Business Process Redesign in the Perioperative Process: A Case Perspective for Digital Transformation

This case study investigates business process redesign within the perioperative process as a method to achieve digital transformation. Specific perioperative sub-processes are targeted for re-design and digitalization, which yield improvement. Based on a 184-month longitudinal study of a large 1,157 registered-bed academic medical center, the observed effects are viewed through a lens of information technology (IT) impact on core capabilities and core strategy to yield a digital transformation framework that supports patient-centric improvement across perioperative sub-processes. This research identifies existing limitations, potential capabilities, and subsequent contextual understanding to minimize perioperative process complexity, target opportunity for improvement, and ultimately yield improved capabilities. Dynamic technological activities of analysis, evaluation, and synthesis applied to specific perioperative patient-centric data collected within integrated hospital information systems yield the organizational resource for process management and control. Conclusions include theoretical and practical implications as well as study limitations

Multi-objective Optimization of Hospital Inpatient Bed Assignment

Choosing which bed to assign an admitted patient to in a hospital is a complex problem. There are numerous factors to consider including the patient’s gender and isolation requirements, current bed availability, and unit configurations. This problem must be solved each time a new patient seeks admission resulting in rearrangement of already admitted patients. Each movement of an already admitted patient increases the workload for hospital staff and also increases the risk of nosocomial infections for the patient. In order to alleviate these problems we propose optimizing the patient admission process through a multi-objective model which first maximizes the overall criticality of patients admitted, then minimizes movements of previously admitted patients while creating space for incoming patients. Using this model we perform three sets of experiments. The first experiments seek to determine the ideal number of private and semi-private rooms in a multi-occupancy unit with a fixed number of total rooms. This results in a tool to enable the unit to manage the tradeoffs between moving previously admitted patients and bed utilization. The second experiments seek to determine the ideal timeframe over which to batch patient admissions. These results suggest more frequent admissions have minimal impact on inpatient rearrangement. The third experiments seek to determine the potential benefit of using a centralized admitting entity and finds managing bed assignment from a central perspective far out performs individual units managing their bed assignments

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Towards optimizing hospital patient transports by automatically identifying interpretable causes of delays

The continuous financial pressure on hospitals forces them to rethink various workflows. We focus on optimizing hospital transports, within the hospital, as they count up to 30% of the overall hospital cost. In this paper, we discuss a self-learning platform that learns the causes of transport delays, in order to avoid these kinds of delays in the future. We pay special attention to the explainability of the self-learning system, such that management understands the learned causes and remains in control over the automated process. This is achieved by providing the learned causes as sentences that can be understood by non-technical personnel and allowing these causes to first be supervised before the system takes them into account. Once approved, the system will calculate how much more time should be assigned to these transports in order to avoid future delays. As a result, the scheduling of patient transportation can be automatically optimized, while management remains in full control of the process

A taxonomy and cultural analysis of intra‐hospital patient transfers

Existing research on intra‐hospital patient transitions focuses chiefly on handoffs, or exchanges of information, between clinicians. Less is known about patient transfers within hospitals, which include but extend beyond the exchange of information. Using participant observations and interviews at a 1,541‐bed, academic, tertiary medical center, we explored the ways in which staff define and understand patient transfers between units. We conducted observations of staff (n = 16) working in four hospital departments and interviewed staff (n = 29) involved in transfers to general medicine floors from either the Emergency Department or the Medical Intensive Care Unit between February and September 2015. The collected data allowed us to understand transfers in the context of several hospital cultural microsystems. Decisions were made through the lens of the specific unit identity to which staff felt they belonged; staff actively strategized to manage workload; and empty beds were treated as a scarce commodity. Staff concepts informed the development of a taxonomy of intra‐hospital transfers that includes five categories of activity: disposition, or determining the right floor and bed for the patient; notification to sending and receiving staff of patient assignment, departure and arrival; preparation to send and receive the patient; communication between sending and receiving units; and coordination to ensure that transfer components occur in a timely and seamless manner. This taxonomy widens the study of intra‐hospital patient transfers from a communication activity to a complex cultural phenomenon with several categories of activity and views them as part of multidimensional hospital culture, as constructed and understood by staff.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145512/1/nur21875.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145512/2/nur21875_am.pd

Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review

Introduction. The intensive care unit (ICU) plays a pivotal role in providing specialized care to patients with severe illnesses or injuries. As a critical aspect of healthcare, ICU admissions demand immediate attention and skilled care from healthcare professionals. However, the intricacies involved in this process necessitate analytical solutions to ensure effective management and optimal patient outcomes. Aim. The aim of this review was to highlight the enhancement of the ICUs through the application of analytics, artificial intelligence, and machine learning. Methods. The review approach was carried out through databases such as MEDLINE, Embase, Web of Science, Scopus, Taylor & Francis, Sage, ProQuest, Science Direct, CINAHL, and Google Scholar. These databases were chosen due to their potential to offer pertinent and comprehensive coverage of the topic while reducing the likelihood of overlooking certain publications. The studies for this review involved the period from 2016 to 2023. Results. Artificial intelligence and machine learning have been instrumental in benchmarking and identifying effective practices to enhance ICU care. These advanced technologies have demonstrated significant improvements in various aspects. Conclusions. Artificial intelligence, machine learning, and data analysis techniques significantly improved critical care, patient outcomes, and healthcare delivery

Implementing and Evaluating a Clinical Information Interface between an Electronic Medical Record and a Patient Classification System

As a result of the Affordable Care Act and the Institute of Medicine’s initiatives, hospitals are challenged to improve outcomes as efficiently as possible. How does the national initiative of RNs partnering with other healthcare professionals to improve the quality of patient care at a lower cost, cascade down to individual organizations? One answer may come by focusing on nurse staffing in acute care hospitals. Considering the impact RNs have on patient quality outcomes and the bottom line of hospitals, appropriate management of the RN workforce is one of the most important areas hospitals can focus on in order to meet the goals of ACA and the IOM. The aim of the project is to create and implement a clinical information interface between two software solutions, by different vendors, that allows electronic medical record (EMR) data to provide source data for the patient classification system (PCS). The end result will be a classification system that is fully automated. The creation and implementation of a clinical interface between software solutions from different industry partners is a very new and innovative approach for advancing the use of software. No template for this work is available. This computerized information interface (CII) will allow Nurse Managers to use timely, accurate and consistent data to make informed decisions to manage the nursing workforce in the in-patient setting

Use of location data for the surveillance, analysis, and optimization of clinical processes

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Includes bibliographical references (leaves 33-35).Location tracking systems in healthcare produce a wealth of data applicable across many aspects of care and management. However, since dedicated location tracking systems, such as the oft mentioned RFID tracking system, are still sparsely deployed, a number of other data sources may be utilized to serve as a proxy for physical location, such as barcodes and manual timestamp entry, and may be better suited to indicate progress through clinical workflows. INCOMING!, a web-based platform that monitors and tracks patient progress from the operating room to the post-anesthesia care unit (PACU), is one such system that utilizes manual timestamps routinely entered as standard process of care in the operating room in order to track a patient's progress through the post-operative period. This integrated real time system facilitates patient flow between the PACU and the surgical ward and eases PACU workload by reducing the effort of discharging patients.(cont.) We have also developed a larger-scale integrated system for perioperative processes that integrates perioperative data from anesthesia and surgical devices and operating room (OR) / hospital information systems, and projects the real-time integrated data as a single, unified, easy to visualize display. The need to optimize perioperative throughput creates a demand for integration of the datastreams and for timely data presentation. The system provides improved context-sensitive information display, improved real-time monitoring of physiological data, real-time access to readiness information, and improved workflow management. These systems provide improved data access and utilization, providing context-aware applications in healthcare that are aware of a user's location, environment, needs, and goals.by Mark A. Meyer.S.M