Patient Flow Optimization in the Department of Medicine at MGH
Patient Flow Optimization in the Department of Medicine at MGH

Author: Elizabeth Ugarph

Publisher:

Published: 2017

Total Pages: 120

ISBN-13:

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In 2015, there were approximately 17,000 General Medicine admissions in the Department of Medicine (DOM) at MGH. General Medicine patients regularly experience significant non-clinical delays caused by bed and care team unavailability, with approximately 25% of patients waiting ten hours or more for a bed. Delays in bed and care team assignments result in decreased patient satisfaction, congestion in the ED and ICUs, and increased overall hospital length-of-stay. This project studies General Medicine patient flow, develops and evaluates interventions to improve this flow, and provides recommendations to hospital leadership. To this end, we construct a discrete-event simulation based on historical data. Intervention effectiveness is measured primarily based on patient-wait-for-bed, the time from when patient is medically ready for an inpatient bed until the bed is assigned to and ready for the patient. We find that the simulation model accurately represents the wait times of General Medicine patients. We propose a new algorithm, which when implemented could reduce overall average patient-wait-for-bed by 9% from 7.36 to 6.67 hours. Implementation of additional capacity and reorganization of the physician care teams (known as the DOM redesign) is shown to result in a further 31% reduction in average wait time (from 6.67 to 4.59 hours). Other interventions tested such as early assignment of patients to care teams based on predicted discharges, and increased flexibility of care teams to cover different units are shown to have modest effects on overall patient-wait-for-bed.

Reducing Intraday Patient Wait Times Through Just-in-time Bed Assignment
Reducing Intraday Patient Wait Times Through Just-in-time Bed Assignment

Author: Sean T. McNichols

Publisher:

Published: 2015

Total Pages: 135

ISBN-13:

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Massachusetts General Hospital (MGH) is the oldest and largest hospital in New England as well as the original and largest teaching hospital of the Harvard Medical School. The neuroscience units experience patient flow issues similar to those observed throughout MGH, including high bed utilization and long intraday patient wait times. This project focuses on the neuroscience units as a microcosm of the hospital. MGH consistently operates near capacity. Patients from the emergency department, the perioperative environment, intensive care units (ICUs) and other sources compete for beds. The admitting department manages the bed assignment process across MGH. Assignments are often made without access to all relevant information, such as expected admission, surgery and discharge timing. As a result of common procedures, patients are frequently assigned to a bed before they are clinically ready to move. Our analysis reveals that suboptimal bed assignment and patient transfer processes are among the leading root causes of intraday patient delays. The primary objective of the project is to develop a bed assignment policy to reduce intraday patient wait times. The policy consists of a bed assignment algorithm and enabling bed management processes. To account for patient acuity, the algorithm segments patients by movement (e.g., ED-to-ICU). The target maximum wait for each segment is the acceptable wait length (AWL). The algorithm ranks patients based on their ready times and the AWLs, and assigns beds primarily on a just-in-time (JIT) basis. The enabling bed management processes include small-scale early discharge and early transfer interventions to better align the intraday timing of demand for inpatient beds with available capacity. A simulation of neuroscience patient flow is used to evaluate different approaches. The model shows that adoption of the JIT policy would increase the percentage of patients who experience bed waits within the AWL for all movement types. Predicted bed waits for patients who require ICU-level care would be 30 minutes or less for 90% of ED patients and 95% of OR patients (improvements from historical baselines of 44% and 91%, respectively). Predicted bed waits for transfers to floor beds would be two hours or less for 81% of ED patients and 93% of OR patients (improvements from historical baselines of 63% and 84%, respectively). The solution significantly reduces intraday patient wait times without a major increase in hospital capacity.

