to infection. (6) Enrollment of older cancer patients in clinical trials for potential antiviral medications if infection does occur. (7) Home health care telemedicine may be an effective strategy for older cancer patients with COVID-19 infection to avoid hospital admission when health care resources become restricted. (8) For selected patients, immunotherapy and targeted therapy may become the systemic therapy of choice for older cancer patients and need to be tested in clinical trials.
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Nassar, Leila, Sarah N. Van Zandt, George Nassar, and Ronya Nassar. "Increasing Breastfeeding Rates Through Continuity of Care." Clinical Lactation 13, no. 1 (February 1, 2022): 32–38. http://dx.doi.org/10.1891/cl.2021-0002.
Full textAbstract:
BackgroundContinuity of care is important in many aspects of medicine, as evidenced by the patient-centered medical home model. The newborn period is an especially important time for continuity of care, as this time sets the stage for lifelong health. Breastfeeding, which is known for its positive health benefits for both mother and child, is the gold standard for infant feeding. While inpatient lactation support is a common amenity available during hospital admission, support can be more difficult to obtain once a patient is discharged. To help eliminate this barrier, a process was put in place within an Eastern Pennsylvania health network’s seven office locations to standardize outreach to the mother-infant dyad.MethodsA quality improvement retrospective chart review comparing pre- and post-intervention rates of exclusive and non-exclusive breastfeeding was completed using one health system’s Electronic Health Record (EHR). A referral process was established to capture dyads born within the health network’s hospital who were going to be followed at one of the seven pediatric offices outpatient. Mothers were called and followed to see how they were progressing with their breastfeeding goals. Any problems were addressed by the health network’s International Board-Certified Lactation Consultants (IBCLCs) and Certified Lactation Counselor (CLC).ResultsLactation rates at the pediatric practice were observed to have increased at both 6 months and 12 months post-intervention compared to pre-intervention.ConclusionProactively scheduling and providing outreach immediately following birth may promote increased breastfeeding rates.
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Metzinger, Candice, Samer Antonios, K. James Kallail, Hayrettin Okut, Rosey Zackula, and Brianna Cline. "Analysis of Patient Handoff between Providers at a Tertiary Urban Medical Center." Kansas Journal of Medicine 14 (August 4, 2021): 192–96. http://dx.doi.org/10.17161/kjm.vol1415170.
Full textAbstract:
Introduction. Few studies have quantified the total number of attending and consulting physicians involved in inpatients’ care, and no other research quantifies the total number of all providers participating in inpatients’ care. The purpose of this study was to calculate the number of attending hand-offs, the attending encounter time, and the total number of providers participating in inpatients’ care for all admitted patients at a tertiary urban medical center.
Methods. The study design was an observational retrospective cohort. Subjects included pediatric and adult patients who were admitted to and discharged from Ascension Via Christi St. Francis (AVCSF) in Wichita, Kansas between November 01, 2019 and January 31, 2020. Data were abstracted from the Cerner Electronic Medical Record. Variables included: patient demographics, admitting diagnosis, diagnosis related group (DRG), admission service, and duration of inpatient stay. Provider variables abstracted included provider type and provider specialty. Categorical variables were presented as frequencies and percentages, while continuous variables were presented as means ± standard deviation.
Results. The sample included information from 200 patient charts. Patients’ ages ranged from 5 to 94 years, with a mean of 61 years. Approximately 52% were female and 74.9% were admitted to a surgical service. The length of all inpatients’ stays ranged from less than 1 day to 31 days, with a mean of 4 days. Seventy-six different DRGs were recorded. The most frequent attending specialties for medical patients were hospital medicine, internal medicine, general surgery, and interventional cardiology. Consulting physicians had more patient encounters than any other healthcare provider. For all inpatients, an average of two attending physicians participated in care over the duration of their stay with a range of one to six attending physicians. There was an average of one hand-off between attending physicians. Patients had an average of five consulting physicians, two resident physicians, two physician assistants, and two nurse practitioners during a stay. There was an average of 10 total providers, with a range of one to 46 total providers participating in care.
Conclusions. Understanding the provider data surrounding an inpatient stay is a foundational step in assessing the quality of the provider-inpatient encounter and potential areas for improvement. In this study, the average number of attending physicians and handoffs was reasonable; however, the total number of providers involved in care was relatively high. Assessment of staffing and scheduling requirements by hospital administration could identify areas of improvement to reduce the potential for medical error caused by multiple providers being involved in patient care.