Journal of Medical Systems

The consideration of natural language as a domain for computer processing has has been one of the long-term projects in the history of data processing. Computers were developed in the late 40's and early 50's in the United States and from the very beginning there was a desire and intent that someday the computer would handle speech and be able to understand normal English literature and text with the coherency of a student or perhaps even an expert. Such breakthroughs have also been anticipated in the medical area. This paper will present a series of design specifications that facilitate the processing of natural language medical databases. Using these techniques it will be possible to expand the use of our traditional tools and exploit the effort that has been invested in these literary creations.

The objective of study is to examine factors affecting the variation in technical and cost efficiency of community health centers (CHCs). A panel study design was formulated to examine the relationships among the contextual, organizational structural, and performance variables. Data Envelopment Analysis (DEA) of technical efficiency and latent growth curve modeling of multi-wave technical and cost efficiency were performed. Regardless of the efficiency measures, CHC efficiency was influenced more by contextual factors than organizational structural factors. The study confirms the independent and additive influences of contextual and organizational predictors on efficiency. The change in CHC technical efficiency positively affects the change in CHC cost efficiency. The practical implication of this finding is that healthcare managers can simultaneously optimize both technical and cost efficiency through appropriate use of inputs to generate optimal outputs. An innovative solution is to employ decision support software to prepare an expert system to assist poorly performing CHCs to achieve better cost efficiency through optimizing technical efficiency.

To safely use medications, professionals and consumers need usable and reliable methods to identify tablets patients are prescribed and taking. Currently, each manufacturer assigns its own identifying codes and symbols. Standardization of the system for identifying solid dosage forms is a goal that has been widely advocated, yet stubbornly resistant to progress. Physicians, pharmacists, and consumers attempting to identify pills must use various methods which have shortcomings in ease of use, availability, and accuracy. Arguments have been advanced, particularly by pharmaceutical manufacturers, that evidence of unworkability of the current system is not compelling, and costs of retooling current manufacturing processes could be prohibitive. These issues are currently being explored by a task force led by the U.S. Pharmacopeia Safe Medication Use, and Pharmaceutical Forms Dosage Expert Committees. This paper presents a fictitious case study of an elderly patient succumbing to digoxin overdose illustrating the dilemmas posed in the tablet-imprint debate.

In the hospital, fast and efficient communication among clinicians and other employees is paramount to ensure optimal patient care, workflow efficiency, patient safety and patient comfort. The implementation of the wireless Vocera® Badge, a hands-free wearable device distributed to perioperative team members, has increased communication efficiency across the perioperative environment at Massachusetts General Hospital (MGH). This quality improvement project, based upon identical pre- and post-implementation surveys, used qualitative and quantitative analysis to determine if and how the Vocera system affected the timeliness of information flow, ease of communication, and operating room noise levels throughout the perioperative environment. Overall, the system increased the speed of information flow and eased communication between coworkers yet was perceived to have raised the overall noise level in and around the operating rooms (ORs). The perceived increase in noise was outweighed by the closed-loop communication between clinicians. Further education of the system’s features in regard to speech recognition and privacy along with expected conversation protocol are necessary to ensure hassle-free communication for all staff.

Left ventricular ejection fraction (LVEF) is an important prognostic indicator of cardiovascular outcomes. It is used clinically to determine the indication for several therapeutic interventions. LVEF is most commonly derived using in-line tools and some manual assessment by cardiologists from standardized echocardiographic views. LVEF is typically documented in free-text reports, and variation in LVEF documentation pose a challenge for the extraction and utilization of LVEF in computer-based clinical workflows. To address this problem, we developed a computerized algorithm to extract LVEF from echocardiography reports for the identification of patients having heart failure with reduced ejection fraction (HFrEF) for therapeutic intervention at a large healthcare system. We processed echocardiogram reports for 57,158 patients with coded diagnosis of Heart Failure that visited the healthcare system over a two-year period. Our algorithm identified a total of 3910 patients with reduced ejection fraction. Of the 46,634 echocardiography reports processed, 97% included a mention of LVEF. Of these reports, 85% contained numerical ejection fraction values, 9% contained ranges, and the remaining 6% contained qualitative descriptions. Overall, 18% of extracted numerical LVEFs were ≤ 40%. Furthermore, manual validation for a sample of 339 reports yielded an accuracy of 1.0. Our study demonstrates that a regular expression-based approach can accurately extract LVEF from echocardiograms, and is useful for delineating heart-failure patients with reduced ejection fraction.

Being the economic powerhouses of most large medical centers, operating rooms (ORs) require the highest levels of teamwork, communication, and efficiency in order to optimize patient safety and reduce hospital waste. A major component of OR waste comes from unused surgical instrumentation; instruments that are frequently prepared for procedures but are never touched by the surgical team still require a full reprocessing cycle at the conclusion of the case. Based on our own previous successes in the perioperative domain, in this work we detail an initiative that reduces surgical instrumentation waste of video-assisted thoracoscopic surgery (VATS) procedures by placing thoracotomy conversion instrumentation in a standby location and designing a specific instrument kit to be used solely for VATS cases. Our estimates suggest that this initiative will reduce at least 91,800 pounds of unnecessary surgical instrumentation from cycling through our ORs and reprocessing department annually, resulting in increased OR team communication without sacrificing the highest standard of patient safety.

The interpolation technique of computed tomography angiography (CTA) image provides the ability for 3D reconstruction, as well as reduces the detect cost and the amount of radiation. However, most of the image interpolation algorithms cannot take the automation and accuracy into account. This study provides a new edge matching interpolation algorithm based on wavelet decomposition of CTA. It includes mark, scale and calculation (MSC). Combining the real clinical image data, this study mainly introduces how to search for proportional factor and use the root mean square operator to find a mean value. Furthermore, we re- synthesize the high frequency and low frequency parts of the processed image by wavelet inverse operation, and get the final interpolation image. MSC can make up for the shortage of the conventional Computed Tomography (CT) and Magnetic Resonance Imaging(MRI) examination. The radiation absorption and the time to check through the proposed synthesized image were significantly reduced. In clinical application, it can help doctor to find hidden lesions in time. Simultaneously, the patients get less economic burden as well as less radiation exposure absorbed.

Medical applications for virtual reality (VR) are just beginning to emerge. These include VR surgical simulators, telepresence surgery, complex medical database visualization, and rehabilitation. These applications are mediated through the computer interface and as such are the embodiment of VR as an integral part of the paradigm shift in the field of medicine. The Green Telepresence Surgery System consists of two components, the surgical workstation and remote worksite. At the remote site there is a 3-D camera system and responsive manipulators with sensory input. At the workstation there is a 3-D monitor and dexterous handles with force feedback. The VR surgical simulator is a stylized recreation of the human abdomen with several essential organs. Using a helmet mounted display and DataGloveTM, a person can learn anatomy from a new perspective by ‘flying’ inside and around the organs, or can practice surgical procedures with a scalpel and clamps. Database visualization creates 3-D images of complex medical data for new perspectives in analysis. Rehabilitation medicine permits impaired individuals to explore worlds not otherwise available to them, allows accurate assessment and therapy for their disabilities, and helps architects understand their critical needs in public or personal space. And to support these advanced technologies, the operating room and hospital of the future will be first designed and tested in virtual reality, bringing together the full power of the digital physician.

A methodology for evaluating the effectiveness of a screening program in terms of early detection of disease is presented. Formulas for predicting the effect of different screening intervals are developed. A case study using patient data from the Johns Hopkins Lung Project exemplifies how the method might be applied to cancer screening.