Merging Data Diversity of Clinical Medical Records to Improve Effectiveness.

Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources.

Simulation analysis of resource flexibility on healthcare processes.

PURPOSE: This paper uses discrete event simulation to explore the best resource flexibility scenario and examine the effect of implementing resource flexibility on different stages of patient treatment process. Specifically we investigate the effect of resource flexibility on patient waiting time and throughput in an orthopedic care process. We further seek to explore on how implementation of resource flexibility on patient treatment processes affects patient access to healthcare services. We focus on two resources, namely, orthopedic surgeon and operating room. METHODS: The observational approach was used to collect process data. The developed model was validated by comparing the simulation output with actual patient data collected from the studied orthopedic care process. We developed different scenarios to identify the best resource flexibility scenario and explore the effect of resource flexibility on patient waiting time, throughput, and future changes in demand. The developed scenarios focused on creating flexibility on service capacity of this care process by altering the amount of additional human resource capacity at different stages of patient care process and extending the use of operating room capacity. RESULTS: The study found that resource flexibility can improve responsiveness to patient demand in the treatment process. Testing different scenarios showed that the introduction of resource flexibility reduces patient waiting time and improves throughput. The simulation results show that patient access to health services can be improved by implementing resource flexibility at different stages of the patient treatment process. CONCLUSION: This study contributes to the current health care literature by explaining how implementing resource flexibility at different stages of patient care processes can improve ability to respond to increasing patients demands. This study was limited to a single patient process; studies focusing on additional processes are recommended.

Improving surgeon utilization in an orthopedic department using simulation modeling.

PURPOSE: Worldwide more than two billion people lack appropriate access to surgical services due to mismatch between existing human resource and patient demands. Improving utilization of existing workforce capacity can reduce the existing gap between surgical demand and available workforce capacity. In this paper, the authors use discrete event simulation to explore the care process at an orthopedic department. Our main focus is improving utilization of surgeons while minimizing patient wait time. METHODS: The authors collaborated with orthopedic department personnel to map the current operations of orthopedic care process in order to identify factors that influence poor surgeons utilization and high patient waiting time. The authors used an observational approach to collect data. The developed model was validated by comparing the simulation output with the actual patient data that were collected from the studied orthopedic care process. The authors developed a proposal scenario to show how to improve surgeon utilization. RESULTS: The simulation results showed that if ancillary services could be performed before the start of clinic examination services, the orthopedic care process could be highly improved. That is, improved surgeon utilization and reduced patient waiting time. Simulation results demonstrate that with improved surgeon utilizations, up to 55% increase of future demand can be accommodated without patients reaching current waiting time at this clinic, thus, improving patient access to health care services. CONCLUSION: This study shows how simulation modeling can be used to improve health care processes. This study was limited to a single care process; however the findings can be applied to improve other orthopedic care process with similar operational characteristics.

Impact of changed management policies on operating room efficiency.

BACKGROUND: To increase operating room (OR) efficiency, a new resource allocation strategy, a new policy for patient urgency classification, and a new system for OR booking was implemented at a tertiary referral hospital. We investigated the impact of these interventions. METHODS: We carried out a before-and-after study using OR data. A total of 23,515 elective (planned) and non-elective (unplanned) orthopaedic and general surgeries were conducted during calendar year 2007 (period 1) and July 2008 to July 2009 (period 2). The Wilcoxon-Mann-Whitney test was used to calculate statistical significance. RESULTS: An increased amount of case time (7.1%, p < 0.05) was conducted without any increase in out-of-hours case time. Despite having three fewer ORs for electives, slightly more elective case time was handled with 26% less use of overtime (p < 0.05). Mean OR utilization was 56% for the 17 mixed ORs, 60% for the 14 elective ORs, and 62% for the 3 dedicated ORs. A 20% growth (p < 0.05) of non-elective case time was primarily absorbed through enhanced daytime surgery, which increased over 48% (p < 0.05). As a result, the proportions of case time on evenings and nights decreased. Specifically, case time at night decreased by 26% (p < 0.05), and the number of nights without surgery increased from 55 to 112 (out of 315 and 316, respectively). Median waiting time for the middle urgencies increased with 1.2 hours, but over 90% received treatment within maximum acceptable waiting time (MAWT) in both periods. Median waiting time for the lowest urgencies was reduced with 12 hours, and the proportion of cases treated within MAWT increased from 70% to 89%. The proportion of high urgency patients (as a proportion of the total) was reduced from 20% to 12%. Consequently, almost 90% of the operations could be planned at least 24 hours in advance. CONCLUSIONS: The redesign facilitated effective daytime surgery and a more selective use of the ORs for high urgency patients out of hours. The synergistic effect probably exceeded the sum of the individual effects of the changes, because the effects of each intervention facilitated the successful implementation of others.