Hospital Operations
Hospital Operations

Author: Wallace J. Hopp

Publisher: Pearson Education

Published: 2013

Total Pages: 638

ISBN-13: 0132908662

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"In Hospital Operations, two leading Operations Management experts and five practicing clinicians demonstrate how to apply new OM advances and metrics to substantially improve any hospital's performance. Replete with examples, Hospital Operations shows how to generate principles-driven breakthrough ideas to systematically improve emergency departments, operating rooms, nursing unites, and diagnostic units." -- Back cover

Improving Surgical Patient Flow Through Simulation of Scheduling Heuristics
Improving Surgical Patient Flow Through Simulation of Scheduling Heuristics

Author: Ashleigh Royalty Range

Publisher:

Published: 2013

Total Pages: 79

ISBN-13:

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Massachusetts General Hospital (MGH) is currently the nation's top ranked hospital and is the largest in New England. With over 900 hospital beds and approximately 38,000 operations performed each year, MGH's operating rooms (ORs) run at 90% utilization and their hospital beds at 99% operational occupancy. MGH is faced with capacity constraints throughout the perioperative (pre-, intra-, and postoperative) process and desires to improve throughput and decrease patient waiting time without adding expensive additional resources. This project focuses on matching the intraday scheduling of elective surgeries with the discharge rate and pattern of patients from the hospital floor by investigating ways surgeons could potentially schedule their cases within a given OR block. To do this, various scheduling rules are modeled to measure the impact of shifting patient flow in each step of the perioperative process. Currently the hospital floor proves to be the biggest bottleneck in the system. Delays in discharging patients result in Same Day Admits (patients that will be admitted to the hospital post-surgery) waiting for hospital beds in the Post Anesthesia Care Unit (PACU). These patients wait more than sixty minutes on average after being medically cleared to depart the PACU. A simulation model is built to evaluate the downstream effects of each scheduling rule and discharge process change. The model takes into account physical and staff resource limitations at each of the upstream and downstream steps in the perioperative process. By scheduling Same Day Admits last in each OR block, patient wait time in the PACU can be reduced up to 49%. By implementing the recommended changes the system will realize lower wait times for patients, less stress on the admitting and nursing staff, and a better overall use of the limited physical resources at MGH.

The Healthcare Imperative
The Healthcare Imperative

Author: Institute of Medicine

Publisher: National Academies Press

Published: 2011-01-17

Total Pages: 852

ISBN-13: 0309144337

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The United States has the highest per capita spending on health care of any industrialized nation but continually lags behind other nations in health care outcomes including life expectancy and infant mortality. National health expenditures are projected to exceed $2.5 trillion in 2009. Given healthcare's direct impact on the economy, there is a critical need to control health care spending. According to The Health Imperative: Lowering Costs and Improving Outcomes, the costs of health care have strained the federal budget, and negatively affected state governments, the private sector and individuals. Healthcare expenditures have restricted the ability of state and local governments to fund other priorities and have contributed to slowing growth in wages and jobs in the private sector. Moreover, the number of uninsured has risen from 45.7 million in 2007 to 46.3 million in 2008. The Health Imperative: Lowering Costs and Improving Outcomes identifies a number of factors driving expenditure growth including scientific uncertainty, perverse economic and practice incentives, system fragmentation, lack of patient involvement, and under-investment in population health. Experts discussed key levers for catalyzing transformation of the delivery system. A few included streamlined health insurance regulation, administrative simplification and clarification and quality and consistency in treatment. The book is an excellent guide for policymakers at all levels of government, as well as private sector healthcare workers.

Improving Surgical Patient Flow in a Congested Recovery Area
Improving Surgical Patient Flow in a Congested Recovery Area

Author: Trevor A. Schwartz

Publisher:

Published: 2012

Total Pages: 63

ISBN-13:

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The recent movement in healthcare reform requires hospitals to care for more patients while simultaneously reducing costs. Medical institutions can no longer afford to simply add beds and hire staff to increase capacity. They must use existing resources more effectively and develop innovative solutions to increase capacity. This project focuses on the redesign of surgical patient flow through multiple Post-Anesthesia Care Units (PACUs) at Massachusetts General Hospital (MGH). The PACU is where surgical patients recover following their procedure that takes place in the Operating Room (OR) suite. Some patients experience delays when leaving the OR due to the lack of a staffed PACU bed. These patients begin the recovery process in the OR which causes delays for to-follow cases. In addition, the OR nursing staff rather than a PACU nurse must monitor recovery, which drives higher costs and frustrates staff members. Therefore this study examined the sources of delay and sought to redesign the flow of surgical patients through the PACUs. Our main recommendation is to incorporate a "Fast Track" for the outpatient population that eliminates delays and expedites outpatient processing in the PACU. Segregating the outpatients and implementing the one-stop "Fast Track" recovery process will reduce average outpatient PACU length of stay (length of stay) by 27%, the equivalent of adding 1.8 beds of capacity. Through the application of operations management techniques, we can decrease the patient processing time or length of stay in the PACU, which in turn increases throughput and creates additional capacity.

