Use of Systems Engineering to Design a Hospital Command Center

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

The working experience of medical staff in the hospital-wide bed-sharing mode: A qualitative study

AIM

The purpose of this study was to provide a comprehensive understanding of the attitudes and experiences of the medical staff regarding the hospital bed-sharing model.

DESIGN

The present research was a qualitative study.

METHODS

This qualitative study used in-depth individual interviews with 7 doctors, 10 clinical nurses and 3 head nurses, which were then transcribed and analysed thematically.

RESULTS

The study identified six overall themes. Issues were raised about the efficient utilization of hospital bed resources, greater challenges for nursing work, adjustment of doctors' work modes, barriers to communication between doctors, nurses, and patients, potential medical risks, and differentiation of patients' medical experience.

IMPLICATIONS FOR NURSING MANAGEMENT

Hospital administrators and nurse managers should work together to solve the challenges that medical staff face, including strengthening nursing training, improving medical-nursing collaboration models, standardizing and effective communication strategies, and improving patient experiences.

Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

Systems approach to health service design, delivery and improvement: a systematic review and meta-analysis

OBJECTIVES

To systematically review the evidence base for a systems approach to healthcare design, delivery or improvement.

DESIGN

Systematic review with meta-analyses.

METHODS

Included were studies in any patients, in any healthcare setting where a systems approach was compared with usual care which reported quantitative results for any outcomes for both groups. We searched Medline, Embase, HMIC, Health Business Elite, Web of Science, Scopus, PsycINFO and CINAHL from inception to 28 May 2019 for relevant studies. These were screened, and data extracted independently and in duplicate. Study outcomes were stratified by study design and whether they reported patient and/or service outcomes. Meta-analysis was conducted with Revman software V.5.3 using ORs-heterogeneity was assessed using I2 statistics.

RESULTS

Of 11 405 records 35 studies were included, of which 28 (80%) were before-and-after design only, five were both before-and-after and concurrent design, and two were randomised controlled trials (RCTs). There was heterogeneity of interventions and wide variation in reported outcome types. Almost all results showed health improvement where systems approaches were used. Study quality varied widely. Exploratory meta-analysis of these suggested favourable effects on both patient outcomes (n=14, OR=0.52 (95% CI 0.38 to 0.71) I2=91%), and service outcomes (n=18, OR=0.40 (95% CI 0.31 to 0.52) I2=97%).

CONCLUSIONS

This study suggests that a systems approaches to healthcare design and delivery results in a statistically significant improvement to both patient and service outcomes. However, better quality studies, particularly RCTs are needed.PROSPERO registration numberCRD42017065920.

Distributed situation awareness: a health-system approach to assessing and designing patient flow management

Patient flow management is a system-wide process but many healthcare providers do not integrate multiple departments into the process to minimise the time between treatments or medical services for maximum patient throughput. This paper presents a case study of applying Distributed Situation Awareness (DSA) to characterise system-wide patient flow management and identify opportunities for improvements in a healthcare system. This case study employed a three-part method of data elicitation, extraction, and representation to investigate DSA. Social, task, and knowledge networks were developed and then combined to characterise patient flow management and identify deficiencies of the command and control centre of a healthcare facility. Social network analysis provided centrality metrics to further characterise patient flow management. The DSA model helped identify design principles and deficiencies in managing patient flow. These findings indicate that DSA is promising for analysing patient flow management from a system-wide perspective. Practitioner summary: This article examines Distribution Situation Awareness (DSA) as an analysis framework to study system-wide patient flow management. The DSA yields social, task, and knowledge networks that can be combined to characterise patient flow and identify deficiencies in the system. DSA appears promising for analysing communication and coordination of complex systems. Abbreviations: CDM: critical decision method; CTaC: carilion transfer and communications center; EAST: event analysis systematic teamwork; ED: emergency department; DES: discrete event simulation; DSA: distributed situation awareness; SA: situation awareness; SNA: social network analysis.

Statewide Real-Time Tracking of Beds and Ventilators During Coronavirus Disease 2019 and Beyond

This brief report describes the rapid deployment of a real-time electronic tracking board for all hospitals in the state of Oregon. In preparation for the coronavirus disease 2019 surge on hospital resources, and in collaboration across health systems, with health authorities and an industry partner, we combined existing infrastructures to create the first automated tracking board for our entire state, including bed types by health system and geographic area, and with granularity to the individual unit level for each participating hospital. At the time of submission, we have a live snapshot of 87% of beds in the state, including real-time ventilator data across eight health systems. The tracking board allows for rapid assessment of available bed and ventilator resources and pulls electronic health record data that is created through normal care processes rather than relying upon manual entry. It is updated every 5 minutes and is drillable from state to unit level. Together these factors make the data actionable, which is essential in a crisis. The new tracking system integrates seamlessly with our preexisting statewide, manually updated tracking board via bidirectional data sharing to ensure existing processes across the state can continue. This new tool allows any health system in our state to visualize occupancy by type and location in real time. Amid pandemic uncertainty, having a reliable tool for tracking critical hospital resources will enhance our statewide ability to maintain healthcare functionality in a world with coronavirus disease 2019.

An Electronic Dashboard to Monitor Patient Flow at the Johns Hopkins Hospital: Communication of Key Performance Indicators Using the Donabedian Model

Efforts to monitoring and managing hospital capacity depend on the ability to extract relevant time-stamped data from electronic medical records and other information technologies. However, the various characterizations of patient flow, cohort decisions, sub-processes, and the diverse stakeholders requiring data visibility create further overlying complexity. We use the Donabedian model to prioritize patient flow metrics and build an electronic dashboard for enabling communication. Ten metrics were identified as key indicators including outcome (length of stay, 30-day readmission, operating room exit delays, capacity-related diversions), process (timely inpatient unit discharge, emergency department disposition), and structural metrics (occupancy, discharge volume, boarding, bed assignation duration). Dashboard users provided real-life examples of how the tool is assisting capacity improvement efforts, and user traffic data revealed an uptrend in dashboard utilization from May to October 2017 (26 to 148 views per month, respectively). Our main contributions are twofold. The former being the results and methods for selecting key performance indicators for a unit, department, and across the entire hospital (i.e., separating signal from noise). The latter being an electronic dashboard deployed and used at The Johns Hopkins Hospital to visualize these ten metrics and communicate systematically to hospital stakeholders. Integration of diverse information technology may create further opportunities for improved hospital capacity.