A Time-Motion Study of Registered Nurses’ Workflow in Intensive Care Unit Remote Monitoring

Utilizing advanced information technology, Intensive Care Unit (ICU) remote monitoring allows highly trained specialists to oversee a large number of patients at multiple sites on a continuous basis. In the current research, we conducted a time-motion study of registered nurses’ work in an ICU remote monitoring facility. Data were collected on seven nurses through 40 hours of observation. The results showed that nurses’ essential tasks were centered on three themes: monitoring patients, maintaining patients’ health records, and managing technology use. In monitoring patients, nurses spent 52% of the time assimilating information embedded in a clinical information system and 15% on monitoring live vitals. System-generated alerts frequently interrupted nurses in their task performance and redirected them to manage suddenly appearing events. These findings provide insight into nurses’ workflow in a new, technology-driven critical care setting and have important implications for system design, work engineering, and personnel selection and training.

Reduction of setup uncertainties and tumor motion in the radiation therapy of lung tumors

Because the goal of radiation therapy is to deliver a lethal dose to the tumor, accurate information on the location of the tumor needs to be known. Margins are placed around the tumor to account for variations in the daily position of the tumor. If tumor motion and patient setup uncertainties can be reduced, margins that account for such uncertainties in tumor location in can be reduced allowing dose escalation, which in turn could potentially improve survival rates. ^ In the first part of this study, we monitor the location of fiducials implanted in the periphery of lung tumors to determine the extent of non-gated and gated fiducial motion, and to quantify patient setup uncertainties. In the second part we determine where the tumor is when different methods of image-guided patient setup and respiratory gating are employed. In the final part we develop, validate, and implement a technique in which patient setup uncertainties are reduced by aligning patients based upon fiducial locations in projection images. ^ Results from the first part indicate that respiratory gating reduces fiducial motion relative to motion during normal respiration and setup uncertainties when the patients were aligned each day using externally placed skin marks are large. The results from the second part indicate that current margins that account for setup uncertainty and tumor motion result in less than 2% of the tumor outside of the planning target volume (PTV) when the patient is aligned using skin marks. In addition, we found that if respiratory gating is going to be used, it is most effective if used in conjunction with image-guided patient setup. From the third part, we successfully developed, validated, and implemented on a patient a technique for aligning a moving target prior to treatment to reduce the uncertainties in tumor location. ^ In conclusion, setup uncertainties and tumor motion are a significant problem when treating tumors located within the thoracic region. Image-guided patient setup in conjunction with treatment delivery using respiratory gating reduces these uncertainties in tumor locations. In doing so, margins around the tumor used to generate the PTV can be reduced, which may allow for dose escalation to the tumor. ^

Interim analysis of clinical trials: Simulation studies of fractional Brownian motion

Interim clinical trial monitoring procedures were motivated by ethical and economic considerations. Classical Brownian motion (Bm) techniques for statistical monitoring of clinical trials were widely used. Conditional power argument and α-spending function based boundary crossing probabilities are popular statistical hypothesis testing procedures under the assumption of Brownian motion. However, it is not rare that the assumptions of Brownian motion are only partially met for trial data. Therefore, I used a more generalized form of stochastic process, called fractional Brownian motion (fBm), to model the test statistics. Fractional Brownian motion does not hold Markov property and future observations depend not only on the present observations but also on the past ones. In this dissertation, we simulated a wide range of fBm data, e.g., H = 0.5 (that is, classical Bm) vs. 0.5< H <1, with treatment effects vs. without treatment effects. Then the performance of conditional power and boundary-crossing based interim analyses were compared by assuming that the data follow Bm or fBm. Our simulation study suggested that the conditional power or boundaries under fBm assumptions are generally higher than those under Bm assumptions when H > 0.5 and also matches better with the empirical results. ^