Optimization-simulation Framework to Optimize Hospital Bed Allocation in Academic Medical Centers
Optimization-simulation Framework to Optimize Hospital Bed Allocation in Academic Medical Centers

Author: Andrew M. Vanden Berg

Publisher:

Published: 2018

Total Pages: 100

ISBN-13:

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Congestion, overcrowding, and increasing patient wait times are major challenges that many large, academic centers currently face. To address these challenges, hospitals must effectively utilize available beds through proper strategic bed allocation and robust operational day-to-day bed assignment policies. Since patient daily demand for beds is highly variable, it is frequent that the physical capacity allocated to a given clinical service is not sufficient to accommodate all of the patients who belong to that service. This situation could lead to extensive wait time of patients in various locations in the hospital (e.g., the emergency department), as well as clinically and operationally undesirable misplacements of patients in hospital floors/beds that are managed by other clinical services than the ones to which the patients belong. In this thesis, we develop an optimization-simulation framework to optimize the bed allocation at Mass General Hospital. Detailed, data-driven simulation suggests that the newly proposed bed allocation would lead to significant reduction in patient intra-day wait time in the emergency department and other hospital locations, as well as a major reduction in the misplacements of patients in the Medicine service, which is the largest service in the hospital. We employ a two-pronged approach. First, we developed a detailed simulation setting of the entire hospital that could be used to assess the effectiveness of day-to-day operational bed assignment policies given a specific bed allocation. However, the simulation does not allow tractable optimization that seeks to find the best bed allocation among all possible allocations. This motivates the development of a network-flow/network design inspired mixed integer program that approximates the operational performance of bed allocations and allows us to effectively search for approximately the best allocation. The mixed integer program can be solved via a scenario sampling approach to provide candidate bed allocations. These are then tested and evaluated via the simulation setting. These tools facilitate expert discussions on how to modify the existing bed allocation at MGH to improve the day-to-day performance of the bed assignment process.

Quantifying the Impact of Care Team Discontinuities on Medically Unnecessary Delays in Inpatient Flow
Quantifying the Impact of Care Team Discontinuities on Medically Unnecessary Delays in Inpatient Flow

Author: Andrew Thomas Johnston (S.M.)

Publisher:

Published: 2016

Total Pages: 93

ISBN-13:

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This thesis quantifies the impact of clinical care team discontinuities on inpatient length-of-stay (LOS) and admission wait time within Massachusetts General Hospital's Department of Medicine (DOM). The DOM is the hospital's largest clinical department by inpatient volume and supports a highly diverse patient population. Like many Academic Medical Centers, the DOM is confronted with increasing inpatient volume (>5% annual growth) and is showing symptoms of being capacity constrained, including rising patient wait times for admission from the Emergency Department. With the goal of informing specific interventions to increase patient throughput, this study evaluates the impact of end-of-rotation Attending physician handoffs (HOFs) on LOS and admission wait time on four, resident-staffed, general care floors with similar patient populations, clinical team configurations, and shift patterns. When combined with independently-distributed patient demand and the randomized assignment of patients to floors, the hospital's residency schedule creates natural randomized experiments through which the impact of HOFs can be isolated. It is found that patients admitted to a floor two days before a HOF spend an average of 0.8 days longer in the hospital than otherwise similar patients, while patients admitted one day before a HOF spend 0.8 fewer days in the hospital (Wilcoxon-Mann-Whitney RS, two-sided, a = 0.05). Further, average admission wait time increases by 15%-34% during the last two days of an Attending's rotation (t-test of means, pooled variance, two-sided, a = 0.05). Finally, a series of regression models that utilize only the information available when a patient is first admitted demonstrate that proximity to a future HOF at point of admission is a significant and robust predictor of LOS across major diagnostic categories (Monte Carlo Cross-Validation, [alpha] = 0.05). The dynamics this study uncovers can be used to attenuate the negative impacts of HOFs on patient LOS by informing the design of clinician rotation schedules, care team structures, and new patient assignment practices.

