Hyo Kyung Lee

Computer modeling of lung cancer diagnosis-to-treatment process.

We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

From production systems to health care delivery systems: a retrospective look on similarities, difficulties and opportunities

Manufacturing systems have attracted substantial research attentions during the last 50 years. In recent years, there has been growing interest in health care systems research to improve efficiency, safety and care quality. The similarities identified between manufacturing systems and health care delivery systems heighten the importance of transferring the experience and knowledge in manufacturing to health care. In this paper, based on the lessons we learned and the experience we obtained during our journey from production systems research to health care delivery systems study, we discuss the similarities between production systems and health care delivery systems in system modelling, design, performance evaluation and continuous improvements and investigate the differences and difficulties that stem from variability, constraints, dynamics and human behaviour. Building upon these, the opportunities encompassing care operations, planning and scheduling, patient transitions, and safety and teamwork in health care delivery systems are discussed. Finally, the challenges and future directions are proposed. We expect this work to serve as a catalyst to stimulate more in-depth and comprehensive studies.

A Markov chain model to evaluate patient transitions in small community hospitals

A patient journey in the hospital may include many departments or units. Making safe and smooth transitions within the hospital is of significant importance. This paper introduces a Markov chain model to study patient transitions between emergency department, intensive or critical care unit, and hospital ward. An iteration method is presented to evaluate the performance of transition process. It is shown that such a method has a high accuracy of estimation and can be used to study patient transitions in small community hospitals.

Staffing Ratio Analysis in Primary Care Redesign: A Simulation Approach

The objective of this paper is to investigate the optimal staffing ratio under various primary clinic settings. Specifically, by using simulations, we investigate the effects of workload shift and identify the proper ratio between medical assistants (MAs) and physicians (MDs) to achieve effective and efficient service level. The results articulate that the optimal staffing ratio is achieved when the workloads of physicians and MAs are balanced.

Reducing Bottlenecks to Improve the Efficiency of the Lung Cancer Care Delivery Process: A Process Engineering Modeling Approach to Patient-Centered Care

The process of lung cancer care from initial lesion detection to treatment is complex, involving multiple steps, each introducing the potential for substantial delays. Identifying the steps with the greatest delays enables a focused effort to improve the timeliness of care-delivery, without sacrificing quality. We retrospectively reviewed clinical events from initial detection, through histologic diagnosis, radiologic and invasive staging, and medical clearance, to surgery for all patients who had an attempted resection of a suspected lung cancer in a community healthcare system. We used a computer process modeling approach to evaluate delays in care delivery, in order to identify potential ‘bottlenecks’ in waiting time, the reduction of which could produce greater care efficiency. We also conducted ‘what-if’ analyses to predict the relative impact of simulated changes in the care delivery process to determine the most efficient pathways to surgery. The waiting time between radiologic lesion detection and diagnostic biopsy, and the waiting time from radiologic staging to surgery were the two most critical bottlenecks impeding efficient care delivery (more than 3 times larger compared to reducing other waiting times). Additionally, instituting surgical consultation prior to cardiac consultation for medical clearance and decreasing the waiting time between CT scans and diagnostic biopsies, were potentially the most impactful measures to reduce care delays before surgery. Rigorous computer simulation modeling, using clinical data, can provide useful information to identify areas for improving the efficiency of care delivery by process engineering, for patients who receive surgery for lung cancer.

Joint visit in primary care clinics: Modeling, analysis, and an application study

ABSTRACT To improve patient flow and reduce provider workload, joint visit has been proposed and implemented in many primary care clinics. In such systems, the provider and medical assistant (MA) visit the patient jointly; while the provider diagnoses and interacts with the patient, the MA handles necessary documentation work. This article introduces Markov chain models of patient flow with joint visits, and investigates the system behavior under different scenarios. To reduce the state space dimension, convergent iterative procedures are proposed. The study is extended to the non-Markovian case by introducing empirical formulas based on the mean and coefficient of variation (CV) of service times. The results have been validated with good accuracy. To illustrate the applicability of the methods, an application study at Dean East Clinic is presented.

Modeling and Analysis of Patient Transitions in Community Hospitals: A Systems Approach

A patient’s stay at a hospital may encompass various departments or units. Since many critical and complex problems occur at the interfaces of healthcare delivery systems, safe and efficient transitions between the departments within a hospital has significant importance. This paper presents a Markov chain-based model to study patient transitions between emergency department, intensive or critical care unit, and hospital ward in small and medium-sized community hospitals. To make the analytical study tractable, an iteration method is introduced to approximate the system performance during transitions, including direct transferring probabilities without waiting, average patient occupancy in each department, and average patient length of stay. In addition, system properties, such as monotonicity and sensitivity, are analyzed. It is shown that such a method has a high accuracy in performance estimation and can be used to study and improve patient transitions in small or medium-sized hospitals.

Reducing COPD readmissions through predictive modeling and incentive-based interventions

This paper introduces a case study at a community hospital to develop a predictive model to quantify readmission risks for patients with chronic obstructive pulmonary disease (COPD), and use it to support decision making for appropriate incentive-based interventions. Data collected from the community hospital’s database are analyzed to identify risk factors and a logistic regression model is developed to predict the readmission risk within 30 days post-discharge of an individual COPD patient. By targeting on the high-risk patients, we investigate the implementability of the incentive policy which encourages patients to take interventions and helps them to overcome the compliance barrier. Specifically, the conditions and scenarios are identified for either achieving the desired readmission rate while minimizing the total cost, or reaching the lowest readmission rate under incentive budget constraint. Currently, such models are under consideration for a pilot study at the community hospital.

An iterative method for analysis of joint visit model at Dean East Clinic

This paper introduces a case study at Dean East Clinic to model patient flow with joint visits by provider and medical assistant (MA). To reduce the state space dimension, a convergent iterative procedure based on Markov chain model of patient flow is proposed. The study is extended to non-Markovian case by introducing an empirical formula based on the mean and coefficient of variation (CV) of service times. The results have been validated with good accuracy using both collected and randomly generated data.