Modeling Neuroscience Patient Flow and Inpatient Bed Management
Modeling Neuroscience Patient Flow and Inpatient Bed Management

Author: Jonas Hiltrop

Publisher:

Published: 2014

Total Pages: 122

ISBN-13:

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Massachusetts General Hospital (MGH) experiences consistently high demand for its more than 900 inpatient beds. On an average weekday, the hospital admits about 220 patients, with the emergency department (ED) and the operating rooms (OR) being the main sources of admissions. Given MGH's high occupancy rates, a comparable number of discharges have to occur daily, and the intraday time distributions of admissions and discharges have to be aligned in order to avoid long wait times for beds. The situation is complicated by the specialization of beds and the medical needs of patients, which place constraints on the possible bed-patient assignments. The hospital currently manages these processes using fairly manual and static approaches, and without clear prioritization rules. The timing of discharges is not aligned with the timing of new admissions, with discharges generally occurring later in the day. For this reason MGH experiences consistent bed capacity constraints, which may cause long wait times for patients, throughput limitations, disruptions in the ED and in the perioperative environment, and adverse clinical outcomes. This project develops a detailed patient flow simulation based on historical data from MGH. The model is focused on the neuroscience clinical specialties as a microcosm of the larger hospital since the neuroscience units (22 ICU beds and 64 floor beds) are directly affected by the hospital's important capacity issues (e.g., patient overflows into other units, ICU-to-floor transfer delays). We use the model to test the effectiveness of the following three interventions: 1. Assigning available inpatient beds to newly admitted patients adaptively on a just-in-time basis; 2. Discharging patients earlier in the day; 3. Reserving beds at inpatient rehabilitation facilities, thereby reducing the MGH length of stay by one or more days for patients who need these services after discharge from the hospital. Intervention effectiveness is measured using several performance metrics, including patient wait times for beds, bed utilization, and delays unrelated to bed availability, which capture the efficiency of bed usage. We find that the simulation model captures the current state of the neuroscience services in terms of intraday wait times, and that all modeled interventions lead to significant wait time reductions for patients in the ED and in the perioperative environment. Just-in-time bed assignments reduce average wait times for patients transferring to the neuroscience floor and ICU beds by up to 35% and 48%, respectively, at current throughput levels. Discharges earlier in the day and multi-day length of stay reductions (i.e., interventions 2 and 3) lead to smaller wait time reductions. However, multi-day length of stay reductions decrease bed utilization by up to 4% under our assumptions, and create capacity for throughput increases. Considering the expected cost of implementing these interventions and the reductions in patient wait times, we recommend adopting just-in-time bed assignments to address some of the existing capacity issues. Our simulation shows that this intervention can be combined effectively with earlier discharges and multi-day length of stay reductions at a later point in order to reduce wait times even further.

Computational Technology for Effective Health Care
Computational Technology for Effective Health Care

Author: National Research Council

Publisher: National Academies Press

Published: 2009-02-24

Total Pages: 121

ISBN-13: 0309155843

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Despite a strong commitment to delivering quality health care, persistent problems involving medical errors and ineffective treatment continue to plague the industry. Many of these problems are the consequence of poor information and technology (IT) capabilities, and most importantly, the lack cognitive IT support. Clinicians spend a great deal of time sifting through large amounts of raw data, when, ideally, IT systems would place raw data into context with current medical knowledge to provide clinicians with computer models that depict the health status of the patient. Computational Technology for Effective Health Care advocates re-balancing the portfolio of investments in health care IT to place a greater emphasis on providing cognitive support for health care providers, patients, and family caregivers; observing proven principles for success in designing and implementing IT; and accelerating research related to health care in the computer and social sciences and in health/biomedical informatics. Health care professionals, patient safety advocates, as well as IT specialists and engineers, will find this book a useful tool in preparation for crossing the health care IT chasm.